From 6336701c9378c23c85d1c0e464b663ca2bbb8e60 Mon Sep 17 00:00:00 2001
From: Galunid <karolek1231456@gmail.com>
Date: Mon, 23 Oct 2023 17:47:03 +0200
Subject: [PATCH 01/79] Fix baichuan convert script not detecing model (#3739)

It seems nobody objects.
---
 convert-baichuan-hf-to-gguf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py
index 3b64ecb8870722..5ee99be73134e6 100755
--- a/convert-baichuan-hf-to-gguf.py
+++ b/convert-baichuan-hf-to-gguf.py
@@ -110,7 +110,7 @@ def parse_args() -> argparse.Namespace:
 with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 print("hello print: ",hparams["architectures"][0])
-if hparams["architectures"][0] != "BaichuanForCausalLM":
+if hparams["architectures"][0] != "BaichuanForCausalLM" and hparams["architectures"][0] != "BaiChuanForCausalLM":
     print("Model architecture not supported: " + hparams["architectures"][0])
 
     sys.exit()

From 5be6c803fa5378f62a1590f3ad8c6b64c7c0c2ce Mon Sep 17 00:00:00 2001
From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
Date: Mon, 23 Oct 2023 12:40:03 -0700
Subject: [PATCH 02/79] llama : remove token functions with `context` args in
 favor of `model` (#3720)

* added `llama_model_token_*` variants to all the `llama_token_*` functions.

* added `LLAMA_API`

* formatting

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* removed old `llama_token` functions

* changed 3 more functions to take in model

- `llama_token_get_text`
- `llama_token_get_score`
- `llama_token_get_type`

* added back docs

* fixed main.cpp

* changed token functions to use new model variants

* changed token functions to use new model variants

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 common/common.cpp                    |  8 ++---
 common/sampling.cpp                  |  4 +--
 common/train.cpp                     |  6 ++--
 examples/batched/batched.cpp         |  2 +-
 examples/beam-search/beam-search.cpp |  2 +-
 examples/infill/infill.cpp           | 30 +++++++++---------
 examples/llama-bench/llama-bench.cpp |  4 +--
 examples/llava/llava-utils.h         |  2 +-
 examples/main/main.cpp               |  8 ++---
 examples/parallel/parallel.cpp       |  2 +-
 examples/perplexity/perplexity.cpp   |  4 +--
 examples/server/server.cpp           | 14 ++++-----
 examples/simple/simple.cpp           |  2 +-
 examples/speculative/speculative.cpp |  2 +-
 llama.cpp                            | 47 ++++++++++++++--------------
 llama.h                              | 21 +++++++------
 16 files changed, 80 insertions(+), 78 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index bbd1518ca29340..44bb76618f5f8b 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -880,13 +880,13 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
     }
 
     if (params.ignore_eos) {
-        params.sparams.logit_bias[llama_token_eos(lctx)] = -INFINITY;
+        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
     }
 
     {
         LOG("warming up the model with an empty run\n");
 
-        std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
+        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
         llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
         llama_kv_cache_tokens_rm(lctx, -1, -1);
         llama_reset_timings(lctx);
@@ -941,7 +941,7 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t
 }
 
 std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
-    const llama_token bos_id = llama_token_bos(ctx);
+    const llama_token bos_id = llama_token_bos(llama_get_model(ctx));
 
     std::string piece;
     std::string result;
@@ -1186,7 +1186,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
     fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
 
-    const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(lctx));
+    const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
     const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
     fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
 
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 6f0af3c4a2afd5..5258d4e8263693 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -147,7 +147,7 @@ llama_token llama_sampling_sample(
 
     // apply penalties
     if (!prev.empty()) {
-        const float nl_logit = logits[llama_token_nl(ctx_main)];
+        const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
 
         llama_sample_repetition_penalties(ctx_main, &cur_p,
                 prev.data() + prev.size() - penalty_last_n,
@@ -155,7 +155,7 @@ llama_token llama_sampling_sample(
 
         if (!penalize_nl) {
             for (size_t idx = 0; idx < cur_p.size; idx++) {
-                if (cur_p.data[idx].id == llama_token_nl(ctx_main)) {
+                if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
                     cur_p.data[idx].logit = nl_logit;
                     break;
                 }
diff --git a/common/train.cpp b/common/train.cpp
index 154ca56e5fa877..3cce5da269637e 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -236,8 +236,8 @@ int64_t get_example_targets_batch(
     int64_t used_samples = 0;
 
     ggml_set_f32(target_probs, 0.0f);
-    llama_token bos = llama_token_bos(lctx);
-    llama_token eos = llama_token_eos(lctx);
+    llama_token bos = llama_token_bos(llama_get_model(lctx));
+    llama_token eos = llama_token_eos(llama_get_model(lctx));
     // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples);
     for (int k=0; k<n_batch; ++k) {
         // printf("%s: batch %d\n", __func__, k);
@@ -924,7 +924,7 @@ size_t tokenize_file(
         for (llama_token token=0; token < n_vocab; ++token) {
             max_token_text_size = std::max(
                 max_token_text_size,
-                strlen(llama_token_get_text(lctx, token)));
+                strlen(llama_token_get_text(llama_get_model(lctx), token)));
         }
 
         // upper bound of context byte length.
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 2797329b4fc57f..75856a81fe9b1b 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -180,7 +180,7 @@ int main(int argc, char ** argv) {
             //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of stream? -> mark the stream as finished
-            if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
+            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
                 i_batch[i] = -1;
                 LOG_TEE("\n");
                 if (n_parallel > 1) {
diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp
index f078ab8a87fa5f..679b382e19b4e0 100644
--- a/examples/beam-search/beam-search.cpp
+++ b/examples/beam-search/beam-search.cpp
@@ -47,7 +47,7 @@ struct beam_search_callback_data {
 // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
 // For example, eob can be flagged due to maximum token length, stop words, etc.
 static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
-    return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx);
+    return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
 }
 
 // Function matching type llama_beam_search_callback_fn_t.
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 6331335e3c423e..9c52b7bbad1dba 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -246,14 +246,14 @@ int main(int argc, char ** argv) {
     if (suff_rm_leading_spc && inp_sfx[0] == space_token) {
         inp_sfx.erase(inp_sfx.begin());
     }
-    inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(ctx));
+    inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
     if (add_bos) {
-        inp_pfx.insert(inp_pfx.begin(), llama_token_bos(ctx));
+        inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
     }
-    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(ctx));
+    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
     embd_inp = inp_pfx;
     embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-    embd_inp.push_back(llama_token_middle(ctx));
+    embd_inp.push_back(llama_token_middle(model));
 
     LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix));
     LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix));
@@ -261,7 +261,7 @@ int main(int argc, char ** argv) {
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(ctx));
+        embd_inp.push_back(llama_token_bos(model));
         LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
     }
 
@@ -577,10 +577,10 @@ int main(int argc, char ** argv) {
         if ((int) embd_inp.size() <= n_consumed) {
 
             // deal with eot token in infill mode
-            if ((llama_sampling_last(ctx_sampling) == llama_token_eot(ctx) || is_interacting) && params.interactive){
+            if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
                 if(is_interacting && !params.interactive_first) {
                     // print an eot token
-                    printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str());
+                    printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
                 }
                 fflush(stdout);
                 printf("\n");
@@ -627,14 +627,14 @@ int main(int argc, char ** argv) {
                 if (suff_rm_leading_spc && inp_sfx[0] == space_token) {
                     inp_sfx.erase(inp_sfx.begin());
                 }
-                inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(ctx));
+                inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
                 if (add_bos) {
-                    inp_pfx.insert(inp_pfx.begin(), llama_token_bos(ctx));
+                    inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
                 }
-                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(ctx));
+                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
                 embd_inp = inp_pfx;
                 embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-                embd_inp.push_back(llama_token_middle(ctx));
+                embd_inp.push_back(llama_token_middle(model));
                 embd.clear();
                 embd_guidance.clear();
                 n_remain = params.n_predict;
@@ -644,7 +644,7 @@ int main(int argc, char ** argv) {
                 is_interacting = false;
             }
             // deal with end of text token in interactive mode
-            else if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) {
+            else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -661,7 +661,7 @@ int main(int argc, char ** argv) {
 
                 if (params.input_prefix_bos) {
                     LOG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_token_bos(ctx));
+                    embd_inp.push_back(llama_token_bos(model));
                 }
 
                 std::string buffer;
@@ -724,7 +724,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !params.interactive) {
+        if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
             break;
         }
 
@@ -736,7 +736,7 @@ int main(int argc, char ** argv) {
         }
     }
     if (!params.interactive && n_remain <= 0) {
-        printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str());
+        printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
         fflush(stdout);
     }
 
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index a04115c962655a..20767d555b206d 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -933,7 +933,7 @@ struct sql_printer : public printer {
 };
 
 static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
-    std::vector<llama_token> tokens(n_batch, llama_token_bos(ctx));
+    std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
     int n_processed = 0;
 
     llama_set_n_threads(ctx, n_threads, n_threads);
@@ -946,7 +946,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
 }
 
 static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
-    llama_token token = llama_token_bos(ctx);
+    llama_token token = llama_token_bos(llama_get_model(ctx));
 
     llama_set_n_threads(ctx, n_threads, n_threads);
 
diff --git a/examples/llava/llava-utils.h b/examples/llava/llava-utils.h
index 45b2b1ad30226c..320c719670b02d 100644
--- a/examples/llava/llava-utils.h
+++ b/examples/llava/llava-utils.h
@@ -137,7 +137,7 @@ inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
 inline const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
     int id = sample_id(ctx_llama, params);
     static std::string ret;
-    if (id == llama_token_eos(ctx_llama)) {
+    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
         ret = "</s>";
     } else {
         ret = llama_token_to_piece(ctx_llama, id);
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 2621bd539875f6..3d9f670b9da7f2 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -248,7 +248,7 @@ int main(int argc, char ** argv) {
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(ctx));
+        embd_inp.push_back(llama_token_bos(model));
         LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
     }
 
@@ -693,7 +693,7 @@ int main(int argc, char ** argv) {
             }
 
             // deal with end of text token in interactive mode
-            if (llama_sampling_last(ctx_sampling) == llama_token_eos(ctx)) {
+            if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -720,7 +720,7 @@ int main(int argc, char ** argv) {
 
                 if (params.input_prefix_bos) {
                     LOG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_token_bos(ctx));
+                    embd_inp.push_back(llama_token_bos(model));
                 }
 
                 std::string buffer;
@@ -804,7 +804,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !(params.instruct || params.interactive)) {
+        if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive)) {
             LOG_TEE(" [end of text]\n");
             break;
         }
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index eb64adef8a3895..9a0b9c183d1070 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -347,7 +347,7 @@ int main(int argc, char ** argv) {
                 //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
 
                 if (client.n_decoded > 2 &&
-                        (id == llama_token_eos(ctx) ||
+                        (id == llama_token_eos(model) ||
                          (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
                          client.response.find("User:") != std::string::npos ||
                          client.response.find('\n') != std::string::npos)) {
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 7d0038bd407573..3c2542e8c105e7 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -227,7 +227,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(ctx);
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
             const auto batch_logits = llama_get_logits(ctx);
@@ -350,7 +350,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(ctx);
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index c3279dbc9c456b..693f9b7735e493 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -726,7 +726,7 @@ struct llama_server_context
 
         if (json_value(data, "ignore_eos", false))
         {
-            slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY;
+            slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
         }
 
         const auto &logit_bias = data.find("logit_bias");
@@ -1056,7 +1056,7 @@ struct llama_server_context
             slot.has_next_token = false;
         }
 
-        if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx))
+        if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
         {
             slot.stopped_eos = true;
             slot.has_next_token = false;
@@ -1130,7 +1130,7 @@ struct llama_server_context
 
     json get_formated_generation(llama_client_slot &slot)
     {
-        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx));
+        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
         const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
                                 eos_bias->second < 0.0f && std::isinf(eos_bias->second);
         return json {
@@ -1555,11 +1555,11 @@ struct llama_server_context
                             suffix_tokens.erase(suffix_tokens.begin());
                         }
 
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(ctx));
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(ctx)); // always add BOS
-                        prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(ctx));
+                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
+                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
+                        prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
                         prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
-                        prefix_tokens.push_back(llama_token_middle(ctx));
+                        prefix_tokens.push_back(llama_token_middle(model));
                         prompt_tokens = prefix_tokens;
                     }
                     else
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index 55385f566aa6f6..f376c050994d19 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -138,7 +138,7 @@ int main(int argc, char ** argv) {
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of stream?
-            if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
+            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
                 LOG_TEE("\n");
 
                 break;
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 894321ce9648cf..92ad27e8e423cc 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -163,7 +163,7 @@ int main(int argc, char ** argv) {
             printf("%s", token_str.c_str());
             fflush(stdout);
 
-            if (id == llama_token_eos(ctx_tgt)) {
+            if (id == llama_token_eos(model_tgt)) {
                 has_eos = true;
             }
 
diff --git a/llama.cpp b/llama.cpp
index c63e6251c76769..61f30c3982f184 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -7493,7 +7493,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
         }
     }
 
-    const llama_token eos = llama_token_eos(ctx);
+    const llama_token eos = llama_token_eos(&ctx->model);
 
     std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
     std::vector<llama_grammar_candidate>                              candidates_grammar;
@@ -7703,7 +7703,7 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
 void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
     const int64_t t_start_sample_us = ggml_time_us();
 
-    if (token == llama_token_eos(ctx)) {
+    if (token == llama_token_eos(&ctx->model)) {
         for (const auto & stack : grammar->stacks) {
             if (stack.empty()) {
                 return;
@@ -8912,7 +8912,7 @@ struct llama_context * llama_new_context_with_model(
             // build worst-case graph
             int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
             int n_past = cparams.n_ctx - n_tokens;
-            llama_token token = llama_token_bos(ctx); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+            llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
             ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0));
 
 #ifdef GGML_USE_METAL
@@ -9673,43 +9673,44 @@ float * llama_get_embeddings(struct llama_context * ctx) {
     return ctx->embedding.data();
 }
 
-const char * llama_token_get_text(const struct llama_context * ctx, llama_token token) {
-    return ctx->model.vocab.id_to_token[token].text.c_str();
+const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
+    return model->vocab.id_to_token[token].text.c_str();
 }
 
-float llama_token_get_score(const struct llama_context * ctx, llama_token token) {
-    return ctx->model.vocab.id_to_token[token].score;
+float llama_token_get_score(const struct llama_model * model, llama_token token) {
+    return model->vocab.id_to_token[token].score;
 }
 
-llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token) {
-    return ctx->model.vocab.id_to_token[token].type;
+llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
+    return model->vocab.id_to_token[token].type;
 }
 
-llama_token llama_token_bos(const struct llama_context * ctx) {
-    return ctx->model.vocab.special_bos_id;
+llama_token llama_token_bos(const struct llama_model * model) {
+    return model->vocab.special_bos_id;
 }
 
-llama_token llama_token_eos(const struct llama_context * ctx) {
-    return ctx->model.vocab.special_eos_id;
+llama_token llama_token_eos(const struct llama_model * model) {
+    return model->vocab.special_eos_id;
 }
 
-llama_token llama_token_nl(const struct llama_context * ctx) {
-    return ctx->model.vocab.linefeed_id;
+llama_token llama_token_nl(const struct llama_model * model) {
+    return model->vocab.linefeed_id;
 }
-llama_token llama_token_prefix(const struct llama_context * ctx) {
-    return ctx->model.vocab.special_prefix_id;
+
+llama_token llama_token_prefix(const struct llama_model * model) {
+    return model->vocab.special_prefix_id;
 }
 
-llama_token llama_token_middle(const struct llama_context * ctx) {
-    return ctx->model.vocab.special_middle_id;
+llama_token llama_token_middle(const struct llama_model * model) {
+    return model->vocab.special_middle_id;
 }
 
-llama_token llama_token_suffix(const struct llama_context * ctx) {
-    return ctx->model.vocab.special_suffix_id;
+llama_token llama_token_suffix(const struct llama_model * model) {
+    return model->vocab.special_suffix_id;
 }
 
-llama_token llama_token_eot(const struct llama_context * ctx) {
-    return ctx->model.vocab.special_eot_id;
+llama_token llama_token_eot(const struct llama_model * model) {
+    return model->vocab.special_eot_id;
 }
 
 int llama_tokenize(
diff --git a/llama.h b/llama.h
index 306f5b383cb111..2f2fee0e2ff9f3 100644
--- a/llama.h
+++ b/llama.h
@@ -494,21 +494,22 @@ extern "C" {
     // Vocab
     //
 
-    LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
+    LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
 
-    LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
+    LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
 
-    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
 
     // Special tokens
-    LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence
-    LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx);  // end-of-sentence
-    LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx);  // next-line
+    LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
+    LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
+    LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
+
     // codellama infill tokens
-    LLAMA_API llama_token llama_token_prefix(const struct llama_context * ctx); // Beginning of infill prefix
-    LLAMA_API llama_token llama_token_middle(const struct llama_context * ctx); // Beginning of infill middle
-    LLAMA_API llama_token llama_token_suffix(const struct llama_context * ctx); // Beginning of infill suffix
-    LLAMA_API llama_token llama_token_eot   (const struct llama_context * ctx); // End of infill middle
+    LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
+    LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
+    LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
+    LLAMA_API llama_token llama_token_eot   (const struct llama_model * model); // End of infill middle
 
     //
     // Tokenization

From 69a6735087c3634963c642fd69f0851ac479cd78 Mon Sep 17 00:00:00 2001
From: Galunid <karolek1231456@gmail.com>
Date: Mon, 23 Oct 2023 21:46:00 +0200
Subject: [PATCH 03/79] Update special token handling in conversion scripts for
 gpt2 derived tokenizers (#3746)

We still have the heads up in `README.md` regarding `bpe` tokenizers and this patch is needed for

- a couple of tokenizer tests
- some more `special` and `non-special` added tokens handling (as far as I understand it)

* Update special token handling

* Add mpt
---
 convert-bloom-hf-to-gguf.py     | 17 +++++++++++++----
 convert-gptneox-hf-to-gguf.py   | 17 +++++++++++++----
 convert-mpt-hf-to-gguf.py       |  6 ++++--
 convert-refact-hf-to-gguf.py    | 17 +++++++++++++----
 convert-starcoder-hf-to-gguf.py | 18 +++++++++++++-----
 5 files changed, 56 insertions(+), 19 deletions(-)

diff --git a/convert-bloom-hf-to-gguf.py b/convert-bloom-hf-to-gguf.py
index 14dbd793c84448..6e866d9434818c 100755
--- a/convert-bloom-hf-to-gguf.py
+++ b/convert-bloom-hf-to-gguf.py
@@ -118,15 +118,24 @@ def parse_args() -> argparse.Namespace:
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
-    scores.append(0.0)  # dummy
-    toktypes.append(gguf.TokenType.NORMAL)
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
 special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py
index f1599b0c44e34a..02d1fdf164eea1 100755
--- a/convert-gptneox-hf-to-gguf.py
+++ b/convert-gptneox-hf-to-gguf.py
@@ -123,15 +123,24 @@ def parse_args() -> argparse.Namespace:
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
-    scores.append(0.0) # dummy
-    toktypes.append(gguf.TokenType.NORMAL)
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
 special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
diff --git a/convert-mpt-hf-to-gguf.py b/convert-mpt-hf-to-gguf.py
index 2d2fa2329dd1bc..70d154b3f5c01e 100755
--- a/convert-mpt-hf-to-gguf.py
+++ b/convert-mpt-hf-to-gguf.py
@@ -136,9 +136,11 @@ def parse_args() -> argparse.Namespace:
         tokens.append(f"[PAD{i}]")
         toktypes.append(gguf.TokenType.USER_DEFINED)
     elif reverse_vocab[i] in added_vocab:
-        # NOTE: wouldn't we like to distinguish CONTROL tokens here?
         tokens.append(reverse_vocab[i])
-        toktypes.append(gguf.TokenType.USER_DEFINED)
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
     else:
         tokens.append(reverse_vocab[i])
         toktypes.append(gguf.TokenType.NORMAL)
diff --git a/convert-refact-hf-to-gguf.py b/convert-refact-hf-to-gguf.py
index 934f3852b24188..f0cfe84d81c8ba 100755
--- a/convert-refact-hf-to-gguf.py
+++ b/convert-refact-hf-to-gguf.py
@@ -139,15 +139,24 @@ def parse_args() -> argparse.Namespace:
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
-    scores.append(0.0) # dummy
-    toktypes.append(gguf.TokenType.NORMAL)
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
 special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
diff --git a/convert-starcoder-hf-to-gguf.py b/convert-starcoder-hf-to-gguf.py
index fe8815cbf6f474..a9bfed85e31bab 100755
--- a/convert-starcoder-hf-to-gguf.py
+++ b/convert-starcoder-hf-to-gguf.py
@@ -111,17 +111,25 @@ def parse_args() -> argparse.Namespace:
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
-    scores.append(0.0) # dummy
-    toktypes.append(gguf.TokenType.NORMAL)
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
-
 special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
 special_vocab.add_to_gguf(gguf_writer)
 

From 9d02956443e5c1ded29b7b5ed8a21bc01ba6f563 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= <yusufsarigoz@gmail.com>
Date: Mon, 23 Oct 2023 22:57:16 +0300
Subject: [PATCH 04/79] issues : separate bug and enhancement template + no
 default title (#3748)

---
 .github/ISSUE_TEMPLATE/{custom.md => bug.md} |  9 +++----
 .github/ISSUE_TEMPLATE/enhancement.md        | 28 ++++++++++++++++++++
 2 files changed, 32 insertions(+), 5 deletions(-)
 rename .github/ISSUE_TEMPLATE/{custom.md => bug.md} (96%)
 create mode 100644 .github/ISSUE_TEMPLATE/enhancement.md

diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/bug.md
similarity index 96%
rename from .github/ISSUE_TEMPLATE/custom.md
rename to .github/ISSUE_TEMPLATE/bug.md
index 8fd95535677803..d7879b232b54dd 100644
--- a/.github/ISSUE_TEMPLATE/custom.md
+++ b/.github/ISSUE_TEMPLATE/bug.md
@@ -1,8 +1,7 @@
 ---
-name: Issue and enhancement template
-about: Used to report issues and request enhancements for llama.cpp
-title: "[User] Insert summary of your issue or enhancement.."
-labels: ''
+name: Bug template
+about: Used to report bugs in llama.cpp
+labels: ["bug"]
 assignees: ''
 
 ---
@@ -46,7 +45,7 @@ $ g++ --version
 
 # Failure Information (for bugs)
 
-Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
+Please help provide information about the failure / bug.
 
 # Steps to Reproduce
 
diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md
new file mode 100644
index 00000000000000..dcffda7500f527
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/enhancement.md
@@ -0,0 +1,28 @@
+---
+name: Enhancement template
+about: Used to request enhancements for llama.cpp
+labels: ["enhancement"]
+assignees: ''
+
+---
+
+# Prerequisites
+
+Please answer the following questions for yourself before submitting an issue.
+
+- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
+- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
+- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
+- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
+
+# Feature Description
+
+Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
+
+# Motivation
+
+Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
+
+# Possible Implementation
+
+If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.

From e3932593d46c30145301a13097895f9376cba509 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 23 Oct 2023 23:46:05 +0300
Subject: [PATCH 05/79] Revert "make : add optional CUDA_NATIVE_ARCH (#2482)"

This reverts commit 96981f37b1e3f450d9e63e571514217bf60f0a7f.

See:

https://github.com/ggerganov/llama.cpp/pull/2482#issuecomment-1775975866
---
 Makefile | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/Makefile b/Makefile
index 705c4acb4a0a98..80179631f95a5b 100644
--- a/Makefile
+++ b/Makefile
@@ -391,12 +391,9 @@ else
 endif #LLAMA_CUDA_NVCC
 ifdef CUDA_DOCKER_ARCH
 	NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
-endif # CUDA_DOCKER_ARCH
-ifdef CUDA_NATIVE_ARCH
-	NVCCFLAGS += -arch=$(CUDA_NATIVE_ARCH)
 else
 	NVCCFLAGS += -arch=native
-endif # CUDA_NATIVE_ARCH
+endif # CUDA_DOCKER_ARCH
 ifdef LLAMA_CUDA_FORCE_DMMV
 	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
 endif # LLAMA_CUDA_FORCE_DMMV

From 469c9addef75893e6be12edda852d12e840bf064 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 24 Oct 2023 09:46:50 +0300
Subject: [PATCH 06/79] metal : handle ggml_scale for n%4 != 0 (close #3754)

ggml-ci
---
 ggml-metal.m     | 18 +++++++++++++-----
 ggml-metal.metal | 10 +++++++++-
 2 files changed, 22 insertions(+), 6 deletions(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index c908106be5e680..c1901dca75269d 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -62,6 +62,7 @@
     GGML_METAL_DECL_KERNEL(mul);
     GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
     GGML_METAL_DECL_KERNEL(scale);
+    GGML_METAL_DECL_KERNEL(scale_4);
     GGML_METAL_DECL_KERNEL(silu);
     GGML_METAL_DECL_KERNEL(relu);
     GGML_METAL_DECL_KERNEL(gelu);
@@ -249,6 +250,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){
         GGML_METAL_ADD_KERNEL(mul);
         GGML_METAL_ADD_KERNEL(mul_row);
         GGML_METAL_ADD_KERNEL(scale);
+        GGML_METAL_ADD_KERNEL(scale_4);
         GGML_METAL_ADD_KERNEL(silu);
         GGML_METAL_ADD_KERNEL(relu);
         GGML_METAL_ADD_KERNEL(gelu);
@@ -347,6 +349,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(mul);
     GGML_METAL_DEL_KERNEL(mul_row);
     GGML_METAL_DEL_KERNEL(scale);
+    GGML_METAL_DEL_KERNEL(scale_4);
     GGML_METAL_DEL_KERNEL(silu);
     GGML_METAL_DEL_KERNEL(relu);
     GGML_METAL_DEL_KERNEL(gelu);
@@ -923,15 +926,20 @@ void ggml_metal_graph_compute(
 
                             const float scale = *(const float *) src1->data;
 
-                            [encoder setComputePipelineState:ctx->pipeline_scale];
+                            int64_t n = ggml_nelements(dst);
+
+                            if (n % 4 == 0) {
+                                n /= 4;
+                                [encoder setComputePipelineState:ctx->pipeline_scale_4];
+                            } else {
+                                [encoder setComputePipelineState:ctx->pipeline_scale];
+                            }
+
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                             [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
 
-                            const int64_t n = ggml_nelements(dst);
-                            GGML_ASSERT(n % 4 == 0);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                         } break;
                     case GGML_OP_UNARY:
                         switch (ggml_get_unary_op(gf->nodes[i])) {
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 69fc7136279ad4..f4b460564453c5 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -125,9 +125,17 @@ kernel void kernel_mul_row(
 }
 
 kernel void kernel_scale(
+        device const float * src0,
+        device       float * dst,
+        constant     float & scale,
+        uint tpig[[thread_position_in_grid]]) {
+    dst[tpig] = src0[tpig] * scale;
+}
+
+kernel void kernel_scale_4(
         device const float4 * src0,
         device       float4 * dst,
-        constant     float & scale,
+        constant     float  & scale,
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] * scale;
 }

From daab3d7f45832e10773c99f3484b0d5b14d86c0c Mon Sep 17 00:00:00 2001
From: Galunid <karolek1231456@gmail.com>
Date: Tue, 24 Oct 2023 09:17:17 +0200
Subject: [PATCH 07/79] Add more tokenizer tests (#3742)

* Add more tokenizer tests

* Add starcoder

* Update test vocab files

* Restrict bpe tokenizer tests to unicode planes

* Update comment

* Comment cosmetics

* Remove bloom vocab/test
---
 models/ggml-vocab-baichuan.gguf  | Bin 0 -> 1340998 bytes
 models/ggml-vocab-gpt-neox.gguf  | Bin 0 -> 1771431 bytes
 models/ggml-vocab-refact.gguf    | Bin 0 -> 1720666 bytes
 models/ggml-vocab-starcoder.gguf | Bin 0 -> 1719281 bytes
 tests/CMakeLists.txt             |   4 ++++
 tests/test-tokenizer-1-bpe.cpp   |  15 ++++++++++++---
 6 files changed, 16 insertions(+), 3 deletions(-)
 create mode 100644 models/ggml-vocab-baichuan.gguf
 create mode 100644 models/ggml-vocab-gpt-neox.gguf
 create mode 100644 models/ggml-vocab-refact.gguf
 create mode 100644 models/ggml-vocab-starcoder.gguf

diff --git a/models/ggml-vocab-baichuan.gguf b/models/ggml-vocab-baichuan.gguf
new file mode 100644
index 0000000000000000000000000000000000000000..7caaf8239b052b1fc2f2b74441fe377a73758bd1
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literal 0
HcmV?d00001

diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 1c73de0a3e92eb..6757ad1cca1a23 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -28,10 +28,14 @@ llama_build_executable(test-tokenizer-0-falcon.cpp)
 llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_build_executable(test-tokenizer-1-llama.cpp)
 llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_test_executable(test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
 llama_build_executable(test-tokenizer-1-bpe.cpp)
 llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
 llama_test_executable(test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+llama_test_executable(test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
+llama_test_executable(test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+llama_test_executable(test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
 llama_build_and_test_executable(test-grammar-parser.cpp)
 llama_build_and_test_executable(test-llama-grammar.cpp)
 llama_build_and_test_executable(test-grad0.cpp) # SLOW
diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp
index 85a59a14dcd536..386530f23f92cc 100644
--- a/tests/test-tokenizer-1-bpe.cpp
+++ b/tests/test-tokenizer-1-bpe.cpp
@@ -91,9 +91,19 @@ int main(int argc, char **argv) {
             }
         }
     }
-    // TODO: why doesn't this work for the full range of Unicodes?
+    // Restrict to assigned unicode planes
     // for (uint32_t cp = 0x10000; cp < 0x0010ffff; ++cp) {
-    for (uint32_t cp = 0x10000; cp < 0x00080000; ++cp) {
+    for (uint32_t cp = 0x10000; cp < 0x00040000; ++cp) {
+        std::string str = codepoint_to_utf8(cp);
+        std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
+        std::string check = llama_detokenize_bpe(ctx, tokens);
+        if (str != check) {
+            fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
+                __func__, cp, check.c_str(), check.length(), str.c_str(), str.length());
+            return 4;
+        }
+    }
+    for (uint32_t cp = 0x000e0000; cp < 0x0010ffff; ++cp) {
         std::string str = codepoint_to_utf8(cp);
         std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
         std::string check = llama_detokenize_bpe(ctx, tokens);
@@ -103,7 +113,6 @@ int main(int argc, char **argv) {
             return 4;
         }
     }
-
     llama_free_model(model);
     llama_free(ctx);
 

From 2b4ea35e56792064598e922e46d081e02bc96b94 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 24 Oct 2023 16:48:37 +0300
Subject: [PATCH 08/79] cuda : add batched cuBLAS GEMM for faster attention
 (#3749)

* cmake : add helper for faster CUDA builds

* batched : add NGL arg

* ggml : skip nops in compute_forward

* cuda : minor indentation

* cuda : batched cuBLAS GEMMs for src0 F16 and src1 F32 (attention ops)

* Apply suggestions from code review

These changes plus:

```c++
#define cublasGemmBatchedEx hipblasGemmBatchedEx
```

are needed to compile with ROCM. I haven't done performance testing, but it seems to work.

I couldn't figure out how to propose a change for lines outside what the pull changed, also this is the first time trying to create a multi-part review so please forgive me if I mess something up.

* cuda : add ROCm / hipBLAS cublasGemmBatchedEx define

* cuda : add cublasGemmStridedBatchedEx for non-broadcasted cases

* cuda : reduce mallocs in cublasGemmBatchedEx branch

* cuda : add TODO for calling cublas from kernel + using mem pool

---------

Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
---
 CMakeLists.txt               |   1 +
 examples/batched/batched.cpp |  11 +-
 ggml-cuda.cu                 | 190 +++++++++++++++++++++++++++++++++--
 ggml.c                       |   4 +
 4 files changed, 193 insertions(+), 13 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6af42a6c266c83..202f260491d393 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -331,6 +331,7 @@ if (LLAMA_CUBLAS)
             set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
         else()
             set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
+            #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work
         endif()
     endif()
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 75856a81fe9b1b..22a4265df77c09 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -11,7 +11,7 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN]\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]);
         return 1 ;
     }
 
@@ -21,6 +21,9 @@ int main(int argc, char ** argv) {
     // total length of the sequences including the prompt
     int n_len = 32;
 
+    // number of layers to offload to the GPU
+    int n_gpu_layers = 0;
+
     if (argc >= 2) {
         params.model = argv[1];
     }
@@ -37,6 +40,10 @@ int main(int argc, char ** argv) {
         n_len = std::atoi(argv[4]);
     }
 
+    if (argc >= 6) {
+        n_gpu_layers = std::atoi(argv[5]);
+    }
+
     if (params.prompt.empty()) {
         params.prompt = "Hello my name is";
     }
@@ -49,7 +56,7 @@ int main(int argc, char ** argv) {
 
     llama_model_params model_params = llama_model_default_params();
 
-    // model_params.n_gpu_layers = 99; // offload all layers to the GPU
+    model_params.n_gpu_layers = n_gpu_layers;
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 654d3632fc179a..db053e3b8a9d81 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -29,6 +29,8 @@
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
 #define cublasCreate hipblasCreate
 #define cublasGemmEx hipblasGemmEx
+#define cublasGemmBatchedEx hipblasGemmBatchedEx
+#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
 #define cublasHandle_t hipblasHandle_t
 #define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
 #define cublasSetStream hipblasSetStream
@@ -4326,13 +4328,13 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
 
     const half * x = (const half *) vx;
 
-    const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
-    const int channel = blockDim.z*blockIdx.z + threadIdx.z;
+    const int row_x     = blockDim.y*blockIdx.y + threadIdx.y;
+    const int channel   = blockDim.z*blockIdx.z + threadIdx.z;
     const int channel_x = channel / channel_x_divisor;
 
-    const int nrows_y = ncols_x;
+    const int nrows_y   = ncols_x;
     const int nrows_dst = nrows_x;
-    const int row_dst = row_x;
+    const int row_dst   = row_x;
 
     const int idst = channel*nrows_dst + row_dst;
 
@@ -4345,13 +4347,13 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
             break;
         }
 
-        const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
-        const float xi = __half2float(x[ix]);
-
         const int row_y = col_x;
 
+        const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
         const int iy = channel*nrows_y + row_y;
 
+        const float xi = __half2float(x[ix]);
+
         tmp += xi * y[iy];
     }
 
@@ -7013,7 +7015,8 @@ static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tens
 }
 
 static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
-    GGML_ASSERT(!ggml_is_contiguous(src0) && ggml_is_contiguous(src1));
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
     GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -7023,11 +7026,11 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
 
-    const int64_t ne12 = src1->ne[2];
-
     const int64_t nb01 = src0->nb[1];
     const int64_t nb02 = src0->nb[2];
 
+    const int64_t ne12 = src1->ne[2];
+
     CUDA_CHECK(ggml_cuda_set_device(g_main_device));
     cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
 
@@ -7046,6 +7049,159 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
     ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
 }
 
+static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00);
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02);
+    const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03);
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+
+    const int64_t nb11 = src1->nb[1];
+    const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
+    const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
+
+    const int64_t ne1 = ggml_nelements(src1);
+    const int64_t ne  = ggml_nelements(dst);
+
+    CUDA_CHECK(ggml_cuda_set_device(g_main_device));
+    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], main_stream));
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    void * src0_ddq = src0_extra->data_device[g_main_device];
+    half * src0_as_f16 = (half *) src0_ddq;
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    // convert src1 to fp16
+    const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
+    GGML_ASSERT(to_fp16_cuda != nullptr);
+
+    size_t src1_as = 0;
+    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
+    to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
+
+    size_t dst_as = 0;
+    half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
+
+    GGML_ASSERT(ne12 % ne02 == 0);
+    GGML_ASSERT(ne13 % ne03 == 0);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    const half alpha_f16 = 1.0f;
+    const half beta_f16  = 0.0f;
+
+#if 0
+    // use cublasGemmEx
+    {
+        for (int i13 = 0; i13 < ne13; ++i13) {
+            for (int i12 = 0; i12 < ne12; ++i12) {
+                int i03 = i13 / r3;
+                int i02 = i12 / r2;
+
+                CUBLAS_CHECK(
+                        cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+                            ne01, ne11, ne10,
+                            &alpha_f16, (const char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3]  , CUDA_R_16F, nb01/sizeof(half),
+                                        (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
+                            &beta_f16,  (      char *)     dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2, CUDA_R_16F, ne01,
+                            CUBLAS_COMPUTE_16F,
+                            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+            }
+        }
+    }
+#else
+    if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
+        // there is no broadcast and src0, src1 are contiguous across dims 2, 3
+        // use cublasGemmStridedBatchedEx
+        CUBLAS_CHECK(
+        cublasGemmStridedBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+                ne01, ne11, ne10,
+                &alpha_f16, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half),  src0->nb[2]/sizeof(half),  // strideA
+                            (const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
+                &beta_f16,  (      char *)     dst_f16, CUDA_R_16F, ne01,                dst->nb[2]/sizeof(float), // strideC
+                ne12*ne13,
+                CUBLAS_COMPUTE_16F,
+                CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+    } else {
+        // use cublasGemmBatchedEx
+        // TODO: https://github.com/ggerganov/llama.cpp/pull/3749#discussion_r1369997000
+        const int ne23 = ne12*ne13;
+
+        // TODO: avoid this alloc
+        void ** ptrs = (void **) malloc(3*ne23*sizeof(void *));
+
+        for (int i13 = 0; i13 < ne13; ++i13) {
+            for (int i12 = 0; i12 < ne12; ++i12) {
+                int i03 = i13 / r3;
+                int i02 = i12 / r2;
+
+                ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3];
+                ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2;
+                ptrs[2*ne23 + i12 + i13*ne12] = (char *)     dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2;
+            }
+        }
+
+        // allocate device memory for pointers
+        void ** ptrs_as = nullptr;
+        CUDA_CHECK(cudaMalloc(&ptrs_as, 3*ne23*sizeof(void *)));
+
+        // TODO: this does not work for some reason -- not sure why?
+        //size_t ptrs_s = 0;
+        //ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s);
+
+        // copy pointers to device
+        CUDA_CHECK(cudaMemcpy(ptrs_as, ptrs, 3*ne23*sizeof(void *), cudaMemcpyHostToDevice));
+
+        free(ptrs);
+
+        CUBLAS_CHECK(
+        cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+                ne01, ne11, ne10,
+                &alpha_f16, (const void **) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
+                            (const void **) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
+                &beta_f16,  (      void **) (ptrs_as + 2*ne23), CUDA_R_16F, ne01,
+                ne23,
+                CUBLAS_COMPUTE_16F,
+                CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+
+        // free device memory for pointers
+        CUDA_CHECK(cudaFree(ptrs_as));
+        //ggml_cuda_pool_free(ptrs_as, ptrs_s);
+    }
+#endif
+
+    const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
+    to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
+
+    ggml_cuda_pool_free(src1_as_f16, src1_as);
+    ggml_cuda_pool_free(dst_f16, dst_as);
+}
+
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     bool all_on_device = (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
         src1->backend == GGML_BACKEND_GPU && dst->backend == GGML_BACKEND_GPU;
@@ -7058,10 +7214,22 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
         }
     }
 
+    // debug helpers
+    //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
+    //printf("      %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
+    //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
+    //printf("      %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
+    //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
+    //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
+
     if (all_on_device && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+        // KQ
         ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (all_on_device && !ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->ne[1] == 1) {
+    } else if (all_on_device && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        // KQV
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
+    } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
+        ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {
         ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
     } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
diff --git a/ggml.c b/ggml.c
index 49f3b7aba31f56..17f0ce48775923 100644
--- a/ggml.c
+++ b/ggml.c
@@ -16602,6 +16602,10 @@ static void ggml_compute_forward_cross_entropy_loss_back(
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
     GGML_ASSERT(params);
 
+    if (tensor->op == GGML_OP_NONE) {
+        return;
+    }
+
 #ifdef GGML_USE_CUBLAS
     bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
     if (skip_cpu) {

From abd21fc99f1d35e2081e4c01dc09c71a86bf3c5a Mon Sep 17 00:00:00 2001
From: John Smith <67539080+kingsidelee@users.noreply.github.com>
Date: Wed, 25 Oct 2023 01:48:45 +0800
Subject: [PATCH 09/79] cmake : add missed dependencies (#3763)

---
 examples/main-cmake-pkg/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt
index 90813188460e08..cb00edbbbe3749 100644
--- a/examples/main-cmake-pkg/CMakeLists.txt
+++ b/examples/main-cmake-pkg/CMakeLists.txt
@@ -16,6 +16,8 @@ add_library(common OBJECT
     ${_common_path}/console.cpp
     ${_common_path}/grammar-parser.h
     ${_common_path}/grammar-parser.cpp
+    ${_common_path}/sampling.h
+    ${_common_path}/sampling.cpp
     )
 
 # WARNING: because build-info.h is auto-generated, it will only

From b2f7e04bd312eaf97eee0523aa09d950d585626b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 24 Oct 2023 21:51:20 +0300
Subject: [PATCH 10/79] sync : ggml (conv ops + cuda MSVC fixes) (#3765)

ggml-ci
---
 ggml-cuda.cu |  10 +-
 ggml.c       | 442 +++++++++++++++++++++++++++++++++++++++++----------
 ggml.h       |  15 +-
 3 files changed, 371 insertions(+), 96 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index db053e3b8a9d81..d1e874b6c778af 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -5664,10 +5664,10 @@ void ggml_init_cublas() {
         GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
         int64_t total_vram = 0;
         fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
-        for (int64_t id = 0; id < g_device_count; ++id) {
+        for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
             CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-            fprintf(stderr, "  Device %ld: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
+            fprintf(stderr, "  Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
 
             g_tensor_split[id] = total_vram;
             total_vram += prop.totalGlobalMem;
@@ -5677,15 +5677,15 @@ void ggml_init_cublas() {
             g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
         }
-        for (int64_t id = 0; id < g_device_count; ++id) {
+        for (int id = 0; id < g_device_count; ++id) {
             g_tensor_split[id] /= total_vram;
         }
 
-        for (int64_t id = 0; id < g_device_count; ++id) {
+        for (int id = 0; id < g_device_count; ++id) {
             CUDA_CHECK(ggml_cuda_set_device(id));
 
             // create cuda streams
-            for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+            for (int is = 0; is < MAX_STREAMS; ++is) {
                 CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking));
             }
 
diff --git a/ggml.c b/ggml.c
index 17f0ce48775923..6f66bab051cea4 100644
--- a/ggml.c
+++ b/ggml.c
@@ -571,7 +571,6 @@ int64_t ggml_cycles_per_ms(void) {
 #define ggml_perf_cycles_per_ms() 0
 #endif
 
-
 //
 // cache line
 //
@@ -1828,7 +1827,6 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
     return type_traits[type];
 }
 
-
 //
 // simd mappings
 //
@@ -4057,16 +4055,17 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "ALIBI",
     "CLAMP",
     "CONV_1D",
+    "CONV_1D_STAGE_0",
+    "CONV_1D_STAGE_1",
     "CONV_TRANSPOSE_1D",
     "CONV_2D",
+    "CONV_2D_STAGE_0",
+    "CONV_2D_STAGE_1",
     "CONV_TRANSPOSE_2D",
     "POOL_1D",
     "POOL_2D",
     "UPSCALE",
 
-    "CONV_1D_STAGE_0",
-    "CONV_1D_STAGE_1",
-
     "FLASH_ATTN",
     "FLASH_FF",
     "FLASH_ATTN_BACK",
@@ -4092,7 +4091,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71");
+static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -4143,16 +4142,17 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "alibi(x)",
     "clamp(x)",
     "conv_1d(x)",
+    "conv_1d_stage_0(x)",
+    "conv_1d_stage_1(x)",
     "conv_transpose_1d(x)",
     "conv_2d(x)",
+    "conv_2d_stage_0(x)",
+    "conv_2d_stage_1(x)",
     "conv_transpose_2d(x)",
     "pool_1d(x)",
     "pool_2d(x)",
     "upscale(x)",
 
-    "conv_1d_stage_0(x)",
-    "conv_1d_stage_1(x)",
-
     "flash_attn(x)",
     "flash_ff(x)",
     "flash_attn_back(x)",
@@ -4178,7 +4178,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71");
+static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4209,8 +4209,10 @@ static void ggml_setup_op_has_task_pass(void) {
         p[GGML_OP_CONV_1D                ] = true;
         p[GGML_OP_CONV_1D_STAGE_0        ] = true;
         p[GGML_OP_CONV_1D_STAGE_1        ] = true;
-        p[GGML_OP_CONV_2D                ] = true;
         p[GGML_OP_CONV_TRANSPOSE_1D      ] = true;
+        p[GGML_OP_CONV_2D                ] = true;
+        p[GGML_OP_CONV_2D_STAGE_0        ] = true;
+        p[GGML_OP_CONV_2D_STAGE_1        ] = true;
         p[GGML_OP_CONV_TRANSPOSE_2D      ] = true;
         p[GGML_OP_FLASH_ATTN_BACK        ] = true;
         p[GGML_OP_CROSS_ENTROPY_LOSS     ] = true;
@@ -5954,7 +5956,6 @@ struct ggml_tensor * ggml_sqrt_inplace(
     return ggml_sqrt_impl(ctx, a, true);
 }
 
-
 // ggml_log
 
 static struct ggml_tensor * ggml_log_impl(
@@ -6008,7 +6009,6 @@ struct ggml_tensor * ggml_sum(
     return result;
 }
 
-
 // ggml_sum_rows
 
 struct ggml_tensor * ggml_sum_rows(
@@ -6640,7 +6640,6 @@ struct ggml_tensor * ggml_set_2d_inplace(
     return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
 }
 
-
 // ggml_cpy
 
 static struct ggml_tensor * ggml_cpy_impl(
@@ -6720,7 +6719,6 @@ struct ggml_tensor * ggml_cont_inplace(
     return ggml_cont_impl(ctx, a, true);
 }
 
-
 // make contiguous, with new shape
 GGML_API struct ggml_tensor * ggml_cont_1d(
         struct ggml_context * ctx,
@@ -7173,7 +7171,6 @@ struct ggml_tensor * ggml_diag(
     return result;
 }
 
-
 // ggml_diag_mask_inf
 
 static struct ggml_tensor * ggml_diag_mask_inf_impl(
@@ -7285,7 +7282,6 @@ struct ggml_tensor * ggml_soft_max_inplace(
     return ggml_soft_max_impl(ctx, a, true);
 }
 
-
 // ggml_soft_max_back
 
 static struct ggml_tensor * ggml_soft_max_back_impl(
@@ -7702,7 +7698,11 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
 
 // ggml_conv_2d
 
-struct ggml_tensor * ggml_conv_2d(
+// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
+// a: [OC，IC, KH, KW]
+// b: [N, IC, IH, IW]
+// result: [N, OH, OW, IC*KH*KW]
+static struct ggml_tensor * ggml_conv_2d_stage_0(
     struct ggml_context * ctx,
     struct ggml_tensor  * a,
     struct ggml_tensor  * b,
@@ -7721,17 +7721,21 @@ struct ggml_tensor * ggml_conv_2d(
         is_node = true;
     }
 
+    const int64_t OH = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1);
+    const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
+
     const int64_t ne[4] = {
-        ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
-        ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
-        a->ne[3], b->ne[3],
+        a->ne[2] * a->ne[1] * a->ne[0],
+        OW,
+        OH,
+        b->ne[3],
     };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne);
 
     int32_t params[] = { s0, s1, p0, p1, d0, d1 };
     ggml_set_op_params(result, params, sizeof(params));
 
-    result->op = GGML_OP_CONV_2D;
+    result->op = GGML_OP_CONV_2D_STAGE_0;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = b;
@@ -7740,8 +7744,61 @@ struct ggml_tensor * ggml_conv_2d(
 
 }
 
-// ggml_conv_2d_sk_p0
+// gemm: [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW]
+// a: [OC, IC, KH, KW]
+// b: [N, OH, OW, IC * KH * KW]
+// result: [N, OC, OH, OW]
+static struct ggml_tensor * ggml_conv_2d_stage_1(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    struct ggml_tensor  * b) {
+
+    bool is_node = false;
 
+    if (a->grad || b->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    const int64_t ne[4] = {
+        b->ne[1],
+        b->ne[2],
+        a->ne[3],
+        b->ne[3],
+    };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    result->op = GGML_OP_CONV_2D_STAGE_1;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+
+}
+
+// a: [OC，IC, KH, KW]
+// b: [N, IC, IH, IW]
+// result: [N, OC, OH, OW]
+struct ggml_tensor * ggml_conv_2d(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    struct ggml_tensor  * b,
+    int                  s0,
+    int                  s1,
+    int                  p0,
+    int                  p1,
+    int                  d0,
+    int                  d1) {
+
+    struct ggml_tensor * result = ggml_conv_2d_stage_0(ctx, a, b, s0, s1, p0, p1, d0, d1); // [N, OH, OW, IC * KH * KW]
+    result = ggml_conv_2d_stage_1(ctx, a, result);
+
+    return result;
+
+}
+
+// ggml_conv_2d_sk_p0
 struct ggml_tensor * ggml_conv_2d_sk_p0(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
@@ -8180,7 +8237,6 @@ static struct ggml_tensor * ggml_add_rel_pos_impl(
     return result;
 }
 
-
 struct ggml_tensor * ggml_add_rel_pos(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
@@ -8625,8 +8681,6 @@ struct ggml_tensor * ggml_map_custom3_inplace(
     return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
 }
 
-
-
 // ggml_cross_entropy_loss
 
 struct ggml_tensor * ggml_cross_entropy_loss(
@@ -9828,7 +9882,6 @@ static void ggml_compute_forward_add1(
     }
 }
 
-
 // ggml_compute_forward_acc
 
 static void ggml_compute_forward_acc_f32(
@@ -9968,7 +10021,6 @@ static void ggml_compute_forward_sub_f32(
             const int i2 = (ir - i3*ne2*ne1)/ne1;
             const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
 
-
 #ifdef GGML_USE_ACCELERATE
             vDSP_vsub(
                     (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
@@ -10149,7 +10201,6 @@ static void ggml_compute_forward_div_f32(
             const int i2 = (ir - i3*ne2*ne1)/ne1;
             const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
 
-
 #ifdef GGML_USE_ACCELERATE
             UNUSED(ggml_vec_div_f32);
 
@@ -10287,7 +10338,6 @@ static void ggml_compute_forward_sqrt(
     }
 }
 
-
 // ggml_compute_forward_log
 
 static void ggml_compute_forward_log_f32(
@@ -12120,7 +12170,6 @@ static void ggml_compute_forward_out_prod_f32(
         }
     }
 
-
     //int64_t t1 = ggml_perf_time_us();
     //static int64_t acc = 0;
     //acc += t1 - t0;
@@ -12316,7 +12365,6 @@ static void ggml_compute_forward_scale_f32(
 
     const size_t nb1 = dst->nb[1];
 
-
     for (int i1 = ir0; i1 < ir1; i1++) {
         if (dst->data != src0->data) {
             // src0 is same shape as dst => same indices
@@ -12714,7 +12762,6 @@ static void ggml_compute_forward_get_rows_back_f32(
     }
 }
 
-
 static void ggml_compute_forward_get_rows_back(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
@@ -13997,6 +14044,7 @@ static void ggml_compute_forward_conv_1d_f32(
     }
 }
 
+// TODO: reuse ggml_mul_mat or implement ggml_im2col and remove stage_0 and stage_1
 static void gemm_f16_out_f32(int64_t m, int64_t n, int64_t k,
                              ggml_fp16_t * A,
                              ggml_fp16_t * B,
@@ -14298,6 +14346,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
             }
         }
 
+        // need to zero dst since we are accumulating into it
+        memset(dst->data, 0, ggml_nbytes(dst));
+
         return;
     }
 
@@ -14370,7 +14421,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
                     const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01);
                     float * dst_data = wdata + i01*ne00*ne02;
                     for (int64_t i00 = 0; i00 < ne00; i00++) {
-                        dst_data[i01*ne00*ne02 + i00*ne02 + i02] = src[i00];
+                        dst_data[i00*ne02 + i02] = src[i00];
                     }
                 }
             }
@@ -14389,6 +14440,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
             }
         }
 
+        // need to zero dst since we are accumulating into it
+        memset(dst->data, 0, ggml_nbytes(dst));
+
         return;
     }
 
@@ -14450,28 +14504,190 @@ static void ggml_compute_forward_conv_transpose_1d(
 
 // ggml_compute_forward_conv_2d
 
-static void ggml_compute_forward_conv_2d_f16_f32(
+// src0: kernel [OC, IC, KH, KW]
+// src1: image [N, IC, IH, IW]
+// dst:  result [N, OH, OW, IC*KH*KW]
+static void ggml_compute_forward_conv_2d_stage_0_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int64_t N = ne13;
+    const int64_t IC = ne12;
+    const int64_t IH = ne11;
+    const int64_t IW = ne10;
+
+    // const int64_t OC = ne03;
+    // const int64_t IC = ne02;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
+
+    const int64_t OH = ne2;
+    const int64_t OW = ne1;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_INIT) {
+        memset(dst->data, 0, ggml_nbytes(dst));
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
+    {
+        ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data;
+
+        for (int64_t in = 0; in < N; in++) {
+            for (int64_t ioh = 0; ioh < OH; ioh++) {
+                for (int64_t iow = 0; iow < OW; iow++) {
+                    for (int64_t iic = ith; iic < IC; iic+=nth) {
+
+                        // micro kernel
+                        ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW]
+                        const float * const src_data = (float *)((char *) src1->data + in*nb13 + iic*nb12); // [IH, IW]
+
+                        for (int64_t ikh = 0; ikh < KH; ikh++) {
+                            for (int64_t ikw = 0; ikw < KW; ikw++) {
+                                const int64_t iiw = iow*s0 + ikw*d0 - p0;
+                                const int64_t iih = ioh*s1 + ikh*d1 - p1;
+
+                                if (!(iih < 0 || iih >= IH || iiw < 0 || iiw >= IW)) {
+                                    dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]);
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+// gemm: [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW]
+// src0: [OC, IC, KH, KW]
+// src1: [N, OH, OW, IC * KH * KW]
+// result: [N, OC, OH, OW]
+static void ggml_compute_forward_conv_2d_stage_1_f16(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
+    if (params->type == GGML_TASK_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
     GGML_TENSOR_BINARY_OP_LOCALS;
 
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb0  == sizeof(float));
+
+    const int N = ne13;
+    const int OH = ne12;
+    const int OW = ne11;
+
+    const int OC = ne03;
+    const int IC = ne02;
+    const int KH = ne01;
+    const int KW = ne00;
+
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int nk0 = ne00;
-    const int nk1 = ne01;
+    int64_t m = OC;
+    int64_t n = OH * OW;
+    int64_t k = IC * KH * KW;
+
+    // [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW]
+    for (int i = 0; i < N; i++) {
+        ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k]
+        ggml_fp16_t * B = (ggml_fp16_t *)src1->data + i * m * k; // [n, k]
+        float * C = (float *)dst->data + i * m * n; // [m, n]
+
+        gemm_f16_out_f32(m, n, k, A, B, C, ith, nth);
+    }
+}
+
+static void ggml_compute_forward_conv_2d_f16_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    // src1: image [N, IC, IH, IW]
+    // src0: kernel [OC, IC, KH, KW]
+    // dst:  result [N, OC, OH, OW]
+    // ne12: IC
+    // ne0: OW
+    // ne1: OH
+    // nk0: KW
+    // nk1: KH
+    // ne13: N
+
+    const int N = ne13;
+    const int IC = ne12;
+    const int IH = ne11;
+    const int IW = ne10;
+
+    const int OC = ne03;
+    // const int IC = ne02;
+    const int KH = ne01;
+    const int KW = ne00;
+
+    const int OH = ne1;
+    const int OW = ne0;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    // const int nk0 = ne00;
+    // const int nk1 = ne01;
 
     // size of the convolution row - the kernel size unrolled across all channels
-    const int ew0 = nk0*nk1*ne02;
+    // const int ew0 = nk0*nk1*ne02;
+    // ew0: IC*KH*KW
 
     const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
     const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
@@ -14487,24 +14703,27 @@ static void ggml_compute_forward_conv_2d_f16_f32(
         memset(params->wdata, 0, params->wsize);
 
         // prepare source data (src1)
+        // im2col: [N, IC, IH, IW] => [N*OH*OW, IC*KH*KW]
+
         {
             ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
 
-            for (int i13 = 0; i13 < ne13; i13++) {
-                for (int i12 = 0; i12 < ne12; i12++) {
-                    const float * const src = (float *)((char *) src1->data + i13*nb13 + i12*nb12);
-                    ggml_fp16_t * dst_data = wdata + i13*(ne1*ne0*ew0);
-
-                    for (int i1 = 0; i1 < ne1; i1++) {
-                        for (int i0 = 0; i0 < ne0; i0++) {
-                            for (int ik1 = 0; ik1 < nk1; ik1++) {
-                                for (int ik0 = 0; ik0 < nk0; ik0++) {
-                                    const int idx0 = i0*s0 + ik0*d0 - p0;
-                                    const int idx1 = i1*s1 + ik1*d1 - p1;
-
-                                    if (!(idx1 < 0 || idx1 >= ne11 || idx0 < 0 || idx0 >= ne10)) {
-                                        dst_data[(i1*ne0 + i0)*ew0 + i12*(nk0*nk1) + ik1*nk0 + ik0] =
-                                            GGML_FP32_TO_FP16(src[idx1*ne10 + idx0]);
+            for (int in = 0; in < N; in++) {
+                for (int iic = 0; iic < IC; iic++) {
+                    for (int ioh = 0; ioh < OH; ioh++) {
+                        for (int iow = 0; iow < OW; iow++) {
+
+                            // micro kernel
+                            ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW]
+                            const float * const src_data = (float *)((char *) src1->data + in*nb13 + iic*nb12); // [IH, IW]
+
+                            for (int ikh = 0; ikh < KH; ikh++) {
+                                for (int ikw = 0; ikw < KW; ikw++) {
+                                    const int iiw = iow*s0 + ikw*d0 - p0;
+                                    const int iih = ioh*s1 + ikh*d1 - p1;
+
+                                    if (!(iih < 0 || iih >= IH || iiw < 0 || iiw >= IW)) {
+                                        dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]);
                                     }
                                 }
                             }
@@ -14521,30 +14740,22 @@ static void ggml_compute_forward_conv_2d_f16_f32(
         return;
     }
 
-    // total patches in dst
-    const int np = ne2;
-
-    // patches per thread
-    const int dp = (np + nth - 1)/nth;
+    ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+    // wdata: [N*OH*OW, IC*KH*KW]
+    // dst: result [N, OC, OH, OW]
+    // src0: kernel [OC, IC, KH, KW]
 
-    // patch range for this thread
-    const int ip0 = dp*ith;
-    const int ip1 = MIN(ip0 + dp, np);
+    int64_t m = OC;
+    int64_t n = OH * OW;
+    int64_t k = IC * KH * KW;
 
-    ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+    // [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW]
+    for (int i = 0; i < N; i++) {
+        ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k]
+        ggml_fp16_t * B = (ggml_fp16_t *)wdata + i * m * k; // [n, k]
+        float * C = (float *)dst->data + i * m * n; // [m * k]
 
-    for (int i3 = 0; i3 < ne3; i3++) {
-        for (int i2 = ip0; i2 < ip1; i2++) {
-            float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2);
-
-            for (int i1 = 0; i1 < ne1; ++i1) {
-                for (int i0 = 0; i0 < ne0; ++i0) {
-                    ggml_vec_dot_f16(ew0, dst_data + i1*ne0 + i0,
-                            (ggml_fp16_t *) ((char *) src0->data + i2*nb03),
-                            (ggml_fp16_t *)                wdata + i3*nb3 + (i1*ne0 + i0)*ew0);
-                }
-            }
-        }
+        gemm_f16_out_f32(m, n, k, A, B, C, ith, nth);
     }
 }
 
@@ -14570,6 +14781,48 @@ static void ggml_compute_forward_conv_2d(
     }
 }
 
+static void ggml_compute_forward_conv_2d_stage_0(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_2d_stage_0_f32(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(false);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_conv_2d_stage_1(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+              struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_2d_stage_1_f16(params, src0, src1, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                GGML_ASSERT(false);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
 // ggml_compute_forward_conv_transpose_2d
 
 static void ggml_compute_forward_conv_transpose_2d(
@@ -14628,6 +14881,8 @@ static void ggml_compute_forward_conv_transpose_2d(
             }
         }
 
+        memset(dst->data, 0, ggml_nbytes(dst));
+
         return;
     }
 
@@ -16126,7 +16381,6 @@ static void ggml_compute_forward_add_rel_pos_f32(
     const int ip0 = dp*ith;
     const int ip1 = MIN(ip0 + dp, np);
 
-
     for (int64_t i13 = ip0; i13 < ip1; ++i13) {
         for (int64_t i12 = 0; i12 < ne12; ++i12) {
             for (int64_t i11 = 0; i11 < ne11; ++i11) {
@@ -16193,7 +16447,6 @@ static void ggml_compute_forward_map_unary_f32(
     }
 }
 
-
 static void ggml_compute_forward_map_unary(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
@@ -16241,7 +16494,6 @@ static void ggml_compute_forward_map_binary_f32(
     }
 }
 
-
 static void ggml_compute_forward_map_binary(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
@@ -16293,7 +16545,6 @@ static void ggml_compute_forward_map_custom2_f32(
     fun(dst, a, b);
 }
 
-
 // ggml_compute_forward_map_custom3
 
 static void ggml_compute_forward_map_custom3_f32(
@@ -16568,7 +16819,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
         ggml_vec_sub_f32(nc, ds0, ds0, s1);
         ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr);
 
-
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
             assert(!isnan(ds0[i]));
@@ -16596,7 +16846,6 @@ static void ggml_compute_forward_cross_entropy_loss_back(
     }
 }
 
-
 /////////////////////////////////
 
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
@@ -16808,6 +17057,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor);
             } break;
+        case GGML_OP_CONV_2D_STAGE_0:
+            {
+                ggml_compute_forward_conv_2d_stage_0(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
+        case GGML_OP_CONV_2D_STAGE_1:
+            {
+                ggml_compute_forward_conv_2d_stage_1(params, tensor->src[0], tensor->src[1], tensor);
+            } break;
         case GGML_OP_CONV_TRANSPOSE_2D:
             {
                 ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
@@ -17737,11 +17994,19 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_CONV_2D:
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
-        case GGML_OP_CONV_TRANSPOSE_1D:
+        case GGML_OP_CONV_2D_STAGE_0:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_2D_STAGE_1:
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
@@ -18670,6 +18935,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     const int64_t ne0 = node->ne[0];
                     const int64_t ne1 = node->ne[1];
                     const int64_t ne2 = node->ne[2];
+                    const int64_t ne3 = node->ne[3];
                     const int64_t nk = ne00*ne01;
                     const int64_t ew0 = nk * ne02;
 
@@ -18680,7 +18946,8 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
 
                     if (node->src[0]->type == GGML_TYPE_F16 &&
                         node->src[1]->type == GGML_TYPE_F32) {
-                        cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0);
+                        // im2col: [N*OH*OW, IC*KH*KW]
+                        cur = sizeof(ggml_fp16_t)*(ne3*ne0*ne1*ew0);
                     } else if (node->src[0]->type == GGML_TYPE_F32 &&
                                node->src[1]->type == GGML_TYPE_F32) {
                         cur = sizeof(float)*      (ne10*ne11*ne12);
@@ -18690,6 +18957,14 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
 
                     work_size = MAX(work_size, cur);
                 } break;
+            case GGML_OP_CONV_2D_STAGE_0:
+                {
+                    n_tasks = n_threads;
+                } break;
+            case GGML_OP_CONV_2D_STAGE_1:
+                {
+                    n_tasks = n_threads;
+                } break;
             case GGML_OP_CONV_TRANSPOSE_2D:
                 {
                     n_tasks = n_threads;
@@ -19878,7 +20153,6 @@ static enum ggml_opt_result ggml_opt_adam(
 
         opt->loss_after = fx;
 
-
         // check convergence
         if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) {
             GGML_PRINT_DEBUG("converged\n");
diff --git a/ggml.h b/ggml.h
index 16aaf169ee8fda..08bff5511c2254 100644
--- a/ggml.h
+++ b/ggml.h
@@ -401,15 +401,16 @@ extern "C" {
         GGML_OP_ALIBI,
         GGML_OP_CLAMP,
         GGML_OP_CONV_1D,
-        GGML_OP_CONV_2D,
+        GGML_OP_CONV_1D_STAGE_0,  // internal
+        GGML_OP_CONV_1D_STAGE_1,  // internal
         GGML_OP_CONV_TRANSPOSE_1D,
+        GGML_OP_CONV_2D,
+        GGML_OP_CONV_2D_STAGE_0, // internal
+        GGML_OP_CONV_2D_STAGE_1, // internal
         GGML_OP_CONV_TRANSPOSE_2D,
         GGML_OP_POOL_1D,
         GGML_OP_POOL_2D,
 
-        GGML_OP_CONV_1D_STAGE_0,  // internal
-        GGML_OP_CONV_1D_STAGE_1,  // internal
-
         GGML_OP_UPSCALE, // nearest interpolate
 
         GGML_OP_FLASH_ATTN,
@@ -1020,9 +1021,9 @@ extern "C" {
             struct ggml_tensor  * b,
             float                 eps);
 
-    // A: n columns, m rows
-    // B: n columns, p rows  (i.e. we transpose it internally)
-    // result is m columns, p rows
+    // A: k columns, n rows => [ne03, ne02, n, k]
+    // B: k columns, m rows  (i.e. we transpose it internally) => [ne03 * x, ne02 * y, m, k]
+    // result is n columns, m rows => [ne03 * x, ne02 * y, m, n]
     GGML_API struct ggml_tensor * ggml_mul_mat(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,

From 1717521cdb976a2219888b0e5cba36e210eee9df Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 24 Oct 2023 23:08:20 +0300
Subject: [PATCH 11/79] server : do not block system prompt update (#3767)

* server : do not block system prompt update

* server : update state machine logic to process system prompts

* server : minor
---
 examples/server/server.cpp | 57 +++++++++++++-------------------------
 1 file changed, 20 insertions(+), 37 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 693f9b7735e493..f52a928c874468 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -454,7 +454,7 @@ struct llama_client_slot
     }
 
     void release() {
-        if (state == PROCESSING)
+        if (state == IDLE || state == PROCESSING)
         {
             t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
             command = RELEASE;
@@ -754,6 +754,7 @@ struct llama_server_context
         }
 
         slot->params.antiprompt.clear();
+
         const auto &stop = data.find("stop");
         if (stop != data.end() && stop->is_array())
         {
@@ -867,7 +868,7 @@ struct llama_server_context
 
         kv_cache_clear();
 
-        for (int32_t i = 0; i < batch.n_tokens; ++i)
+        for (int i = 0; i < (int) system_tokens.size(); ++i)
         {
             llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
         }
@@ -894,16 +895,8 @@ struct llama_server_context
         {
             slot.release();
         }
-        wait_all_are_idle();
-        all_slots_are_idle = true;
 
-        // wait until system prompt load
         system_need_update = true;
-        while (system_need_update)
-        {
-            std::this_thread::sleep_for(std::chrono::milliseconds(5));
-        }
-        // system prompt loaded, continue
     }
 
     void process_system_prompt_data(const json &sys_props) {
@@ -915,26 +908,6 @@ struct llama_server_context
         {
             notify_system_prompt_changed();
         }
-        else
-        {
-            system_need_update = true;
-        }
-    }
-
-    void wait_all_are_idle() {
-        bool wait = true;
-        while (wait)
-        {
-            wait = false;
-            for (auto &slot : slots)
-            {
-                if (!slot.available())
-                {
-                    wait = true;
-                    break;
-                }
-            }
-        }
     }
 
     static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
@@ -965,7 +938,6 @@ struct llama_server_context
                     slot.has_next_token = false;
                 }
                 stop_pos = pos;
-
             }
         }
 
@@ -1444,7 +1416,7 @@ struct llama_server_context
         process_tasks();
 
         // update the system prompt wait until all slots are idle state
-        if (system_need_update)
+        if (system_need_update && all_slots_are_idle)
         {
             LOG_TEE("updating system prompt\n");
             update_system_prompt();
@@ -1498,7 +1470,7 @@ struct llama_server_context
         for (auto & slot : slots)
         {
             // release the slot
-            if (slot.state == PROCESSING && slot.command == RELEASE)
+            if (slot.command == RELEASE)
             {
                 slot.state = IDLE;
                 slot.command = NONE;
@@ -1509,7 +1481,7 @@ struct llama_server_context
                 continue;
             }
 
-            if (slot.state == IDLE || slot.command == RELEASE)
+            if (slot.state == IDLE)
             {
                 continue;
             }
@@ -1530,6 +1502,17 @@ struct llama_server_context
         {
             for (auto & slot : slots)
             {
+                const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty());
+
+                // empty prompt passed -> release the slot and send empty response
+                if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt)
+                {
+                    slot.release();
+                    slot.print_timings();
+                    send_final_response(slot);
+                    continue;
+                }
+
                 // need process the prompt
                 if (slot.state == IDLE && slot.command == LOAD_PROMPT)
                 {
@@ -1749,8 +1732,8 @@ struct llama_server_context
                 if (!process_token(result, slot))
                 {
                     slot.release();
-                    send_final_response(slot);
                     slot.print_timings();
+                    send_final_response(slot);
                 }
 
                 slot.i_batch = -1;
@@ -2285,7 +2268,7 @@ int main(int argc, char **argv)
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
                     task_result result = llama.next_result(task_id);
-                    if(!result.error && result.stop) {
+                    if (!result.error && result.stop) {
                         res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json");
                     }
                     else
@@ -2312,7 +2295,7 @@ int main(int argc, char **argv)
                                 {
                                     return false;
                                 }
-                                if(result.stop) {
+                                if (result.stop) {
                                     break;
                                 }
                             } else {

From ad939626577cd25b462e8026cc543efb71528472 Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Tue, 24 Oct 2023 16:10:43 -0400
Subject: [PATCH 12/79] server : add parameter -tb N, --threads-batch N (#3584)
 (#3768)

Co-authored-by: Michael Coppola <m18coppola@gmail.com>
Co-authored-by: Michael Coppola <info@michaeljcoppola.com>
---
 examples/server/server.cpp | 28 +++++++++++++++++++---------
 1 file changed, 19 insertions(+), 9 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index f52a928c874468..b4c4d0a20ad0a7 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1749,15 +1749,16 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("usage: %s [options]\n", argv0);
     printf("\n");
     printf("options:\n");
-    printf("  -h, --help            show this help message and exit\n");
-    printf("  -v, --verbose         verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
-    printf("  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
-    printf("  -c N, --ctx-size N    size of the prompt context (default: %d)\n", params.n_ctx);
-    printf("  --rope-freq-base N    RoPE base frequency (default: loaded from model)\n");
-    printf("  --rope-freq-scale N   RoPE frequency scaling factor (default: loaded from model)\n");
-    printf("  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
-    printf("  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
-    printf("                        not recommended: doubles context memory required and no measurable increase in quality\n");
+    printf("  -h, --help                show this help message and exit\n");
+    printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
+    printf("  -t N,  --threads N        number of threads to use during computation (default: %d)\n", params.n_threads);
+    printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
+    printf("  -c N,  --ctx-size N       size of the prompt context (default: %d)\n", params.n_ctx);
+    printf("  --rope-freq-base N        RoPE base frequency (default: loaded from model)\n");
+    printf("  --rope-freq-scale N       RoPE frequency scaling factor (default: loaded from model)\n");
+    printf("  -b N,  --batch-size N     batch size for prompt processing (default: %d)\n", params.n_batch);
+    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
+    printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
     if (llama_mlock_supported())
     {
         printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
@@ -1907,6 +1908,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.n_threads = std::stoi(argv[i]);
         }
+        else if (arg == "--threads-batch" || arg == "-tb")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+            params.n_threads_batch = std::stoi(argv[i]);
+        }
         else if (arg == "-b" || arg == "--batch-size")
         {
             if (++i >= argc)

From cc448774866e6479c750bd7c135cd8f92cedee67 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 25 Oct 2023 10:09:16 +0300
Subject: [PATCH 13/79] log : disable pid in log filenames

---
 common/log.h | 35 ++++++++++++++++++-----------------
 1 file changed, 18 insertions(+), 17 deletions(-)

diff --git a/common/log.h b/common/log.h
index 70e7e4ca215e30..d2c864ceab7be6 100644
--- a/common/log.h
+++ b/common/log.h
@@ -97,22 +97,23 @@
     #define LOG_TEE_TARGET stderr
 #endif
 
+// NOTE: currently disabled as it produces too many log files
 // Utility to obtain "pid" like unique process id and use it when creating log files.
-inline std::string log_get_pid()
-{
-    static std::string pid;
-    if (pid.empty())
-    {
-        // std::this_thread::get_id() is the most portable way of obtaining a "process id"
-        //  it's not the same as "pid" but is unique enough to solve multiple instances
-        //  trying to write to the same log.
-        std::stringstream ss;
-        ss << std::this_thread::get_id();
-        pid = ss.str();
-    }
-
-    return pid;
-}
+//inline std::string log_get_pid()
+//{
+//    static std::string pid;
+//    if (pid.empty())
+//    {
+//        // std::this_thread::get_id() is the most portable way of obtaining a "process id"
+//        //  it's not the same as "pid" but is unique enough to solve multiple instances
+//        //  trying to write to the same log.
+//        std::stringstream ss;
+//        ss << std::this_thread::get_id();
+//        pid = ss.str();
+//    }
+//
+//    return pid;
+//}
 
 // Utility function for generating log file names with unique id based on thread id.
 //  invocation with log_filename_generator( "llama", "log" ) creates a string "llama.<number>.log"
@@ -126,8 +127,8 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base
     std::stringstream buf;
 
     buf << log_file_basename;
-    buf << ".";
-    buf << log_get_pid();
+    //buf << ".";
+    //buf << log_get_pid();
     buf << ".";
     buf << log_file_extension;
 

From 6961c4bd0b5176e10ab03b35394f1e9eab761792 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 25 Oct 2023 10:26:27 +0300
Subject: [PATCH 14/79] batched-bench : print params at start

---
 examples/batched-bench/batched-bench.cpp |  4 ++++
 ggml-cuda.cu                             | 12 ++++++------
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index c552eaa738becf..43f9c971d18465 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -154,6 +154,10 @@ int main(int argc, char ** argv) {
         }
     }
 
+    LOG_TEE("\n");
+    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq);
+    LOG_TEE("\n");
+
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
     LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
 
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index d1e874b6c778af..ba0cd5a7d3f1eb 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -6254,16 +6254,15 @@ inline void ggml_cuda_op_mul_mat_cublas(
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, const cudaStream_t & stream) {
 
-    GGML_ASSERT(src0_dd_i != nullptr);
+    GGML_ASSERT(src0_dd_i  != nullptr);
     GGML_ASSERT(src1_ddf_i != nullptr);
-    GGML_ASSERT(dst_dd_i != nullptr);
-
+    GGML_ASSERT(dst_dd_i   != nullptr);
 
     const int64_t ne00 = src0->ne[0];
-
     const int64_t ne10 = src1->ne[0];
 
     const int64_t ne0 = dst->ne[0];
+
     const int64_t row_diff = row_high - row_low;
 
     int id;
@@ -7223,12 +7222,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
     if (all_on_device && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
-        // KQ
+        // KQ single-batch
         ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
     } else if (all_on_device && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
-        // KQV
+        // KQV single-batch
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
     } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
+        // KQ + KQV multi-batch
         ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {
         ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);

From 34b2a5e1ee4fe6295fb4420eb91131d743694c65 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 26 Oct 2023 22:53:37 +0300
Subject: [PATCH 15/79] server : do not release slot on image input (#3798)

---
 examples/server/server.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index b4c4d0a20ad0a7..5b7e4139de551e 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1502,7 +1502,7 @@ struct llama_server_context
         {
             for (auto & slot : slots)
             {
-                const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty());
+                const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();
 
                 // empty prompt passed -> release the slot and send empty response
                 if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt)

From 2f9ec7e271220a78fe27c9e6ccbcc0dda31cda0f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 27 Oct 2023 17:01:23 +0300
Subject: [PATCH 16/79] cuda : improve text-generation and batched decoding
 performance (#3776)

* cuda : prints wip

* cuda : new cublas gemm branch for multi-batch quantized src0

* cuda : add F32 sgemm branch

* cuda : fine-tune >= VOLTA params + use MMQ only for small batches

* cuda : remove duplicated cuBLAS GEMM code

* cuda : add CUDA_USE_TENSOR_CORES and GGML_CUDA_FORCE_MMQ macros

* build : add compile option to force use of MMQ kernels
---
 CMakeLists.txt |   7 +++
 Makefile       |   3 ++
 ggml-cuda.cu   | 130 +++++++++++++++++++++++++++++++++++++++++++------
 llama.cpp      |   2 -
 llama.h        |   2 +-
 5 files changed, 125 insertions(+), 19 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 202f260491d393..d9fc86237b15cc 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -82,6 +82,7 @@ set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 option(LLAMA_CUBLAS                          "llama: use CUDA"                                  OFF)
 #option(LLAMA_CUDA_CUBLAS                     "llama: use cuBLAS for prompt processing"          OFF)
 option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
+option(LLAMA_CUDA_FORCE_MMQ                  "llama: use mmq kernels instead of cuBLAS"         OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
 set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some calculations"   OFF)
@@ -305,6 +306,9 @@ if (LLAMA_CUBLAS)
         if (LLAMA_CUDA_FORCE_DMMV)
             add_compile_definitions(GGML_CUDA_FORCE_DMMV)
         endif()
+        if (LLAMA_CUDA_FORCE_MMQ)
+            add_compile_definitions(GGML_CUDA_FORCE_MMQ)
+        endif()
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
         add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
         if (DEFINED LLAMA_CUDA_DMMV_Y)
@@ -405,6 +409,9 @@ if (LLAMA_HIPBLAS)
         if (LLAMA_CUDA_FORCE_DMMV)
             target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV)
         endif()
+        if (LLAMA_CUDA_FORCE_MMQ)
+            target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_MMQ)
+        endif()
         target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
         target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
         target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
diff --git a/Makefile b/Makefile
index 80179631f95a5b..68069f9ff331e9 100644
--- a/Makefile
+++ b/Makefile
@@ -397,6 +397,9 @@ endif # CUDA_DOCKER_ARCH
 ifdef LLAMA_CUDA_FORCE_DMMV
 	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
 endif # LLAMA_CUDA_FORCE_DMMV
+ifdef LLAMA_CUDA_FORCE_MMQ
+	NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
+endif # LLAMA_CUDA_FORCE_MMQ
 ifdef LLAMA_CUDA_DMMV_X
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
 else
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index ba0cd5a7d3f1eb..1ba951f688d82c 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -87,6 +87,24 @@
 #define CC_OFFSET_AMD 1000000
 #define CC_RDNA2      (CC_OFFSET_AMD + 1030)
 
+// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
+// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
+// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
+// -  7B quantum model: +100-200 MB
+// - 13B quantum model: +200-400 MB
+//
+//#define GGML_CUDA_FORCE_MMQ
+
+// TODO: improve this to be correct for more hardware
+//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
+//       probably other such cases, and not sure what happens on AMD hardware
+#if !defined(GGML_CUDA_FORCE_MMQ)
+#define CUDA_USE_TENSOR_CORES
+#endif
+
+// max batch size to use MMQ kernels when tensor cores are available
+#define MMQ_MAX_BATCH_SIZE 32
+
 #if defined(GGML_USE_HIPBLAS)
 #define __CUDA_ARCH__ 1300
 
@@ -470,7 +488,6 @@ static int g_device_count = -1;
 static int g_main_device = 0;
 static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
 static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
-static bool g_mul_mat_q = true;
 
 static void * g_scratch_buffer = nullptr;
 static size_t g_scratch_size = 0; // disabled by default
@@ -3554,9 +3571,15 @@ static __device__ __forceinline__ void mul_mat_q(
 #define  MMQ_X_Q4_0_RDNA1  64
 #define  MMQ_Y_Q4_0_RDNA1  64
 #define NWARPS_Q4_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_0_AMPERE 4
+#define  MMQ_Y_Q4_0_AMPERE 32
+#define NWARPS_Q4_0_AMPERE 4
+#else
 #define  MMQ_X_Q4_0_AMPERE 64
 #define  MMQ_Y_Q4_0_AMPERE 128
 #define NWARPS_Q4_0_AMPERE 4
+#endif
 #define  MMQ_X_Q4_0_PASCAL 64
 #define  MMQ_Y_Q4_0_PASCAL 64
 #define NWARPS_Q4_0_PASCAL 8
@@ -3615,9 +3638,15 @@ template <bool need_check> static __global__ void
 #define  MMQ_X_Q4_1_RDNA1  64
 #define  MMQ_Y_Q4_1_RDNA1  64
 #define NWARPS_Q4_1_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_1_AMPERE 4
+#define  MMQ_Y_Q4_1_AMPERE 32
+#define NWARPS_Q4_1_AMPERE 4
+#else
 #define  MMQ_X_Q4_1_AMPERE 64
 #define  MMQ_Y_Q4_1_AMPERE 128
 #define NWARPS_Q4_1_AMPERE 4
+#endif
 #define  MMQ_X_Q4_1_PASCAL 64
 #define  MMQ_Y_Q4_1_PASCAL 64
 #define NWARPS_Q4_1_PASCAL 8
@@ -3678,9 +3707,15 @@ template <bool need_check> static __global__ void
 #define  MMQ_X_Q5_0_RDNA1  64
 #define  MMQ_Y_Q5_0_RDNA1  64
 #define NWARPS_Q5_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_0_AMPERE 4
+#define  MMQ_Y_Q5_0_AMPERE 32
+#define NWARPS_Q5_0_AMPERE 4
+#else
 #define  MMQ_X_Q5_0_AMPERE 128
 #define  MMQ_Y_Q5_0_AMPERE 64
 #define NWARPS_Q5_0_AMPERE 4
+#endif
 #define  MMQ_X_Q5_0_PASCAL 64
 #define  MMQ_Y_Q5_0_PASCAL 64
 #define NWARPS_Q5_0_PASCAL 8
@@ -3739,9 +3774,15 @@ template <bool need_check> static __global__ void
 #define  MMQ_X_Q5_1_RDNA1  64
 #define  MMQ_Y_Q5_1_RDNA1  64
 #define NWARPS_Q5_1_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_1_AMPERE 4
+#define  MMQ_Y_Q5_1_AMPERE 32
+#define NWARPS_Q5_1_AMPERE 4
+#else
 #define  MMQ_X_Q5_1_AMPERE 128
 #define  MMQ_Y_Q5_1_AMPERE 64
 #define NWARPS_Q5_1_AMPERE 4
+#endif
 #define  MMQ_X_Q5_1_PASCAL 64
 #define  MMQ_Y_Q5_1_PASCAL 64
 #define NWARPS_Q5_1_PASCAL 8
@@ -3800,9 +3841,15 @@ mul_mat_q5_1(
 #define  MMQ_X_Q8_0_RDNA1  64
 #define  MMQ_Y_Q8_0_RDNA1  64
 #define NWARPS_Q8_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q8_0_AMPERE 4
+#define  MMQ_Y_Q8_0_AMPERE 32
+#define NWARPS_Q8_0_AMPERE 4
+#else
 #define  MMQ_X_Q8_0_AMPERE 128
 #define  MMQ_Y_Q8_0_AMPERE 64
 #define NWARPS_Q8_0_AMPERE 4
+#endif
 #define  MMQ_X_Q8_0_PASCAL 64
 #define  MMQ_Y_Q8_0_PASCAL 64
 #define NWARPS_Q8_0_PASCAL 8
@@ -3861,9 +3908,15 @@ template <bool need_check> static __global__ void
 #define  MMQ_X_Q2_K_RDNA1  128
 #define  MMQ_Y_Q2_K_RDNA1  32
 #define NWARPS_Q2_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q2_K_AMPERE 4
+#define  MMQ_Y_Q2_K_AMPERE 32
+#define NWARPS_Q2_K_AMPERE 4
+#else
 #define  MMQ_X_Q2_K_AMPERE 64
 #define  MMQ_Y_Q2_K_AMPERE 128
 #define NWARPS_Q2_K_AMPERE 4
+#endif
 #define  MMQ_X_Q2_K_PASCAL 64
 #define  MMQ_Y_Q2_K_PASCAL 64
 #define NWARPS_Q2_K_PASCAL 8
@@ -3922,9 +3975,15 @@ mul_mat_q2_K(
 #define  MMQ_X_Q3_K_RDNA1  32
 #define  MMQ_Y_Q3_K_RDNA1  128
 #define NWARPS_Q3_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q3_K_AMPERE 4
+#define  MMQ_Y_Q3_K_AMPERE 32
+#define NWARPS_Q3_K_AMPERE 4
+#else
 #define  MMQ_X_Q3_K_AMPERE 128
 #define  MMQ_Y_Q3_K_AMPERE 128
 #define NWARPS_Q3_K_AMPERE 4
+#endif
 #define  MMQ_X_Q3_K_PASCAL 64
 #define  MMQ_Y_Q3_K_PASCAL 64
 #define NWARPS_Q3_K_PASCAL 8
@@ -3985,9 +4044,15 @@ template <bool need_check> static __global__ void
 #define  MMQ_X_Q4_K_RDNA1  32
 #define  MMQ_Y_Q4_K_RDNA1  64
 #define NWARPS_Q4_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_K_AMPERE 4
+#define  MMQ_Y_Q4_K_AMPERE 32
+#define NWARPS_Q4_K_AMPERE 4
+#else
 #define  MMQ_X_Q4_K_AMPERE 64
 #define  MMQ_Y_Q4_K_AMPERE 128
 #define NWARPS_Q4_K_AMPERE 4
+#endif
 #define  MMQ_X_Q4_K_PASCAL 64
 #define  MMQ_Y_Q4_K_PASCAL 64
 #define NWARPS_Q4_K_PASCAL 8
@@ -4048,9 +4113,15 @@ template <bool need_check> static __global__ void
 #define  MMQ_X_Q5_K_RDNA1  32
 #define  MMQ_Y_Q5_K_RDNA1  64
 #define NWARPS_Q5_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_K_AMPERE 4
+#define  MMQ_Y_Q5_K_AMPERE 32
+#define NWARPS_Q5_K_AMPERE 4
+#else
 #define  MMQ_X_Q5_K_AMPERE 64
 #define  MMQ_Y_Q5_K_AMPERE 128
 #define NWARPS_Q5_K_AMPERE 4
+#endif
 #define  MMQ_X_Q5_K_PASCAL 64
 #define  MMQ_Y_Q5_K_PASCAL 64
 #define NWARPS_Q5_K_PASCAL 8
@@ -4109,9 +4180,15 @@ mul_mat_q5_K(
 #define  MMQ_X_Q6_K_RDNA1  32
 #define  MMQ_Y_Q6_K_RDNA1  64
 #define NWARPS_Q6_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q6_K_AMPERE 4
+#define  MMQ_Y_Q6_K_AMPERE 32
+#define NWARPS_Q6_K_AMPERE 4
+#else
 #define  MMQ_X_Q6_K_AMPERE 64
 #define  MMQ_Y_Q6_K_AMPERE 64
 #define NWARPS_Q6_K_AMPERE 4
+#endif
 #define  MMQ_X_Q6_K_PASCAL 64
 #define  MMQ_Y_Q6_K_PASCAL 64
 #define NWARPS_Q6_K_PASCAL 8
@@ -5663,6 +5740,16 @@ void ggml_init_cublas() {
         CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
         GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
         int64_t total_vram = 0;
+#if defined(GGML_CUDA_FORCE_MMQ)
+        fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   yes\n", __func__);
+#else
+        fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   no\n", __func__);
+#endif
+#if defined(CUDA_USE_TENSOR_CORES)
+        fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
+#else
+        fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
+#endif
         fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
         for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
@@ -6347,7 +6434,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
             cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                     row_diff, src1_ncols, ne10,
                     &alpha, src0_ddf_i, ne00,
-                            src1_ddf_i,  ne10,
+                            src1_ddf_i, ne10,
                     &beta,  dst_dd_i,   ldc));
 
         if (src0_as != 0) {
@@ -7048,9 +7135,10 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
     ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
 }
 
-static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
+
     GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
@@ -7202,17 +7290,24 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
 }
 
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    bool all_on_device = (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
-        src1->backend == GGML_BACKEND_GPU && dst->backend == GGML_BACKEND_GPU;
+    const bool all_on_device =
+        (src0->backend == GGML_BACKEND_GPU) &&
+        (src1->backend == GGML_BACKEND_GPU) &&
+        ( dst->backend == GGML_BACKEND_GPU);
 
     int64_t min_compute_capability = INT_MAX;
     for (int64_t id = 0; id < g_device_count; ++id) {
-        if (min_compute_capability > g_compute_capabilities[id]
-                && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
+        if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
             min_compute_capability = g_compute_capabilities[id];
         }
     }
 
+#ifdef CUDA_USE_TENSOR_CORES
+    const bool use_tensor_cores = true;
+#else
+    const bool use_tensor_cores = false;
+#endif
+
     // debug helpers
     //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
     //printf("      %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
@@ -7221,20 +7316,19 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (all_on_device && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+    if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
         ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (all_on_device && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+    } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
+    } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
         // KQ + KQV multi-batch
         ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {
         ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
     } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
         if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
-
 #ifdef GGML_CUDA_FORCE_DMMV
             const bool use_mul_mat_vec_q = false;
 #else
@@ -7247,7 +7341,15 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
             }
         } else {
-            if (g_mul_mat_q && ggml_is_quantized(src0->type) && min_compute_capability >= MIN_CC_DP4A) {
+            bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
+
+            // when tensor cores are available, use them for large batch size
+            // ref: https://github.com/ggerganov/llama.cpp/pull/3776
+            if (use_tensor_cores && min_compute_capability >= CC_VOLTA && src1->ne[1] > MMQ_MAX_BATCH_SIZE) {
+                use_mul_mat_q = false;
+            }
+
+            if (use_mul_mat_q) {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
             } else {
                 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
@@ -7601,10 +7703,6 @@ void ggml_cuda_set_main_device(const int main_device) {
     }
 }
 
-void ggml_cuda_set_mul_mat_q(const bool mul_mat_q) {
-    g_mul_mat_q = mul_mat_q;
-}
-
 void ggml_cuda_set_scratch_size(const size_t scratch_size) {
     // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously
     // it still won't always work as expected, but it's better than nothing
diff --git a/llama.cpp b/llama.cpp
index 61f30c3982f184..cc8669b0e9e23d 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -5959,8 +5959,6 @@ static int llama_decode_internal(
         }
     }
 
-    ggml_cuda_set_mul_mat_q(cparams.mul_mat_q);
-
     // HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed
     if (!lctx.embedding.empty()) {
         embeddings->backend = GGML_BACKEND_CPU;
diff --git a/llama.h b/llama.h
index 2f2fee0e2ff9f3..beac9a0cedd76c 100644
--- a/llama.h
+++ b/llama.h
@@ -178,7 +178,7 @@ extern "C" {
         float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
-        bool mul_mat_q;  // if true, use experimental mul_mat_q kernels
+        bool mul_mat_q;  // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
         bool f16_kv;     // use fp16 for KV cache, fp32 otherwise
         bool logits_all; // the llama_eval() call computes all logits, not just the last one
         bool embedding;  // embedding mode only

From c8d6a1f34ab6f1b6bd468d256e535a61f98f114c Mon Sep 17 00:00:00 2001
From: Thibault Terrasson <thibault.terrasson@gmail.com>
Date: Fri, 27 Oct 2023 16:37:41 +0200
Subject: [PATCH 17/79] simple : fix batch handling (#3803)

---
 examples/simple/simple.cpp | 18 ++++--------------
 1 file changed, 4 insertions(+), 14 deletions(-)

diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index f376c050994d19..374aef6f16189c 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -95,13 +95,8 @@ int main(int argc, char ** argv) {
     llama_batch batch = llama_batch_init(512, 0, 1);
 
     // evaluate the initial prompt
-    batch.n_tokens = tokens_list.size();
-
-    for (int32_t i = 0; i < batch.n_tokens; i++) {
-        batch.token[i]  = tokens_list[i];
-        batch.pos[i]    = i;
-        batch.seq_id[i] = 0;
-        batch.logits[i] = false;
+    for (size_t i = 0; i < tokens_list.size(); i++) {
+        llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
     }
 
     // llama_decode will output logits only for the last token of the prompt
@@ -148,15 +143,10 @@ int main(int argc, char ** argv) {
             fflush(stdout);
 
             // prepare the next batch
-            batch.n_tokens = 0;
+            llama_batch_clear(batch);
 
             // push this new token for next evaluation
-            batch.token [batch.n_tokens] = new_token_id;
-            batch.pos   [batch.n_tokens] = n_cur;
-            batch.seq_id[batch.n_tokens] = 0;
-            batch.logits[batch.n_tokens] = true;
-
-            batch.n_tokens += 1;
+            llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
 
             n_decode += 1;
         }

From 6d459cbfbe5a011dfca94f9550527a504b6f9aa1 Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Fri, 27 Oct 2023 17:33:53 -0400
Subject: [PATCH 18/79] llama : correctly report GGUFv3 format (#3818)

---
 llama.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/llama.cpp b/llama.cpp
index cc8669b0e9e23d..408533d8a62f0b 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1578,12 +1578,14 @@ static void llama_kv_cache_seq_shift(
 enum llama_fver {
     GGUF_FILE_VERSION_V1 = 1,
     GGUF_FILE_VERSION_V2 = 2,
+    GGUF_FILE_VERSION_V3 = 3,
 };
 
 static const char * llama_file_version_name(llama_fver version) {
     switch (version) {
         case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
-        case GGUF_FILE_VERSION_V2: return "GGUF V2 (latest)";
+        case GGUF_FILE_VERSION_V2: return "GGUF V2";
+        case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
     }
 
     return "unknown";

From 41aee4df821854f37d90a45281f03b6db8d27de2 Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Fri, 27 Oct 2023 15:40:07 -0600
Subject: [PATCH 19/79] speculative : ensure draft and target model vocab
 matches (#3812)

* speculative: Ensure draft and target model vocab matches

* Tolerate small differences when checking dft vs tgt vocab
---
 examples/speculative/speculative.cpp | 33 +++++++++++++++++++++++++++-
 1 file changed, 32 insertions(+), 1 deletion(-)

diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 92ad27e8e423cc..f921b78455a72c 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -8,6 +8,9 @@
 #include <string>
 #include <vector>
 
+#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  100
+#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
+
 struct seq_draft {
     bool active   = false;
     bool drafting = false;
@@ -64,6 +67,33 @@ int main(int argc, char ** argv) {
     params.n_gpu_layers = params.n_gpu_layers_draft;
     std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
 
+    {
+        const int n_vocab_tgt = llama_n_vocab(model_tgt);
+        const int n_vocab_dft = llama_n_vocab(model_dft);
+        const int vocab_diff  = n_vocab_tgt > n_vocab_dft
+            ? n_vocab_tgt - n_vocab_dft
+            : n_vocab_dft - n_vocab_tgt;
+
+        if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
+            fprintf(stderr, "%s: error: draft model vocab must closely match target model to use speculation but ", __func__);
+            fprintf(stderr, "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
+                    n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
+            return 1;
+        }
+
+        for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
+            const char * token_text_tgt = llama_token_get_text(model_tgt, i);
+            const char * token_text_dft = llama_token_get_text(model_dft, i);
+            if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
+                fprintf(stderr, "%s: error: draft model vocab must match target model to use speculation but ", __func__);
+                fprintf(stderr, "token %d content differs - target '%s', draft '%s'\n", i,
+                        llama_token_to_piece(ctx_tgt, i).c_str(),
+                        llama_token_to_piece(ctx_dft, i).c_str());
+                return 1;
+            }
+        }
+    }
+
     // tokenize the prompt
     std::vector<llama_token> inp;
     inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
@@ -227,6 +257,7 @@ int main(int argc, char ** argv) {
             llama_batch_add  (batch_dft, id, n_past_dft, { 0 }, true);
 
             llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
+            // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
             llama_decode         (ctx_dft, batch_dft);
 
             ++n_past_dft;
@@ -370,7 +401,7 @@ int main(int argc, char ** argv) {
                 llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1);
             }
 
-            //LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt));
+            // LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str());
             llama_decode(ctx_tgt, batch_tgt);
             ++n_past_tgt;
         }

From fdee152e4eebb78c191df0b074857111d7f2aba7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 28 Oct 2023 12:06:08 +0300
Subject: [PATCH 20/79] starcoder : add GPU offloading (#3827)

* starcoder : do not GPU split 1D bias tensors

* starcoder : offload layers to GPU

ggml-ci
---
 llama.cpp | 106 +++++++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 85 insertions(+), 21 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 408533d8a62f0b..6caa58960cf3c3 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2695,8 +2695,8 @@ static void llm_load_tensors(
                 } break;
             case LLM_ARCH_STARCODER:
                 {
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
-                    model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
+                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab},             GGML_BACKEND_CPU);
+                    model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"),   {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
 
                     // output
                     {
@@ -2747,19 +2747,19 @@ static void llm_load_tensors(
                         layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
 
                         layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend_split);
+                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);
 
                         layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend_split);
+                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend);
 
                         layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                         layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
 
                         layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
+                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
 
                         layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
+                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
@@ -4616,6 +4616,8 @@ static struct ggml_cgraph * llm_build_starcoder(
 
     const float norm_eps = hparams.f_norm_eps;
 
+    const int n_gpu_layers = model.n_gpu_layers;
+
     const int32_t n_tokens = batch.n_tokens;
     const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
     const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
@@ -4660,6 +4662,27 @@ static struct ggml_cgraph * llm_build_starcoder(
         }
     }
 
+    const int i_gpu_start = n_layer - n_gpu_layers;
+    (void) i_gpu_start;
+
+    // offload functions set the tensor output backend to GPU
+    // tensors are GPU-accelerated if any input or the output has been offloaded
+    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
+    offload_func_t offload_func_kq = llama_nop;
+    offload_func_t offload_func_v  = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+    if (n_gpu_layers > n_layer) {
+        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 1) {
+        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
+    }
+    if (n_gpu_layers > n_layer + 2) {
+        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
+    }
+#endif // GGML_USE_CUBLAS
+
     {
         // Compute position embeddings.
         struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
@@ -4685,6 +4708,7 @@ static struct ggml_cgraph * llm_build_starcoder(
     // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
     ggml_set_name(KQ_mask, "KQ_mask");
+    offload_func_kq(KQ_mask);
     ggml_allocr_alloc(lctx.alloc, KQ_mask);
     if (!ggml_allocr_is_measure(lctx.alloc)) {
         float * data = (float *) KQ_mask->data;
@@ -4708,44 +4732,67 @@ static struct ggml_cgraph * llm_build_starcoder(
     ggml_set_name(inpL, "inpL");
 
     for (int il = 0; il < n_layer; ++il) {
+        offload_func_t offload_func = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+        if (il >= i_gpu_start) {
+            offload_func = ggml_cuda_assign_buffers_no_alloc;
+        }
+#endif // GGML_USE_CUBLAS
+
         {
             // Norm
             cur = ggml_norm(ctx0, inpL, norm_eps);
+            offload_func(cur);
+
             cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
+            offload_func(cur);
         }
 
         {
             // Self Attention
-            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            offload_func_kq(cur);
 
-            struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
-            struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd);
-            struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            offload_func_kq(cur);
 
-            struct ggml_tensor * Qcur = tmpq;
+            struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            struct ggml_tensor * tmpv = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+            ggml_set_name(tmpq, "tmpq");
+            ggml_set_name(tmpk, "tmpk");
+            ggml_set_name(tmpv, "tmpv");
+
+            offload_func_kq(tmpq);
+            offload_func_kq(tmpk);
+            offload_func_v (tmpv);
+
+            struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
             struct ggml_tensor * Kcur = tmpk;
 
             {
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
+                struct ggml_tensor * Vcur = ggml_transpose(ctx0, tmpv);
+                offload_func_v(Vcur);
                 ggml_set_name(Vcur, "Vcur");
 
                 struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+                offload_func_kq(k);
                 ggml_set_name(k, "k");
 
                 struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
                         (   n_ctx)*ggml_element_size(kv_self.v),
                         (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+                offload_func_v(v);
+                ggml_set_name(v, "v");
 
                 ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
                 ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
             }
 
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        ggml_cpy(ctx0,
-                            Qcur,
-                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)),
-                        0, 2, 1, 3);
+            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            offload_func_kq(Q);
             ggml_set_name(Q, "Q");
 
             struct ggml_tensor * K =
@@ -4754,23 +4801,28 @@ static struct ggml_cgraph * llm_build_starcoder(
                         ggml_element_size(kv_self.k)*n_embd_gqa,
                         ggml_element_size(kv_self.k)*n_embd_head,
                         ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+            offload_func_kq(K);
             ggml_set_name(K, "K");
 
             // K * Q
             struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            offload_func_kq(KQ);
             ggml_set_name(KQ, "KQ");
 
             // KQ_scaled = KQ / sqrt(n_embd_head)
             // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
             struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
+            offload_func_kq(KQ_scaled);
             ggml_set_name(KQ_scaled, "KQ_scaled");
 
             // KQ_masked = mask_past(KQ_scaled)
             struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
+            offload_func_kq(KQ_masked);
             ggml_set_name(KQ_masked, "KQ_masked");
 
             // KQ = soft_max(KQ_masked)
             struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
+            offload_func_v(KQ_soft_max);
             ggml_set_name(KQ_soft_max, "KQ_soft_max");
 
             // split cached V into n_head heads
@@ -4783,22 +4835,25 @@ static struct ggml_cgraph * llm_build_starcoder(
             ggml_set_name(V, "V");
 
             struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
+            offload_func_v(KQV);
             ggml_set_name(KQV, "KQV");
 
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
             struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+            offload_func_v(KQV_merged);
             ggml_set_name(KQV_merged, "KQV_merged");
 
-            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
             cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
+            offload_func_v(cur);
             ggml_set_name(cur, "KQV_merged_contiguous");
         }
 
         // Projection
         cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
+        offload_func(cur);
 
         // Add the input
         cur = ggml_add(ctx0, cur, inpL);
+        offload_func(cur);
 
         struct ggml_tensor * inpFF = cur;
 
@@ -4807,27 +4862,36 @@ static struct ggml_cgraph * llm_build_starcoder(
             // Norm
             {
                 cur = ggml_norm(ctx0, inpFF, norm_eps);
+                offload_func_nr(cur);
+
                 cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
+                offload_func_nr(cur);
             }
 
             cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
+            offload_func(cur);
 
             // GELU activation
             cur = ggml_gelu(ctx0, cur);
+            offload_func(cur);
 
             // Projection
             cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
+            offload_func(cur);
         }
 
         inpL = ggml_add(ctx0, cur, inpFF);
+
     }
 
     // Output Norm
     {
         cur = ggml_norm(ctx0, inpL, norm_eps);
+        offload_func_nr(cur);
+
         cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
+        ggml_set_name(cur, "result_norm");
     }
-    ggml_set_name(cur, "result_norm");
 
     cur = ggml_mul_mat(ctx0, model.output, cur);
     ggml_set_name(cur, "result_output");

From 177461104b454163473dced2a5038f4e016cdb7e Mon Sep 17 00:00:00 2001
From: Henk Poley <HenkPoley@gmail.com>
Date: Sat, 28 Oct 2023 12:16:33 +0200
Subject: [PATCH 21/79] common : print that one line of the syntax help *also*
 to standard output (#3823)

---
 common/common.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/common.cpp b/common/common.cpp
index 44bb76618f5f8b..c0d4924e2d4a53 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -743,7 +743,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
 #endif // GGML_USE_CUBLAS
 #endif
     printf("  --verbose-prompt      print prompt before generation\n");
-    fprintf(stderr, "  --simple-io           use basic IO for better compatibility in subprocesses and limited consoles\n");
+    printf("  --simple-io           use basic IO for better compatibility in subprocesses and limited consoles\n");
     printf("  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
     printf("  --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
     printf("  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");

From ee1a0ec9cb367ba41d138134795cbbbe93d2bf1c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 28 Oct 2023 14:23:11 +0300
Subject: [PATCH 22/79] llama : add option for greedy sampling with probs
 (#3813)

* llama : add option for greedy sampling with probs

* llama : add comment about llama_sample_token_greedy() missing probs

* sampling : temp == 0.0 -> no probs, temp < 0.0 -> probs
---
 common/common.cpp                    | 1 +
 common/sampling.cpp                  | 8 ++++++--
 examples/speculative/speculative.cpp | 2 +-
 llama.h                              | 1 +
 4 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index c0d4924e2d4a53..f81f4d354bc017 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -224,6 +224,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             sparams.temp = std::stof(argv[i]);
+            sparams.temp = std::max(sparams.temp, 0.0f);
         } else if (arg == "--tfs") {
             if (++i >= argc) {
                 invalid_param = true;
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 5258d4e8263693..c4996c9857d8ac 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -167,8 +167,12 @@ llama_token llama_sampling_sample(
         llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
     }
 
-    if (temp <= 0) {
-        // greedy sampling
+    if (temp < 0.0) {
+        // greedy sampling, with probs
+        llama_sample_softmax(ctx_main, &cur_p);
+        id = cur_p.data[0].id;
+    } else if (temp == 0.0) {
+        // greedy sampling, no probs
         id = llama_sample_token_greedy(ctx_main, &cur_p);
     } else {
         if (mirostat == 1) {
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index f921b78455a72c..323c74652c9a69 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -148,7 +148,7 @@ int main(int argc, char ** argv) {
     std::vector<seq_draft> drafts(n_seq_dft);
 
     params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
-    params.sparams.temp = std::max(0.01f, params.sparams.temp);
+    params.sparams.temp = -1.0f;    // force greedy sampling with probs for the draft model
 
     for (int s = 0; s < n_seq_dft; ++s) {
         drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
diff --git a/llama.h b/llama.h
index beac9a0cedd76c..d901dcd9116d3d 100644
--- a/llama.h
+++ b/llama.h
@@ -658,6 +658,7 @@ extern "C" {
                            float * mu);
 
     /// @details Selects the token with the highest probability.
+    ///          Does not compute the token probabilities. Use llama_sample_softmax() instead.
     LLAMA_API llama_token llama_sample_token_greedy(
             struct llama_context * ctx,
           llama_token_data_array * candidates);

From bd6d9e205982b34e0ba2c3b22bbf31a1ef1a1bb5 Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Sat, 28 Oct 2023 05:54:24 -0600
Subject: [PATCH 23/79] llama : allow quantizing k-quants to fall back when
 tensor size incompatible (#3747)

* Allow quantizing k-quants to fall back when tensor size incompatible

* quantizing: Add warning when tensors were incompatible with k-quants

Clean up k-quants state passing a bit
---
 llama.cpp | 108 ++++++++++++++++++++++++++++++++----------------------
 1 file changed, 65 insertions(+), 43 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 6caa58960cf3c3..3d431ee7bf5260 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -8049,6 +8049,24 @@ struct no_init {
     no_init() { /* do nothing */ }
 };
 
+struct quantize_state_internal {
+    const llama_model                 & model;
+    const llama_model_quantize_params * params;
+#ifdef GGML_USE_K_QUANTS
+    int n_attention_wv    = 0;
+    int n_feed_forward_w2 = 0;
+    int i_attention_wv    = 0;
+    int i_feed_forward_w2 = 0;
+
+    int n_k_quantized     = 0;
+    int n_fallback        = 0;
+#endif
+    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
+        : model(model)
+        , params(params)
+        {}
+};
+
 static void llama_convert_tensor_internal(
     struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
     const size_t nelements, const int nthread
@@ -8109,12 +8127,13 @@ static void llama_convert_tensor_internal(
 
 #ifdef GGML_USE_K_QUANTS
 static ggml_type get_k_quant_type(
-    ggml_type new_type, const ggml_tensor * tensor, const llama_model & model, llama_ftype ftype, int * i_attention_wv,
-    int n_attention_wv, int * i_feed_forward_w2, int n_feed_forward_w2
+    quantize_state_internal & qs,
+    ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype
 ) {
     const std::string name = ggml_get_name(tensor);
     // TODO: avoid hardcoded tensor names - use the TN_* constants
-    const auto tn = LLM_TN(model.arch);
+    const llm_arch arch = qs.model.arch;
+    const auto       tn = LLM_TN(arch);
 
     auto use_more_bits = [](int i_layer, int num_layers) -> bool {
         return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
@@ -8122,7 +8141,7 @@ static ggml_type get_k_quant_type(
 
     if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
         int nx = tensor->ne[0];
-        if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+        if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
             new_type = GGML_TYPE_Q8_0;
         }
         else if (new_type != GGML_TYPE_Q8_0) {
@@ -8131,46 +8150,46 @@ static ggml_type get_k_quant_type(
     } else if (name.find("attn_v.weight") != std::string::npos) {
         if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-            new_type = *i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+            new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
-                use_more_bits(*i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && *i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
+                use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
         else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
-                (*i_attention_wv < n_attention_wv/8 || *i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
-        if (model.type == MODEL_70B) {
+                (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
+        if (qs.model.type == MODEL_70B) {
             // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
             // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
             // nearly negligible increase in model size by quantizing this tensor with more bits:
             if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
         }
-        ++*i_attention_wv;
+        ++qs.i_attention_wv;
     } else if (name.find("ffn_down.weight") != std::string::npos) {
         if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-            new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
-                     : model.arch != LLM_ARCH_FALCON || use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K
+            new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
+                     : arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K
                      : GGML_TYPE_Q3_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
-            new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
+            new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
-            if (model.arch == LLM_ARCH_FALCON) {
-                new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
-                           use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+            if (arch == LLM_ARCH_FALCON) {
+                new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
+                           use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
             } else {
-                if (use_more_bits(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+                if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
             }
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && *i_feed_forward_w2 < 4) {
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < 4) {
             new_type = GGML_TYPE_Q5_K;
         }
-        ++*i_feed_forward_w2;
+        ++qs.i_feed_forward_w2;
     } else if (name.find("attn_output.weight") != std::string::npos) {
-        if (model.arch != LLM_ARCH_FALCON) {
+        if (arch != LLM_ARCH_FALCON) {
             if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
             else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
             else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
@@ -8197,20 +8216,23 @@ static ggml_type get_k_quant_type(
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];
         if (nx % QK_K != 0) {
-            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K);
+            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
             convert_incompatible_tensor = true;
+        } else {
+            ++qs.n_k_quantized;
         }
     }
     if (convert_incompatible_tensor) {
-        if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
-            new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing.
-            LLAMA_LOG_WARN("F16 will be used for this tensor instead.\n");
-        } else if (name == tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
-            new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing.
-            LLAMA_LOG_WARN("Q4_0 will be used for this tensor instead.\n");
-        } else {
-            throw std::runtime_error("Unsupported tensor size encountered\n");
+        switch (new_type) {
+            case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break;
+            case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break;
+            case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
+            case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
+            case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
+            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
         }
+        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
+        ++qs.n_fallback;
     }
 
     return new_type;
@@ -8268,6 +8290,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     llm_load_arch(ml, model);
     llm_load_hparams(ml, model);
 
+    struct quantize_state_internal qs(model, params);
+
     if (params->only_copy) {
         ftype = model.ftype;
     }
@@ -8281,9 +8305,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     gguf_set_val_u32(ctx_out, "general.file_type", ftype);
 
 #ifdef GGML_USE_K_QUANTS
-    int n_attention_wv    = 0;
-    int n_feed_forward_w2 = 0;
-
     for (int i = 0; i < ml.n_tensors; ++i) {
         struct ggml_tensor * meta = ml.get_tensor_meta(i);
 
@@ -8291,19 +8312,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
         // TODO: avoid hardcoded tensor names - use the TN_* constants
         if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
-            ++n_attention_wv;
+            ++qs.n_attention_wv;
         }
         else if (name.find("ffn_down.weight") != std::string::npos) {
-            ++n_feed_forward_w2;
+            ++qs.n_feed_forward_w2;
         }
     }
-    if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) {
+    if (qs.n_attention_wv != qs.n_feed_forward_w2 || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) {
         LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
-                __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer);
+                __func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer);
     }
-
-    int i_attention_wv = 0;
-    int i_feed_forward_w2 = 0;
 #endif
 
     size_t total_size_org = 0;
@@ -8370,9 +8388,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         if (quantize) {
             new_type = quantized_type;
 #ifdef GGML_USE_K_QUANTS
-            new_type = get_k_quant_type(
-                new_type, tensor, model, ftype, &i_attention_wv, n_attention_wv, &i_feed_forward_w2, n_feed_forward_w2
-            );
+            new_type = get_k_quant_type(qs, new_type, tensor, ftype);
 #endif
             // If we've decided to quantize to the same type the tensor is already
             // in then there's nothing to do.
@@ -8498,6 +8514,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             LLAMA_LOG_INFO("\n");
         }
     }
+#ifdef GGML_USE_K_QUANTS
+    if (qs.n_fallback > 0) {
+        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
+                __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
+    }
+#endif
 }
 
 static int llama_apply_lora_from_file_internal(

From 8a2f2fea2914aaa3f4b2f82800c7de15f15bdb09 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 28 Oct 2023 15:25:15 +0300
Subject: [PATCH 24/79] convert : ignore tokens if their IDs are within [0,
 vocab_size) (#3831)

---
 convert.py | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/convert.py b/convert.py
index 0680f71ea73e89..bfbfab283f6ae7 100755
--- a/convert.py
+++ b/convert.py
@@ -366,16 +366,19 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> No
             added_tokens = {}
 
         vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
-        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
-        actual_ids   = sorted(added_tokens.values())
-        if expected_ids != actual_ids:
-            raise Exception(f"Expected added token IDs to be sequential and start at {vocab_size}; got {actual_ids}")
 
-        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
-        self.added_tokens_list = [text for (text, idx) in items]
-        self.vocab_size_base: int = vocab_size
-        self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer = fname_tokenizer
+        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
+        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
+        actual_new_ids   = sorted(new_tokens.keys())
+
+        if expected_new_ids != actual_new_ids:
+            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
+
+        # Token pieces that were added to the base vocabulary.
+        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
+        self.vocab_size_base    = vocab_size
+        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer    = fname_tokenizer
         self.fname_added_tokens = fname_added_tokens
 
     def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:

From ba231e8a6dd8ad82acfe0e4d492ff7cef6b3f0a1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 28 Oct 2023 15:25:33 +0300
Subject: [PATCH 25/79] issues : change label from bug to bug-unconfirmed
 (#3748)

---
 .github/ISSUE_TEMPLATE/bug.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md
index d7879b232b54dd..c003fe7c136274 100644
--- a/.github/ISSUE_TEMPLATE/bug.md
+++ b/.github/ISSUE_TEMPLATE/bug.md
@@ -1,7 +1,7 @@
 ---
 name: Bug template
 about: Used to report bugs in llama.cpp
-labels: ["bug"]
+labels: ["bug-unconfirmed"]
 assignees: ''
 
 ---

From 82a6646e0221216c41edcdf99f5a44bb051391f5 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sat, 28 Oct 2023 15:43:01 +0300
Subject: [PATCH 26/79] metal : try cwd for ggml-metal.metal if bundle lookup
 fails (#3793)

* Try cwd for ggml-metal if bundle lookup fails

When building with `-DBUILD_SHARED_LIBS=ON -DLLAMA_METAL=ON -DLLAMA_BUILD_SERVER=ON`,
`server` would fail to load `ggml-metal.metal` because `[bundle pathForResource:...]`
returns `nil`.  In that case, fall back to `ggml-metal.metal` in the cwd instead of
passing `null` as a path.

Follows up on #1782

* Update ggml-metal.m

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml-metal.m | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml-metal.m b/ggml-metal.m
index c1901dca75269d..2380c431001408 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -210,6 +210,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){
             GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
 
             NSString * sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+            if (sourcePath == nil) {
+                GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
+                sourcePath = @"ggml-metal.metal";
+            }
             GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
             NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
             if (error) {

From ff3bad83e29e3009010cbc923bebd769055eaa7f Mon Sep 17 00:00:00 2001
From: Erik Scholz <Green-Sky@users.noreply.github.com>
Date: Sat, 28 Oct 2023 16:41:07 +0200
Subject: [PATCH 27/79] flake : update flake.lock for newer transformers
 version + provide extra dev shell (#3797)

* flake : update flake.lock for newer transformers version + provide extra dev shell with torch and transformers (for most convert-xxx.py scripts)
---
 flake.lock | 6 +++---
 flake.nix  | 7 +++++++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/flake.lock b/flake.lock
index a7777d05d5c0cd..070f0e1613fc30 100644
--- a/flake.lock
+++ b/flake.lock
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1692913444,
-        "narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=",
+        "lastModified": 1698134075,
+        "narHash": "sha256-foCD+nuKzfh49bIoiCBur4+Fx1nozo+4C/6k8BYk4sg=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "18324978d632ffc55ef1d928e81630c620f4f447",
+        "rev": "8efd5d1e283604f75a808a20e6cde0ef313d07d4",
         "type": "github"
       },
       "original": {
diff --git a/flake.nix b/flake.nix
index cfc4776a469381..fa34394b2f0593 100644
--- a/flake.nix
+++ b/flake.nix
@@ -51,6 +51,9 @@
         };
         llama-python =
           pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]);
+        # TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime
+        llama-python-extra =
+          pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece torchWithoutCuda transformers ]);
         postPatch = ''
           substituteInPlace ./ggml-metal.m \
             --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
@@ -126,5 +129,9 @@
           buildInputs = [ llama-python ];
           packages = nativeBuildInputs ++ osSpecific;
         };
+        devShells.extra = pkgs.mkShell {
+          buildInputs = [ llama-python-extra ];
+          packages = nativeBuildInputs ++ osSpecific;
+        };
       });
 }

From d69d777c02b9ac405a95f3cbfba219a990caefff Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 29 Oct 2023 18:32:28 +0200
Subject: [PATCH 28/79] ggml : quantization refactoring (#3833)

* ggml : factor all quantization code in ggml-quants

ggml-ci

* ggml-quants : fix Zig and Swift builds + quantize tool

ggml-ci

* quantize : --pure option for disabling k-quant mixtures

---------

Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
---
 CMakeLists.txt                 |   12 +-
 Makefile                       |   18 +-
 Package.swift                  |    3 +-
 build.zig                      |   21 +-
 examples/quantize/quantize.cpp |    9 +-
 k_quants.c => ggml-quants.c    | 4310 ++++++++++++++++++++++++--------
 k_quants.h => ggml-quants.h    |  103 +-
 ggml.c                         | 3641 +++++----------------------
 ggml.h                         |    7 +
 llama.cpp                      |   34 +-
 llama.h                        |    1 +
 11 files changed, 4073 insertions(+), 4086 deletions(-)
 rename k_quants.c => ggml-quants.c (71%)
 rename k_quants.h => ggml-quants.h (63%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d9fc86237b15cc..3659279e2d7d09 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -94,7 +94,6 @@ option(LLAMA_CLBLAST                         "llama: use CLBlast"
 option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
 option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
 option(LLAMA_MPI                             "llama: use MPI"                                   OFF)
-option(LLAMA_K_QUANTS                        "llama: use k-quants"                              ON)
 option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
 
 option(LLAMA_BUILD_TESTS                "llama: build tests"    ${LLAMA_STANDALONE})
@@ -278,13 +277,8 @@ if (LLAMA_BLAS)
     endif()
 endif()
 
-if (LLAMA_K_QUANTS)
-    set(GGML_HEADERS_EXTRA k_quants.h)
-    set(GGML_SOURCES_EXTRA k_quants.c)
-    add_compile_definitions(GGML_USE_K_QUANTS)
-    if (LLAMA_QKK_64)
-        add_compile_definitions(GGML_QKK_64)
-    endif()
+if (LLAMA_QKK_64)
+    add_compile_definitions(GGML_QKK_64)
 endif()
 
 if (LLAMA_CUBLAS)
@@ -673,6 +667,8 @@ add_library(ggml OBJECT
             ggml-alloc.h
             ggml-backend.c
             ggml-backend.h
+            ggml-quants.c
+            ggml-quants.h
             ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
             ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
             ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
diff --git a/Makefile b/Makefile
index 68069f9ff331e9..2cecc2216c87b6 100644
--- a/Makefile
+++ b/Makefile
@@ -342,13 +342,9 @@ else
 	MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d
 endif
 
-ifndef LLAMA_NO_K_QUANTS
-	MK_CPPFLAGS += -DGGML_USE_K_QUANTS
-	OBJS     += k_quants.o
 ifdef LLAMA_QKK_64
 	MK_CPPFLAGS += -DGGML_QKK_64
 endif
-endif
 
 ifndef LLAMA_NO_ACCELERATE
 	# Mac OS - include Accelerate framework.
@@ -365,7 +361,7 @@ ifdef LLAMA_MPI
 	MK_CPPFLAGS += -DGGML_USE_MPI
 	MK_CFLAGS   += -Wno-cast-qual
 	MK_CXXFLAGS += -Wno-cast-qual
-	OBJS     += ggml-mpi.o
+	OBJS        += ggml-mpi.o
 endif # LLAMA_MPI
 
 ifdef LLAMA_OPENBLAS
@@ -382,7 +378,7 @@ endif # LLAMA_BLIS
 ifdef LLAMA_CUBLAS
 	MK_CPPFLAGS  += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
 	MK_LDFLAGS   += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
-	OBJS      += ggml-cuda.o
+	OBJS         += ggml-cuda.o
 	NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
 ifdef LLAMA_CUDA_NVCC
 	NVCC = $(LLAMA_CUDA_NVCC)
@@ -497,11 +493,6 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
 	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_MPI
 
-ifndef LLAMA_NO_K_QUANTS
-k_quants.o: k_quants.c k_quants.h
-	$(CC) $(CFLAGS) -c $< -o $@
-endif # LLAMA_NO_K_QUANTS
-
 # combine build flags with cmdline overrides
 override CFLAGS        := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
 override CXXFLAGS      := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
@@ -542,7 +533,10 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
 ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-OBJS += ggml-alloc.o ggml-backend.o
+ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
+	$(CC) $(CFLAGS)    -c $< -o $@
+
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
 
 llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
diff --git a/Package.swift b/Package.swift
index 4ab055b19da2e5..5b3bd72cafe196 100644
--- a/Package.swift
+++ b/Package.swift
@@ -42,13 +42,12 @@ let package = Package(
                 "llama.cpp",
                 "ggml-alloc.c",
                 "ggml-backend.c",
-                "k_quants.c",
+                "ggml-quants.c",
             ] + additionalSources,
             resources: resources,
             publicHeadersPath: "spm-headers",
             cSettings: [
                 .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
-                .define("GGML_USE_K_QUANTS"),
                 .define("GGML_USE_ACCELERATE")
                 // NOTE: NEW_LAPACK will required iOS version 16.4+
                 // We should consider add this in the future when we drop support for iOS 14
diff --git a/build.zig b/build.zig
index dcfa3dd6b97643..9b58b74ca858b6 100644
--- a/build.zig
+++ b/build.zig
@@ -116,15 +116,10 @@ pub fn build(b: *std.build.Builder) !void {
     var make = try Maker.init(b);
     make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false;
 
-    if (b.option(bool, "k-quants", "Enable K-quants, (default: true)") orelse true) {
-        try make.addFlag("-DGGML_USE_K_QUANTS");
-        const k_quants = make.obj("k_quants", "k_quants.c");
-        try make.objs.append(k_quants);
-    }
-
     const ggml = make.obj("ggml", "ggml.c");
     const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
     const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
+    const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
     const llama = make.obj("llama", "llama.cpp");
     const common = make.obj("common", "common/common.cpp");
     const console = make.obj("console", "common/console.cpp");
@@ -133,14 +128,14 @@ pub fn build(b: *std.build.Builder) !void {
     const train = make.obj("train", "common/train.cpp");
     const clip = make.obj("clip", "examples/llava/clip.cpp");
 
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
-    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, console, grammar_parser });
+    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
+    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
+    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
+    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train });
+    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train });
 
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip });
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, grammar_parser, clip });
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index c7dd0d894634cb..be0b2fe1eb963f 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -18,7 +18,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
     { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
     { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
-#ifdef GGML_USE_K_QUANTS
     { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
     { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
     { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
@@ -31,7 +30,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", },
     { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
     { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", },
-#endif
     { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
     { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
     { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
@@ -70,13 +68,14 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 }
 
 // usage:
-//  ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
+//  ./quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
 //
 [[noreturn]]
 static void usage(const char * executable) {
-    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
+    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
     printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
     printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
+    printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
     printf("\nAllowed quantization types:\n");
     for (auto & it : QUANT_OPTIONS) {
         if (it.name != "COPY") {
@@ -103,6 +102,8 @@ int main(int argc, char ** argv) {
             params.quantize_output_tensor = false;
         } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
             params.allow_requantize = true;
+        } else if (strcmp(argv[arg_idx], "--pure") == 0) {
+            params.pure = true;
         } else {
             usage(argv[0]);
         }
diff --git a/k_quants.c b/ggml-quants.c
similarity index 71%
rename from k_quants.c
rename to ggml-quants.c
index 801941fbee075a..fd4ee1be64befa 100644
--- a/k_quants.c
+++ b/ggml-quants.c
@@ -1,9 +1,10 @@
-#include "k_quants.h"
+#include "ggml-quants.h"
 #include "ggml.h"
 
 #include <math.h>
 #include <string.h>
 #include <assert.h>
+#include <float.h>
 
 #ifdef __ARM_NEON
 
@@ -65,1251 +66,3478 @@ inline static int32_t vaddvq_s32(int32x4_t v) {
 
 #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
 
-//
-// 2-6 bit quantization in super-blocks
-//
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
+// multiply int8_t, add results pairwise twice
+static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
+    // Get absolute values of x vectors
+    const __m128i ax = _mm_sign_epi8(x, x);
+    // Sign the values of the y vectors
+    const __m128i sy = _mm_sign_epi8(y, x);
+    // Perform multiplication and create 16-bit values
+    const __m128i dot = _mm_maddubs_epi16(ax, sy);
+    const __m128i ones = _mm_set1_epi16(1);
+    return _mm_madd_epi16(ones, dot);
+}
 
-//
-// ===================== Helper functions
-//
-static inline int nearest_int(float fval) {
-    assert(fval <= 4194303.f);
-    float val = fval + 12582912.f;
-    int i; memcpy(&i, &val, sizeof(int));
-    return (i & 0x007fffff) - 0x00400000;
+#if __AVX__ || __AVX2__ || __AVX512F__
+// horizontally add 8 floats
+static inline float hsum_float_8(const __m256 x) {
+    __m128 res = _mm256_extractf128_ps(x, 1);
+    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
+    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
+    res = _mm_add_ss(res, _mm_movehdup_ps(res));
+    return _mm_cvtss_f32(res);
 }
 
-static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type) {
-    float max = 0;
-    float amax = 0;
-    for (int i = 0; i < n; ++i) {
-        float ax = fabsf(x[i]);
-        if (ax > amax) { amax = ax; max = x[i]; }
-    }
-    if (amax < 1e-30f) { // all zero
-        for (int i = 0; i < n; ++i) {
-            L[i] = 0;
+// horizontally add 8 int32_t
+static inline int hsum_i32_8(const __m256i a) {
+    const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+    const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128);
+    const __m128i sum64 = _mm_add_epi32(hi64, sum128);
+    const __m128i hi32  = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
+    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
+}
+
+// horizontally add 4 int32_t
+static inline int hsum_i32_4(const __m128i a) {
+    const __m128i hi64 = _mm_unpackhi_epi64(a, a);
+    const __m128i sum64 = _mm_add_epi32(hi64, a);
+    const __m128i hi32  = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
+    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
+}
+
+#if defined(__AVX2__) || defined(__AVX512F__)
+// spread 32 bits to 32 bytes { 0x00, 0xFF }
+static inline __m256i bytes_from_bits_32(const uint8_t * x) {
+    uint32_t x32;
+    memcpy(&x32, x, sizeof(uint32_t));
+    const __m256i shuf_mask = _mm256_set_epi64x(
+            0x0303030303030303, 0x0202020202020202,
+            0x0101010101010101, 0x0000000000000000);
+    __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask);
+    const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe);
+    bytes = _mm256_or_si256(bytes, bit_mask);
+    return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1));
+}
+
+// Unpack 32 4-bit fields into 32 bytes
+// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
+static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
+{
+    const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi);
+    const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp);
+    const __m256i lowMask = _mm256_set1_epi8( 0xF );
+    return _mm256_and_si256(lowMask, bytes);
+}
+
+// add int16_t pairwise and return as float vector
+static inline __m256 sum_i16_pairs_float(const __m256i x) {
+    const __m256i ones = _mm256_set1_epi16(1);
+    const __m256i summed_pairs = _mm256_madd_epi16(ones, x);
+    return _mm256_cvtepi32_ps(summed_pairs);
+}
+
+static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
+#if __AVXVNNI__
+    const __m256i zero = _mm256_setzero_si256();
+    const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
+    return _mm256_cvtepi32_ps(summed_pairs);
+#else
+    // Perform multiplication and create 16-bit values
+    const __m256i dot = _mm256_maddubs_epi16(ax, sy);
+    return sum_i16_pairs_float(dot);
+#endif
+}
+
+// multiply int8_t, add results pairwise twice and return as float vector
+static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
+#if __AVXVNNIINT8__
+    const __m256i zero = _mm256_setzero_si256();
+    const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
+    return _mm256_cvtepi32_ps(summed_pairs);
+#else
+    // Get absolute values of x vectors
+    const __m256i ax = _mm256_sign_epi8(x, x);
+    // Sign the values of the y vectors
+    const __m256i sy = _mm256_sign_epi8(y, x);
+    return mul_sum_us8_pairs_float(ax, sy);
+#endif
+}
+
+static inline __m128i packNibbles( __m256i bytes )
+{
+    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
+#if __AVX512F__
+    const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4);   // 0000_0000_abcd_0000
+    bytes = _mm256_or_si256(bytes, bytes_srli_4);               // 0000_abcd_abcd_efgh
+    return _mm256_cvtepi16_epi8(bytes);                         // abcd_efgh
+#else
+    const __m256i lowByte = _mm256_set1_epi16( 0xFF );
+    __m256i high = _mm256_andnot_si256( lowByte, bytes );
+    __m256i low = _mm256_and_si256( lowByte, bytes );
+    high = _mm256_srli_epi16( high, 4 );
+    bytes = _mm256_or_si256( low, high );
+
+    // Compress uint16_t lanes into bytes
+    __m128i r0 = _mm256_castsi256_si128( bytes );
+    __m128i r1 = _mm256_extracti128_si256( bytes, 1 );
+    return _mm_packus_epi16( r0, r1 );
+#endif
+}
+#elif defined(__AVX__)
+// spread 32 bits to 32 bytes { 0x00, 0xFF }
+static inline __m256i bytes_from_bits_32(const uint8_t * x) {
+    uint32_t x32;
+    memcpy(&x32, x, sizeof(uint32_t));
+    const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000);
+    const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202);
+    __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl);
+    __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh);
+    const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe);
+    bytesl = _mm_or_si128(bytesl, bit_mask);
+    bytesh = _mm_or_si128(bytesh, bit_mask);
+    bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1));
+    bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1));
+    return MM256_SET_M128I(bytesh, bytesl);
+}
+
+// Unpack 32 4-bit fields into 32 bytes
+// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
+static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
+{
+    // Load 16 bytes from memory
+    __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi);
+    __m128i tmph = _mm_srli_epi16(tmpl, 4);
+    const __m128i lowMask = _mm_set1_epi8(0xF);
+    tmpl = _mm_and_si128(lowMask, tmpl);
+    tmph = _mm_and_si128(lowMask, tmph);
+    return MM256_SET_M128I(tmph, tmpl);
+}
+
+// add int16_t pairwise and return as float vector
+static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
+    const __m128i ones = _mm_set1_epi16(1);
+    const __m128i summed_pairsl = _mm_madd_epi16(ones, xl);
+    const __m128i summed_pairsh = _mm_madd_epi16(ones, xh);
+    const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl);
+    return _mm256_cvtepi32_ps(summed_pairs);
+}
+
+static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
+    const __m128i axl = _mm256_castsi256_si128(ax);
+    const __m128i axh = _mm256_extractf128_si256(ax, 1);
+    const __m128i syl = _mm256_castsi256_si128(sy);
+    const __m128i syh = _mm256_extractf128_si256(sy, 1);
+    // Perform multiplication and create 16-bit values
+    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
+    const __m128i doth = _mm_maddubs_epi16(axh, syh);
+    return sum_i16_pairs_float(doth, dotl);
+}
+
+// multiply int8_t, add results pairwise twice and return as float vector
+static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
+    const __m128i xl = _mm256_castsi256_si128(x);
+    const __m128i xh = _mm256_extractf128_si256(x, 1);
+    const __m128i yl = _mm256_castsi256_si128(y);
+    const __m128i yh = _mm256_extractf128_si256(y, 1);
+    // Get absolute values of x vectors
+    const __m128i axl = _mm_sign_epi8(xl, xl);
+    const __m128i axh = _mm_sign_epi8(xh, xh);
+    // Sign the values of the y vectors
+    const __m128i syl = _mm_sign_epi8(yl, xl);
+    const __m128i syh = _mm_sign_epi8(yh, xh);
+    // Perform multiplication and create 16-bit values
+    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
+    const __m128i doth = _mm_maddubs_epi16(axh, syh);
+    return sum_i16_pairs_float(doth, dotl);
+}
+
+static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 )
+{
+    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
+    const __m128i lowByte = _mm_set1_epi16( 0xFF );
+    __m128i high = _mm_andnot_si128( lowByte, bytes1 );
+    __m128i low = _mm_and_si128( lowByte, bytes1 );
+    high = _mm_srli_epi16( high, 4 );
+    bytes1 = _mm_or_si128( low, high );
+    high = _mm_andnot_si128( lowByte, bytes2 );
+    low = _mm_and_si128( lowByte, bytes2 );
+    high = _mm_srli_epi16( high, 4 );
+    bytes2 = _mm_or_si128( low, high );
+
+    return _mm_packus_epi16( bytes1, bytes2);
+}
+#endif
+#elif defined(__SSSE3__)
+// horizontally add 4x4 floats
+static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) {
+    __m128 res_0 =_mm_hadd_ps(a, b);
+    __m128 res_1 =_mm_hadd_ps(c, d);
+    __m128 res =_mm_hadd_ps(res_0, res_1);
+    res =_mm_hadd_ps(res, res);
+    res =_mm_hadd_ps(res, res);
+
+    return _mm_cvtss_f32(res);
+}
+#endif // __AVX__ || __AVX2__ || __AVX512F__
+#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
+
+#if defined(__ARM_NEON)
+
+#if !defined(__aarch64__)
+
+inline static int32_t vaddvq_s32(int32x4_t v) {
+    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
+}
+
+inline static float vaddvq_f32(float32x4_t v) {
+    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
+}
+
+inline static float vmaxvq_f32(float32x4_t v) {
+    return
+        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
+            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
+}
+
+inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
+    int32x4_t res;
+
+    res[0] = roundf(vgetq_lane_f32(v, 0));
+    res[1] = roundf(vgetq_lane_f32(v, 1));
+    res[2] = roundf(vgetq_lane_f32(v, 2));
+    res[3] = roundf(vgetq_lane_f32(v, 3));
+
+    return res;
+}
+
+#endif
+#endif
+
+#if defined(__ARM_NEON) || defined(__wasm_simd128__)
+#define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
+#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
+#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s)
+#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s)
+#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s)
+#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s)
+#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s)
+#define B8(c,s  ) B7(c,s,     c), B7(c,s,     s)
+
+// precomputed tables for expanding 8bits to 8 bytes:
+static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4
+static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
+#endif
+
+// reference implementation for deterministic creation of model files
+void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) {
+    static const int qk = QK4_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+            if (amax < fabsf(v)) {
+                amax = fabsf(v);
+                max  = v;
+            }
         }
-        return 0.f;
-    }
-    float iscale = -nmax / max;
-    if (rmse_type == 0) {
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale * x[i]);
-            L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
+
+        const float d  = max / -8;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = x[i*qk + 0    + j]*id;
+            const float x1 = x[i*qk + qk/2 + j]*id;
+
+            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
+            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));
+
+            y[i].qs[j]  = xi0;
+            y[i].qs[j] |= xi1 << 4;
         }
-        return 1/iscale;
-    }
-    bool return_early = false;
-    if (rmse_type < 0) {
-        rmse_type = -rmse_type;
-        return_early = true;
-    }
-    int weight_type = rmse_type%2;
-    float sumlx = 0;
-    float suml2 = 0;
-    for (int i = 0; i < n; ++i) {
-        int l = nearest_int(iscale * x[i]);
-        l = MAX(-nmax, MIN(nmax-1, l));
-        L[i] = l + nmax;
-        float w = weight_type == 1 ? x[i] * x[i] : 1;
-        sumlx += w*x[i]*l;
-        suml2 += w*l*l;
     }
-    float scale = sumlx/suml2;
-    if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale;
-    float best = scale * sumlx;
-    for (int is = -9; is <= 9; ++is) {
-        if (is == 0) {
-            continue;
+}
+
+void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q4_0_reference(x, y, k);
+}
+
+void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
+    const int qk = QK4_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float min = FLT_MAX;
+        float max = -FLT_MAX;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+
+            if (v < min) min = v;
+            if (v > max) max = v;
         }
-        iscale = -(nmax + 0.1f*is) / max;
-        sumlx = suml2 = 0;
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale * x[i]);
-            l = MAX(-nmax, MIN(nmax-1, l));
-            float w = weight_type == 1 ? x[i] * x[i] : 1;
-            sumlx += w*x[i]*l;
-            suml2 += w*l*l;
+
+        const float d  = (max - min) / ((1 << 4) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].m = ggml_fp32_to_fp16(min);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = (x[i*qk + 0    + j] - min)*id;
+            const float x1 = (x[i*qk + qk/2 + j] - min)*id;
+
+            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
+            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));
+
+            y[i].qs[j]  = xi0;
+            y[i].qs[j] |= xi1 << 4;
         }
-        if (suml2 > 0 && sumlx*sumlx > best*suml2) {
-            for (int i = 0; i < n; ++i) {
-                int l = nearest_int(iscale * x[i]);
-                L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
+    }
+}
+
+void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q4_1_reference(x, y, k);
+}
+
+void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) {
+    static const int qk = QK5_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+            if (amax < fabsf(v)) {
+                amax = fabsf(v);
+                max  = v;
             }
-            scale = sumlx/suml2; best = scale*sumlx;
         }
+
+        const float d  = max / -16;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+
+        uint32_t qh = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = x[i*qk + 0    + j]*id;
+            const float x1 = x[i*qk + qk/2 + j]*id;
+
+            const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
+            const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
+
+            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
+
+            // get the 5-th bit and store it in qh at the right position
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2);
+        }
+
+        memcpy(&y[i].qh, &qh, sizeof(qh));
     }
-    return scale;
 }
 
-static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, bool do_rmse) {
-    float max = 0;
-    float amax = 0;
-    for (int i = 0; i < n; ++i) {
-        float ax = fabsf(x[i]);
-        if (ax > amax) { amax = ax; max = x[i]; }
+void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q5_0_reference(x, y, k);
+}
+
+void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) {
+    const int qk = QK5_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float min = FLT_MAX;
+        float max = -FLT_MAX;
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+
+            if (v < min) min = v;
+            if (v > max) max = v;
+        }
+
+        const float d  = (max - min) / ((1 << 5) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].m = ggml_fp32_to_fp16(min);
+
+        uint32_t qh = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const float x0 = (x[i*qk + 0    + j] - min)*id;
+            const float x1 = (x[i*qk + qk/2 + j] - min)*id;
+
+            const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+            const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
+
+            // get the 5-th bit and store it in qh at the right position
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2);
+        }
+
+        memcpy(&y[i].qh, &qh, sizeof(y[i].qh));
     }
-    if (!amax) { // all zero
-        for (int i = 0; i < n; ++i) { L[i] = 0; }
-        return 0.f;
+}
+
+void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q5_1_reference(x, y, k);
+}
+
+// reference implementation for deterministic creation of model files
+void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        for (int j = 0; j < QK8_0; j++) {
+            const float v = x[i*QK8_0 + j];
+            amax = MAX(amax, fabsf(v));
+        }
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+
+        for (int j = 0; j < QK8_0; ++j) {
+            const float x0 = x[i*QK8_0 + j]*id;
+
+            y[i].qs[j] = roundf(x0);
+        }
     }
-    float iscale = -nmax / max;
-    if (do_rmse) {
-        float sumlx = 0;
-        float suml2 = 0;
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale * x[i]);
-            l = MAX(-nmax, MIN(nmax-1, l));
-            L[i] = l;
-            float w = x[i]*x[i];
-            sumlx += w*x[i]*l;
-            suml2 += w*l*l;
+}
+
+void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
+    assert(QK8_0 == 32);
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    for (int i = 0; i < nb; i++) {
+        float32x4_t srcv [8];
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = vld1q_f32(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = vmaxvq_f32(amaxv[0]);
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+
+        for (int j = 0; j < 8; j++) {
+            const float32x4_t v  = vmulq_n_f32(srcv[j], id);
+            const int32x4_t   vi = vcvtnq_s32_f32(v);
+
+            y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3);
         }
-        for (int itry = 0; itry < 5; ++itry) {
-            int n_changed = 0;
-            for (int i = 0; i < n; ++i) {
-                float w = x[i]*x[i];
-                float slx = sumlx - w*x[i]*L[i];
-                if (slx > 0) {
-                    float sl2 = suml2 - w*L[i]*L[i];
-                    int new_l = nearest_int(x[i] * sl2 / slx);
-                    new_l = MAX(-nmax, MIN(nmax-1, new_l));
-                    if (new_l != L[i]) {
-                        slx += w*x[i]*new_l;
-                        sl2 += w*new_l*new_l;
-                        if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) {
-                            L[i] = new_l; sumlx = slx; suml2 = sl2;
-                            ++n_changed;
-                        }
-                    }
-                }
+    }
+#elif defined(__wasm_simd128__)
+    for (int i = 0; i < nb; i++) {
+        v128_t srcv [8];
+        v128_t asrcv[8];
+        v128_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = wasm_v128_load(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0),
+                                   wasm_f32x4_extract_lane(amaxv[0], 1)),
+                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2),
+                                   wasm_f32x4_extract_lane(amaxv[0], 3)));
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+
+        for (int j = 0; j < 8; j++) {
+            const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
+            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);
+
+            y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0);
+            y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1);
+            y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2);
+            y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3);
+        }
+    }
+#elif defined(__AVX2__) || defined(__AVX__)
+    for (int i = 0; i < nb; i++) {
+        // Load elements into 4 AVX vectors
+        __m256 v0 = _mm256_loadu_ps( x );
+        __m256 v1 = _mm256_loadu_ps( x + 8 );
+        __m256 v2 = _mm256_loadu_ps( x + 16 );
+        __m256 v3 = _mm256_loadu_ps( x + 24 );
+        x += 32;
+
+        // Compute max(abs(e)) for the block
+        const __m256 signBit = _mm256_set1_ps( -0.0f );
+        __m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
+
+        __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
+        max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
+        max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
+        const float maxScalar = _mm_cvtss_f32( max4 );
+
+        // Quantize these floats
+        const float d = maxScalar / 127.f;
+        y[i].d = ggml_fp32_to_fp16(d);
+        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
+        const __m256 mul = _mm256_set1_ps( id );
+
+        // Apply the multiplier
+        v0 = _mm256_mul_ps( v0, mul );
+        v1 = _mm256_mul_ps( v1, mul );
+        v2 = _mm256_mul_ps( v2, mul );
+        v3 = _mm256_mul_ps( v3, mul );
+
+        // Round to nearest integer
+        v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
+        v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
+        v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
+        v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
+
+        // Convert floats to integers
+        __m256i i0 = _mm256_cvtps_epi32( v0 );
+        __m256i i1 = _mm256_cvtps_epi32( v1 );
+        __m256i i2 = _mm256_cvtps_epi32( v2 );
+        __m256i i3 = _mm256_cvtps_epi32( v3 );
+
+#if defined(__AVX2__)
+        // Convert int32 to int16
+        i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
+        i2 = _mm256_packs_epi32( i2, i3 );	// 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
+                                            // Convert int16 to int8
+        i0 = _mm256_packs_epi16( i0, i2 );	// 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
+
+        // We got our precious signed bytes, but the order is now wrong
+        // These AVX2 pack instructions process 16-byte pieces independently
+        // The following instruction is fixing the order
+        const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
+        i0 = _mm256_permutevar8x32_epi32( i0, perm );
+
+        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
+#else
+        // Since we don't have in AVX some necessary functions,
+        // we split the registers in half and call AVX2 analogs from SSE
+        __m128i ni0 = _mm256_castsi256_si128( i0 );
+        __m128i ni1 = _mm256_extractf128_si256( i0, 1);
+        __m128i ni2 = _mm256_castsi256_si128( i1 );
+        __m128i ni3 = _mm256_extractf128_si256( i1, 1);
+        __m128i ni4 = _mm256_castsi256_si128( i2 );
+        __m128i ni5 = _mm256_extractf128_si256( i2, 1);
+        __m128i ni6 = _mm256_castsi256_si128( i3 );
+        __m128i ni7 = _mm256_extractf128_si256( i3, 1);
+
+        // Convert int32 to int16
+        ni0 = _mm_packs_epi32( ni0, ni1 );
+        ni2 = _mm_packs_epi32( ni2, ni3 );
+        ni4 = _mm_packs_epi32( ni4, ni5 );
+        ni6 = _mm_packs_epi32( ni6, ni7 );
+        // Convert int16 to int8
+        ni0 = _mm_packs_epi16( ni0, ni2 );
+        ni4 = _mm_packs_epi16( ni4, ni6 );
+
+        _mm_storeu_si128((__m128i *)(y[i].qs +  0), ni0);
+        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
+#endif
+    }
+#elif defined(__riscv_v_intrinsic)
+
+    size_t vl = __riscv_vsetvl_e32m4(QK8_0);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vfloat32m4_t v_x   = __riscv_vle32_v_f32m4(x+i*QK8_0, vl);
+
+        vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl);
+        vfloat32m1_t tmp   = __riscv_vfmv_v_f_f32m1(0.0f, vl);
+        vfloat32m1_t vmax  = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl);
+        float amax = __riscv_vfmv_f_s_f32m1_f32(vmax);
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = ggml_fp32_to_fp16(d);
+
+        vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
+
+        // convert to integer
+        vint16m2_t   vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl);
+        vint8m1_t    vs = __riscv_vncvt_x_x_w_i8m1(vi, vl);
+
+        // store result
+        __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
+    }
+#else
+    // scalar
+    quantize_row_q8_0_reference(x, y, k);
+#endif
+}
+
+// reference implementation for deterministic creation of model files
+void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) {
+    assert(QK8_1 == 32);
+    assert(k % QK8_1 == 0);
+    const int nb = k / QK8_1;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        for (int j = 0; j < QK8_1; j++) {
+            const float v = x[i*QK8_1 + j];
+            amax = MAX(amax, fabsf(v));
+        }
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        int sum = 0;
+
+        for (int j = 0; j < QK8_1/2; ++j) {
+            const float v0 = x[i*QK8_1           + j]*id;
+            const float v1 = x[i*QK8_1 + QK8_1/2 + j]*id;
+
+            y[i].qs[          j] = roundf(v0);
+            y[i].qs[QK8_1/2 + j] = roundf(v1);
+
+            sum += y[i].qs[          j];
+            sum += y[i].qs[QK8_1/2 + j];
+        }
+
+        y[i].s = sum*d;
+    }
+}
+
+void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK8_1 == 0);
+    const int nb = k / QK8_1;
+
+    block_q8_1 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    for (int i = 0; i < nb; i++) {
+        float32x4_t srcv [8];
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = vld1q_f32(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = vmaxvq_f32(amaxv[0]);
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        int32x4_t accv = vdupq_n_s32(0);
+
+        for (int j = 0; j < 8; j++) {
+            const float32x4_t v  = vmulq_n_f32(srcv[j], id);
+            const int32x4_t   vi = vcvtnq_s32_f32(v);
+
+            y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3);
+
+            accv = vaddq_s32(accv, vi);
+        }
+
+        y[i].s = d * vaddvq_s32(accv);
+    }
+#elif defined(__wasm_simd128__)
+    for (int i = 0; i < nb; i++) {
+        v128_t srcv [8];
+        v128_t asrcv[8];
+        v128_t amaxv[8];
+
+        for (int j = 0; j < 8; j++) srcv[j]  = wasm_v128_load(x + i*32 + 4*j);
+        for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]);
+
+        for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]);
+        for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]);
+        for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]);
+
+        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0),
+                                   wasm_f32x4_extract_lane(amaxv[0], 1)),
+                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2),
+                                   wasm_f32x4_extract_lane(amaxv[0], 3)));
+
+        const float d = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        v128_t accv = wasm_i32x4_splat(0);
+
+        for (int j = 0; j < 8; j++) {
+            const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
+            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);
+
+            y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0);
+            y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1);
+            y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2);
+            y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3);
+
+            accv = wasm_i32x4_add(accv, vi);
+        }
+
+        y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) +
+                      wasm_i32x4_extract_lane(accv, 1) +
+                      wasm_i32x4_extract_lane(accv, 2) +
+                      wasm_i32x4_extract_lane(accv, 3));
+    }
+#elif defined(__AVX2__) || defined(__AVX__)
+    for (int i = 0; i < nb; i++) {
+        // Load elements into 4 AVX vectors
+        __m256 v0 = _mm256_loadu_ps( x );
+        __m256 v1 = _mm256_loadu_ps( x + 8 );
+        __m256 v2 = _mm256_loadu_ps( x + 16 );
+        __m256 v3 = _mm256_loadu_ps( x + 24 );
+        x += 32;
+
+        // Compute max(abs(e)) for the block
+        const __m256 signBit = _mm256_set1_ps( -0.0f );
+        __m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
+        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
+
+        __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
+        max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
+        max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
+        const float maxScalar = _mm_cvtss_f32( max4 );
+
+        // Quantize these floats
+        const float d = maxScalar / 127.f;
+        y[i].d = d;
+        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
+        const __m256 mul = _mm256_set1_ps( id );
+
+        // Apply the multiplier
+        v0 = _mm256_mul_ps( v0, mul );
+        v1 = _mm256_mul_ps( v1, mul );
+        v2 = _mm256_mul_ps( v2, mul );
+        v3 = _mm256_mul_ps( v3, mul );
+
+        // Round to nearest integer
+        v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
+        v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
+        v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
+        v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
+
+        // Convert floats to integers
+        __m256i i0 = _mm256_cvtps_epi32( v0 );
+        __m256i i1 = _mm256_cvtps_epi32( v1 );
+        __m256i i2 = _mm256_cvtps_epi32( v2 );
+        __m256i i3 = _mm256_cvtps_epi32( v3 );
+
+#if defined(__AVX2__)
+        // Compute the sum of the quants and set y[i].s
+        y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));
+
+        // Convert int32 to int16
+        i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
+        i2 = _mm256_packs_epi32( i2, i3 );	// 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
+                                            // Convert int16 to int8
+        i0 = _mm256_packs_epi16( i0, i2 );	// 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
+
+        // We got our precious signed bytes, but the order is now wrong
+        // These AVX2 pack instructions process 16-byte pieces independently
+        // The following instruction is fixing the order
+        const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
+        i0 = _mm256_permutevar8x32_epi32( i0, perm );
+
+        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
+#else
+        // Since we don't have in AVX some necessary functions,
+        // we split the registers in half and call AVX2 analogs from SSE
+        __m128i ni0 = _mm256_castsi256_si128( i0 );
+        __m128i ni1 = _mm256_extractf128_si256( i0, 1);
+        __m128i ni2 = _mm256_castsi256_si128( i1 );
+        __m128i ni3 = _mm256_extractf128_si256( i1, 1);
+        __m128i ni4 = _mm256_castsi256_si128( i2 );
+        __m128i ni5 = _mm256_extractf128_si256( i2, 1);
+        __m128i ni6 = _mm256_castsi256_si128( i3 );
+        __m128i ni7 = _mm256_extractf128_si256( i3, 1);
+
+        // Compute the sum of the quants and set y[i].s
+        const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3));
+        const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7));
+        y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1));
+
+        // Convert int32 to int16
+        ni0 = _mm_packs_epi32( ni0, ni1 );
+        ni2 = _mm_packs_epi32( ni2, ni3 );
+        ni4 = _mm_packs_epi32( ni4, ni5 );
+        ni6 = _mm_packs_epi32( ni6, ni7 );
+        // Convert int16 to int8
+        ni0 = _mm_packs_epi16( ni0, ni2 );
+        ni4 = _mm_packs_epi16( ni4, ni6 );
+
+        _mm_storeu_si128((__m128i *)(y[i].qs +  0), ni0);
+        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
+#endif
+    }
+#elif defined(__riscv_v_intrinsic)
+
+    size_t vl = __riscv_vsetvl_e32m4(QK8_1);
+
+    for (int i = 0; i < nb; i++) {
+        // load elements
+        vfloat32m4_t v_x   = __riscv_vle32_v_f32m4(x+i*QK8_1, vl);
+
+        vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl);
+        vfloat32m1_t tmp   = __riscv_vfmv_v_f_f32m1(0.0, vl);
+        vfloat32m1_t vmax  = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl);
+        float amax = __riscv_vfmv_f_s_f32m1_f32(vmax);
+
+        const float d  = amax / ((1 << 7) - 1);
+        const float id = d ? 1.0f/d : 0.0f;
+
+        y[i].d = d;
+
+        vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
+
+        // convert to integer
+        vint16m2_t   vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl);
+        vint8m1_t    vs = __riscv_vncvt_x_x_w_i8m1(vi, vl);
+
+        // store result
+        __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
+
+        // compute sum for y[i].s
+        vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl);
+        vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl);
+
+        // set y[i].s
+        int sum = __riscv_vmv_x_s_i16m1_i16(vwrs);
+        y[i].s = sum*d;
+    }
+#else
+    // scalar
+    quantize_row_q8_1_reference(x, y, k);
+#endif
+}
+
+void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) {
+    static const int qk = QK4_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = ggml_fp16_to_fp32(x[i].d);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int x0 = (x[i].qs[j] & 0x0F) - 8;
+            const int x1 = (x[i].qs[j] >>   4) - 8;
+
+            y[i*qk + j + 0   ] = x0*d;
+            y[i*qk + j + qk/2] = x1*d;
+        }
+    }
+}
+
+void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) {
+    static const int qk = QK4_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float m = ggml_fp16_to_fp32(x[i].m);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int x0 = (x[i].qs[j] & 0x0F);
+            const int x1 = (x[i].qs[j] >>   4);
+
+            y[i*qk + j + 0   ] = x0*d + m;
+            y[i*qk + j + qk/2] = x1*d + m;
+        }
+    }
+}
+
+void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) {
+    static const int qk = QK5_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = ggml_fp16_to_fp32(x[i].d);
+
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
+            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
+
+            y[i*qk + j + 0   ] = x0*d;
+            y[i*qk + j + qk/2] = x1*d;
+        }
+    }
+}
+
+void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) {
+    static const int qk = QK5_1;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float m = ggml_fp16_to_fp32(x[i].m);
+
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int x0 = (x[i].qs[j] & 0x0F) | xh_0;
+            const int x1 = (x[i].qs[j] >>   4) | xh_1;
+
+            y[i*qk + j + 0   ] = x0*d + m;
+            y[i*qk + j + qk/2] = x1*d + m;
+        }
+    }
+}
+
+void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k) {
+    static const int qk = QK8_0;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = ggml_fp16_to_fp32(x[i].d);
+
+        for (int j = 0; j < qk; ++j) {
+            y[i*qk + j] = x[i].qs[j]*d;
+        }
+    }
+}
+
+//
+// 2-6 bit quantization in super-blocks
+//
+
+//
+// ===================== Helper functions
+//
+static inline int nearest_int(float fval) {
+    assert(fval <= 4194303.f);
+    float val = fval + 12582912.f;
+    int i; memcpy(&i, &val, sizeof(int));
+    return (i & 0x007fffff) - 0x00400000;
+}
+
+static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type) {
+    float max = 0;
+    float amax = 0;
+    for (int i = 0; i < n; ++i) {
+        float ax = fabsf(x[i]);
+        if (ax > amax) { amax = ax; max = x[i]; }
+    }
+    if (amax < 1e-30f) { // all zero
+        for (int i = 0; i < n; ++i) {
+            L[i] = 0;
+        }
+        return 0.f;
+    }
+    float iscale = -nmax / max;
+    if (rmse_type == 0) {
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale * x[i]);
+            L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
+        }
+        return 1/iscale;
+    }
+    bool return_early = false;
+    if (rmse_type < 0) {
+        rmse_type = -rmse_type;
+        return_early = true;
+    }
+    int weight_type = rmse_type%2;
+    float sumlx = 0;
+    float suml2 = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale * x[i]);
+        l = MAX(-nmax, MIN(nmax-1, l));
+        L[i] = l + nmax;
+        float w = weight_type == 1 ? x[i] * x[i] : 1;
+        sumlx += w*x[i]*l;
+        suml2 += w*l*l;
+    }
+    float scale = sumlx/suml2;
+    if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale;
+    float best = scale * sumlx;
+    for (int is = -9; is <= 9; ++is) {
+        if (is == 0) {
+            continue;
+        }
+        iscale = -(nmax + 0.1f*is) / max;
+        sumlx = suml2 = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale * x[i]);
+            l = MAX(-nmax, MIN(nmax-1, l));
+            float w = weight_type == 1 ? x[i] * x[i] : 1;
+            sumlx += w*x[i]*l;
+            suml2 += w*l*l;
+        }
+        if (suml2 > 0 && sumlx*sumlx > best*suml2) {
+            for (int i = 0; i < n; ++i) {
+                int l = nearest_int(iscale * x[i]);
+                L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
+            }
+            scale = sumlx/suml2; best = scale*sumlx;
+        }
+    }
+    return scale;
+}
+
+static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, bool do_rmse) {
+    float max = 0;
+    float amax = 0;
+    for (int i = 0; i < n; ++i) {
+        float ax = fabsf(x[i]);
+        if (ax > amax) { amax = ax; max = x[i]; }
+    }
+    if (!amax) { // all zero
+        for (int i = 0; i < n; ++i) { L[i] = 0; }
+        return 0.f;
+    }
+    float iscale = -nmax / max;
+    if (do_rmse) {
+        float sumlx = 0;
+        float suml2 = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale * x[i]);
+            l = MAX(-nmax, MIN(nmax-1, l));
+            L[i] = l;
+            float w = x[i]*x[i];
+            sumlx += w*x[i]*l;
+            suml2 += w*l*l;
+        }
+        for (int itry = 0; itry < 5; ++itry) {
+            int n_changed = 0;
+            for (int i = 0; i < n; ++i) {
+                float w = x[i]*x[i];
+                float slx = sumlx - w*x[i]*L[i];
+                if (slx > 0) {
+                    float sl2 = suml2 - w*L[i]*L[i];
+                    int new_l = nearest_int(x[i] * sl2 / slx);
+                    new_l = MAX(-nmax, MIN(nmax-1, new_l));
+                    if (new_l != L[i]) {
+                        slx += w*x[i]*new_l;
+                        sl2 += w*new_l*new_l;
+                        if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) {
+                            L[i] = new_l; sumlx = slx; suml2 = sl2;
+                            ++n_changed;
+                        }
+                    }
+                }
+            }
+            if (!n_changed) {
+                break;
+            }
+        }
+        for (int i = 0; i < n; ++i) {
+            L[i] += nmax;
+        }
+        return sumlx / suml2;
+    }
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale * x[i]);
+        l = MAX(-nmax, MIN(nmax-1, l));
+        L[i] = l + nmax;
+    }
+    return 1/iscale;
+}
+
+static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min,
+        int ntry, float alpha) {
+    float min = x[0];
+    float max = x[0];
+    for (int i = 1; i < n; ++i) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+    }
+    if (max == min) {
+        for (int i = 0; i < n; ++i) L[i] = 0;
+        *the_min = 0;
+        return 0.f;
+    }
+    if (min > 0) min = 0;
+    float iscale = nmax/(max - min);
+    float scale = 1/iscale;
+    for (int itry = 0; itry < ntry; ++itry) {
+        float sumlx = 0; int suml2 = 0;
+        bool did_change = false;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale*(x[i] - min));
+            l = MAX(0, MIN(nmax, l));
+            if (l != L[i]) {
+                L[i] = l;
+                did_change = true;
+            }
+            sumlx += (x[i] - min)*l;
+            suml2 += l*l;
+        }
+        scale = sumlx/suml2;
+        float sum = 0;
+        for (int i = 0; i < n; ++i) {
+            sum += x[i] - scale*L[i];
+        }
+        min = alpha*min + (1 - alpha)*sum/n;
+        if (min > 0) min = 0;
+        iscale = 1/scale;
+        if (!did_change) break;
+    }
+    *the_min = -min;
+    return scale;
+}
+
+static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
+        uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
+        float rmin, float rdelta, int nstep, bool use_mad) {
+    float min = x[0];
+    float max = x[0];
+    float sum_w = weights[0];
+    float sum_x = sum_w * x[0];
+    for (int i = 1; i < n; ++i) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+        float w = weights[i];
+        sum_w += w;
+        sum_x += w * x[i];
+    }
+    if (min > 0) min = 0;
+    if (max == min) {
+        for (int i = 0; i < n; ++i) L[i] = 0;
+        *the_min = -min;
+        return 0.f;
+    }
+    float iscale = nmax/(max - min);
+    float scale = 1/iscale;
+    float best_mad = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale*(x[i] - min));
+        L[i] = MAX(0, MIN(nmax, l));
+        float diff = scale * L[i] + min - x[i];
+        diff = use_mad ? fabsf(diff) : diff * diff;
+        float w = weights[i];
+        best_mad += w * diff;
+    }
+    if (nstep < 1) {
+        *the_min = -min;
+        return scale;
+    }
+    for (int is = 0; is <= nstep; ++is) {
+        iscale = (rmin + rdelta*is + nmax)/(max - min);
+        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale*(x[i] - min));
+            l = MAX(0, MIN(nmax, l));
+            Laux[i] = l;
+            float w = weights[i];
+            sum_l += w*l;
+            sum_l2 += w*l*l;
+            sum_xl += w*l*x[i];
+        }
+        float D = sum_w * sum_l2 - sum_l * sum_l;
+        if (D > 0) {
+            float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D;
+            float this_min   = (sum_l2 * sum_x - sum_l * sum_xl)/D;
+            if (this_min > 0) {
+                this_min = 0;
+                this_scale = sum_xl / sum_l2;
+            }
+            float mad = 0;
+            for (int i = 0; i < n; ++i) {
+                float diff = this_scale * Laux[i] + this_min - x[i];
+                diff = use_mad ? fabsf(diff) : diff * diff;
+                float w = weights[i];
+                mad += w * diff;
+            }
+            if (mad < best_mad) {
+                for (int i = 0; i < n; ++i) {
+                    L[i] = Laux[i];
+                }
+                best_mad = mad;
+                scale = this_scale;
+                min = this_min;
+            }
+        }
+    }
+    *the_min = -min;
+    return scale;
+}
+
+#if QK_K == 256
+static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) {
+    if (j < 4) {
+        *d = q[j] & 63; *m = q[j + 4] & 63;
+    } else {
+        *d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        *m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+//========================- 2-bit (de)-quantization
+
+void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[16];
+    float   weights[16];
+    float mins[QK_K/16];
+    float scales[QK_K/16];
+
+    const float q4scale = 15.f;
+
+    for (int i = 0; i < nb; i++) {
+        float max_scale = 0; // as we are deducting the min, scales are always positive
+        float max_min = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 16; ++l) weights[l] = fabsf(x[16*j + l]);
+            scales[j] = make_qkx2_quants(16, 3, x + 16*j, weights, L + 16*j, &mins[j], Laux, -0.5f, 0.1f, 15, true);
+            float scale = scales[j];
+            if (scale > max_scale) {
+                max_scale = scale;
+            }
+            float min = mins[j];
+            if (min > max_min) {
+                max_min = min;
+            }
+        }
+
+        if (max_scale > 0) {
+            float iscale = q4scale/max_scale;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(iscale*scales[j]);
+                y[i].scales[j] = l;
+            }
+            y[i].d = ggml_fp32_to_fp16(max_scale/q4scale);
+        } else {
+            for (int j = 0; j < QK_K/16; ++j) y[i].scales[j] = 0;
+            y[i].d = ggml_fp32_to_fp16(0.f);
+        }
+        if (max_min > 0) {
+            float iscale = q4scale/max_min;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(iscale*mins[j]);
+                y[i].scales[j] |= (l << 4);
+            }
+            y[i].dmin = ggml_fp32_to_fp16(max_min/q4scale);
+        } else {
+            y[i].dmin = ggml_fp32_to_fp16(0.f);
+        }
+        for (int j = 0; j < QK_K/16; ++j) {
+            const float d = ggml_fp16_to_fp32(y[i].d) * (y[i].scales[j] & 0xF);
+            if (!d) continue;
+            const float dm = ggml_fp16_to_fp32(y[i].dmin) * (y[i].scales[j] >> 4);
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int((x[16*j + ii] + dm)/d);
+                l = MAX(0, MIN(3, l));
+                L[16*j + ii] = l;
+            }
+        }
+
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+            }
+        }
+#else
+        for (int l = 0; l < 16; ++l) {
+            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float min = ggml_fp16_to_fp32(x[i].dmin);
+
+        const uint8_t * q = x[i].qs;
+
+#if QK_K == 256
+        int is = 0;
+        float dl, ml;
+        for (int n = 0; n < QK_K; n += 128) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+
+                uint8_t sc = x[i].scales[is++];
+                dl = d * (sc & 0xF); ml = min * (sc >> 4);
+                for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l] >> shift) & 3)) - ml;
+
+                sc = x[i].scales[is++];
+                dl = d * (sc & 0xF); ml = min * (sc >> 4);
+                for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3)) - ml;
+
+                shift += 2;
+            }
+            q += 32;
+        }
+#else
+        float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4);
+        float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4);
+        float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4);
+        float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4);
+        for (int l = 0; l < 16; ++l) {
+            y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1;
+            y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2;
+            y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3;
+            y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4;
+        }
+        y += QK_K;
+#endif
+    }
+}
+
+void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) {
+    quantize_row_q2_K_reference(x, vy, k);
+}
+
+size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K;
+        quantize_row_q2_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q2_K));
+}
+
+//========================= 3-bit (de)-quantization
+
+void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    int8_t L[QK_K];
+    float scales[QK_K / 16];
+
+    for (int i = 0; i < nb; i++) {
+
+        float max_scale = 0;
+        float amax = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            scales[j] = make_q3_quants(16, 4, x + 16*j, L + 16*j, true);
+            float scale = fabsf(scales[j]);
+            if (scale > amax) {
+                amax = scale; max_scale = scales[j];
+            }
+        }
+
+#if QK_K == 256
+        memset(y[i].scales, 0, 12);
+        if (max_scale) {
+            float iscale = -32.f/max_scale;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int8_t l = nearest_int(iscale*scales[j]);
+                l = MAX(-32, MIN(31, l)) + 32;
+                if (j < 8) {
+                    y[i].scales[j] = l & 0xF;
+                } else {
+                    y[i].scales[j-8] |= ((l & 0xF) << 4);
+                }
+                l >>= 4;
+                y[i].scales[j%4 + 8] |= (l << (2*(j/4)));
+            }
+            y[i].d = ggml_fp32_to_fp16(1/iscale);
+        } else {
+            y[i].d = ggml_fp32_to_fp16(0.f);
+        }
+
+        int8_t sc;
+        for (int j = 0; j < QK_K/16; ++j) {
+            sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4;
+            sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32;
+            float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-4, MIN(3, l));
+                L[16*j + ii] = l + 4;
+            }
+        }
+#else
+        if (max_scale) {
+            float iscale = -8.f/max_scale;
+            for (int j = 0; j < QK_K/16; j+=2) {
+                int l1 = nearest_int(iscale*scales[j]);
+                l1 = 8 + MAX(-8, MIN(7, l1));
+                int l2 = nearest_int(iscale*scales[j+1]);
+                l2 = 8 + MAX(-8, MIN(7, l2));
+                y[i].scales[j/2] = l1 | (l2 << 4);
+            }
+            y[i].d = ggml_fp32_to_fp16(1/iscale);
+        } else {
+            for (int j = 0; j < QK_K/16; j+=2) {
+                y[i].scales[j/2] = 0;
+            }
+            y[i].d = ggml_fp32_to_fp16(0.f);
+        }
+        for (int j = 0; j < QK_K/16; ++j) {
+            int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4;
+            float d = ggml_fp16_to_fp32(y[i].d) * (s - 8);
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-4, MIN(3, l));
+                L[16*j + ii] = l + 4;
+            }
+        }
+#endif
+
+        memset(y[i].hmask, 0, QK_K/8);
+        // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc.
+        int m = 0;
+        uint8_t hm = 1;
+        for (int j = 0; j < QK_K; ++j) {
+            if (L[j] > 3) {
+                y[i].hmask[m] |= hm;
+                L[j] -= 4;
+            }
+            if (++m == QK_K/8) {
+                m = 0; hm <<= 1;
+            }
+        }
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+            }
+        }
+#else
+        for (int l = 0; l < 16; ++l) {
+            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
+        }
+#endif
+
+        x += QK_K;
+    }
+}
+
+#if QK_K == 256
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    const uint32_t kmask1 = 0x03030303;
+    const uint32_t kmask2 = 0x0f0f0f0f;
+
+    uint32_t aux[4];
+    const int8_t * scales = (const int8_t*)aux;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d_all = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q = x[i].qs;
+        const uint8_t * restrict hm = x[i].hmask;
+        uint8_t m = 1;
+
+        memcpy(aux, x[i].scales, 12);
+        uint32_t tmp = aux[2];
+        aux[2] = ((aux[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        aux[3] = ((aux[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        aux[0] = (aux[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        aux[1] = (aux[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+
+        int is = 0;
+        float dl;
+        for (int n = 0; n < QK_K; n += 128) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+
+                dl = d_all * (scales[is++] - 32);
+                for (int l = 0; l < 16; ++l) {
+                    *y++ = dl * ((int8_t)((q[l+ 0] >> shift) & 3) - ((hm[l+ 0] & m) ? 0 : 4));
+                }
+
+                dl = d_all * (scales[is++] - 32);
+                for (int l = 0; l < 16; ++l) {
+                    *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3) - ((hm[l+16] & m) ? 0 : 4));
+                }
+
+                shift += 2;
+                m <<= 1;
+            }
+            q += 32;
+        }
+
+    }
+}
+#else
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    assert(QK_K == 64);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d_all = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict q = x[i].qs;
+        const uint8_t * restrict hm = x[i].hmask;
+
+        const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8);
+        const float d2 = d_all * ((x[i].scales[0] >>  4) - 8);
+        const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8);
+        const float d4 = d_all * ((x[i].scales[1] >>  4) - 8);
+
+        for (int l=0; l<8; ++l) {
+            uint8_t h = hm[l];
+            y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4));
+            y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4));
+            y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4));
+            y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4));
+            y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4));
+            y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4));
+            y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4));
+            y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4));
+        }
+        y += QK_K;
+    }
+}
+#endif
+
+void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) {
+    quantize_row_q3_K_reference(x, vy, k);
+}
+
+size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K;
+        quantize_row_q3_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q3_K));
+}
+
+// ====================== 4-bit (de)-quantization
+
+void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    float   weights[32];
+    float mins[QK_K/32];
+    float scales[QK_K/32];
+
+    for (int i = 0; i < nb; i++) {
+
+        float max_scale = 0; // as we are deducting the min, scales are always positive
+        float max_min = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            //scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
+            float sum_x2 = 0;
+            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
+            float av_x = sqrtf(sum_x2/32);
+            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
+            float scale = scales[j];
+            if (scale > max_scale) {
+                max_scale = scale;
+            }
+            float min = mins[j];
+            if (min > max_min) {
+                max_min = min;
+            }
+        }
+
+#if QK_K == 256
+        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
+        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
+        for (int j = 0; j < QK_K/32; ++j) {
+            uint8_t ls = nearest_int(inv_scale*scales[j]);
+            uint8_t lm = nearest_int(inv_min*mins[j]);
+            ls = MIN(63, ls);
+            lm = MIN(63, lm);
+            if (j < 4) {
+                y[i].scales[j] = ls;
+                y[i].scales[j+4] = lm;
+            } else {
+                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
+                y[i].scales[j-4] |= ((ls >> 4) << 6);
+                y[i].scales[j-0] |= ((lm >> 4) << 6);
+            }
+        }
+        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
+        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K/32; ++j) {
+            get_scale_min_k4(j, y[i].scales, &sc, &m);
+            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            if (!d) continue;
+            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + dm)/d);
+                l = MAX(0, MIN(15, l));
+                L[32*j + ii] = l;
+            }
+        }
+#else
+        const float s_factor = 15.f;
+        float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f;
+        float inv_min   = max_min   > 0 ? s_factor/max_min   : 0.f;
+        int d1 = nearest_int(inv_scale*scales[0]);
+        int m1 = nearest_int(inv_min*mins[0]);
+        int d2 = nearest_int(inv_scale*scales[1]);
+        int m2 = nearest_int(inv_min*mins[1]);
+        y[i].scales[0] = d1 | (m1 << 4);
+        y[i].scales[1] = d2 | (m2 << 4);
+        y[i].d[0] = ggml_fp32_to_fp16(max_scale/s_factor);
+        y[i].d[1] = ggml_fp32_to_fp16(max_min/s_factor);
+
+        float sumlx = 0;
+        int   suml2 = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            const uint8_t sd = y[i].scales[j] & 0xF;
+            const uint8_t sm = y[i].scales[j] >>  4;
+            const float d = ggml_fp16_to_fp32(y[i].d[0]) * sd;
+            if (!d) continue;
+            const float m = ggml_fp16_to_fp32(y[i].d[1]) * sm;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + m)/d);
+                l = MAX(0, MIN(15, l));
+                L[32*j + ii] = l;
+                sumlx += (x[32*j + ii] + m)*l*sd;
+                suml2 += l*l*sd*sd;
+            }
+        }
+        if (suml2) {
+            y[i].d[0] = ggml_fp32_to_fp16(sumlx/suml2);
+        }
+#endif
+        uint8_t * q = y[i].qs;
+        for (int j = 0; j < QK_K; j += 64) {
+            for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4);
+            q += 32;
+        }
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * q = x[i].qs;
+
+#if QK_K == 256
+
+        const float d   = ggml_fp16_to_fp32(x[i].d);
+        const float min = ggml_fp16_to_fp32(x[i].dmin);
+
+        int is = 0;
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K; j += 64) {
+            get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
+            const float d1 = d * sc; const float m1 = min * m;
+            get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
+            const float d2 = d * sc; const float m2 = min * m;
+            for (int l = 0; l < 32; ++l) *y++ = d1 * (q[l] & 0xF) - m1;
+            for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l]  >> 4) - m2;
+            q += 32; is += 2;
+        }
+#else
+        const float dall = ggml_fp16_to_fp32(x[i].d[0]);
+        const float mall = ggml_fp16_to_fp32(x[i].d[1]);
+        const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4);
+        const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4);
+        for (int l = 0; l < 32; ++l) {
+            y[l+ 0] = d1 * (q[l] & 0xF) - m1;
+            y[l+32] = d2 * (q[l] >>  4) - m2;
+        }
+        y += QK_K;
+#endif
+
+    }
+}
+
+void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_q4_K * restrict y = vy;
+    quantize_row_q4_K_reference(x, y, k);
+}
+
+size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    assert(k % QK_K == 0);
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K;
+        quantize_row_q4_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q4_K));
+}
+
+// ====================== 5-bit (de)-quantization
+
+void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+#if QK_K == 256
+    uint8_t L[QK_K];
+    float mins[QK_K/32];
+    float scales[QK_K/32];
+    float weights[32];
+    uint8_t Laux[32];
+#else
+    int8_t L[QK_K];
+    float scales[QK_K/16];
+#endif
+
+    for (int i = 0; i < nb; i++) {
+
+#if QK_K == 256
+
+        float max_scale = 0; // as we are deducting the min, scales are always positive
+        float max_min = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            //scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
+            float sum_x2 = 0;
+            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
+            float av_x = sqrtf(sum_x2/32);
+            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            scales[j] = make_qkx2_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.5f, 0.1f, 15, false);
+            float scale = scales[j];
+            if (scale > max_scale) {
+                max_scale = scale;
+            }
+            float min = mins[j];
+            if (min > max_min) {
+                max_min = min;
+            }
+        }
+
+        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
+        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
+        for (int j = 0; j < QK_K/32; ++j) {
+            uint8_t ls = nearest_int(inv_scale*scales[j]);
+            uint8_t lm = nearest_int(inv_min*mins[j]);
+            ls = MIN(63, ls);
+            lm = MIN(63, lm);
+            if (j < 4) {
+                y[i].scales[j] = ls;
+                y[i].scales[j+4] = lm;
+            } else {
+                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
+                y[i].scales[j-4] |= ((ls >> 4) << 6);
+                y[i].scales[j-0] |= ((lm >> 4) << 6);
             }
-            if (!n_changed) {
-                break;
+        }
+        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
+        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K/32; ++j) {
+            get_scale_min_k4(j, y[i].scales, &sc, &m);
+            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            if (!d) continue;
+            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + dm)/d);
+                l = MAX(0, MIN(31, l));
+                L[32*j + ii] = l;
+            }
+        }
+
+        uint8_t * restrict qh = y[i].qh;
+        uint8_t * restrict ql = y[i].qs;
+        memset(qh, 0, QK_K/8);
+
+        uint8_t m1 = 1, m2 = 2;
+        for (int n = 0; n < QK_K; n += 64) {
+            for (int j = 0; j < 32; ++j) {
+                int l1 = L[n + j];
+                if (l1 > 15) {
+                    l1 -= 16; qh[j] |= m1;
+                }
+                int l2 = L[n + j + 32];
+                if (l2 > 15) {
+                    l2 -= 16; qh[j] |= m2;
+                }
+                ql[j] = l1 | (l2 << 4);
+            }
+            m1 <<= 2; m2 <<= 2;
+            ql += 32;
+        }
+#else
+        float max_scale = 0, amax = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1);
+            float abs_scale = fabsf(scales[j]);
+            if (abs_scale > amax) {
+                amax = abs_scale;
+                max_scale = scales[j];
+            }
+        }
+
+        float iscale = -128.f/max_scale;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int l = nearest_int(iscale*scales[j]);
+            y[i].scales[j] = MAX(-128, MIN(127, l));
+        }
+        y[i].d = ggml_fp32_to_fp16(1/iscale);
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            const float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
+            if (!d) continue;
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-16, MIN(15, l));
+                L[16*j + ii] = l + 16;
+            }
+        }
+
+        uint8_t * restrict qh = y[i].qh;
+        uint8_t * restrict ql = y[i].qs;
+        memset(qh, 0, QK_K/8);
+
+        for (int j = 0; j < 32; ++j) {
+            int jm = j%8;
+            int is = j/8;
+            int l1 = L[j];
+            if (l1 > 15) {
+                l1 -= 16; qh[jm] |= (1 << is);
+            }
+            int l2 = L[j + 32];
+            if (l2 > 15) {
+                l2 -= 16; qh[jm] |= (1 << (4 + is));
+            }
+            ql[j] = l1 | (l2 << 4);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * ql = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+
+#if QK_K == 256
+
+        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float min = ggml_fp16_to_fp32(x[i].dmin);
+
+        int is = 0;
+        uint8_t sc, m;
+        uint8_t u1 = 1, u2 = 2;
+        for (int j = 0; j < QK_K; j += 64) {
+            get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
+            const float d1 = d * sc; const float m1 = min * m;
+            get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
+            const float d2 = d * sc; const float m2 = min * m;
+            for (int l = 0; l < 32; ++l) *y++ = d1 * ((ql[l] & 0xF) + (qh[l] & u1 ? 16 : 0)) - m1;
+            for (int l = 0; l < 32; ++l) *y++ = d2 * ((ql[l]  >> 4) + (qh[l] & u2 ? 16 : 0)) - m2;
+            ql += 32; is += 2;
+            u1 <<= 2; u2 <<= 2;
+        }
+#else
+        float d = ggml_fp16_to_fp32(x[i].d);
+        const int8_t * restrict s = x[i].scales;
+        for (int l = 0; l < 8; ++l) {
+            y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16));
+            y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16));
+            y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16));
+            y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16));
+            y[l+32] = d * s[2] * ((ql[l+ 0] >>  4) - (qh[l] & 0x10 ? 0 : 16));
+            y[l+40] = d * s[2] * ((ql[l+ 8] >>  4) - (qh[l] & 0x20 ? 0 : 16));
+            y[l+48] = d * s[3] * ((ql[l+16] >>  4) - (qh[l] & 0x40 ? 0 : 16));
+            y[l+56] = d * s[3] * ((ql[l+24] >>  4) - (qh[l] & 0x80 ? 0 : 16));
+        }
+        y += QK_K;
+#endif
+    }
+}
+
+void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_q5_K * restrict y = vy;
+    quantize_row_q5_K_reference(x, y, k);
+}
+
+size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
+    assert(k % QK_K == 0);
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K;
+        quantize_row_q5_K_reference(src + j, y, k);
+    }
+    return (n/QK_K*sizeof(block_q5_K));
+}
+
+// ====================== 6-bit (de)-quantization
+
+void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    int8_t L[QK_K];
+    float   scales[QK_K/16];
+
+    for (int i = 0; i < nb; i++) {
+
+        float max_scale = 0;
+        float max_abs_scale = 0;
+
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+
+            const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1);
+            scales[ib] = scale;
+
+            const float abs_scale = fabsf(scale);
+            if (abs_scale > max_abs_scale) {
+                max_abs_scale = abs_scale;
+                max_scale = scale;
+            }
+
+        }
+
+        if (!max_abs_scale) {
+            memset(&y[i], 0, sizeof(block_q6_K));
+            y[i].d = ggml_fp32_to_fp16(0.f);
+            x += QK_K;
+            continue;
+        }
+
+        float iscale = -128.f/max_scale;
+        y[i].d = ggml_fp32_to_fp16(1/iscale);
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib]));
+        }
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-32, MIN(31, l));
+                L[16*j + ii] = l + 32;
+            }
+        }
+
+        uint8_t * restrict ql = y[i].ql;
+        uint8_t * restrict qh = y[i].qh;
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                const uint8_t q1 = L[j + l +  0] & 0xF;
+                const uint8_t q2 = L[j + l + 32] & 0xF;
+                const uint8_t q3 = L[j + l + 64] & 0xF;
+                const uint8_t q4 = L[j + l + 96] & 0xF;
+                ql[l+ 0] = q1 | (q3 << 4);
+                ql[l+32] = q2 | (q4 << 4);
+                qh[l] = (L[j + l] >> 4) | ((L[j + l + 32] >> 4) << 2) | ((L[j + l + 64] >> 4) << 4) | ((L[j + l + 96] >> 4) << 6);
+            }
+            ql += 64;
+            qh += 32;
+        }
+#else
+        for (int l = 0; l < 32; ++l) {
+            const uint8_t q1 = L[l +  0] & 0xF;
+            const uint8_t q2 = L[l + 32] & 0xF;
+            ql[l] = q1 | (q2 << 4);
+        }
+        for (int l = 0; l < 16; ++l) {
+            qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = ggml_fp16_to_fp32(x[i].d);
+
+        const uint8_t * restrict ql = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const int8_t  * restrict sc = x[i].scales;
+
+#if QK_K == 256
+        for (int n = 0; n < QK_K; n += 128) {
+            for (int l = 0; l < 32; ++l) {
+                int is = l/16;
+                const int8_t q1 = (int8_t)((ql[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                const int8_t q2 = (int8_t)((ql[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                const int8_t q3 = (int8_t)((ql[l +  0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                const int8_t q4 = (int8_t)((ql[l + 32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+                y[l +  0] = d * sc[is + 0] * q1;
+                y[l + 32] = d * sc[is + 2] * q2;
+                y[l + 64] = d * sc[is + 4] * q3;
+                y[l + 96] = d * sc[is + 6] * q4;
             }
+            y  += 128;
+            ql += 64;
+            qh += 32;
+            sc += 8;
         }
-        for (int i = 0; i < n; ++i) {
-            L[i] += nmax;
+#else
+        for (int l = 0; l < 16; ++l) {
+            const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+            const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+            const int8_t q3 = (int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+            const int8_t q4 = (int8_t)((ql[l+16]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            y[l+ 0] = d * sc[0] * q1;
+            y[l+16] = d * sc[1] * q2;
+            y[l+32] = d * sc[2] * q3;
+            y[l+48] = d * sc[3] * q4;
         }
-        return sumlx / suml2;
-    }
-    for (int i = 0; i < n; ++i) {
-        int l = nearest_int(iscale * x[i]);
-        l = MAX(-nmax, MIN(nmax-1, l));
-        L[i] = l + nmax;
+        y  += 64;
+#endif
+
     }
-    return 1/iscale;
 }
 
-static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min,
-        int ntry, float alpha) {
-    float min = x[0];
-    float max = x[0];
-    for (int i = 1; i < n; ++i) {
-        if (x[i] < min) min = x[i];
-        if (x[i] > max) max = x[i];
-    }
-    if (max == min) {
-        for (int i = 0; i < n; ++i) L[i] = 0;
-        *the_min = 0;
-        return 0.f;
+void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_q6_K * restrict y = vy;
+    quantize_row_q6_K_reference(x, y, k);
+}
+
+size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) {
+    assert(k % QK_K == 0);
+    (void)hist; // TODO: collect histograms
+
+    for (int j = 0; j < n; j += k) {
+        block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K;
+        quantize_row_q6_K_reference(src + j, y, k);
     }
-    if (min > 0) min = 0;
-    float iscale = nmax/(max - min);
-    float scale = 1/iscale;
-    for (int itry = 0; itry < ntry; ++itry) {
-        float sumlx = 0; int suml2 = 0;
-        bool did_change = false;
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale*(x[i] - min));
-            l = MAX(0, MIN(nmax, l));
-            if (l != L[i]) {
-                L[i] = l;
-                did_change = true;
+    return (n/QK_K*sizeof(block_q6_K));
+}
+
+//===================================== Q8_K ==============================================
+
+void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        float max = 0;
+        float amax = 0;
+        for (int j = 0; j < QK_K; ++j) {
+            float ax = fabsf(x[j]);
+            if (ax > amax) {
+                amax = ax; max = x[j];
             }
-            sumlx += (x[i] - min)*l;
-            suml2 += l*l;
         }
-        scale = sumlx/suml2;
-        float sum = 0;
-        for (int i = 0; i < n; ++i) {
-            sum += x[i] - scale*L[i];
+        if (!amax) {
+            y[i].d = 0;
+            memset(y[i].qs, 0, QK_K);
+            x += QK_K;
+            continue;
         }
-        min = alpha*min + (1 - alpha)*sum/n;
-        if (min > 0) min = 0;
-        iscale = 1/scale;
-        if (!did_change) break;
-    }
-    *the_min = -min;
-    return scale;
-}
-
-static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
-        uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
-        float rmin, float rdelta, int nstep, bool use_mad) {
-    float min = x[0];
-    float max = x[0];
-    float sum_w = weights[0];
-    float sum_x = sum_w * x[0];
-    for (int i = 1; i < n; ++i) {
-        if (x[i] < min) min = x[i];
-        if (x[i] > max) max = x[i];
-        float w = weights[i];
-        sum_w += w;
-        sum_x += w * x[i];
-    }
-    if (min > 0) min = 0;
-    if (max == min) {
-        for (int i = 0; i < n; ++i) L[i] = 0;
-        *the_min = -min;
-        return 0.f;
-    }
-    float iscale = nmax/(max - min);
-    float scale = 1/iscale;
-    float best_mad = 0;
-    for (int i = 0; i < n; ++i) {
-        int l = nearest_int(iscale*(x[i] - min));
-        L[i] = MAX(0, MIN(nmax, l));
-        float diff = scale * L[i] + min - x[i];
-        diff = use_mad ? fabsf(diff) : diff * diff;
-        float w = weights[i];
-        best_mad += w * diff;
-    }
-    if (nstep < 1) {
-        *the_min = -min;
-        return scale;
-    }
-    for (int is = 0; is <= nstep; ++is) {
-        iscale = (rmin + rdelta*is + nmax)/(max - min);
-        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale*(x[i] - min));
-            l = MAX(0, MIN(nmax, l));
-            Laux[i] = l;
-            float w = weights[i];
-            sum_l += w*l;
-            sum_l2 += w*l*l;
-            sum_xl += w*l*x[i];
+        const float iscale = -128.f/max;
+        for (int j = 0; j < QK_K; ++j) {
+            int v = nearest_int(iscale*x[j]);
+            y[i].qs[j] = MIN(127, v);
         }
-        float D = sum_w * sum_l2 - sum_l * sum_l;
-        if (D > 0) {
-            float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D;
-            float this_min   = (sum_l2 * sum_x - sum_l * sum_xl)/D;
-            if (this_min > 0) {
-                this_min = 0;
-                this_scale = sum_xl / sum_l2;
-            }
-            float mad = 0;
-            for (int i = 0; i < n; ++i) {
-                float diff = this_scale * Laux[i] + this_min - x[i];
-                diff = use_mad ? fabsf(diff) : diff * diff;
-                float w = weights[i];
-                mad += w * diff;
-            }
-            if (mad < best_mad) {
-                for (int i = 0; i < n; ++i) {
-                    L[i] = Laux[i];
-                }
-                best_mad = mad;
-                scale = this_scale;
-                min = this_min;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int sum = 0;
+            for (int ii = 0; ii < 16; ++ii) {
+                sum += y[i].qs[j*16 + ii];
             }
+            y[i].bsums[j] = sum;
         }
+        y[i].d = 1/iscale;
+        x += QK_K;
     }
-    *the_min = -min;
-    return scale;
 }
 
-#if QK_K == 256
-static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) {
-    if (j < 4) {
-        *d = q[j] & 63; *m = q[j + 4] & 63;
-    } else {
-        *d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        *m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+        for (int j = 0; j < QK_K; ++j) {
+            *y++ = x[i].d * x[i].qs[j];
+        }
     }
 }
+
+void quantize_row_q8_K(const float * restrict x, void * restrict y, int k) {
+    quantize_row_q8_K_reference(x, y, k);
+}
+
+//===================================== Dot ptoducts =================================
+
+//
+// Helper functions
+//
+#if __AVX__ || __AVX2__ || __AVX512F__
+
+// shuffles to pick the required scales in dot products
+static inline __m256i get_scale_shuffle_q3k(int i) {
+    static const uint8_t k_shuffle[128] = {
+         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,     2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
+         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,     6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
+         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,    10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
+        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,    14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,
+    };
+    return _mm256_loadu_si256((const __m256i*)k_shuffle + i);
+}
+static inline __m256i get_scale_shuffle_k4(int i) {
+    static const uint8_t k_shuffle[256] = {
+         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
+         2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
+         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,
+         6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
+         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,
+        10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
+        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,
+        14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15
+    };
+    return _mm256_loadu_si256((const __m256i*)k_shuffle + i);
+}
+static inline __m128i get_scale_shuffle(int i) {
+    static const uint8_t k_shuffle[128] = {
+         0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+         2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
+         4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,
+         6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
+         8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9,
+        10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11,
+        12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13,
+        14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15
+    };
+    return _mm_loadu_si128((const __m128i*)k_shuffle + i);
+}
 #endif
 
-//========================- 2-bit (de)-quantization
+void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+
+    const block_q4_0 * restrict x = vx;
+    const block_q8_0 * restrict y = vy;
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-    uint8_t L[QK_K];
-    uint8_t Laux[16];
-    float   weights[16];
-    float mins[QK_K/16];
-    float scales[QK_K/16];
+    assert(nb % 2 == 0); // TODO: handle odd nb
 
-    const float q4scale = 15.f;
+    for (int i = 0; i < nb; i += 2) {
+        const block_q4_0 * restrict x0 = &x[i + 0];
+        const block_q4_0 * restrict x1 = &x[i + 1];
+        const block_q8_0 * restrict y0 = &y[i + 0];
+        const block_q8_0 * restrict y1 = &y[i + 1];
 
-    for (int i = 0; i < nb; i++) {
-        float max_scale = 0; // as we are deducting the min, scales are always positive
-        float max_min = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            for (int l = 0; l < 16; ++l) weights[l] = fabsf(x[16*j + l]);
-            scales[j] = make_qkx2_quants(16, 3, x + 16*j, weights, L + 16*j, &mins[j], Laux, -0.5f, 0.1f, 15, true);
-            float scale = scales[j];
-            if (scale > max_scale) {
-                max_scale = scale;
-            }
-            float min = mins[j];
-            if (min > max_min) {
-                max_min = min;
-            }
-        }
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+        const int8x16_t  s8b = vdupq_n_s8(0x8);
 
-        if (max_scale > 0) {
-            float iscale = q4scale/max_scale;
-            for (int j = 0; j < QK_K/16; ++j) {
-                int l = nearest_int(iscale*scales[j]);
-                y[i].scales[j] = l;
-            }
-            y[i].d = ggml_fp32_to_fp16(max_scale/q4scale);
-        } else {
-            for (int j = 0; j < QK_K/16; ++j) y[i].scales[j] = 0;
-            y[i].d = ggml_fp32_to_fp16(0.f);
-        }
-        if (max_min > 0) {
-            float iscale = q4scale/max_min;
-            for (int j = 0; j < QK_K/16; ++j) {
-                int l = nearest_int(iscale*mins[j]);
-                y[i].scales[j] |= (l << 4);
-            }
-            y[i].dmin = ggml_fp32_to_fp16(max_min/q4scale);
-        } else {
-            y[i].dmin = ggml_fp32_to_fp16(0.f);
-        }
-        for (int j = 0; j < QK_K/16; ++j) {
-            const float d = ggml_fp16_to_fp32(y[i].d) * (y[i].scales[j] & 0xF);
-            if (!d) continue;
-            const float dm = ggml_fp16_to_fp32(y[i].dmin) * (y[i].scales[j] >> 4);
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int((x[16*j + ii] + dm)/d);
-                l = MAX(0, MIN(3, l));
-                L[16*j + ii] = l;
-            }
-        }
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
 
-#if QK_K == 256
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
-            }
-        }
+        // 4-bit -> 8-bit
+        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // sub 8
+        const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
+        const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
+        const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
+        const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        // dot product into int32x4_t
+        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
+        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
 #else
-        for (int l = 0; l < 16; ++l) {
-            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
-        }
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
 #endif
+    }
 
-        x += QK_K;
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__AVX2__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        /* Compute combined scale for the block */
+        const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) );
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
 
+        // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
+        const __m256i off = _mm256_set1_epi8( 8 );
+        bx = _mm256_sub_epi8( bx, off );
+
+        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        /* Multiply q with scale and accumulate */
+        acc = _mm256_fmadd_ps( d, q, acc );
     }
-}
 
-void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    *s = hsum_float_8(acc);
+#elif defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
 
-    for (int i = 0; i < nb; i++) {
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        // Compute combined scale for the block
+        const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) );
 
-        const float d = ggml_fp16_to_fp32(x[i].d);
-        const float min = ggml_fp16_to_fp32(x[i].dmin);
+        const __m128i lowMask = _mm_set1_epi8(0xF);
+        const __m128i off = _mm_set1_epi8(8);
 
-        const uint8_t * q = x[i].qs;
+        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
 
-#if QK_K == 256
-        int is = 0;
-        float dl, ml;
-        for (int n = 0; n < QK_K; n += 128) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
+        __m128i bx = _mm_and_si128(lowMask, tmp);
+        __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs);
+        bx = _mm_sub_epi8(bx, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx, by);
 
-                uint8_t sc = x[i].scales[is++];
-                dl = d * (sc & 0xF); ml = min * (sc >> 4);
-                for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l] >> shift) & 3)) - ml;
+        bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
+        by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        bx = _mm_sub_epi8(bx, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx, by);
 
-                sc = x[i].scales[is++];
-                dl = d * (sc & 0xF); ml = min * (sc >> 4);
-                for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3)) - ml;
+        // Convert int32_t to float
+        __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1));
 
-                shift += 2;
-            }
-            q += 32;
-        }
-#else
-        float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4);
-        float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4);
-        float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4);
-        float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4);
-        for (int l = 0; l < 16; ++l) {
-            y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1;
-            y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2;
-            y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3;
-            y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4;
-        }
-        y += QK_K;
-#endif
+        // Apply the scale, and accumulate
+        acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc);
     }
-}
 
-void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) {
-    quantize_row_q2_K_reference(x, vy, k);
-}
+    *s = hsum_float_8(acc);
+#elif defined(__SSSE3__)
+    // set constants
+    const __m128i lowMask = _mm_set1_epi8(0xF);
+    const __m128i off = _mm_set1_epi8(8);
+
+    // Initialize accumulator with zeros
+    __m128 acc_0 = _mm_setzero_ps();
+    __m128 acc_1 = _mm_setzero_ps();
+    __m128 acc_2 = _mm_setzero_ps();
+    __m128 acc_3 = _mm_setzero_ps();
+
+    // First round without accumulation
+    {
+        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 0 and 1
+        const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[0].d) * ggml_fp16_to_fp32(y[0].d) );
+
+        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
+
+        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
+        bx_0 = _mm_sub_epi8(bx_0, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
+
+        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
+        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
+        bx_1 = _mm_sub_epi8(bx_1, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
+
+        _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 2 and 3
+        const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[1].d) * ggml_fp16_to_fp32(y[1].d) );
+
+        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
+
+        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
+        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
+        bx_2 = _mm_sub_epi8(bx_2, off);
+        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
+
+        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
+        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
+        bx_3 = _mm_sub_epi8(bx_3, off);
+        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
+
+        // Convert int32_t to float
+        __m128 p0 = _mm_cvtepi32_ps(i32_0);
+        __m128 p1 = _mm_cvtepi32_ps(i32_1);
+        __m128 p2 = _mm_cvtepi32_ps(i32_2);
+        __m128 p3 = _mm_cvtepi32_ps(i32_3);
+
+        // Apply the scale
+        acc_0 = _mm_mul_ps( d_0_1, p0 );
+        acc_1 = _mm_mul_ps( d_0_1, p1 );
+        acc_2 = _mm_mul_ps( d_2_3, p2 );
+        acc_3 = _mm_mul_ps( d_2_3, p3 );
+    }
 
-size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    (void)hist; // TODO: collect histograms
+    assert(nb % 2 == 0); // TODO: handle odd nb
 
-    for (int j = 0; j < n; j += k) {
-        block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K;
-        quantize_row_q2_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q2_K));
-}
+    // Main loop
+    for (int i = 2; i < nb; i+=2) {
+        _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
 
-//========================= 3-bit (de)-quantization
+        // Compute combined scale for the block 0 and 1
+        const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) );
 
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
 
-    int8_t L[QK_K];
-    float scales[QK_K / 16];
+        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
+        bx_0 = _mm_sub_epi8(bx_0, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
+
+        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
+        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        bx_1 = _mm_sub_epi8(bx_1, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
+
+        _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
+
+        // Compute combined scale for the block 2 and 3
+        const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[i + 1].d) * ggml_fp16_to_fp32(y[i + 1].d) );
+
+        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
+
+        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
+        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
+        bx_2 = _mm_sub_epi8(bx_2, off);
+        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
+
+        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
+        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
+        bx_3 = _mm_sub_epi8(bx_3, off);
+        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
+
+        // Convert int32_t to float
+        __m128 p0 = _mm_cvtepi32_ps(i32_0);
+        __m128 p1 = _mm_cvtepi32_ps(i32_1);
+        __m128 p2 = _mm_cvtepi32_ps(i32_2);
+        __m128 p3 = _mm_cvtepi32_ps(i32_3);
+
+        // Apply the scale
+        __m128 p0_d = _mm_mul_ps( d_0_1, p0 );
+        __m128 p1_d = _mm_mul_ps( d_0_1, p1 );
+        __m128 p2_d = _mm_mul_ps( d_2_3, p2 );
+        __m128 p3_d = _mm_mul_ps( d_2_3, p3 );
+
+        // Acummulate
+        acc_0 = _mm_add_ps(p0_d, acc_0);
+        acc_1 = _mm_add_ps(p1_d, acc_1);
+        acc_2 = _mm_add_ps(p2_d, acc_2);
+        acc_3 = _mm_add_ps(p3_d, acc_3);
+    }
+
+    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
     for (int i = 0; i < nb; i++) {
+        // load elements
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
 
-        float max_scale = 0;
-        float amax = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            scales[j] = make_q3_quants(16, 4, x + 16*j, L + 16*j, true);
-            float scale = fabsf(scales[j]);
-            if (scale > amax) {
-                amax = scale; max_scale = scales[j];
-            }
-        }
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
 
-#if QK_K == 256
-        memset(y[i].scales, 0, 12);
-        if (max_scale) {
-            float iscale = -32.f/max_scale;
-            for (int j = 0; j < QK_K/16; ++j) {
-                int8_t l = nearest_int(iscale*scales[j]);
-                l = MAX(-32, MIN(31, l)) + 32;
-                if (j < 8) {
-                    y[i].scales[j] = l & 0xF;
-                } else {
-                    y[i].scales[j-8] |= ((l & 0xF) << 4);
-                }
-                l >>= 4;
-                y[i].scales[j%4 + 8] |= (l << (2*(j/4)));
-            }
-            y[i].d = ggml_fp32_to_fp16(1/iscale);
-        } else {
-            y[i].d = ggml_fp32_to_fp16(0.f);
-        }
+        // mask and store lower part of x, and then upper part
+        vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
 
-        int8_t sc;
-        for (int j = 0; j < QK_K/16; ++j) {
-            sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4;
-            sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32;
-            float d = ggml_fp16_to_fp32(y[i].d) * sc;
-            if (!d) {
-                continue;
-            }
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int(x[16*j + ii]/d);
-                l = MAX(-4, MIN(3, l));
-                L[16*j + ii] = l + 4;
-            }
-        }
-#else
-        if (max_scale) {
-            float iscale = -8.f/max_scale;
-            for (int j = 0; j < QK_K/16; j+=2) {
-                int l1 = nearest_int(iscale*scales[j]);
-                l1 = 8 + MAX(-8, MIN(7, l1));
-                int l2 = nearest_int(iscale*scales[j+1]);
-                l2 = 8 + MAX(-8, MIN(7, l2));
-                y[i].scales[j/2] = l1 | (l2 << 4);
-            }
-            y[i].d = ggml_fp32_to_fp16(1/iscale);
-        } else {
-            for (int j = 0; j < QK_K/16; j+=2) {
-                y[i].scales[j/2] = 0;
-            }
-            y[i].d = ggml_fp32_to_fp16(0.f);
-        }
-        for (int j = 0; j < QK_K/16; ++j) {
-            int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4;
-            float d = ggml_fp16_to_fp32(y[i].d) * (s - 8);
-            if (!d) {
-                continue;
-            }
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int(x[16*j + ii]/d);
-                l = MAX(-4, MIN(3, l));
-                L[16*j + ii] = l + 4;
-            }
-        }
-#endif
+        vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
 
-        memset(y[i].hmask, 0, QK_K/8);
-        // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc.
-        int m = 0;
-        uint8_t hm = 1;
-        for (int j = 0; j < QK_K; ++j) {
-            if (L[j] > 3) {
-                y[i].hmask[m] |= hm;
-                L[j] -= 4;
-            }
-            if (++m == QK_K/8) {
-                m = 0; hm <<= 1;
-            }
-        }
-#if QK_K == 256
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
-            }
-        }
+        // subtract offset
+        vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl);
+        vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d);
+    }
+
+    *s = sumf;
 #else
-        for (int l = 0; l < 16; ++l) {
-            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[i].qs[j] & 0x0F) - 8;
+            const int v1 = (x[i].qs[j] >>   4) - 8;
+
+            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
         }
-#endif
 
-        x += QK_K;
+        sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d);
     }
+
+    *s = sumf;
+#endif
 }
 
-#if QK_K == 256
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_1;
+    const int nb = n / qk;
 
-    const uint32_t kmask1 = 0x03030303;
-    const uint32_t kmask2 = 0x0f0f0f0f;
+    assert(n % qk == 0);
 
-    uint32_t aux[4];
-    const int8_t * scales = (const int8_t*)aux;
+    const block_q4_1 * restrict x = vx;
+    const block_q8_1 * restrict y = vy;
 
-    for (int i = 0; i < nb; i++) {
+    // TODO: add WASM SIMD
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-        const float d_all = ggml_fp16_to_fp32(x[i].d);
+    float summs = 0;
 
-        const uint8_t * restrict q = x[i].qs;
-        const uint8_t * restrict hm = x[i].hmask;
-        uint8_t m = 1;
+    assert(nb % 2 == 0); // TODO: handle odd nb
 
-        memcpy(aux, x[i].scales, 12);
-        uint32_t tmp = aux[2];
-        aux[2] = ((aux[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
-        aux[3] = ((aux[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
-        aux[0] = (aux[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
-        aux[1] = (aux[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+    for (int i = 0; i < nb; i += 2) {
+        const block_q4_1 * restrict x0 = &x[i + 0];
+        const block_q4_1 * restrict x1 = &x[i + 1];
+        const block_q8_1 * restrict y0 = &y[i + 0];
+        const block_q8_1 * restrict y1 = &y[i + 1];
 
-        int is = 0;
-        float dl;
-        for (int n = 0; n < QK_K; n += 128) {
-            int shift = 0;
-            for (int j = 0; j < 4; ++j) {
+        summs += ggml_fp16_to_fp32(x0->m) * y0->s + ggml_fp16_to_fp32(x1->m) * y1->s;
 
-                dl = d_all * (scales[is++] - 32);
-                for (int l = 0; l < 16; ++l) {
-                    *y++ = dl * ((int8_t)((q[l+ 0] >> shift) & 3) - ((hm[l+ 0] & m) ? 0 : 4));
-                }
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
-                dl = d_all * (scales[is++] - 32);
-                for (int l = 0; l < 16; ++l) {
-                    *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3) - ((hm[l+16] & m) ? 0 : 4));
-                }
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
 
-                shift += 2;
-                m <<= 1;
-            }
-            q += 32;
-        }
+        // 4-bit -> 8-bit
+        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
 
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        // dot product into int32x4_t
+        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
+        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), ggml_fp16_to_fp32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), ggml_fp16_to_fp32(x1->d)*y1->d);
+#else
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*y1->d);
+#endif
     }
-}
+
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
+#elif defined(__AVX2__) || defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0;
+
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        const float d0 = ggml_fp16_to_fp32(x[i].d);
+        const float d1 = y[i].d;
+
+        summs += ggml_fp16_to_fp32(x[i].m) * y[i].s;
+
+        const __m256 d0v = _mm256_set1_ps( d0 );
+        const __m256 d1v = _mm256_set1_ps( d1 );
+
+        // Compute combined scales
+        const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
+
+        // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
+        const __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
+
+        const __m256 xy = mul_sum_us8_pairs_float(bx, by);
+
+        // Accumulate d0*d1*x*y
+#if defined(__AVX2__)
+        acc = _mm256_fmadd_ps( d0d1, xy, acc );
 #else
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    assert(QK_K == 64);
-    const int nb = k / QK_K;
+        acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc );
+#endif
+    }
+
+    *s = hsum_float_8(acc) + summs;
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
     for (int i = 0; i < nb; i++) {
+        // load elements
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
 
-        const float d_all = ggml_fp16_to_fp32(x[i].d);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
 
-        const uint8_t * restrict q = x[i].qs;
-        const uint8_t * restrict hm = x[i].hmask;
+        // mask and store lower part of x, and then upper part
+        vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
 
-        const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8);
-        const float d2 = d_all * ((x[i].scales[0] >>  4) - 8);
-        const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8);
-        const float d4 = d_all * ((x[i].scales[1] >>  4) - 8);
+        vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
 
-        for (int l=0; l<8; ++l) {
-            uint8_t h = hm[l];
-            y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4));
-            y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4));
-            y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4));
-            y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4));
-            y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4));
-            y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4));
-            y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4));
-            y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4));
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
+    }
+
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
+
+    for (int i = 0; i < nb; i++) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[i].qs[j] & 0x0F);
+            const int v1 = (x[i].qs[j] >>   4);
+
+            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
         }
-        y += QK_K;
+
+        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
     }
+
+    *s = sumf;
+#endif
 }
+
+void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+
+    assert(n % qk == 0);
+    assert(qk == QK5_0);
+
+    const block_q5_0 * restrict x = vx;
+    const block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    uint32_t qh0;
+    uint32_t qh1;
+
+    uint64_t tmp0[4];
+    uint64_t tmp1[4];
+
+    assert(nb % 2 == 0); // TODO: handle odd nb
+
+    for (int i = 0; i < nb; i += 2) {
+        const block_q5_0 * restrict x0 = &x[i];
+        const block_q5_0 * restrict x1 = &x[i + 1];
+        const block_q8_0 * restrict y0 = &y[i];
+        const block_q8_0 * restrict y1 = &y[i + 1];
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+
+        // extract the 5th bit via lookup table ((!b) << 4)
+        memcpy(&qh0, x0->qh, sizeof(qh0));
+        memcpy(&qh1, x1->qh, sizeof(qh1));
+
+        tmp0[0] = table_b2b_1[(qh0 >>  0) & 0xFF];
+        tmp0[1] = table_b2b_1[(qh0 >>  8) & 0xFF];
+        tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF];
+        tmp0[3] = table_b2b_1[(qh0 >> 24)       ];
+
+        tmp1[0] = table_b2b_1[(qh1 >>  0) & 0xFF];
+        tmp1[1] = table_b2b_1[(qh1 >>  8) & 0xFF];
+        tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF];
+        tmp1[3] = table_b2b_1[(qh1 >> 24)       ];
+
+        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
+        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
+        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
+        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));
+
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
+
+        // 4-bit -> 8-bit
+        int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
+        const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0);
+        const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0);
+        const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1);
+        const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1);
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
+                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
+                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+#else
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
 #endif
+    }
 
-void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) {
-    quantize_row_q3_K_reference(x, vy, k);
-}
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__wasm_simd128__)
+    v128_t sumv = wasm_f32x4_splat(0.0f);
 
-size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    (void)hist; // TODO: collect histograms
+    uint32_t qh;
+    uint64_t tmp[4];
 
-    for (int j = 0; j < n; j += k) {
-        block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K;
-        quantize_row_q3_K_reference(src + j, y, k);
+    // TODO: check if unrolling this is better
+    for (int i = 0; i < nb; ++i) {
+        const block_q5_0 * restrict x0 = &x[i];
+        const block_q8_0 * restrict y0 = &y[i];
+
+        const v128_t m4b  = wasm_i8x16_splat(0x0F);
+
+        // extract the 5th bit
+        memcpy(&qh, x0->qh, sizeof(qh));
+
+        tmp[0] = table_b2b_1[(qh >>  0) & 0xFF];
+        tmp[1] = table_b2b_1[(qh >>  8) & 0xFF];
+        tmp[2] = table_b2b_1[(qh >> 16) & 0xFF];
+        tmp[3] = table_b2b_1[(qh >> 24)       ];
+
+        const v128_t qhl = wasm_v128_load(tmp + 0);
+        const v128_t qhh = wasm_v128_load(tmp + 2);
+
+        const v128_t v0 = wasm_v128_load(x0->qs);
+
+        // 4-bit -> 8-bit
+        const v128_t v0l = wasm_v128_and (v0, m4b);
+        const v128_t v0h = wasm_u8x16_shr(v0, 4);
+
+        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
+        const v128_t v0lf = wasm_i8x16_sub(v0l, qhl);
+        const v128_t v0hf = wasm_i8x16_sub(v0h, qhh);
+
+        // load y
+        const v128_t v1l = wasm_v128_load(y0->qs);
+        const v128_t v1h = wasm_v128_load(y0->qs + 16);
+
+        // int8x16 -> int16x8
+        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
+        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
+        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
+        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
+
+        const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
+        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
+        const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
+        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
+
+        // dot product
+        sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(
+                        wasm_i32x4_add(
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
+                                           wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
+                                           wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
+                    wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * ggml_fp16_to_fp32(y0->d))));
     }
-    return (n/QK_K*sizeof(block_q3_K));
-}
 
-// ====================== 4-bit (de)-quantization
+    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
+#elif defined(__AVX2__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
 
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    // Main loop
+    for (int i = 0; i < nb; i++) {
+        /* Compute combined scale for the block */
+        const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d));
 
-    uint8_t L[QK_K];
-    uint8_t Laux[32];
-    float   weights[32];
-    float mins[QK_K/32];
-    float scales[QK_K/32];
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
+        bx = _mm256_or_si256(bx, bxhi);
+
+        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        /* Multiply q with scale and accumulate */
+        acc = _mm256_fmadd_ps(d, q, acc);
+    }
+
+    *s = hsum_float_8(acc);
+#elif defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+    __m128i mask = _mm_set1_epi8((char)0xF0);
 
+    // Main loop
     for (int i = 0; i < nb; i++) {
+        /* Compute combined scale for the block */
+        const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d));
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m128i bxhil = _mm256_castsi256_si128(bxhi);
+        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
+        bxhil = _mm_andnot_si128(bxhil, mask);
+        bxhih = _mm_andnot_si128(bxhih, mask);
+        __m128i bxl = _mm256_castsi256_si128(bx);
+        __m128i bxh = _mm256_extractf128_si256(bx, 1);
+        bxl = _mm_or_si128(bxl, bxhil);
+        bxh = _mm_or_si128(bxh, bxhih);
+        bx = MM256_SET_M128I(bxh, bxl);
+
+        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+
+        /* Multiply q with scale and accumulate */
+        acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
+    }
 
-        float max_scale = 0; // as we are deducting the min, scales are always positive
-        float max_min = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            //scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
-            float sum_x2 = 0;
-            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
-            float av_x = sqrtf(sum_x2/32);
-            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
-            scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
-            float scale = scales[j];
-            if (scale > max_scale) {
-                max_scale = scale;
-            }
-            float min = mins[j];
-            if (min > max_min) {
-                max_min = min;
-            }
-        }
+    *s = hsum_float_8(acc);
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
 
-#if QK_K == 256
-        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
-        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
-        for (int j = 0; j < QK_K/32; ++j) {
-            uint8_t ls = nearest_int(inv_scale*scales[j]);
-            uint8_t lm = nearest_int(inv_min*mins[j]);
-            ls = MIN(63, ls);
-            lm = MIN(63, lm);
-            if (j < 4) {
-                y[i].scales[j] = ls;
-                y[i].scales[j+4] = lm;
-            } else {
-                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
-                y[i].scales[j-4] |= ((ls >> 4) << 6);
-                y[i].scales[j-0] |= ((lm >> 4) << 6);
-            }
-        }
-        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
-        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+    uint32_t qh;
 
-        uint8_t sc, m;
-        for (int j = 0; j < QK_K/32; ++j) {
-            get_scale_min_k4(j, y[i].scales, &sc, &m);
-            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
-            if (!d) continue;
-            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
-            for (int ii = 0; ii < 32; ++ii) {
-                int l = nearest_int((x[32*j + ii] + dm)/d);
-                l = MAX(0, MIN(15, l));
-                L[32*j + ii] = l;
-            }
-        }
-#else
-        const float s_factor = 15.f;
-        float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f;
-        float inv_min   = max_min   > 0 ? s_factor/max_min   : 0.f;
-        int d1 = nearest_int(inv_scale*scales[0]);
-        int m1 = nearest_int(inv_min*mins[0]);
-        int d2 = nearest_int(inv_scale*scales[1]);
-        int m2 = nearest_int(inv_min*mins[1]);
-        y[i].scales[0] = d1 | (m1 << 4);
-        y[i].scales[1] = d2 | (m2 << 4);
-        y[i].d[0] = ggml_fp32_to_fp16(max_scale/s_factor);
-        y[i].d[1] = ggml_fp32_to_fp16(max_min/s_factor);
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
-        float sumlx = 0;
-        int   suml2 = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            const uint8_t sd = y[i].scales[j] & 0xF;
-            const uint8_t sm = y[i].scales[j] >>  4;
-            const float d = ggml_fp16_to_fp32(y[i].d[0]) * sd;
-            if (!d) continue;
-            const float m = ggml_fp16_to_fp32(y[i].d[1]) * sm;
-            for (int ii = 0; ii < 32; ++ii) {
-                int l = nearest_int((x[32*j + ii] + m)/d);
-                l = MAX(0, MIN(15, l));
-                L[32*j + ii] = l;
-                sumlx += (x[32*j + ii] + m)*l*sd;
-                suml2 += l*l*sd*sd;
-            }
-        }
-        if (suml2) {
-            y[i].d[0] = ggml_fp32_to_fp16(sumlx/suml2);
-        }
-#endif
-        uint8_t * q = y[i].qs;
-        for (int j = 0; j < QK_K; j += 64) {
-            for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4);
-            q += 32;
-        }
+    // These tempory registers are for masking and shift operations
+    vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
+    vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl);
+
+    vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl);
+    vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
+
+    for (int i = 0; i < nb; i++) {
+        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+
+        // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+        vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl);
+        vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl);
+        vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl);
+
+        // ((qh & (1u << (j + 16))) >> (j + 12));
+        vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl);
+        vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl);
+
+        // narrowing
+        vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl);
+        vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl);
+
+        vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl);
+        vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
+
+        // load
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+
+        vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
+
+        vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl);
+        vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl);
+
+        vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
 
-        x += QK_K;
+        vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl);
+        vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
 
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi;
     }
-}
 
-void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
 
     for (int i = 0; i < nb; i++) {
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
 
-        const uint8_t * q = x[i].qs;
+        int sumi = 0;
 
-#if QK_K == 256
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
 
-        const float d   = ggml_fp16_to_fp32(x[i].d);
-        const float min = ggml_fp16_to_fp32(x[i].dmin);
+            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
+            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
 
-        int is = 0;
-        uint8_t sc, m;
-        for (int j = 0; j < QK_K; j += 64) {
-            get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
-            const float d1 = d * sc; const float m1 = min * m;
-            get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
-            const float d2 = d * sc; const float m2 = min * m;
-            for (int l = 0; l < 32; ++l) *y++ = d1 * (q[l] & 0xF) - m1;
-            for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l]  >> 4) - m2;
-            q += 32; is += 2;
-        }
-#else
-        const float dall = ggml_fp16_to_fp32(x[i].d[0]);
-        const float mall = ggml_fp16_to_fp32(x[i].d[1]);
-        const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4);
-        const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4);
-        for (int l = 0; l < 32; ++l) {
-            y[l+ 0] = d1 * (q[l] & 0xF) - m1;
-            y[l+32] = d2 * (q[l] >>  4) - m2;
+            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
         }
-        y += QK_K;
-#endif
 
+        sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi;
     }
-}
 
-void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) {
-    assert(k % QK_K == 0);
-    block_q4_K * restrict y = vy;
-    quantize_row_q4_K_reference(x, y, k);
+    *s = sumf;
+#endif
 }
 
-size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_1;
+    const int nb = n / qk;
 
-    for (int j = 0; j < n; j += k) {
-        block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K;
-        quantize_row_q4_K_reference(src + j, y, k);
-    }
-    return (n/QK_K*sizeof(block_q4_K));
-}
+    assert(n % qk == 0);
+    assert(qk == QK5_1);
 
-// ====================== 5-bit (de)-quantization
+    const block_q5_1 * restrict x = vx;
+    const block_q8_1 * restrict y = vy;
 
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-#if QK_K == 256
-    uint8_t L[QK_K];
-    float mins[QK_K/32];
-    float scales[QK_K/32];
-    float weights[32];
-    uint8_t Laux[32];
+    float summs0 = 0.0f;
+    float summs1 = 0.0f;
+
+    uint32_t qh0;
+    uint32_t qh1;
+
+    uint64_t tmp0[4];
+    uint64_t tmp1[4];
+
+    assert(nb % 2 == 0); // TODO: handle odd nb
+
+    for (int i = 0; i < nb; i += 2) {
+        const block_q5_1 * restrict x0 = &x[i];
+        const block_q5_1 * restrict x1 = &x[i + 1];
+        const block_q8_1 * restrict y0 = &y[i];
+        const block_q8_1 * restrict y1 = &y[i + 1];
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0F);
+
+        summs0 += ggml_fp16_to_fp32(x0->m) * y0->s;
+        summs1 += ggml_fp16_to_fp32(x1->m) * y1->s;
+
+        // extract the 5th bit via lookup table ((b) << 4)
+        memcpy(&qh0, x0->qh, sizeof(qh0));
+        memcpy(&qh1, x1->qh, sizeof(qh1));
+
+        tmp0[0] = table_b2b_0[(qh0 >>  0) & 0xFF];
+        tmp0[1] = table_b2b_0[(qh0 >>  8) & 0xFF];
+        tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF];
+        tmp0[3] = table_b2b_0[(qh0 >> 24)       ];
+
+        tmp1[0] = table_b2b_0[(qh1 >>  0) & 0xFF];
+        tmp1[1] = table_b2b_0[(qh1 >>  8) & 0xFF];
+        tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF];
+        tmp1[3] = table_b2b_0[(qh1 >> 24)       ];
+
+        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
+        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
+        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
+        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));
+
+        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
+        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
+
+        // 4-bit -> 8-bit
+        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+        // add high bit
+        const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0);
+        const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0);
+        const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1);
+        const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1);
+
+        // load y
+        const int8x16_t v1_0l = vld1q_s8(y0->qs);
+        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
+        const int8x16_t v1_1l = vld1q_s8(y1->qs);
+        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
+                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), ggml_fp16_to_fp32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
+                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), ggml_fp16_to_fp32(x1->d)*y1->d);
 #else
-    int8_t L[QK_K];
-    float scales[QK_K/16];
+        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
+        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
+        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
+        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
+
+        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
+        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
+        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
+        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
+
+        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
+        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
+        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
+        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*y1->d);
 #endif
+    }
 
-    for (int i = 0; i < nb; i++) {
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
+#elif defined(__wasm_simd128__)
+    v128_t sumv = wasm_f32x4_splat(0.0f);
 
-#if QK_K == 256
+    float summs = 0.0f;
 
-        float max_scale = 0; // as we are deducting the min, scales are always positive
-        float max_min = 0;
-        for (int j = 0; j < QK_K/32; ++j) {
-            //scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
-            float sum_x2 = 0;
-            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
-            float av_x = sqrtf(sum_x2/32);
-            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
-            scales[j] = make_qkx2_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.5f, 0.1f, 15, false);
-            float scale = scales[j];
-            if (scale > max_scale) {
-                max_scale = scale;
-            }
-            float min = mins[j];
-            if (min > max_min) {
-                max_min = min;
-            }
-        }
+    uint32_t qh;
+    uint64_t tmp[4];
 
-        float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
-        float inv_min   = max_min   > 0 ? 63.f/max_min   : 0.f;
-        for (int j = 0; j < QK_K/32; ++j) {
-            uint8_t ls = nearest_int(inv_scale*scales[j]);
-            uint8_t lm = nearest_int(inv_min*mins[j]);
-            ls = MIN(63, ls);
-            lm = MIN(63, lm);
-            if (j < 4) {
-                y[i].scales[j] = ls;
-                y[i].scales[j+4] = lm;
-            } else {
-                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
-                y[i].scales[j-4] |= ((ls >> 4) << 6);
-                y[i].scales[j-0] |= ((lm >> 4) << 6);
-            }
-        }
-        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
-        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+    // TODO: check if unrolling this is better
+    for (int i = 0; i < nb; ++i) {
+        const block_q5_1 * restrict x0 = &x[i];
+        const block_q8_1 * restrict y0 = &y[i];
 
-        uint8_t sc, m;
-        for (int j = 0; j < QK_K/32; ++j) {
-            get_scale_min_k4(j, y[i].scales, &sc, &m);
-            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
-            if (!d) continue;
-            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
-            for (int ii = 0; ii < 32; ++ii) {
-                int l = nearest_int((x[32*j + ii] + dm)/d);
-                l = MAX(0, MIN(31, l));
-                L[32*j + ii] = l;
-            }
-        }
+        summs += ggml_fp16_to_fp32(x0->m) * y0->s;
 
-        uint8_t * restrict qh = y[i].qh;
-        uint8_t * restrict ql = y[i].qs;
-        memset(qh, 0, QK_K/8);
+        const v128_t m4b = wasm_i8x16_splat(0x0F);
 
-        uint8_t m1 = 1, m2 = 2;
-        for (int n = 0; n < QK_K; n += 64) {
-            for (int j = 0; j < 32; ++j) {
-                int l1 = L[n + j];
-                if (l1 > 15) {
-                    l1 -= 16; qh[j] |= m1;
-                }
-                int l2 = L[n + j + 32];
-                if (l2 > 15) {
-                    l2 -= 16; qh[j] |= m2;
-                }
-                ql[j] = l1 | (l2 << 4);
-            }
-            m1 <<= 2; m2 <<= 2;
-            ql += 32;
-        }
-#else
-        float max_scale = 0, amax = 0;
-        for (int j = 0; j < QK_K/16; ++j) {
-            scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1);
-            float abs_scale = fabsf(scales[j]);
-            if (abs_scale > amax) {
-                amax = abs_scale;
-                max_scale = scales[j];
-            }
-        }
+        // extract the 5th bit
+        memcpy(&qh, x0->qh, sizeof(qh));
 
-        float iscale = -128.f/max_scale;
-        for (int j = 0; j < QK_K/16; ++j) {
-            int l = nearest_int(iscale*scales[j]);
-            y[i].scales[j] = MAX(-128, MIN(127, l));
-        }
-        y[i].d = ggml_fp32_to_fp16(1/iscale);
+        tmp[0] = table_b2b_0[(qh >>  0) & 0xFF];
+        tmp[1] = table_b2b_0[(qh >>  8) & 0xFF];
+        tmp[2] = table_b2b_0[(qh >> 16) & 0xFF];
+        tmp[3] = table_b2b_0[(qh >> 24)       ];
 
-        for (int j = 0; j < QK_K/16; ++j) {
-            const float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
-            if (!d) continue;
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int(x[16*j + ii]/d);
-                l = MAX(-16, MIN(15, l));
-                L[16*j + ii] = l + 16;
-            }
-        }
+        const v128_t qhl = wasm_v128_load(tmp + 0);
+        const v128_t qhh = wasm_v128_load(tmp + 2);
 
-        uint8_t * restrict qh = y[i].qh;
-        uint8_t * restrict ql = y[i].qs;
-        memset(qh, 0, QK_K/8);
+        const v128_t v0 = wasm_v128_load(x0->qs);
 
-        for (int j = 0; j < 32; ++j) {
-            int jm = j%8;
-            int is = j/8;
-            int l1 = L[j];
-            if (l1 > 15) {
-                l1 -= 16; qh[jm] |= (1 << is);
-            }
-            int l2 = L[j + 32];
-            if (l2 > 15) {
-                l2 -= 16; qh[jm] |= (1 << (4 + is));
-            }
-            ql[j] = l1 | (l2 << 4);
-        }
-#endif
+        // 4-bit -> 8-bit
+        const v128_t v0l = wasm_v128_and (v0, m4b);
+        const v128_t v0h = wasm_u8x16_shr(v0, 4);
 
-        x += QK_K;
+        // add high bit
+        const v128_t v0lf = wasm_v128_or(v0l, qhl);
+        const v128_t v0hf = wasm_v128_or(v0h, qhh);
+
+        // load y
+        const v128_t v1l = wasm_v128_load(y0->qs);
+        const v128_t v1h = wasm_v128_load(y0->qs + 16);
 
+        // int8x16 -> int16x8
+        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
+        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
+        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
+        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
+
+        const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
+        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
+        const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
+        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
+
+        // dot product
+        sumv = wasm_f32x4_add(sumv,
+                wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add(
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
+                                           wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
+                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
+                                           wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
+                    wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * y0->d)));
     }
-}
 
-void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
+#elif defined(__AVX2__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+
+    float summs = 0.0f;
 
+    // Main loop
     for (int i = 0; i < nb; i++) {
+        const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d));
 
-        const uint8_t * ql = x[i].qs;
-        const uint8_t * qh = x[i].qh;
+        summs += ggml_fp16_to_fp32(x[i].m) * y[i].s;
 
-#if QK_K == 256
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
+        bx = _mm256_or_si256(bx, bxhi);
 
-        const float d = ggml_fp16_to_fp32(x[i].d);
-        const float min = ggml_fp16_to_fp32(x[i].dmin);
+        const __m256 dy = _mm256_set1_ps(y[i].d);
+        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        int is = 0;
-        uint8_t sc, m;
-        uint8_t u1 = 1, u2 = 2;
-        for (int j = 0; j < QK_K; j += 64) {
-            get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
-            const float d1 = d * sc; const float m1 = min * m;
-            get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
-            const float d2 = d * sc; const float m2 = min * m;
-            for (int l = 0; l < 32; ++l) *y++ = d1 * ((ql[l] & 0xF) + (qh[l] & u1 ? 16 : 0)) - m1;
-            for (int l = 0; l < 32; ++l) *y++ = d2 * ((ql[l]  >> 4) + (qh[l] & u2 ? 16 : 0)) - m2;
-            ql += 32; is += 2;
-            u1 <<= 2; u2 <<= 2;
-        }
-#else
-        float d = ggml_fp16_to_fp32(x[i].d);
-        const int8_t * restrict s = x[i].scales;
-        for (int l = 0; l < 8; ++l) {
-            y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16));
-            y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16));
-            y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16));
-            y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16));
-            y[l+32] = d * s[2] * ((ql[l+ 0] >>  4) - (qh[l] & 0x10 ? 0 : 16));
-            y[l+40] = d * s[2] * ((ql[l+ 8] >>  4) - (qh[l] & 0x20 ? 0 : 16));
-            y[l+48] = d * s[3] * ((ql[l+16] >>  4) - (qh[l] & 0x40 ? 0 : 16));
-            y[l+56] = d * s[3] * ((ql[l+24] >>  4) - (qh[l] & 0x80 ? 0 : 16));
-        }
-        y += QK_K;
-#endif
+        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+
+        acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
     }
-}
 
-void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) {
-    assert(k % QK_K == 0);
-    block_q5_K * restrict y = vy;
-    quantize_row_q5_K_reference(x, y, k);
-}
+    *s = hsum_float_8(acc) + summs;
+#elif defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
+    __m128i mask = _mm_set1_epi8(0x10);
 
-size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+    float summs = 0.0f;
 
-    for (int j = 0; j < n; j += k) {
-        block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K;
-        quantize_row_q5_K_reference(src + j, y, k);
+    // Main loop
+    for (int i = 0; i < nb; i++) {
+        const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d));
+
+        summs += ggml_fp16_to_fp32(x[i].m) * y[i].s;
+
+        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m128i bxhil = _mm256_castsi256_si128(bxhi);
+        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
+        bxhil = _mm_and_si128(bxhil, mask);
+        bxhih = _mm_and_si128(bxhih, mask);
+        __m128i bxl = _mm256_castsi256_si128(bx);
+        __m128i bxh = _mm256_extractf128_si256(bx, 1);
+        bxl = _mm_or_si128(bxl, bxhil);
+        bxh = _mm_or_si128(bxh, bxhih);
+        bx = MM256_SET_M128I(bxh, bxl);
+
+        const __m256 dy = _mm256_set1_ps(y[i].d);
+        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+
+        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+
+        acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
     }
-    return (n/QK_K*sizeof(block_q5_K));
-}
 
-// ====================== 6-bit (de)-quantization
+    *s = hsum_float_8(acc) + summs;
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
 
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    uint32_t qh;
 
-    int8_t L[QK_K];
-    float   scales[QK_K/16];
+    size_t vl = __riscv_vsetvl_e8m1(qk/2);
+
+    // temporary registers for shift operations
+    vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
+    vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
 
     for (int i = 0; i < nb; i++) {
+        memcpy(&qh, x[i].qh, sizeof(uint32_t));
 
-        float max_scale = 0;
-        float max_abs_scale = 0;
+        // load qh
+        vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl);
 
-        for (int ib = 0; ib < QK_K/16; ++ib) {
+        // ((qh >> (j +  0)) << 4) & 0x10;
+        vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl);
+        vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl);
+        vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl);
 
-            const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1);
-            scales[ib] = scale;
+        // ((qh >> (j + 12))     ) & 0x10;
+        vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl);
+        vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl);
 
-            const float abs_scale = fabsf(scale);
-            if (abs_scale > max_abs_scale) {
-                max_abs_scale = abs_scale;
-                max_scale = scale;
-            }
+        // narrowing
+        vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl);
+        vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl);
 
-        }
+        vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl);
+        vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
 
-        if (!max_abs_scale) {
-            memset(&y[i], 0, sizeof(block_q6_K));
-            y[i].d = ggml_fp32_to_fp16(0.f);
-            x += QK_K;
-            continue;
-        }
+        // load
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
 
-        float iscale = -128.f/max_scale;
-        y[i].d = ggml_fp32_to_fp16(1/iscale);
-        for (int ib = 0; ib < QK_K/16; ++ib) {
-            y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib]));
-        }
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
 
-        for (int j = 0; j < QK_K/16; ++j) {
-            float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
-            if (!d) {
-                continue;
-            }
-            for (int ii = 0; ii < 16; ++ii) {
-                int l = nearest_int(x[16*j + ii]/d);
-                l = MAX(-32, MIN(31, l));
-                L[16*j + ii] = l + 32;
-            }
-        }
+        vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
+        vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
 
-        uint8_t * restrict ql = y[i].ql;
-        uint8_t * restrict qh = y[i].qh;
-#if QK_K == 256
-        for (int j = 0; j < QK_K; j += 128) {
-            for (int l = 0; l < 32; ++l) {
-                const uint8_t q1 = L[j + l +  0] & 0xF;
-                const uint8_t q2 = L[j + l + 32] & 0xF;
-                const uint8_t q3 = L[j + l + 64] & 0xF;
-                const uint8_t q4 = L[j + l + 96] & 0xF;
-                ql[l+ 0] = q1 | (q3 << 4);
-                ql[l+32] = q2 | (q4 << 4);
-                qh[l] = (L[j + l] >> 4) | ((L[j + l + 32] >> 4) << 2) | ((L[j + l + 64] >> 4) << 4) | ((L[j + l + 96] >> 4) << 6);
-            }
-            ql += 64;
-            qh += 32;
-        }
-#else
-        for (int l = 0; l < 32; ++l) {
-            const uint8_t q1 = L[l +  0] & 0xF;
-            const uint8_t q2 = L[l + 32] & 0xF;
-            ql[l] = q1 | (q2 << 4);
-        }
-        for (int l = 0; l < 16; ++l) {
-            qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6);
-        }
-#endif
+        vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl);
+        vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl);
 
-        x += QK_K;
+        vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
+        vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
+
+        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
+        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+
+        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+
+        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
+        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
 
+        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+
+        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
     }
-}
 
-void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
 
     for (int i = 0; i < nb; i++) {
+        uint32_t qh;
+        memcpy(&qh, x[i].qh, sizeof(qh));
 
-        const float d = ggml_fp16_to_fp32(x[i].d);
+        int sumi = 0;
 
-        const uint8_t * restrict ql = x[i].ql;
-        const uint8_t * restrict qh = x[i].qh;
-        const int8_t  * restrict sc = x[i].scales;
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
 
-#if QK_K == 256
-        for (int n = 0; n < QK_K; n += 128) {
-            for (int l = 0; l < 32; ++l) {
-                int is = l/16;
-                const int8_t q1 = (int8_t)((ql[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-                const int8_t q2 = (int8_t)((ql[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-                const int8_t q3 = (int8_t)((ql[l +  0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-                const int8_t q4 = (int8_t)((ql[l + 32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-                y[l +  0] = d * sc[is + 0] * q1;
-                y[l + 32] = d * sc[is + 2] * q2;
-                y[l + 64] = d * sc[is + 4] * q3;
-                y[l + 96] = d * sc[is + 6] * q4;
-            }
-            y  += 128;
-            ql += 64;
-            qh += 32;
-            sc += 8;
-        }
-#else
-        for (int l = 0; l < 16; ++l) {
-            const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
-            const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
-            const int8_t q3 = (int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
-            const int8_t q4 = (int8_t)((ql[l+16]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
-            y[l+ 0] = d * sc[0] * q1;
-            y[l+16] = d * sc[1] * q2;
-            y[l+32] = d * sc[2] * q3;
-            y[l+48] = d * sc[3] * q4;
+            const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[i].qs[j] >>  4) | xh_1;
+
+            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
         }
-        y  += 64;
-#endif
 
+        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
     }
-}
 
-void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) {
-    assert(k % QK_K == 0);
-    block_q6_K * restrict y = vy;
-    quantize_row_q6_K_reference(x, y, k);
+    *s = sumf;
+#endif
 }
 
-size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
 
-    for (int j = 0; j < n; j += k) {
-        block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K;
-        quantize_row_q6_K_reference(src + j, y, k);
+    assert(n % qk == 0);
+
+    const block_q8_0 * restrict x = vx;
+    const block_q8_0 * restrict y = vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t sumv0 = vdupq_n_f32(0.0f);
+    float32x4_t sumv1 = vdupq_n_f32(0.0f);
+
+    assert(nb % 2 == 0); // TODO: handle odd nb
+
+    for (int i = 0; i < nb; i += 2) {
+        const block_q8_0 * restrict x0 = &x[i + 0];
+        const block_q8_0 * restrict x1 = &x[i + 1];
+        const block_q8_0 * restrict y0 = &y[i + 0];
+        const block_q8_0 * restrict y1 = &y[i + 1];
+
+        const int8x16_t x0_0 = vld1q_s8(x0->qs);
+        const int8x16_t x0_1 = vld1q_s8(x0->qs + 16);
+        const int8x16_t x1_0 = vld1q_s8(x1->qs);
+        const int8x16_t x1_1 = vld1q_s8(x1->qs + 16);
+
+        // load y
+        const int8x16_t y0_0 = vld1q_s8(y0->qs);
+        const int8x16_t y0_1 = vld1q_s8(y0->qs + 16);
+        const int8x16_t y1_0 = vld1q_s8(y1->qs);
+        const int8x16_t y1_1 = vld1q_s8(y1->qs + 16);
+
+#if defined(__ARM_FEATURE_DOTPROD)
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
+                        vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
+                        vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
+                        vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+
+#else
+        const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
+        const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
+        const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1));
+        const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));
+
+        const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0));
+        const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0));
+        const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1));
+        const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1));
+
+        const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
+        const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
+        const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
+        const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
+
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+#endif
     }
-    return (n/QK_K*sizeof(block_q6_K));
-}
 
-//===================================== Q8_K ==============================================
+    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+#elif defined(__AVX2__) || defined(__AVX__)
+    // Initialize accumulator with zeros
+    __m256 acc = _mm256_setzero_ps();
 
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    // Main loop
+    for (int i = 0; i < nb; ++i) {
+        // Compute combined scale for the block
+        const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d));
+        __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
+        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-    for (int i = 0; i < nb; i++) {
+        const __m256 q = mul_sum_i8_pairs_float(bx, by);
 
-        float max = 0;
-        float amax = 0;
-        for (int j = 0; j < QK_K; ++j) {
-            float ax = fabsf(x[j]);
-            if (ax > amax) {
-                amax = ax; max = x[j];
-            }
-        }
-        if (!amax) {
-            y[i].d = 0;
-            memset(y[i].qs, 0, QK_K);
-            x += QK_K;
-            continue;
-        }
-        const float iscale = -128.f/max;
-        for (int j = 0; j < QK_K; ++j) {
-            int v = nearest_int(iscale*x[j]);
-            y[i].qs[j] = MIN(127, v);
-        }
-        for (int j = 0; j < QK_K/16; ++j) {
-            int sum = 0;
-            for (int ii = 0; ii < 16; ++ii) {
-                sum += y[i].qs[j*16 + ii];
-            }
-            y[i].bsums[j] = sum;
-        }
-        y[i].d = 1/iscale;
-        x += QK_K;
+        // Multiply q with scale and accumulate
+#if defined(__AVX2__)
+        acc = _mm256_fmadd_ps( d, q, acc );
+#else
+        acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc );
+#endif
     }
-}
 
-void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k) {
-    assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    *s = hsum_float_8(acc);
+#elif defined(__riscv_v_intrinsic)
+    float sumf = 0.0;
+    size_t vl = __riscv_vsetvl_e8m1(qk);
 
     for (int i = 0; i < nb; i++) {
-        for (int j = 0; j < QK_K; ++j) {
-            *y++ = x[i].d * x[i].qs[j];
-        }
+        // load elements
+        vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl);
+        vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl);
+
+        vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl);
+
+        vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl);
+        vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl);
+
+        int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);
+
+        sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d));
     }
-}
 
-void quantize_row_q8_K(const float * restrict x, void * restrict y, int k) {
-    quantize_row_q8_K_reference(x, y, k);
-}
+    *s = sumf;
+#else
+    // scalar
+    float sumf = 0.0;
 
-//===================================== Dot ptoducts =================================
+    for (int i = 0; i < nb; i++) {
+        int sumi = 0;
 
-//
-// Helper functions
-//
-#if __AVX__ || __AVX2__ || __AVX512F__
+        for (int j = 0; j < qk; j++) {
+            sumi += x[i].qs[j]*y[i].qs[j];
+        }
 
-// horizontally add 8 floats
-static inline float hsum_float_8(const __m256 x) {
-    __m128 res = _mm256_extractf128_ps(x, 1);
-    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
-    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
-    res = _mm_add_ss(res, _mm_movehdup_ps(res));
-    return _mm_cvtss_f32(res);
-}
+        sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d));
+    }
 
-// shuffles to pick the required scales in dot products
-static inline __m256i get_scale_shuffle_q3k(int i) {
-    static const uint8_t k_shuffle[128] = {
-         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,     2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
-         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,     6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
-         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,    10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
-        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,    14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,
-    };
-    return _mm256_loadu_si256((const __m256i*)k_shuffle + i);
-}
-static inline __m256i get_scale_shuffle_k4(int i) {
-    static const uint8_t k_shuffle[256] = {
-         0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
-         2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,
-         4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5,
-         6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7,
-         8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9,
-        10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,
-        12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,
-        14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15
-    };
-    return _mm256_loadu_si256((const __m256i*)k_shuffle + i);
-}
-static inline __m128i get_scale_shuffle(int i) {
-    static const uint8_t k_shuffle[128] = {
-         0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
-         2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
-         4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,
-         6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7,
-         8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9,
-        10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11,
-        12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13,
-        14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15
-    };
-    return _mm_loadu_si128((const __m128i*)k_shuffle + i);
-}
+    *s = sumf;
 #endif
+}
 
 #if QK_K == 256
 void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
diff --git a/k_quants.h b/ggml-quants.h
similarity index 63%
rename from k_quants.h
rename to ggml-quants.h
index 9de089e7a47195..d88f99e331f1dd 100644
--- a/k_quants.h
+++ b/ggml-quants.h
@@ -1,20 +1,14 @@
 #pragma once
 
+// This is a private API for quantization and dequantization
+// Should not be used directly, use ggml.h instead
+
 #include "ggml.h"
 
 #include <stdint.h>
 #include <assert.h>
 #include <stddef.h>
 
-// Super-block size
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
 #ifndef static_assert
 #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
 #define static_assert(cond, msg) _Static_assert(cond, msg)
@@ -23,10 +17,66 @@
 #endif
 #endif
 
+#define QK4_0 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    uint8_t qs[QK4_0 / 2];  // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define QK4_1 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    ggml_fp16_t m;          // min
+    uint8_t qs[QK4_1 / 2];  // nibbles / quants
+} block_q4_1;
+static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
+
+#define QK5_0 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2]; // nibbles / quants
+} block_q5_0;
+static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
+
+#define QK5_1 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    ggml_fp16_t m;         // min
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2]; // nibbles / quants
+} block_q5_1;
+static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+
+#define QK8_0 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    int8_t  qs[QK8_0];     // quants
+} block_q8_0;
+static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
+
+#define QK8_1 32
+typedef struct {
+    float d;               // delta
+    float s;               // d * sum(qs[i])
+    int8_t  qs[QK8_1];     // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
+
 //
 // Super-block quantization structures
 //
 
+// Super-block size
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif
+
 // 2-bit quantization
 // weight is represented as x = a * q + b
 // 16 blocks of 16 elements each
@@ -127,6 +177,13 @@ static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_
 
 
 // Quantization
+void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
+void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
+void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k);
+void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k);
+void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k);
+void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k);
+
 void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
 void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
 void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
@@ -134,6 +191,13 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
 void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
 void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
 
+void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
+void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
+void quantize_row_q5_0(const float * restrict x, void * restrict y, int k);
+void quantize_row_q5_1(const float * restrict x, void * restrict y, int k);
+void quantize_row_q8_0(const float * restrict x, void * restrict y, int k);
+void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
+
 void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
 void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
 void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
@@ -142,6 +206,13 @@ void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
 void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
 
 // Dequantization
+void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
+void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k);
+void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k);
+void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k);
+void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k);
+//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k);
+
 void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
 void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
 void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
@@ -150,16 +221,14 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int
 void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
 
 // Dot product
+void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+
 void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-
-// Quantization with histogram collection
-size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
-size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
-
diff --git a/ggml.c b/ggml.c
index 6f66bab051cea4..95f72c35e8f205 100644
--- a/ggml.c
+++ b/ggml.c
@@ -1,10 +1,7 @@
 #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
 
 #include "ggml.h"
-
-#ifdef GGML_USE_K_QUANTS
-#include "k_quants.h"
-#endif
+#include "ggml-quants.h"
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
@@ -443,21 +440,6 @@ static ggml_fp16_t table_exp_f16[1 << 16];
 // precomputed f32 table for f16 (256 KB)
 static float table_f32_f16[1 << 16];
 
-#if defined(__ARM_NEON) || defined(__wasm_simd128__)
-#define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
-#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
-#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s)
-#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s)
-#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s)
-#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s)
-#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s)
-#define B8(c,s  ) B7(c,s,     c), B7(c,s,     s)
-
-// precomputed tables for expanding 8bits to 8 bytes:
-static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4
-static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
-#endif
-
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
 // This is also true for POWER9.
@@ -587,3071 +569,794 @@ int64_t ggml_cycles_per_ms(void) {
 
 static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
 
-//
-// quantization
-//
-
-#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
-
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
-// multiply int8_t, add results pairwise twice
-static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
-    // Get absolute values of x vectors
-    const __m128i ax = _mm_sign_epi8(x, x);
-    // Sign the values of the y vectors
-    const __m128i sy = _mm_sign_epi8(y, x);
-    // Perform multiplication and create 16-bit values
-    const __m128i dot = _mm_maddubs_epi16(ax, sy);
-    const __m128i ones = _mm_set1_epi16(1);
-    return _mm_madd_epi16(ones, dot);
-}
-
-#if __AVX__ || __AVX2__ || __AVX512F__
-// horizontally add 8 floats
-static inline float hsum_float_8(const __m256 x) {
-    __m128 res = _mm256_extractf128_ps(x, 1);
-    res = _mm_add_ps(res, _mm256_castps256_ps128(x));
-    res = _mm_add_ps(res, _mm_movehl_ps(res, res));
-    res = _mm_add_ss(res, _mm_movehdup_ps(res));
-    return _mm_cvtss_f32(res);
-}
-
-// horizontally add 8 int32_t
-static inline int hsum_i32_8(const __m256i a) {
-    const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
-    const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128);
-    const __m128i sum64 = _mm_add_epi32(hi64, sum128);
-    const __m128i hi32  = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
-    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
-}
-
-// horizontally add 4 int32_t
-static inline int hsum_i32_4(const __m128i a) {
-    const __m128i hi64 = _mm_unpackhi_epi64(a, a);
-    const __m128i sum64 = _mm_add_epi32(hi64, a);
-    const __m128i hi32  = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
-    return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
-}
-
-#if defined(__AVX2__) || defined(__AVX512F__)
-// spread 32 bits to 32 bytes { 0x00, 0xFF }
-static inline __m256i bytes_from_bits_32(const uint8_t * x) {
-    uint32_t x32;
-    memcpy(&x32, x, sizeof(uint32_t));
-    const __m256i shuf_mask = _mm256_set_epi64x(
-            0x0303030303030303, 0x0202020202020202,
-            0x0101010101010101, 0x0000000000000000);
-    __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask);
-    const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe);
-    bytes = _mm256_or_si256(bytes, bit_mask);
-    return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1));
-}
-
-// Unpack 32 4-bit fields into 32 bytes
-// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
-static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
-{
-    const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi);
-    const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp);
-    const __m256i lowMask = _mm256_set1_epi8( 0xF );
-    return _mm256_and_si256(lowMask, bytes);
-}
-
-// add int16_t pairwise and return as float vector
-static inline __m256 sum_i16_pairs_float(const __m256i x) {
-    const __m256i ones = _mm256_set1_epi16(1);
-    const __m256i summed_pairs = _mm256_madd_epi16(ones, x);
-    return _mm256_cvtepi32_ps(summed_pairs);
-}
-
-static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
-#if __AVXVNNI__
-    const __m256i zero = _mm256_setzero_si256();
-    const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
-    return _mm256_cvtepi32_ps(summed_pairs);
-#else
-    // Perform multiplication and create 16-bit values
-    const __m256i dot = _mm256_maddubs_epi16(ax, sy);
-    return sum_i16_pairs_float(dot);
-#endif
-}
-
-// multiply int8_t, add results pairwise twice and return as float vector
-static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
-#if __AVXVNNIINT8__
-    const __m256i zero = _mm256_setzero_si256();
-    const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
-    return _mm256_cvtepi32_ps(summed_pairs);
-#else
-    // Get absolute values of x vectors
-    const __m256i ax = _mm256_sign_epi8(x, x);
-    // Sign the values of the y vectors
-    const __m256i sy = _mm256_sign_epi8(y, x);
-    return mul_sum_us8_pairs_float(ax, sy);
-#endif
-}
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
 
-static inline __m128i packNibbles( __m256i bytes )
-{
-    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
-#if __AVX512F__
-    const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4);   // 0000_0000_abcd_0000
-    bytes = _mm256_or_si256(bytes, bytes_srli_4);               // 0000_abcd_abcd_efgh
-    return _mm256_cvtepi16_epi8(bytes);                         // abcd_efgh
-#else
-    const __m256i lowByte = _mm256_set1_epi16( 0xFF );
-    __m256i high = _mm256_andnot_si256( lowByte, bytes );
-    __m256i low = _mm256_and_si256( lowByte, bytes );
-    high = _mm256_srli_epi16( high, 4 );
-    bytes = _mm256_or_si256( low, high );
-
-    // Compress uint16_t lanes into bytes
-    __m128i r0 = _mm256_castsi256_si128( bytes );
-    __m128i r1 = _mm256_extracti128_si256( bytes, 1 );
-    return _mm_packus_epi16( r0, r1 );
-#endif
-}
-#elif defined(__AVX__)
-// spread 32 bits to 32 bytes { 0x00, 0xFF }
-static inline __m256i bytes_from_bits_32(const uint8_t * x) {
-    uint32_t x32;
-    memcpy(&x32, x, sizeof(uint32_t));
-    const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000);
-    const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202);
-    __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl);
-    __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh);
-    const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe);
-    bytesl = _mm_or_si128(bytesl, bit_mask);
-    bytesh = _mm_or_si128(bytesh, bit_mask);
-    bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1));
-    bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1));
-    return MM256_SET_M128I(bytesh, bytesl);
-}
-
-// Unpack 32 4-bit fields into 32 bytes
-// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
-static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi)
-{
-    // Load 16 bytes from memory
-    __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi);
-    __m128i tmph = _mm_srli_epi16(tmpl, 4);
-    const __m128i lowMask = _mm_set1_epi8(0xF);
-    tmpl = _mm_and_si128(lowMask, tmpl);
-    tmph = _mm_and_si128(lowMask, tmph);
-    return MM256_SET_M128I(tmph, tmpl);
-}
-
-// add int16_t pairwise and return as float vector
-static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
-    const __m128i ones = _mm_set1_epi16(1);
-    const __m128i summed_pairsl = _mm_madd_epi16(ones, xl);
-    const __m128i summed_pairsh = _mm_madd_epi16(ones, xh);
-    const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl);
-    return _mm256_cvtepi32_ps(summed_pairs);
-}
-
-static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
-    const __m128i axl = _mm256_castsi256_si128(ax);
-    const __m128i axh = _mm256_extractf128_si256(ax, 1);
-    const __m128i syl = _mm256_castsi256_si128(sy);
-    const __m128i syh = _mm256_extractf128_si256(sy, 1);
-    // Perform multiplication and create 16-bit values
-    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
-    const __m128i doth = _mm_maddubs_epi16(axh, syh);
-    return sum_i16_pairs_float(doth, dotl);
-}
-
-// multiply int8_t, add results pairwise twice and return as float vector
-static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
-    const __m128i xl = _mm256_castsi256_si128(x);
-    const __m128i xh = _mm256_extractf128_si256(x, 1);
-    const __m128i yl = _mm256_castsi256_si128(y);
-    const __m128i yh = _mm256_extractf128_si256(y, 1);
-    // Get absolute values of x vectors
-    const __m128i axl = _mm_sign_epi8(xl, xl);
-    const __m128i axh = _mm_sign_epi8(xh, xh);
-    // Sign the values of the y vectors
-    const __m128i syl = _mm_sign_epi8(yl, xl);
-    const __m128i syh = _mm_sign_epi8(yh, xh);
-    // Perform multiplication and create 16-bit values
-    const __m128i dotl = _mm_maddubs_epi16(axl, syl);
-    const __m128i doth = _mm_maddubs_epi16(axh, syh);
-    return sum_i16_pairs_float(doth, dotl);
-}
-
-static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 )
-{
-    // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
-    const __m128i lowByte = _mm_set1_epi16( 0xFF );
-    __m128i high = _mm_andnot_si128( lowByte, bytes1 );
-    __m128i low = _mm_and_si128( lowByte, bytes1 );
-    high = _mm_srli_epi16( high, 4 );
-    bytes1 = _mm_or_si128( low, high );
-    high = _mm_andnot_si128( lowByte, bytes2 );
-    low = _mm_and_si128( lowByte, bytes2 );
-    high = _mm_srli_epi16( high, 4 );
-    bytes2 = _mm_or_si128( low, high );
-
-    return _mm_packus_epi16( bytes1, bytes2);
-}
-#endif
-#elif defined(__SSSE3__)
-// horizontally add 4x4 floats
-static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) {
-    __m128 res_0 =_mm_hadd_ps(a, b);
-    __m128 res_1 =_mm_hadd_ps(c, d);
-    __m128 res =_mm_hadd_ps(res_0, res_1);
-    res =_mm_hadd_ps(res, res);
-    res =_mm_hadd_ps(res, res);
+static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+    [GGML_TYPE_I8] = {
+        .type_name                = "i8",
+        .blck_size                = 1,
+        .type_size                = sizeof(int8_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I16] = {
+        .type_name                = "i16",
+        .blck_size                = 1,
+        .type_size                = sizeof(int16_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_I32] = {
+        .type_name                = "i32",
+        .blck_size                = 1,
+        .type_size                = sizeof(int32_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_F32] = {
+        .type_name                = "f32",
+        .blck_size                = 1,
+        .type_size                = sizeof(float),
+        .is_quantized             = false,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
+        .vec_dot_type             = GGML_TYPE_F32,
+    },
+    [GGML_TYPE_F16] = {
+        .type_name                = "f16",
+        .blck_size                = 1,
+        .type_size                = sizeof(ggml_fp16_t),
+        .is_quantized             = false,
+        .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
+        .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
+        .vec_dot_type             = GGML_TYPE_F16,
+    },
+    [GGML_TYPE_Q4_0] = {
+        .type_name                = "q4_0",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
+        .from_float               = quantize_row_q4_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
+        .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+    },
+    [GGML_TYPE_Q4_1] = {
+        .type_name                = "q4_1",
+        .blck_size                = QK4_1,
+        .type_size                = sizeof(block_q4_1),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
+        .from_float               = quantize_row_q4_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
+        .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+    },
+    [GGML_TYPE_Q5_0] = {
+        .type_name                = "q5_0",
+        .blck_size                = QK5_0,
+        .type_size                = sizeof(block_q5_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
+        .from_float               = quantize_row_q5_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
+        .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+    },
+    [GGML_TYPE_Q5_1] = {
+        .type_name                = "q5_1",
+        .blck_size                = QK5_1,
+        .type_size                = sizeof(block_q5_1),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
+        .from_float               = quantize_row_q5_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
+        .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+    },
+    [GGML_TYPE_Q8_0] = {
+        .type_name                = "q8_0",
+        .blck_size                = QK8_0,
+        .type_size                = sizeof(block_q8_0),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q8_0,
+        .from_float               = quantize_row_q8_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
+        .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+    },
+    [GGML_TYPE_Q8_1] = {
+        .type_name                = "q8_1",
+        .blck_size                = QK8_1,
+        .type_size                = sizeof(block_q8_1),
+        .is_quantized             = true,
+        .from_float               = quantize_row_q8_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
+        .vec_dot_type             = GGML_TYPE_Q8_1,
+    },
+    [GGML_TYPE_Q2_K] = {
+        .type_name                = "q2_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q2_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
+        .from_float               = quantize_row_q2_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
+        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q3_K] = {
+        .type_name                = "q3_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q3_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
+        .from_float               = quantize_row_q3_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
+        .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q4_K] = {
+        .type_name                = "q4_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q4_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
+        .from_float               = quantize_row_q4_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
+        .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q5_K] = {
+        .type_name                = "q5_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q5_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
+        .from_float               = quantize_row_q5_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
+        .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q6_K] = {
+        .type_name                = "q6_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q6_K),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
+        .from_float               = quantize_row_q6_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
+        .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+    },
+    [GGML_TYPE_Q8_K] = {
+        .type_name                = "q8_K",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_q8_K),
+        .is_quantized             = true,
+        .from_float               = quantize_row_q8_K,
+    }
+};
 
-    return _mm_cvtss_f32(res);
+// For internal test use
+ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
+    GGML_ASSERT(type < GGML_TYPE_COUNT);
+    return type_traits[type];
 }
-#endif // __AVX__ || __AVX2__ || __AVX512F__
-#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
-
-#if defined(__ARM_NEON)
 
-#if !defined(__aarch64__)
+//
+// simd mappings
+//
 
-inline static int32_t vaddvq_s32(int32x4_t v) {
-    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
-}
+// we define a common set of C macros which map to specific intrinsics based on the current architecture
+// we then implement the fundamental computation operations below using only these macros
+// adding support for new architectures requires to define the corresponding SIMD macros
+//
+// GGML_F32_STEP / GGML_F16_STEP
+//   number of elements to process in a single step
+//
+// GGML_F32_EPR / GGML_F16_EPR
+//   number of elements to fit in a single register
+//
 
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
 
-inline static float vmaxvq_f32(float32x4_t v) {
-    return
-        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
-            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
-}
+#define GGML_SIMD
 
-inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
-    int32x4_t res;
+// F32 NEON
 
-    res[0] = roundf(vgetq_lane_f32(v, 0));
-    res[1] = roundf(vgetq_lane_f32(v, 1));
-    res[2] = roundf(vgetq_lane_f32(v, 2));
-    res[3] = roundf(vgetq_lane_f32(v, 3));
+#define GGML_F32_STEP 16
+#define GGML_F32_EPR  4
 
-    return res;
+#define GGML_F32x4              float32x4_t
+#define GGML_F32x4_ZERO         vdupq_n_f32(0.0f)
+#define GGML_F32x4_SET1(x)      vdupq_n_f32(x)
+#define GGML_F32x4_LOAD         vld1q_f32
+#define GGML_F32x4_STORE        vst1q_f32
+#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c)
+#define GGML_F32x4_ADD          vaddq_f32
+#define GGML_F32x4_MUL          vmulq_f32
+#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
+#define GGML_F32x4_REDUCE(res, x)              \
+{                                              \
+    int offset = GGML_F32_ARR >> 1;            \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vaddq_f32(x[i], x[offset+i]);   \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vaddq_f32(x[i], x[offset+i]);   \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vaddq_f32(x[i], x[offset+i]);   \
+    }                                          \
+    res = GGML_F32x4_REDUCE_ONE(x[0]);         \
 }
 
-#endif
-#endif
-
-#define QK4_0 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    ggml_fp16_t m;          // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    ggml_fp16_t m;         // min
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2]; // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    int8_t  qs[QK8_0];     // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-typedef struct {
-    float d;               // delta
-    float s;               // d * sum(qs[i])
-    int8_t  qs[QK8_1];     // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
-
-// reference implementation for deterministic creation of model files
-static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k) {
-    static const int qk = QK4_0;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f; // absolute max
-        float max  = 0.0f;
-
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i*qk + j];
-            if (amax < fabsf(v)) {
-                amax = fabsf(v);
-                max  = v;
-            }
-        }
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
 
-        const float d  = max / -8;
-        const float id = d ? 1.0f/d : 0.0f;
+// F16 NEON
 
-        y[i].d = GGML_FP32_TO_FP16(d);
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    #define GGML_F16_STEP 32
+    #define GGML_F16_EPR  8
 
-        for (int j = 0; j < qk/2; ++j) {
-            const float x0 = x[i*qk + 0    + j]*id;
-            const float x1 = x[i*qk + qk/2 + j]*id;
-
-            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
-            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));
-
-            y[i].qs[j]  = xi0;
-            y[i].qs[j] |= xi1 << 4;
-        }
-    }
-}
-
-static void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
-    quantize_row_q4_0_reference(x, y, k);
-}
-
-static void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
-    const int qk = QK4_1;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        float min = FLT_MAX;
-        float max = -FLT_MAX;
-
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i*qk + j];
-
-            if (v < min) min = v;
-            if (v > max) max = v;
-        }
-
-        const float d  = (max - min) / ((1 << 4) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-        y[i].m = GGML_FP32_TO_FP16(min);
-
-        for (int j = 0; j < qk/2; ++j) {
-            const float x0 = (x[i*qk + 0    + j] - min)*id;
-            const float x1 = (x[i*qk + qk/2 + j] - min)*id;
-
-            const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
-            const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));
-
-            y[i].qs[j]  = xi0;
-            y[i].qs[j] |= xi1 << 4;
-        }
-    }
-}
-
-static void quantize_row_q4_1(const float * restrict x, void * restrict y, int k) {
-    quantize_row_q4_1_reference(x, y, k);
-}
-
-static void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k) {
-    static const int qk = QK5_0;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f; // absolute max
-        float max  = 0.0f;
-
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i*qk + j];
-            if (amax < fabsf(v)) {
-                amax = fabsf(v);
-                max  = v;
-            }
-        }
-
-        const float d  = max / -16;
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-
-        uint32_t qh = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const float x0 = x[i*qk + 0    + j]*id;
-            const float x1 = x[i*qk + qk/2 + j]*id;
-
-            const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
-            const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
-
-            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
-
-            // get the 5-th bit and store it in qh at the right position
-            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
-            qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2);
-        }
-
-        memcpy(&y[i].qh, &qh, sizeof(qh));
-    }
-}
-
-static void quantize_row_q5_0(const float * restrict x, void * restrict y, int k) {
-    quantize_row_q5_0_reference(x, y, k);
-}
-
-static void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k) {
-    const int qk = QK5_1;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        float min = FLT_MAX;
-        float max = -FLT_MAX;
-
-        for (int j = 0; j < qk; j++) {
-            const float v = x[i*qk + j];
-
-            if (v < min) min = v;
-            if (v > max) max = v;
-        }
-
-        const float d  = (max - min) / ((1 << 5) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-        y[i].m = GGML_FP32_TO_FP16(min);
-
-        uint32_t qh = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const float x0 = (x[i*qk + 0    + j] - min)*id;
-            const float x1 = (x[i*qk + qk/2 + j] - min)*id;
-
-            const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
-            const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
-
-            y[i].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
-
-            // get the 5-th bit and store it in qh at the right position
-            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
-            qh |= ((xi1 & 0x10u) >> 4) << (j + qk/2);
-        }
-
-        memcpy(&y[i].qh, &qh, sizeof(y[i].qh));
-    }
-}
-
-static void quantize_row_q5_1(const float * restrict x, void * restrict y, int k) {
-    quantize_row_q5_1_reference(x, y, k);
-}
-
-// reference implementation for deterministic creation of model files
-static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
-    assert(k % QK8_0 == 0);
-    const int nb = k / QK8_0;
-
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f; // absolute max
-
-        for (int j = 0; j < QK8_0; j++) {
-            const float v = x[i*QK8_0 + j];
-            amax = MAX(amax, fabsf(v));
-        }
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-
-        for (int j = 0; j < QK8_0; ++j) {
-            const float x0 = x[i*QK8_0 + j]*id;
-
-            y[i].qs[j] = roundf(x0);
-        }
-    }
-}
-
-static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
-    assert(QK8_0 == 32);
-    assert(k % QK8_0 == 0);
-    const int nb = k / QK8_0;
-
-    block_q8_0 * restrict y = vy;
-
-#if defined(__ARM_NEON)
-    for (int i = 0; i < nb; i++) {
-        float32x4_t srcv [8];
-        float32x4_t asrcv[8];
-        float32x4_t amaxv[8];
-
-        for (int j = 0; j < 8; j++) srcv[j]  = vld1q_f32(x + i*32 + 4*j);
-        for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]);
-
-        for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]);
-        for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]);
-        for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]);
-
-        const float amax = vmaxvq_f32(amaxv[0]);
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-
-        for (int j = 0; j < 8; j++) {
-            const float32x4_t v  = vmulq_n_f32(srcv[j], id);
-            const int32x4_t   vi = vcvtnq_s32_f32(v);
-
-            y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0);
-            y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1);
-            y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2);
-            y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3);
-        }
-    }
-#elif defined(__wasm_simd128__)
-    for (int i = 0; i < nb; i++) {
-        v128_t srcv [8];
-        v128_t asrcv[8];
-        v128_t amaxv[8];
-
-        for (int j = 0; j < 8; j++) srcv[j]  = wasm_v128_load(x + i*32 + 4*j);
-        for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]);
-
-        for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]);
-        for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]);
-        for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]);
-
-        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0),
-                                   wasm_f32x4_extract_lane(amaxv[0], 1)),
-                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2),
-                                   wasm_f32x4_extract_lane(amaxv[0], 3)));
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-
-        for (int j = 0; j < 8; j++) {
-            const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
-            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);
-
-            y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0);
-            y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1);
-            y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2);
-            y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3);
-        }
-    }
-#elif defined(__AVX2__) || defined(__AVX__)
-    for (int i = 0; i < nb; i++) {
-        // Load elements into 4 AVX vectors
-        __m256 v0 = _mm256_loadu_ps( x );
-        __m256 v1 = _mm256_loadu_ps( x + 8 );
-        __m256 v2 = _mm256_loadu_ps( x + 16 );
-        __m256 v3 = _mm256_loadu_ps( x + 24 );
-        x += 32;
-
-        // Compute max(abs(e)) for the block
-        const __m256 signBit = _mm256_set1_ps( -0.0f );
-        __m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
-        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
-        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
-        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
-
-        __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
-        max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
-        max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
-        const float maxScalar = _mm_cvtss_f32( max4 );
-
-        // Quantize these floats
-        const float d = maxScalar / 127.f;
-        y[i].d = GGML_FP32_TO_FP16(d);
-        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
-        const __m256 mul = _mm256_set1_ps( id );
-
-        // Apply the multiplier
-        v0 = _mm256_mul_ps( v0, mul );
-        v1 = _mm256_mul_ps( v1, mul );
-        v2 = _mm256_mul_ps( v2, mul );
-        v3 = _mm256_mul_ps( v3, mul );
-
-        // Round to nearest integer
-        v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
-        v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
-        v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
-        v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
-
-        // Convert floats to integers
-        __m256i i0 = _mm256_cvtps_epi32( v0 );
-        __m256i i1 = _mm256_cvtps_epi32( v1 );
-        __m256i i2 = _mm256_cvtps_epi32( v2 );
-        __m256i i3 = _mm256_cvtps_epi32( v3 );
-
-#if defined(__AVX2__)
-        // Convert int32 to int16
-        i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
-        i2 = _mm256_packs_epi32( i2, i3 );	// 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
-                                            // Convert int16 to int8
-        i0 = _mm256_packs_epi16( i0, i2 );	// 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
-
-        // We got our precious signed bytes, but the order is now wrong
-        // These AVX2 pack instructions process 16-byte pieces independently
-        // The following instruction is fixing the order
-        const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
-        i0 = _mm256_permutevar8x32_epi32( i0, perm );
-
-        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
-#else
-        // Since we don't have in AVX some necessary functions,
-        // we split the registers in half and call AVX2 analogs from SSE
-        __m128i ni0 = _mm256_castsi256_si128( i0 );
-        __m128i ni1 = _mm256_extractf128_si256( i0, 1);
-        __m128i ni2 = _mm256_castsi256_si128( i1 );
-        __m128i ni3 = _mm256_extractf128_si256( i1, 1);
-        __m128i ni4 = _mm256_castsi256_si128( i2 );
-        __m128i ni5 = _mm256_extractf128_si256( i2, 1);
-        __m128i ni6 = _mm256_castsi256_si128( i3 );
-        __m128i ni7 = _mm256_extractf128_si256( i3, 1);
-
-        // Convert int32 to int16
-        ni0 = _mm_packs_epi32( ni0, ni1 );
-        ni2 = _mm_packs_epi32( ni2, ni3 );
-        ni4 = _mm_packs_epi32( ni4, ni5 );
-        ni6 = _mm_packs_epi32( ni6, ni7 );
-        // Convert int16 to int8
-        ni0 = _mm_packs_epi16( ni0, ni2 );
-        ni4 = _mm_packs_epi16( ni4, ni6 );
-
-        _mm_storeu_si128((__m128i *)(y[i].qs +  0), ni0);
-        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
-#endif
-    }
-#elif defined(__riscv_v_intrinsic)
-
-    size_t vl = __riscv_vsetvl_e32m4(QK8_0);
-
-    for (int i = 0; i < nb; i++) {
-        // load elements
-        vfloat32m4_t v_x   = __riscv_vle32_v_f32m4(x+i*QK8_0, vl);
-
-        vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl);
-        vfloat32m1_t tmp   = __riscv_vfmv_v_f_f32m1(0.0f, vl);
-        vfloat32m1_t vmax  = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl);
-        float amax = __riscv_vfmv_f_s_f32m1_f32(vmax);
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = GGML_FP32_TO_FP16(d);
-
-        vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
-
-        // convert to integer
-        vint16m2_t   vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl);
-        vint8m1_t    vs = __riscv_vncvt_x_x_w_i8m1(vi, vl);
-
-        // store result
-        __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
-    }
-#else
-    // scalar
-    quantize_row_q8_0_reference(x, y, k);
-#endif
-}
-
-// reference implementation for deterministic creation of model files
-static void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k) {
-    assert(QK8_1 == 32);
-    assert(k % QK8_1 == 0);
-    const int nb = k / QK8_1;
-
-    for (int i = 0; i < nb; i++) {
-        float amax = 0.0f; // absolute max
-
-        for (int j = 0; j < QK8_1; j++) {
-            const float v = x[i*QK8_1 + j];
-            amax = MAX(amax, fabsf(v));
-        }
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = d;
-
-        int sum = 0;
-
-        for (int j = 0; j < QK8_1/2; ++j) {
-            const float v0 = x[i*QK8_1           + j]*id;
-            const float v1 = x[i*QK8_1 + QK8_1/2 + j]*id;
-
-            y[i].qs[          j] = roundf(v0);
-            y[i].qs[QK8_1/2 + j] = roundf(v1);
-
-            sum += y[i].qs[          j];
-            sum += y[i].qs[QK8_1/2 + j];
-        }
-
-        y[i].s = sum*d;
-    }
-}
-
-static void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
-    assert(k % QK8_1 == 0);
-    const int nb = k / QK8_1;
-
-    block_q8_1 * restrict y = vy;
-
-#if defined(__ARM_NEON)
-    for (int i = 0; i < nb; i++) {
-        float32x4_t srcv [8];
-        float32x4_t asrcv[8];
-        float32x4_t amaxv[8];
-
-        for (int j = 0; j < 8; j++) srcv[j]  = vld1q_f32(x + i*32 + 4*j);
-        for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]);
-
-        for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]);
-        for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]);
-        for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]);
-
-        const float amax = vmaxvq_f32(amaxv[0]);
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = d;
-
-        int32x4_t accv = vdupq_n_s32(0);
-
-        for (int j = 0; j < 8; j++) {
-            const float32x4_t v  = vmulq_n_f32(srcv[j], id);
-            const int32x4_t   vi = vcvtnq_s32_f32(v);
-
-            y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0);
-            y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1);
-            y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2);
-            y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3);
-
-            accv = vaddq_s32(accv, vi);
-        }
-
-        y[i].s = d * vaddvq_s32(accv);
-    }
-#elif defined(__wasm_simd128__)
-    for (int i = 0; i < nb; i++) {
-        v128_t srcv [8];
-        v128_t asrcv[8];
-        v128_t amaxv[8];
-
-        for (int j = 0; j < 8; j++) srcv[j]  = wasm_v128_load(x + i*32 + 4*j);
-        for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]);
-
-        for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]);
-        for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]);
-        for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]);
-
-        const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0),
-                                   wasm_f32x4_extract_lane(amaxv[0], 1)),
-                               MAX(wasm_f32x4_extract_lane(amaxv[0], 2),
-                                   wasm_f32x4_extract_lane(amaxv[0], 3)));
-
-        const float d = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = d;
-
-        v128_t accv = wasm_i32x4_splat(0);
-
-        for (int j = 0; j < 8; j++) {
-            const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
-            const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v);
-
-            y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0);
-            y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1);
-            y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2);
-            y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3);
-
-            accv = wasm_i32x4_add(accv, vi);
-        }
-
-        y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) +
-                      wasm_i32x4_extract_lane(accv, 1) +
-                      wasm_i32x4_extract_lane(accv, 2) +
-                      wasm_i32x4_extract_lane(accv, 3));
-    }
-#elif defined(__AVX2__) || defined(__AVX__)
-    for (int i = 0; i < nb; i++) {
-        // Load elements into 4 AVX vectors
-        __m256 v0 = _mm256_loadu_ps( x );
-        __m256 v1 = _mm256_loadu_ps( x + 8 );
-        __m256 v2 = _mm256_loadu_ps( x + 16 );
-        __m256 v3 = _mm256_loadu_ps( x + 24 );
-        x += 32;
-
-        // Compute max(abs(e)) for the block
-        const __m256 signBit = _mm256_set1_ps( -0.0f );
-        __m256 maxAbs = _mm256_andnot_ps( signBit, v0 );
-        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) );
-        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) );
-        maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) );
-
-        __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) );
-        max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) );
-        max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) );
-        const float maxScalar = _mm_cvtss_f32( max4 );
-
-        // Quantize these floats
-        const float d = maxScalar / 127.f;
-        y[i].d = d;
-        const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
-        const __m256 mul = _mm256_set1_ps( id );
-
-        // Apply the multiplier
-        v0 = _mm256_mul_ps( v0, mul );
-        v1 = _mm256_mul_ps( v1, mul );
-        v2 = _mm256_mul_ps( v2, mul );
-        v3 = _mm256_mul_ps( v3, mul );
-
-        // Round to nearest integer
-        v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST );
-        v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST );
-        v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST );
-        v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST );
-
-        // Convert floats to integers
-        __m256i i0 = _mm256_cvtps_epi32( v0 );
-        __m256i i1 = _mm256_cvtps_epi32( v1 );
-        __m256i i2 = _mm256_cvtps_epi32( v2 );
-        __m256i i3 = _mm256_cvtps_epi32( v3 );
-
-#if defined(__AVX2__)
-        // Compute the sum of the quants and set y[i].s
-        y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));
-
-        // Convert int32 to int16
-        i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
-        i2 = _mm256_packs_epi32( i2, i3 );	// 16, 17, 18, 19,  24, 25, 26, 27,  20, 21, 22, 23, 28, 29, 30, 31
-                                            // Convert int16 to int8
-        i0 = _mm256_packs_epi16( i0, i2 );	// 0, 1, 2, 3,  8, 9, 10, 11,  16, 17, 18, 19,  24, 25, 26, 27,  4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
-
-        // We got our precious signed bytes, but the order is now wrong
-        // These AVX2 pack instructions process 16-byte pieces independently
-        // The following instruction is fixing the order
-        const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 );
-        i0 = _mm256_permutevar8x32_epi32( i0, perm );
-
-        _mm256_storeu_si256((__m256i *)y[i].qs, i0);
-#else
-        // Since we don't have in AVX some necessary functions,
-        // we split the registers in half and call AVX2 analogs from SSE
-        __m128i ni0 = _mm256_castsi256_si128( i0 );
-        __m128i ni1 = _mm256_extractf128_si256( i0, 1);
-        __m128i ni2 = _mm256_castsi256_si128( i1 );
-        __m128i ni3 = _mm256_extractf128_si256( i1, 1);
-        __m128i ni4 = _mm256_castsi256_si128( i2 );
-        __m128i ni5 = _mm256_extractf128_si256( i2, 1);
-        __m128i ni6 = _mm256_castsi256_si128( i3 );
-        __m128i ni7 = _mm256_extractf128_si256( i3, 1);
-
-        // Compute the sum of the quants and set y[i].s
-        const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3));
-        const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7));
-        y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1));
-
-        // Convert int32 to int16
-        ni0 = _mm_packs_epi32( ni0, ni1 );
-        ni2 = _mm_packs_epi32( ni2, ni3 );
-        ni4 = _mm_packs_epi32( ni4, ni5 );
-        ni6 = _mm_packs_epi32( ni6, ni7 );
-        // Convert int16 to int8
-        ni0 = _mm_packs_epi16( ni0, ni2 );
-        ni4 = _mm_packs_epi16( ni4, ni6 );
-
-        _mm_storeu_si128((__m128i *)(y[i].qs +  0), ni0);
-        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
-#endif
-    }
-#elif defined(__riscv_v_intrinsic)
-
-    size_t vl = __riscv_vsetvl_e32m4(QK8_1);
-
-    for (int i = 0; i < nb; i++) {
-        // load elements
-        vfloat32m4_t v_x   = __riscv_vle32_v_f32m4(x+i*QK8_1, vl);
-
-        vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl);
-        vfloat32m1_t tmp   = __riscv_vfmv_v_f_f32m1(0.0, vl);
-        vfloat32m1_t vmax  = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl);
-        float amax = __riscv_vfmv_f_s_f32m1_f32(vmax);
-
-        const float d  = amax / ((1 << 7) - 1);
-        const float id = d ? 1.0f/d : 0.0f;
-
-        y[i].d = d;
-
-        vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
-
-        // convert to integer
-        vint16m2_t   vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl);
-        vint8m1_t    vs = __riscv_vncvt_x_x_w_i8m1(vi, vl);
-
-        // store result
-        __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
-
-        // compute sum for y[i].s
-        vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl);
-        vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl);
-
-        // set y[i].s
-        int sum = __riscv_vmv_x_s_i16m1_i16(vwrs);
-        y[i].s = sum*d;
-    }
-#else
-    // scalar
-    quantize_row_q8_1_reference(x, y, k);
-#endif
-}
-
-static void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k) {
-    static const int qk = QK4_0;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        const float d = GGML_FP16_TO_FP32(x[i].d);
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int x0 = (x[i].qs[j] & 0x0F) - 8;
-            const int x1 = (x[i].qs[j] >>   4) - 8;
-
-            y[i*qk + j + 0   ] = x0*d;
-            y[i*qk + j + qk/2] = x1*d;
-        }
-    }
-}
-
-static void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k) {
-    static const int qk = QK4_1;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        const float d = GGML_FP16_TO_FP32(x[i].d);
-        const float m = GGML_FP16_TO_FP32(x[i].m);
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int x0 = (x[i].qs[j] & 0x0F);
-            const int x1 = (x[i].qs[j] >>   4);
-
-            y[i*qk + j + 0   ] = x0*d + m;
-            y[i*qk + j + qk/2] = x1*d + m;
-        }
-    }
-}
-
-static void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k) {
-    static const int qk = QK5_0;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        const float d = GGML_FP16_TO_FP32(x[i].d);
-
-        uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
-            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
-
-            y[i*qk + j + 0   ] = x0*d;
-            y[i*qk + j + qk/2] = x1*d;
-        }
-    }
-}
-
-static void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k) {
-    static const int qk = QK5_1;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    for (int i = 0; i < nb; i++) {
-        const float d = GGML_FP16_TO_FP32(x[i].d);
-        const float m = GGML_FP16_TO_FP32(x[i].m);
-
-        uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int x0 = (x[i].qs[j] & 0x0F) | xh_0;
-            const int x1 = (x[i].qs[j] >>   4) | xh_1;
-
-            y[i*qk + j + 0   ] = x0*d + m;
-            y[i*qk + j + qk/2] = x1*d + m;
-        }
-    }
-}
-
-static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, int k) {
-    static const int qk = QK8_0;
-
-    assert(k % qk == 0);
-
-    const int nb = k / qk;
-
-    const block_q8_0 * restrict x = vx;
-
-    for (int i = 0; i < nb; i++) {
-        const float d = GGML_FP16_TO_FP32(x[i].d);
-
-        for (int j = 0; j < qk; ++j) {
-            y[i*qk + j] = x[i].qs[j]*d;
-        }
-    }
-}
-
-static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
-static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
-static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-
-static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
-    [GGML_TYPE_I8] = {
-        .type_name                = "i8",
-        .blck_size                = 1,
-        .type_size                = sizeof(int8_t),
-        .is_quantized             = false,
-    },
-    [GGML_TYPE_I16] = {
-        .type_name                = "i16",
-        .blck_size                = 1,
-        .type_size                = sizeof(int16_t),
-        .is_quantized             = false,
-    },
-    [GGML_TYPE_I32] = {
-        .type_name                = "i32",
-        .blck_size                = 1,
-        .type_size                = sizeof(int32_t),
-        .is_quantized             = false,
-    },
-    [GGML_TYPE_F32] = {
-        .type_name                = "f32",
-        .blck_size                = 1,
-        .type_size                = sizeof(float),
-        .is_quantized             = false,
-        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
-        .vec_dot_type             = GGML_TYPE_F32,
-    },
-    [GGML_TYPE_F16] = {
-        .type_name                = "f16",
-        .blck_size                = 1,
-        .type_size                = sizeof(ggml_fp16_t),
-        .is_quantized             = false,
-        .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
-        .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
-        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
-        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
-        .vec_dot_type             = GGML_TYPE_F16,
-    },
-    [GGML_TYPE_Q4_0] = {
-        .type_name                = "q4_0",
-        .blck_size                = QK4_0,
-        .type_size                = sizeof(block_q4_0),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
-        .from_float               = quantize_row_q4_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
-        .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-    },
-    [GGML_TYPE_Q4_1] = {
-        .type_name                = "q4_1",
-        .blck_size                = QK4_1,
-        .type_size                = sizeof(block_q4_1),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
-        .from_float               = quantize_row_q4_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
-        .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
-        .vec_dot_type             = GGML_TYPE_Q8_1,
-    },
-    [GGML_TYPE_Q5_0] = {
-        .type_name                = "q5_0",
-        .blck_size                = QK5_0,
-        .type_size                = sizeof(block_q5_0),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
-        .from_float               = quantize_row_q5_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
-        .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-    },
-    [GGML_TYPE_Q5_1] = {
-        .type_name                = "q5_1",
-        .blck_size                = QK5_1,
-        .type_size                = sizeof(block_q5_1),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
-        .from_float               = quantize_row_q5_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
-        .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
-        .vec_dot_type             = GGML_TYPE_Q8_1,
-    },
-    [GGML_TYPE_Q8_0] = {
-        .type_name                = "q8_0",
-        .blck_size                = QK8_0,
-        .type_size                = sizeof(block_q8_0),
-        .is_quantized             = true,
-        .to_float                 = dequantize_row_q8_0,
-        .from_float               = quantize_row_q8_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
-        .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-    },
-    [GGML_TYPE_Q8_1] = {
-        .type_name                = "q8_1",
-        .blck_size                = QK8_1,
-        .type_size                = sizeof(block_q8_1),
-        .is_quantized             = true,
-        .from_float               = quantize_row_q8_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
-        .vec_dot_type             = GGML_TYPE_Q8_1,
-    },
-#ifdef GGML_USE_K_QUANTS
-    [GGML_TYPE_Q2_K] = {
-        .type_name                = "q2_K",
-        .blck_size                = QK_K,
-        .type_size                = sizeof(block_q2_K),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
-        .from_float               = quantize_row_q2_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
-        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
-        .vec_dot_type             = GGML_TYPE_Q8_K,
-    },
-    [GGML_TYPE_Q3_K] = {
-        .type_name                = "q3_K",
-        .blck_size                = QK_K,
-        .type_size                = sizeof(block_q3_K),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
-        .from_float               = quantize_row_q3_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
-        .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
-        .vec_dot_type             = GGML_TYPE_Q8_K,
-    },
-    [GGML_TYPE_Q4_K] = {
-        .type_name                = "q4_K",
-        .blck_size                = QK_K,
-        .type_size                = sizeof(block_q4_K),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
-        .from_float               = quantize_row_q4_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
-        .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
-        .vec_dot_type             = GGML_TYPE_Q8_K,
-    },
-    [GGML_TYPE_Q5_K] = {
-        .type_name                = "q5_K",
-        .blck_size                = QK_K,
-        .type_size                = sizeof(block_q5_K),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
-        .from_float               = quantize_row_q5_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
-        .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
-        .vec_dot_type             = GGML_TYPE_Q8_K,
-    },
-    [GGML_TYPE_Q6_K] = {
-        .type_name                = "q6_K",
-        .blck_size                = QK_K,
-        .type_size                = sizeof(block_q6_K),
-        .is_quantized             = true,
-        .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
-        .from_float               = quantize_row_q6_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
-        .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
-        .vec_dot_type             = GGML_TYPE_Q8_K,
-    },
-    [GGML_TYPE_Q8_K] = {
-        .type_name                = "q8_K",
-        .blck_size                = QK_K,
-        .type_size                = sizeof(block_q8_K),
-        .is_quantized             = true,
-        .from_float               = quantize_row_q8_K,
-    }
-#endif
-};
-
-// For internal test use
-ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
-    GGML_ASSERT(type < GGML_TYPE_COUNT);
-    return type_traits[type];
-}
-
-//
-// simd mappings
-//
-
-// we define a common set of C macros which map to specific intrinsics based on the current architecture
-// we then implement the fundamental computation operations below using only these macros
-// adding support for new architectures requires to define the corresponding SIMD macros
-//
-// GGML_F32_STEP / GGML_F16_STEP
-//   number of elements to process in a single step
-//
-// GGML_F32_EPR / GGML_F16_EPR
-//   number of elements to fit in a single register
-//
-
-#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
-
-#define GGML_SIMD
-
-// F32 NEON
-
-#define GGML_F32_STEP 16
-#define GGML_F32_EPR  4
-
-#define GGML_F32x4              float32x4_t
-#define GGML_F32x4_ZERO         vdupq_n_f32(0.0f)
-#define GGML_F32x4_SET1(x)      vdupq_n_f32(x)
-#define GGML_F32x4_LOAD         vld1q_f32
-#define GGML_F32x4_STORE        vst1q_f32
-#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c)
-#define GGML_F32x4_ADD          vaddq_f32
-#define GGML_F32x4_MUL          vmulq_f32
-#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x)
-#define GGML_F32x4_REDUCE(res, x)              \
-{                                              \
-    int offset = GGML_F32_ARR >> 1;            \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vaddq_f32(x[i], x[offset+i]);   \
-    }                                          \
-    offset >>= 1;                              \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vaddq_f32(x[i], x[offset+i]);   \
-    }                                          \
-    offset >>= 1;                              \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vaddq_f32(x[i], x[offset+i]);   \
-    }                                          \
-    res = GGML_F32x4_REDUCE_ONE(x[0]);         \
-}
-
-#define GGML_F32_VEC        GGML_F32x4
-#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
-#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
-#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
-#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
-#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
-#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
-#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
-#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
-
-// F16 NEON
-
-#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-    #define GGML_F16_STEP 32
-    #define GGML_F16_EPR  8
-
-    #define GGML_F16x8              float16x8_t
-    #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
-    #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
-    #define GGML_F16x8_LOAD         vld1q_f16
-    #define GGML_F16x8_STORE        vst1q_f16
-    #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
-    #define GGML_F16x8_ADD          vaddq_f16
-    #define GGML_F16x8_MUL          vmulq_f16
-    #define GGML_F16x8_REDUCE(res, x)                             \
-    do {                                                          \
-        int offset = GGML_F16_ARR >> 1;                           \
-        for (int i = 0; i < offset; ++i) {                        \
-            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
-        }                                                         \
-        offset >>= 1;                                             \
-        for (int i = 0; i < offset; ++i) {                        \
-            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
-        }                                                         \
-        offset >>= 1;                                             \
-        for (int i = 0; i < offset; ++i) {                        \
-            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
-        }                                                         \
-        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
-        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
-        res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
-    } while (0)
-
-    #define GGML_F16_VEC                GGML_F16x8
-    #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
-    #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
-    #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
-    #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
-    #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
-    #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
-    #define GGML_F16_VEC_REDUCE         GGML_F16x8_REDUCE
-#else
-    // if FP16 vector arithmetic is not supported, we use FP32 instead
-    // and take advantage of the vcvt_ functions to convert to/from FP16
-
-    #define GGML_F16_STEP 16
-    #define GGML_F16_EPR  4
-
-    #define GGML_F32Cx4              float32x4_t
-    #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
-    #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
-    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16(x))
-    #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
-    #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
-    #define GGML_F32Cx4_ADD          vaddq_f32
-    #define GGML_F32Cx4_MUL          vmulq_f32
-    #define GGML_F32Cx4_REDUCE       GGML_F32x4_REDUCE
-
-    #define GGML_F16_VEC                GGML_F32Cx4
-    #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
-    #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
-    #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
-    #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
-    #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
-    #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
-    #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
-#endif
-
-#elif defined(__AVX__)
-
-#define GGML_SIMD
-
-// F32 AVX
-
-#define GGML_F32_STEP 32
-#define GGML_F32_EPR  8
-
-#define GGML_F32x8         __m256
-#define GGML_F32x8_ZERO    _mm256_setzero_ps()
-#define GGML_F32x8_SET1(x) _mm256_set1_ps(x)
-#define GGML_F32x8_LOAD    _mm256_loadu_ps
-#define GGML_F32x8_STORE   _mm256_storeu_ps
-#if defined(__FMA__)
-    #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a)
-#else
-    #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a)
-#endif
-#define GGML_F32x8_ADD     _mm256_add_ps
-#define GGML_F32x8_MUL     _mm256_mul_ps
-#define GGML_F32x8_REDUCE(res, x)                                 \
-do {                                                              \
-    int offset = GGML_F32_ARR >> 1;                               \
-    for (int i = 0; i < offset; ++i) {                            \
-        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
-    }                                                             \
-    offset >>= 1;                                                 \
-    for (int i = 0; i < offset; ++i) {                            \
-        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
-    }                                                             \
-    offset >>= 1;                                                 \
-    for (int i = 0; i < offset; ++i) {                            \
-        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
-    }                                                             \
-    const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]),    \
-                                 _mm256_extractf128_ps(x[0], 1)); \
-    const __m128 t1 = _mm_hadd_ps(t0, t0);                        \
-    res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));                     \
-} while (0)
-// TODO: is this optimal ?
-
-#define GGML_F32_VEC        GGML_F32x8
-#define GGML_F32_VEC_ZERO   GGML_F32x8_ZERO
-#define GGML_F32_VEC_SET1   GGML_F32x8_SET1
-#define GGML_F32_VEC_LOAD   GGML_F32x8_LOAD
-#define GGML_F32_VEC_STORE  GGML_F32x8_STORE
-#define GGML_F32_VEC_FMA    GGML_F32x8_FMA
-#define GGML_F32_VEC_ADD    GGML_F32x8_ADD
-#define GGML_F32_VEC_MUL    GGML_F32x8_MUL
-#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE
-
-// F16 AVX
-
-#define GGML_F16_STEP 32
-#define GGML_F16_EPR  8
-
-// F16 arithmetic is not supported by AVX, so we use F32 instead
-
-#define GGML_F32Cx8             __m256
-#define GGML_F32Cx8_ZERO        _mm256_setzero_ps()
-#define GGML_F32Cx8_SET1(x)     _mm256_set1_ps(x)
-
-#if defined(__F16C__)
-// the  _mm256_cvt intrinsics require F16C
-#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
-#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
-#else
-static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
-    float tmp[8];
-
-    for (int i = 0; i < 8; i++) {
-        tmp[i] = GGML_FP16_TO_FP32(x[i]);
-    }
-
-    return _mm256_loadu_ps(tmp);
-}
-static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
-    float arr[8];
-
-    _mm256_storeu_ps(arr, y);
-
-    for (int i = 0; i < 8; i++)
-        x[i] = GGML_FP32_TO_FP16(arr[i]);
-}
-#define GGML_F32Cx8_LOAD(x)     __avx_f32cx8_load(x)
-#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y)
-#endif
-
-#define GGML_F32Cx8_FMA         GGML_F32x8_FMA
-#define GGML_F32Cx8_ADD         _mm256_add_ps
-#define GGML_F32Cx8_MUL         _mm256_mul_ps
-#define GGML_F32Cx8_REDUCE      GGML_F32x8_REDUCE
-
-#define GGML_F16_VEC                GGML_F32Cx8
-#define GGML_F16_VEC_ZERO           GGML_F32Cx8_ZERO
-#define GGML_F16_VEC_SET1           GGML_F32Cx8_SET1
-#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx8_LOAD(p)
-#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i])
-#define GGML_F16_VEC_FMA            GGML_F32Cx8_FMA
-#define GGML_F16_VEC_ADD            GGML_F32Cx8_ADD
-#define GGML_F16_VEC_MUL            GGML_F32Cx8_MUL
-#define GGML_F16_VEC_REDUCE         GGML_F32Cx8_REDUCE
-
-#elif defined(__POWER9_VECTOR__)
-
-#define GGML_SIMD
-
-// F32 POWER9
-
-#define GGML_F32_STEP 32
-#define GGML_F32_EPR  4
-
-#define GGML_F32x4              vector float
-#define GGML_F32x4_ZERO         0.0f
-#define GGML_F32x4_SET1         vec_splats
-#define GGML_F32x4_LOAD(p)      vec_xl(0, p)
-#define GGML_F32x4_STORE(p, r)  vec_xst(r, 0, p)
-#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a)
-#define GGML_F32x4_ADD          vec_add
-#define GGML_F32x4_MUL          vec_mul
-#define GGML_F32x4_REDUCE(res, x)              \
-{                                              \
-    int offset = GGML_F32_ARR >> 1;            \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vec_add(x[i], x[offset+i]);     \
-    }                                          \
-    offset >>= 1;                              \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vec_add(x[i], x[offset+i]);     \
-    }                                          \
-    offset >>= 1;                              \
-    for (int i = 0; i < offset; ++i) {         \
-        x[i] = vec_add(x[i], x[offset+i]);     \
-    }                                          \
-    res = vec_extract(x[0], 0) +               \
-          vec_extract(x[0], 1) +               \
-          vec_extract(x[0], 2) +               \
-          vec_extract(x[0], 3);                \
-}
-
-#define GGML_F32_VEC        GGML_F32x4
-#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
-#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
-#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
-#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
-#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
-#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
-#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
-#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
-
-// F16 POWER9
-#define GGML_F16_STEP       GGML_F32_STEP
-#define GGML_F16_EPR        GGML_F32_EPR
-#define GGML_F16_VEC        GGML_F32x4
-#define GGML_F16_VEC_ZERO   GGML_F32x4_ZERO
-#define GGML_F16_VEC_SET1   GGML_F32x4_SET1
-#define GGML_F16_VEC_FMA    GGML_F32x4_FMA
-#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE
-// Use vec_xl, not vec_ld, in case the load address is not aligned.
-#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ?                   \
-  vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \
-  vec_extract_fp32_from_shortl(vec_xl(0, p))
-#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i]
-#define GGML_F16_VEC_STORE(p, r, i)                             \
-  if (i & 0x1)                                                  \
-    vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)],  \
-                                   r[i - GGML_ENDIAN_BYTE(0)]), \
-            0, p - GGML_F16_EPR)
-
-#elif defined(__wasm_simd128__)
-
-#define GGML_SIMD
-
-// F32 WASM
-
-#define GGML_F32_STEP 16
-#define GGML_F32_EPR  4
-
-#define GGML_F32x4              v128_t
-#define GGML_F32x4_ZERO         wasm_f32x4_splat(0.0f)
-#define GGML_F32x4_SET1(x)      wasm_f32x4_splat(x)
-#define GGML_F32x4_LOAD         wasm_v128_load
-#define GGML_F32x4_STORE        wasm_v128_store
-#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a)
-#define GGML_F32x4_ADD          wasm_f32x4_add
-#define GGML_F32x4_MUL          wasm_f32x4_mul
-#define GGML_F32x4_REDUCE(res, x)                  \
-{                                                  \
-    int offset = GGML_F32_ARR >> 1;                \
-    for (int i = 0; i < offset; ++i) {             \
-        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
-    }                                              \
-    offset >>= 1;                                  \
-    for (int i = 0; i < offset; ++i) {             \
-        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
-    }                                              \
-    offset >>= 1;                                  \
-    for (int i = 0; i < offset; ++i) {             \
-        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
-    }                                              \
-    res = wasm_f32x4_extract_lane(x[0], 0) +       \
-          wasm_f32x4_extract_lane(x[0], 1) +       \
-          wasm_f32x4_extract_lane(x[0], 2) +       \
-          wasm_f32x4_extract_lane(x[0], 3);        \
-}
-
-#define GGML_F32_VEC        GGML_F32x4
-#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
-#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
-#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
-#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
-#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
-#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
-#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
-#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
-
-// F16 WASM
-
-#define GGML_F16_STEP 16
-#define GGML_F16_EPR  4
-
-inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) {
-    float tmp[4];
-
-    tmp[0] = GGML_FP16_TO_FP32(p[0]);
-    tmp[1] = GGML_FP16_TO_FP32(p[1]);
-    tmp[2] = GGML_FP16_TO_FP32(p[2]);
-    tmp[3] = GGML_FP16_TO_FP32(p[3]);
-
-    return wasm_v128_load(tmp);
-}
-
-inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
-    float tmp[4];
-
-    wasm_v128_store(tmp, x);
-
-    p[0] = GGML_FP32_TO_FP16(tmp[0]);
-    p[1] = GGML_FP32_TO_FP16(tmp[1]);
-    p[2] = GGML_FP32_TO_FP16(tmp[2]);
-    p[3] = GGML_FP32_TO_FP16(tmp[3]);
-}
-
-#define GGML_F16x4             v128_t
-#define GGML_F16x4_ZERO        wasm_f32x4_splat(0.0f)
-#define GGML_F16x4_SET1(x)     wasm_f32x4_splat(x)
-#define GGML_F16x4_LOAD(x)     __wasm_f16x4_load(x)
-#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y)
-#define GGML_F16x4_FMA         GGML_F32x4_FMA
-#define GGML_F16x4_ADD         wasm_f32x4_add
-#define GGML_F16x4_MUL         wasm_f32x4_mul
-#define GGML_F16x4_REDUCE(res, x)                  \
-{                                                  \
-    int offset = GGML_F16_ARR >> 1;                \
-    for (int i = 0; i < offset; ++i) {             \
-        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
-    }                                              \
-    offset >>= 1;                                  \
-    for (int i = 0; i < offset; ++i) {             \
-        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
-    }                                              \
-    offset >>= 1;                                  \
-    for (int i = 0; i < offset; ++i) {             \
-        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
-    }                                              \
-    res = wasm_f32x4_extract_lane(x[0], 0) +       \
-          wasm_f32x4_extract_lane(x[0], 1) +       \
-          wasm_f32x4_extract_lane(x[0], 2) +       \
-          wasm_f32x4_extract_lane(x[0], 3);        \
-}
-
-#define GGML_F16_VEC                GGML_F16x4
-#define GGML_F16_VEC_ZERO           GGML_F16x4_ZERO
-#define GGML_F16_VEC_SET1           GGML_F16x4_SET1
-#define GGML_F16_VEC_LOAD(p, i)     GGML_F16x4_LOAD(p)
-#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i])
-#define GGML_F16_VEC_FMA            GGML_F16x4_FMA
-#define GGML_F16_VEC_ADD            GGML_F16x4_ADD
-#define GGML_F16_VEC_MUL            GGML_F16x4_MUL
-#define GGML_F16_VEC_REDUCE         GGML_F16x4_REDUCE
-
-#elif defined(__SSE3__)
-
-#define GGML_SIMD
-
-// F32 SSE
-
-#define GGML_F32_STEP 32
-#define GGML_F32_EPR  4
-
-#define GGML_F32x4         __m128
-#define GGML_F32x4_ZERO    _mm_setzero_ps()
-#define GGML_F32x4_SET1(x) _mm_set1_ps(x)
-#define GGML_F32x4_LOAD    _mm_loadu_ps
-#define GGML_F32x4_STORE   _mm_storeu_ps
-#if defined(__FMA__)
-    // TODO: Does this work?
-    #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a)
-#else
-    #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a)
-#endif
-#define GGML_F32x4_ADD     _mm_add_ps
-#define GGML_F32x4_MUL     _mm_mul_ps
-#define GGML_F32x4_REDUCE(res, x)                                 \
-{                                                                 \
-    int offset = GGML_F32_ARR >> 1;                               \
-    for (int i = 0; i < offset; ++i) {                            \
-        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
-    }                                                             \
-    offset >>= 1;                                                 \
-    for (int i = 0; i < offset; ++i) {                            \
-        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
-    }                                                             \
-    offset >>= 1;                                                 \
-    for (int i = 0; i < offset; ++i) {                            \
-        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
-    }                                                             \
-    const __m128 t0 = _mm_hadd_ps(x[0], x[0]);                    \
-    res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0));                     \
-}
-// TODO: is this optimal ?
-
-#define GGML_F32_VEC        GGML_F32x4
-#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
-#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
-#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
-#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
-#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
-#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
-#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
-#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
-
-// F16 SSE
-
-#define GGML_F16_STEP 32
-#define GGML_F16_EPR  4
-
-static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
-    float tmp[4];
-
-    tmp[0] = GGML_FP16_TO_FP32(x[0]);
-    tmp[1] = GGML_FP16_TO_FP32(x[1]);
-    tmp[2] = GGML_FP16_TO_FP32(x[2]);
-    tmp[3] = GGML_FP16_TO_FP32(x[3]);
-
-    return _mm_loadu_ps(tmp);
-}
-
-static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
-    float arr[4];
-
-    _mm_storeu_ps(arr, y);
-
-    x[0] = GGML_FP32_TO_FP16(arr[0]);
-    x[1] = GGML_FP32_TO_FP16(arr[1]);
-    x[2] = GGML_FP32_TO_FP16(arr[2]);
-    x[3] = GGML_FP32_TO_FP16(arr[3]);
-}
-
-#define GGML_F32Cx4             __m128
-#define GGML_F32Cx4_ZERO        _mm_setzero_ps()
-#define GGML_F32Cx4_SET1(x)     _mm_set1_ps(x)
-#define GGML_F32Cx4_LOAD(x)     __sse_f16x4_load(x)
-#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y)
-#define GGML_F32Cx4_FMA         GGML_F32x4_FMA
-#define GGML_F32Cx4_ADD         _mm_add_ps
-#define GGML_F32Cx4_MUL         _mm_mul_ps
-#define GGML_F32Cx4_REDUCE      GGML_F32x4_REDUCE
-
-#define GGML_F16_VEC                 GGML_F32Cx4
-#define GGML_F16_VEC_ZERO            GGML_F32Cx4_ZERO
-#define GGML_F16_VEC_SET1            GGML_F32Cx4_SET1
-#define GGML_F16_VEC_LOAD(p, i)      GGML_F32Cx4_LOAD(p)
-#define GGML_F16_VEC_STORE(p, r, i)  GGML_F32Cx4_STORE(p, r[i])
-#define GGML_F16_VEC_FMA             GGML_F32Cx4_FMA
-#define GGML_F16_VEC_ADD             GGML_F32Cx4_ADD
-#define GGML_F16_VEC_MUL             GGML_F32Cx4_MUL
-#define GGML_F16_VEC_REDUCE          GGML_F32Cx4_REDUCE
-
-#endif
-
-// GGML_F32_ARR / GGML_F16_ARR
-//   number of registers to use per step
-#ifdef GGML_SIMD
-#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR)
-#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR)
-#endif
-
-//
-// fundamental operations
-//
-
-inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-
-inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-
-inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-
-inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-
-inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
-inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float   v) { for (int i = 0; i < n; ++i) z[i]  = x[i] + v;    }
-inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i] += x[i];        }
-inline static void ggml_vec_acc1_f32(const int n, float * y, const float   v)                  { for (int i = 0; i < n; ++i) y[i] += v;           }
-inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] - y[i]; }
-inline static void ggml_vec_set_f32 (const int n, float * x, const float   v)                  { for (int i = 0; i < n; ++i) x[i]  = v;           }
-inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = x[i];        }
-inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = -x[i];       }
-inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
-inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
-
-static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
-#ifdef GGML_SIMD
-    float sumf = 0.0f;
-    const int np = (n & ~(GGML_F32_STEP - 1));
-
-    GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
-
-    GGML_F32_VEC ax[GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
-
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-
-            sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
-        }
-    }
-
-    // reduce sum0..sum3 to sum0
-    GGML_F32_VEC_REDUCE(sumf, sum);
-
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        sumf += x[i]*y[i];
-    }
-#else
-    // scalar
-    ggml_float sumf = 0.0;
-    for (int i = 0; i < n; ++i) {
-        sumf += (ggml_float)(x[i]*y[i]);
-    }
-#endif
-
-    *s = sumf;
-}
-
-static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
-    ggml_float sumf = 0.0;
-
-#if defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F16_STEP - 1));
-
-    GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
-
-    GGML_F16_VEC ax[GGML_F16_ARR];
-    GGML_F16_VEC ay[GGML_F16_ARR];
-
-    for (int i = 0; i < np; i += GGML_F16_STEP) {
-        for (int j = 0; j < GGML_F16_ARR; j++) {
-            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
-            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-
-            sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
-        }
-    }
-
-    // reduce sum0..sum3 to sum0
-    GGML_F16_VEC_REDUCE(sumf, sum);
-
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
-    }
-#else
-    for (int i = 0; i < n; ++i) {
-        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
-    }
-#endif
-
-    *s = sumf;
-}
-
-static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const int qk = QK8_0;
-    const int nb = n / qk;
-
-    assert(n % qk == 0);
-
-    const block_q4_0 * restrict x = vx;
-    const block_q8_0 * restrict y = vy;
-
-#if defined(__ARM_NEON)
-    float32x4_t sumv0 = vdupq_n_f32(0.0f);
-    float32x4_t sumv1 = vdupq_n_f32(0.0f);
-
-    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_0 * restrict x0 = &x[i + 0];
-        const block_q4_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
-
-        const uint8x16_t m4b = vdupq_n_u8(0x0F);
-        const int8x16_t  s8b = vdupq_n_s8(0x8);
-
-        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
-        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
-
-        // 4-bit -> 8-bit
-        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
-        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
-        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
-        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
-
-        // sub 8
-        const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
-        const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
-        const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
-        const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
-
-        // load y
-        const int8x16_t v1_0l = vld1q_s8(y0->qs);
-        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
-        const int8x16_t v1_1l = vld1q_s8(y1->qs);
-        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
-
-#if defined(__ARM_FEATURE_DOTPROD)
-        // dot product into int32x4_t
-        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
-        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
-#else
-        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
-        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
-        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0h));
-        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
-
-        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1l));
-        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
-        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1h));
-        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));
-
-        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
-        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
-        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
-        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
-#endif
-    }
-
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
-#elif defined(__AVX2__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
-
-    // Main loop
-    for (int i = 0; i < nb; ++i) {
-        /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
-
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
-
-        // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
-        const __m256i off = _mm256_set1_epi8( 8 );
-        bx = _mm256_sub_epi8( bx, off );
-
-        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
-
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
-
-        /* Multiply q with scale and accumulate */
-        acc = _mm256_fmadd_ps( d, q, acc );
-    }
-
-    *s = hsum_float_8(acc);
-#elif defined(__AVX__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
-
-    // Main loop
-    for (int i = 0; i < nb; ++i) {
-        // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
-
-        const __m128i lowMask = _mm_set1_epi8(0xF);
-        const __m128i off = _mm_set1_epi8(8);
-
-        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
-
-        __m128i bx = _mm_and_si128(lowMask, tmp);
-        __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs);
-        bx = _mm_sub_epi8(bx, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx, by);
-
-        bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
-        by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
-        bx = _mm_sub_epi8(bx, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx, by);
-
-        // Convert int32_t to float
-        __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1));
-
-        // Apply the scale, and accumulate
-        acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc);
-    }
-
-    *s = hsum_float_8(acc);
-#elif defined(__SSSE3__)
-    // set constants
-    const __m128i lowMask = _mm_set1_epi8(0xF);
-    const __m128i off = _mm_set1_epi8(8);
-
-    // Initialize accumulator with zeros
-    __m128 acc_0 = _mm_setzero_ps();
-    __m128 acc_1 = _mm_setzero_ps();
-    __m128 acc_2 = _mm_setzero_ps();
-    __m128 acc_3 = _mm_setzero_ps();
-
-    // First round without accumulation
-    {
-        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
-
-        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
-
-        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
-        bx_0 = _mm_sub_epi8(bx_0, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
-
-        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
-        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
-        bx_1 = _mm_sub_epi8(bx_1, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
-
-        _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
-
-        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
-
-        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
-        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
-        bx_2 = _mm_sub_epi8(bx_2, off);
-        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
-
-        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
-        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
-        bx_3 = _mm_sub_epi8(bx_3, off);
-        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
-
-        // Convert int32_t to float
-        __m128 p0 = _mm_cvtepi32_ps(i32_0);
-        __m128 p1 = _mm_cvtepi32_ps(i32_1);
-        __m128 p2 = _mm_cvtepi32_ps(i32_2);
-        __m128 p3 = _mm_cvtepi32_ps(i32_3);
-
-        // Apply the scale
-        acc_0 = _mm_mul_ps( d_0_1, p0 );
-        acc_1 = _mm_mul_ps( d_0_1, p1 );
-        acc_2 = _mm_mul_ps( d_2_3, p2 );
-        acc_3 = _mm_mul_ps( d_2_3, p3 );
-    }
-
-    // Main loop
-    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
-    for (int i = 2; i < nb; i+=2) {
-        _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
-
-        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
-
-        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
-        bx_0 = _mm_sub_epi8(bx_0, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
-
-        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
-        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
-        bx_1 = _mm_sub_epi8(bx_1, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
-
-        _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
-
-        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
-
-        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
-        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
-        bx_2 = _mm_sub_epi8(bx_2, off);
-        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
-
-        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
-        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
-        bx_3 = _mm_sub_epi8(bx_3, off);
-        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
-
-        // Convert int32_t to float
-        __m128 p0 = _mm_cvtepi32_ps(i32_0);
-        __m128 p1 = _mm_cvtepi32_ps(i32_1);
-        __m128 p2 = _mm_cvtepi32_ps(i32_2);
-        __m128 p3 = _mm_cvtepi32_ps(i32_3);
-
-        // Apply the scale
-        __m128 p0_d = _mm_mul_ps( d_0_1, p0 );
-        __m128 p1_d = _mm_mul_ps( d_0_1, p1 );
-        __m128 p2_d = _mm_mul_ps( d_2_3, p2 );
-        __m128 p3_d = _mm_mul_ps( d_2_3, p3 );
-
-        // Acummulate
-        acc_0 = _mm_add_ps(p0_d, acc_0);
-        acc_1 = _mm_add_ps(p1_d, acc_1);
-        acc_2 = _mm_add_ps(p2_d, acc_2);
-        acc_3 = _mm_add_ps(p3_d, acc_3);
-    }
-
-    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
-#elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
-    size_t vl = __riscv_vsetvl_e8m1(qk/2);
-
-    for (int i = 0; i < nb; i++) {
-        // load elements
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
-
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
-
-        // mask and store lower part of x, and then upper part
-        vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
-        vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
-
-        vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
-        vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
-
-        // subtract offset
-        vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl);
-        vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl);
-
-        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
-        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
-
-        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
-
-        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
-        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
-
-        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
-
-        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
-    }
-
-    *s = sumf;
-#else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[i].qs[j] & 0x0F) - 8;
-            const int v1 = (x[i].qs[j] >>   4) - 8;
-
-            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
-        }
-
-        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
-    }
-
-    *s = sumf;
-#endif
-}
-
-static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const int qk = QK8_1;
-    const int nb = n / qk;
-
-    assert(n % qk == 0);
-
-    const block_q4_1 * restrict x = vx;
-    const block_q8_1 * restrict y = vy;
-
-    // TODO: add WASM SIMD
-#if defined(__ARM_NEON)
-    float32x4_t sumv0 = vdupq_n_f32(0.0f);
-    float32x4_t sumv1 = vdupq_n_f32(0.0f);
-
-    float summs = 0;
-
-    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_1 * restrict x0 = &x[i + 0];
-        const block_q4_1 * restrict x1 = &x[i + 1];
-        const block_q8_1 * restrict y0 = &y[i + 0];
-        const block_q8_1 * restrict y1 = &y[i + 1];
-
-        summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
-
-        const uint8x16_t m4b = vdupq_n_u8(0x0F);
-
-        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
-        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
-
-        // 4-bit -> 8-bit
-        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
-        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
-        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
-        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
-
-        // load y
-        const int8x16_t v1_0l = vld1q_s8(y0->qs);
-        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
-        const int8x16_t v1_1l = vld1q_s8(y1->qs);
-        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
-
-#if defined(__ARM_FEATURE_DOTPROD)
-        // dot product into int32x4_t
-        const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
-        const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
-#else
-        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
-        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
-        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0h));
-        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0h));
-
-        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1l));
-        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1l));
-        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1h));
-        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1h));
-
-        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
-        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
-        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
-        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
-#endif
-    }
-
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
-#elif defined(__AVX2__) || defined(__AVX__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
-
-    float summs = 0;
-
-    // Main loop
-    for (int i = 0; i < nb; ++i) {
-        const float d0 = GGML_FP16_TO_FP32(x[i].d);
-        const float d1 = y[i].d;
-
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
-
-        const __m256 d0v = _mm256_set1_ps( d0 );
-        const __m256 d1v = _mm256_set1_ps( d1 );
-
-        // Compute combined scales
-        const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
-
-        // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
-        const __m256i bx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
-
-        const __m256 xy = mul_sum_us8_pairs_float(bx, by);
-
-        // Accumulate d0*d1*x*y
-#if defined(__AVX2__)
-        acc = _mm256_fmadd_ps( d0d1, xy, acc );
-#else
-        acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc );
-#endif
-    }
-
-    *s = hsum_float_8(acc) + summs;
-#elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
-    size_t vl = __riscv_vsetvl_e8m1(qk/2);
-
-    for (int i = 0; i < nb; i++) {
-        // load elements
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
-
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
-
-        // mask and store lower part of x, and then upper part
-        vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
-        vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
-
-        vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
-        vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
-
-        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
-        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
-
-        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
-
-        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
-        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
-
-        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
-
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
-    }
+    #define GGML_F16x8              float16x8_t
+    #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
+    #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
+    #define GGML_F16x8_LOAD         vld1q_f16
+    #define GGML_F16x8_STORE        vst1q_f16
+    #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
+    #define GGML_F16x8_ADD          vaddq_f16
+    #define GGML_F16x8_MUL          vmulq_f16
+    #define GGML_F16x8_REDUCE(res, x)                             \
+    do {                                                          \
+        int offset = GGML_F16_ARR >> 1;                           \
+        for (int i = 0; i < offset; ++i) {                        \
+            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
+        }                                                         \
+        offset >>= 1;                                             \
+        for (int i = 0; i < offset; ++i) {                        \
+            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
+        }                                                         \
+        offset >>= 1;                                             \
+        for (int i = 0; i < offset; ++i) {                        \
+            x[i] = vaddq_f16(x[i], x[offset+i]);                  \
+        }                                                         \
+        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
+        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
+        res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
+    } while (0)
 
-    *s = sumf;
+    #define GGML_F16_VEC                GGML_F16x8
+    #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
+    #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
+    #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
+    #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F16x8_REDUCE
 #else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[i].qs[j] & 0x0F);
-            const int v1 = (x[i].qs[j] >>   4);
+    // if FP16 vector arithmetic is not supported, we use FP32 instead
+    // and take advantage of the vcvt_ functions to convert to/from FP16
 
-            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
-        }
+    #define GGML_F16_STEP 16
+    #define GGML_F16_EPR  4
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
-    }
+    #define GGML_F32Cx4              float32x4_t
+    #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
+    #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16(x))
+    #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
+    #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
+    #define GGML_F32Cx4_ADD          vaddq_f32
+    #define GGML_F32Cx4_MUL          vmulq_f32
+    #define GGML_F32Cx4_REDUCE       GGML_F32x4_REDUCE
 
-    *s = sumf;
+    #define GGML_F16_VEC                GGML_F32Cx4
+    #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
+    #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
+    #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
+    #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
 #endif
-}
 
-static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const int qk = QK8_0;
-    const int nb = n / qk;
+#elif defined(__AVX__)
 
-    assert(n % qk == 0);
-    assert(qk == QK5_0);
+#define GGML_SIMD
 
-    const block_q5_0 * restrict x = vx;
-    const block_q8_0 * restrict y = vy;
+// F32 AVX
 
-#if defined(__ARM_NEON)
-    float32x4_t sumv0 = vdupq_n_f32(0.0f);
-    float32x4_t sumv1 = vdupq_n_f32(0.0f);
-
-    uint32_t qh0;
-    uint32_t qh1;
-
-    uint64_t tmp0[4];
-    uint64_t tmp1[4];
-
-    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
-    for (int i = 0; i < nb; i += 2) {
-        const block_q5_0 * restrict x0 = &x[i];
-        const block_q5_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i];
-        const block_q8_0 * restrict y1 = &y[i + 1];
-
-        const uint8x16_t m4b = vdupq_n_u8(0x0F);
-
-        // extract the 5th bit via lookup table ((!b) << 4)
-        memcpy(&qh0, x0->qh, sizeof(qh0));
-        memcpy(&qh1, x1->qh, sizeof(qh1));
-
-        tmp0[0] = table_b2b_1[(qh0 >>  0) & 0xFF];
-        tmp0[1] = table_b2b_1[(qh0 >>  8) & 0xFF];
-        tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF];
-        tmp0[3] = table_b2b_1[(qh0 >> 24)       ];
-
-        tmp1[0] = table_b2b_1[(qh1 >>  0) & 0xFF];
-        tmp1[1] = table_b2b_1[(qh1 >>  8) & 0xFF];
-        tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF];
-        tmp1[3] = table_b2b_1[(qh1 >> 24)       ];
-
-        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
-        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
-        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
-        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));
-
-        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
-        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
-
-        // 4-bit -> 8-bit
-        int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
-        int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
-        int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
-        int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
-
-        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
-        const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0);
-        const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0);
-        const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1);
-        const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1);
-
-        // load y
-        const int8x16_t v1_0l = vld1q_s8(y0->qs);
-        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
-        const int8x16_t v1_1l = vld1q_s8(y1->qs);
-        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
-
-#if defined(__ARM_FEATURE_DOTPROD)
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  8
+
+#define GGML_F32x8         __m256
+#define GGML_F32x8_ZERO    _mm256_setzero_ps()
+#define GGML_F32x8_SET1(x) _mm256_set1_ps(x)
+#define GGML_F32x8_LOAD    _mm256_loadu_ps
+#define GGML_F32x8_STORE   _mm256_storeu_ps
+#if defined(__FMA__)
+    #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a)
 #else
-        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
-        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
-        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
-        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
-
-        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
-        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
-        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
-        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
-
-        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
-        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
-        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
-        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+    #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a)
 #endif
-    }
-
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
-#elif defined(__wasm_simd128__)
-    v128_t sumv = wasm_f32x4_splat(0.0f);
-
-    uint32_t qh;
-    uint64_t tmp[4];
-
-    // TODO: check if unrolling this is better
-    for (int i = 0; i < nb; ++i) {
-        const block_q5_0 * restrict x0 = &x[i];
-        const block_q8_0 * restrict y0 = &y[i];
-
-        const v128_t m4b  = wasm_i8x16_splat(0x0F);
-
-        // extract the 5th bit
-        memcpy(&qh, x0->qh, sizeof(qh));
-
-        tmp[0] = table_b2b_1[(qh >>  0) & 0xFF];
-        tmp[1] = table_b2b_1[(qh >>  8) & 0xFF];
-        tmp[2] = table_b2b_1[(qh >> 16) & 0xFF];
-        tmp[3] = table_b2b_1[(qh >> 24)       ];
-
-        const v128_t qhl = wasm_v128_load(tmp + 0);
-        const v128_t qhh = wasm_v128_load(tmp + 2);
-
-        const v128_t v0 = wasm_v128_load(x0->qs);
-
-        // 4-bit -> 8-bit
-        const v128_t v0l = wasm_v128_and (v0, m4b);
-        const v128_t v0h = wasm_u8x16_shr(v0, 4);
-
-        // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero)
-        const v128_t v0lf = wasm_i8x16_sub(v0l, qhl);
-        const v128_t v0hf = wasm_i8x16_sub(v0h, qhh);
-
-        // load y
-        const v128_t v1l = wasm_v128_load(y0->qs);
-        const v128_t v1h = wasm_v128_load(y0->qs + 16);
-
-        // int8x16 -> int16x8
-        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
-        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
-        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
-        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
-
-        const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
-        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
-        const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
-        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
-
-        // dot product
-        sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4(
-                        wasm_i32x4_add(
-                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
-                                           wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
-                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
-                                           wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
-                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
-    }
-
-    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
-         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
-#elif defined(__AVX2__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
-
-    // Main loop
-    for (int i = 0; i < nb; i++) {
-        /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
-
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
-        bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
-        bx = _mm256_or_si256(bx, bxhi);
-
-        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
-
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+#define GGML_F32x8_ADD     _mm256_add_ps
+#define GGML_F32x8_MUL     _mm256_mul_ps
+#define GGML_F32x8_REDUCE(res, x)                                 \
+do {                                                              \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm256_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]),    \
+                                 _mm256_extractf128_ps(x[0], 1)); \
+    const __m128 t1 = _mm_hadd_ps(t0, t0);                        \
+    res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));                     \
+} while (0)
+// TODO: is this optimal ?
 
-        /* Multiply q with scale and accumulate */
-        acc = _mm256_fmadd_ps(d, q, acc);
-    }
+#define GGML_F32_VEC        GGML_F32x8
+#define GGML_F32_VEC_ZERO   GGML_F32x8_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x8_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x8_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x8_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x8_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x8_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x8_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE
 
-    *s = hsum_float_8(acc);
-#elif defined(__AVX__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
-    __m128i mask = _mm_set1_epi8((char)0xF0);
+// F16 AVX
 
-    // Main loop
-    for (int i = 0; i < nb; i++) {
-        /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+#define GGML_F16_STEP 32
+#define GGML_F16_EPR  8
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
-        __m128i bxhil = _mm256_castsi256_si128(bxhi);
-        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
-        bxhil = _mm_andnot_si128(bxhil, mask);
-        bxhih = _mm_andnot_si128(bxhih, mask);
-        __m128i bxl = _mm256_castsi256_si128(bx);
-        __m128i bxh = _mm256_extractf128_si256(bx, 1);
-        bxl = _mm_or_si128(bxl, bxhil);
-        bxh = _mm_or_si128(bxh, bxhih);
-        bx = MM256_SET_M128I(bxh, bxl);
+// F16 arithmetic is not supported by AVX, so we use F32 instead
 
-        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+#define GGML_F32Cx8             __m256
+#define GGML_F32Cx8_ZERO        _mm256_setzero_ps()
+#define GGML_F32Cx8_SET1(x)     _mm256_set1_ps(x)
 
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+#if defined(__F16C__)
+// the  _mm256_cvt intrinsics require F16C
+#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
+#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
+#else
+static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
+    float tmp[8];
 
-        /* Multiply q with scale and accumulate */
-        acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
+    for (int i = 0; i < 8; i++) {
+        tmp[i] = GGML_FP16_TO_FP32(x[i]);
     }
 
-    *s = hsum_float_8(acc);
-#elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
-    uint32_t qh;
-
-    size_t vl = __riscv_vsetvl_e8m1(qk/2);
-
-    // These tempory registers are for masking and shift operations
-    vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
-    vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl);
-
-    vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl);
-    vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
-
-    for (int i = 0; i < nb; i++) {
-        memcpy(&qh, x[i].qh, sizeof(uint32_t));
-
-        // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
-        vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl);
-        vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl);
-        vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl);
-
-        // ((qh & (1u << (j + 16))) >> (j + 12));
-        vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl);
-        vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl);
-
-        // narrowing
-        vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl);
-        vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl);
-
-        vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl);
-        vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
-
-        // load
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
-
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
-
-        vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
-        vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
-
-        vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl);
-        vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl);
-
-        vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
-        vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
-
-        vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl);
-        vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl);
-
-        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
-        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
-
-        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
-
-        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
-        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
-
-        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+    return _mm256_loadu_ps(tmp);
+}
+static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
+    float arr[8];
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
-    }
+    _mm256_storeu_ps(arr, y);
 
-    *s = sumf;
-#else
-    // scalar
-    float sumf = 0.0;
+    for (int i = 0; i < 8; i++)
+        x[i] = GGML_FP32_TO_FP16(arr[i]);
+}
+#define GGML_F32Cx8_LOAD(x)     __avx_f32cx8_load(x)
+#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y)
+#endif
 
-    for (int i = 0; i < nb; i++) {
-        uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
+#define GGML_F32Cx8_FMA         GGML_F32x8_FMA
+#define GGML_F32Cx8_ADD         _mm256_add_ps
+#define GGML_F32Cx8_MUL         _mm256_mul_ps
+#define GGML_F32Cx8_REDUCE      GGML_F32x8_REDUCE
 
-        int sumi = 0;
+#define GGML_F16_VEC                GGML_F32Cx8
+#define GGML_F16_VEC_ZERO           GGML_F32Cx8_ZERO
+#define GGML_F16_VEC_SET1           GGML_F32Cx8_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx8_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F32Cx8_FMA
+#define GGML_F16_VEC_ADD            GGML_F32Cx8_ADD
+#define GGML_F16_VEC_MUL            GGML_F32Cx8_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F32Cx8_REDUCE
 
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
-            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
+#elif defined(__POWER9_VECTOR__)
 
-            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
-            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
+#define GGML_SIMD
 
-            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
-        }
+// F32 POWER9
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
-    }
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  4
 
-    *s = sumf;
-#endif
+#define GGML_F32x4              vector float
+#define GGML_F32x4_ZERO         0.0f
+#define GGML_F32x4_SET1         vec_splats
+#define GGML_F32x4_LOAD(p)      vec_xl(0, p)
+#define GGML_F32x4_STORE(p, r)  vec_xst(r, 0, p)
+#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a)
+#define GGML_F32x4_ADD          vec_add
+#define GGML_F32x4_MUL          vec_mul
+#define GGML_F32x4_REDUCE(res, x)              \
+{                                              \
+    int offset = GGML_F32_ARR >> 1;            \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vec_add(x[i], x[offset+i]);     \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vec_add(x[i], x[offset+i]);     \
+    }                                          \
+    offset >>= 1;                              \
+    for (int i = 0; i < offset; ++i) {         \
+        x[i] = vec_add(x[i], x[offset+i]);     \
+    }                                          \
+    res = vec_extract(x[0], 0) +               \
+          vec_extract(x[0], 1) +               \
+          vec_extract(x[0], 2) +               \
+          vec_extract(x[0], 3);                \
 }
 
-static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const int qk = QK8_1;
-    const int nb = n / qk;
-
-    assert(n % qk == 0);
-    assert(qk == QK5_1);
-
-    const block_q5_1 * restrict x = vx;
-    const block_q8_1 * restrict y = vy;
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
 
-#if defined(__ARM_NEON)
-    float32x4_t sumv0 = vdupq_n_f32(0.0f);
-    float32x4_t sumv1 = vdupq_n_f32(0.0f);
-
-    float summs0 = 0.0f;
-    float summs1 = 0.0f;
-
-    uint32_t qh0;
-    uint32_t qh1;
-
-    uint64_t tmp0[4];
-    uint64_t tmp1[4];
-
-    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
-    for (int i = 0; i < nb; i += 2) {
-        const block_q5_1 * restrict x0 = &x[i];
-        const block_q5_1 * restrict x1 = &x[i + 1];
-        const block_q8_1 * restrict y0 = &y[i];
-        const block_q8_1 * restrict y1 = &y[i + 1];
-
-        const uint8x16_t m4b = vdupq_n_u8(0x0F);
-
-        summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s;
-        summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s;
-
-        // extract the 5th bit via lookup table ((b) << 4)
-        memcpy(&qh0, x0->qh, sizeof(qh0));
-        memcpy(&qh1, x1->qh, sizeof(qh1));
-
-        tmp0[0] = table_b2b_0[(qh0 >>  0) & 0xFF];
-        tmp0[1] = table_b2b_0[(qh0 >>  8) & 0xFF];
-        tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF];
-        tmp0[3] = table_b2b_0[(qh0 >> 24)       ];
-
-        tmp1[0] = table_b2b_0[(qh1 >>  0) & 0xFF];
-        tmp1[1] = table_b2b_0[(qh1 >>  8) & 0xFF];
-        tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF];
-        tmp1[3] = table_b2b_0[(qh1 >> 24)       ];
-
-        const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0));
-        const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2));
-        const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0));
-        const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2));
-
-        const uint8x16_t v0_0 = vld1q_u8(x0->qs);
-        const uint8x16_t v0_1 = vld1q_u8(x1->qs);
-
-        // 4-bit -> 8-bit
-        const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
-        const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
-        const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
-        const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
-
-        // add high bit
-        const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0);
-        const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0);
-        const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1);
-        const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1);
-
-        // load y
-        const int8x16_t v1_0l = vld1q_s8(y0->qs);
-        const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
-        const int8x16_t v1_1l = vld1q_s8(y1->qs);
-        const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
-
-#if defined(__ARM_FEATURE_DOTPROD)
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
-#else
-        const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
-        const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
-        const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hf), vget_low_s8 (v1_0h));
-        const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hf), vget_high_s8(v1_0h));
-
-        const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lf), vget_low_s8 (v1_1l));
-        const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lf), vget_high_s8(v1_1l));
-        const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hf), vget_low_s8 (v1_1h));
-        const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hf), vget_high_s8(v1_1h));
-
-        const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
-        const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
-        const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
-        const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
-#endif
-    }
+// F16 POWER9
+#define GGML_F16_STEP       GGML_F32_STEP
+#define GGML_F16_EPR        GGML_F32_EPR
+#define GGML_F16_VEC        GGML_F32x4
+#define GGML_F16_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F16_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F16_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE
+// Use vec_xl, not vec_ld, in case the load address is not aligned.
+#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ?                   \
+  vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \
+  vec_extract_fp32_from_shortl(vec_xl(0, p))
+#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i]
+#define GGML_F16_VEC_STORE(p, r, i)                             \
+  if (i & 0x1)                                                  \
+    vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)],  \
+                                   r[i - GGML_ENDIAN_BYTE(0)]), \
+            0, p - GGML_F16_EPR)
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
 #elif defined(__wasm_simd128__)
-    v128_t sumv = wasm_f32x4_splat(0.0f);
-
-    float summs = 0.0f;
-
-    uint32_t qh;
-    uint64_t tmp[4];
-
-    // TODO: check if unrolling this is better
-    for (int i = 0; i < nb; ++i) {
-        const block_q5_1 * restrict x0 = &x[i];
-        const block_q8_1 * restrict y0 = &y[i];
 
-        summs += GGML_FP16_TO_FP32(x0->m) * y0->s;
-
-        const v128_t m4b = wasm_i8x16_splat(0x0F);
-
-        // extract the 5th bit
-        memcpy(&qh, x0->qh, sizeof(qh));
+#define GGML_SIMD
 
-        tmp[0] = table_b2b_0[(qh >>  0) & 0xFF];
-        tmp[1] = table_b2b_0[(qh >>  8) & 0xFF];
-        tmp[2] = table_b2b_0[(qh >> 16) & 0xFF];
-        tmp[3] = table_b2b_0[(qh >> 24)       ];
+// F32 WASM
 
-        const v128_t qhl = wasm_v128_load(tmp + 0);
-        const v128_t qhh = wasm_v128_load(tmp + 2);
+#define GGML_F32_STEP 16
+#define GGML_F32_EPR  4
 
-        const v128_t v0 = wasm_v128_load(x0->qs);
+#define GGML_F32x4              v128_t
+#define GGML_F32x4_ZERO         wasm_f32x4_splat(0.0f)
+#define GGML_F32x4_SET1(x)      wasm_f32x4_splat(x)
+#define GGML_F32x4_LOAD         wasm_v128_load
+#define GGML_F32x4_STORE        wasm_v128_store
+#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a)
+#define GGML_F32x4_ADD          wasm_f32x4_add
+#define GGML_F32x4_MUL          wasm_f32x4_mul
+#define GGML_F32x4_REDUCE(res, x)                  \
+{                                                  \
+    int offset = GGML_F32_ARR >> 1;                \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    res = wasm_f32x4_extract_lane(x[0], 0) +       \
+          wasm_f32x4_extract_lane(x[0], 1) +       \
+          wasm_f32x4_extract_lane(x[0], 2) +       \
+          wasm_f32x4_extract_lane(x[0], 3);        \
+}
 
-        // 4-bit -> 8-bit
-        const v128_t v0l = wasm_v128_and (v0, m4b);
-        const v128_t v0h = wasm_u8x16_shr(v0, 4);
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
 
-        // add high bit
-        const v128_t v0lf = wasm_v128_or(v0l, qhl);
-        const v128_t v0hf = wasm_v128_or(v0h, qhh);
+// F16 WASM
 
-        // load y
-        const v128_t v1l = wasm_v128_load(y0->qs);
-        const v128_t v1h = wasm_v128_load(y0->qs + 16);
+#define GGML_F16_STEP 16
+#define GGML_F16_EPR  4
 
-        // int8x16 -> int16x8
-        const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf);
-        const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf);
-        const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf);
-        const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf);
+inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) {
+    float tmp[4];
 
-        const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l);
-        const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l);
-        const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h);
-        const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h);
+    tmp[0] = GGML_FP16_TO_FP32(p[0]);
+    tmp[1] = GGML_FP16_TO_FP32(p[1]);
+    tmp[2] = GGML_FP16_TO_FP32(p[2]);
+    tmp[3] = GGML_FP16_TO_FP32(p[3]);
 
-        // dot product
-        sumv = wasm_f32x4_add(sumv,
-                wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add(
-                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll),
-                                           wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
-                            wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
-                                           wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
-                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d)));
-    }
+    return wasm_v128_load(tmp);
+}
 
-    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
-         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
-#elif defined(__AVX2__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
+inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
+    float tmp[4];
 
-    float summs = 0.0f;
+    wasm_v128_store(tmp, x);
 
-    // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+    p[0] = GGML_FP32_TO_FP16(tmp[0]);
+    p[1] = GGML_FP32_TO_FP16(tmp[1]);
+    p[2] = GGML_FP32_TO_FP16(tmp[2]);
+    p[3] = GGML_FP32_TO_FP16(tmp[3]);
+}
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+#define GGML_F16x4             v128_t
+#define GGML_F16x4_ZERO        wasm_f32x4_splat(0.0f)
+#define GGML_F16x4_SET1(x)     wasm_f32x4_splat(x)
+#define GGML_F16x4_LOAD(x)     __wasm_f16x4_load(x)
+#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y)
+#define GGML_F16x4_FMA         GGML_F32x4_FMA
+#define GGML_F16x4_ADD         wasm_f32x4_add
+#define GGML_F16x4_MUL         wasm_f32x4_mul
+#define GGML_F16x4_REDUCE(res, x)                  \
+{                                                  \
+    int offset = GGML_F16_ARR >> 1;                \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    offset >>= 1;                                  \
+    for (int i = 0; i < offset; ++i) {             \
+        x[i] = wasm_f32x4_add(x[i], x[offset+i]);  \
+    }                                              \
+    res = wasm_f32x4_extract_lane(x[0], 0) +       \
+          wasm_f32x4_extract_lane(x[0], 1) +       \
+          wasm_f32x4_extract_lane(x[0], 2) +       \
+          wasm_f32x4_extract_lane(x[0], 3);        \
+}
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
-        bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
-        bx = _mm256_or_si256(bx, bxhi);
+#define GGML_F16_VEC                GGML_F16x4
+#define GGML_F16_VEC_ZERO           GGML_F16x4_ZERO
+#define GGML_F16_VEC_SET1           GGML_F16x4_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F16x4_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F16x4_FMA
+#define GGML_F16_VEC_ADD            GGML_F16x4_ADD
+#define GGML_F16_VEC_MUL            GGML_F16x4_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F16x4_REDUCE
 
-        const __m256 dy = _mm256_set1_ps(y[i].d);
-        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+#elif defined(__SSE3__)
 
-        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+#define GGML_SIMD
 
-        acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
-    }
+// F32 SSE
 
-    *s = hsum_float_8(acc) + summs;
-#elif defined(__AVX__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
-    __m128i mask = _mm_set1_epi8(0x10);
+#define GGML_F32_STEP 32
+#define GGML_F32_EPR  4
 
-    float summs = 0.0f;
+#define GGML_F32x4         __m128
+#define GGML_F32x4_ZERO    _mm_setzero_ps()
+#define GGML_F32x4_SET1(x) _mm_set1_ps(x)
+#define GGML_F32x4_LOAD    _mm_loadu_ps
+#define GGML_F32x4_STORE   _mm_storeu_ps
+#if defined(__FMA__)
+    // TODO: Does this work?
+    #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a)
+#else
+    #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a)
+#endif
+#define GGML_F32x4_ADD     _mm_add_ps
+#define GGML_F32x4_MUL     _mm_mul_ps
+#define GGML_F32x4_REDUCE(res, x)                                 \
+{                                                                 \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
+    }                                                             \
+    const __m128 t0 = _mm_hadd_ps(x[0], x[0]);                    \
+    res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0));                     \
+}
+// TODO: is this optimal ?
 
-    // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+#define GGML_F32_VEC        GGML_F32x4
+#define GGML_F32_VEC_ZERO   GGML_F32x4_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x4_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x4_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x4_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x4_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x4_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x4_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+// F16 SSE
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
-        __m128i bxhil = _mm256_castsi256_si128(bxhi);
-        __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
-        bxhil = _mm_and_si128(bxhil, mask);
-        bxhih = _mm_and_si128(bxhih, mask);
-        __m128i bxl = _mm256_castsi256_si128(bx);
-        __m128i bxh = _mm256_extractf128_si256(bx, 1);
-        bxl = _mm_or_si128(bxl, bxhil);
-        bxh = _mm_or_si128(bxh, bxhih);
-        bx = MM256_SET_M128I(bxh, bxl);
+#define GGML_F16_STEP 32
+#define GGML_F16_EPR  4
 
-        const __m256 dy = _mm256_set1_ps(y[i].d);
-        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
+    float tmp[4];
 
-        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+    tmp[0] = GGML_FP16_TO_FP32(x[0]);
+    tmp[1] = GGML_FP16_TO_FP32(x[1]);
+    tmp[2] = GGML_FP16_TO_FP32(x[2]);
+    tmp[3] = GGML_FP16_TO_FP32(x[3]);
 
-        acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
-    }
+    return _mm_loadu_ps(tmp);
+}
 
-    *s = hsum_float_8(acc) + summs;
-#elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
+static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
+    float arr[4];
 
-    uint32_t qh;
+    _mm_storeu_ps(arr, y);
 
-    size_t vl = __riscv_vsetvl_e8m1(qk/2);
+    x[0] = GGML_FP32_TO_FP16(arr[0]);
+    x[1] = GGML_FP32_TO_FP16(arr[1]);
+    x[2] = GGML_FP32_TO_FP16(arr[2]);
+    x[3] = GGML_FP32_TO_FP16(arr[3]);
+}
 
-    // temporary registers for shift operations
-    vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
-    vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
+#define GGML_F32Cx4             __m128
+#define GGML_F32Cx4_ZERO        _mm_setzero_ps()
+#define GGML_F32Cx4_SET1(x)     _mm_set1_ps(x)
+#define GGML_F32Cx4_LOAD(x)     __sse_f16x4_load(x)
+#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y)
+#define GGML_F32Cx4_FMA         GGML_F32x4_FMA
+#define GGML_F32Cx4_ADD         _mm_add_ps
+#define GGML_F32Cx4_MUL         _mm_mul_ps
+#define GGML_F32Cx4_REDUCE      GGML_F32x4_REDUCE
 
-    for (int i = 0; i < nb; i++) {
-        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+#define GGML_F16_VEC                 GGML_F32Cx4
+#define GGML_F16_VEC_ZERO            GGML_F32Cx4_ZERO
+#define GGML_F16_VEC_SET1            GGML_F32Cx4_SET1
+#define GGML_F16_VEC_LOAD(p, i)      GGML_F32Cx4_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i)  GGML_F32Cx4_STORE(p, r[i])
+#define GGML_F16_VEC_FMA             GGML_F32Cx4_FMA
+#define GGML_F16_VEC_ADD             GGML_F32Cx4_ADD
+#define GGML_F16_VEC_MUL             GGML_F32Cx4_MUL
+#define GGML_F16_VEC_REDUCE          GGML_F32Cx4_REDUCE
 
-        // load qh
-        vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl);
+#endif
 
-        // ((qh >> (j +  0)) << 4) & 0x10;
-        vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl);
-        vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl);
-        vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl);
+// GGML_F32_ARR / GGML_F16_ARR
+//   number of registers to use per step
+#ifdef GGML_SIMD
+#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR)
+#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR)
+#endif
 
-        // ((qh >> (j + 12))     ) & 0x10;
-        vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl);
-        vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl);
+//
+// fundamental operations
+//
 
-        // narrowing
-        vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl);
-        vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl);
+inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 
-        vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl);
-        vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
+inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 
-        // load
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 
-        vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
-        vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
+inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
+inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float   v) { for (int i = 0; i < n; ++i) z[i]  = x[i] + v;    }
+inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i] += x[i];        }
+inline static void ggml_vec_acc1_f32(const int n, float * y, const float   v)                  { for (int i = 0; i < n; ++i) y[i] += v;           }
+inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] - y[i]; }
+inline static void ggml_vec_set_f32 (const int n, float * x, const float   v)                  { for (int i = 0; i < n; ++i) x[i]  = v;           }
+inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = x[i];        }
+inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = -x[i];       }
+inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
+inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
 
-        vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl);
-        vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl);
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
+#ifdef GGML_SIMD
+    float sumf = 0.0f;
+    const int np = (n & ~(GGML_F32_STEP - 1));
 
-        vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a);
-        vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l);
+    GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
 
-        vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl);
-        vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl);
+    GGML_F32_VEC ax[GGML_F32_ARR];
+    GGML_F32_VEC ay[GGML_F32_ARR];
 
-        vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl);
+    for (int i = 0; i < np; i += GGML_F32_STEP) {
+        for (int j = 0; j < GGML_F32_ARR; j++) {
+            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
 
-        vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl);
-        vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl);
+            sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
+        }
+    }
 
-        int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
+    // reduce sum0..sum3 to sum0
+    GGML_F32_VEC_REDUCE(sumf, sum);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        sumf += x[i]*y[i];
     }
-
-    *s = sumf;
 #else
     // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
-        uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
-
-        int sumi = 0;
-
-        for (int j = 0; j < qk/2; ++j) {
-            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
-            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
-
-            const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[i].qs[j] >>  4) | xh_1;
-
-            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
-        }
-
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+    ggml_float sumf = 0.0;
+    for (int i = 0; i < n; ++i) {
+        sumf += (ggml_float)(x[i]*y[i]);
     }
+#endif
 
     *s = sumf;
-#endif
 }
 
-static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const int qk = QK8_0;
-    const int nb = n / qk;
-
-    assert(n % qk == 0);
-
-    const block_q8_0 * restrict x = vx;
-    const block_q8_0 * restrict y = vy;
-
-#if defined(__ARM_NEON)
-    float32x4_t sumv0 = vdupq_n_f32(0.0f);
-    float32x4_t sumv1 = vdupq_n_f32(0.0f);
-
-    GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb
-    for (int i = 0; i < nb; i += 2) {
-        const block_q8_0 * restrict x0 = &x[i + 0];
-        const block_q8_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
-
-        const int8x16_t x0_0 = vld1q_s8(x0->qs);
-        const int8x16_t x0_1 = vld1q_s8(x0->qs + 16);
-        const int8x16_t x1_0 = vld1q_s8(x1->qs);
-        const int8x16_t x1_1 = vld1q_s8(x1->qs + 16);
-
-        // load y
-        const int8x16_t y0_0 = vld1q_s8(y0->qs);
-        const int8x16_t y0_1 = vld1q_s8(y0->qs + 16);
-        const int8x16_t y1_0 = vld1q_s8(y1->qs);
-        const int8x16_t y1_1 = vld1q_s8(y1->qs + 16);
-
-#if defined(__ARM_FEATURE_DOTPROD)
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
-                        vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
-                        vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
-                        vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
+    ggml_float sumf = 0.0;
 
-#else
-        const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
-        const int16x8_t p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
-        const int16x8_t p0_2 = vmull_s8(vget_low_s8 (x0_1), vget_low_s8 (y0_1));
-        const int16x8_t p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));
-
-        const int16x8_t p1_0 = vmull_s8(vget_low_s8 (x1_0), vget_low_s8 (y1_0));
-        const int16x8_t p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0));
-        const int16x8_t p1_2 = vmull_s8(vget_low_s8 (x1_1), vget_low_s8 (y1_1));
-        const int16x8_t p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_1));
-
-        const int32x4_t p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
-        const int32x4_t p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
-        const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
-        const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
-
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
-#endif
-    }
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
-#elif defined(__AVX2__) || defined(__AVX__)
-    // Initialize accumulator with zeros
-    __m256 acc = _mm256_setzero_ps();
+    GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
 
-    // Main loop
-    for (int i = 0; i < nb; ++i) {
-        // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
-        __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
-        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
 
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
 
-        // Multiply q with scale and accumulate
-#if defined(__AVX2__)
-        acc = _mm256_fmadd_ps( d, q, acc );
-#else
-        acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc );
-#endif
+            sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
+        }
     }
 
-    *s = hsum_float_8(acc);
-#elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-    size_t vl = __riscv_vsetvl_e8m1(qk);
-
-    for (int i = 0; i < nb; i++) {
-        // load elements
-        vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl);
-        vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl);
-
-        vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl);
-
-        vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl);
-        vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl);
-
-        int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);
+    // reduce sum0..sum3 to sum0
+    GGML_F16_VEC_REDUCE(sumf, sum);
 
-        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
     }
-
-    *s = sumf;
 #else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
-        int sumi = 0;
-
-        for (int j = 0; j < qk; j++) {
-            sumi += x[i].qs[j]*y[i].qs[j];
-        }
-
-        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+    for (int i = 0; i < n; ++i) {
+        sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
     }
+#endif
 
     *s = sumf;
-#endif
 }
 
 // compute GGML_VEC_DOT_UNROLL dot products at once
@@ -21001,7 +18706,6 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
                 block_q8_0 * block = (block_q8_0*)dst + start / QK8_0;
                 result = ggml_quantize_q8_0(src + start, block, n, n, hist);
             } break;
-#ifdef GGML_USE_K_QUANTS
         case GGML_TYPE_Q2_K:
             {
                 GGML_ASSERT(start % QK_K == 0);
@@ -21032,7 +18736,6 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
                 block_q6_K * block = (block_q6_K*)dst + start / QK_K;
                 result = ggml_quantize_q6_K(src + start, block, n, n, hist);
             } break;
-#endif
         case GGML_TYPE_F16:
             {
                 int elemsize = sizeof(ggml_fp16_t);
diff --git a/ggml.h b/ggml.h
index 08bff5511c2254..8c954904e5a007 100644
--- a/ggml.h
+++ b/ggml.h
@@ -1930,12 +1930,19 @@ extern "C" {
     // quantization
     //
 
+    // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
     GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
     GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
 
+    GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
+
     GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
 
     //
diff --git a/llama.cpp b/llama.cpp
index 3d431ee7bf5260..1d1db8fc97fa38 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -19,13 +19,11 @@
 #ifdef GGML_USE_MPI
 #  include "ggml-mpi.h"
 #endif
-#ifdef GGML_USE_K_QUANTS
-#  ifndef QK_K
-#    ifdef GGML_QKK_64
-#      define QK_K 64
-#    else
-#      define QK_K 256
-#    endif
+#ifndef QK_K
+#  ifdef GGML_QKK_64
+#    define QK_K 64
+#  else
+#    define QK_K 256
 #  endif
 #endif
 
@@ -8052,7 +8050,7 @@ struct no_init {
 struct quantize_state_internal {
     const llama_model                 & model;
     const llama_model_quantize_params * params;
-#ifdef GGML_USE_K_QUANTS
+
     int n_attention_wv    = 0;
     int n_feed_forward_w2 = 0;
     int i_attention_wv    = 0;
@@ -8060,7 +8058,7 @@ struct quantize_state_internal {
 
     int n_k_quantized     = 0;
     int n_fallback        = 0;
-#endif
+
     quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
         : model(model)
         , params(params)
@@ -8125,7 +8123,6 @@ static void llama_convert_tensor_internal(
     workers.clear();
 }
 
-#ifdef GGML_USE_K_QUANTS
 static ggml_type get_k_quant_type(
     quantize_state_internal & qs,
     ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype
@@ -8237,7 +8234,6 @@ static ggml_type get_k_quant_type(
 
     return new_type;
 }
-#endif
 
 static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
     ggml_type quantized_type;
@@ -8252,7 +8248,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_F16:  quantized_type = GGML_TYPE_F16;  break;
         case LLAMA_FTYPE_ALL_F32:     quantized_type = GGML_TYPE_F32;  break;
 
-#ifdef GGML_USE_K_QUANTS
         // K-quants
         case LLAMA_FTYPE_MOSTLY_Q2_K:   quantized_type = GGML_TYPE_Q2_K; break;
         case LLAMA_FTYPE_MOSTLY_Q3_K_S:
@@ -8263,7 +8258,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_Q5_K_S:
         case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
         case LLAMA_FTYPE_MOSTLY_Q6_K:   quantized_type = GGML_TYPE_Q6_K; break;
-#endif
+
         default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
     }
 
@@ -8304,7 +8299,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
     gguf_set_val_u32(ctx_out, "general.file_type", ftype);
 
-#ifdef GGML_USE_K_QUANTS
     for (int i = 0; i < ml.n_tensors; ++i) {
         struct ggml_tensor * meta = ml.get_tensor_meta(i);
 
@@ -8322,7 +8316,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
                 __func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer);
     }
-#endif
 
     size_t total_size_org = 0;
     size_t total_size_new = 0;
@@ -8387,9 +8380,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
         if (quantize) {
             new_type = quantized_type;
-#ifdef GGML_USE_K_QUANTS
-            new_type = get_k_quant_type(qs, new_type, tensor, ftype);
-#endif
+            if (!params->pure) {
+                new_type = get_k_quant_type(qs, new_type, tensor, ftype);
+            }
+
             // If we've decided to quantize to the same type the tensor is already
             // in then there's nothing to do.
             quantize = tensor->type != new_type;
@@ -8514,12 +8508,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             LLAMA_LOG_INFO("\n");
         }
     }
-#ifdef GGML_USE_K_QUANTS
+
     if (qs.n_fallback > 0) {
         LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
                 __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
     }
-#endif
 }
 
 static int llama_apply_lora_from_file_internal(
@@ -8844,6 +8837,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
         /*.allow_requantize            =*/ false,
         /*.quantize_output_tensor      =*/ true,
         /*.only_copy                   =*/ false,
+        /*.pure                        =*/ false,
     };
 
     return result;
diff --git a/llama.h b/llama.h
index d901dcd9116d3d..6927bd6010dd7a 100644
--- a/llama.h
+++ b/llama.h
@@ -191,6 +191,7 @@ extern "C" {
         bool allow_requantize;       // allow quantizing non-f32/f16 tensors
         bool quantize_output_tensor; // quantize output.weight
         bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+        bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
     } llama_model_quantize_params;
 
     // grammar types

From 71a09da301705b9c5ad4ca3cf3fbd966dd3f1ec5 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 29 Oct 2023 18:32:51 +0200
Subject: [PATCH 29/79] llama : fix kv shift bug (#3835)

ggml-ci
---
 llama.cpp | 27 ++++++++++++++++++---------
 1 file changed, 18 insertions(+), 9 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 1d1db8fc97fa38..d8510a5cf01254 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1552,14 +1552,14 @@ static void llama_kv_cache_seq_shift(
 
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
-            cache.cells[i].pos += delta;
+            cache.has_shift = true;
+            cache.cells[i].pos   += delta;
+            cache.cells[i].delta += delta;
+
             if (cache.cells[i].pos < 0) {
                 cache.cells[i].pos = -1;
                 cache.cells[i].seq_id.clear();
                 if (new_head == cache.size) new_head = i;
-            } else {
-                cache.has_shift = true;
-                cache.cells[i].delta = delta;
             }
         }
     }
@@ -6073,11 +6073,20 @@ static int llama_decode_internal(
 #endif
 
     // update the kv ring buffer
-    lctx.kv_self.has_shift  = false;
-    lctx.kv_self.head      += n_tokens;
-    // Ensure kv cache head points to a valid index.
-    if (lctx.kv_self.head >= lctx.kv_self.size) {
-        lctx.kv_self.head = 0;
+    {
+        if (kv_self.has_shift) {
+            kv_self.has_shift = false;
+            for (uint32_t i = 0; i < kv_self.size; ++i) {
+                kv_self.cells[i].delta = 0;
+            }
+        }
+
+        kv_self.head += n_tokens;
+
+        // Ensure kv cache head points to a valid index.
+        if (kv_self.head >= kv_self.size) {
+            kv_self.head = 0;
+        }
     }
 
 #ifdef GGML_PERF

From 2046eb4345e62c4575b3cdc0115a51db89f3fb70 Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Sun, 29 Oct 2023 12:33:47 -0400
Subject: [PATCH 30/79] make : remove unnecessary dependency on build-info.h
 (#3842)

---
 Makefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 2cecc2216c87b6..c53c1e7260fef4 100644
--- a/Makefile
+++ b/Makefile
@@ -541,10 +541,10 @@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
 llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-COMMON_H_DEPS = common/common.h common/sampling.h build-info.h common/log.h
-COMMON_DEPS   = $(COMMON_H_DEPS) common.o sampling.o grammar-parser.o
+COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
+COMMON_DEPS   = common.o sampling.o grammar-parser.o
 
-common.o: common/common.cpp $(COMMON_H_DEPS)
+common.o: common/common.cpp build-info.h $(COMMON_H_DEPS)
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 sampling.o: common/sampling.cpp $(COMMON_H_DEPS)

From 6e08281e588bbba1a5d180290a94a43f167f3a1a Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Sun, 29 Oct 2023 11:31:40 -0600
Subject: [PATCH 31/79] Extend llama_kv_cache_seq_rm to allow matching any
 sequence (#3843)

* Extend llama_kv_cache_seq_rm to allow matichng any sequence

* Replace llama_kv_cache_tokens_rm with llama_kv_cache_clear

Use llama_kv_cache_clear for cache clearing

Change calls to llama_kv_cache_tokens_rm that want to delete by position to use llama_kv_cache_seq_rm functionality
---
 common/common.cpp                        |  2 +-
 examples/batched-bench/batched-bench.cpp |  2 +-
 examples/llama-bench/llama-bench.cpp     |  4 ++--
 examples/main/main.cpp                   |  2 +-
 examples/perplexity/perplexity.cpp       |  6 ++---
 examples/server/server.cpp               |  2 +-
 llama.cpp                                | 29 ++++++++++++------------
 llama.h                                  | 15 +++++-------
 8 files changed, 30 insertions(+), 32 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index f81f4d354bc017..c187128d6ede3d 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -889,7 +889,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 
         std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
         llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
-        llama_kv_cache_tokens_rm(lctx, -1, -1);
+        llama_kv_cache_clear(lctx);
         llama_reset_timings(lctx);
     }
 
diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index 43f9c971d18465..533c55c17aad17 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -185,7 +185,7 @@ int main(int argc, char ** argv) {
 
                 const auto t_pp_start = ggml_time_us();
 
-                llama_kv_cache_tokens_rm(ctx, -1, -1);
+                llama_kv_cache_clear(ctx);
 
                 if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
                     LOG_TEE("%s: llama_decode() failed\n", __func__);
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 20767d555b206d..780398184d2215 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -1037,7 +1037,7 @@ int main(int argc, char ** argv) {
 
         test t(inst, lmodel, ctx);
 
-        llama_kv_cache_tokens_rm(ctx, -1, -1);
+        llama_kv_cache_clear(ctx);
 
         // warmup run
         if (t.n_prompt > 0) {
@@ -1048,7 +1048,7 @@ int main(int argc, char ** argv) {
         }
 
         for (int i = 0; i < params.reps; i++) {
-            llama_kv_cache_tokens_rm(ctx, -1, -1);
+            llama_kv_cache_clear(ctx);
 
             uint64_t t_start = get_time_ns();
             if (t.n_prompt > 0) {
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 3d9f670b9da7f2..8a43b6ab878a5f 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -298,7 +298,7 @@ int main(int argc, char ** argv) {
         }
 
         // remove any "future" tokens that we might have inherited from the previous session
-        llama_kv_cache_tokens_rm(ctx, n_matching_session_tokens, -1);
+        llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);
     }
 
     LOGLN(
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 3c2542e8c105e7..bd2c73d87875fe 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -210,7 +210,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
         const auto t_start = std::chrono::high_resolution_clock::now();
 
         // clear the KV cache
-        llama_kv_cache_tokens_rm(ctx, -1, -1);
+        llama_kv_cache_clear(ctx);
 
         for (int j = 0; j < num_batches; ++j) {
             const int batch_start = start + j * n_batch;
@@ -339,7 +339,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
         const auto t_start = std::chrono::high_resolution_clock::now();
 
         // clear the KV cache
-        llama_kv_cache_tokens_rm(ctx, -1, -1);
+        llama_kv_cache_clear(ctx);
 
         for (int j = 0; j < num_batches; ++j) {
             const int batch_start = start + j * n_batch;
@@ -573,7 +573,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         }
 
         // clear the KV cache
-        llama_kv_cache_tokens_rm(ctx, -1, -1);
+        llama_kv_cache_clear(ctx);
 
         auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab);
         if (logits.empty()) {
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 5b7e4139de551e..c163c7f8ec0dd1 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -857,7 +857,7 @@ struct llama_server_context
 
     void kv_cache_clear() {
         // clear the entire KV cache
-        llama_kv_cache_tokens_rm(ctx, -1, -1);
+        llama_kv_cache_clear(ctx);
         clean_kv_cache = false;
     }
 
diff --git a/llama.cpp b/llama.cpp
index d8510a5cf01254..a4340d5277b09c 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1466,17 +1466,12 @@ static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
     return 0;
 }
 
-static void llama_kv_cache_tokens_rm(struct llama_kv_cache & cache, int32_t c0, int32_t c1) {
-    if (c0 < 0) c0 = 0;
-    if (c1 < 0) c1 = cache.size;
-
-    for (int32_t i = c0; i < c1; ++i) {
+static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
+    for (int32_t i = 0; i < cache.size; ++i) {
         cache.cells[i].pos = -1;
         cache.cells[i].seq_id.clear();
     }
-
-    // Searching for a free slot can start here since we know it will be empty.
-    cache.head = uint32_t(c0);
+    cache.head = 0;
 }
 
 static void llama_kv_cache_seq_rm(
@@ -1490,8 +1485,14 @@ static void llama_kv_cache_seq_rm(
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
     for (uint32_t i = 0; i < cache.size; ++i) {
-        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
-            cache.cells[i].seq_id.erase(seq_id);
+        if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+            if (seq_id < 0) {
+                cache.cells[i].seq_id.clear();
+            } else if (cache.cells[i].has_seq_id(seq_id)) {
+                cache.cells[i].seq_id.erase(seq_id);
+            } else {
+                continue;
+            }
             if (cache.cells[i].seq_id.empty()) {
                 cache.cells[i].pos = -1;
                 if (new_head == cache.size) new_head = i;
@@ -9207,8 +9208,8 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
     return ctx->kv_self.head;
 }
 
-void llama_kv_cache_tokens_rm(struct llama_context * ctx, int32_t c0, int32_t c1) {
-    llama_kv_cache_tokens_rm(ctx->kv_self, c0, c1);
+void llama_kv_cache_clear(struct llama_context * ctx) {
+    llama_kv_cache_clear(ctx->kv_self);
 }
 
 void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
@@ -9654,7 +9655,7 @@ int llama_eval(
                  llama_token * tokens,
                      int32_t   n_tokens,
                          int   n_past) {
-    llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1);
+    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
 
     const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
     if (ret < 0) {
@@ -9669,7 +9670,7 @@ int llama_eval_embd(
                            float * embd,
                          int32_t   n_tokens,
                              int   n_past) {
-    llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1);
+    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
 
     llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
 
diff --git a/llama.h b/llama.h
index 6927bd6010dd7a..d727dbd9fd915d 100644
--- a/llama.h
+++ b/llama.h
@@ -334,17 +334,14 @@ extern "C" {
     LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
             "avoid using this, it will be removed in the future, instead - count the tokens in user code");
 
-    // Remove all tokens data of cells in [c0, c1)
-    // c0 < 0 : [0,  c1]
-    // c1 < 0 : [c0, inf)
-    LLAMA_API void llama_kv_cache_tokens_rm(
-            struct llama_context * ctx,
-                         int32_t   c0,
-                         int32_t   c1);
+    // Clear the KV cache
+    LLAMA_API void llama_kv_cache_clear(
+            struct llama_context * ctx);
 
     // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
-    // p0 < 0 : [0,  p1]
-    // p1 < 0 : [p0, inf)
+    // seq_id < 0 : match any sequence
+    // p0 < 0     : [0,  p1]
+    // p1 < 0     : [p0, inf)
     LLAMA_API void llama_kv_cache_seq_rm(
             struct llama_context * ctx,
                     llama_seq_id   seq_id,

From 207b51900e15cc7f89763a3bb1c565fe11cbb45d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 30 Oct 2023 19:19:15 +0200
Subject: [PATCH 32/79] ggml : move FP16 <-> FP32 code to ggml-impl.h (#3861)

* ggml : move FP16 <-> FP32 stuff to ggml-impl.h

ggml-ci

* tests : fix ARM build

* ggml : explicitly initialize deprecated type traits

* ggml : add math.h to ggml-impl.h

* ggml : remove duplicate static assert macros

* ggml : prefix lookup tables with ggml_

ggml-ci

* ggml-impl : move extern "C" to start of file
---
 ggml-impl.h                 | 237 ++++++++++++++++++++++++
 ggml-quants.c               | 350 ++++++++++++++++++------------------
 ggml-quants.h               |  14 +-
 ggml.c                      | 282 +++++------------------------
 llama.cpp                   |   2 +-
 tests/test-double-float.cpp |   2 +-
 tests/test-quantize-fns.cpp |   7 +
 7 files changed, 470 insertions(+), 424 deletions(-)
 create mode 100644 ggml-impl.h

diff --git a/ggml-impl.h b/ggml-impl.h
new file mode 100644
index 00000000000000..5ec18a50c8da57
--- /dev/null
+++ b/ggml-impl.h
@@ -0,0 +1,237 @@
+#pragma once
+
+#include "ggml.h"
+
+// GGML internal header
+
+#include <assert.h>
+#include <stddef.h>
+#include <stdbool.h>
+#include <string.h> // memcpy
+#include <math.h>   // fabsf
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// static_assert should be a #define, but if it's not,
+// fall back to the _Static_assert C11 keyword.
+// if C99 - static_assert is noop
+// ref: https://stackoverflow.com/a/53923785/4039976
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+
+// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
+#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
+#ifndef __FMA__
+#define __FMA__
+#endif
+#ifndef __F16C__
+#define __F16C__
+#endif
+#ifndef __SSE3__
+#define __SSE3__
+#endif
+#endif
+
+#undef MIN
+#undef MAX
+
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+// 16-bit float
+// on Arm, we use __fp16
+// on x86, we use uint16_t
+#if defined(__ARM_NEON) && !defined(_MSC_VER)
+
+// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
+//
+//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
+//
+#include <arm_neon.h>
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
+#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
+
+#define GGML_FP16_TO_FP32(x) ((float) (x))
+#define GGML_FP32_TO_FP16(x) (x)
+
+#else
+
+#ifdef __wasm_simd128__
+#include <wasm_simd128.h>
+#else
+#ifdef __POWER9_VECTOR__
+#include <altivec.h>
+#undef bool
+#define bool _Bool
+#else
+#if defined(_MSC_VER) || defined(__MINGW32__)
+#include <intrin.h>
+#else
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
+#if !defined(__riscv)
+#include <immintrin.h>
+#endif
+#endif
+#endif
+#endif
+#endif
+
+#ifdef __riscv_v_intrinsic
+#include <riscv_vector.h>
+#endif
+
+#ifdef __F16C__
+
+#ifdef _MSC_VER
+#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x)))
+#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0)
+#else
+#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
+#endif
+
+#elif defined(__POWER9_VECTOR__)
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+/* the inline asm below is about 12% faster than the lookup method */
+#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    register float f;
+    register double d;
+    __asm__(
+        "mtfprd %0,%2\n"
+        "xscvhpdp %0,%0\n"
+        "frsp %1,%0\n" :
+        /* temp */ "=d"(d),
+        /* out */  "=f"(f):
+        /* in */   "r"(h));
+    return f;
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+    register double d;
+    register ggml_fp16_t r;
+    __asm__( /* xscvdphp can work on double or single precision */
+        "xscvdphp %0,%2\n"
+        "mffprd %1,%0\n" :
+        /* temp */ "=d"(d),
+        /* out */  "=r"(r):
+        /* in */   "f"(f));
+    return r;
+}
+
+#else
+
+// FP16 <-> FP32
+// ref: https://github.com/Maratyszcza/FP16
+
+static inline float fp32_from_bits(uint32_t w) {
+    union {
+        uint32_t as_bits;
+        float as_value;
+    } fp32;
+    fp32.as_bits = w;
+    return fp32.as_value;
+}
+
+static inline uint32_t fp32_to_bits(float f) {
+    union {
+        float as_value;
+        uint32_t as_bits;
+    } fp32;
+    fp32.as_value = f;
+    return fp32.as_bits;
+}
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    const uint32_t w = (uint32_t) h << 16;
+    const uint32_t sign = w & UINT32_C(0x80000000);
+    const uint32_t two_w = w + w;
+
+    const uint32_t exp_offset = UINT32_C(0xE0) << 23;
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
+    const float exp_scale = 0x1.0p-112f;
+#else
+    const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
+#endif
+    const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
+
+    const uint32_t magic_mask = UINT32_C(126) << 23;
+    const float magic_bias = 0.5f;
+    const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
+
+    const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
+    const uint32_t result = sign |
+        (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
+    return fp32_from_bits(result);
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
+    const float scale_to_inf = 0x1.0p+112f;
+    const float scale_to_zero = 0x1.0p-110f;
+#else
+    const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
+    const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
+#endif
+    float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
+
+    const uint32_t w = fp32_to_bits(f);
+    const uint32_t shl1_w = w + w;
+    const uint32_t sign = w & UINT32_C(0x80000000);
+    uint32_t bias = shl1_w & UINT32_C(0xFF000000);
+    if (bias < UINT32_C(0x71000000)) {
+        bias = UINT32_C(0x71000000);
+    }
+
+    base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
+    const uint32_t bits = fp32_to_bits(base);
+    const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
+    const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
+    const uint32_t nonsign = exp_bits + mantissa_bits;
+    return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
+}
+
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+
+#endif // __F16C__
+
+#endif // __ARM_NEON
+
+// precomputed f32 table for f16 (256 KB)
+// defined in ggml.c, initialized in ggml_init()
+extern float ggml_table_f32_f16[1 << 16];
+
+// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
+// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
+// This is also true for POWER9.
+#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
+
+inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
+    uint16_t s;
+    memcpy(&s, &f, sizeof(uint16_t));
+    return ggml_table_f32_f16[s];
+}
+
+#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+
+#endif
+
+    // TODO: backend v2 PR
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml-quants.c b/ggml-quants.c
index fd4ee1be64befa..72159446738e30 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -1,5 +1,5 @@
 #include "ggml-quants.h"
-#include "ggml.h"
+#include "ggml-impl.h"
 
 #include <math.h>
 #include <string.h>
@@ -352,7 +352,7 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict
         const float d  = max / -8;
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         for (int j = 0; j < qk/2; ++j) {
             const float x0 = x[i*qk + 0    + j]*id;
@@ -392,8 +392,8 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict
         const float d  = (max - min) / ((1 << 4) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
-        y[i].m = ggml_fp32_to_fp16(min);
+        y[i].d = GGML_FP32_TO_FP16(d);
+        y[i].m = GGML_FP32_TO_FP16(min);
 
         for (int j = 0; j < qk/2; ++j) {
             const float x0 = (x[i*qk + 0    + j] - min)*id;
@@ -434,7 +434,7 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict
         const float d  = max / -16;
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         uint32_t qh = 0;
 
@@ -481,8 +481,8 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict
         const float d  = (max - min) / ((1 << 5) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
-        y[i].m = ggml_fp32_to_fp16(min);
+        y[i].d = GGML_FP32_TO_FP16(d);
+        y[i].m = GGML_FP32_TO_FP16(min);
 
         uint32_t qh = 0;
 
@@ -524,7 +524,7 @@ void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         for (int j = 0; j < QK8_0; ++j) {
             const float x0 = x[i*QK8_0 + j]*id;
@@ -559,7 +559,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         for (int j = 0; j < 8; j++) {
             const float32x4_t v  = vmulq_n_f32(srcv[j], id);
@@ -592,7 +592,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         for (int j = 0; j < 8; j++) {
             const v128_t v  = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id));
@@ -627,7 +627,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
 
         // Quantize these floats
         const float d = maxScalar / 127.f;
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
         const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
         const __m256 mul = _mm256_set1_ps( id );
 
@@ -704,7 +704,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = ggml_fp32_to_fp16(d);
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
 
@@ -982,7 +982,7 @@ void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int
     const int nb = k / qk;
 
     for (int i = 0; i < nb; i++) {
-        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
 
         for (int j = 0; j < qk/2; ++j) {
             const int x0 = (x[i].qs[j] & 0x0F) - 8;
@@ -1002,8 +1002,8 @@ void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int
     const int nb = k / qk;
 
     for (int i = 0; i < nb; i++) {
-        const float d = ggml_fp16_to_fp32(x[i].d);
-        const float m = ggml_fp16_to_fp32(x[i].m);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const float m = GGML_FP16_TO_FP32(x[i].m);
 
         for (int j = 0; j < qk/2; ++j) {
             const int x0 = (x[i].qs[j] & 0x0F);
@@ -1023,7 +1023,7 @@ void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int
     const int nb = k / qk;
 
     for (int i = 0; i < nb; i++) {
-        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
 
         uint32_t qh;
         memcpy(&qh, x[i].qh, sizeof(qh));
@@ -1049,8 +1049,8 @@ void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int
     const int nb = k / qk;
 
     for (int i = 0; i < nb; i++) {
-        const float d = ggml_fp16_to_fp32(x[i].d);
-        const float m = ggml_fp16_to_fp32(x[i].m);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const float m = GGML_FP16_TO_FP32(x[i].m);
 
         uint32_t qh;
         memcpy(&qh, x[i].qh, sizeof(qh));
@@ -1076,7 +1076,7 @@ void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int
     const int nb = k / qk;
 
     for (int i = 0; i < nb; i++) {
-        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
 
         for (int j = 0; j < qk; ++j) {
             y[i*qk + j] = x[i].qs[j]*d;
@@ -1387,10 +1387,10 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
                 int l = nearest_int(iscale*scales[j]);
                 y[i].scales[j] = l;
             }
-            y[i].d = ggml_fp32_to_fp16(max_scale/q4scale);
+            y[i].d = GGML_FP32_TO_FP16(max_scale/q4scale);
         } else {
             for (int j = 0; j < QK_K/16; ++j) y[i].scales[j] = 0;
-            y[i].d = ggml_fp32_to_fp16(0.f);
+            y[i].d = GGML_FP32_TO_FP16(0.f);
         }
         if (max_min > 0) {
             float iscale = q4scale/max_min;
@@ -1398,14 +1398,14 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
                 int l = nearest_int(iscale*mins[j]);
                 y[i].scales[j] |= (l << 4);
             }
-            y[i].dmin = ggml_fp32_to_fp16(max_min/q4scale);
+            y[i].dmin = GGML_FP32_TO_FP16(max_min/q4scale);
         } else {
-            y[i].dmin = ggml_fp32_to_fp16(0.f);
+            y[i].dmin = GGML_FP32_TO_FP16(0.f);
         }
         for (int j = 0; j < QK_K/16; ++j) {
-            const float d = ggml_fp16_to_fp32(y[i].d) * (y[i].scales[j] & 0xF);
+            const float d = GGML_FP16_TO_FP32(y[i].d) * (y[i].scales[j] & 0xF);
             if (!d) continue;
-            const float dm = ggml_fp16_to_fp32(y[i].dmin) * (y[i].scales[j] >> 4);
+            const float dm = GGML_FP16_TO_FP32(y[i].dmin) * (y[i].scales[j] >> 4);
             for (int ii = 0; ii < 16; ++ii) {
                 int l = nearest_int((x[16*j + ii] + dm)/d);
                 l = MAX(0, MIN(3, l));
@@ -1436,8 +1436,8 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int
 
     for (int i = 0; i < nb; i++) {
 
-        const float d = ggml_fp16_to_fp32(x[i].d);
-        const float min = ggml_fp16_to_fp32(x[i].dmin);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const float min = GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * q = x[i].qs;
 
@@ -1526,16 +1526,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict
                 l >>= 4;
                 y[i].scales[j%4 + 8] |= (l << (2*(j/4)));
             }
-            y[i].d = ggml_fp32_to_fp16(1/iscale);
+            y[i].d = GGML_FP32_TO_FP16(1/iscale);
         } else {
-            y[i].d = ggml_fp32_to_fp16(0.f);
+            y[i].d = GGML_FP32_TO_FP16(0.f);
         }
 
         int8_t sc;
         for (int j = 0; j < QK_K/16; ++j) {
             sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4;
             sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32;
-            float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            float d = GGML_FP16_TO_FP32(y[i].d) * sc;
             if (!d) {
                 continue;
             }
@@ -1555,16 +1555,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict
                 l2 = 8 + MAX(-8, MIN(7, l2));
                 y[i].scales[j/2] = l1 | (l2 << 4);
             }
-            y[i].d = ggml_fp32_to_fp16(1/iscale);
+            y[i].d = GGML_FP32_TO_FP16(1/iscale);
         } else {
             for (int j = 0; j < QK_K/16; j+=2) {
                 y[i].scales[j/2] = 0;
             }
-            y[i].d = ggml_fp32_to_fp16(0.f);
+            y[i].d = GGML_FP32_TO_FP16(0.f);
         }
         for (int j = 0; j < QK_K/16; ++j) {
             int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4;
-            float d = ggml_fp16_to_fp32(y[i].d) * (s - 8);
+            float d = GGML_FP16_TO_FP32(y[i].d) * (s - 8);
             if (!d) {
                 continue;
             }
@@ -1618,7 +1618,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int
 
     for (int i = 0; i < nb; i++) {
 
-        const float d_all = ggml_fp16_to_fp32(x[i].d);
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q = x[i].qs;
         const uint8_t * restrict hm = x[i].hmask;
@@ -1663,7 +1663,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int
 
     for (int i = 0; i < nb; i++) {
 
-        const float d_all = ggml_fp16_to_fp32(x[i].d);
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q = x[i].qs;
         const uint8_t * restrict hm = x[i].hmask;
@@ -1753,15 +1753,15 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
                 y[i].scales[j-0] |= ((lm >> 4) << 6);
             }
         }
-        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
-        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+        y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
+        y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
 
         uint8_t sc, m;
         for (int j = 0; j < QK_K/32; ++j) {
             get_scale_min_k4(j, y[i].scales, &sc, &m);
-            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            const float d = GGML_FP16_TO_FP32(y[i].d) * sc;
             if (!d) continue;
-            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
+            const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m;
             for (int ii = 0; ii < 32; ++ii) {
                 int l = nearest_int((x[32*j + ii] + dm)/d);
                 l = MAX(0, MIN(15, l));
@@ -1778,17 +1778,17 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
         int m2 = nearest_int(inv_min*mins[1]);
         y[i].scales[0] = d1 | (m1 << 4);
         y[i].scales[1] = d2 | (m2 << 4);
-        y[i].d[0] = ggml_fp32_to_fp16(max_scale/s_factor);
-        y[i].d[1] = ggml_fp32_to_fp16(max_min/s_factor);
+        y[i].d[0] = GGML_FP32_TO_FP16(max_scale/s_factor);
+        y[i].d[1] = GGML_FP32_TO_FP16(max_min/s_factor);
 
         float sumlx = 0;
         int   suml2 = 0;
         for (int j = 0; j < QK_K/32; ++j) {
             const uint8_t sd = y[i].scales[j] & 0xF;
             const uint8_t sm = y[i].scales[j] >>  4;
-            const float d = ggml_fp16_to_fp32(y[i].d[0]) * sd;
+            const float d = GGML_FP16_TO_FP32(y[i].d[0]) * sd;
             if (!d) continue;
-            const float m = ggml_fp16_to_fp32(y[i].d[1]) * sm;
+            const float m = GGML_FP16_TO_FP32(y[i].d[1]) * sm;
             for (int ii = 0; ii < 32; ++ii) {
                 int l = nearest_int((x[32*j + ii] + m)/d);
                 l = MAX(0, MIN(15, l));
@@ -1798,7 +1798,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
             }
         }
         if (suml2) {
-            y[i].d[0] = ggml_fp32_to_fp16(sumlx/suml2);
+            y[i].d[0] = GGML_FP32_TO_FP16(sumlx/suml2);
         }
 #endif
         uint8_t * q = y[i].qs;
@@ -1822,8 +1822,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int
 
 #if QK_K == 256
 
-        const float d   = ggml_fp16_to_fp32(x[i].d);
-        const float min = ggml_fp16_to_fp32(x[i].dmin);
+        const float d   = GGML_FP16_TO_FP32(x[i].d);
+        const float min = GGML_FP16_TO_FP32(x[i].dmin);
 
         int is = 0;
         uint8_t sc, m;
@@ -1837,8 +1837,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int
             q += 32; is += 2;
         }
 #else
-        const float dall = ggml_fp16_to_fp32(x[i].d[0]);
-        const float mall = ggml_fp16_to_fp32(x[i].d[1]);
+        const float dall = GGML_FP16_TO_FP32(x[i].d[0]);
+        const float mall = GGML_FP16_TO_FP32(x[i].d[1]);
         const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4);
         const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4);
         for (int l = 0; l < 32; ++l) {
@@ -1924,15 +1924,15 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
                 y[i].scales[j-0] |= ((lm >> 4) << 6);
             }
         }
-        y[i].d = ggml_fp32_to_fp16(max_scale/63.f);
-        y[i].dmin = ggml_fp32_to_fp16(max_min/63.f);
+        y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
+        y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
 
         uint8_t sc, m;
         for (int j = 0; j < QK_K/32; ++j) {
             get_scale_min_k4(j, y[i].scales, &sc, &m);
-            const float d = ggml_fp16_to_fp32(y[i].d) * sc;
+            const float d = GGML_FP16_TO_FP32(y[i].d) * sc;
             if (!d) continue;
-            const float dm = ggml_fp16_to_fp32(y[i].dmin) * m;
+            const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m;
             for (int ii = 0; ii < 32; ++ii) {
                 int l = nearest_int((x[32*j + ii] + dm)/d);
                 l = MAX(0, MIN(31, l));
@@ -1976,10 +1976,10 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
             int l = nearest_int(iscale*scales[j]);
             y[i].scales[j] = MAX(-128, MIN(127, l));
         }
-        y[i].d = ggml_fp32_to_fp16(1/iscale);
+        y[i].d = GGML_FP32_TO_FP16(1/iscale);
 
         for (int j = 0; j < QK_K/16; ++j) {
-            const float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
+            const float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j];
             if (!d) continue;
             for (int ii = 0; ii < 16; ++ii) {
                 int l = nearest_int(x[16*j + ii]/d);
@@ -2023,8 +2023,8 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int
 
 #if QK_K == 256
 
-        const float d = ggml_fp16_to_fp32(x[i].d);
-        const float min = ggml_fp16_to_fp32(x[i].dmin);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const float min = GGML_FP16_TO_FP32(x[i].dmin);
 
         int is = 0;
         uint8_t sc, m;
@@ -2040,7 +2040,7 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int
             u1 <<= 2; u2 <<= 2;
         }
 #else
-        float d = ggml_fp16_to_fp32(x[i].d);
+        float d = GGML_FP16_TO_FP32(x[i].d);
         const int8_t * restrict s = x[i].scales;
         for (int l = 0; l < 8; ++l) {
             y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16));
@@ -2103,19 +2103,19 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
 
         if (!max_abs_scale) {
             memset(&y[i], 0, sizeof(block_q6_K));
-            y[i].d = ggml_fp32_to_fp16(0.f);
+            y[i].d = GGML_FP32_TO_FP16(0.f);
             x += QK_K;
             continue;
         }
 
         float iscale = -128.f/max_scale;
-        y[i].d = ggml_fp32_to_fp16(1/iscale);
+        y[i].d = GGML_FP32_TO_FP16(1/iscale);
         for (int ib = 0; ib < QK_K/16; ++ib) {
             y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib]));
         }
 
         for (int j = 0; j < QK_K/16; ++j) {
-            float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j];
+            float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j];
             if (!d) {
                 continue;
             }
@@ -2164,7 +2164,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int
 
     for (int i = 0; i < nb; i++) {
 
-        const float d = ggml_fp16_to_fp32(x[i].d);
+        const float d = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict ql = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -2371,8 +2371,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h);
         const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h);
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
 #else
         const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0l));
         const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
@@ -2389,8 +2389,8 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
         const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
 #endif
     }
 
@@ -2402,7 +2402,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
     // Main loop
     for (int i = 0; i < nb; ++i) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) );
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
 
         __m256i bx = bytes_from_nibbles_32(x[i].qs);
 
@@ -2426,7 +2426,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
     // Main loop
     for (int i = 0; i < nb; ++i) {
         // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) );
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
 
         const __m128i lowMask = _mm_set1_epi8(0xF);
         const __m128i off = _mm_set1_epi8(8);
@@ -2468,7 +2468,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[0].d) * ggml_fp16_to_fp32(y[0].d) );
+        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
 
         const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
 
@@ -2486,7 +2486,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[1].d) * ggml_fp16_to_fp32(y[1].d) );
+        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
 
         const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
 
@@ -2521,7 +2521,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d) );
+        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
 
         const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
 
@@ -2539,7 +2539,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( ggml_fp16_to_fp32(x[i + 1].d) * ggml_fp16_to_fp32(y[i + 1].d) );
+        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
 
         const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
 
@@ -2606,7 +2606,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d);
+        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
     }
 
     *s = sumf;
@@ -2624,7 +2624,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
             sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += sumi*ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d);
+        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
     }
 
     *s = sumf;
@@ -2655,7 +2655,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const block_q8_1 * restrict y0 = &y[i + 0];
         const block_q8_1 * restrict y1 = &y[i + 1];
 
-        summs += ggml_fp16_to_fp32(x0->m) * y0->s + ggml_fp16_to_fp32(x1->m) * y1->s;
+        summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -2679,8 +2679,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
         const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), ggml_fp16_to_fp32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), ggml_fp16_to_fp32(x1->d)*y1->d);
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
 #else
         const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0l));
         const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0l));
@@ -2697,8 +2697,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
         const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*y1->d);
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
 #endif
     }
 
@@ -2711,10 +2711,10 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
 
     // Main loop
     for (int i = 0; i < nb; ++i) {
-        const float d0 = ggml_fp16_to_fp32(x[i].d);
+        const float d0 = GGML_FP16_TO_FP32(x[i].d);
         const float d1 = y[i].d;
 
-        summs += ggml_fp16_to_fp32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
 
         const __m256 d0v = _mm256_set1_ps( d0 );
         const __m256 d1v = _mm256_set1_ps( d1 );
@@ -2766,7 +2766,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
     }
 
     *s = sumf;
@@ -2784,7 +2784,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
             sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
     }
 
     *s = sumf;
@@ -2864,10 +2864,10 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
 #if defined(__ARM_FEATURE_DOTPROD)
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
                         vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
                         vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
 #else
         const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
         const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
@@ -2884,8 +2884,8 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
         const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
         const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
 #endif
     }
 
@@ -2946,7 +2946,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
                                            wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
                             wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
                                            wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
-                    wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * ggml_fp16_to_fp32(y0->d))));
+                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
     *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
@@ -2958,7 +2958,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
     // Main loop
     for (int i = 0; i < nb; i++) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
 
         __m256i bx = bytes_from_nibbles_32(x[i].qs);
         __m256i bxhi = bytes_from_bits_32(x[i].qh);
@@ -2982,7 +2982,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
     // Main loop
     for (int i = 0; i < nb; i++) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
 
         __m256i bx = bytes_from_nibbles_32(x[i].qs);
         const __m256i bxhi = bytes_from_bits_32(x[i].qh);
@@ -3066,7 +3066,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
     }
 
     *s = sumf;
@@ -3090,7 +3090,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
             sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d)) * sumi;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
     }
 
     *s = sumf;
@@ -3130,8 +3130,8 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
-        summs0 += ggml_fp16_to_fp32(x0->m) * y0->s;
-        summs1 += ggml_fp16_to_fp32(x1->m) * y1->s;
+        summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s;
+        summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s;
 
         // extract the 5th bit via lookup table ((b) << 4)
         memcpy(&qh0, x0->qh, sizeof(qh0));
@@ -3176,10 +3176,10 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 #if defined(__ARM_FEATURE_DOTPROD)
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
                         vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), ggml_fp16_to_fp32(x0->d)*y0->d);
+                        vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
                         vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), ggml_fp16_to_fp32(x1->d)*y1->d);
+                        vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
 #else
         const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lf), vget_low_s8 (v1_0l));
         const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lf), vget_high_s8(v1_0l));
@@ -3196,8 +3196,8 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
         const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
         const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), ggml_fp16_to_fp32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), ggml_fp16_to_fp32(x1->d)*y1->d);
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(pl0, ph0)), GGML_FP16_TO_FP32(x0->d)*y0->d);
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(pl1, ph1)), GGML_FP16_TO_FP32(x1->d)*y1->d);
 #endif
     }
 
@@ -3215,7 +3215,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
         const block_q5_1 * restrict x0 = &x[i];
         const block_q8_1 * restrict y0 = &y[i];
 
-        summs += ggml_fp16_to_fp32(x0->m) * y0->s;
+        summs += GGML_FP16_TO_FP32(x0->m) * y0->s;
 
         const v128_t m4b = wasm_i8x16_splat(0x0F);
 
@@ -3262,7 +3262,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
                                            wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
                             wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
                                            wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
-                    wasm_f32x4_splat(ggml_fp16_to_fp32(x0->d) * y0->d)));
+                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d)));
     }
 
     *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
@@ -3275,9 +3275,9 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
     // Main loop
     for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d));
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
 
-        summs += ggml_fp16_to_fp32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
 
         __m256i bx = bytes_from_nibbles_32(x[i].qs);
         __m256i bxhi = bytes_from_bits_32(x[i].qh);
@@ -3302,9 +3302,9 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
     // Main loop
     for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d));
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
 
-        summs += ggml_fp16_to_fp32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
 
         __m256i bx = bytes_from_nibbles_32(x[i].qs);
         const __m256i bxhi = bytes_from_bits_32(x[i].qh);
@@ -3385,7 +3385,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
     }
 
     *s = sumf;
@@ -3409,7 +3409,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
             sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (ggml_fp16_to_fp32(x[i].d)*y[i].d)*sumi + ggml_fp16_to_fp32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
     }
 
     *s = sumf;
@@ -3451,11 +3451,11 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
 #if defined(__ARM_FEATURE_DOTPROD)
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
                         vdotq_s32(vdupq_n_s32(0), x0_0, y0_0),
-                        vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
+                        vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
 
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
                         vdotq_s32(vdupq_n_s32(0), x1_0, y1_0),
-                        vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+                        vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
 
 #else
         const int16x8_t p0_0 = vmull_s8(vget_low_s8 (x0_0), vget_low_s8 (y0_0));
@@ -3473,8 +3473,8 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
         const int32x4_t p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
         const int32x4_t p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), ggml_fp16_to_fp32(x0->d)*ggml_fp16_to_fp32(y0->d));
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), ggml_fp16_to_fp32(x1->d)*ggml_fp16_to_fp32(y1->d));
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(p0, p1)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(p2, p3)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
 #endif
     }
 
@@ -3486,7 +3486,7 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
     // Main loop
     for (int i = 0; i < nb; ++i) {
         // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps(ggml_fp16_to_fp32(x[i].d) * ggml_fp16_to_fp32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
         __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
         __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
@@ -3517,7 +3517,7 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);
 
-        sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d));
+        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
     }
 
     *s = sumf;
@@ -3532,7 +3532,7 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
             sumi += x[i].qs[j]*y[i].qs[j];
         }
 
-        sumf += sumi*(ggml_fp16_to_fp32(x[i].d)*ggml_fp16_to_fp32(y[i].d));
+        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
     }
 
     *s = sumf;
@@ -3562,8 +3562,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -3641,8 +3641,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -3708,8 +3708,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -3816,8 +3816,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         const  int8_t * q8 = y[i].qs;
         const uint8_t * sc = x[i].scales;
 
-        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         size_t vl = 16;
 
@@ -3903,8 +3903,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
             summs += y[i].bsums[j] * (sc[j] >> 4);
         }
 
-        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         int isum = 0;
         int is = 0;
@@ -4021,8 +4021,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4073,8 +4073,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4188,8 +4188,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
             summs += y[i].bsums[j] * (sc[j] >> 4);
         }
 
-        const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         isum[0] = isum[1] = isum[2] = isum[3] = 0;
         for (int l =  0; l < 16; ++l) {
@@ -4242,7 +4242,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q3 = x[i].qs;
         const uint8_t * restrict qh = x[i].hmask;
@@ -4350,7 +4350,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q3 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4455,7 +4455,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q3 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4676,7 +4676,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
         }
 
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
 
         sumf += d*sum_t;
 
@@ -4741,7 +4741,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
             for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
             q8 += 8; a += 8;
         }
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -4843,7 +4843,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q3 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4914,7 +4914,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q3 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -5099,7 +5099,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
             q8 += 8; a += 8;
             for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l];
         }
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -5139,8 +5139,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8));
 
@@ -5222,8 +5222,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
    for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         memcpy(utmp, x[i].scales, 12);
         utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
@@ -5288,8 +5288,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
    for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q4 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -5371,8 +5371,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
         size_t vl = 8;
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl);
         vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl);
@@ -5482,9 +5482,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
             for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
             q8 += 8; a += 8;
         }
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d;
+        const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
         sumf -= dmin * sumi;
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -5586,8 +5586,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d;
-        const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d;
+        const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d;
         const __m256 vd = _mm256_set1_ps(d);
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
@@ -5632,8 +5632,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d;
-        const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d;
+        const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d;
         const __m256 vd = _mm256_set1_ps(d);
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
@@ -5689,8 +5689,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
         s16[0] = b[0] & 0x0f0f;
         s16[1] = (b[0] >> 4) & 0x0f0f;
 
-        sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]);
+        sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
 
         size_t vl = 32;
 
@@ -5739,9 +5739,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
         s16[0] = b[0] & 0x0f0f;
         s16[1] = (b[0] >> 4) & 0x0f0f;
 
-        sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
+        sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]));
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
 
         for (int j = 0; j < QK_K/32; ++j) {
             for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l];
@@ -5789,8 +5789,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8));
 
@@ -5878,8 +5878,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         const int8_t  * restrict q8 = y[i].qs;
 
 #if QK_K == 256
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         memcpy(utmp, x[i].scales, 12);
         utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
@@ -5960,8 +5960,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
-        const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q5 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -6065,8 +6065,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         const uint8_t * restrict hm = x[i].qh;
         const  int8_t * restrict q8 = y[i].qs;
 
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
-        const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
 
         vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl);
         vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl);
@@ -6188,9 +6188,9 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
             for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
             q8 += 8; a += 8;
         }
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
-        const float dmin = ggml_fp16_to_fp32(x[i].dmin) * y[i].d;
+        const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
         sumf -= dmin * sumi;
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -6288,7 +6288,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         const uint8_t * restrict q5 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
 
@@ -6334,7 +6334,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
         const uint8_t * restrict q5 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5);
 
@@ -6471,7 +6471,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
             for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16);
         }
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const int8_t * restrict sc = x[i].scales;
 
         for (int j = 0; j < QK_K/16; ++j) {
@@ -6514,7 +6514,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d_all = ggml_fp16_to_fp32(x[i].d);
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -6646,7 +6646,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q4 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -6726,7 +6726,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q4 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -6838,7 +6838,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
     float sumf = 0;
     for (int i = 0; i < nb; ++i) {
 
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -6955,7 +6955,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
             for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
             q8 += 8; a += 8;
         }
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -7053,7 +7053,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q4 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -7110,7 +7110,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * ggml_fp16_to_fp32(x[i].d);
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q4 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -7269,7 +7269,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
             for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
             q8 += 8; a += 8;
         }
-        const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d;
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
         for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
     }
     for (int l = 0; l < 8; ++l) sumf += sums[l];
diff --git a/ggml-quants.h b/ggml-quants.h
index d88f99e331f1dd..70c12c27465e80 100644
--- a/ggml-quants.h
+++ b/ggml-quants.h
@@ -1,22 +1,12 @@
 #pragma once
 
-// This is a private API for quantization and dequantization
-// Should not be used directly, use ggml.h instead
+#include "ggml-impl.h"
 
-#include "ggml.h"
+// GGML internal header
 
 #include <stdint.h>
-#include <assert.h>
 #include <stddef.h>
 
-#ifndef static_assert
-#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
-#define static_assert(cond, msg) _Static_assert(cond, msg)
-#else
-#define static_assert(cond, msg) struct global_scope_noop_trick
-#endif
-#endif
-
 #define QK4_0 32
 typedef struct {
     ggml_fp16_t d;          // delta
diff --git a/ggml.c b/ggml.c
index 95f72c35e8f205..84407b1224226f 100644
--- a/ggml.c
+++ b/ggml.c
@@ -1,6 +1,6 @@
 #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
 
-#include "ggml.h"
+#include "ggml-impl.h"
 #include "ggml-quants.h"
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
@@ -27,18 +27,6 @@
 #include <unistd.h>
 #endif
 
-// static_assert should be a #define, but if it's not,
-// fall back to the _Static_assert C11 keyword.
-// if C99 - static_assert is noop
-// ref: https://stackoverflow.com/a/53923785/4039976
-#ifndef static_assert
-#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
-#define static_assert(cond, msg) _Static_assert(cond, msg)
-#else
-#define static_assert(cond, msg) struct global_scope_noop_trick
-#endif
-#endif
-
 #if defined(_MSC_VER)
 // disable "possible loss of data" to avoid hundreds of casts
 // we should just be careful :)
@@ -106,23 +94,11 @@ typedef void * thread_ret_t;
 #include <unistd.h>
 
 #endif
+
 #ifdef GGML_USE_CPU_HBM
 #include <hbwmalloc.h>
 #endif
 
-// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
-#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
-#ifndef __FMA__
-#define __FMA__
-#endif
-#ifndef __F16C__
-#define __F16C__
-#endif
-#ifndef __SSE3__
-#define __SSE3__
-#endif
-#endif
-
 /*#define GGML_PERF*/
 #define GGML_DEBUG 0
 #define GGML_GELU_FP16
@@ -248,213 +224,27 @@ inline static void * ggml_aligned_malloc(size_t size) {
 #include "ggml-opencl.h"
 #endif
 
-#undef MIN
-#undef MAX
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-
 // floating point type used to accumulate sums
 typedef double ggml_float;
 
-// 16-bit float
-// on Arm, we use __fp16
-// on x86, we use uint16_t
-#if defined(__ARM_NEON) && !defined(_MSC_VER)
-
-// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
-//
-//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
-//
-#include <arm_neon.h>
-
-#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
-#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
-
-#define GGML_FP16_TO_FP32(x) ((float) (x))
-#define GGML_FP32_TO_FP16(x) (x)
-
-#else
-
-#ifdef __wasm_simd128__
-#include <wasm_simd128.h>
-#else
-#ifdef __POWER9_VECTOR__
-#include <altivec.h>
-#undef bool
-#define bool _Bool
-#else
-#if defined(_MSC_VER) || defined(__MINGW32__)
-#include <intrin.h>
-#else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
-#if !defined(__riscv)
-#include <immintrin.h>
-#endif
-#endif
-#endif
-#endif
-#endif
-
-#ifdef __riscv_v_intrinsic
-#include <riscv_vector.h>
-#endif
-
-#ifdef __F16C__
-
-#ifdef _MSC_VER
-#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x)))
-#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0)
-#else
-#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x)
-#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0)
-#endif
-
-#elif defined(__POWER9_VECTOR__)
-
-#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
-#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
-/* the inline asm below is about 12% faster than the lookup method */
-#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
-#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
-
-static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
-    register float f;
-    register double d;
-    __asm__(
-        "mtfprd %0,%2\n"
-        "xscvhpdp %0,%0\n"
-        "frsp %1,%0\n" :
-        /* temp */ "=d"(d),
-        /* out */  "=f"(f):
-        /* in */   "r"(h));
-    return f;
-}
-
-static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
-    register double d;
-    register ggml_fp16_t r;
-    __asm__( /* xscvdphp can work on double or single precision */
-        "xscvdphp %0,%2\n"
-        "mffprd %1,%0\n" :
-        /* temp */ "=d"(d),
-        /* out */  "=r"(r):
-        /* in */   "f"(f));
-    return r;
-}
-
-#else
-
-// FP16 <-> FP32
-// ref: https://github.com/Maratyszcza/FP16
-
-static inline float fp32_from_bits(uint32_t w) {
-    union {
-        uint32_t as_bits;
-        float as_value;
-    } fp32;
-    fp32.as_bits = w;
-    return fp32.as_value;
-}
-
-static inline uint32_t fp32_to_bits(float f) {
-    union {
-        float as_value;
-        uint32_t as_bits;
-    } fp32;
-    fp32.as_value = f;
-    return fp32.as_bits;
-}
-
-static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
-    const uint32_t w = (uint32_t) h << 16;
-    const uint32_t sign = w & UINT32_C(0x80000000);
-    const uint32_t two_w = w + w;
-
-    const uint32_t exp_offset = UINT32_C(0xE0) << 23;
-#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
-    const float exp_scale = 0x1.0p-112f;
-#else
-    const float exp_scale = fp32_from_bits(UINT32_C(0x7800000));
-#endif
-    const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
-
-    const uint32_t magic_mask = UINT32_C(126) << 23;
-    const float magic_bias = 0.5f;
-    const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
-
-    const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
-    const uint32_t result = sign |
-        (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value));
-    return fp32_from_bits(result);
-}
-
-static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
-#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)
-    const float scale_to_inf = 0x1.0p+112f;
-    const float scale_to_zero = 0x1.0p-110f;
-#else
-    const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000));
-    const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000));
-#endif
-    float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
-
-    const uint32_t w = fp32_to_bits(f);
-    const uint32_t shl1_w = w + w;
-    const uint32_t sign = w & UINT32_C(0x80000000);
-    uint32_t bias = shl1_w & UINT32_C(0xFF000000);
-    if (bias < UINT32_C(0x71000000)) {
-        bias = UINT32_C(0x71000000);
-    }
-
-    base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
-    const uint32_t bits = fp32_to_bits(base);
-    const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
-    const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
-    const uint32_t nonsign = exp_bits + mantissa_bits;
-    return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign);
-}
-
-#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
-#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
-
-#endif // __F16C__
-
-#endif // __ARM_NEON
-
 //
 // global data
 //
 
 // precomputed gelu table for f16 (128 KB)
-static ggml_fp16_t table_gelu_f16[1 << 16];
+static ggml_fp16_t ggml_table_gelu_f16[1 << 16];
 
 // precomputed quick gelu table for f16 (128 KB)
-static ggml_fp16_t table_gelu_quick_f16[1 << 16];
+static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16];
 
 // precomputed silu table for f16 (128 KB)
-static ggml_fp16_t table_silu_f16[1 << 16];
+static ggml_fp16_t ggml_table_silu_f16[1 << 16];
 
 // precomputed exp table for f16 (128 KB)
-static ggml_fp16_t table_exp_f16[1 << 16];
-
-// precomputed f32 table for f16 (256 KB)
-static float table_f32_f16[1 << 16];
+static ggml_fp16_t ggml_table_exp_f16[1 << 16];
 
-// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
-// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
-// This is also true for POWER9.
-#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
-
-inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
-    uint16_t s;
-    memcpy(&s, &f, sizeof(uint16_t));
-    return table_f32_f16[s];
-}
-
-#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
-#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
-
-#endif
+// precomputed f32 table for f16 (256 KB) (ggml-impl.h)
+float ggml_table_f32_f16[1 << 16];
 
 // note: do not use these inside ggml.c
 // these are meant to be used via the ggml.h API
@@ -632,6 +422,28 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
+    [4] = { // GGML_TYPE_Q4_2
+        .type_name                = "DEPRECATED",
+        .blck_size                = 0,
+        .type_size                = 0,
+        .is_quantized             = false,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_COUNT,
+    },
+    [5] = { // GGML_TYPE_Q4_3
+        .type_name                = "DEPRECATED",
+        .blck_size                = 0,
+        .type_size                = 0,
+        .is_quantized             = false,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_COUNT,
+    },
     [GGML_TYPE_Q5_0] = {
         .type_name                = "q5_0",
         .blck_size                = QK5_0,
@@ -1551,7 +1363,7 @@ inline static float ggml_gelu_f32(float x) {
 inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
     const uint16_t * i16 = (const uint16_t *) x;
     for (int i = 0; i < n; ++i) {
-        y[i] = table_gelu_f16[i16[i]];
+        y[i] = ggml_table_gelu_f16[i16[i]];
     }
 }
 
@@ -1561,7 +1373,7 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
     for (int i = 0; i < n; ++i) {
         ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
         memcpy(&t, &fp16, sizeof(uint16_t));
-        y[i] = GGML_FP16_TO_FP32(table_gelu_f16[t]);
+        y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
     }
 }
 #else
@@ -1579,7 +1391,7 @@ inline static float ggml_gelu_quick_f32(float x) {
 //inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
 //    const uint16_t * i16 = (const uint16_t *) x;
 //    for (int i = 0; i < n; ++i) {
-//        y[i] = table_gelu_quick_f16[i16[i]];
+//        y[i] = ggml_table_gelu_quick_f16[i16[i]];
 //    }
 //}
 
@@ -1589,7 +1401,7 @@ inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float *
     for (int i = 0; i < n; ++i) {
         ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
         memcpy(&t, &fp16, sizeof(uint16_t));
-        y[i] = GGML_FP16_TO_FP32(table_gelu_quick_f16[t]);
+        y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]);
     }
 }
 #else
@@ -1608,7 +1420,7 @@ inline static float ggml_silu_f32(float x) {
 //inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
 //    const uint16_t * i16 = (const uint16_t *) x;
 //    for (int i = 0; i < n; ++i) {
-//        y[i] = table_silu_f16[i16[i]];
+//        y[i] = ggml_table_silu_f16[i16[i]];
 //    }
 //}
 
@@ -1618,7 +1430,7 @@ inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) {
     for (int i = 0; i < n; ++i) {
         ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
         memcpy(&t, &fp16, sizeof(uint16_t));
-        y[i] = GGML_FP16_TO_FP32(table_silu_f16[t]);
+        y[i] = GGML_FP16_TO_FP32(ggml_table_silu_f16[t]);
     }
 }
 #else
@@ -2334,11 +2146,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
             for (int i = 0; i < (1 << 16); ++i) {
                 uint16_t ui = i;
                 memcpy(&ii, &ui, sizeof(ii));
-                const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
-                table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
-                table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
-                table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
-                table_exp_f16[i]  = GGML_FP32_TO_FP16(expf(f));
+                const float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
+                ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
+                ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
+                ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
+                ggml_table_exp_f16[i]  = GGML_FP32_TO_FP16(expf(f));
             }
 
             const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
@@ -10701,7 +10513,7 @@ static void ggml_compute_forward_soft_max_f32(
                 // const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max);
                 ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max);
                 memcpy(&scvt, &s, sizeof(scvt));
-                const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+                const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]);
                 sum += (ggml_float)val;
                 dp[i] = val;
             }
@@ -12990,7 +12802,7 @@ static void ggml_compute_forward_flash_attn_f32(
 #else
                             ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
                             memcpy(&scvt[j], &s, sizeof(uint16_t));
-                            const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+                            const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]);
 #endif
                             sump[j] += (ggml_float)val;
                             SS[j] = val;
@@ -13192,7 +13004,7 @@ static void ggml_compute_forward_flash_attn_f16(
                         } else {
                             ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
                             memcpy(&scvt[j], &s, sizeof(uint16_t));
-                            const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+                            const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]);
                             sump[j] += (ggml_float)val;
                             SS[j] = val;
                         }
@@ -13643,7 +13455,7 @@ static void ggml_compute_forward_flash_attn_back_f32(
 #else
                                     ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max);
                                     memcpy(&scvt[j], &s, sizeof(uint16_t));
-                                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
+                                    const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]);
 #endif
                                     sump[j] += (ggml_float)val;
                                     SW[j] = val;
@@ -14393,7 +14205,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
 #else
                     ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
                     memcpy(&scvt, &s, sizeof(scvt));
-                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+                    const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]);
 #endif
                     sum += (ggml_float)val;
                     st[i] = val;
@@ -14507,7 +14319,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
 #else
                     ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
                     memcpy(&scvt, &s, sizeof(scvt));
-                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+                    const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]);
 #endif
                     sum += (ggml_float)val;
                     ds0[i] = val;
diff --git a/llama.cpp b/llama.cpp
index a4340d5277b09c..e599917a81eb1d 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1467,7 +1467,7 @@ static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
 }
 
 static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
-    for (int32_t i = 0; i < cache.size; ++i) {
+    for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
         cache.cells[i].pos = -1;
         cache.cells[i].seq_id.clear();
     }
diff --git a/tests/test-double-float.cpp b/tests/test-double-float.cpp
index afd7bf77fcb552..753dae911b0cb3 100644
--- a/tests/test-double-float.cpp
+++ b/tests/test-double-float.cpp
@@ -4,7 +4,7 @@
 
 #undef NDEBUG
 #include <cassert>
-#if !defined(__riscv) && !defined(__s390__)
+#if !defined(__riscv) && !defined(__s390__) && !defined(__ARM_NEON)
 #include <immintrin.h>
 #endif
 #include <cmath>
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index 884af40548fb79..a2459a2867c5c0 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -129,6 +129,13 @@ int main(int argc, char * argv[]) {
         ggml_type type = (ggml_type) i;
         ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
 
+        // deprecated - skip
+        if (qfns.blck_size == 0) {
+            continue;
+        }
+
+        printf("Testing %s\n", ggml_type_name((ggml_type) i));
+
         if (qfns.from_float && qfns.to_float) {
             const float total_error = total_quantization_error(qfns, test_size, test_data.data());
             const float max_quantization_error =

From 07178c98e1b61a5e2af39d347add12e7eb9e08e1 Mon Sep 17 00:00:00 2001
From: Tungsten842 <886724vf@anonaddy.me>
Date: Tue, 31 Oct 2023 18:24:03 +0100
Subject: [PATCH 33/79] flake.nix: fix for rocm 5.7 (#3853)

---
 flake.lock | 12 ++++++------
 flake.nix  | 10 ++++++----
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/flake.lock b/flake.lock
index 070f0e1613fc30..0455f65617a2dc 100644
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
         "systems": "systems"
       },
       "locked": {
-        "lastModified": 1692799911,
-        "narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=",
+        "lastModified": 1694529238,
+        "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44",
+        "rev": "ff7b65b44d01cf9ba6a71320833626af21126384",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1698134075,
-        "narHash": "sha256-foCD+nuKzfh49bIoiCBur4+Fx1nozo+4C/6k8BYk4sg=",
+        "lastModified": 1698318101,
+        "narHash": "sha256-gUihHt3yPD7bVqg+k/UVHgngyaJ3DMEBchbymBMvK1E=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "8efd5d1e283604f75a808a20e6cde0ef313d07d4",
+        "rev": "63678e9f3d3afecfeafa0acead6239cdb447574c",
         "type": "github"
       },
       "original": {
diff --git a/flake.nix b/flake.nix
index fa34394b2f0593..4cf28d5c11c0fd 100644
--- a/flake.nix
+++ b/flake.nix
@@ -11,8 +11,7 @@
         meta.mainProgram = "llama";
         inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin;
         buildInputs = with pkgs; [ openmpi ];
-        osSpecific = with pkgs; buildInputs ++
-        (
+        osSpecific = with pkgs; buildInputs ++ (
           if isAarch64 && isDarwin then
             with pkgs.darwin.apple_sdk_11_0.frameworks; [
               Accelerate
@@ -96,12 +95,15 @@
         };
         packages.rocm = pkgs.stdenv.mkDerivation {
           inherit name src meta postPatch nativeBuildInputs postInstall;
-          buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ];
+          buildInputs = with pkgs.rocmPackages; buildInputs ++ [ clr hipblas rocblas ];
           cmakeFlags = cmakeFlags ++ [
             "-DLLAMA_HIPBLAS=1"
             "-DCMAKE_C_COMPILER=hipcc"
             "-DCMAKE_CXX_COMPILER=hipcc"
-            "-DCMAKE_POSITION_INDEPENDENT_CODE=ON"
+            # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM
+            # in github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt
+            # and select the line that matches the current nixpkgs version of rocBLAS.
+            "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
           ];
         };
         apps.llama-server = {

From 238657db2364cfb728c694470a4a81702afea760 Mon Sep 17 00:00:00 2001
From: kalomaze <66376113+kalomaze@users.noreply.github.com>
Date: Tue, 31 Oct 2023 14:44:49 -0500
Subject: [PATCH 34/79] samplers : Min-P sampler implementation [alternative to
 Top P/Top K] (#3841)

* Introduce the new Min-P sampler by @kalomaze
   The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token.

* Min-P enabled and set to 0.05 default

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
---
 common/common.cpp       |  8 ++++++++
 common/sampling.cpp     |  6 ++++--
 common/sampling.h       |  1 +
 examples/main/README.md |  8 ++++++++
 llama.cpp               | 26 ++++++++++++++++++++++++++
 llama.h                 |  7 +++++++
 6 files changed, 54 insertions(+), 2 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index c187128d6ede3d..dc4865e80b1544 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -218,6 +218,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             sparams.top_p = std::stof(argv[i]);
+        } else if (arg == "--min-p") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.min_p = std::stof(argv[i]);
         } else if (arg == "--temp") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -679,6 +685,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     printf("  --top-k N             top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
     printf("  --top-p N             top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
+    printf("  --min-p N             min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
     printf("  --tfs N               tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z);
     printf("  --typical N           locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p);
     printf("  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n);
@@ -1275,6 +1282,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
     fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
     fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
+    fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
     fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
 }
diff --git a/common/sampling.cpp b/common/sampling.cpp
index c4996c9857d8ac..673d67a6d5380e 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -89,10 +89,10 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
 
     snprintf(result, sizeof(result),
             "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
-            "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, typical_p = %.3f, temp = %.3f\n"
+            "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
             "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
             params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
-            params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp,
+            params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,
             params.mirostat, params.mirostat_eta, params.mirostat_tau);
 
     return std::string(result);
@@ -110,6 +110,7 @@ llama_token llama_sampling_sample(
     const float   temp            = params.temp;
     const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
     const float   top_p           = params.top_p;
+    const float   min_p           = params.min_p;
     const float   tfs_z           = params.tfs_z;
     const float   typical_p       = params.typical_p;
     const int32_t penalty_last_n  = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
@@ -190,6 +191,7 @@ llama_token llama_sampling_sample(
             llama_sample_tail_free(ctx_main, &cur_p, tfs_z,     min_keep);
             llama_sample_typical  (ctx_main, &cur_p, typical_p, min_keep);
             llama_sample_top_p    (ctx_main, &cur_p, top_p,     min_keep);
+            llama_sample_min_p    (ctx_main, &cur_p, min_p,     min_keep);
             llama_sample_temp     (ctx_main, &cur_p, temp);
 
             id = llama_sample_token(ctx_main, &cur_p);
diff --git a/common/sampling.h b/common/sampling.h
index 62ea6d4cfb7e5e..7c9b8dcf23bcbf 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -14,6 +14,7 @@ typedef struct llama_sampling_params {
     int32_t n_probs           = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
     int32_t top_k             = 40;    // <= 0 to use vocab size
     float   top_p             = 0.95f; // 1.0 = disabled
+    float   min_p             = 0.05f; // 0.0 = disabled
     float   tfs_z             = 1.00f; // 1.0 = disabled
     float   typical_p         = 1.00f; // 1.0 = disabled
     float   temp              = 0.80f; // 1.0 = disabled
diff --git a/examples/main/README.md b/examples/main/README.md
index a9561c383c0cba..a3428b48763d0b 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -208,6 +208,14 @@ Top-p sampling, also known as nucleus sampling, is another text generation metho
 
 Example usage: `--top-p 0.95`
 
+### Min P Sampling
+
+-   `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.05).
+
+The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.
+
+Example usage: `--min-p 0.05`
+
 ### Tail Free Sampling (TFS)
 
 -   `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
diff --git a/llama.cpp b/llama.cpp
index e599917a81eb1d..7ee5892989f0a9 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -7368,6 +7368,32 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can
     }
 }
 
+void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p <= 0.0f || !candidates->size) {
+        return;
+    }
+
+    llama_sample_softmax(ctx, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    float scale = candidates->data[0].p; // scale by max prob
+    size_t i = 1; // first token always matches
+
+    for (; i < candidates->size; ++i) {
+        if (candidates->data[i].p < p * scale && i >= min_keep) {
+            break; // prob too small
+        }
+    }
+
+    // Resize the output vector to keep only the matching tokens
+    candidates->size = i;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
 void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
     if (z >= 1.0f || candidates->size <= 2) {
         return;
diff --git a/llama.h b/llama.h
index d727dbd9fd915d..75fe391ef2e733 100644
--- a/llama.h
+++ b/llama.h
@@ -598,6 +598,13 @@ extern "C" {
                            float   p,
                           size_t   min_keep);
 
+    /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
+    LLAMA_API void llama_sample_min_p(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   p,
+                          size_t   min_keep);
+
     /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
     LLAMA_API void llama_sample_tail_free(
             struct llama_context * ctx,

From 71e3718abdb2771b50c9606d3a7569623a0b0afe Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 08:04:02 +0200
Subject: [PATCH 35/79] llama : refactor graph build code (#3837)

* llama : factor out ggml-alloc from graph graph build functions

ggml-ci

* metal : disable kernel load log

* llama : factor out tensor offloading outside the build call (wip)

ggml-ci

* llama : offload rest of the models

ggml-ci

* llama : update offload log messages to print node index

* llama : comments

* llama : support offloading result_norm + comments

* llama : factor graph input into a function

* llama : do tensor offload only with CUDA

* llama : fix res_norm offloading

* llama : try to optimize offloading code

* llama : fix non-CUDA build

* llama : try to fix build

* llama : move refact in correct place + optimize graph input

* llama : refactor tensor offloading as callback

* llama : add layer index to all tensor names

* llama : add functional header

* llama : comment

ggml-ci

* llama : remove obsolete map for layer counting

* llama : add llm_build helper functions (#3848)

* llama : add llm_build_norm helper function

ggml-ci

* llama : add llm_build_ffn helper function (#3849)

ggml-ci

* llama : add llm_build_k_shift helper

ggml-ci

* llama : fix offloading after recent changes

* llama : add llm_build_kv_store helper

ggml-ci

* llama : remove obsolete offload names

* llama : fix llm_build_k_shift to use n_head_kv instead of n_head

* llama : simplify falcon Q, K, V computation

* llama : remove obsolete comments in build graphs

* llama : add llm_build_kqv helper

ggml-ci

* llama : minor

* llama : add LLAMA_OFFLOAD_DEBUG + fix starcoder offloading

* llama : fix input allocation logic

* llama : update offload functions for KQ tensors

* llama : normalize tensor names

ggml-ci

* llama : enable warning about not offloaded tensors

* llama : remove extra ; + deduplicate gate_b logic

* llama : add llm_build_inp_embd helper
---
 ggml-metal.m |   11 +-
 ggml.h       |    2 +-
 llama.cpp    | 3741 ++++++++++++++++++++------------------------------
 3 files changed, 1520 insertions(+), 2234 deletions(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index 2380c431001408..bc881395a7aadc 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -238,14 +238,17 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){
     // load kernels
     {
         NSError * error = nil;
-#define GGML_METAL_ADD_KERNEL(name) \
-        ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
-        ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
+
+        /*
         GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
                 (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
                 (int) ctx->pipeline_##name.threadExecutionWidth); \
+        */
+#define GGML_METAL_ADD_KERNEL(name) \
+        ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
+        ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
         if (error) { \
-          GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
+            GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
             return NULL; \
         }
 
diff --git a/ggml.h b/ggml.h
index 8c954904e5a007..9d16c5a72fda0e 100644
--- a/ggml.h
+++ b/ggml.h
@@ -709,7 +709,7 @@ extern "C" {
     // Context tensor enumeration and lookup
     GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx);
     GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor);
-    GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
+    GGML_API struct ggml_tensor * ggml_get_tensor      (struct ggml_context * ctx, const char * name);
 
     GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
diff --git a/llama.cpp b/llama.cpp
index 7ee5892989f0a9..ead1d421d243dc 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -60,7 +60,9 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <forward_list>
 #include <fstream>
+#include <functional>
 #include <initializer_list>
 #include <map>
 #include <memory>
@@ -69,11 +71,10 @@
 #include <queue>
 #include <random>
 #include <regex>
+#include <set>
 #include <sstream>
 #include <thread>
 #include <unordered_map>
-#include <set>
-#include <forward_list>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -969,7 +970,7 @@ struct llama_mlock {
 
 typedef void (*offload_func_t)(struct ggml_tensor * tensor);
 
-static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
+static void ggml_offload_nop(struct ggml_tensor * tensor) {
     (void) tensor;
 }
 
@@ -1113,13 +1114,13 @@ struct llama_layer {
     struct ggml_tensor * ffn_norm_b;
 
     // ff
-    struct ggml_tensor * w1; // ffn_gate
-    struct ggml_tensor * w2; // ffn_down
-    struct ggml_tensor * w3; // ffn_up
+    struct ggml_tensor * ffn_gate; // w1
+    struct ggml_tensor * ffn_down; // w2
+    struct ggml_tensor * ffn_up;   // w3
 
     // ff bias
-    struct ggml_tensor * b2; // ffn_down
-    struct ggml_tensor * b3; // ffn_up
+    struct ggml_tensor * ffn_down_b; // b2
+    struct ggml_tensor * ffn_up_b;   // b3
 };
 
 struct llama_kv_cell {
@@ -1225,8 +1226,8 @@ struct llama_model {
     llama_hparams hparams = {};
     llama_vocab   vocab;
 
-    struct ggml_tensor * tok_embeddings;
-    struct ggml_tensor * pos_embeddings;
+    struct ggml_tensor * tok_embd;
+    struct ggml_tensor * pos_embd;
     struct ggml_tensor * tok_norm;
     struct ggml_tensor * tok_norm_b;
 
@@ -2482,7 +2483,7 @@ static void llm_load_tensors(
             case LLM_ARCH_LLAMA:
             case LLM_ARCH_REFACT:
                 {
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
 
                     // output
                     {
@@ -2536,21 +2537,21 @@ static void llm_load_tensors(
 
                         layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
 
-                        layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
-                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)       +
-                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
-                                ggml_nbytes(layer.w1)        + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq)       + ggml_nbytes(layer.wk)       +
+                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo)       + ggml_nbytes(layer.ffn_norm) +
+                                ggml_nbytes(layer.ffn_gate)  + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
                         }
                     }
                 } break;
             case LLM_ARCH_BAICHUAN:
                 {
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
                     {
                         ggml_backend_type backend_norm;
                         ggml_backend_type backend_output;
@@ -2602,15 +2603,15 @@ static void llm_load_tensors(
 
                         layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
 
-                        layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
-                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)       +
-                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
-                                ggml_nbytes(layer.w1)        + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
+                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq)       + ggml_nbytes(layer.wk)       +
+                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo)       + ggml_nbytes(layer.ffn_norm) +
+                                ggml_nbytes(layer.ffn_gate)  + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
                         }
                     }
                 } break;
@@ -2618,7 +2619,7 @@ static void llm_load_tensors(
                 {
                     // TODO: CPU-only for now
 
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
 
                     // output
                     {
@@ -2681,21 +2682,21 @@ static void llm_load_tensors(
                         layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
                         layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
 
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
                                 ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
                                 ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.wo)          +
-                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.w3);
+                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_up);
                         }
                     }
                 } break;
             case LLM_ARCH_STARCODER:
                 {
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab},             GGML_BACKEND_CPU);
-                    model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"),   {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab},             GGML_BACKEND_CPU);
+                    model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"),   {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
 
                     // output
                     {
@@ -2754,11 +2755,11 @@ static void llm_load_tensors(
                         layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                         layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
 
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
+                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
+                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
 
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);
+                        layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
@@ -2766,14 +2767,14 @@ static void llm_load_tensors(
                                 ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.bqkv)        +
                                 ggml_nbytes(layer.wo)        + ggml_nbytes(layer.bo)          +
                                 ggml_nbytes(layer.ffn_norm)  + ggml_nbytes(layer.ffn_norm_b)  +
-                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.b2)          +
-                                ggml_nbytes(layer.w3)        + ggml_nbytes(layer.b3);
+                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_down_b)  +
+                                ggml_nbytes(layer.ffn_up)    + ggml_nbytes(layer.ffn_up_b);
                         }
                     }
                 } break;
             case LLM_ARCH_PERSIMMON:
                 {
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab}, GGML_BACKEND_CPU);
 
                     {
                         ggml_backend_type backend_norm;
@@ -2814,31 +2815,31 @@ static void llm_load_tensors(
                         const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
                         const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
                         auto & layer = model.layers[i];
-                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, backend);
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend_split);
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend_split);
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
-                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
-                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.attn_norm     = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
+                        layer.attn_norm_b   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd}, backend);
+                        layer.wqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
+                        layer.bqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa},         backend_split);
+                        layer.wo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd},   backend_split);
+                        layer.bo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd},           backend_split);
+                        layer.ffn_down      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd}, backend_split);
+                        layer.ffn_down_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd},       backend_split);
+                        layer.ffn_up        = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_up_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff},           backend_split);
+                        layer.ffn_norm      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, backend);
+                        layer.ffn_norm_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd}, backend);
                         layer.attn_q_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
-                        layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),   {64}, backend);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64}, backend);
                         layer.attn_k_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),   {64}, backend);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64}, backend);
                     }
                 } break;
             case LLM_ARCH_BLOOM:
                 {
                     // TODO: CPU-only for now
 
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
-                    model.tok_norm       = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd},          GGML_BACKEND_CPU);
-                    model.tok_norm_b     = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd},          GGML_BACKEND_CPU);
+                    model.tok_embd   = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd},          GGML_BACKEND_CPU);
+                    model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd},          GGML_BACKEND_CPU);
 
                     // output
                     {
@@ -2897,11 +2898,11 @@ static void llm_load_tensors(
                         layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                         layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
 
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
+                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
+                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
 
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
+                        layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
@@ -2909,14 +2910,14 @@ static void llm_load_tensors(
                                 ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.bqkv)        +
                                 ggml_nbytes(layer.wo)        + ggml_nbytes(layer.bo)          +
                                 ggml_nbytes(layer.ffn_norm)  + ggml_nbytes(layer.ffn_norm_b)  +
-                                ggml_nbytes(layer.w3)        + ggml_nbytes(layer.b3)          +
-                                ggml_nbytes(layer.w2)        + ggml_nbytes(layer.b2);
+                                ggml_nbytes(layer.ffn_up)    + ggml_nbytes(layer.ffn_up_b)    +
+                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_down_b);
                         }
                     }
                 } break;
             case LLM_ARCH_MPT:
                 {
-                    model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
 
                     // output
                     {
@@ -2967,8 +2968,8 @@ static void llm_load_tensors(
 
                         layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
 
-                        layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
+                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
@@ -2976,8 +2977,8 @@ static void llm_load_tensors(
                                 ggml_nbytes(layer.wqkv)      +
                                 ggml_nbytes(layer.wo)        +
                                 ggml_nbytes(layer.ffn_norm)  +
-                                ggml_nbytes(layer.w2)        +
-                                ggml_nbytes(layer.w3);
+                                ggml_nbytes(layer.ffn_down)  +
+                                ggml_nbytes(layer.ffn_up);
                         }
                     }
                 } break;
@@ -3007,10 +3008,10 @@ static void llm_load_tensors(
 
 #ifdef GGML_USE_CUBLAS
         const int max_backend_supported_layers = hparams.n_layer + 3;
-        const int max_offloadable_layers = hparams.n_layer + 3;
-#elif defined(GGML_USE_CLBLAST)
+        const int max_offloadable_layers       = hparams.n_layer + 3;
+#elif GGML_USE_CLBLAST
         const int max_backend_supported_layers = hparams.n_layer + 1;
-        const int max_offloadable_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers       = hparams.n_layer + 1;
 #endif // GGML_USE_CUBLAS
 
         LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
@@ -3089,9 +3090,359 @@ static bool llama_model_load(
     return true;
 }
 
+using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
+
+enum llm_rope_type {
+    LLM_ROPE,
+    LLM_ROPE_NEOX,
+    LLM_ROPE_GLM,
+};
+
+static struct ggml_tensor * llm_build_inp_embd(
+        struct ggml_context * ctx,
+          const llama_batch & batch,
+         struct ggml_tensor * tok_embd,
+                    int64_t   n_embd,
+                    int32_t   n_tokens,
+         const llm_build_cb & cb) {
+    struct ggml_tensor * inpL;
+
+    if (batch.token) {
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_tokens);
+        cb(inp_tokens, "inp_tokens", -1);
+
+        inpL = ggml_get_rows(ctx, tok_embd, inp_tokens);
+    } else {
+#ifdef GGML_USE_MPI
+        GGML_ASSERT(false && "not implemented");
+#endif
+
+        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
+    }
+
+    return inpL;
+}
+
+// Persimmon: n_rot = n_embd_head/2
+// Other:     n_rot = n_embd_head
+static void llm_build_k_shift(
+        const llama_context & lctx,
+        struct ggml_context * ctx,
+         struct ggml_cgraph * graph,
+                    int64_t   n_rot,
+              llm_rope_type   type,
+         const llm_build_cb & cb) {
+    const auto & model   = lctx.model;
+    const auto & kv_self = lctx.kv_self;
+    const auto & cparams = lctx.cparams;
+
+    const auto & hparams = model.hparams;
+
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+    const int64_t n_embd_head = hparams.n_embd_head();
+
+    const int64_t n_ctx = lctx.cparams.n_ctx;
+
+    const float freq_base  = cparams.rope_freq_base;
+    const float freq_scale = cparams.rope_freq_scale;
+
+    GGML_ASSERT(n_embd_head % n_rot == 0);
+
+    struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
+    cb(K_shift, "K_shift", -1);
+
+    int rope_type = 0;
+
+    switch (type) {
+        case LLM_ROPE:      rope_type = 0; break;
+        case LLM_ROPE_NEOX: rope_type = 2; break;
+        case LLM_ROPE_GLM:  rope_type = 4; break;
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * tmp =
+            // we rotate only the first n_rot dimensions
+            ggml_rope_custom_inplace(ctx,
+                    ggml_view_3d(ctx, kv_self.k,
+                        n_rot, n_head_kv, n_ctx,
+                        ggml_element_size(kv_self.k)*n_embd_head,
+                        ggml_element_size(kv_self.k)*n_embd_gqa,
+                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
+                    K_shift, n_rot, rope_type, 0, freq_base, freq_scale);
+        cb(tmp, "K_shifted", il);
+        ggml_build_forward_expand(graph, tmp);
+    }
+}
+
+static void llm_build_kv_store(
+        const llama_context & lctx,
+        struct ggml_context * ctx,
+         struct ggml_cgraph * graph,
+         struct ggml_tensor * k_cur,
+         struct ggml_tensor * v_cur,
+                    int32_t   n_tokens,
+                    int32_t   kv_head,
+         const llm_build_cb & cb,
+                    int64_t   il) {
+    const auto & model   = lctx.model;
+    const auto & kv_self = lctx.kv_self;
+    const auto & cparams = lctx.cparams;
+
+    const auto & hparams = model.hparams;
+
+    const int64_t n_ctx      = cparams.n_ctx;
+    const int64_t n_embd_gqa = hparams.n_embd_gqa();
+
+    // compute the transposed [n_tokens, n_embd] V matrix
+    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens));
+    //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
+    cb(v_cur_t, "v_cur_t", il);
+
+    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv_self.k, n_tokens*n_embd_gqa,
+                (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+    cb(k_cache_view, "k_cache_view", il);
+
+    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv_self.v, n_tokens, n_embd_gqa,
+            (   n_ctx)*ggml_element_size(kv_self.v),
+            (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+    cb(v_cache_view, "v_cache_view", il);
+
+    // important: storing RoPE-ed version of K in the KV cache!
+    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur,   k_cache_view));
+    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
+}
+
+enum llm_norm_type {
+    LLM_NORM,
+    LLM_NORM_RMS,
+};
+
+static struct ggml_tensor * llm_build_norm(
+        struct ggml_context * ctx,
+         struct ggml_tensor * cur,
+         struct ggml_tensor * mw,
+         struct ggml_tensor * mb,
+              llm_norm_type   type,
+                      float   eps,
+         const llm_build_cb & cb,
+                        int   il) {
+    switch (type) {
+        case LLM_NORM:     cur = ggml_norm    (ctx, cur, eps); break;
+        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, eps); break;
+    }
+
+    if (mw || mb) {
+        cb(cur, "norm", il);
+    }
+
+    if (mw) {
+        cur = ggml_mul(ctx, cur, mw);
+        if (mb) {
+            cb(cur, "norm_w", il);
+        }
+    }
+
+    if (mb) {
+        cur = ggml_add(ctx, cur, mb);
+    }
+
+    return cur;
+}
+
+enum llm_ffn_op_type {
+    LLM_FFN_SILU,
+    LLM_FFN_GELU,
+    LLM_FFN_RELU,
+    LLM_FFN_RELU_SQR,
+};
+
+enum llm_ffn_gate_type {
+    LLM_FFN_SEQ,
+    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
+};
+
+static struct ggml_tensor * llm_build_ffn(
+        struct ggml_context * ctx,
+         struct ggml_tensor * cur,
+         struct ggml_tensor * up,
+         struct ggml_tensor * up_b,
+         struct ggml_tensor * gate,
+         struct ggml_tensor * gate_b,
+         struct ggml_tensor * down,
+         struct ggml_tensor * down_b,
+            llm_ffn_op_type   type_op,
+          llm_ffn_gate_type   type_gate,
+         const llm_build_cb & cb,
+                        int   il) {
+    struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
+    cb(tmp, "ffn_up", il);
+
+    if (up_b) {
+        tmp = ggml_add(ctx, tmp, up_b);
+        cb(tmp, "ffn_up_b", il);
+    }
+
+    if (gate) {
+        switch (type_gate) {
+            case LLM_FFN_SEQ:
+                {
+                    cur = ggml_mul_mat(ctx, gate, tmp);
+                    cb(cur, "ffn_gate", il);
+                } break;
+            case LLM_FFN_PAR:
+                {
+                    cur = ggml_mul_mat(ctx, gate, cur);
+                    cb(cur, "ffn_gate", il);
+                } break;
+        }
+
+        if (gate_b) {
+            cur = ggml_add(ctx, cur, gate_b);
+            cb(cur, "ffn_gate_b", il);
+        }
+    } else {
+        cur = tmp;
+    }
+
+    switch (type_op) {
+        case LLM_FFN_SILU:
+            {
+                cur = ggml_silu(ctx, cur);
+                cb(cur, "ffn_silu", il);
+            } break;
+        case LLM_FFN_GELU:
+            {
+                cur = ggml_gelu(ctx, cur);
+                cb(cur, "ffn_gelu", il);
+            } break;
+        case LLM_FFN_RELU:
+            {
+                cur = ggml_relu(ctx, cur);
+                cb(cur, "ffn_relu", il);
+            } break;
+        case LLM_FFN_RELU_SQR:
+            {
+                cur = ggml_relu(ctx, cur);
+                cb(cur, "ffn_relu", il);
+
+                cur = ggml_sqr(ctx, cur);
+                cb(cur, "ffn_sqr(relu)", il);
+            } break;
+    }
+
+    if (type_gate == LLM_FFN_PAR) {
+        cur = ggml_mul(ctx, cur, tmp);
+        cb(cur, "ffn_gate_par", il);
+    }
+
+    cur = ggml_mul_mat(ctx, down, cur);
+    if (down_b) {
+        cb(cur, "ffn_down", il);
+    }
+
+    if (down_b) {
+        cur = ggml_add(ctx, cur, down_b);
+    }
+
+    return cur;
+}
+
+// if max_alibi_bias > 0 then apply ALiBi
+static struct ggml_tensor * llm_build_kqv(
+        const llama_context & lctx,
+        struct ggml_context * ctx,
+         struct ggml_tensor * cur,
+         struct ggml_tensor * wo,
+         struct ggml_tensor * wo_b,
+         struct ggml_tensor * q_cur,
+         struct ggml_tensor * kq_scale,
+         struct ggml_tensor * kq_mask,
+                    int32_t   n_tokens,
+                    int32_t   n_kv,
+                      float   alibi_bias_max,
+         const llm_build_cb & cb,
+         int   il) {
+    const auto & model   = lctx.model;
+    const auto & kv_self = lctx.kv_self;
+    const auto & cparams = lctx.cparams;
+
+    const auto & hparams = model.hparams;
+
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+
+    struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
+    cb(q, "q", il);
+
+    struct ggml_tensor * k =
+        ggml_view_3d(ctx, kv_self.k,
+                n_embd_head, n_kv, n_head_kv,
+                ggml_element_size(kv_self.k)*n_embd_gqa,
+                ggml_element_size(kv_self.k)*n_embd_head,
+                ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+    cb(k, "k", il);
+
+    struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
+    cb(kq, "kq", il);
+
+    kq = ggml_scale(ctx, kq, kq_scale);
+    cb(kq, "kq_scaled", il);
+
+    if (alibi_bias_max > 0.0f) {
+        // TODO: n_head or n_head_kv
+        // TODO: K-shift is likely not working
+        // TODO: change to ggml_add
+        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, alibi_bias_max);
+        cb(kq, "kq_scaled_alibi", il);
+    }
+
+    kq = ggml_add(ctx, kq, kq_mask);
+    cb(kq, "kq_masked", il);
+
+    kq = ggml_soft_max(ctx, kq);
+    cb(kq, "kq_soft_max", il);
+
+    // split cached v into n_head heads
+    struct ggml_tensor * v =
+        ggml_view_3d(ctx, kv_self.v,
+                n_kv, n_embd_head, n_head_kv,
+                ggml_element_size(kv_self.v)*n_ctx,
+                ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
+                ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+    cb(v, "v", il);
+
+    struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
+    cb(kqv, "kqv", il);
+
+    struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
+    cb(kqv_merged, "kqv_merged", il);
+
+    cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens);
+    cb(cur, "kqv_merged_cont", il);
+
+    cur = ggml_mul_mat(ctx, wo, cur);
+    if (wo_b) {
+        cb(cur, "kqv_wo", il);
+    }
+
+    if (wo_b) {
+        cur = ggml_add(ctx, cur, wo_b);
+    }
+
+    return cur;
+}
+
 static struct ggml_cgraph * llm_build_llama(
-    llama_context & lctx,
-    const llama_batch & batch) {
+        llama_context  & lctx,
+    const llama_batch  & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
@@ -3106,7 +3457,6 @@ static struct ggml_cgraph * llm_build_llama(
     const int64_t n_head      = hparams.n_head;
     const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
 
     GGML_ASSERT(n_embd_head == hparams.n_rot);
 
@@ -3114,13 +3464,11 @@ static struct ggml_cgraph * llm_build_llama(
     const float freq_scale   = cparams.rope_freq_scale;
     const float norm_rms_eps = hparams.f_norm_rms_eps;
 
-    const int n_gpu_layers = model.n_gpu_layers;
-
     const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
 
-    const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+    const bool do_rope_shift = worst_case || kv_self.has_shift;
 
     //printf("n_kv = %d\n", n_kv);
 
@@ -3139,314 +3487,81 @@ static struct ggml_cgraph * llm_build_llama(
     struct ggml_tensor * cur;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-
-        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inpL);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
-        }
-    }
-
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
-
-    // offload functions set the tensor output backend to GPU
-    // tensors are GPU-accelerated if any input or the output has been offloaded
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
 
-#ifdef GGML_USE_CUBLAS
-    if (n_gpu_layers > n_layer) {
-        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 1) {
-        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 2) {
-        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
-    }
-#endif // GGML_USE_CUBLAS
+    // inp_pos - contains the positions
+    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    cb(inp_pos, "inp_pos", -1);
 
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
-    }
+    cb(KQ_scale, "KQ_scale", -1);
 
     // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    offload_func_kq(KQ_mask);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
-
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    offload_func_kq(KQ_pos);
-    ggml_set_name(KQ_pos, "KQ_pos");
-    ggml_allocr_alloc(lctx.alloc, KQ_pos);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        int * data = (int *) KQ_pos->data;
-        for (int i = 0; i < n_tokens; ++i) {
-            data[i] = batch.pos[i];
-        }
-    }
+    cb(KQ_mask, "KQ_mask", -1);
 
     // shift the entire K-cache if needed
     if (do_rope_shift) {
-        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
-        offload_func_kq(K_shift);
-        ggml_set_name(K_shift, "K_shift");
-        ggml_allocr_alloc(lctx.alloc, K_shift);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            int * data = (int *) K_shift->data;
-            for (int i = 0; i < n_ctx; ++i) {
-                data[i] = kv_self.cells[i].delta;
-            }
-        }
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * tmp =
-                    ggml_rope_custom_inplace(ctx0,
-                        ggml_view_3d(ctx0, kv_self.k,
-                            n_embd_head, n_head_kv, n_ctx,
-                            ggml_element_size(kv_self.k)*n_embd_head,
-                            ggml_element_size(kv_self.k)*n_embd_gqa,
-                            ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
-                        K_shift, n_embd_head, 0, 0, freq_base, freq_scale);
-            offload_func_kq(tmp);
-            ggml_build_forward_expand(gf, tmp);
-        }
+        llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb);
     }
 
     for (int il = 0; il < n_layer; ++il) {
-        ggml_format_name(inpL, "layer_inp_%d", il);
-
-        offload_func_t offload_func = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-        if (il >= i_gpu_start) {
-            offload_func = ggml_cuda_assign_buffers_no_alloc;
-        }
-#endif // GGML_USE_CUBLAS
-
         struct ggml_tensor * inpSA = inpL;
 
         // norm
-        {
-            cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
-            offload_func(cur);
-            ggml_set_name(cur, "rms_norm_0");
-
-            // cur = cur*attn_norm(broadcasted)
-            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
-            offload_func(cur);
-            ggml_set_name(cur, "attention_norm_0");
-        }
+        cur = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, norm_rms_eps, cb, il);
+        cb(cur, "attn_norm", il);
 
         // self-attention
         {
             // compute Q and K and RoPE them
-            struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            offload_func_kq(tmpk);
-            ggml_set_name(tmpk, "tmpk");
-
-            struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            offload_func_kq(tmpq);
-            ggml_set_name(tmpq, "tmpq");
-
-            struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-            offload_func_kq(Kcur);
-            ggml_set_name(Kcur, "Kcur");
-
-            struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-            offload_func_kq(Qcur);
-            ggml_set_name(Qcur, "Qcur");
-
-            // store key and value to memory
-            {
-                // compute the transposed [n_tokens, n_embd] V matrix
-
-                struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                offload_func_v(tmpv);
-                ggml_set_name(tmpv, "tmpv");
-
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
-                offload_func_v(Vcur);
-                ggml_set_name(Vcur, "Vcur");
-
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
-
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-                ggml_set_name(v, "v");
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
 
-                // important: storing RoPE-ed version of K in the KV cache!
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
+            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
 
-            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-            offload_func_kq(Q);
-            ggml_set_name(Q, "Q");
+            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
 
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            offload_func_kq(K);
-            ggml_set_name(K, "K");
-
-            // K * Q
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            // KQ_scaled = KQ / sqrt(n_embd_head)
-            // KQ_scaled shape [n_kv, n_tokens, n_head, 1]
-            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
-            offload_func_kq(KQ_masked);
-            ggml_set_name(KQ_masked, "KQ_masked");
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-            offload_func_v(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
-
-            // split cached V into n_head heads
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            offload_func_v(V);
-            ggml_set_name(V, "V");
-
-#if 1
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
-#else
-            // make V contiguous in memory to speed up the matmul, however we waste time on the copy
-            // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
-            // is there a better way?
-            struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
-#endif
+            Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+            cb(Qcur, "Qcur", il);
 
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
+            Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+            cb(Kcur, "Kcur", il);
 
-            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
 
-            // projection (no bias)
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].wo,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_wo");
+            cur = llm_build_kqv(lctx, ctx0, cur,
+                    model.layers[il].wo, NULL,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
+            cb(cur, "kqv_out", il);
         }
 
-        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
-        offload_func(inpFF);
-        ggml_set_name(inpFF, "inpFF");
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
 
         // feed-forward network
         {
-            // norm
-            {
-                cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
-                offload_func(cur);
-                ggml_set_name(cur, "rms_norm_1");
-
-                // cur = cur*ffn_norm(broadcasted)
-                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
-                offload_func(cur);
-                ggml_set_name(cur, "ffn_norm");
-            }
-
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model.layers[il].w3,
-                    cur);
-            offload_func(tmp);
-            ggml_set_name(tmp, "result_w3");
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, norm_rms_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].w1,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_w1");
-
-            // SILU activation
-            cur = ggml_silu(ctx0, cur);
-            offload_func(cur);
-            ggml_set_name(cur, "silu");
-
-            cur = ggml_mul(ctx0, cur, tmp);
-            offload_func(cur);
-            ggml_set_name(cur, "silu_x_result_w3");
-
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].w2,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_w2");
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
         }
 
-        cur = ggml_add(ctx0, cur, inpFF);
-        offload_func(cur);
-        ggml_set_name(cur, "inpFF_+_result_w2");
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
 
         // input for next layer
         inpL = cur;
@@ -3454,21 +3569,14 @@ static struct ggml_cgraph * llm_build_llama(
 
     cur = inpL;
 
-    // norm
-    {
-        cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
-        offload_func_nr(cur);
-        ggml_set_name(cur, "rms_norm_2");
-
-        // cur = cur*norm(broadcasted)
-        cur = ggml_mul(ctx0, cur, model.output_norm);
-        // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
-        ggml_set_name(cur, "result_norm");
-    }
+    cur = llm_build_norm(ctx0, cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, norm_rms_eps, cb, -1);
+    cb(cur, "result_norm", -1);
 
     // lm_head
     cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+    cb(cur, "result_output", -1);
 
     ggml_build_forward_expand(gf, cur);
 
@@ -3479,7 +3587,9 @@ static struct ggml_cgraph * llm_build_llama(
 
 static struct ggml_cgraph * llm_build_baichaun(
          llama_context & lctx,
-     const llama_batch & batch) {
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
@@ -3494,7 +3604,6 @@ static struct ggml_cgraph * llm_build_baichaun(
     const int64_t n_head      = hparams.n_head;
     const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
 
     GGML_ASSERT(n_embd_head == hparams.n_rot);
 
@@ -3502,13 +3611,11 @@ static struct ggml_cgraph * llm_build_baichaun(
     const float freq_scale   = cparams.rope_freq_scale;
     const float norm_rms_eps = hparams.f_norm_rms_eps;
 
-    const int n_gpu_layers = model.n_gpu_layers;
-
     const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
 
-    const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+    const bool do_rope_shift = worst_case || kv_self.has_shift;
 
     auto & buf_compute = lctx.buf_compute;
 
@@ -3525,331 +3632,91 @@ static struct ggml_cgraph * llm_build_baichaun(
     struct ggml_tensor * cur;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-
-        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inpL);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
-        }
-    }
-
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
-
-    // offload functions set the tensor output backend to GPU
-    // tensors are GPU-accelerated if any input or the output has been offloaded
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
 
-#ifdef GGML_USE_CUBLAS
-    if (n_gpu_layers > n_layer) {
-        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 1) {
-        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 2) {
-        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
-    }
-#endif // GGML_USE_CUBLAS
+    // inp_pos - contains the positions
+    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    cb(inp_pos, "inp_pos", -1);
 
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
-    }
+    cb(KQ_scale, "KQ_scale", -1);
 
     // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    offload_func_kq(KQ_mask);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
-
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    offload_func_kq(KQ_pos);
-    ggml_set_name(KQ_pos, "KQ_pos");
-    ggml_allocr_alloc(lctx.alloc, KQ_pos);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        int * data = (int *) KQ_pos->data;
-        for (int i = 0; i < n_tokens; ++i) {
-            data[i] = batch.pos[i];
-        }
-    }
+    cb(KQ_mask, "KQ_mask", -1);
 
     // shift the entire K-cache if needed
     if (do_rope_shift) {
-        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
-        offload_func_kq(K_shift);
-        ggml_set_name(K_shift, "K_shift");
-        ggml_allocr_alloc(lctx.alloc, K_shift);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            int * data = (int *) K_shift->data;
-            for (int i = 0; i < n_ctx; ++i) {
-                data[i] = kv_self.cells[i].delta;
-            }
-        }
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * tmp =
-                    ggml_rope_custom_inplace(ctx0,
-                        ggml_view_3d(ctx0, kv_self.k,
-                            n_embd_head, n_head_kv, n_ctx,
-                            ggml_element_size(kv_self.k)*n_embd_head,
-                            ggml_element_size(kv_self.k)*n_embd_gqa,
-                            ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
-                        K_shift, n_embd_head, 0, 0, freq_base, freq_scale);
-            offload_func_kq(tmp);
-            ggml_build_forward_expand(gf, tmp);
-        }
+        llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb);
     }
 
     for (int il = 0; il < n_layer; ++il) {
-        ggml_format_name(inpL, "layer_inp_%d", il);
-
-        offload_func_t offload_func = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-        if (il >= i_gpu_start) {
-            offload_func = ggml_cuda_assign_buffers_no_alloc;
-        }
-#endif // GGML_USE_CUBLAS
-
         struct ggml_tensor * inpSA = inpL;
 
-        // norm
-        {
-            cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
-            offload_func(cur);
-            ggml_set_name(cur, "rms_norm_0");
-
-            // cur = cur*attn_norm(broadcasted)
-            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
-            offload_func(cur);
-            ggml_set_name(cur, "attention_norm_0");
-        }
+        cur = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, norm_rms_eps, cb, il);
+        cb(cur, "attn_norm", il);
 
         // self-attention
         {
-            // compute Q and K and RoPE them
-            struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            offload_func_kq(tmpk);
-            ggml_set_name(tmpk, "tmpk");
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
 
-            struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            offload_func_kq(tmpq);
-            ggml_set_name(tmpq, "tmpq");
+            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
 
-            struct ggml_tensor * Kcur;
-            struct ggml_tensor * Qcur;
             switch (model.type) {
                 case MODEL_7B:
-                    Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-                    Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens),    KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                    Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens),    inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                    Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
                     break;
                 case MODEL_13B:
-                    Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, n_tokens);
-                    Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, n_tokens);
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
                     break;
                 default:
                     GGML_ASSERT(false);
             }
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
 
-            offload_func_kq(Kcur);
-            ggml_set_name(Kcur, "Kcur");
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
 
-            offload_func_kq(Qcur);
-            ggml_set_name(Qcur, "Qcur");
+            // apply ALiBi for 13B model
+            const float alibi_bias_max = model.type == MODEL_13B ? 8.0f : -1.0f;
 
-            // store key and value to memory
-            {
-                // compute the transposed [n_tokens, n_embd] V matrix
+            cur = llm_build_kqv(lctx, ctx0, cur,
+                    model.layers[il].wo, NULL,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, alibi_bias_max, cb, il);
+            cb(cur, "kqv_out", il);
+        }
 
-                struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                offload_func_v(tmpv);
-                ggml_set_name(tmpv, "tmpv");
-
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
-                offload_func_v(Vcur);
-                ggml_set_name(Vcur, "Vcur");
-
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
-
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-                ggml_set_name(v, "v");
-
-                // important: storing RoPE-ed version of K in the KV cache!
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
-
-            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-            offload_func_kq(Q);
-            ggml_set_name(Q, "Q");
-
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            offload_func_kq(K);
-            ggml_set_name(K, "K");
-
-            // K * Q
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            // KQ_scaled = KQ / sqrt(n_embd_head)
-            // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
-            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            struct ggml_tensor * KQ_masked;
-            struct ggml_tensor * KQ_scaled_alibi;
-
-            switch (model.type) {
-                case MODEL_7B:
-                    KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
-                    break;
-                case MODEL_13B:
-                    // TODO: replace with ggml_add()
-                    KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
-                    ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
-                    KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
-                    break;
-                default:
-                    GGML_ASSERT(false);
-            }
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-            offload_func_v(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
-
-            // split cached V into n_head heads
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            offload_func_v(V);
-            ggml_set_name(V, "V");
-
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
-
-            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
-
-            // projection (no bias)
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].wo,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_wo");
-        }
-
-        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
-        offload_func(inpFF);
-        ggml_set_name(inpFF, "inpFF");
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
 
         // feed-forward network
         {
-            // norm
-            {
-                cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
-                offload_func(cur);
-                ggml_set_name(cur, "rms_norm_1");
-
-                // cur = cur*ffn_norm(broadcasted)
-                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
-                offload_func(cur);
-                ggml_set_name(cur, "ffn_norm");
-            }
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, norm_rms_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model.layers[il].w3,
-                    cur);
-            offload_func(tmp);
-            ggml_set_name(tmp, "result_w3");
-
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].w1,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_w1");
-
-            // SILU activation
-            cur = ggml_silu(ctx0, cur);
-            offload_func(cur);
-            ggml_set_name(cur, "silu");
-
-            cur = ggml_mul(ctx0, cur, tmp);
-            offload_func(cur);
-            ggml_set_name(cur, "silu_x_result_w3");
-
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].w2,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_w2");
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
         }
 
-        cur = ggml_add(ctx0, cur, inpFF);
-        offload_func(cur);
-        ggml_set_name(cur, "inpFF_+_result_w2");
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
 
         // input for next layer
         inpL = cur;
@@ -3857,21 +3724,14 @@ static struct ggml_cgraph * llm_build_baichaun(
 
     cur = inpL;
 
-    // norm
-    {
-        cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
-        offload_func_nr(cur);
-        ggml_set_name(cur, "rms_norm_2");
-
-        // cur = cur*norm(broadcasted)
-        cur = ggml_mul(ctx0, cur, model.output_norm);
-        // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
-        ggml_set_name(cur, "result_norm");
-    }
+    cur = llm_build_norm(ctx0, cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, norm_rms_eps, cb, -1);
+    cb(cur, "result_norm", -1);
 
     // lm_head
     cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+    cb(cur, "result_output", -1);
 
     ggml_build_forward_expand(gf, cur);
 
@@ -3880,9 +3740,11 @@ static struct ggml_cgraph * llm_build_baichaun(
     return gf;
 }
 
-static struct ggml_cgraph * llm_build_refact(
+static struct ggml_cgraph * llm_build_falcon(
          llama_context & lctx,
-     const llama_batch & batch) {
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
@@ -3899,15 +3761,20 @@ static struct ggml_cgraph * llm_build_refact(
     const int64_t n_embd_head = hparams.n_embd_head();
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
 
-    const float norm_rms_eps = hparams.f_norm_rms_eps;
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-    const int n_gpu_layers = model.n_gpu_layers;
+    const float freq_base  = cparams.rope_freq_base;
+    const float freq_scale = cparams.rope_freq_scale;
+    const float norm_eps   = hparams.f_norm_eps;
 
     const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
 
-    // printf("n_kv = %d\n", n_kv);
+    const bool do_rope_shift = worst_case || kv_self.has_shift;
+
+    //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
+    //        kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
 
     auto & buf_compute = lctx.buf_compute;
 
@@ -3924,277 +3791,94 @@ static struct ggml_cgraph * llm_build_refact(
     struct ggml_tensor * cur;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-
-        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inpL);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
-        }
-    }
-
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
 
-    // offload functions set the tensor output backend to GPU
-    // tensors are GPU-accelerated if any input or the output has been offloaded
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-    if (n_gpu_layers > n_layer) {
-        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 1) {
-        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 2) {
-        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
-    }
-#endif // GGML_USE_CUBLAS
+    // inp_pos - contains the positions
+    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    cb(inp_pos, "inp_pos", -1);
 
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
-    }
+    cb(KQ_scale, "KQ_scale", -1);
 
     // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    offload_func_kq(KQ_mask);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
+    cb(KQ_mask, "KQ_mask", -1);
+
+    // shift the entire K-cache if needed
+    if (do_rope_shift) {
+        llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE_NEOX, cb);
     }
 
     for (int il = 0; il < n_layer; ++il) {
-        ggml_format_name(inpL, "layer_inp_%d", il);
-
-        offload_func_t offload_func = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-        if (il >= i_gpu_start) {
-            offload_func = ggml_cuda_assign_buffers_no_alloc;
-        }
-#endif // GGML_USE_CUBLAS
-
-        struct ggml_tensor * inpSA = inpL;
-
-        // norm
-        {
-            cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
-            offload_func(cur);
-            ggml_set_name(cur, "rms_norm_0");
+        struct ggml_tensor * attn_norm;
 
-            // cur = cur*attn_norm(broadcasted)
-            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
-            offload_func(cur);
-            ggml_set_name(cur, "attention_norm_0");
-        }
+        attn_norm = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, norm_eps, cb, il);
+        cb(attn_norm, "attn_norm", il);
 
         // self-attention
         {
-            // compute Q and K
-            struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            offload_func_kq(tmpk);
-            ggml_set_name(tmpk, "tmpk");
-
-            struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            offload_func_kq(tmpq);
-            ggml_set_name(tmpq, "tmpq");
-
-            struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
-            offload_func_kq(Kcur);
-            ggml_set_name(Kcur, "Kcur");
-
-            struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens);
-            offload_func_kq(Qcur);
-            ggml_set_name(Qcur, "Qcur");
-
-            // store key and value to memory
-            {
-                // compute the transposed [n_tokens, n_embd] V matrix
-
-                struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                offload_func_v(tmpv);
-                ggml_set_name(tmpv, "tmpv");
-
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
-                offload_func_v(Vcur);
-                ggml_set_name(Vcur, "Vcur");
+            if (model.layers[il].attn_norm_2) {
+                // Falcon-40B
+                cur = llm_build_norm(ctx0, attn_norm,
+                        model.layers[il].attn_norm_2,
+                        model.layers[il].attn_norm_2_b,
+                        LLM_NORM, norm_eps, cb, il);
+                cb(cur, "attn_norm_2", il);
+            } else {
+                cur = attn_norm;
+            }
 
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
 
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-                ggml_set_name(v, "v");
+            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
 
-            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-            offload_func_kq(Q);
-            ggml_set_name(Q, "Q");
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            offload_func_kq(K);
-            ggml_set_name(K, "K");
-
-            // K * Q
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            // KQ_scaled = KQ / sqrt(n_embd_head)
-            // KQ_scaled shape [n_kv, n_tokens, n_head, 1]
-            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
-            ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
-
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
-            offload_func_kq(KQ_masked);
-            ggml_set_name(KQ_masked, "KQ_masked");
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-            offload_func_v(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
-
-            // split cached V into n_head heads
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            offload_func_v(V);
-            ggml_set_name(V, "V");
-
-#if 1
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
-#else
-            // make V contiguous in memory to speed up the matmul, however we waste time on the copy
-            // on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
-            // is there a better way?
-            struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
-#endif
+            // using mode = 2 for neox mode
+            Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+            cb(Qcur, "Qcur", il);
 
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
+            Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+            cb(Kcur, "Kcur", il);
 
-            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
 
-            // projection (no bias)
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].wo,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_wo");
+            cur = llm_build_kqv(lctx, ctx0, attn_norm,
+                    model.layers[il].wo, NULL,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
+            cb(cur, "kqv_out", il);
         }
 
-        struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
-        offload_func(inpFF);
-        ggml_set_name(inpFF, "inpFF");
+        struct ggml_tensor * ffn_inp = cur;
 
-        // feed-forward network
+        // feed forward
         {
-            // norm
-            {
-                cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
-                offload_func(cur);
-                ggml_set_name(cur, "rms_norm_1");
-
-                // cur = cur*ffn_norm(broadcasted)
-                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
-                offload_func(cur);
-                ggml_set_name(cur, "ffn_norm");
-            }
-
-            struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
-                    model.layers[il].w3,
-                    cur);
-            offload_func(tmp);
-            ggml_set_name(tmp, "result_w3");
-
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].w1,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_w1");
-
-            // SILU activation
-            cur = ggml_silu(ctx0, cur);
-            offload_func(cur);
-            ggml_set_name(cur, "silu");
-
-            cur = ggml_mul(ctx0, cur, tmp);
-            offload_func(cur);
-            ggml_set_name(cur, "silu_x_result_w3");
-
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].w2,
-                    cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_w2");
+            cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
+                    model.layers[il].ffn_up,   NULL,
+                    NULL,                      NULL,
+                    model.layers[il].ffn_down, NULL,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
         }
 
-        cur = ggml_add(ctx0, cur, inpFF);
-        offload_func(cur);
-        ggml_set_name(cur, "inpFF_+_result_w2");
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
+
+        cur = ggml_add(ctx0, cur, inpL);
+        cb(cur, "l_out", il);
 
         // input for next layer
         inpL = cur;
@@ -4203,20 +3887,14 @@ static struct ggml_cgraph * llm_build_refact(
     cur = inpL;
 
     // norm
-    {
-        cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
-        offload_func_nr(cur);
-        ggml_set_name(cur, "rms_norm_2");
-
-        // cur = cur*norm(broadcasted)
-        cur = ggml_mul(ctx0, cur, model.output_norm);
-        // offload_func_nr(cur); // TODO CPU + GPU mirrored backend
-        ggml_set_name(cur, "result_norm");
-    }
+    cur = llm_build_norm(ctx0, cur,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, norm_eps, cb, -1);
+    cb(cur, "result_norm", -1);
 
-    // lm_head
     cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+    cb(cur, "result_output", -1);
 
     ggml_build_forward_expand(gf, cur);
 
@@ -4225,9 +3903,11 @@ static struct ggml_cgraph * llm_build_refact(
     return gf;
 }
 
-static struct ggml_cgraph * llm_build_falcon(
+static struct ggml_cgraph * llm_build_starcoder(
          llama_context & lctx,
-     const llama_batch & batch) {
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
@@ -4240,26 +3920,16 @@ static struct ggml_cgraph * llm_build_falcon(
     const int64_t n_layer     = hparams.n_layer;
     const int64_t n_ctx       = cparams.n_ctx;
     const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_head = hparams.n_embd_head();
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
 
     GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-    const float freq_base  = cparams.rope_freq_base;
-    const float freq_scale = cparams.rope_freq_scale;
-    const float norm_eps   = hparams.f_norm_eps;
-
-    const int n_gpu_layers = model.n_gpu_layers;
+    const float norm_eps = hparams.f_norm_eps;
 
     const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
-
-    const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
-
-    //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
-    //        kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
 
     auto & buf_compute = lctx.buf_compute;
 
@@ -4274,352 +3944,133 @@ static struct ggml_cgraph * llm_build_falcon(
     ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     struct ggml_tensor * cur;
+    struct ggml_tensor * pos;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-
-        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
 
-        ggml_allocr_alloc(lctx.alloc, inpL);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
-        }
-    }
-
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
-
-    // offload functions set the tensor output backend to GPU
-    // tensors are GPU-accelerated if any input or the output has been offloaded
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-    if (n_gpu_layers > n_layer) {
-        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 1) {
-        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 2) {
-        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
-    }
-#endif // GGML_USE_CUBLAS
+    // inp_pos - contains the positions
+    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    cb(inp_pos, "inp_pos", -1);
 
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
-    }
+    cb(KQ_scale, "KQ_scale", -1);
 
     // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    offload_func_kq(KQ_mask);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
+    cb(KQ_mask, "KQ_mask", -1);
 
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    offload_func_kq(KQ_pos);
-    ggml_set_name(KQ_pos, "KQ_pos");
-    ggml_allocr_alloc(lctx.alloc, KQ_pos);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        int * data = (int *) KQ_pos->data;
-        for (int i = 0; i < n_tokens; ++i) {
-            data[i] = batch.pos[i];
-        }
-    }
-
-    // shift the entire K-cache if needed
-    if (do_rope_shift) {
-        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
-        offload_func_kq(K_shift);
-        ggml_set_name(K_shift, "K_shift");
-        ggml_allocr_alloc(lctx.alloc, K_shift);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            int * data = (int *) K_shift->data;
-            for (int i = 0; i < n_ctx; ++i) {
-                data[i] = kv_self.cells[i].delta;
-            }
-        }
+    pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+    cb(pos, "pos_embd", -1);
 
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * tmp =
-                    ggml_rope_custom_inplace(ctx0,
-                        ggml_view_3d(ctx0, kv_self.k,
-                            n_embd_head, n_head_kv, n_ctx,
-                            ggml_element_size(kv_self.k)*n_embd_head,
-                            ggml_element_size(kv_self.k)*n_embd_gqa,
-                            ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
-                        K_shift, n_embd_head, 2, 0, freq_base, freq_scale);
-            offload_func_kq(tmp);
-            ggml_build_forward_expand(gf, tmp);
-        }
-    }
+    inpL = ggml_add(ctx0, inpL, pos);
+    cb(inpL, "inpL", -1);
 
     for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * attn_norm;
-
-        offload_func_t offload_func = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-        if (il >= i_gpu_start) {
-            offload_func = ggml_cuda_assign_buffers_no_alloc;
-        }
-#endif // GGML_USE_CUBLAS
+        cur = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, norm_eps, cb, il);
+        cb(cur, "attn_norm", il);
 
         // self-attention
-        // TODO: refactor into common function (shared with LLaMA)
         {
-            attn_norm = ggml_norm(ctx0, inpL, norm_eps);
-            offload_func(attn_norm);
-
-            attn_norm = ggml_add(ctx0,
-                    ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm),
-                    model.layers[il].attn_norm_b);
-            offload_func(attn_norm->src[0]);
-            offload_func(attn_norm);
-
-            if (model.layers[il].attn_norm_2) { // Falcon-40B
-                cur = ggml_norm(ctx0, inpL, norm_eps);
-                offload_func(cur);
-
-                cur = ggml_add(ctx0,
-                        ggml_mul(ctx0, cur, model.layers[il].attn_norm_2),
-                        model.layers[il].attn_norm_2_b);
-                offload_func(cur->src[0]);
-                offload_func(cur);
-            } else { // Falcon 7B
-                cur = attn_norm;
-            }
-
-            // compute QKV
-
             cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            offload_func_kq(cur);
-
-            // Note that the strides for Kcur, Vcur are set up so that the
-            // resulting views are misaligned with the tensor's storage
-            // (by applying the K/V offset we shift the tensor's original
-            // view to stick out behind the viewed QKV tensor's allocated
-            // memory, so to say). This is ok because no actual accesses
-            // happen to that out-of-range memory, but it can require some
-            // trickery when trying to accurately dump these views for
-            // debugging.
-
-            const size_t wsize = ggml_type_size(cur->type);
-
-            // TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
-            //       non-contiguous views is added for the rope operator
-            struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
-                ctx0, cur, n_embd_head, n_head, n_tokens,
-                wsize * n_embd_head,
-                wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                0));
-            offload_func_kq(tmpq);
-
-            struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
-                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
-                wsize * n_embd_head,
-                wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                wsize * n_embd_head *  n_head));
-            offload_func_kq(tmpk);
-
-            struct ggml_tensor * tmpv = ggml_view_3d(
-                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
-                wsize * n_embd_head,
-                wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                wsize * n_embd_head * (n_head +     n_head_kv));
-            offload_func_v(tmpv);
-
-            // using mode = 2 for neox mode
-            struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale);
-            offload_func_kq(Qcur);
-            struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale);
-            offload_func_kq(Kcur);
-
-            {
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
-                offload_func_v(Vcur);
-                offload_func_v(Vcur->src[0]->src[0]);
-                ggml_set_name(Vcur, "Vcur");
-
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
-
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
+            cb(cur, "wqkv", il);
 
-            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-            offload_func_kq(Q);
-            ggml_set_name(Q, "Q");
-
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            offload_func_kq(K);
-            ggml_set_name(K, "K");
-
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
-            offload_func_kq(KQ_masked);
-            ggml_set_name(KQ_masked, "KQ_masked");
-
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-            offload_func_v(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
 
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            offload_func_v(V);
-            ggml_set_name(V, "V");
+            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
 
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_wo");
+            cur = llm_build_kqv(lctx, ctx0, cur,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
+            cb(cur, "kqv_out", il);
         }
 
-        struct ggml_tensor * attn_out = cur;
+        // add the input
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
 
-        // feed forward
+        // FF
         {
-            struct ggml_tensor * inpFF = attn_norm;
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, norm_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].w3, inpFF);
-            offload_func(cur);
-
-            cur = ggml_gelu(ctx0, cur);
-            offload_func(cur);
-            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
-            offload_func(cur);
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                    NULL,                      NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
         }
 
-        cur = ggml_add(ctx0, cur, attn_out);
-        offload_func(cur);
-        cur = ggml_add(ctx0, cur, inpL);
-        offload_func(cur);
-
-        // input for next layer
-        inpL = cur;
+        inpL = ggml_add(ctx0, cur, ffn_inp);
+        cb(inpL, "l_out", il);
     }
 
-    cur = inpL;
-
-    // norm
-    {
-        cur = ggml_norm(ctx0, cur, norm_eps);
-        offload_func_nr(cur);
-
-        cur = ggml_add(ctx0,
-                ggml_mul(ctx0, cur, model.output_norm),
-                model.output_norm_b);
-        ggml_set_name(cur, "result_norm");
-    }
+    cur = llm_build_norm(ctx0, inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, norm_eps, cb, -1);
+    cb(cur, "result_norm", -1);
 
     cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+    cb(cur, "result_output", -1);
 
     ggml_build_forward_expand(gf, cur);
-
     ggml_free(ctx0);
 
     return gf;
 }
 
-static struct ggml_cgraph * llm_build_starcoder(
+static struct ggml_cgraph * llm_build_persimmon(
          llama_context & lctx,
-     const llama_batch & batch) {
-    const auto & model   = lctx.model;
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
+    const auto & model = lctx.model;
     const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
 
     const auto & kv_self = lctx.kv_self;
 
     GGML_ASSERT(!!kv_self.ctx);
 
+    const auto & cparams = lctx.cparams;
+
     const int64_t n_embd      = hparams.n_embd;
     const int64_t n_layer     = hparams.n_layer;
     const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
     const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_head      = hparams.n_head;
     const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    const int64_t n_rot       = n_embd_head / 2;
 
-    const float norm_eps = hparams.f_norm_eps;
+    const float freq_base  = cparams.rope_freq_base;
+    const float freq_scale = cparams.rope_freq_scale;
+    const float norm_eps   = hparams.f_norm_eps;
 
-    const int n_gpu_layers = model.n_gpu_layers;
+    const int32_t n_tokens    = batch.n_tokens;
+    const int32_t n_kv        = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head     = worst_case ? n_ctx - n_tokens : kv_self.head;
 
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    const bool do_rope_shift  = worst_case || kv_self.has_shift;
 
     auto & buf_compute = lctx.buf_compute;
 
@@ -4634,313 +4085,376 @@ static struct ggml_cgraph * llm_build_starcoder(
     ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     struct ggml_tensor * cur;
-    struct ggml_tensor * token;
-    struct ggml_tensor * position;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-
-        token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-
-        token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, token);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
-        }
-    }
-
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
-
-    // offload functions set the tensor output backend to GPU
-    // tensors are GPU-accelerated if any input or the output has been offloaded
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-    if (n_gpu_layers > n_layer) {
-        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 1) {
-        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 2) {
-        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
-    }
-#endif // GGML_USE_CUBLAS
-
-    {
-        // Compute position embeddings.
-        struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        ggml_allocr_alloc(lctx.alloc, inp_positions);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            for (int i = 0; i < n_tokens; ++i) {
-                ((int32_t *) inp_positions->data)[i] = batch.pos[i];
-            }
-        }
-        ggml_set_name(inp_positions, "inp_positions");
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "imp_embd", -1);
 
-        position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions);
-    }
+    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    cb(inp_pos, "inp_pos", -1);
 
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
-    }
+    cb(KQ_scale, "KQ_scale", -1);
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    offload_func_kq(KQ_mask);
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
+    cb(KQ_mask, "KQ_mask", -1);
 
-    inpL = ggml_add(ctx0, token, position);
-    ggml_set_name(inpL, "inpL");
+    if (do_rope_shift) {
+        llm_build_k_shift(lctx, ctx0, gf, n_rot, LLM_ROPE_NEOX, cb);
+    }
 
     for (int il = 0; il < n_layer; ++il) {
-        offload_func_t offload_func = llama_nop;
+        struct ggml_tensor * residual = inpL;
 
-#ifdef GGML_USE_CUBLAS
-        if (il >= i_gpu_start) {
-            offload_func = ggml_cuda_assign_buffers_no_alloc;
-        }
-#endif // GGML_USE_CUBLAS
+        cur = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, norm_eps, cb, il);
+        cb(cur, "attn_norm", il);
 
+        // self attention
         {
-            // Norm
-            cur = ggml_norm(ctx0, inpL, norm_eps);
-            offload_func(cur);
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
+
+            // split qkv
+            GGML_ASSERT(n_head_kv == n_head);
+
+            struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
+            cb(tmpqkv, "tmpqkv", il);
+
+            struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
+            cb(tmpqkv_perm, "tmpqkv", il);
+
+            struct ggml_tensor * tmpq = ggml_view_3d(
+                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                    0
+                );
+            cb(tmpq, "tmpq", il);
+
+            struct ggml_tensor * tmpk = ggml_view_3d(
+                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
+                );
+            cb(tmpk, "tmpk", il);
+
+            // Q/K Layernorm
+            tmpq = llm_build_norm(ctx0, tmpq,
+                    model.layers[il].attn_q_norm,
+                    model.layers[il].attn_q_norm_b,
+                    LLM_NORM, norm_eps, cb, il);
+            cb(tmpq, "tmpq", il);
+
+            tmpk = llm_build_norm(ctx0, tmpk,
+                    model.layers[il].attn_k_norm,
+                    model.layers[il].attn_k_norm_b,
+                    LLM_NORM, norm_eps, cb, il);
+            cb(tmpk, "tmpk", il);
+
+            // RoPE the first n_rot of q/k, pass the other half, and concat.
+            struct ggml_tensor * qrot = ggml_view_3d(
+                ctx0, tmpq, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpq) * n_embd_head,
+                ggml_element_size(tmpq) * n_embd_head * n_head,
+                0
+            );
+            cb(qrot, "qrot", il);
+
+            struct ggml_tensor * krot = ggml_view_3d(
+                ctx0, tmpk, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpk) * n_embd_head,
+                ggml_element_size(tmpk) * n_embd_head * n_head,
+                0
+            );
+            cb(krot, "krot", il);
+
+            // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
+            struct ggml_tensor * qpass = ggml_view_3d(
+                ctx0, tmpq, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpq) * n_embd_head,
+                ggml_element_size(tmpq) * n_embd_head * n_head,
+                ggml_element_size(tmpq) * n_rot
+            );
+            cb(qpass, "qpass", il);
+
+            struct ggml_tensor * kpass = ggml_view_3d(
+                ctx0, tmpk, n_rot, n_head, n_tokens,
+                ggml_element_size(tmpk) * n_embd_head,
+                ggml_element_size(tmpk) * n_embd_head * n_head,
+                ggml_element_size(tmpk) * n_rot
+            );
+            cb(kpass, "kpass", il);
+
+            struct ggml_tensor * qrotated = ggml_rope_custom(
+                    ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
+            );
+            cb(qrotated, "qrotated", il);
+
+            struct ggml_tensor * krotated = ggml_rope_custom(
+                    ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
+            );
+            cb(krotated, "krotated", il);
+
+            // ggml currently only supports concatenation on dim=2
+            // so we need to permute qrot, qpass, concat, then permute back.
+            qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
+            cb(qrotated, "qrotated", il);
+
+            krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
+            cb(krotated, "krotated", il);
+
+            qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
+            cb(qpass, "qpass", il);
+
+            kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
+            cb(kpass, "kpass", il);
+
+            struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
+            cb(Qcur, "Qcur", il);
 
-            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
-            offload_func(cur);
+            struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
+            cb(Kcur, "Kcur", il);
+
+            struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
+            cb(Q, "Q", il);
+
+            Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
+            cb(Kcur, "Kcur", il);
+
+            struct ggml_tensor * Vcur = ggml_view_3d(
+                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
+                    );
+            cb(Vcur, "Vcur", il);
+
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+            // TODO: not tested, could be broken
+            cur = llm_build_kqv(lctx, ctx0, Q,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Q, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
+            cb(cur, "kqv_out", il);
         }
 
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
         {
-            // Self Attention
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            offload_func_kq(cur);
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, norm_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-            offload_func_kq(cur);
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                    NULL,                      NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
 
-            struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-            struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-            struct ggml_tensor * tmpv = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+        inpL = cur;
+    }
 
-            ggml_set_name(tmpq, "tmpq");
-            ggml_set_name(tmpk, "tmpk");
-            ggml_set_name(tmpv, "tmpv");
+    cur = inpL;
 
-            offload_func_kq(tmpq);
-            offload_func_kq(tmpk);
-            offload_func_v (tmpv);
+    cur = llm_build_norm(ctx0, cur,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, norm_eps, cb, -1);
+    cb(cur, "result_norm", -1);
 
-            struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
-            struct ggml_tensor * Kcur = tmpk;
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    cb(cur, "result_output", -1);
 
-            {
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, tmpv);
-                offload_func_v(Vcur);
-                ggml_set_name(Vcur, "Vcur");
-
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
-
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-                ggml_set_name(v, "v");
-
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
+    ggml_build_forward_expand(gf, cur);
 
-            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-            offload_func_kq(Q);
-            ggml_set_name(Q, "Q");
+    ggml_free(ctx0);
 
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            offload_func_kq(K);
-            ggml_set_name(K, "K");
-
-            // K * Q
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            // KQ_scaled = KQ / sqrt(n_embd_head)
-            // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
-            struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
-            offload_func_kq(KQ_masked);
-            ggml_set_name(KQ_masked, "KQ_masked");
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-            offload_func_v(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
-
-            // split cached V into n_head heads
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            ggml_set_name(V, "V");
-
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
-
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
-
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
-        }
-
-        // Projection
-        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
-        offload_func(cur);
+    return gf;
+}
 
-        // Add the input
-        cur = ggml_add(ctx0, cur, inpL);
-        offload_func(cur);
+static struct ggml_cgraph * llm_build_refact(
+         llama_context & lctx,
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
 
-        struct ggml_tensor * inpFF = cur;
+    const auto & kv_self = lctx.kv_self;
 
-        // FF
+    GGML_ASSERT(!!kv_self.ctx);
+
+    const int64_t n_embd      = hparams.n_embd;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t n_ctx       = cparams.n_ctx;
+    const int64_t n_head      = hparams.n_head;
+    const int64_t n_head_kv   = hparams.n_head_kv;
+    const int64_t n_embd_head = hparams.n_embd_head();
+
+    const float norm_rms_eps = hparams.f_norm_rms_eps;
+
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
+
+    auto & buf_compute = lctx.buf_compute;
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ buf_compute.size,
+        /*.mem_buffer =*/ buf_compute.data,
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    struct ggml_tensor * cur;
+    struct ggml_tensor * inpL;
+
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
+
+    // KQ_scale
+    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    cb(KQ_scale, "KQ_scale", -1);
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+    cb(KQ_mask, "KQ_mask", -1);
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * inpSA = inpL;
+
+        cur = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, norm_rms_eps, cb, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
         {
-            // Norm
-            {
-                cur = ggml_norm(ctx0, inpFF, norm_eps);
-                offload_func_nr(cur);
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
 
-                cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
-                offload_func_nr(cur);
-            }
+            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
 
-            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
-            offload_func(cur);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            cb(Kcur, "Kcur", il);
 
-            // GELU activation
-            cur = ggml_gelu(ctx0, cur);
-            offload_func(cur);
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+            cb(Qcur, "Qcur", il);
 
-            // Projection
-            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
-            offload_func(cur);
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+            cur = llm_build_kqv(lctx, ctx0, Qcur,
+                    model.layers[il].wo, NULL,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il);
+            cb(cur, "kqv_out", il);
         }
 
-        inpL = ggml_add(ctx0, cur, inpFF);
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
 
-    }
+        // feed-forward network
+        {
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, norm_rms_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-    // Output Norm
-    {
-        cur = ggml_norm(ctx0, inpL, norm_eps);
-        offload_func_nr(cur);
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
 
-        cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
-        ggml_set_name(cur, "result_norm");
+        // input for next layer
+        inpL = cur;
     }
 
+    cur = inpL;
+
+    cur = llm_build_norm(ctx0, cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, norm_rms_eps, cb, -1);
+    cb(cur, "result_norm", -1);
+
+    // lm_head
     cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+    cb(cur, "result_output", -1);
 
     ggml_build_forward_expand(gf, cur);
+
     ggml_free(ctx0);
 
     return gf;
 }
 
-static struct ggml_cgraph * llm_build_persimmon(
+static struct ggml_cgraph * llm_build_bloom(
          llama_context & lctx,
-     const llama_batch & batch) {
-    const auto & model = lctx.model;
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
+    const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
 
     const auto & kv_self = lctx.kv_self;
 
     GGML_ASSERT(!!kv_self.ctx);
 
-    const auto & cparams = lctx.cparams;
     const int64_t n_embd      = hparams.n_embd;
     const int64_t n_layer     = hparams.n_layer;
     const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_head      = hparams.n_head;
     const int64_t n_embd_head = hparams.n_embd_head();
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-    const size_t n_rot        = n_embd_head / 2;
-
-    const float freq_base  = cparams.rope_freq_base;
-    const float freq_scale = cparams.rope_freq_scale;
-    const float norm_eps = hparams.f_norm_eps;
-
-    const int n_gpu_layers = model.n_gpu_layers;
 
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-    const int32_t n_tokens    = batch.n_tokens;
-    const int32_t n_kv        = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    const float norm_eps = hparams.f_norm_eps;
 
-    const bool do_rope_shift  = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
+    const int32_t n_tokens = batch.n_tokens;
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
 
     auto & buf_compute = lctx.buf_compute;
+
     struct ggml_init_params params = {
         /*.mem_size   =*/ buf_compute.size,
         /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
+        /*.no_alloc   =*/ false,
     };
 
+    params.no_alloc = true;
+
     struct ggml_context * ctx0 = ggml_init(params);
 
     ggml_cgraph * gf = ggml_new_graph(ctx0);
@@ -4948,359 +4462,101 @@ static struct ggml_cgraph * llm_build_persimmon(
     struct ggml_tensor * cur;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
 
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
-        ggml_allocr_alloc(lctx.alloc, inpL);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
-        }
-    }
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
-    }
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
+    cb(KQ_scale, "KQ_scale", -1);
+
+    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    offload_func_kq(KQ_mask);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
+    cb(KQ_mask, "KQ_mask", -1);
 
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    offload_func_kq(KQ_pos);
-    ggml_set_name(KQ_pos, "KQ_pos");
-    ggml_allocr_alloc(lctx.alloc, KQ_pos);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        int * data = (int *) KQ_pos->data;
-        for (int i = 0; i < n_tokens; ++i) {
-            data[i] = batch.pos[i];
-        }
-    }
-    if (do_rope_shift) {
-        struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
-        offload_func_kq(K_shift);
-        ggml_set_name(K_shift, "K_shift");
-        ggml_allocr_alloc(lctx.alloc, K_shift);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            int * data = (int *) K_shift->data;
-            for (int i = 0; i < n_ctx; ++i) {
-                data[i] = kv_self.cells[i].delta;
-            }
-        }
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * tmp =
-                    // we rotate only the first n_rot dimensions.
-                    ggml_rope_custom_inplace(ctx0,
-                        ggml_view_3d(ctx0, kv_self.k,
-                            n_rot, n_head, n_ctx,
-                            ggml_element_size(kv_self.k)*n_embd_gqa,
-                            ggml_element_size(kv_self.k)*n_embd_head,
-                            ggml_element_size(kv_self.k)*(n_embd_head*n_ctx*il)
-                        ),
-                        K_shift, n_rot, 2, 0, freq_base, freq_scale);
-            offload_func_kq(tmp);
-            ggml_build_forward_expand(gf, tmp);
-        }
-    }
-    for (int il=0; il < n_layer; ++il) {
-        struct ggml_tensor * residual = inpL;
-        offload_func_t offload_func = llama_nop;
-        {
-            cur = ggml_norm(ctx0, inpL, norm_eps);
-            offload_func(cur);
-            cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
-            offload_func(cur);
-            cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
-            offload_func(cur);
-            ggml_format_name(cur, "input_layernorm_%d", il);
-        }
-        // self attention
+    inpL = llm_build_norm(ctx0, inpL,
+            model.tok_norm,
+            model.tok_norm_b,
+            LLM_NORM, norm_eps, cb, -1);
+    cb(inpL, "inp_norm", -1);
+
+    for (int il = 0; il < n_layer; ++il) {
+        cur = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, norm_eps, cb, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
         {
             cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            offload_func_kq(cur);
-            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-            offload_func_kq(cur);
+            cb(cur, "wqkv", il);
 
-            // split qkv
-            GGML_ASSERT(n_head_kv == n_head);
-            ggml_set_name(cur, format("qkv_%d", il).c_str());
-            struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
-            offload_func_kq(tmpqkv);
-            struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
-            offload_func_kq(tmpqkv_perm);
-            ggml_format_name(tmpqkv_perm, "tmpqkv_perm_%d", il);
-            struct ggml_tensor * tmpq = ggml_view_3d(
-                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                    0
-                );
-            offload_func_kq(tmpq);
-            struct ggml_tensor * tmpk = ggml_view_3d(
-                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
-                );
-            offload_func_kq(tmpk);
-            // Q/K Layernorm
-            tmpq = ggml_norm(ctx0, tmpq, norm_eps);
-            offload_func_kq(tmpq);
-            tmpq =  ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm);
-            offload_func_kq(tmpq);
-            tmpq =  ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b);
-            offload_func_kq(tmpq);
-
-            tmpk = ggml_norm(ctx0, tmpk, norm_eps);
-            offload_func_v(tmpk);
-            tmpk =  ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
-            offload_func_v(tmpk);
-            tmpk =  ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
-            offload_func_v(tmpk);
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
 
-            // RoPE the first n_rot of q/k, pass the other half, and concat.
-            struct ggml_tensor * qrot = ggml_view_3d(
-                ctx0, tmpq, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpq) * n_embd_head,
-                ggml_element_size(tmpq) * n_embd_head * n_head,
-                0
-            );
-            offload_func_kq(qrot);
-            ggml_format_name(qrot, "qrot_%d", il);
-            struct ggml_tensor * krot = ggml_view_3d(
-                ctx0, tmpk, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpk) * n_embd_head,
-                ggml_element_size(tmpk) * n_embd_head * n_head,
-                0
-            );
-            offload_func_kq(krot);
-            ggml_format_name(krot, "krot_%d", il);
+            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-            // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
-            struct ggml_tensor * qpass = ggml_view_3d(
-                ctx0, tmpq, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpq) * n_embd_head,
-                ggml_element_size(tmpq) * n_embd_head * n_head,
-                ggml_element_size(tmpq) * n_rot
-            );
-            offload_func_kq(qpass);
-            ggml_format_name(qpass, "qpass_%d", il);
-            struct ggml_tensor * kpass = ggml_view_3d(
-                ctx0, tmpk, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpk) * n_embd_head,
-                ggml_element_size(tmpk) * n_embd_head * n_head,
-                ggml_element_size(tmpk) * n_rot
-            );
-            offload_func_kq(kpass);
-            ggml_format_name(kpass, "kpass_%d", il);
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
 
-            struct ggml_tensor * qrotated =  ggml_rope_custom(
-                    ctx0, qrot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
-            );
-            offload_func_kq(qrotated);
-            struct ggml_tensor * krotated = ggml_rope_custom(
-                    ctx0, krot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
-            );
-            offload_func_kq(krotated);
-            // ggml currently only supports concatenation on dim=2
-            // so we need to permute qrot, qpass, concat, then permute back.
-            qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
-            offload_func_kq(qrotated);
-            krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
-            offload_func_kq(krotated);
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-            qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
-            offload_func_kq(qpass);
-            kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
-            offload_func_kq(kpass);
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
 
-            struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
-            offload_func_kq(Qcur);
-            struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
-            offload_func_kq(Kcur);
+            cur = llm_build_kqv(lctx, ctx0, Qcur,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il);
+            cb(cur, "kqv_out", il);
+        }
 
-            struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
-            offload_func_kq(Q);
+        // Add the input
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
 
-            Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
-            offload_func_kq(Kcur);
-            {
-                struct ggml_tensor * tmpv = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
-                    );
-                offload_func_v(tmpv);
-                // store K, V in cache
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
-                offload_func_v(Vcur);
-                ggml_set_name(Vcur, "Vcur");
-
-                struct ggml_tensor * k = ggml_view_1d(
-                    ctx0, kv_self.k, n_tokens*n_embd_gqa,
-                    (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)
-                );
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
-
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-                ggml_set_name(v, "v");
-
-                // important: storing RoPE-ed version of K in the KV cache!
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
-            struct ggml_tensor * K = ggml_view_3d(ctx0, kv_self.k,
-                    n_embd_head, n_kv, n_head_kv,
-                    ggml_element_size(kv_self.k)*n_embd_gqa,
-                    ggml_element_size(kv_self.k)*n_embd_head,
-                    ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-
-            offload_func_kq(K);
-            ggml_format_name(K, "K_%d", il);
-
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
-            offload_func_kq(KQ_masked);
-            ggml_set_name(KQ_masked, "KQ_masked");
-
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-            offload_func_kq(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
-
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            offload_func_v(V);
-            ggml_set_name(V, "V");
-
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
-
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
-
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
-
-            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
-            offload_func(cur);
-            cur = ggml_add(ctx0, cur, model.layers[il].bo);
-            offload_func(cur);
-            ggml_set_name(cur, "result_wo");
-        }
-
-        struct ggml_tensor * inpFF = ggml_add(ctx0, residual, cur);
-        offload_func(inpFF);
-        ggml_set_name(inpFF, "inpFF");
+        // FF
         {
-            // MLP
-            {
-                // Norm
-                cur = ggml_norm(ctx0, inpFF, norm_eps);
-                offload_func(cur);
-                cur = ggml_add(ctx0,
-                    ggml_mul(ctx0, cur, model.layers[il].ffn_norm),
-                    model.layers[il].ffn_norm_b
-                );
-                ggml_set_name(cur, "ffn_norm");
-                offload_func(cur);
-            }
-            cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
-            offload_func(cur);
-
-            cur = ggml_add(ctx0, cur, model.layers[il].b3);
-            offload_func(cur);
-            ggml_set_name(cur, "result_ffn_up");
-
-            cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur));
-            ggml_set_name(cur, "result_ffn_act");
-            offload_func(cur);
-            offload_func(cur->src[0]);
-
-            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
-            offload_func(cur);
-            cur = ggml_add(ctx0,
-                cur,
-                model.layers[il].b2);
-            offload_func(cur);
-            ggml_set_name(cur, "outFF");
-        }
-        cur = ggml_add(ctx0, cur, inpFF);
-        offload_func(cur);
-        ggml_set_name(cur, "inpFF_+_outFF");
-        inpL = cur;
-    }
-    cur = inpL;
-    {
-        cur = ggml_norm(ctx0, cur, norm_eps);
-        offload_func_nr(cur);
-        cur = ggml_mul(ctx0, cur, model.output_norm);
-        offload_func_nr(cur);
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, norm_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-        cur = ggml_add(ctx0, cur, model.output_norm_b);
-        // offload_func_nr(cur);
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                    NULL,                      NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
+        }
 
-        ggml_set_name(cur, "result_norm");
+        inpL = ggml_add(ctx0, cur, ffn_inp);
+        cb(inpL, "l_out", il);
     }
+
+    cur = llm_build_norm(ctx0, inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, norm_eps, cb, -1);
+    cb(cur, "result_norm", -1);
+
     cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+    cb(cur, "result_output", -1);
+
     ggml_build_forward_expand(gf, cur);
+
     ggml_free(ctx0);
+
     return gf;
 }
 
-static struct ggml_cgraph * llm_build_bloom(
+static struct ggml_cgraph * llm_build_mpt(
          llama_context & lctx,
-     const llama_batch & batch) {
+     const llama_batch & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) {
     const auto & model   = lctx.model;
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
@@ -5313,17 +4569,16 @@ static struct ggml_cgraph * llm_build_bloom(
     const int64_t n_layer     = hparams.n_layer;
     const int64_t n_ctx       = cparams.n_ctx;
     const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_head = hparams.n_embd_head();
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
 
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-    const float norm_eps = hparams.f_norm_eps;
+    const float norm_eps       = hparams.f_norm_eps;
+    const float clamp_kqv      = hparams.f_clamp_kqv;
+    const float max_alibi_bias = hparams.f_max_alibi_bias;
 
     const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
+    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
 
     auto & buf_compute = lctx.buf_compute;
 
@@ -5340,567 +4595,591 @@ static struct ggml_cgraph * llm_build_bloom(
     ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     struct ggml_tensor * cur;
-    struct ggml_tensor * token;
     struct ggml_tensor * inpL;
 
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-        }
-        ggml_set_name(inp_tokens, "inp_tokens");
-
-        token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-
-        token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
-
-        ggml_allocr_alloc(lctx.alloc, token);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
-        }
-    }
+    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
+    cb(inpL, "inp_embd", -1);
 
     // KQ_scale
     struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
-    }
+    cb(KQ_scale, "KQ_scale", -1);
 
     // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
     struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
+    cb(KQ_mask, "KQ_mask", -1);
 
-    // norm
-    {
-        inpL = ggml_norm(ctx0, token, norm_eps);
-        inpL = ggml_add(ctx0, ggml_mul(ctx0, inpL, model.tok_norm), model.tok_norm_b);
-    }
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * attn_norm;
 
-    ggml_set_name(inpL, "inpL");
+        attn_norm = llm_build_norm(ctx0, inpL,
+                model.layers[il].attn_norm,
+                NULL,
+                LLM_NORM, norm_eps, cb, il);
+        cb(attn_norm, "attn_norm", il);
 
-    for (int il = 0; il < n_layer; ++il) {
+        // self-attention
         {
-            // Norm
-            cur = ggml_norm(ctx0, inpL, norm_eps);
-            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
+            cur = attn_norm;
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            if (clamp_kqv > 0.0f) {
+                cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv);
+                cb(cur, "wqkv_clamped", il);
+            }
+
+            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+            cur = llm_build_kqv(lctx, ctx0, Qcur,
+                    model.layers[il].wo, NULL,
+                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, max_alibi_bias, cb, il);
+            cb(cur, "kqv_out", il);
         }
 
-        {
-            // Self Attention
-            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
+        // Add the input
+        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
 
-            struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
-            struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd);
-            struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
+        // feed forward
+        {
+            cur = llm_build_norm(ctx0, ffn_inp,
+                    model.layers[il].ffn_norm,
+                    NULL,
+                    LLM_NORM, norm_eps, cb, il);
+            cb(cur, "ffn_norm", il);
 
-            struct ggml_tensor * Qcur = tmpq;
-            struct ggml_tensor * Kcur = tmpk;
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    NULL,                      NULL,
+                    model.layers[il].ffn_down, NULL,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
+        }
 
-            // store key and value to memory
-            {
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
-                ggml_set_name(Vcur, "Vcur");
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
 
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                ggml_set_name(k, "k");
+        // input for next layer
+        inpL = cur;
+    }
 
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+    cur = inpL;
 
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
-            }
+    cur = llm_build_norm(ctx0, cur,
+            model.output_norm,
+            NULL,
+            LLM_NORM, norm_eps, cb, -1);
+    cb(cur, "result_norm", -1);
 
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        ggml_cpy(ctx0,
-                            Qcur,
-                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)),
-                        0, 2, 1, 3);
-            ggml_set_name(Q, "Q");
-
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            ggml_set_name(K, "K");
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    cb(cur, "result_output", -1);
 
-            // K * Q
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            ggml_set_name(KQ, "KQ");
+    ggml_build_forward_expand(gf, cur);
 
-            // KQ_scaled = KQ / sqrt(n_embd_head)
-            // KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
-            struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
+    ggml_free(ctx0);
 
-            struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ kv_head, n_head, 8);
-            ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
+    return gf;
+}
 
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
-            ggml_set_name(KQ_masked, "KQ_masked");
+//
+// tensor offloading helpers
+//
+// TODO: will be removed with backend v2
+
+enum llm_offload_func_e {
+    OFFLOAD_FUNC_NOP,
+    OFFLOAD_FUNC,
+    OFFLOAD_FUNC_KQ,
+    OFFLOAD_FUNC_V,
+    OFFLOAD_FUNC_NR,
+    OFFLOAD_FUNC_EMB,
+    OFFLOAD_FUNC_OUT,
+};
 
-            // KQ = soft_max(KQ_masked)
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
+// TODO: will be removed with backend v2
+struct llm_offload_trie {
+    struct node {
+        ~node() {
+            for (int i = 0; i < 256; ++i) {
+                if (children[i]) {
+                    delete children[i];
+                }
+            }
+        }
 
-            // split cached V into n_head heads
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            ggml_set_name(V, "V");
+        node * children[256] = { nullptr };
+        llm_offload_func_e func = OFFLOAD_FUNC_NOP;
+    };
 
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            ggml_set_name(KQV, "KQV");
+    llm_offload_trie() {
+        root = new node;
+    }
 
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            ggml_set_name(KQV_merged, "KQV_merged");
+    llm_offload_trie(const std::unordered_map<const char *, llm_offload_func_e> & map) {
+        root = new node;
 
-            // cur = KQV_merged.contiguous().view(n_embd, n_tokens)
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            ggml_set_name(cur, "KQV_merged_contiguous");
+        for (const auto & kv : map) {
+            add(kv.first, kv.second);
         }
+    }
 
-        // Projection
-        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
+    ~llm_offload_trie() {
+        delete root;
+    }
 
-        // Add the input
-        cur = ggml_add(ctx0, cur, inpL);
+    void add(const char * name, llm_offload_func_e func) {
+        node * cur = root;
 
-        struct ggml_tensor * inpFF = cur;
+        for (int i = 0; ; ++i) {
+            const uint8_t c = name[i];
 
-        // FF
-        {
-            // Norm
-            {
-                cur = ggml_norm(ctx0, inpFF, norm_eps);
-                cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
+            if (!c) {
+                break;
             }
 
-            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
-
-            // GELU activation
-            cur = ggml_gelu(ctx0, cur);
+            if (!cur->children[c]) {
+                cur->children[c] = new node;
+            }
 
-            // Projection
-            cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
+            cur = cur->children[c];
         }
 
-        inpL = ggml_add(ctx0, cur, inpFF);
+        cur->func = func;
     }
 
-    // Output Norm
-    {
-        cur = ggml_norm(ctx0, inpL, norm_eps);
-        cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
-    }
-    ggml_set_name(cur, "result_norm");
+    llm_offload_func_e find(const char * name) const {
+        const node * cur = root;
 
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+        for (int i = 0; ; ++i) {
+            const uint8_t c = name[i];
 
-    ggml_build_forward_expand(gf, cur);
+            if (!c) {
+                break;
+            }
 
-    ggml_free(ctx0);
+            if (!cur->children[c]) {
+                return OFFLOAD_FUNC_NOP;
+            }
 
-    return gf;
-}
+            cur = cur->children[c];
+        }
 
-static struct ggml_cgraph * llm_build_mpt(
-         llama_context & lctx,
-     const llama_batch & batch) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
+        return cur->func;
+    }
 
-    const auto & kv_self = lctx.kv_self;
+    node * root = nullptr;
+};
 
-    GGML_ASSERT(!!kv_self.ctx);
+// TODO: will be removed with backend v2
+static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map = {
+  //{ "inp_tokens",                 OFFLOAD_FUNC_NR  }, // TODO: missing K-quants get_rows kernel
+  //{ "inp_embd",                   OFFLOAD_FUNC_NR  }, // TODO: missing K-quants get_rows kernel
+    { "pos_embd",                   OFFLOAD_FUNC_NR  },
+
+    { "inp_pos",                    OFFLOAD_FUNC_KQ  }, // this is often used for KQ ops (e.g. rope)
+    { "KQ_scale",                   OFFLOAD_FUNC_KQ  },
+    { "KQ_mask",                    OFFLOAD_FUNC_KQ  },
+    { "K_shift",                    OFFLOAD_FUNC_KQ  },
+    { "K_shifted",                  OFFLOAD_FUNC_KQ  },
+
+    { "inp_norm",                   OFFLOAD_FUNC_NR  },
+    { "inp_norm_w",                 OFFLOAD_FUNC_NR  },
+    { "inp_norm_wb",                OFFLOAD_FUNC_NR  },
+
+    { "norm",                       OFFLOAD_FUNC     },
+    { "norm_w",                     OFFLOAD_FUNC     },
+    { "norm_wb",                    OFFLOAD_FUNC     },
+
+    { "attn_norm",                  OFFLOAD_FUNC     },
+    { "attn_norm_2",                OFFLOAD_FUNC     },
+
+    { "wqkv",                       OFFLOAD_FUNC_KQ  },
+    { "bqkv",                       OFFLOAD_FUNC_KQ  },
+    { "wqkv_clamped",               OFFLOAD_FUNC_KQ  },
+
+    { "tmpk",                       OFFLOAD_FUNC_KQ  },
+    { "tmpq",                       OFFLOAD_FUNC_KQ  },
+    { "tmpv",                       OFFLOAD_FUNC_V   },
+    { "Kcur",                       OFFLOAD_FUNC_KQ  },
+    { "Qcur",                       OFFLOAD_FUNC_KQ  },
+    { "Vcur",                       OFFLOAD_FUNC_V   },
+
+    { "krot",                       OFFLOAD_FUNC_KQ  },
+    { "qrot",                       OFFLOAD_FUNC_KQ  },
+    { "kpass",                      OFFLOAD_FUNC_KQ  },
+    { "qpass",                      OFFLOAD_FUNC_KQ  },
+    { "krotated",                   OFFLOAD_FUNC_KQ  },
+    { "qrotated",                   OFFLOAD_FUNC_KQ  },
+
+    { "q",                          OFFLOAD_FUNC_KQ  },
+    { "k",                          OFFLOAD_FUNC_KQ  },
+    { "kq",                         OFFLOAD_FUNC_KQ  },
+    { "kq_scaled",                  OFFLOAD_FUNC_KQ  },
+    { "kq_scaled_alibi",            OFFLOAD_FUNC_KQ  },
+    { "kq_masked",                  OFFLOAD_FUNC_KQ  },
+    { "kq_soft_max",                OFFLOAD_FUNC_V   },
+    { "v",                          OFFLOAD_FUNC_V   },
+    { "kqv",                        OFFLOAD_FUNC_V   },
+    { "kqv_merged",                 OFFLOAD_FUNC_V   },
+    { "kqv_merged_cont",            OFFLOAD_FUNC_V   },
+    { "kqv_wo",                     OFFLOAD_FUNC_V   },
+    { "kqv_out",                    OFFLOAD_FUNC_V   },
+
+    { "ffn_inp",                    OFFLOAD_FUNC     },
+    { "ffn_norm",                   OFFLOAD_FUNC     },
+
+    { "ffn_up",                     OFFLOAD_FUNC     },
+    { "ffn_up_b",                   OFFLOAD_FUNC     },
+    { "ffn_gate",                   OFFLOAD_FUNC     },
+    { "ffn_gate_b",                 OFFLOAD_FUNC     },
+    { "ffn_gate_par",               OFFLOAD_FUNC     },
+    { "ffn_down",                   OFFLOAD_FUNC     },
+    { "ffn_down_b",                 OFFLOAD_FUNC     },
+    { "ffn_out",                    OFFLOAD_FUNC     },
+
+    { "ffn_silu",                   OFFLOAD_FUNC     },
+    { "ffn_gelu",                   OFFLOAD_FUNC     },
+    { "ffn_relu",                   OFFLOAD_FUNC     },
+    { "ffn_sqr(relu)",              OFFLOAD_FUNC     },
+
+    { "l_out",                      OFFLOAD_FUNC     },
+
+    { "result_norm",                OFFLOAD_FUNC_EMB },
+    { "result_output",              OFFLOAD_FUNC_OUT },
+};
 
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
+static llm_offload_trie k_offload_func_trie(k_offload_map);
 
-    const float norm_eps       = hparams.f_norm_eps;
-    const float clamp_kqv      = hparams.f_clamp_kqv;
-    const float max_alibi_bias = hparams.f_max_alibi_bias;
+static struct ggml_cgraph * llama_build_graph(
+         llama_context & lctx,
+     const llama_batch & batch) {
+    const auto & model = lctx.model;
 
-    const int n_gpu_layers = model.n_gpu_layers;
+    // check if we should build the worst-case graph (for memory measurement)
+    const bool worst_case = ggml_allocr_is_measure(lctx.alloc);
 
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = ggml_allocr_is_measure(lctx.alloc) ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
+    // keep track of the input that has already been allocated
+    bool alloc_inp_tokens   = false;
+    bool alloc_inp_embd     = false;
+    bool alloc_inp_pos      = false;
+    bool alloc_inp_KQ_scale = false;
+    bool alloc_inp_KQ_mask  = false;
+    bool alloc_inp_K_shift  = false;
 
-    auto & buf_compute = lctx.buf_compute;
+#ifdef GGML_USE_CUBLAS
+    const bool do_offload = true;
+#else
+    const bool do_offload = true; // TODO: set to false after finishing refactoring
+#endif
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ false,
-    };
+    int n_non_view = 0; // number of non-view tensors that have been processed by the callback
 
-    params.no_alloc = true;
+    // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
+    // TODO: will be removed with backend v2
+    llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
+        if (il >= 0) {
+            ggml_format_name(cur, "%s-%d", name, il);
+        } else {
+            ggml_set_name(cur, name);
+        }
 
-    struct ggml_context * ctx0 = ggml_init(params);
+        //
+        // allocate input tensors and set input data
+        //
+        // TODO: will be removed with backend v2
 
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+        if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) {
+            ggml_allocr_alloc(lctx.alloc, cur);
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+            if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) {
+                const int64_t n_tokens = cur->ne[0];
 
-    //int warmup = 0;
-    if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+                memcpy(cur->data, batch.token, n_tokens*ggml_element_size(cur));
+            }
 
-        ggml_allocr_alloc(lctx.alloc, inp_tokens);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
-            //warmup = ((uint32_t*) inp_tokens->data)[0] == 0;
+            alloc_inp_tokens = true;
         }
 
-        ggml_set_name(inp_tokens, "inp_tokens");
+        if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) {
+            ggml_allocr_alloc(lctx.alloc, cur);
 
-        inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
-    } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
+            if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) {
+                const int64_t n_embd   = cur->ne[0];
+                const int64_t n_tokens = cur->ne[1];
 
-        inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+                memcpy(cur->data, batch.embd, n_tokens*n_embd*ggml_element_size(cur));
+            }
 
-        ggml_allocr_alloc(lctx.alloc, inpL);
-        if (!ggml_allocr_is_measure(lctx.alloc)) {
-            memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
+            alloc_inp_embd = true;
         }
-    }
 
-    const int i_gpu_start = n_layer - n_gpu_layers;
-    (void) i_gpu_start;
+        if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) {
+            ggml_allocr_alloc(lctx.alloc, cur);
 
-    // offload functions set the tensor output backend to GPU
-    // tensors are GPU-accelerated if any input or the output has been offloaded
-    offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
-    offload_func_t offload_func_kq = llama_nop;
-    offload_func_t offload_func_v  = llama_nop;
-
-#ifdef GGML_USE_CUBLAS
-    if (n_gpu_layers > n_layer) {
-        offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 1) {
-        offload_func_v  = ggml_cuda_assign_buffers_no_alloc;
-    }
-    if (n_gpu_layers > n_layer + 2) {
-        offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
-    }
-#endif // GGML_USE_CUBLAS
+            if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) {
+                const int64_t n_tokens = cur->ne[0];
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
-    ggml_allocr_alloc(lctx.alloc, KQ_scale);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
-    }
+                int32_t * data = (int32_t *) cur->data;
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    offload_func_kq(KQ_mask);
-    ggml_set_name(KQ_mask, "KQ_mask");
-    ggml_allocr_alloc(lctx.alloc, KQ_mask);
-    if (!ggml_allocr_is_measure(lctx.alloc)) {
-        float * data = (float *) KQ_mask->data;
-        memset(data, 0, ggml_nbytes(KQ_mask));
-
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                const llama_pos    pos    = batch.pos[j];
-                const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                for (int i = 0; i < n_kv; ++i) {
-                    if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
-                    }
+                for (int i = 0; i < n_tokens; ++i) {
+                    data[i] = batch.pos[i];
                 }
             }
+
+            alloc_inp_pos = true;
         }
-    }
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * attn_norm;
+        if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) {
+            ggml_allocr_alloc(lctx.alloc, cur);
 
-        offload_func_t offload_func = llama_nop;
+            if (!ggml_allocr_is_measure(lctx.alloc)) {
+                const int64_t n_embd_head = model.hparams.n_embd_head();
+                ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head)));
+            }
 
-#ifdef GGML_USE_CUBLAS
-        if (il >= i_gpu_start) {
-            offload_func = ggml_cuda_assign_buffers_no_alloc;
+            alloc_inp_KQ_scale = true;
         }
-#endif // GGML_USE_CUBLAS
-
-        // self-attention
-        // TODO: refactor into common function (shared with LLaMA)
-        {
-            attn_norm = ggml_norm(ctx0, inpL, norm_eps);
-            offload_func(attn_norm);
 
-            attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm);
-            offload_func(attn_norm);
+        if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) {
+            ggml_allocr_alloc(lctx.alloc, cur);
 
-            if (1) {
-                cur = attn_norm;
-            }
+            if (!ggml_allocr_is_measure(lctx.alloc)) {
+                const int64_t n_kv     = cur->ne[0];
+                const int64_t n_tokens = cur->ne[1];
 
-            // compute QKV
+                float * data = (float *) cur->data;
+                memset(data, 0, ggml_nbytes(cur));
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            offload_func_kq(cur);
+                for (int h = 0; h < 1; ++h) {
+                    for (int j = 0; j < n_tokens; ++j) {
+                        const llama_pos    pos    = batch.pos[j];
+                        const llama_seq_id seq_id = batch.seq_id[j][0];
 
-            if (clamp_kqv > 0.0f) {
-                cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv);
-                offload_func_kq(cur);
+                        for (int i = 0; i < n_kv; ++i) {
+                            if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
+                                data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
+                            }
+                        }
+                    }
+                }
             }
 
-            const size_t wsize = ggml_type_size(cur->type);
-
-            struct ggml_tensor * Qcur = ggml_view_3d(
-                ctx0, cur, n_embd_head, n_head, n_tokens,
-                wsize * n_embd_head,
-                wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                0);
-            offload_func_kq(Qcur);
+            alloc_inp_KQ_mask = true;
+        }
 
-            struct ggml_tensor * Kcur = ggml_view_3d(
-                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
-                wsize * n_embd_head,
-                wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                wsize * n_embd_head *  n_head);
-            offload_func_kq(Kcur);
+        if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) {
+            ggml_allocr_alloc(lctx.alloc, cur);
 
-            struct ggml_tensor * tmpv = ggml_view_3d(
-                ctx0, cur, n_embd_head, n_head_kv, n_tokens,
-                wsize * n_embd_head,
-                wsize * n_embd_head * (n_head + 2 * n_head_kv),
-                wsize * n_embd_head * (n_head +     n_head_kv));
-            offload_func_kq(Kcur);
+            if (!ggml_allocr_is_measure(lctx.alloc)) {
+                const int64_t n_ctx = cur->ne[0];
 
-            ggml_set_name(Qcur, "Qcur");
-            ggml_set_name(Kcur, "Kcur");
+                int32_t * data = (int32_t *) cur->data;
 
-            {
-                struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
-                offload_func_v(Vcur);
-                offload_func_v(Vcur->src[0]->src[0]);
-                ggml_set_name(Vcur, "Vcur");
-
-                struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
-                offload_func_kq(k);
-                ggml_set_name(k, "k");
-
-                struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
-                        (   n_ctx)*ggml_element_size(kv_self.v),
-                        (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
-                offload_func_v(v);
-
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
+                for (int i = 0; i < n_ctx; ++i) {
+                    data[i] = lctx.kv_self.cells[i].delta;
+                }
             }
 
-            struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-            offload_func_kq(Q);
-            ggml_set_name(Q, "Q");
+            alloc_inp_K_shift = true;
+        }
 
-            struct ggml_tensor * K =
-                ggml_view_3d(ctx0, kv_self.k,
-                        n_embd_head, n_kv, n_head_kv,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
-            offload_func_kq(K);
-            ggml_set_name(K, "K");
-
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-            offload_func_kq(KQ);
-            ggml_set_name(KQ, "KQ");
-
-            struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
-            offload_func_kq(KQ_scaled);
-            ggml_set_name(KQ_scaled, "KQ_scaled");
-
-            // TODO: replace with ggml_add()
-            struct ggml_tensor * KQ_scaled_alibi =
-                ggml_alibi(ctx0, KQ_scaled, 0, n_head, max_alibi_bias);
-            offload_func_kq(KQ_scaled_alibi);
-            ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
-
-            struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
-            offload_func_kq(KQ_masked);
-            ggml_set_name(KQ_masked, "KQ_masked");
-
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-            offload_func_v(KQ_soft_max);
-            ggml_set_name(KQ_soft_max, "KQ_soft_max");
-
-            struct ggml_tensor * V =
-                ggml_view_3d(ctx0, kv_self.v,
-                        n_kv, n_embd_head, n_head_kv,
-                        ggml_element_size(kv_self.v)*n_ctx,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                        ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
-            offload_func_v(V);
-            ggml_set_name(V, "V");
-
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-            offload_func_v(KQV);
-            ggml_set_name(KQV, "KQV");
-
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-            offload_func_v(KQV_merged);
-            ggml_set_name(KQV_merged, "KQV_merged");
-
-            cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
-            offload_func_v(cur);
-            ggml_set_name(cur, "KQV_merged_contiguous");
-
-            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
-            offload_func(cur);
-            ggml_set_name(cur, "result_wo");
+        // view tensors are not processed further
+        if (cur->view_src != nullptr) {
+            return;
         }
 
-        // Add the input
-        cur = ggml_add(ctx0, cur, inpL);
-        offload_func(cur);
+        if (cur->op != GGML_OP_NONE) {
+            n_non_view++;
+        }
 
-        struct ggml_tensor * attn_out = cur;
+        //
+        // offload layers
+        //
+        // TODO: will be removed with backend v2
 
-        // feed forward
-        {
-            // Norm
-            {
-                cur = ggml_norm(ctx0, attn_out, norm_eps);
-                offload_func(cur);
+//#define LLAMA_OFFLOAD_DEBUG
 
-                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
-                offload_func(cur);
-            }
+        if (!do_offload) {
+            return;
+        }
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
-            offload_func(cur);
+        const int n_layer = model.hparams.n_layer;
 
-            cur = ggml_gelu(ctx0, cur);
-            offload_func(cur);
-            cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
-            offload_func(cur);
-        }
+        const int n_gpu_layers = model.n_gpu_layers;
+        const int i_gpu_start  = n_layer - n_gpu_layers;
 
-        cur = ggml_add(ctx0, cur, attn_out);
-        offload_func(cur);
-        // input for next layer
-        inpL = cur;
-    }
+        // should we offload the final norm? yes if we are not computing embeddings
+        const bool offload_emb = lctx.embedding.empty();
 
-    cur = inpL;
+        static const std::unordered_map<llm_offload_func_e, std::string, std::hash<int>> k_offload_func_name = {
+            { OFFLOAD_FUNC_NOP, "CPU" },
+            { OFFLOAD_FUNC_OUT, "CPU" },
+#ifdef GGML_USE_CUBLAS
+            { OFFLOAD_FUNC,     "GPU (CUDA)" },
+            { OFFLOAD_FUNC_KQ,  "GPU (CUDA) KQ" },
+            { OFFLOAD_FUNC_V,   "GPU (CUDA) V" },
+            { OFFLOAD_FUNC_NR,  "GPU (CUDA) NR" },
+            { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" },
+#else
+            { OFFLOAD_FUNC,     "CPU" },
+            { OFFLOAD_FUNC_KQ,  "CPU" },
+            { OFFLOAD_FUNC_V,   "CPU" },
+            { OFFLOAD_FUNC_NR,  "CPU" },
+            { OFFLOAD_FUNC_EMB, "CPU" },
+#endif // GGML_USE_CUBLAS
+        };
 
-    // norm
-    {
-        cur = ggml_norm(ctx0, cur, norm_eps);
-        offload_func_nr(cur);
+        // check the global map for what offload function to use for this tensor
+        llm_offload_func_e func_e = k_offload_func_trie.find(name);
 
-        cur = ggml_mul(ctx0, cur, model.output_norm);
-        ggml_set_name(cur, "result_norm");
-    }
+        if (func_e == OFFLOAD_FUNC_NOP) {
+#ifdef LLAMA_OFFLOAD_DEBUG
+            // if a tensor hasn't been offloaded, we warn the user
+            if (worst_case) {
+                LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__,
+                        cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837");
+            }
+#endif
 
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    ggml_set_name(cur, "result_output");
+            return;
+        }
 
-    ggml_build_forward_expand(gf, cur);
+        // count the number of layers and respect the provided n_gpu_layers
+        switch (func_e) {
+            case OFFLOAD_FUNC_NOP:
+            case OFFLOAD_FUNC_OUT:
+                break;
+            case OFFLOAD_FUNC:
+                if (n_gpu_layers < n_layer) {
+                    if (il < i_gpu_start) {
+                        func_e = OFFLOAD_FUNC_NOP;
+                    }
+                }
+                break;
+            case OFFLOAD_FUNC_NR:
+                if (n_gpu_layers <= n_layer + 0) {
+                    func_e = OFFLOAD_FUNC_NOP;
+                }
+                break;
+            case OFFLOAD_FUNC_V:
+                if (n_gpu_layers <= n_layer + 1) {
+                    func_e = OFFLOAD_FUNC_NOP;
+                }
+                break;
+            case OFFLOAD_FUNC_KQ:
+                if (n_gpu_layers <= n_layer + 2) {
+                    func_e = OFFLOAD_FUNC_NOP;
+                }
+                break;
+            case OFFLOAD_FUNC_EMB:
+                if (!offload_emb || n_gpu_layers < n_layer) {
+                    func_e = OFFLOAD_FUNC_NOP;
+                }
+                break;
+            default: GGML_ASSERT(false);
+        }
 
-    ggml_free(ctx0);
+        offload_func_t func = ggml_offload_nop;
 
-    return gf;
-}
+        // this is needed for compatibility with Metal for example
+#ifdef GGML_USE_CUBLAS
+        static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc;
+#else
+        static offload_func_t ggml_offload_gpu = ggml_offload_nop;
+#endif
 
-static struct ggml_cgraph * llama_build_graph(
-         llama_context & lctx,
-     const llama_batch & batch) {
-    const auto & model = lctx.model;
+        switch (func_e) {
+            case OFFLOAD_FUNC_NOP:
+            case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break;
+            case OFFLOAD_FUNC:
+            case OFFLOAD_FUNC_KQ:
+            case OFFLOAD_FUNC_V:
+            case OFFLOAD_FUNC_NR:
+            case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break;
+            default: GGML_ASSERT(false);
+        }
+
+        // apply offload function to the tensor
+        func(cur);
+
+#ifdef LLAMA_OFFLOAD_DEBUG
+        if (worst_case) {
+            LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str());
+        }
+#endif
+    };
 
     struct ggml_cgraph * result = NULL;
 
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
             {
-                result = llm_build_llama(lctx, batch);
+                result = llm_build_llama(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_BAICHUAN:
             {
-                result = llm_build_baichaun(lctx, batch);
+                result = llm_build_baichaun(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_FALCON:
             {
-                result = llm_build_falcon(lctx, batch);
+                result = llm_build_falcon(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_STARCODER:
             {
-                result = llm_build_starcoder(lctx, batch);
+                result = llm_build_starcoder(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_PERSIMMON:
             {
-                result = llm_build_persimmon(lctx, batch);
+                result = llm_build_persimmon(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_REFACT:
             {
-                result = llm_build_refact(lctx, batch);
+                result = llm_build_refact(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_BLOOM:
             {
-                result = llm_build_bloom(lctx, batch);
+                result = llm_build_bloom(lctx, batch, cb, worst_case);
             } break;
         case LLM_ARCH_MPT:
             {
-                result = llm_build_mpt(lctx, batch);
+                result = llm_build_mpt(lctx, batch, cb, worst_case);
             } break;
         default:
             GGML_ASSERT(false);
     }
 
+    if (worst_case) {
+        int n_non_view_total = 0;
+
+        for (int i = 0; i < result->n_nodes; ++i) {
+            if (result->nodes[i]->view_src == nullptr) {
+                n_non_view_total++;
+            }
+        }
+
+        LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total);
+
+        if (n_non_view != n_non_view_total) {
+            LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
+            LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n",     __func__);
+            LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n",    __func__);
+            LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n",                     __func__);
+            LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n",            __func__);
+            LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
+        }
+    }
+
     return result;
 }
 
@@ -6043,11 +5322,13 @@ static int llama_decode_internal(
     }
 
     // If all tensors can be run on the GPU then using more than 1 thread is detrimental.
-    const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
+    const bool full_offload_supported =
+        model.arch == LLM_ARCH_LLAMA    ||
         model.arch == LLM_ARCH_BAICHUAN ||
-        model.arch == LLM_ARCH_FALCON ||
-        model.arch == LLM_ARCH_REFACT ||
+        model.arch == LLM_ARCH_FALCON   ||
+        model.arch == LLM_ARCH_REFACT   ||
         model.arch == LLM_ARCH_MPT;
+
     const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
     if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
         n_threads = 1;
@@ -6102,6 +5383,8 @@ static int llama_decode_internal(
     //}
 
     // extract logits
+    // TODO: do not compute and extract logits if only embeddings are needed
+    //       need to update the graphs to skip "result_output"
     {
         auto & logits_out = lctx.logits;
 
@@ -8713,8 +7996,8 @@ static int llama_apply_lora_from_file_internal(
 
             ggml_tensor * dest_t = model_tensors[base_name];
 
-            offload_func_t offload_func = llama_nop;
-            offload_func_t offload_func_force_inplace = llama_nop;
+            offload_func_t offload_func               = ggml_offload_nop;
+            offload_func_t offload_func_force_inplace = ggml_offload_nop;
 
 #ifdef GGML_USE_CUBLAS
             if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {

From ca190bca8e844d171020d6147687e71472d71734 Mon Sep 17 00:00:00 2001
From: Adrian Hesketh <a-h@users.noreply.github.com>
Date: Wed, 1 Nov 2023 09:28:28 +0000
Subject: [PATCH 36/79] server : re-enable completion and embedded at the same
 time (#3876)

---
 .gitignore                 |  1 +
 examples/server/server.cpp | 16 ++++++++++------
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/.gitignore b/.gitignore
index 545c2872632234..5d7c5479ef67ae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,6 +15,7 @@
 .DS_Store
 .build/
 .cache/
+.ccls-cache/
 .direnv/
 .envrc
 .swiftpm
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index c163c7f8ec0dd1..47ae0d55856cf8 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -149,6 +149,7 @@ struct task_server {
     task_type type;
     json data;
     bool infill_mode = false;
+    bool embedding_mode = false;
 };
 
 struct task_result {
@@ -371,6 +372,7 @@ struct llama_client_slot
     std::vector<completion_token_output> generated_token_probs;
 
     bool infill = false;
+    bool embedding = false;
     bool has_next_token = true;
     bool truncated = false;
     bool stopped_eos = false;
@@ -1244,13 +1246,14 @@ struct llama_server_context
         queue_results.push_back(res);
     }
 
-    int request_completion(json data, bool infill)
+    int request_completion(json data, bool infill, bool embedding)
     {
         std::lock_guard<std::mutex> lock(mutex_tasks);
         task_server task;
         task.id = id_gen++;
         task.data = data;
         task.infill_mode = infill;
+        task.embedding_mode = embedding;
         task.type = COMPLETION_TASK;
         queue_tasks.push_back(task);
         return task.id;
@@ -1376,7 +1379,7 @@ struct llama_server_context
                     {
                         LOG_TEE("slot unavailable\n");
                         // send error result
-                        send_error(task.id, "slot unavaliable");
+                        send_error(task.id, "slot unavailable");
                         return;
                     }
 
@@ -1388,6 +1391,7 @@ struct llama_server_context
                     slot->reset();
 
                     slot->infill = task.infill_mode;
+                    slot->embedding = task.embedding_mode;
                     slot->task_id = task.id;
 
                     if (!launch_slot_with_data(slot, task.data))
@@ -1695,7 +1699,7 @@ struct llama_server_context
                 }
 
                 // prompt evaluated for embedding
-                if (params.embedding)
+                if (slot.embedding)
                 {
                     send_embedding(slot);
                     slot.release();
@@ -2274,7 +2278,7 @@ int main(int argc, char **argv)
     svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
             {
                 json data = json::parse(req.body);
-                const int task_id = llama.request_completion(data, false);
+                const int task_id = llama.request_completion(data, false, false);
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
                     task_result result = llama.next_result(task_id);
@@ -2329,7 +2333,7 @@ int main(int argc, char **argv)
     svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
             {
                 json data = json::parse(req.body);
-                const int task_id = llama.request_completion(data, true);
+                const int task_id = llama.request_completion(data, true, false);
                 if (!json_value(data, "stream", false)) {
                     std::string completion_text;
                     task_result result = llama.next_result(task_id);
@@ -2433,7 +2437,7 @@ int main(int argc, char **argv)
                 {
                     prompt = "";
                 }
-                const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false);
+                const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true);
                 task_result result = llama.next_result(task_id);
                 return res.set_content(result.result_json.dump(), "application/json");
             });

From f0e209324a7f663225791897877bf610f1af152d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 11:29:07 +0200
Subject: [PATCH 37/79] scripts : add server-llm.sh (#3868)

* scripts : add deploy-server.sh

* scripts : rename to server-llm.sh

* scripts : working curl pipe
---
 scripts/server-llm.sh | 391 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 391 insertions(+)
 create mode 100644 scripts/server-llm.sh

diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh
new file mode 100644
index 00000000000000..7bf0929bb8ca11
--- /dev/null
+++ b/scripts/server-llm.sh
@@ -0,0 +1,391 @@
+#!/bin/bash
+#
+# Helper script for deploying llama.cpp server with a single Bash command
+#
+# - Works on Linux and macOS
+# - Supports: CPU, CUDA, Metal, OpenCL
+# - Can run all GGUF models from HuggingFace
+# - Can serve requests in parallel
+# - Always builds latest llama.cpp from GitHub
+#
+# Limitations
+#
+# - Chat templates are poorly supported (base models recommended)
+# - Might be unstable!
+#
+# Usage:
+#   ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]
+#
+#   --port:       port number, default is 8888
+#   --repo:       path to a repo containing GGUF model files
+#   --wtype:      weights type (f16, q8_0, q4_0, q4_1), default is user-input
+#   --backend:    cpu, cuda, metal, opencl, depends on the OS
+#   --gpu-id:     gpu id, default is 0
+#   --n-parallel: number of parallel requests, default is 8
+#   --n-kv:       KV cache size, default is 4096
+#   --verbose:    verbose output
+#
+# Example:
+#
+#   bash -c "$(curl -s https://ggml.ai/server-llm.sh)"
+#
+
+set -e
+
+# required utils: curl, git, make
+if ! command -v curl &> /dev/null; then
+    printf "[-] curl not found\n"
+    exit 1
+fi
+if ! command -v git &> /dev/null; then
+    printf "[-] git not found\n"
+    exit 1
+fi
+if ! command -v make &> /dev/null; then
+    printf "[-] make not found\n"
+    exit 1
+fi
+
+# parse arguments
+port=8888
+repo=""
+wtype=""
+backend="cpu"
+
+# if macOS, use metal backend by default
+if [[ "$OSTYPE" == "darwin"* ]]; then
+    backend="metal"
+elif command -v nvcc &> /dev/null; then
+    backend="cuda"
+fi
+
+gpu_id=0
+n_parallel=8
+n_kv=4096
+verbose=0
+
+function print_usage {
+    printf "Usage:\n"
+    printf "  ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n"
+    printf "  --port:       port number, default is 8888\n"
+    printf "  --repo:       path to a repo containing GGUF model files\n"
+    printf "  --wtype:      weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
+    printf "  --backend:    cpu, cuda, metal, opencl, depends on the OS\n"
+    printf "  --gpu-id:     gpu id, default is 0\n"
+    printf "  --n-parallel: number of parallel requests, default is 8\n"
+    printf "  --n-kv:       KV cache size, default is 4096\n"
+    printf "  --verbose:    verbose output\n\n"
+    printf "Example:\n\n"
+    printf '  bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n'
+}
+
+while [[ $# -gt 0 ]]; do
+    key="$1"
+    case $key in
+        --port)
+            port="$2"
+            shift
+            shift
+            ;;
+        --repo)
+            repo="$2"
+            shift
+            shift
+            ;;
+        --wtype)
+            wtype="$2"
+            shift
+            shift
+            ;;
+        --backend)
+            backend="$2"
+            shift
+            shift
+            ;;
+        --gpu-id)
+            gpu_id="$2"
+            shift
+            shift
+            ;;
+        --n-parallel)
+            n_parallel="$2"
+            shift
+            shift
+            ;;
+        --n-kv)
+            n_kv="$2"
+            shift
+            shift
+            ;;
+        --verbose)
+            verbose=1
+            shift
+            ;;
+        --help)
+            print_usage
+            exit 0
+            ;;
+        *)
+            echo "Unknown argument: $key"
+            print_usage
+            exit 1
+            ;;
+    esac
+done
+
+# available weights types
+wtypes=("F16" "Q8_0" "Q4_0" "Q4_1" "Q5_0" "Q5_1" "Q6_K" "Q5_K_M" "Q5_K_S" "Q4_K_M" "Q4_K_S" "Q3_K_L" "Q3_K_M" "Q3_K_S" "Q2_K")
+
+wfiles=()
+for wt in "${wtypes[@]}"; do
+    wfiles+=("")
+done
+
+# sample repos
+repos=(
+    "https://huggingface.co/TheBloke/Llama-2-7B-GGUF"
+    "https://huggingface.co/TheBloke/Llama-2-13B-GGUF"
+    "https://huggingface.co/TheBloke/Llama-2-70B-GGUF"
+    "https://huggingface.co/TheBloke/CodeLlama-7B-GGUF"
+    "https://huggingface.co/TheBloke/CodeLlama-13B-GGUF"
+    "https://huggingface.co/TheBloke/CodeLlama-34B-GGUF"
+    "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF"
+    "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF"
+    "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF"
+    "https://huggingface.co/TheBloke/CausalLM-7B-GGUF"
+)
+
+printf "\n"
+printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
+printf "    Based on the options that follow, the script might download a model file\n"
+printf "    from the internet, which can be a few GBs in size. The script will also\n"
+printf "    build the latest llama.cpp source code from GitHub, which can be unstable.\n"
+printf "\n"
+printf "    Upon success, an HTTP server will be started and it will serve the selected\n"
+printf "    model using llama.cpp for demonstration purposes.\n"
+printf "\n"
+printf "    Please note:\n"
+printf "\n"
+printf "    - All new data will be stored in the current folder\n"
+printf "    - The server will be listening on all network interfaces\n"
+printf "    - The server will run with default settings which are not always optimal\n"
+printf "    - Do not judge the quality of a model based on the results from this script\n"
+printf "    - Do not use this script to benchmark llama.cpp\n"
+printf "    - Do not use this script in production\n"
+printf "    - This script is only for demonstration purposes\n"
+printf "\n"
+printf "    If you don't know what you are doing, please press Ctrl-C to abort now\n"
+printf "\n"
+printf "    Press Enter to continue ...\n\n"
+
+read
+
+if [[ -z "$repo" ]]; then
+    printf "[+] No repo provided from the command line\n"
+    printf "    Please select a number from the list below or enter an URL:\n\n"
+
+    is=0
+    for r in "${repos[@]}"; do
+        printf "    %2d) %s\n" $is "$r"
+        is=$((is+1))
+    done
+
+    # ask for repo until index of sample repo is provided or an URL
+    while [[ -z "$repo" ]]; do
+        printf "\n    Or choose one from: https://huggingface.co/models?sort=trending&search=gguf\n\n"
+        read -p "[+] Select repo: " repo
+
+        # check if the input is a number
+        if [[ "$repo" =~ ^[0-9]+$ ]]; then
+            if [[ "$repo" -ge 0 && "$repo" -lt ${#repos[@]} ]]; then
+                repo="${repos[$repo]}"
+            else
+                printf "[-] Invalid repo index: %s\n" "$repo"
+                repo=""
+            fi
+        elif [[ "$repo" =~ ^https?:// ]]; then
+            repo="$repo"
+        else
+            printf "[-] Invalid repo URL: %s\n" "$repo"
+            repo=""
+        fi
+    done
+fi
+
+# remove suffix
+repo=$(echo "$repo" | sed -E 's/\/tree\/main$//g')
+
+printf "[+] Checking for GGUF model files in %s\n" "$repo"
+
+# find GGUF files in the source
+# TODO: better logic
+model_tree="${repo%/}/tree/main"
+model_files=$(curl -s "$model_tree" | grep -i "\\.gguf</span>" | sed -E 's/.*<span class="truncate group-hover:underline">(.*)<\/span><\/a>/\1/g')
+
+# list all files in the provided git repo
+printf "[+] Model files:\n\n"
+for file in $model_files; do
+    # determine iw by grepping the filename with wtypes
+    iw=-1
+    is=0
+    for wt in "${wtypes[@]}"; do
+        # uppercase
+        ufile=$(echo "$file" | tr '[:lower:]' '[:upper:]')
+        if [[ "$ufile" =~ "$wt" ]]; then
+            iw=$is
+            break
+        fi
+        is=$((is+1))
+    done
+
+    if [[ $iw -eq -1 ]]; then
+        continue
+    fi
+
+    wfiles[$iw]="$file"
+
+    have=" "
+    if [[ -f "$file" ]]; then
+        have="*"
+    fi
+
+    printf "    %2d) %s %s\n" $iw "$have" "$file"
+done
+
+# ask for weights type until provided and available
+while [[ -z "$wtype" ]]; do
+    printf "\n"
+    read -p "[+] Select weight type: " wtype
+    wfile="${wfiles[$wtype]}"
+
+    if [[ -z "$wfile" ]]; then
+        printf "[-] Invalid weight type: %s\n" "$wtype"
+        wtype=""
+    fi
+done
+
+printf "[+] Selected weight type: %s (%s)\n" "$wtype" "$wfile"
+
+url="${repo%/}/resolve/main/$wfile"
+
+# check file if the model has been downloaded before
+chk="$wfile.chk"
+
+# check if we should download the file
+# - if $wfile does not exist
+# - if $wfile exists but $chk does not exist
+# - if $wfile exists and $chk exists but $wfile is newer than $chk
+# TODO: better logic using git lfs info
+
+do_download=0
+
+if [[ ! -f "$wfile" ]]; then
+    do_download=1
+elif [[ ! -f "$chk" ]]; then
+    do_download=1
+elif [[ "$wfile" -nt "$chk" ]]; then
+    do_download=1
+fi
+
+if [[ $do_download -eq 1 ]]; then
+    printf "[+] Downloading weights from %s\n" "$url"
+
+    # download the weights file
+    curl -o "$wfile" -# -L "$url"
+
+    # create a check file if successful
+    if [[ $? -eq 0 ]]; then
+        printf "[+] Creating check file %s\n" "$chk"
+        touch "$chk"
+    fi
+else
+    printf "[+] Using cached weights %s\n" "$wfile"
+fi
+
+# get latest llama.cpp and build
+
+printf "[+] Downloading latest llama.cpp\n"
+
+llama_cpp_dir="__llama_cpp_port_${port}__"
+
+if [[ -d "$llama_cpp_dir" && ! -f "$llama_cpp_dir/__ggml_script__" ]]; then
+    # if the dir exists and there isn't a file "__ggml_script__" in it, abort
+    printf "[-] Directory %s already exists\n" "$llama_cpp_dir"
+    printf "[-] Please remove it and try again\n"
+    exit 1
+elif [[ -d "$llama_cpp_dir" ]]; then
+    printf "[+] Directory %s already exists\n" "$llama_cpp_dir"
+    printf "[+] Using cached llama.cpp\n"
+
+    cd "$llama_cpp_dir"
+    git reset --hard
+    git fetch
+    git checkout origin/master
+
+    cd ..
+else
+    printf "[+] Cloning llama.cpp\n"
+
+    git clone https://github.com/ggerganov/llama.cpp "$llama_cpp_dir"
+fi
+
+# mark that that the directory is made by this script
+touch "$llama_cpp_dir/__ggml_script__"
+
+if [[ $verbose -eq 1 ]]; then
+    set -x
+fi
+
+# build
+cd "$llama_cpp_dir"
+
+make clean
+
+log="--silent"
+if [[ $verbose -eq 1 ]]; then
+    log=""
+fi
+
+if [[ "$backend" == "cuda" ]]; then
+    printf "[+] Building with CUDA backend\n"
+    LLAMA_CUBLAS=1 make -j server $log
+elif [[ "$backend" == "cpu" ]]; then
+    printf "[+] Building with CPU backend\n"
+    make -j server $log
+elif [[ "$backend" == "metal" ]]; then
+    printf "[+] Building with Metal backend\n"
+    make -j server $log
+elif [[ "$backend" == "opencl" ]]; then
+    printf "[+] Building with OpenCL backend\n"
+    LLAMA_CLBLAST=1 make -j server $log
+else
+    printf "[-] Unknown backend: %s\n" "$backend"
+    exit 1
+fi
+
+# run the server
+
+printf "[+] Running server\n"
+
+args=""
+if [[ "$backend" == "cuda" ]]; then
+    export CUDA_VISIBLE_DEVICES=$gpu_id
+    args="-ngl 999"
+elif [[ "$backend" == "cpu" ]]; then
+    args="-ngl 0"
+elif [[ "$backend" == "metal" ]]; then
+    args="-ngl 999"
+elif [[ "$backend" == "opencl" ]]; then
+    args="-ngl 999"
+else
+    printf "[-] Unknown backend: %s\n" "$backend"
+    exit 1
+fi
+
+if [[ $verbose -eq 1 ]]; then
+    args="$args --verbose"
+fi
+
+./server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args
+
+exit 0

From 73bdcb395ef9a997d9c02950c7cd4249546162cd Mon Sep 17 00:00:00 2001
From: Andrew Godfrey <AndrewGodfrey@users.noreply.github.com>
Date: Wed, 1 Nov 2023 04:49:04 -0700
Subject: [PATCH 38/79] finetune : add -ngl parameter (#3762)

* Add '-ngl' support to finetune.cpp

* Add fprintf in ggml_cuda_op_add

When I tried CUDA offloading during finetuning following the readme, I got an assert here.
This probably isn't an important case because inference later gives a warning saying you should use f16 or f32 instead when using lora

* Add 'finetune.sh', which currently fails when using GPU

"error: operator (): Finetuning on tensors with type 'f16' is not yet supported"

* tweak finetune.sh

* Suppress some warnings in ggml.c

* Add f16 implementation to ggml_compute_forward_add_f16_f32

* Add an f16 case to ggml_add_cast_impl and llama_build_lora_finetune_graphs

* finetune.sh: Edit comments

* Add "add_f16_f32_f32_cuda"

* Tweak an error message

* finetune.sh: Add an optional LLAMA_MODEL_DIR variable

* finetune.sh: Add an optional LLAMA_TRAINING_DIR variable

* train : minor

* tabs to spaces

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
---
 common/train.cpp               |  2 ++
 common/train.h                 |  1 +
 examples/finetune/finetune.cpp | 14 ++++++++-
 examples/finetune/finetune.sh  | 34 ++++++++++++++++++++++
 ggml-cuda.cu                   | 17 +++++++++++
 ggml-quants.c                  |  2 ++
 ggml.c                         | 53 ++++++++++++++++++++++++----------
 llama.cpp                      |  2 +-
 8 files changed, 108 insertions(+), 17 deletions(-)
 create mode 100644 examples/finetune/finetune.sh

diff --git a/common/train.cpp b/common/train.cpp
index 3cce5da269637e..bc15b7a03c0cd4 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -1045,6 +1045,7 @@ struct train_params_common get_default_train_params_common() {
     params.n_batch    =    8;
     params.n_gradient_accumulation = 1;
     params.n_epochs   = -1;
+    params.n_gpu_layers = 0;
 
     params.custom_n_ctx = false;
 
@@ -1080,6 +1081,7 @@ struct train_params_common get_default_train_params_common() {
     params.adam_beta2          = 0.999f;
     params.adam_gclip          = 1.0f;
     params.adam_eps_f          = 0.0f;
+
     return params;
 }
 
diff --git a/common/train.h b/common/train.h
index 42fa704b897ae5..d86c93cc4f1472 100644
--- a/common/train.h
+++ b/common/train.h
@@ -44,6 +44,7 @@ struct train_params_common {
     int n_batch;
     int n_gradient_accumulation;
     int n_epochs;
+    int n_gpu_layers;
 
     bool custom_n_ctx;
 
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 35824cd2d786a7..60c7faa797028a 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -652,7 +652,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
     GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
 
     auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
-        if (ggml_is_quantized(a->type)) {
+        if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) {
             return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
         } else if (a->type == GGML_TYPE_F32) {
             return ggml_add(ctx, a, b);
@@ -1459,6 +1459,17 @@ static bool train_params_parse(int argc, char ** argv, struct train_params * par
             }
             params->n_rank_w3 = std::stoi(argv[i]);
             params->custom_n_rank_w3 = true;
+        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
+            params->common.n_gpu_layers = std::stoi(argv[i]);
+#else
+            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
+            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+#endif
         } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             train_print_usage(argc, argv, &default_params);
@@ -1545,6 +1556,7 @@ int main(int argc, char ** argv) {
     srand(params.common.seed);
 
     struct llama_model_params llama_mparams = llama_model_default_params();
+    llama_mparams.n_gpu_layers = params.common.n_gpu_layers;
     llama_mparams.vocab_only = false;
 
     printf("%s: model base = '%s'\n", __func__, params.fn_model_base);
diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh
new file mode 100644
index 00000000000000..079bfa1139d5b5
--- /dev/null
+++ b/examples/finetune/finetune.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+cd `dirname $0`
+cd ../..
+
+EXE="./finetune"
+
+if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi
+if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi
+
+# MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf" # This is the model the readme uses.
+MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "main --lora" with GPU inferencing.
+
+while getopts "dg" opt; do
+  case $opt in
+    d)
+      DEBUGGER="gdb --args"
+      ;;
+    g)
+      EXE="./build/bin/Release/finetune"
+      GPUARG="--gpu-layers 25"
+      ;;
+  esac
+done
+
+$DEBUGGER $EXE \
+        --model-base $MODEL \
+        $GPUARG \
+        --checkpoint-in  chk-ol3b-shakespeare-LATEST.gguf \
+        --checkpoint-out chk-ol3b-shakespeare-ITERATION.gguf \
+        --lora-out lora-ol3b-shakespeare-ITERATION.bin \
+        --train-data "$LLAMA_TRAINING_DIR\shakespeare.txt" \
+        --save-every 10 \
+        --threads 10 --adam-iter 30 --batch 4 --ctx 64 \
+        --use-checkpointing
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 1ba951f688d82c..4e6e7cd94892b1 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -513,6 +513,15 @@ static __global__ void add_f16_f32_f16(const half * x, const float * y, half * d
     dst[i] = __hadd(x[i], __float2half(y[i]));
 }
 
+static __global__ void add_f16_f32_f32(const half * x, const float * y, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = __half2float(x[i]) + y[i];
+}
+
 static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
     const int i = blockDim.x*blockIdx.x + threadIdx.x;
 
@@ -4693,6 +4702,11 @@ static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, co
     add_f16_f32_f16<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
 }
 
+static void add_f16_f32_f32_cuda(const half * x, const float * y, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
+    add_f16_f32_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
+}
+
 static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
     const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
     mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
@@ -5996,7 +6010,10 @@ inline void ggml_cuda_op_add(
         add_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(src0), ne10*ne11, main_stream);
     } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
         add_f16_f32_f16_cuda((const half *) src0_dd, src1_dd, (half *) dst_dd, ggml_nelements(src0), main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+        add_f16_f32_f32_cuda((const half *) src0_dd, src1_dd, dst_dd, ggml_nelements(src0), main_stream);
     } else {
+        fprintf(stderr, "src0->type: %d  dst->type: %d\n", src0->type, dst->type);
         GGML_ASSERT(false);
     }
 
diff --git a/ggml-quants.c b/ggml-quants.c
index 72159446738e30..255c89b6a7ab84 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -716,6 +716,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
         __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
     }
 #else
+    UNUSED(nb);
     // scalar
     quantize_row_q8_0_reference(x, y, k);
 #endif
@@ -969,6 +970,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         y[i].s = sum*d;
     }
 #else
+    UNUSED(nb);
     // scalar
     quantize_row_q8_1_reference(x, y, k);
 #endif
diff --git a/ggml.c b/ggml.c
index 84407b1224226f..80d682255328c3 100644
--- a/ggml.c
+++ b/ggml.c
@@ -3153,7 +3153,7 @@ static struct ggml_tensor * ggml_add_cast_impl(
     // TODO: support less-strict constraint
     //       GGML_ASSERT(ggml_can_repeat(b, a));
     GGML_ASSERT(ggml_can_repeat_rows(b, a));
-    GGML_ASSERT(ggml_is_quantized(a->type)); // currently only supported for quantized input
+    GGML_ASSERT(ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16); // currently only supported for quantized input and f16
 
     bool is_node = false;
 
@@ -6927,9 +6927,15 @@ static void ggml_compute_forward_add_f16_f32(
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
 
-    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    if (dst->type == GGML_TYPE_F32) {
+        GGML_ASSERT( nb0 == sizeof(float));
+    }
+    else {
+        GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+        GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    }
+
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
 
     // rows per thread
@@ -6940,18 +6946,35 @@ static void ggml_compute_forward_add_f16_f32(
     const int ir1 = MIN(ir0 + dr, nr);
 
     if (nb10 == sizeof(float)) {
-        for (int ir = ir0; ir < ir1; ++ir) {
-            // src0, src1 and dst are same shape => same indices
-            const int i3 = ir/(ne2*ne1);
-            const int i2 = (ir - i3*ne2*ne1)/ne1;
-            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
-
-            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
-            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
-            float *       src1_ptr = (float *)       ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
-
-            for (int i = 0; i < ne0; i++) {
-                dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]);
+        if (dst->type == GGML_TYPE_F16) {
+            for (int ir = ir0; ir < ir1; ++ir) {
+                // src0, src1 and dst are same shape => same indices
+                const int i3 = ir/(ne2*ne1);
+                const int i2 = (ir - i3*ne2*ne1)/ne1;
+                const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+                ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+                ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+                float *       src1_ptr = (float *)       ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+                for (int i = 0; i < ne0; i++) {
+                    dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]);
+                }
+            }
+        } else {
+            for (int ir = ir0; ir < ir1; ++ir) {
+                // src0, src1 and dst are same shape => same indices
+                const int i3 = ir/(ne2*ne1);
+                const int i2 = (ir - i3*ne2*ne1)/ne1;
+                const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+                float *       dst_ptr  = (float *)       ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
+                ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
+                float *       src1_ptr = (float *)       ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+
+                for (int i = 0; i < ne0; i++) {
+                    dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i];
+                }
             }
         }
     }
diff --git a/llama.cpp b/llama.cpp
index ead1d421d243dc..42cedc7a1cd592 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -8003,7 +8003,7 @@ static int llama_apply_lora_from_file_internal(
             if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
                 if (dest_t->type != GGML_TYPE_F16) {
                     throw std::runtime_error(format(
-                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
+                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type));
                 }
                 offload_func = ggml_cuda_assign_buffers;
                 offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;

From 9a3b4f6c86503c9cfc049d4d0fdeafef12806f5e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 13:50:45 +0200
Subject: [PATCH 39/79] ggml : fix UNUSED macro (#3762)

---
 ggml-quants.c | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml-quants.c b/ggml-quants.c
index 255c89b6a7ab84..740be6dc5c7981 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -716,7 +716,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
         __riscv_vse8_v_i8m1(y[i].qs , vs, vl);
     }
 #else
-    UNUSED(nb);
+    GGML_UNUSED(nb);
     // scalar
     quantize_row_q8_0_reference(x, y, k);
 #endif
@@ -970,7 +970,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         y[i].s = sum*d;
     }
 #else
-    UNUSED(nb);
+    GGML_UNUSED(nb);
     // scalar
     quantize_row_q8_1_reference(x, y, k);
 #endif

From e75dfdd31b6a3dfa0627ba4ac3bb4b36e9db588e Mon Sep 17 00:00:00 2001
From: l3utterfly <gc.pthzfoldr@gmail.com>
Date: Wed, 1 Nov 2023 21:40:43 +0800
Subject: [PATCH 40/79] sampling : null grammar field after reset (#3885)

---
 common/sampling.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index 673d67a6d5380e..1317024c2c11cf 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -39,6 +39,7 @@ void llama_sampling_free(struct llama_sampling_context * ctx) {
 void llama_sampling_reset(llama_sampling_context * ctx) {
     if (ctx->grammar != NULL) {
         llama_grammar_free(ctx->grammar);
+        ctx->grammar = NULL;
     }
 
     if (!ctx->parsed_grammar.rules.empty()) {

From a2758d08e44ce3624d233af4d23c6843e2e735b5 Mon Sep 17 00:00:00 2001
From: staviq <staviq@gmail.com>
Date: Wed, 1 Nov 2023 15:18:27 +0100
Subject: [PATCH 41/79] log : make generating separate log files optional
 (#3787)

* impl --log-new, --log-append

* Update common/log.h

Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>

* Update common/log.h

Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>

* Apply suggestions from code review

Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>

---------

Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
---
 common/log.h | 122 ++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 82 insertions(+), 40 deletions(-)

diff --git a/common/log.h b/common/log.h
index d2c864ceab7be6..c0e814861e0c69 100644
--- a/common/log.h
+++ b/common/log.h
@@ -97,38 +97,56 @@
     #define LOG_TEE_TARGET stderr
 #endif
 
-// NOTE: currently disabled as it produces too many log files
+// Utility for synchronizing log configuration state
+//  since std::optional was introduced only in c++17
+enum LogTriState
+{
+    LogTriStateSame,
+    LogTriStateFalse,
+    LogTriStateTrue
+};
+
 // Utility to obtain "pid" like unique process id and use it when creating log files.
-//inline std::string log_get_pid()
-//{
-//    static std::string pid;
-//    if (pid.empty())
-//    {
-//        // std::this_thread::get_id() is the most portable way of obtaining a "process id"
-//        //  it's not the same as "pid" but is unique enough to solve multiple instances
-//        //  trying to write to the same log.
-//        std::stringstream ss;
-//        ss << std::this_thread::get_id();
-//        pid = ss.str();
-//    }
-//
-//    return pid;
-//}
+inline std::string log_get_pid()
+{
+   static std::string pid;
+   if (pid.empty())
+   {
+       // std::this_thread::get_id() is the most portable way of obtaining a "process id"
+       //  it's not the same as "pid" but is unique enough to solve multiple instances
+       //  trying to write to the same log.
+       std::stringstream ss;
+       ss << std::this_thread::get_id();
+       pid = ss.str();
+   }
+
+   return pid;
+}
 
 // Utility function for generating log file names with unique id based on thread id.
 //  invocation with log_filename_generator( "llama", "log" ) creates a string "llama.<number>.log"
 //  where the number is a runtime id of the current thread.
 
-#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(log_file_basename, log_file_extension)
+#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(LogTriStateSame, log_file_basename, log_file_extension)
 
 // INTERNAL, DO NOT USE
-inline std::string log_filename_generator_impl(const std::string & log_file_basename, const std::string & log_file_extension)
+inline std::string log_filename_generator_impl(LogTriState multilog, const std::string & log_file_basename, const std::string & log_file_extension)
 {
+    static bool _multilog = false;
+
+    if (multilog != LogTriStateSame)
+    {
+        _multilog = multilog == LogTriStateTrue;
+    }
+
     std::stringstream buf;
 
     buf << log_file_basename;
-    //buf << ".";
-    //buf << log_get_pid();
+    if (_multilog)
+    {
+        buf << ".";
+        buf << log_get_pid();
+    }
     buf << ".";
     buf << log_file_extension;
 
@@ -213,15 +231,6 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base
     #define LOG_TEE_FLF_VAL ,""
 #endif
 
-// Utility for synchronizing log configuration state
-//  since std::optional was introduced only in c++17
-enum LogTriState
-{
-    LogTriStateSame,
-    LogTriStateFalse,
-    LogTriStateTrue
-};
-
 // INTERNAL, DO NOT USE
 //  USE LOG() INSTEAD
 //
@@ -315,16 +324,23 @@ enum LogTriState
 #endif
 
 // INTERNAL, DO NOT USE
-inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr)
+inline FILE *log_handler1_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr)
 {
-    static bool _initialized{false};
-    static bool _disabled{(filename.empty() && target == nullptr)};
+    static bool _initialized = false;
+    static bool _append = false;
+    static bool _disabled = filename.empty() && target == nullptr;
     static std::string log_current_filename{filename};
     static FILE *log_current_target{target};
     static FILE *logfile = nullptr;
 
     if (change)
     {
+        if (append != LogTriStateSame)
+        {
+            _append = append == LogTriStateTrue;
+            return logfile;
+        }
+
         if (disable == LogTriStateTrue)
         {
             // Disable primary target
@@ -377,7 +393,7 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri
             }
         }
 
-        logfile = fopen(filename.c_str(), "w");
+        logfile = fopen(filename.c_str(), _append ? "a" : "w");
     }
 
     if (!logfile)
@@ -398,9 +414,9 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri
 }
 
 // INTERNAL, DO NOT USE
-inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME)
+inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME)
 {
-    return log_handler1_impl(change, disable, filename, target);
+    return log_handler1_impl(change, append, disable, filename, target);
 }
 
 // Disables logs entirely at runtime.
@@ -411,7 +427,7 @@ inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTri
 // INTERNAL, DO NOT USE
 inline FILE *log_disable_impl()
 {
-    return log_handler1_impl(true, LogTriStateTrue);
+    return log_handler1_impl(true, LogTriStateSame, LogTriStateTrue);
 }
 
 // Enables logs at runtime.
@@ -420,19 +436,31 @@ inline FILE *log_disable_impl()
 // INTERNAL, DO NOT USE
 inline FILE *log_enable_impl()
 {
-    return log_handler1_impl(true, LogTriStateFalse);
+    return log_handler1_impl(true, LogTriStateSame, LogTriStateFalse);
 }
 
 // Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*)
 #define log_set_target(target) log_set_target_impl(target)
 
 // INTERNAL, DO NOT USE
-inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, filename); }
-inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, target); }
+inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, LogTriStateSame, filename); }
+inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, LogTriStateSame, target); }
 
 // INTERNAL, DO NOT USE
 inline FILE *log_handler() { return log_handler1_impl(); }
 
+// Enable or disable creating separate log files for each run.
+//  can ONLY be invoked BEFORE first log use.
+#define log_multilog(enable) log_filename_generator_impl((enable) ? LogTriStateTrue : LogTriStateFalse, "", "")
+// Enable or disable append mode for log file.
+//  can ONLY be invoked BEFORE first log use.
+#define log_append(enable) log_append_impl(enable)
+// INTERNAL, DO NOT USE
+inline FILE *log_append_impl(bool enable)
+{
+    return log_handler1_impl(true, enable ? LogTriStateTrue : LogTriStateFalse, LogTriStateSame);
+}
+
 inline void log_test()
 {
     log_disable();
@@ -494,6 +522,18 @@ inline bool log_param_single_parse(const std::string & param)
         return true;
     }
 
+    if (param == "--log-new")
+    {
+        log_multilog(true);
+        return true;
+    }
+
+    if (param == "--log-append")
+    {
+        log_append(true);
+        return true;
+    }
+
     return false;
 }
 
@@ -523,7 +563,9 @@ inline void log_print_usage()
     printf("  --log-disable         Disable trace logs\n");
     printf("  --log-enable          Enable trace logs\n");
     printf("  --log-file            Specify a log filename (without extension)\n");
-    printf("                        Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /*  */
+    printf("  --log-new             Create a separate new log file on start. "
+                                   "Each log file will have unique name: \"<name>.<ID>.log\"\n");
+    printf("  --log-append          Don't truncate the old log file.\n");
 }
 
 #define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)

From 0e40806c1cb3bdf9955ed807ffbe212be85b4c67 Mon Sep 17 00:00:00 2001
From: bandoti <141645996+bandoti@users.noreply.github.com>
Date: Wed, 1 Nov 2023 14:42:01 -0300
Subject: [PATCH 42/79] common : allow caller to handle help/argument
 exceptions (#3715)

* Allow caller to handle help/argument exceptions

* Prepend newline to usage output

* Add new gpt_params_parse_ex function to hide arg-parse impl

* Fix issue blocking success case

* exit instead of returning false

* Update common/common.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update common/common.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 common/common.cpp | 41 ++++++++++++++++++++++++++---------------
 common/common.h   |  2 ++
 2 files changed, 28 insertions(+), 15 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index dc4865e80b1544..89be4126185db5 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -103,9 +103,24 @@ void process_escapes(std::string& input) {
 }
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
+    bool result = true;
+    try {
+        if (!gpt_params_parse_ex(argc, argv, params)) {
+            gpt_print_usage(argc, argv, gpt_params());
+            exit(0);
+        }
+    }
+    catch (const std::invalid_argument& ex) {
+        fprintf(stderr, ex.what());
+        gpt_print_usage(argc, argv, gpt_params());
+        exit(1);
+    }
+    return result;
+}
+
+bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
     bool invalid_param = false;
     std::string arg;
-    gpt_params default_params;
     const std::string arg_prefix = "--";
     llama_sampling_params & sparams = params.sparams;
 
@@ -554,11 +569,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
         } else if (arg == "-h" || arg == "--help") {
-            gpt_print_usage(argc, argv, default_params);
-#ifndef LOG_DISABLE_LOGS
-            log_print_usage();
-#endif // LOG_DISABLE_LOGS
-            exit(0);
+            return false;
+
         } else if (arg == "--random-prompt") {
             params.random_prompt = true;
         } else if (arg == "--in-prefix-bos") {
@@ -617,22 +629,17 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         // End of Parse args for logging parameters
 #endif // LOG_DISABLE_LOGS
         } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            gpt_print_usage(argc, argv, default_params);
-            exit(1);
+            throw std::invalid_argument("error: unknown argument: " + arg);
         }
     }
     if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
-        gpt_print_usage(argc, argv, default_params);
-        exit(1);
+        throw std::invalid_argument("error: invalid parameter for argument: " + arg);
     }
     if (params.prompt_cache_all &&
             (params.interactive || params.interactive_first ||
              params.instruct)) {
-        fprintf(stderr, "error: --prompt-cache-all not supported in interactive mode yet\n");
-        gpt_print_usage(argc, argv, default_params);
-        exit(1);
+
+        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
     if (params.escape) {
@@ -651,6 +658,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
 void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     const llama_sampling_params & sparams = params.sparams;
 
+    printf("\n");
     printf("usage: %s [options]\n", argv[0]);
     printf("\n");
     printf("options:\n");
@@ -762,6 +770,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -ld LOGDIR, --logdir LOGDIR\n");
     printf("                        path under which to save YAML logs (no logging if unset)\n");
     printf("\n");
+#ifndef LOG_DISABLE_LOGS
+    log_print_usage();
+#endif // LOG_DISABLE_LOGS
 }
 
 std::string get_system_info(const gpt_params & params) {
diff --git a/common/common.h b/common/common.h
index 84523a4fbf460a..343b272177c7ec 100644
--- a/common/common.h
+++ b/common/common.h
@@ -110,6 +110,8 @@ struct gpt_params {
     std::string image = ""; // path to an image file
 };
 
+bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
+
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
 
 void gpt_print_usage(int argc, char ** argv, const gpt_params & params);

From 50337961a678fce4081554b24e56e86b67660163 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 20:11:02 +0200
Subject: [PATCH 43/79] llm : add llm_build_context (#3881)

* llm : add llm_build_context

* llm : deduce norm eps based on type + explict max_alibi_bias, clamp_kqv

* llm : restore the non-graph llm_build_ functional API

ggml-ci

* llm : cleanup + comments
---
 llama.cpp | 1986 +++++++++++++++++++++++------------------------------
 1 file changed, 866 insertions(+), 1120 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 42cedc7a1cd592..d0c4ef10151828 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3090,6 +3090,10 @@ static bool llama_model_load(
     return true;
 }
 
+//
+// llm_build
+//
+
 using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
 
 enum llm_rope_type {
@@ -3098,17 +3102,35 @@ enum llm_rope_type {
     LLM_ROPE_GLM,
 };
 
+enum llm_ffn_op_type {
+    LLM_FFN_SILU,
+    LLM_FFN_GELU,
+    LLM_FFN_RELU,
+    LLM_FFN_RELU_SQR,
+};
+
+enum llm_ffn_gate_type {
+    LLM_FFN_SEQ,
+    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
+};
+
+enum llm_norm_type {
+    LLM_NORM,
+    LLM_NORM_RMS,
+};
+
 static struct ggml_tensor * llm_build_inp_embd(
         struct ggml_context * ctx,
+        const llama_hparams & hparams,
           const llama_batch & batch,
          struct ggml_tensor * tok_embd,
-                    int64_t   n_embd,
-                    int32_t   n_tokens,
          const llm_build_cb & cb) {
+    const int64_t n_embd = hparams.n_embd;
+
     struct ggml_tensor * inpL;
 
     if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_tokens);
+        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
         cb(inp_tokens, "inp_tokens", -1);
 
         inpL = ggml_get_rows(ctx, tok_embd, inp_tokens);
@@ -3117,7 +3139,7 @@ static struct ggml_tensor * llm_build_inp_embd(
         GGML_ASSERT(false && "not implemented");
 #endif
 
-        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
+        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
     }
 
     return inpL;
@@ -3126,28 +3148,21 @@ static struct ggml_tensor * llm_build_inp_embd(
 // Persimmon: n_rot = n_embd_head/2
 // Other:     n_rot = n_embd_head
 static void llm_build_k_shift(
-        const llama_context & lctx,
-        struct ggml_context * ctx,
-         struct ggml_cgraph * graph,
-                    int64_t   n_rot,
-              llm_rope_type   type,
-         const llm_build_cb & cb) {
-    const auto & model   = lctx.model;
-    const auto & kv_self = lctx.kv_self;
-    const auto & cparams = lctx.cparams;
-
-    const auto & hparams = model.hparams;
-
+      struct ggml_context * ctx,
+      const llama_hparams & hparams,
+     const llama_kv_cache & kv,
+       struct ggml_cgraph * graph,
+            llm_rope_type   type,
+                  int64_t   n_ctx,
+                  int64_t   n_rot,
+                  float     freq_base,
+                  float     freq_scale,
+       const llm_build_cb & cb) {
     const int64_t n_layer     = hparams.n_layer;
     const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
     const int64_t n_embd_head = hparams.n_embd_head();
 
-    const int64_t n_ctx = lctx.cparams.n_ctx;
-
-    const float freq_base  = cparams.rope_freq_base;
-    const float freq_scale = cparams.rope_freq_scale;
-
     GGML_ASSERT(n_embd_head % n_rot == 0);
 
     struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
@@ -3165,11 +3180,11 @@ static void llm_build_k_shift(
         struct ggml_tensor * tmp =
             // we rotate only the first n_rot dimensions
             ggml_rope_custom_inplace(ctx,
-                    ggml_view_3d(ctx, kv_self.k,
+                    ggml_view_3d(ctx, kv.k,
                         n_rot, n_head_kv, n_ctx,
-                        ggml_element_size(kv_self.k)*n_embd_head,
-                        ggml_element_size(kv_self.k)*n_embd_gqa,
-                        ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
+                        ggml_element_size(kv.k)*n_embd_head,
+                        ggml_element_size(kv.k)*n_embd_gqa,
+                        ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il),
                     K_shift, n_rot, rope_type, 0, freq_base, freq_scale);
         cb(tmp, "K_shifted", il);
         ggml_build_forward_expand(graph, tmp);
@@ -3177,22 +3192,17 @@ static void llm_build_k_shift(
 }
 
 static void llm_build_kv_store(
-        const llama_context & lctx,
         struct ggml_context * ctx,
+        const llama_hparams & hparams,
+       const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
          struct ggml_tensor * k_cur,
          struct ggml_tensor * v_cur,
+                    int64_t   n_ctx,
                     int32_t   n_tokens,
                     int32_t   kv_head,
          const llm_build_cb & cb,
                     int64_t   il) {
-    const auto & model   = lctx.model;
-    const auto & kv_self = lctx.kv_self;
-    const auto & cparams = lctx.cparams;
-
-    const auto & hparams = model.hparams;
-
-    const int64_t n_ctx      = cparams.n_ctx;
     const int64_t n_embd_gqa = hparams.n_embd_gqa();
 
     // compute the transposed [n_tokens, n_embd] V matrix
@@ -3200,13 +3210,13 @@ static void llm_build_kv_store(
     //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
     cb(v_cur_t, "v_cur_t", il);
 
-    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv_self.k, n_tokens*n_embd_gqa,
-                (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
+    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k, n_tokens*n_embd_gqa,
+            (ggml_element_size(kv.k)*n_embd_gqa)*(il*n_ctx + kv_head));
     cb(k_cache_view, "k_cache_view", il);
 
-    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv_self.v, n_tokens, n_embd_gqa,
-            (   n_ctx)*ggml_element_size(kv_self.v),
-            (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
+    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v, n_tokens, n_embd_gqa,
+            (   n_ctx)*ggml_element_size(kv.v),
+            (il*n_ctx)*ggml_element_size(kv.v)*n_embd_gqa + kv_head*ggml_element_size(kv.v));
     cb(v_cache_view, "v_cache_view", il);
 
     // important: storing RoPE-ed version of K in the KV cache!
@@ -3214,23 +3224,18 @@ static void llm_build_kv_store(
     ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
 }
 
-enum llm_norm_type {
-    LLM_NORM,
-    LLM_NORM_RMS,
-};
-
 static struct ggml_tensor * llm_build_norm(
         struct ggml_context * ctx,
          struct ggml_tensor * cur,
+        const llama_hparams & hparams,
          struct ggml_tensor * mw,
          struct ggml_tensor * mb,
               llm_norm_type   type,
-                      float   eps,
          const llm_build_cb & cb,
                         int   il) {
     switch (type) {
-        case LLM_NORM:     cur = ggml_norm    (ctx, cur, eps); break;
-        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, eps); break;
+        case LLM_NORM:     cur = ggml_norm    (ctx, cur, hparams.f_norm_eps);     break;
+        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
     }
 
     if (mw || mb) {
@@ -3251,18 +3256,6 @@ static struct ggml_tensor * llm_build_norm(
     return cur;
 }
 
-enum llm_ffn_op_type {
-    LLM_FFN_SILU,
-    LLM_FFN_GELU,
-    LLM_FFN_RELU,
-    LLM_FFN_RELU_SQR,
-};
-
-enum llm_ffn_gate_type {
-    LLM_FFN_SEQ,
-    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
-};
-
 static struct ggml_tensor * llm_build_ffn(
         struct ggml_context * ctx,
          struct ggml_tensor * cur,
@@ -3351,26 +3344,21 @@ static struct ggml_tensor * llm_build_ffn(
 
 // if max_alibi_bias > 0 then apply ALiBi
 static struct ggml_tensor * llm_build_kqv(
-        const llama_context & lctx,
         struct ggml_context * ctx,
          struct ggml_tensor * cur,
+        const llama_hparams & hparams,
+       const llama_kv_cache & kv,
          struct ggml_tensor * wo,
          struct ggml_tensor * wo_b,
          struct ggml_tensor * q_cur,
          struct ggml_tensor * kq_scale,
          struct ggml_tensor * kq_mask,
+                    int64_t   n_ctx,
                     int32_t   n_tokens,
                     int32_t   n_kv,
-                      float   alibi_bias_max,
+                    float     max_alibi_bias,
          const llm_build_cb & cb,
-         int   il) {
-    const auto & model   = lctx.model;
-    const auto & kv_self = lctx.kv_self;
-    const auto & cparams = lctx.cparams;
-
-    const auto & hparams = model.hparams;
-
-    const int64_t n_ctx       = cparams.n_ctx;
+                    int       il) {
     const int64_t n_embd      = hparams.n_embd;
     const int64_t n_head      = hparams.n_head;
     const int64_t n_head_kv   = hparams.n_head_kv;
@@ -3381,11 +3369,11 @@ static struct ggml_tensor * llm_build_kqv(
     cb(q, "q", il);
 
     struct ggml_tensor * k =
-        ggml_view_3d(ctx, kv_self.k,
+        ggml_view_3d(ctx, kv.k,
                 n_embd_head, n_kv, n_head_kv,
-                ggml_element_size(kv_self.k)*n_embd_gqa,
-                ggml_element_size(kv_self.k)*n_embd_head,
-                ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
+                ggml_element_size(kv.k)*n_embd_gqa,
+                ggml_element_size(kv.k)*n_embd_head,
+                ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il);
     cb(k, "k", il);
 
     struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
@@ -3394,11 +3382,11 @@ static struct ggml_tensor * llm_build_kqv(
     kq = ggml_scale(ctx, kq, kq_scale);
     cb(kq, "kq_scaled", il);
 
-    if (alibi_bias_max > 0.0f) {
+    if (max_alibi_bias > 0.0f) {
         // TODO: n_head or n_head_kv
         // TODO: K-shift is likely not working
         // TODO: change to ggml_add
-        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, alibi_bias_max);
+        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
         cb(kq, "kq_scaled_alibi", il);
     }
 
@@ -3410,11 +3398,11 @@ static struct ggml_tensor * llm_build_kqv(
 
     // split cached v into n_head heads
     struct ggml_tensor * v =
-        ggml_view_3d(ctx, kv_self.v,
+        ggml_view_3d(ctx, kv.v,
                 n_kv, n_embd_head, n_head_kv,
-                ggml_element_size(kv_self.v)*n_ctx,
-                ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
-                ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
+                ggml_element_size(kv.v)*n_ctx,
+                ggml_element_size(kv.v)*n_ctx*n_embd_head,
+                ggml_element_size(kv.v)*n_ctx*n_embd_gqa*il);
     cb(v, "v", il);
 
     struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
@@ -3438,1259 +3426,1011 @@ static struct ggml_tensor * llm_build_kqv(
     return cur;
 }
 
-static struct ggml_cgraph * llm_build_llama(
-        llama_context  & lctx,
-    const llama_batch  & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
+struct llm_build_context {
+    const llama_model    & model;
+    const llama_hparams  & hparams;
+    const llama_cparams  & cparams;
+    const llama_batch    & batch;
+    const llama_kv_cache & kv_self;
 
-    GGML_ASSERT(!!kv_self.ctx);
+    const int64_t n_embd;
+    const int64_t n_layer;
+    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
+    const int64_t n_head;
+    const int64_t n_head_kv;
+    const int64_t n_embd_head;
+    const int64_t n_embd_gqa;
 
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
+    const float freq_base;
+    const float freq_scale;
+    const float norm_eps;
+    const float norm_rms_eps;
 
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+    const int32_t n_tokens;
+    const int32_t n_kv;     // size of KV cache to consider (n_kv <= n_ctx)
+    const int32_t kv_head;  // index of where we store new KV data in the cache
 
-    const float freq_base    = cparams.rope_freq_base;
-    const float freq_scale   = cparams.rope_freq_scale;
-    const float norm_rms_eps = hparams.f_norm_rms_eps;
+    const bool do_rope_shift;
 
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
+    const llm_build_cb & cb;
 
-    const bool do_rope_shift = worst_case || kv_self.has_shift;
+    llama_buffer & buf_compute;
 
-    //printf("n_kv = %d\n", n_kv);
+    struct ggml_context * ctx0 = nullptr;
 
-    auto & buf_compute = lctx.buf_compute;
-
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
-    };
-
-    struct ggml_context * ctx0 = ggml_init(params);
-
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
-
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
-
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
-
-    // inp_pos - contains the positions
-    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    cb(inp_pos, "inp_pos", -1);
-
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
-
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+    // TODO: consider making the entire interface noexcept
+    llm_build_context(
+        llama_context  & lctx,
+    const llama_batch  & batch,
+    const llm_build_cb & cb,
+                  bool   worst_case) :
+        model         (lctx.model),
+        hparams       (model.hparams),
+        cparams       (lctx.cparams),
+        batch         (batch),
+        kv_self       (lctx.kv_self),
+        n_embd        (hparams.n_embd),
+        n_layer       (hparams.n_layer),
+        n_ctx         (cparams.n_ctx),
+        n_head        (hparams.n_head),
+        n_head_kv     (hparams.n_head_kv),
+        n_embd_head   (hparams.n_embd_head()),
+        n_embd_gqa    (hparams.n_embd_gqa()),
+        freq_base     (cparams.rope_freq_base),
+        freq_scale    (cparams.rope_freq_scale),
+        norm_eps      (hparams.f_norm_eps),
+        norm_rms_eps  (hparams.f_norm_rms_eps),
+        n_tokens      (batch.n_tokens),
+        n_kv          (worst_case ? n_ctx            : kv_self.n),
+        kv_head       (worst_case ? n_ctx - n_tokens : kv_self.head),
+        do_rope_shift (worst_case || kv_self.has_shift),
+        cb            (cb),
+        buf_compute   (lctx.buf_compute) {
+            GGML_ASSERT(!!kv_self.ctx);
+
+            // all initializations should be done in init()
+        }
+
+    void init() {
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ buf_compute.size,
+            /*.mem_buffer =*/ buf_compute.data,
+            /*.no_alloc   =*/ true,
+        };
 
-    // shift the entire K-cache if needed
-    if (do_rope_shift) {
-        llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb);
+        ctx0 = ggml_init(params);
     }
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
-
-        // norm
-        cur = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm, NULL,
-                LLM_NORM_RMS, norm_rms_eps, cb, il);
-        cb(cur, "attn_norm", il);
-
-        // self-attention
-        {
-            // compute Q and K and RoPE them
-            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
-
-            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
-
-            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-            cb(Vcur, "Vcur", il);
-
-            Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-            cb(Qcur, "Qcur", il);
-
-            Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-            cb(Kcur, "Kcur", il);
-
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
-
-            cur = llm_build_kqv(lctx, ctx0, cur,
-                    model.layers[il].wo, NULL,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
-            cb(cur, "kqv_out", il);
-        }
-
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
-
-        // feed-forward network
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, norm_rms_eps, cb, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-            cb(cur, "ffn_out", il);
+    void free() {
+        if (ctx0) {
+            ggml_free(ctx0);
+            ctx0 = nullptr;
         }
-
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "l_out", il);
-
-        // input for next layer
-        inpL = cur;
     }
 
-    cur = inpL;
-
-    cur = llm_build_norm(ctx0, cur,
-            model.output_norm, NULL,
-            LLM_NORM_RMS, norm_rms_eps, cb, -1);
-    cb(cur, "result_norm", -1);
-
-    // lm_head
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
-
-    ggml_build_forward_expand(gf, cur);
-
-    ggml_free(ctx0);
-
-    return gf;
-}
-
-static struct ggml_cgraph * llm_build_baichaun(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
-
-    GGML_ASSERT(!!kv_self.ctx);
-
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
-
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-    const float freq_base    = cparams.rope_freq_base;
-    const float freq_scale   = cparams.rope_freq_scale;
-    const float norm_rms_eps = hparams.f_norm_rms_eps;
+    struct ggml_cgraph * build_llama() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-    const bool do_rope_shift = worst_case || kv_self.has_shift;
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    auto & buf_compute = lctx.buf_compute;
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
-    };
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(inp_pos, "inp_pos", -1);
 
-    struct ggml_context * ctx0 = ggml_init(params);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+        // shift the entire K-cache if needed
+        if (do_rope_shift) {
+            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+        }
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-    // inp_pos - contains the positions
-    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    cb(inp_pos, "inp_pos", -1);
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
 
-    // shift the entire K-cache if needed
-    if (do_rope_shift) {
-        llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE, cb);
-    }
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
+                Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                cb(Qcur, "Qcur", il);
 
-        cur = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm, NULL,
-                LLM_NORM_RMS, norm_rms_eps, cb, il);
-        cb(cur, "attn_norm", il);
+                Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                cb(Kcur, "Kcur", il);
 
-        // self-attention
-        {
-            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
+                cur = llm_build_kqv(ctx0, cur, hparams, kv_self,
+                        model.layers[il].wo, NULL,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
+                cb(cur, "kqv_out", il);
+            }
 
-            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-            cb(Vcur, "Vcur", il);
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
 
-            switch (model.type) {
-                case MODEL_7B:
-                    Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens),    inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-                    Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-                    break;
-                case MODEL_13B:
-                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
-                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
-                    break;
-                default:
-                    GGML_ASSERT(false);
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
             }
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-            // apply ALiBi for 13B model
-            const float alibi_bias_max = model.type == MODEL_13B ? 8.0f : -1.0f;
-
-            cur = llm_build_kqv(lctx, ctx0, cur,
-                    model.layers[il].wo, NULL,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, alibi_bias_max, cb, il);
-            cb(cur, "kqv_out", il);
+            // input for next layer
+            inpL = cur;
         }
 
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
+        cur = inpL;
 
-        // feed-forward network
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, norm_rms_eps, cb, il);
-            cb(cur, "ffn_norm", il);
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "l_out", il);
+        ggml_build_forward_expand(gf, cur);
 
-        // input for next layer
-        inpL = cur;
+        return gf;
     }
 
-    cur = inpL;
+    struct ggml_cgraph * build_baichuan() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    cur = llm_build_norm(ctx0, cur,
-            model.output_norm, NULL,
-            LLM_NORM_RMS, norm_rms_eps, cb, -1);
-    cb(cur, "result_norm", -1);
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    // lm_head
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    ggml_build_forward_expand(gf, cur);
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(inp_pos, "inp_pos", -1);
 
-    ggml_free(ctx0);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    return gf;
-}
-
-static struct ggml_cgraph * llm_build_falcon(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
-
-    GGML_ASSERT(!!kv_self.ctx);
-
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-    const float freq_base  = cparams.rope_freq_base;
-    const float freq_scale = cparams.rope_freq_scale;
-    const float norm_eps   = hparams.f_norm_eps;
-
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
-
-    const bool do_rope_shift = worst_case || kv_self.has_shift;
-
-    //printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
-    //        kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
-
-    auto & buf_compute = lctx.buf_compute;
-
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
-    };
-
-    struct ggml_context * ctx0 = ggml_init(params);
-
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+        // shift the entire K-cache if needed
+        if (do_rope_shift) {
+            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+        }
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-    // inp_pos - contains the positions
-    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    cb(inp_pos, "inp_pos", -1);
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
 
-    // shift the entire K-cache if needed
-    if (do_rope_shift) {
-        llm_build_k_shift(lctx, ctx0, gf, n_embd_head, LLM_ROPE_NEOX, cb);
-    }
+                switch (model.type) {
+                    case MODEL_7B:
+                        Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens),    inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                        Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                        break;
+                    case MODEL_13B:
+                        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
+                        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
+                        break;
+                    default:
+                        GGML_ASSERT(false);
+                }
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * attn_norm;
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-        attn_norm = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm,
-                model.layers[il].attn_norm_b,
-                LLM_NORM, norm_eps, cb, il);
-        cb(attn_norm, "attn_norm", il);
+                // apply ALiBi for 13B model
+                const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f;
 
-        // self-attention
-        {
-            if (model.layers[il].attn_norm_2) {
-                // Falcon-40B
-                cur = llm_build_norm(ctx0, attn_norm,
-                        model.layers[il].attn_norm_2,
-                        model.layers[il].attn_norm_2_b,
-                        LLM_NORM, norm_eps, cb, il);
-                cb(cur, "attn_norm_2", il);
-            } else {
-                cur = attn_norm;
+                cur = llm_build_kqv(ctx0, cur, hparams, kv_self,
+                        model.layers[il].wo, NULL,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il);
+                cb(cur, "kqv_out", il);
             }
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            cb(cur, "wqkv", il);
-
-            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
-
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
 
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-
-            // using mode = 2 for neox mode
-            Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
-            cb(Qcur, "Qcur", il);
-
-            Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
-            cb(Kcur, "Kcur", il);
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-            cur = llm_build_kqv(lctx, ctx0, attn_norm,
-                    model.layers[il].wo, NULL,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
-            cb(cur, "kqv_out", il);
+            // input for next layer
+            inpL = cur;
         }
 
-        struct ggml_tensor * ffn_inp = cur;
+        cur = inpL;
 
-        // feed forward
-        {
-            cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
-                    model.layers[il].ffn_up,   NULL,
-                    NULL,                      NULL,
-                    model.layers[il].ffn_down, NULL,
-                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "l_out", il);
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        cur = ggml_add(ctx0, cur, inpL);
-        cb(cur, "l_out", il);
+        ggml_build_forward_expand(gf, cur);
 
-        // input for next layer
-        inpL = cur;
+        return gf;
     }
 
-    cur = inpL;
-
-    // norm
-    cur = llm_build_norm(ctx0, cur,
-            model.output_norm,
-            model.output_norm_b,
-            LLM_NORM, norm_eps, cb, -1);
-    cb(cur, "result_norm", -1);
-
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
+    struct ggml_cgraph * build_falcon() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    ggml_build_forward_expand(gf, cur);
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    ggml_free(ctx0);
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    return gf;
-}
-
-static struct ggml_cgraph * llm_build_starcoder(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
-
-    GGML_ASSERT(!!kv_self.ctx);
-
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-    const float norm_eps = hparams.f_norm_eps;
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(inp_pos, "inp_pos", -1);
 
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    auto & buf_compute = lctx.buf_compute;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
-    };
-
-    struct ggml_context * ctx0 = ggml_init(params);
+        // shift the entire K-cache if needed
+        if (do_rope_shift) {
+            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+        }
 
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * attn_norm;
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * pos;
-    struct ggml_tensor * inpL;
+            attn_norm = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(attn_norm, "attn_norm", il);
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
+            // self-attention
+            {
+                if (model.layers[il].attn_norm_2) {
+                    // Falcon-40B
+                    cur = llm_build_norm(ctx0, attn_norm, hparams,
+                            model.layers[il].attn_norm_2,
+                            model.layers[il].attn_norm_2_b,
+                            LLM_NORM, cb, il);
+                    cb(cur, "attn_norm_2", il);
+                } else {
+                    cur = attn_norm;
+                }
 
-    // inp_pos - contains the positions
-    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    cb(inp_pos, "inp_pos", -1);
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
 
-    pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
-    cb(pos, "pos_embd", -1);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-    inpL = ggml_add(ctx0, inpL, pos);
-    cb(inpL, "inpL", -1);
+                // using mode = 2 for neox mode
+                Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+                cb(Qcur, "Qcur", il);
 
-    for (int il = 0; il < n_layer; ++il) {
-        cur = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm,
-                model.layers[il].attn_norm_b,
-                LLM_NORM, norm_eps, cb, il);
-        cb(cur, "attn_norm", il);
+                Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+                cb(Kcur, "Kcur", il);
 
-        // self-attention
-        {
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            cb(cur, "wqkv", il);
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-            cb(cur, "bqkv", il);
+                cur = llm_build_kqv(ctx0, attn_norm, hparams, kv_self,
+                        model.layers[il].wo, NULL,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
+                cb(cur, "kqv_out", il);
+            }
 
-            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+            struct ggml_tensor * ffn_inp = cur;
 
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
+            // feed forward
+            {
+                cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
+                        model.layers[il].ffn_up,   NULL,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            cur = ggml_add(ctx0, cur, inpL);
+            cb(cur, "l_out", il);
 
-            cur = llm_build_kqv(lctx, ctx0, cur,
-                    model.layers[il].wo, model.layers[il].bo,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
-            cb(cur, "kqv_out", il);
+            // input for next layer
+            inpL = cur;
         }
 
-        // add the input
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-        cb(ffn_inp, "ffn_inp", il);
+        cur = inpL;
 
-        // FF
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm,
-                    model.layers[il].ffn_norm_b,
-                    LLM_NORM, norm_eps, cb, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                    NULL,                      NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
-                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        // norm
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
 
-        inpL = ggml_add(ctx0, cur, ffn_inp);
-        cb(inpL, "l_out", il);
-    }
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-    cur = llm_build_norm(ctx0, inpL,
-            model.output_norm,
-            model.output_norm_b,
-            LLM_NORM, norm_eps, cb, -1);
-    cb(cur, "result_norm", -1);
+        ggml_build_forward_expand(gf, cur);
 
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
+        return gf;
+    }
 
-    ggml_build_forward_expand(gf, cur);
-    ggml_free(ctx0);
+    struct ggml_cgraph * build_starcoder() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    return gf;
-}
+        struct ggml_tensor * cur;
+        struct ggml_tensor * pos;
+        struct ggml_tensor * inpL;
 
-static struct ggml_cgraph * llm_build_persimmon(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model = lctx.model;
-    const auto & hparams = model.hparams;
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    const auto & kv_self = lctx.kv_self;
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(inp_pos, "inp_pos", -1);
 
-    GGML_ASSERT(!!kv_self.ctx);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    const auto & cparams = lctx.cparams;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_rot       = n_embd_head / 2;
+        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+        cb(pos, "pos_embd", -1);
 
-    const float freq_base  = cparams.rope_freq_base;
-    const float freq_scale = cparams.rope_freq_scale;
-    const float norm_eps   = hparams.f_norm_eps;
+        inpL = ggml_add(ctx0, inpL, pos);
+        cb(inpL, "inpL", -1);
 
-    const int32_t n_tokens    = batch.n_tokens;
-    const int32_t n_kv        = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head     = worst_case ? n_ctx - n_tokens : kv_self.head;
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
 
-    const bool do_rope_shift  = worst_case || kv_self.has_shift;
+            // self-attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-    auto & buf_compute = lctx.buf_compute;
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
-    };
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-    struct ggml_context * ctx0 = ggml_init(params);
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
 
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "imp_embd", -1);
+                cur = llm_build_kqv(ctx0, cur, hparams, kv_self,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
+                cb(cur, "kqv_out", il);
+            }
 
-    struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-    cb(inp_pos, "inp_pos", -1);
+            // add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+            // FF
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
+        }
 
-    if (do_rope_shift) {
-        llm_build_k_shift(lctx, ctx0, gf, n_rot, LLM_ROPE_NEOX, cb);
-    }
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * residual = inpL;
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        cur = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm,
-                model.layers[il].attn_norm_b,
-                LLM_NORM, norm_eps, cb, il);
-        cb(cur, "attn_norm", il);
+        ggml_build_forward_expand(gf, cur);
 
-        // self attention
-        {
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            cb(cur, "wqkv", il);
-
-            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-            cb(cur, "bqkv", il);
-
-            // split qkv
-            GGML_ASSERT(n_head_kv == n_head);
-
-            struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
-            cb(tmpqkv, "tmpqkv", il);
-
-            struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
-            cb(tmpqkv_perm, "tmpqkv", il);
-
-            struct ggml_tensor * tmpq = ggml_view_3d(
-                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                    0
-                );
-            cb(tmpq, "tmpq", il);
-
-            struct ggml_tensor * tmpk = ggml_view_3d(
-                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
-                );
-            cb(tmpk, "tmpk", il);
-
-            // Q/K Layernorm
-            tmpq = llm_build_norm(ctx0, tmpq,
-                    model.layers[il].attn_q_norm,
-                    model.layers[il].attn_q_norm_b,
-                    LLM_NORM, norm_eps, cb, il);
-            cb(tmpq, "tmpq", il);
-
-            tmpk = llm_build_norm(ctx0, tmpk,
-                    model.layers[il].attn_k_norm,
-                    model.layers[il].attn_k_norm_b,
-                    LLM_NORM, norm_eps, cb, il);
-            cb(tmpk, "tmpk", il);
-
-            // RoPE the first n_rot of q/k, pass the other half, and concat.
-            struct ggml_tensor * qrot = ggml_view_3d(
-                ctx0, tmpq, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpq) * n_embd_head,
-                ggml_element_size(tmpq) * n_embd_head * n_head,
-                0
-            );
-            cb(qrot, "qrot", il);
+        return gf;
+    }
 
-            struct ggml_tensor * krot = ggml_view_3d(
-                ctx0, tmpk, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpk) * n_embd_head,
-                ggml_element_size(tmpk) * n_embd_head * n_head,
-                0
-            );
-            cb(krot, "krot", il);
-
-            // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
-            struct ggml_tensor * qpass = ggml_view_3d(
-                ctx0, tmpq, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpq) * n_embd_head,
-                ggml_element_size(tmpq) * n_embd_head * n_head,
-                ggml_element_size(tmpq) * n_rot
-            );
-            cb(qpass, "qpass", il);
+    struct ggml_cgraph * build_persimmon() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-            struct ggml_tensor * kpass = ggml_view_3d(
-                ctx0, tmpk, n_rot, n_head, n_tokens,
-                ggml_element_size(tmpk) * n_embd_head,
-                ggml_element_size(tmpk) * n_embd_head * n_head,
-                ggml_element_size(tmpk) * n_rot
-            );
-            cb(kpass, "kpass", il);
+        const int64_t n_rot = n_embd_head / 2;
 
-            struct ggml_tensor * qrotated = ggml_rope_custom(
-                    ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
-            );
-            cb(qrotated, "qrotated", il);
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-            struct ggml_tensor * krotated = ggml_rope_custom(
-                    ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
-            );
-            cb(krotated, "krotated", il);
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "imp_embd", -1);
 
-            // ggml currently only supports concatenation on dim=2
-            // so we need to permute qrot, qpass, concat, then permute back.
-            qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
-            cb(qrotated, "qrotated", il);
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(inp_pos, "inp_pos", -1);
 
-            krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
-            cb(krotated, "krotated", il);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-            qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
-            cb(qpass, "qpass", il);
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-            kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
-            cb(kpass, "kpass", il);
+        if (do_rope_shift) {
+            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+        }
 
-            struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
-            cb(Qcur, "Qcur", il);
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * residual = inpL;
 
-            struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
-            cb(Kcur, "Kcur", il);
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
 
-            struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
-            cb(Q, "Q", il);
+            // self attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                // split qkv
+                GGML_ASSERT(n_head_kv == n_head);
+
+                struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
+                cb(tmpqkv, "tmpqkv", il);
+
+                struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
+                cb(tmpqkv_perm, "tmpqkv", il);
+
+                struct ggml_tensor * tmpq = ggml_view_3d(
+                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                        0
+                        );
+                cb(tmpq, "tmpq", il);
+
+                struct ggml_tensor * tmpk = ggml_view_3d(
+                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
+                        );
+                cb(tmpk, "tmpk", il);
+
+                // Q/K Layernorm
+                tmpq = llm_build_norm(ctx0, tmpq, hparams,
+                        model.layers[il].attn_q_norm,
+                        model.layers[il].attn_q_norm_b,
+                        LLM_NORM, cb, il);
+                cb(tmpq, "tmpq", il);
+
+                tmpk = llm_build_norm(ctx0, tmpk, hparams,
+                        model.layers[il].attn_k_norm,
+                        model.layers[il].attn_k_norm_b,
+                        LLM_NORM, cb, il);
+                cb(tmpk, "tmpk", il);
+
+                // RoPE the first n_rot of q/k, pass the other half, and concat.
+                struct ggml_tensor * qrot = ggml_view_3d(
+                        ctx0, tmpq, n_rot, n_head, n_tokens,
+                        ggml_element_size(tmpq) * n_embd_head,
+                        ggml_element_size(tmpq) * n_embd_head * n_head,
+                        0
+                        );
+                cb(qrot, "qrot", il);
+
+                struct ggml_tensor * krot = ggml_view_3d(
+                        ctx0, tmpk, n_rot, n_head, n_tokens,
+                        ggml_element_size(tmpk) * n_embd_head,
+                        ggml_element_size(tmpk) * n_embd_head * n_head,
+                        0
+                        );
+                cb(krot, "krot", il);
+
+                // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
+                struct ggml_tensor * qpass = ggml_view_3d(
+                        ctx0, tmpq, n_rot, n_head, n_tokens,
+                        ggml_element_size(tmpq) * n_embd_head,
+                        ggml_element_size(tmpq) * n_embd_head * n_head,
+                        ggml_element_size(tmpq) * n_rot
+                        );
+                cb(qpass, "qpass", il);
+
+                struct ggml_tensor * kpass = ggml_view_3d(
+                        ctx0, tmpk, n_rot, n_head, n_tokens,
+                        ggml_element_size(tmpk) * n_embd_head,
+                        ggml_element_size(tmpk) * n_embd_head * n_head,
+                        ggml_element_size(tmpk) * n_rot
+                        );
+                cb(kpass, "kpass", il);
+
+                struct ggml_tensor * qrotated = ggml_rope_custom(
+                        ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
+                        );
+                cb(qrotated, "qrotated", il);
+
+                struct ggml_tensor * krotated = ggml_rope_custom(
+                        ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
+                        );
+                cb(krotated, "krotated", il);
+
+                // ggml currently only supports concatenation on dim=2
+                // so we need to permute qrot, qpass, concat, then permute back.
+                qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
+                cb(qrotated, "qrotated", il);
+
+                krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
+                cb(krotated, "krotated", il);
+
+                qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
+                cb(qpass, "qpass", il);
+
+                kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
+                cb(kpass, "kpass", il);
+
+                struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
+                cb(Q, "Q", il);
+
+                Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_view_3d(
+                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
+                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
+                        );
+                cb(Vcur, "Vcur", il);
+
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
+
+                // TODO: not tested, could be broken
+                cur = llm_build_kqv(ctx0, Q, hparams, kv_self,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
+                cb(cur, "kqv_out", il);
+            }
 
-            Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
-            cb(Kcur, "Kcur", il);
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
+            cb(ffn_inp, "ffn_inp", il);
 
-            struct ggml_tensor * Vcur = ggml_view_3d(
-                    ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                    ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
-                    );
-            cb(Vcur, "Vcur", il);
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-            // TODO: not tested, could be broken
-            cur = llm_build_kqv(lctx, ctx0, Q,
-                    model.layers[il].wo, model.layers[il].bo,
-                    Q, KQ_scale, KQ_mask, n_tokens, n_kv, -1.0f, cb, il);
-            cb(cur, "kqv_out", il);
+            inpL = cur;
         }
 
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
-        cb(ffn_inp, "ffn_inp", il);
+        cur = inpL;
 
-        // feed-forward network
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm,
-                    model.layers[il].ffn_norm_b,
-                    LLM_NORM, norm_eps, cb, il);
-            cb(cur, "ffn_norm", il);
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                    NULL,                      NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
-                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "l_out", il);
+        ggml_build_forward_expand(gf, cur);
 
-        inpL = cur;
+        return gf;
     }
 
-    cur = inpL;
-
-    cur = llm_build_norm(ctx0, cur,
-            model.output_norm,
-            model.output_norm_b,
-            LLM_NORM, norm_eps, cb, -1);
-    cb(cur, "result_norm", -1);
-
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
-
-    ggml_build_forward_expand(gf, cur);
-
-    ggml_free(ctx0);
-
-    return gf;
-}
-
-static struct ggml_cgraph * llm_build_refact(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
-
-    GGML_ASSERT(!!kv_self.ctx);
-
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_head_kv   = hparams.n_head_kv;
-    const int64_t n_embd_head = hparams.n_embd_head();
-
-    const float norm_rms_eps = hparams.f_norm_rms_eps;
-
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
+    struct ggml_cgraph * build_refact() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    auto & buf_compute = lctx.buf_compute;
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ true,
-    };
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    struct ggml_context * ctx0 = ggml_init(params);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * inpSA = inpL;
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
 
-        cur = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm, NULL,
-                LLM_NORM_RMS, norm_rms_eps, cb, il);
-        cb(cur, "attn_norm", il);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                cb(Kcur, "Kcur", il);
 
-        // self-attention
-        {
-            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                cb(Qcur, "Qcur", il);
 
-            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-            cb(Vcur, "Vcur", il);
+                cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self,
+                        model.layers[il].wo, NULL,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
+                cb(cur, "kqv_out", il);
+            }
 
-            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-            cb(Kcur, "Kcur", il);
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
 
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-            cb(Qcur, "Qcur", il);
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-            cur = llm_build_kqv(lctx, ctx0, Qcur,
-                    model.layers[il].wo, NULL,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il);
-            cb(cur, "kqv_out", il);
+            // input for next layer
+            inpL = cur;
         }
 
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
+        cur = inpL;
 
-        // feed-forward network
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, norm_rms_eps, cb, il);
-            cb(cur, "ffn_norm", il);
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "l_out", il);
+        ggml_build_forward_expand(gf, cur);
 
-        // input for next layer
-        inpL = cur;
+        return gf;
     }
 
-    cur = inpL;
-
-    cur = llm_build_norm(ctx0, cur,
-            model.output_norm, NULL,
-            LLM_NORM_RMS, norm_rms_eps, cb, -1);
-    cb(cur, "result_norm", -1);
-
-    // lm_head
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
-
-    ggml_build_forward_expand(gf, cur);
-
-    ggml_free(ctx0);
-
-    return gf;
-}
-
-static struct ggml_cgraph * llm_build_bloom(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
-
-    GGML_ASSERT(!!kv_self.ctx);
-
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-
-    GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-    const float norm_eps = hparams.f_norm_eps;
-
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
-
-    auto & buf_compute = lctx.buf_compute;
-
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ false,
-    };
+    struct ggml_cgraph * build_bloom() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    params.no_alloc = true;
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    struct ggml_context * ctx0 = ggml_init(params);
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
+        inpL = llm_build_norm(ctx0, inpL, hparams,
+                model.tok_norm,
+                model.tok_norm_b,
+                LLM_NORM, cb, -1);
+        cb(inpL, "inp_norm", -1);
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+            // self-attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-    inpL = llm_build_norm(ctx0, inpL,
-            model.tok_norm,
-            model.tok_norm_b,
-            LLM_NORM, norm_eps, cb, -1);
-    cb(inpL, "inp_norm", -1);
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
 
-    for (int il = 0; il < n_layer; ++il) {
-        cur = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm,
-                model.layers[il].attn_norm_b,
-                LLM_NORM, norm_eps, cb, il);
-        cb(cur, "attn_norm", il);
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-        // self-attention
-        {
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            cb(cur, "wqkv", il);
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
 
-            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-            cb(cur, "bqkv", il);
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
+                cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
+                cb(cur, "kqv_out", il);
+            }
 
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            // Add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            // FF
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-            cur = llm_build_kqv(lctx, ctx0, Qcur,
-                    model.layers[il].wo, model.layers[il].bo,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, 8.0f, cb, il);
-            cb(cur, "kqv_out", il);
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
         }
 
-        // Add the input
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-        cb(ffn_inp, "ffn_inp", il);
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
 
-        // FF
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm,
-                    model.layers[il].ffn_norm_b,
-                    LLM_NORM, norm_eps, cb, il);
-            cb(cur, "ffn_norm", il);
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                    NULL,                      NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b,
-                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        ggml_build_forward_expand(gf, cur);
 
-        inpL = ggml_add(ctx0, cur, ffn_inp);
-        cb(inpL, "l_out", il);
+        return gf;
     }
 
-    cur = llm_build_norm(ctx0, inpL,
-            model.output_norm,
-            model.output_norm_b,
-            LLM_NORM, norm_eps, cb, -1);
-    cb(cur, "result_norm", -1);
+    struct ggml_cgraph * build_mpt() {
+        struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-    ggml_build_forward_expand(gf, cur);
+        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
+        cb(inpL, "inp_embd", -1);
 
-    ggml_free(ctx0);
+        // KQ_scale
+        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+        cb(KQ_scale, "KQ_scale", -1);
 
-    return gf;
-}
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
+        cb(KQ_mask, "KQ_mask", -1);
 
-static struct ggml_cgraph * llm_build_mpt(
-         llama_context & lctx,
-     const llama_batch & batch,
-    const llm_build_cb & cb,
-                  bool   worst_case) {
-    const auto & model   = lctx.model;
-    const auto & hparams = model.hparams;
-    const auto & cparams = lctx.cparams;
-
-    const auto & kv_self = lctx.kv_self;
-
-    GGML_ASSERT(!!kv_self.ctx);
-
-    const int64_t n_embd      = hparams.n_embd;
-    const int64_t n_layer     = hparams.n_layer;
-    const int64_t n_ctx       = cparams.n_ctx;
-    const int64_t n_head      = hparams.n_head;
-    const int64_t n_embd_head = hparams.n_embd_head();
-    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
-
-    const float norm_eps       = hparams.f_norm_eps;
-    const float clamp_kqv      = hparams.f_clamp_kqv;
-    const float max_alibi_bias = hparams.f_max_alibi_bias;
-
-    const int32_t n_tokens = batch.n_tokens;
-    const int32_t n_kv     = worst_case ? n_ctx            : kv_self.n;
-    const int32_t kv_head  = worst_case ? n_ctx - n_tokens : kv_self.head;
-
-    auto & buf_compute = lctx.buf_compute;
-
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_compute.size,
-        /*.mem_buffer =*/ buf_compute.data,
-        /*.no_alloc   =*/ false,
-    };
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * attn_norm;
 
-    params.no_alloc = true;
-
-    struct ggml_context * ctx0 = ggml_init(params);
-
-    ggml_cgraph * gf = ggml_new_graph(ctx0);
-
-    struct ggml_tensor * cur;
-    struct ggml_tensor * inpL;
+            attn_norm = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    NULL,
+                    LLM_NORM, cb, il);
+            cb(attn_norm, "attn_norm", il);
 
-    inpL = llm_build_inp_embd(ctx0, batch, model.tok_embd, n_embd, n_tokens, cb);
-    cb(inpL, "inp_embd", -1);
+            // self-attention
+            {
+                cur = attn_norm;
 
-    // KQ_scale
-    struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
-    cb(KQ_scale, "KQ_scale", -1);
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
 
-    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-    struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-    cb(KQ_mask, "KQ_mask", -1);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    cb(cur, "wqkv_clamped", il);
+                }
 
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * attn_norm;
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
 
-        attn_norm = llm_build_norm(ctx0, inpL,
-                model.layers[il].attn_norm,
-                NULL,
-                LLM_NORM, norm_eps, cb, il);
-        cb(attn_norm, "attn_norm", il);
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
 
-        // self-attention
-        {
-            cur = attn_norm;
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-            cb(cur, "wqkv", il);
+                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-            if (clamp_kqv > 0.0f) {
-                cur = ggml_clamp(ctx0, cur, -clamp_kqv, clamp_kqv);
-                cb(cur, "wqkv_clamped", il);
+                cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self,
+                        model.layers[il].wo, NULL,
+                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il);
+                cb(cur, "kqv_out", il);
             }
 
-            struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-            struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
-
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
+            // Add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
 
-            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            // feed forward
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        NULL,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+            }
 
-            llm_build_kv_store(lctx, ctx0, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
 
-            cur = llm_build_kqv(lctx, ctx0, Qcur,
-                    model.layers[il].wo, NULL,
-                    Qcur, KQ_scale, KQ_mask, n_tokens, n_kv, max_alibi_bias, cb, il);
-            cb(cur, "kqv_out", il);
+            // input for next layer
+            inpL = cur;
         }
 
-        // Add the input
-        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-        cb(ffn_inp, "ffn_inp", il);
+        cur = inpL;
 
-        // feed forward
-        {
-            cur = llm_build_norm(ctx0, ffn_inp,
-                    model.layers[il].ffn_norm,
-                    NULL,
-                    LLM_NORM, norm_eps, cb, il);
-            cb(cur, "ffn_norm", il);
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm,
+                NULL,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
 
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    NULL,                      NULL,
-                    model.layers[il].ffn_down, NULL,
-                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
-            cb(cur, "ffn_out", il);
-        }
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
 
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "l_out", il);
+        ggml_build_forward_expand(gf, cur);
 
-        // input for next layer
-        inpL = cur;
+        return gf;
     }
-
-    cur = inpL;
-
-    cur = llm_build_norm(ctx0, cur,
-            model.output_norm,
-            NULL,
-            LLM_NORM, norm_eps, cb, -1);
-    cb(cur, "result_norm", -1);
-
-    cur = ggml_mul_mat(ctx0, model.output, cur);
-    cb(cur, "result_output", -1);
-
-    ggml_build_forward_expand(gf, cur);
-
-    ggml_free(ctx0);
-
-    return gf;
-}
+};
 
 //
 // tensor offloading helpers
@@ -5122,43 +4862,49 @@ static struct ggml_cgraph * llama_build_graph(
 
     struct ggml_cgraph * result = NULL;
 
+    struct llm_build_context llm(lctx, batch, cb, worst_case);
+
+    llm.init();
+
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
             {
-                result = llm_build_llama(lctx, batch, cb, worst_case);
+                result = llm.build_llama();
             } break;
         case LLM_ARCH_BAICHUAN:
             {
-                result = llm_build_baichaun(lctx, batch, cb, worst_case);
+                result = llm.build_baichuan();
             } break;
         case LLM_ARCH_FALCON:
             {
-                result = llm_build_falcon(lctx, batch, cb, worst_case);
+                result = llm.build_falcon();
             } break;
         case LLM_ARCH_STARCODER:
             {
-                result = llm_build_starcoder(lctx, batch, cb, worst_case);
+                result = llm.build_starcoder();
             } break;
         case LLM_ARCH_PERSIMMON:
             {
-                result = llm_build_persimmon(lctx, batch, cb, worst_case);
+                result = llm.build_persimmon();
             } break;
         case LLM_ARCH_REFACT:
             {
-                result = llm_build_refact(lctx, batch, cb, worst_case);
+                result = llm.build_refact();
             } break;
         case LLM_ARCH_BLOOM:
             {
-                result = llm_build_bloom(lctx, batch, cb, worst_case);
+                result = llm.build_bloom();
             } break;
         case LLM_ARCH_MPT:
             {
-                result = llm_build_mpt(lctx, batch, cb, worst_case);
+                result = llm.build_mpt();
             } break;
         default:
             GGML_ASSERT(false);
     }
 
+    llm.free();
+
     if (worst_case) {
         int n_non_view_total = 0;
 

From ff8f9a88da0018972dfdf6fe64b5c8992caabd9c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 21:15:55 +0200
Subject: [PATCH 44/79] common : minor (#3715)

---
 common/common.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 89be4126185db5..7a48e9d11e8593 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -110,8 +110,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             exit(0);
         }
     }
-    catch (const std::invalid_argument& ex) {
-        fprintf(stderr, ex.what());
+    catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
         gpt_print_usage(argc, argv, gpt_params());
         exit(1);
     }

From e16b9fa4baa8a09c6619b116159830e898050942 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 21:25:00 +0200
Subject: [PATCH 45/79] metal : multi-simd softmax (#3710)

ggml-ci
---
 ggml-metal.m     |   9 +++-
 ggml-metal.metal | 129 +++++++++++++++++++++++++++++++++++++----------
 2 files changed, 108 insertions(+), 30 deletions(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index bc881395a7aadc..1f034150788e26 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1001,11 +1001,15 @@ void ggml_metal_graph_compute(
                         } break;
                     case GGML_OP_SOFT_MAX:
                         {
-                            const int nth = MIN(32, ne00);
+                            int nth = 32; // SIMD width
 
                             if (ne00%4 == 0) {
                                 [encoder setComputePipelineState:ctx->pipeline_soft_max_4];
                             } else {
+                                do {
+                                    nth *= 2;
+                                } while (nth <= ne00 && nth <= 1024);
+                                nth /= 2;
                                 [encoder setComputePipelineState:ctx->pipeline_soft_max];
                             }
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@@ -1013,8 +1017,9 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
                             [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
                             [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+                            [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0];
 
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
                     case GGML_OP_DIAG_MASK_INF:
                         {
diff --git a/ggml-metal.metal b/ggml-metal.metal
index f4b460564453c5..f3152778ae48c3 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -184,36 +184,73 @@ kernel void kernel_soft_max(
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
+        threadgroup float  * buf [[threadgroup(0)]],
+        uint  tgpig[[threadgroup_position_in_grid]],
+        uint  tpitg[[thread_position_in_threadgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint    ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = (tgpig) / (ne02*ne01);
+    const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
+    const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
 
     device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
     device       float * pdst  = dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
 
     // parallel max
-    float lmax = tpitg[0] < ne00 ? psrc0[tpitg[0]] : -INFINITY;
-    for (int i00 = tpitg[0] + ntg[0]; i00 < ne00; i00 += ntg[0]) {
+    float lmax = tpitg < ne00 ? psrc0[tpitg] : -INFINITY;
+
+    for (int i00 = tpitg + ntg; i00 < ne00; i00 += ntg) {
         lmax = MAX(lmax, psrc0[i00]);
     }
-    const float max = simd_max(lmax);
+
+    float max = simd_max(lmax);
+    if (tiisg == 0) {
+        buf[sgitg] = max;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]);
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    max = buf[0];
 
     // parallel sum
     float lsum = 0.0f;
-    for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
+    for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
         const float exp_psrc0 = exp(psrc0[i00] - max);
         lsum += exp_psrc0;
         // Remember the result of exp here. exp is expensive, so we really do not
-        // whish to compute it twice.
+        // wish to compute it twice.
         pdst[i00] = exp_psrc0;
     }
 
-    const float sum = simd_sum(lsum);
+    float sum = simd_sum(lsum);
+    if (tiisg == 0) {
+        buf[sgitg] = sum;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] += buf[tpitg + i];
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    sum = buf[0];
 
-    for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
+    for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
         pdst[i00] /= sum;
     }
 }
@@ -224,37 +261,73 @@ kernel void kernel_soft_max_4(
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
+        threadgroup float  * buf [[threadgroup(0)]],
+        uint  tgpig[[threadgroup_position_in_grid]],
+        uint  tpitg[[thread_position_in_threadgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint    ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = (tgpig) / (ne02*ne01);
+    const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
+    const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
 
     device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
     device       float4 * pdst4 = (device       float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
 
     // parallel max
-    float4 lmax4 = tpitg[0] < ne00/4 ? psrc4[tpitg[0]] : -INFINITY;
-    for (int i00 = tpitg[0] + ntg[0]; i00 < ne00/4; i00 += ntg[0]) {
+    float4 lmax4 = tpitg < ne00/4 ? psrc4[tpitg] : -INFINITY;
+
+    for (int i00 = tpitg + ntg; i00 < ne00/4; i00 += ntg) {
         lmax4 = fmax(lmax4, psrc4[i00]);
     }
-    float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
 
-    const float max = simd_max(lmax);
+    const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
+    float max = simd_max(lmax);
+    if (tiisg == 0) {
+        buf[sgitg] = max;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] = MAX(buf[tpitg], buf[tpitg + i]);
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    max = buf[0];
 
     // parallel sum
     float4 lsum4 = 0.0f;
-    for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) {
+    for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
         const float4 exp_psrc4 = exp(psrc4[i00] - max);
         lsum4 += exp_psrc4;
         pdst4[i00] = exp_psrc4;
     }
-    float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
 
-    const float sum = simd_sum(lsum);
+    const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
+    float sum = simd_sum(lsum);
+    if (tiisg == 0) {
+        buf[sgitg] = sum;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // broadcast, simd group number is ntg / 32
+    for (uint i = ntg / 32 / 2; i > 0; i /= 2) {
+       if (tpitg < i) {
+           buf[tpitg] += buf[tpitg + i];
+       }
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    sum = buf[0];
 
-    for (int i00 = tpitg[0]; i00 < ne00/4; i00 += ntg[0]) {
+    for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
         pdst4[i00] /= sum;
     }
 }
@@ -274,7 +347,7 @@ kernel void kernel_diag_mask_inf(
         dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY;
     } else {
         dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00];
-     }
+    }
 }
 
 kernel void kernel_diag_mask_inf_8(

From 523e49b11174368cd73460fa5eae7b39d856f300 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 23:00:50 +0200
Subject: [PATCH 46/79] llm : fix falcon norm after refactoring (#3837)

---
 llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/llama.cpp b/llama.cpp
index d0c4ef10151828..17cf364bb20eac 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3763,7 +3763,7 @@ struct llm_build_context {
             {
                 if (model.layers[il].attn_norm_2) {
                     // Falcon-40B
-                    cur = llm_build_norm(ctx0, attn_norm, hparams,
+                    cur = llm_build_norm(ctx0, inpL, hparams,
                             model.layers[il].attn_norm_2,
                             model.layers[il].attn_norm_2_b,
                             LLM_NORM, cb, il);

From c43c2da8afacaddfe51c09b21dbd9922cd0ea46b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 1 Nov 2023 23:08:30 +0200
Subject: [PATCH 47/79] llm : fix llm_build_kqv taking unused tensor (benign,
 #3837)

---
 llama.cpp | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 17cf364bb20eac..1c6d482f8fe1bc 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -3345,7 +3345,6 @@ static struct ggml_tensor * llm_build_ffn(
 // if max_alibi_bias > 0 then apply ALiBi
 static struct ggml_tensor * llm_build_kqv(
         struct ggml_context * ctx,
-         struct ggml_tensor * cur,
         const llama_hparams & hparams,
        const llama_kv_cache & kv,
          struct ggml_tensor * wo,
@@ -3411,7 +3410,7 @@ static struct ggml_tensor * llm_build_kqv(
     struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
     cb(kqv_merged, "kqv_merged", il);
 
-    cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens);
+    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens);
     cb(cur, "kqv_merged_cont", il);
 
     cur = ggml_mul_mat(ctx, wo, cur);
@@ -3565,7 +3564,7 @@ struct llm_build_context {
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-                cur = llm_build_kqv(ctx0, cur, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, NULL,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                 cb(cur, "kqv_out", il);
@@ -3677,7 +3676,7 @@ struct llm_build_context {
                 // apply ALiBi for 13B model
                 const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f;
 
-                cur = llm_build_kqv(ctx0, cur, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, NULL,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il);
                 cb(cur, "kqv_out", il);
@@ -3795,7 +3794,7 @@ struct llm_build_context {
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-                cur = llm_build_kqv(ctx0, attn_norm, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, NULL,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                 cb(cur, "kqv_out", il);
@@ -3895,7 +3894,7 @@ struct llm_build_context {
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-                cur = llm_build_kqv(ctx0, cur, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, model.layers[il].bo,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                 cb(cur, "kqv_out", il);
@@ -4100,7 +4099,7 @@ struct llm_build_context {
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
                 // TODO: not tested, could be broken
-                cur = llm_build_kqv(ctx0, Q, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, model.layers[il].bo,
                         Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                 cb(cur, "kqv_out", il);
@@ -4191,7 +4190,7 @@ struct llm_build_context {
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-                cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, NULL,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
                 cb(cur, "kqv_out", il);
@@ -4288,7 +4287,7 @@ struct llm_build_context {
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-                cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, model.layers[il].bo,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
                 cb(cur, "kqv_out", il);
@@ -4382,7 +4381,7 @@ struct llm_build_context {
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
 
-                cur = llm_build_kqv(ctx0, Qcur, hparams, kv_self,
+                cur = llm_build_kqv(ctx0, hparams, kv_self,
                         model.layers[il].wo, NULL,
                         Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il);
                 cb(cur, "kqv_out", il);

From 898aeca90a9bb992f506234cf3b8b7f7fa28a1df Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Wed, 1 Nov 2023 18:04:33 -0400
Subject: [PATCH 48/79] llama : implement YaRN RoPE scaling (#2268)

Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
Co-authored-by: Jeffrey Quesnelle <jquesnelle@gmail.com>
---
 common/common.cpp                             |  79 +++++-
 common/common.h                               |   7 +
 convert-baichuan-hf-to-gguf.py                |   3 +-
 convert.py                                    |  97 ++++---
 examples/finetune/finetune.cpp                |   5 +-
 examples/server/server.cpp                    |  59 ++++-
 .../train-text-from-scratch.cpp               |   6 +-
 ggml-cuda.cu                                  | 153 ++++++++---
 ggml-metal.m                                  |  24 +-
 ggml-metal.metal                              |  61 ++++-
 ggml.c                                        | 241 +++++++++++++-----
 ggml.h                                        |  20 +-
 gguf-py/gguf/gguf.py                          |  29 ++-
 llama.cpp                                     | 220 ++++++++++++----
 llama.h                                       |  18 +-
 15 files changed, 764 insertions(+), 258 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 7a48e9d11e8593..b182ffaaef48ec 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -219,12 +219,52 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.rope_freq_scale = std::stof(argv[i]);
+        } else if (arg == "--rope-scaling") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::string value(argv[i]);
+            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
+            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
+            else { invalid_param = true; break; }
         } else if (arg == "--rope-scale") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_scale = 1.0f/std::stof(argv[i]);
+        } else if (arg == "--yarn-orig-ctx") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_orig_ctx = std::stoi(argv[i]);
+        } else if (arg == "--yarn-ext-factor") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_ext_factor = std::stof(argv[i]);
+        } else if (arg == "--yarn-attn-factor") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_attn_factor = std::stof(argv[i]);
+        } else if (arg == "--yarn-beta-fast") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_beta_fast = std::stof(argv[i]);
+        } else if (arg == "--yarn-beta-slow") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_beta_slow = std::stof(argv[i]);
         } else if (arg == "--memory-f32") {
             params.memory_f16 = false;
         } else if (arg == "--top-p") {
@@ -716,9 +756,16 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --cfg-negative-prompt-file FNAME\n");
     printf("                        negative prompt file to use for guidance. (default: empty)\n");
     printf("  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale);
-    printf("  --rope-scale N        RoPE context linear scaling factor, inverse of --rope-freq-scale\n");
+    printf("  --rope-scaling {none,linear,yarn}\n");
+    printf("                        RoPE frequency scaling method, defaults to linear unless specified by the model\n");
+    printf("  --rope-scale N        RoPE context scaling factor, expands context by a factor of N\n");
     printf("  --rope-freq-base N    RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n");
-    printf("  --rope-freq-scale N   RoPE frequency linear scaling factor (default: loaded from model)\n");
+    printf("  --rope-freq-scale N   RoPE frequency scaling factor, expands context by a factor of 1/N\n");
+    printf("  --yarn-orig-ctx N     YaRN: original context size of model (default: 0 = model training context size)\n");
+    printf("  --yarn-ext-factor N   YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
+    printf("  --yarn-attn-factor N  YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
+    printf("  --yarn-beta-slow N    YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
+    printf("  --yarn-beta-fast N    YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
     printf("  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
     printf("  --no-penalize-nl      do not penalize newline token\n");
     printf("  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
@@ -826,17 +873,23 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
     auto cparams = llama_context_default_params();
 
-    cparams.n_ctx           = params.n_ctx;
-    cparams.n_batch         = params.n_batch;
-    cparams.n_threads       = params.n_threads;
-    cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
-    cparams.mul_mat_q       = params.mul_mat_q;
-    cparams.seed            = params.seed;
-    cparams.f16_kv          = params.memory_f16;
-    cparams.logits_all      = params.logits_all;
-    cparams.embedding       = params.embedding;
-    cparams.rope_freq_base  = params.rope_freq_base;
-    cparams.rope_freq_scale = params.rope_freq_scale;
+    cparams.n_ctx             = params.n_ctx;
+    cparams.n_batch           = params.n_batch;
+    cparams.n_threads         = params.n_threads;
+    cparams.n_threads_batch   = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    cparams.mul_mat_q         = params.mul_mat_q;
+    cparams.seed              = params.seed;
+    cparams.f16_kv            = params.memory_f16;
+    cparams.logits_all        = params.logits_all;
+    cparams.embedding         = params.embedding;
+    cparams.rope_scaling_type = params.rope_scaling_type;
+    cparams.rope_freq_base    = params.rope_freq_base;
+    cparams.rope_freq_scale   = params.rope_freq_scale;
+    cparams.yarn_ext_factor   = params.yarn_ext_factor;
+    cparams.yarn_attn_factor  = params.yarn_attn_factor;
+    cparams.yarn_beta_fast    = params.yarn_beta_fast;
+    cparams.yarn_beta_slow    = params.yarn_beta_slow;
+    cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
 
     return cparams;
 }
diff --git a/common/common.h b/common/common.h
index 343b272177c7ec..7be69f925bc2be 100644
--- a/common/common.h
+++ b/common/common.h
@@ -9,6 +9,7 @@
 #define LOG_NO_FILE_LINE_FUNCTION
 #include "log.h"
 
+#include <cmath>
 #include <string>
 #include <vector>
 #include <random>
@@ -54,6 +55,12 @@ struct gpt_params {
     int32_t n_beams                         = 0;    // if non-zero then use beam search of given width.
     float   rope_freq_base                  = 0.0f; // RoPE base frequency
     float   rope_freq_scale                 = 0.0f; // RoPE frequency scaling factor
+    float   yarn_ext_factor                 = NAN;  // YaRN extrapolation mix factor
+    float   yarn_attn_factor                = 1.0f; // YaRN magnitude scaling factor
+    float   yarn_beta_fast                  = 32.0f;// YaRN low correction dim
+    float   yarn_beta_slow                  = 1.0f; // YaRN high correction dim
+    int32_t yarn_orig_ctx                   = 0;    // YaRN original context length
+    int8_t  rope_scaling_type               = LLAMA_ROPE_SCALING_UNSPECIFIED;
 
     // // sampling parameters
     struct llama_sampling_params sparams;
diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py
index 5ee99be73134e6..67ccbe99f132af 100755
--- a/convert-baichuan-hf-to-gguf.py
+++ b/convert-baichuan-hf-to-gguf.py
@@ -163,7 +163,8 @@ def parse_args() -> argparse.Namespace:
 if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
     if "type" in hparams["rope_scaling"]:
         if hparams["rope_scaling"]["type"] == "linear":
-            gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
+            gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+            gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
 
 
 # TOKENIZATION
diff --git a/convert.py b/convert.py
index bfbfab283f6ae7..9110f15806c6bc 100755
--- a/convert.py
+++ b/convert.py
@@ -151,8 +151,11 @@ class Params:
     n_head_kv:  int
     f_norm_eps: float
 
+    rope_scaling_type: gguf.RopeScalingType | None = None
     f_rope_freq_base: float | None = None
     f_rope_scale: float | None = None
+    n_orig_ctx: int | None = None
+    rope_finetuned: bool | None = None
 
     ftype: GGMLFileType | None = None
 
@@ -198,20 +201,20 @@ def guessed(model: LazyModel) -> Params:
     def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
         config = json.load(open(config_path))
 
-        n_vocab          = config["vocab_size"]
-        n_embd           = config["hidden_size"]
-        n_layer          = config["num_hidden_layers"]
-        n_ff             = config["intermediate_size"]
-        n_head           = config["num_attention_heads"]
-        n_head_kv        = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
-        f_norm_eps       = config["rms_norm_eps"]
-        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
-
+        rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
         rope_scaling = config.get("rope_scaling")
-        if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear":
-            f_rope_scale = config["rope_scaling"].get("factor")
-        else:
-            f_rope_scale = None
+
+        if rope_scaling is not None and (typ := rope_scaling.get("type")):
+            rope_factor = rope_scaling.get("factor")
+            f_rope_scale = rope_factor
+            if typ == "linear":
+                rope_scaling_type = gguf.RopeScalingType.LINEAR
+            elif typ == "yarn":
+                rope_scaling_type = gguf.RopeScalingType.YARN
+                n_orig_ctx = rope_scaling['original_max_position_embeddings']
+                rope_finetuned = rope_scaling['finetuned']
+            else:
+                raise NotImplementedError(f'Unknown rope scaling type: {typ}')
 
         if "max_sequence_length" in config:
             n_ctx = config["max_sequence_length"]
@@ -222,16 +225,19 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
                             "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
 
         return Params(
-            n_vocab          = n_vocab,
-            n_embd           = n_embd,
-            n_layer          = n_layer,
-            n_ctx            = n_ctx,
-            n_ff             = n_ff,
-            n_head           = n_head,
-            n_head_kv        = n_head_kv,
-            f_norm_eps       = f_norm_eps,
-            f_rope_freq_base = f_rope_freq_base,
-            f_rope_scale     = f_rope_scale,
+            n_vocab           = config["vocab_size"],
+            n_embd            = config["hidden_size"],
+            n_layer           = config["num_hidden_layers"],
+            n_ctx             = n_ctx,
+            n_ff              = config["intermediate_size"],
+            n_head            = (n_head := config["num_attention_heads"]),
+            n_head_kv         = config.get("num_key_value_heads", n_head),
+            f_norm_eps        = config["rms_norm_eps"],
+            f_rope_freq_base  = config.get("rope_theta"),
+            rope_scaling_type = rope_scaling_type,
+            f_rope_scale      = f_rope_scale,
+            n_orig_ctx        = n_orig_ctx,
+            rope_finetuned    = rope_finetuned,
         )
 
     # LLaMA v2 70B params.json
@@ -240,17 +246,8 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
     def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
         config = json.load(open(config_path))
 
-        n_vocab          = config["vocab_size"] if "vocab_size" in config else -1
-        n_embd           = config["dim"]
-        n_layer          = config["n_layers"]
-        n_ff             = -1
-        n_head           = config["n_heads"]
-        n_head_kv        = config["n_kv_heads"] if "n_kv_heads" in config else n_head
-        f_norm_eps       = config["norm_eps"]
-        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
-
         # hack to determine LLaMA v1 vs v2 vs CodeLlama
-        if f_rope_freq_base == 1000000:
+        if config.get("rope_theta") == 1000000:
             # CodeLlama
             n_ctx = 16384
         elif config["norm_eps"] == 1e-05:
@@ -260,22 +257,16 @@ def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
             # LLaMA v1
             n_ctx = 2048
 
-        if n_vocab == -1:
-            n_vocab = model["tok_embeddings.weight"].shape[0]
-
-        if n_ff == -1:
-            n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
-
         return Params(
-            n_vocab          = n_vocab,
-            n_embd           = n_embd,
-            n_layer          = n_layer,
+            n_vocab          = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
+            n_embd           = config["dim"],
+            n_layer          = config["n_layers"],
             n_ctx            = n_ctx,
-            n_ff             = n_ff,
-            n_head           = n_head,
-            n_head_kv        = n_head_kv,
-            f_norm_eps       = f_norm_eps,
-            f_rope_freq_base = f_rope_freq_base,
+            n_ff             = model["layers.0.feed_forward.w1.weight"].shape[0],
+            n_head           = (n_head := config["n_heads"]),
+            n_head_kv        = config.get("n_kv_heads", n_head),
+            f_norm_eps       = config["norm_eps"],
+            f_rope_freq_base = config.get("rope_theta"),
         )
 
     @staticmethod
@@ -831,8 +822,16 @@ def add_meta_arch(self, params: Params) -> None:
         if params.f_rope_freq_base is not None:
             self.gguf.add_rope_freq_base(params.f_rope_freq_base)
 
-        if params.f_rope_scale is not None:
-            self.gguf.add_rope_scale_linear(params.f_rope_scale)
+        if params.rope_scaling_type:
+            assert params.f_rope_scale is not None
+            self.gguf.add_rope_scaling_type(params.rope_scaling_type)
+            self.gguf.add_rope_scaling_factor(params.f_rope_scale)
+
+        if params.n_orig_ctx is not None:
+            self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)
+
+        if params.rope_finetuned is not None:
+            self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)
 
         if params.ftype is not None:
             self.gguf.add_file_type(params.ftype)
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index 60c7faa797028a..649a3b7c1941e5 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -642,8 +642,9 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
         const int rope_mode = 0;
 
         return ggml_rope_custom(ctx,
-            t, KQ_pos, n_rot, rope_mode, n_ctx,
-            rope_freq_base, rope_freq_scale);
+            t, KQ_pos, n_rot, rope_mode, n_ctx, 0,
+            rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f
+        );
     };
 
     set_name(tokens_input, "tokens_input");
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 47ae0d55856cf8..84b04d5a0493a6 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1755,12 +1755,18 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("options:\n");
     printf("  -h, --help                show this help message and exit\n");
     printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
-    printf("  -t N,  --threads N        number of threads to use during computation (default: %d)\n", params.n_threads);
+    printf("  -t N, --threads N         number of threads to use during computation (default: %d)\n", params.n_threads);
     printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
-    printf("  -c N,  --ctx-size N       size of the prompt context (default: %d)\n", params.n_ctx);
+    printf("  -c N, --ctx-size N        size of the prompt context (default: %d)\n", params.n_ctx);
+    printf("  --rope-scaling {none,linear,yarn}\n");
+    printf("                            RoPE frequency scaling method, defaults to linear unless specified by the model\n");
     printf("  --rope-freq-base N        RoPE base frequency (default: loaded from model)\n");
-    printf("  --rope-freq-scale N       RoPE frequency scaling factor (default: loaded from model)\n");
-    printf("  -b N,  --batch-size N     batch size for prompt processing (default: %d)\n", params.n_batch);
+    printf("  --rope-freq-scale N       RoPE frequency scaling factor, expands context by a factor of 1/N\n");
+    printf("  --yarn-ext-factor N       YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
+    printf("  --yarn-attn-factor N      YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
+    printf("  --yarn-beta-slow N        YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
+    printf("  --yarn-beta-fast N        YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
+    printf("  -b N, --batch-size N      batch size for prompt processing (default: %d)\n", params.n_batch);
     printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
     printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
     if (llama_mlock_supported())
@@ -1881,6 +1887,19 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.n_ctx = std::stoi(argv[i]);
         }
+        else if (arg == "--rope-scaling")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+            std::string value(argv[i]);
+            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
+            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
+            else { invalid_param = true; break; }
+        }
         else if (arg == "--rope-freq-base")
         {
             if (++i >= argc)
@@ -1899,6 +1918,38 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.rope_freq_scale = std::stof(argv[i]);
         }
+        else if (arg == "--yarn-ext-factor")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_ext_factor = std::stof(argv[i]);
+        }
+        else if (arg == "--yarn-attn-factor")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_attn_factor = std::stof(argv[i]);
+        }
+        else if (arg == "--yarn-beta-fast")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_beta_fast = std::stof(argv[i]);
+        }
+        else if (arg == "--yarn-beta-slow")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.yarn_beta_slow = std::stof(argv[i]);
+        }
         else if (arg == "--memory-f32" || arg == "--memory_f32")
         {
             params.memory_f16 = false;
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 1ce6cef29cfd06..2a257e63215e3c 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -349,9 +349,9 @@ static struct ggml_tensor * llama_build_train_graphs(
         // not capturing these, to silcence warnings
         const int rope_mode = 0;
 
-        return ggml_rope_custom(ctx,
-            t, KQ_pos, n_rot, rope_mode, n_ctx,
-            rope_freq_base, rope_freq_scale);
+        return ggml_rope_custom(
+            ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
+        );
     };
 
     set_name(tokens_input, "tokens_input");
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 4e6e7cd94892b1..12ee10e3d9bdcd 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -4493,11 +4493,41 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
     cpy_1(cx + x_offset, cdst + dst_offset);
 }
 
-// rope == RoPE == rotary positional embedding
+static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+struct rope_corr_dims {
+    float v[4];
+};
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static __device__ void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
 
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+    *cos_theta = cosf(theta) * mscale;
+    *sin_theta = sinf(theta) * mscale;
+}
+
+// rope == RoPE == rotary positional embedding
 template<typename T, bool has_pos>
-static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale,
-                            const int p_delta_rows, const float theta_scale) {
+static __global__ void rope(
+    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims
+) {
     const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (col >= ncols) {
@@ -4509,10 +4539,10 @@ static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t
     const int i2 = row/p_delta_rows;
 
     const int p = has_pos ? pos[i2] : 0;
-    const float p0 = p*freq_scale;
-    const float theta = p0*powf(theta_scale, col/2);
-    const float sin_theta = sinf(theta);
-    const float cos_theta = cosf(theta);
+    const float theta_base = p*powf(freq_base, -col/ncols);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
     const float x0 = x[i + 0];
     const float x1 = x[i + 1];
@@ -4522,8 +4552,10 @@ static __global__ void rope(const T * x, T * dst, const int ncols, const int32_t
 }
 
 template<typename T, bool has_pos>
-static __global__ void rope_neox(const T * x, T * dst, const int ncols, const int32_t * pos, const float freq_scale,
-                                 const int p_delta_rows, const float theta_scale) {
+static __global__ void rope_neox(
+    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims
+) {
     const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (col >= ncols) {
@@ -4534,11 +4566,14 @@ static __global__ void rope_neox(const T * x, T * dst, const int ncols, const in
     const int i = row*ncols + col/2;
     const int i2 = row/p_delta_rows;
 
+    // simplified from `(row * ncols + col) * (-1 / ncols)`
+    const float cur_rot = -col/ncols - row;
+
     const int p = has_pos ? pos[i2] : 0;
-    const float p0 = p*freq_scale;
-    const float theta = p0*powf(theta_scale, col/2);
-    const float sin_theta = sinf(theta);
-    const float cos_theta = cosf(theta);
+    const float theta_base = p*powf(freq_base, cur_rot);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
     const float x0 = x[i + 0];
     const float x1 = x[i + ncols/2];
@@ -4547,8 +4582,10 @@ static __global__ void rope_neox(const T * x, T * dst, const int ncols, const in
     dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
 }
 
-static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const int32_t * pos, const float freq_scale,
-                                    const int p_delta_rows, const float theta_scale, const int n_ctx) {
+static __global__ void rope_glm_f32(
+    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    int n_ctx
+) {
     const int col = blockDim.x*blockIdx.x + threadIdx.x;
     const int half_n_dims = ncols/4;
 
@@ -4560,7 +4597,7 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
     const int i = row*ncols + col;
     const int i2 = row/p_delta_rows;
 
-    const float col_theta_scale = powf(theta_scale, col);
+    const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
      // FIXME: this is likely wrong
     const int p = pos != nullptr ? pos[i2] : 0;
 
@@ -5584,40 +5621,54 @@ static void clamp_f32_cuda(const float * x, float * dst, const float min, const
 }
 
 template<typename T>
-static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
-                          const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+static void rope_cuda(
+    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
     GGML_ASSERT(ncols % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
     if (pos == nullptr) {
-        rope<T, false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+        rope<T, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
     } else {
-        rope<T, true><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+        rope<T, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
     }
 }
 
 template<typename T>
-static void rope_neox_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
-                          const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+static void rope_neox_cuda(
+    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
     GGML_ASSERT(ncols % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
     const dim3 block_nums(nrows, num_blocks_x, 1);
     if (pos == nullptr) {
-        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
     } else {
-        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale);
+        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
     }
 }
 
-static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
-                              const int p_delta_rows, const float theta_scale, const int n_ctx, cudaStream_t stream) {
+static void rope_glm_f32_cuda(
+    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, int n_ctx, cudaStream_t stream
+) {
     GGML_ASSERT(ncols % 4 == 0);
     const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
     const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
     const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, theta_scale, n_ctx);
+    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
 }
 
 static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
@@ -6477,17 +6528,20 @@ inline void ggml_cuda_op_rope(
     const int64_t ne2 = dst->ne[2];
     const int64_t nrows = ggml_nrows(src0);
 
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_dims = ((int32_t *) dst->op_params)[1];
-    const int mode   = ((int32_t *) dst->op_params)[2];
-    const int n_ctx  = ((int32_t *) dst->op_params)[3];
-    // RoPE alteration for extended context
-
-    float freq_base, freq_scale;
-    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
-    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+    //const int n_past      = ((int32_t *) dst->op_params)[0];
+    const int n_dims      = ((int32_t *) dst->op_params)[1];
+    const int mode        = ((int32_t *) dst->op_params)[2];
+    const int n_ctx       = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
 
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    // RoPE alteration for extended context
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
 
     const int32_t * pos = nullptr;
     if ((mode & 1) == 0) {
@@ -6499,24 +6553,39 @@ inline void ggml_cuda_op_rope(
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
 
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
+
     // compute
     if (is_glm) {
         GGML_ASSERT(false);
-        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, n_ctx, main_stream);
+        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
     } else if (is_neox) {
         GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
         if (src0->type == GGML_TYPE_F32) {
-            rope_neox_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+            rope_neox_cuda(
+                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, main_stream
+            );
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+            rope_neox_cuda(
+                (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, main_stream
+            );
         } else {
             GGML_ASSERT(false);
         }
     } else {
         if (src0->type == GGML_TYPE_F32) {
-            rope_cuda((const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+            rope_cuda(
+                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, main_stream
+            );
         } else if (src0->type == GGML_TYPE_F16) {
-            rope_cuda((const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, theta_scale, main_stream);
+            rope_cuda(
+                (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, main_stream
+            );
         } else {
             GGML_ASSERT(false);
         }
diff --git a/ggml-metal.m b/ggml-metal.m
index 1f034150788e26..611d5e173681eb 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1400,14 +1400,18 @@ void ggml_metal_graph_compute(
 
                             const int nth = MIN(1024, ne00);
 
-                            const int n_past = ((int32_t *) dst->op_params)[0];
-                            const int n_dims = ((int32_t *) dst->op_params)[1];
-                            const int mode   = ((int32_t *) dst->op_params)[2];
-
-                            float freq_base;
-                            float freq_scale;
-                            memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
-                            memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+                            const int n_past     = ((int32_t *) dst->op_params)[0];
+                            const int n_dims     = ((int32_t *) dst->op_params)[1];
+                            const int mode       = ((int32_t *) dst->op_params)[2];
+                            const int n_orig_ctx = ((int32_t *) dst->op_params)[3];
+
+                            float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+                            memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+                            memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+                            memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+                            memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+                            memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+                            memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
 
                             switch (src0->type) {
                                 case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break;
@@ -1439,6 +1443,10 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&mode    length:sizeof(     int) atIndex:21];
                             [encoder setBytes:&freq_base  length:sizeof(float) atIndex:22];
                             [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23];
+                            [encoder setBytes:&ext_factor  length:sizeof(float) atIndex:24];
+                            [encoder setBytes:&attn_factor length:sizeof(float) atIndex:25];
+                            [encoder setBytes:&beta_fast   length:sizeof(float) atIndex:26];
+                            [encoder setBytes:&beta_slow   length:sizeof(float) atIndex:27];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
diff --git a/ggml-metal.metal b/ggml-metal.metal
index f3152778ae48c3..471d7d390f8138 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -1061,6 +1061,45 @@ kernel void kernel_alibi_f32(
     }
 }
 
+static float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static void rope_yarn(
+    float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+    *cos_theta = cosf(theta) * mscale;
+    *sin_theta = sinf(theta) * mscale;
+}
+
+// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
+// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
+static float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) {
+    return n_dims * log(n_orig_ctx / (n_rot * 2 * M_PI_F)) / (2 * log(base));
+}
+
+static void rope_yarn_corr_dims(
+    int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
+) {
+    // start and end correction dims
+    dims[0] = max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base)));
+    dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base)));
+}
+
 typedef void (rope_t)(
         device const    void * src0,
         device const int32_t * src1,
@@ -1116,6 +1155,10 @@ kernel void kernel_rope(
         constant         int & mode,
         constant       float & freq_base,
         constant       float & freq_scale,
+        constant       float & ext_factor,
+        constant       float & attn_factor,
+        constant       float & beta_fast,
+        constant       float & beta_slow,
         uint  tiitg[[thread_index_in_threadgroup]],
         uint3 tptg[[threads_per_threadgroup]],
         uint3 tgpig[[threadgroup_position_in_grid]]) {
@@ -1125,19 +1168,22 @@ kernel void kernel_rope(
 
     const bool is_neox = mode & 2;
 
+    float corr_dims[2];
+    rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+
     device const int32_t * pos = src1;
 
     const int64_t p = pos[i2];
 
-    const float theta_0 = freq_scale * (float)p;
+    const float theta_0 = (float)p;
     const float inv_ndims = -1.f/n_dims;
 
     if (!is_neox) {
         for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
 
             const float theta = theta_0 * pow(freq_base, inv_ndims*i0);
-            const float cos_theta = cos(theta);
-            const float sin_theta = sin(theta);
+            float cos_theta, sin_theta;
+            rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
             device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
             device       T * dst_data  = (device T *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
@@ -1152,9 +1198,12 @@ kernel void kernel_rope(
         for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
             for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) {
 
-                const float theta = theta_0 * pow(freq_base, inv_ndims*ic - ib);
-                const float cos_theta = cos(theta);
-                const float sin_theta = sin(theta);
+                // simplified from `(ib * n_dims + ic) * inv_ndims`
+                const float cur_rot = inv_ndims*ic - ib;
+
+                const float theta = theta_0 * pow(freq_base, cur_rot);
+                float cos_theta, sin_theta;
+                rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
 
                 const int64_t i0 = ib*n_dims + ic/2;
 
diff --git a/ggml.c b/ggml.c
index 80d682255328c3..2c7fe476b176d5 100644
--- a/ggml.c
+++ b/ggml.c
@@ -1,4 +1,5 @@
 #define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
+#define _USE_MATH_DEFINES // For M_PI on MSVC
 
 #include "ggml-impl.h"
 #include "ggml-quants.h"
@@ -4845,8 +4846,13 @@ static struct ggml_tensor * ggml_rope_impl(
         int                   n_dims,
         int                   mode,
         int                   n_ctx,
+        int                   n_orig_ctx,
         float                 freq_base,
         float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow,
         float                 xpos_base,
         bool                  xpos_down,
         bool                  inplace) {
@@ -4862,11 +4868,15 @@ static struct ggml_tensor * ggml_rope_impl(
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
-    memcpy(params + 4, &freq_base,  sizeof(float));
-    memcpy(params + 5, &freq_scale, sizeof(float));
-    memcpy(params + 6, &xpos_base,  sizeof(float));
-    memcpy(params + 7, &xpos_down,  sizeof(bool));
+    int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
+    memcpy(params +  5, &freq_base,    sizeof(float));
+    memcpy(params +  6, &freq_scale,   sizeof(float));
+    memcpy(params +  7, &ext_factor,   sizeof(float));
+    memcpy(params +  8, &attn_factor,  sizeof(float));
+    memcpy(params +  9, &beta_fast,    sizeof(float));
+    memcpy(params + 10, &beta_slow,    sizeof(float));
+    memcpy(params + 11, &xpos_base,    sizeof(float));
+    memcpy(params + 12, &xpos_down,    sizeof(bool));
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_ROPE;
@@ -4884,7 +4894,9 @@ struct ggml_tensor * ggml_rope(
         int                   n_dims,
         int                   mode,
         int                   n_ctx) {
-    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false);
+    return ggml_rope_impl(
+        ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false
+    );
 }
 
 struct ggml_tensor * ggml_rope_inplace(
@@ -4894,7 +4906,9 @@ struct ggml_tensor * ggml_rope_inplace(
         int                   n_dims,
         int                   mode,
         int                   n_ctx) {
-    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true);
+    return ggml_rope_impl(
+        ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true
+    );
 }
 
 struct ggml_tensor * ggml_rope_custom(
@@ -4904,9 +4918,17 @@ struct ggml_tensor * ggml_rope_custom(
         int                   n_dims,
         int                   mode,
         int                   n_ctx,
+        int                   n_orig_ctx,
         float                 freq_base,
-        float                 freq_scale) {
-    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false);
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
+    return ggml_rope_impl(
+        ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false
+    );
 }
 
 struct ggml_tensor * ggml_rope_custom_inplace(
@@ -4916,9 +4938,17 @@ struct ggml_tensor * ggml_rope_custom_inplace(
         int                   n_dims,
         int                   mode,
         int                   n_ctx,
+        int                   n_orig_ctx,
         float                 freq_base,
-        float                 freq_scale) {
-    return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true);
+        float                 freq_scale,
+        float                 ext_factor,
+        float                 attn_factor,
+        float                 beta_fast,
+        float                 beta_slow) {
+    return ggml_rope_impl(
+        ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
+        ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true
+    );
 }
 
 struct ggml_tensor * ggml_rope_xpos_inplace(
@@ -4928,7 +4958,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace(
         int                   n_dims,
         float                 base,
         bool                  down) {
-    return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true);
+    return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true);
 }
 
 // ggml_rope_back
@@ -10901,6 +10931,45 @@ static void ggml_compute_forward_clamp(
 
 // ggml_compute_forward_rope
 
+static float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / MAX(0.001f, high - low);
+    return 1 - MIN(1, MAX(0, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static void rope_yarn(
+    float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+    *cos_theta = cosf(theta) * mscale;
+    *sin_theta = sinf(theta) * mscale;
+}
+
+// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
+// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
+static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) {
+    return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
+}
+
+void ggml_rope_yarn_corr_dims(
+    int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
+) {
+    // start and end correction dims
+    dims[0] = MAX(0,         floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)));
+    dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)));
+}
+
 static void ggml_compute_forward_rope_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
@@ -10910,21 +10979,26 @@ static void ggml_compute_forward_rope_f32(
         return;
     }
 
-    float freq_base;
-    float freq_scale;
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
 
     // these two only relevant for xPos RoPE:
     float xpos_base;
     bool  xpos_down;
 
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_dims = ((int32_t *) dst->op_params)[1];
-    const int mode   = ((int32_t *) dst->op_params)[2];
-    const int n_ctx  = ((int32_t *) dst->op_params)[3];
-    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
-    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
-    memcpy(&xpos_base,  (int32_t *) dst->op_params + 6, sizeof(float));
-    memcpy(&xpos_down,  (int32_t *) dst->op_params + 7, sizeof(bool));
+    //const int n_past     = ((int32_t *) dst->op_params)[0];
+    const int n_dims     = ((int32_t *) dst->op_params)[1];
+    const int mode       = ((int32_t *) dst->op_params)[2];
+    const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&xpos_base,   (int32_t *) dst->op_params + 11, sizeof(float));
+    memcpy(&xpos_down,   (int32_t *) dst->op_params + 12, sizeof(bool));
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
@@ -10952,6 +11026,9 @@ static void ggml_compute_forward_rope_f32(
     int ir = 0;
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float inv_ndims = -1.f/n_dims;
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
 
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
@@ -10965,18 +11042,18 @@ static void ggml_compute_forward_rope_f32(
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta = freq_scale * (float)p;
+                float theta_base = (float)p;
 
                 if (is_glm) {
-                    theta = MIN(p, n_ctx - 2);
+                    theta_base = MIN(p, n_ctx - 2);
                     float block_theta = MAX(p - (n_ctx - 2), 0);
                     for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
-                        const float cos_theta = cosf(theta);
-                        const float sin_theta = sinf(theta);
+                        const float cos_theta = cosf(theta_base);
+                        const float sin_theta = sinf(theta_base);
                         const float cos_block_theta = cosf(block_theta);
                         const float sin_block_theta = sinf(block_theta);
 
-                        theta *= theta_scale;
+                        theta_base *= theta_scale;
                         block_theta *= theta_scale;
 
                         const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
@@ -10994,13 +11071,16 @@ static void ggml_compute_forward_rope_f32(
                     }
                 } else if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        const float cos_theta = cosf(theta);
-                        const float sin_theta = sinf(theta);
+                        float cos_theta, sin_theta;
+                        rope_yarn(
+                            theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
+                        );
+
                         // zeta scaling for xPos only:
                         float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
                         if (xpos_down) zeta = 1.0f / zeta;
 
-                        theta *= theta_scale;
+                        theta_base *= theta_scale;
 
                         const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
                               float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
@@ -11014,12 +11094,19 @@ static void ggml_compute_forward_rope_f32(
                 } else {
                     // TODO: this might be wrong for ne0 != n_dims - need double check
                     // ref:  https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
+                    theta_base *= freq_scale;
                     for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
                         for (int64_t ic = 0; ic < n_dims; ic += 2) {
-                            const float cos_theta = cosf(theta);
-                            const float sin_theta = sinf(theta);
+                            // simplified from `(ib * n_dims + ic) * inv_ndims`
+                            float cur_rot = inv_ndims * ic - ib;
+
+                            float cos_theta, sin_theta;
+                            rope_yarn(
+                                theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
+                                &cos_theta, &sin_theta
+                            );
 
-                            theta *= theta_scale;
+                            theta_base *= theta_scale;
 
                             const int64_t i0 = ib*n_dims + ic/2;
 
@@ -11048,15 +11135,19 @@ static void ggml_compute_forward_rope_f16(
         return;
     }
 
-    float freq_base;
-    float freq_scale;
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
 
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_dims = ((int32_t *) dst->op_params)[1];
-    const int mode   = ((int32_t *) dst->op_params)[2];
-    const int n_ctx  = ((int32_t *) dst->op_params)[3];
-    memcpy(&freq_base,  (int32_t *) dst->op_params + 4, sizeof(float));
-    memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
+    //const int n_past     = ((int32_t *) dst->op_params)[0];
+    const int n_dims     = ((int32_t *) dst->op_params)[1];
+    const int mode       = ((int32_t *) dst->op_params)[2];
+    const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
@@ -11084,6 +11175,9 @@ static void ggml_compute_forward_rope_f16(
     int ir = 0;
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float inv_ndims = -1.f/n_dims;
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
 
     const bool is_neox = mode & 2;
     const bool is_glm  = mode & 4;
@@ -11097,18 +11191,18 @@ static void ggml_compute_forward_rope_f16(
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta = freq_scale * (float)p;
+                float theta_base = (float)p;
 
                 if (is_glm) {
-                    theta = MIN(p, n_ctx - 2);
+                    theta_base = MIN(p, n_ctx - 2);
                     float block_theta = MAX(p - (n_ctx - 2), 0);
                     for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
-                        const float cos_theta = cosf(theta);
-                        const float sin_theta = sinf(theta);
+                        const float cos_theta = cosf(theta_base);
+                        const float sin_theta = sinf(theta_base);
                         const float cos_block_theta = cosf(block_theta);
                         const float sin_block_theta = sinf(block_theta);
 
-                        theta *= theta_scale;
+                        theta_base *= theta_scale;
                         block_theta *= theta_scale;
 
                         const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
@@ -11126,10 +11220,12 @@ static void ggml_compute_forward_rope_f16(
                     }
                 } else if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        const float cos_theta = cosf(theta);
-                        const float sin_theta = sinf(theta);
+                        float cos_theta, sin_theta;
+                        rope_yarn(
+                            theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
+                        );
 
-                        theta *= theta_scale;
+                        theta_base *= theta_scale;
 
                         const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
                               ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
@@ -11143,12 +11239,19 @@ static void ggml_compute_forward_rope_f16(
                 } else {
                     // TODO: this might be wrong for ne0 != n_dims - need double check
                     // ref:  https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
+                    theta_base *= freq_scale;
                     for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
                         for (int64_t ic = 0; ic < n_dims; ic += 2) {
-                            const float cos_theta = cosf(theta);
-                            const float sin_theta = sinf(theta);
+                            // simplified from `(ib * n_dims + ic) * inv_ndims`
+                            float cur_rot = inv_ndims * ic - ib;
 
-                            theta *= theta_scale;
+                            float cos_theta, sin_theta;
+                            rope_yarn(
+                                theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
+                                &cos_theta, &sin_theta
+                            );
+
+                            theta_base *= theta_scale;
 
                             const int64_t i0 = ib*n_dims + ic/2;
 
@@ -11256,17 +11359,18 @@ static void ggml_compute_forward_rope_back_f32(
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta = freq_scale * (float)p;
+                float theta_base = freq_scale * (float)p;
 
                 if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        const float cos_theta = cosf(theta);
-                        const float sin_theta = sinf(theta);
+                        const float cos_theta = cosf(theta_base);
+                        const float sin_theta = sinf(theta_base);
+
                         // zeta scaling for xPos only:
                         float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
                         if (xpos_down) zeta = 1.0f / zeta;
 
-                        theta *= theta_scale;
+                        theta_base *= theta_scale;
 
                         const float * const dy  = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
                               float *       dx  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
@@ -11280,10 +11384,10 @@ static void ggml_compute_forward_rope_back_f32(
                 } else {
                     for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
                         for (int64_t ic = 0; ic < n_dims; ic += 2) {
-                            const float cos_theta = cosf(theta);
-                            const float sin_theta = sinf(theta);
+                            const float cos_theta = cosf(theta_base);
+                            const float sin_theta = sinf(theta_base);
 
-                            theta *= theta_scale;
+                            theta_base *= theta_scale;
 
                             const int64_t i0 = ib*n_dims + ic/2;
 
@@ -11356,14 +11460,14 @@ static void ggml_compute_forward_rope_back_f16(
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
 
-                float theta = (float)p;
+                float theta_base = (float)p;
 
                 if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        const float cos_theta = cosf(theta);
-                        const float sin_theta = sinf(theta);
+                        const float cos_theta = cosf(theta_base);
+                        const float sin_theta = sinf(theta_base);
 
-                        theta *= theta_scale;
+                        theta_base *= theta_scale;
 
                         const ggml_fp16_t * const dy  = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
                               ggml_fp16_t *       dx  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
@@ -11377,10 +11481,10 @@ static void ggml_compute_forward_rope_back_f16(
                 } else {
                     for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
                         for (int64_t ic = 0; ic < n_dims; ic += 2) {
-                            const float cos_theta = cosf(theta);
-                            const float sin_theta = sinf(theta);
+                            const float cos_theta = cosf(theta_base);
+                            const float sin_theta = sinf(theta_base);
 
-                            theta *= theta_scale;
+                            theta_base *= theta_scale;
 
                             const int64_t i0 = ib*n_dims + ic/2;
 
@@ -15505,9 +15609,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                                 src1,
                                 n_dims,
                                 mode,
+                                0,
                                 n_ctx,
                                 freq_base,
                                 freq_scale,
+                                0.0f,
+                                1.0f,
+                                0.0f,
+                                0.0f,
                                 xpos_base,
                                 xpos_down,
                                 false),
diff --git a/ggml.h b/ggml.h
index 9d16c5a72fda0e..70eb25a6bf3afc 100644
--- a/ggml.h
+++ b/ggml.h
@@ -219,7 +219,7 @@
 #define GGML_MAX_CONTEXTS      64
 #define GGML_MAX_SRC           6
 #define GGML_MAX_NAME          64
-#define GGML_MAX_OP_PARAMS     32
+#define GGML_MAX_OP_PARAMS     64
 #define GGML_DEFAULT_N_THREADS 4
 
 #if UINTPTR_MAX == 0xFFFFFFFF
@@ -1326,8 +1326,13 @@ extern "C" {
             int                   n_dims,
             int                   mode,
             int                   n_ctx,
+            int                   n_orig_ctx,
             float                 freq_base,
-            float                 freq_scale);
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
 
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
@@ -1337,8 +1342,17 @@ extern "C" {
             int                   n_dims,
             int                   mode,
             int                   n_ctx,
+            int                   n_orig_ctx,
             float                 freq_base,
-            float                 freq_scale);
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+    // compute correction dims for YaRN RoPE scaling
+    void ggml_rope_yarn_corr_dims(
+        int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
 
     // xPos RoPE, in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py
index 6b7d654294a3e0..727b4e55495a76 100644
--- a/gguf-py/gguf/gguf.py
+++ b/gguf-py/gguf/gguf.py
@@ -7,7 +7,7 @@
 import struct
 import sys
 import tempfile
-from enum import IntEnum, auto
+from enum import Enum, IntEnum, auto
 from io import BufferedWriter
 from pathlib import Path
 from typing import IO, Any, BinaryIO, Callable, Sequence
@@ -53,9 +53,12 @@
 KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
 
 # RoPE
-KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
-KEY_ROPE_FREQ_BASE       = "{arch}.rope.freq_base"
-KEY_ROPE_SCALE_LINEAR    = "{arch}.rope.scale_linear"
+KEY_ROPE_DIMENSION_COUNT         = "{arch}.rope.dimension_count"
+KEY_ROPE_FREQ_BASE               = "{arch}.rope.freq_base"
+KEY_ROPE_SCALING_TYPE            = "{arch}.rope.scaling.type"
+KEY_ROPE_SCALING_FACTOR          = "{arch}.rope.scaling.factor"
+KEY_ROPE_SCALING_ORIG_CTX_LEN    = "{arch}.rope.scaling.original_context_length"
+KEY_ROPE_SCALING_FINETUNED       = "{arch}.rope.scaling.finetuned"
 
 # tokenization
 KEY_TOKENIZER_MODEL      = "tokenizer.ggml.model"
@@ -577,6 +580,11 @@ class TokenType(IntEnum):
     UNUSED       = 5
     BYTE         = 6
 
+class RopeScalingType(Enum):
+    NONE   = 'none'
+    LINEAR = 'linear'
+    YARN   = 'yarn'
+
 #
 # implementation
 #
@@ -948,8 +956,17 @@ def add_rope_dimension_count(self, count: int):
     def add_rope_freq_base(self, value: float):
         self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value)
 
-    def add_rope_scale_linear(self, value: float):
-        self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value)
+    def add_rope_scaling_type(self, value: RopeScalingType):
+        self.add_string(KEY_ROPE_SCALING_TYPE.format(arch=self.arch), value.value)
+
+    def add_rope_scaling_factor(self, value: float):
+        self.add_float32(KEY_ROPE_SCALING_FACTOR.format(arch=self.arch), value)
+
+    def add_rope_scaling_orig_ctx_len(self, value: int):
+        self.add_uint32(KEY_ROPE_SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
+
+    def add_rope_scaling_finetuned(self, value: bool):
+        self.add_bool(KEY_ROPE_SCALING_FINETUNED.format(arch=self.arch), value)
 
     def add_tokenizer_model(self, model: str):
         self.add_string(KEY_TOKENIZER_MODEL, model)
diff --git a/llama.cpp b/llama.cpp
index 1c6d482f8fe1bc..685882c201921d 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -54,6 +54,7 @@
 #include <cassert>
 #include <cinttypes>
 #include <climits>
+#include <cmath>
 #include <cstdarg>
 #include <cstddef>
 #include <cstdint>
@@ -235,6 +236,10 @@ enum llm_kv {
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_SCALE_LINEAR,
+    LLM_KV_ROPE_SCALING_TYPE,
+    LLM_KV_ROPE_SCALING_FACTOR,
+    LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
+    LLM_KV_ROPE_SCALING_FINETUNED,
 
     LLM_KV_TOKENIZER_MODEL,
     LLM_KV_TOKENIZER_LIST,
@@ -276,9 +281,13 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
 
-    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count" },
-    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"       },
-    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"    },
+    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
+    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
+    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
+    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
 
     { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
     { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
@@ -552,6 +561,22 @@ do { \
     } \
 } while (0)
 
+static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
+    { LLAMA_ROPE_SCALING_NONE,   "none"   },
+    { LLAMA_ROPE_SCALING_LINEAR, "linear" },
+    { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
+};
+
+static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
+    for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
+        if (kv.second == name) {
+            return kv.first;
+        }
+    }
+
+    return LLAMA_ROPE_SCALING_UNSPECIFIED;
+}
+
 //
 // ggml helpers
 //
@@ -1035,8 +1060,11 @@ struct llama_hparams {
     float f_norm_eps;
     float f_norm_rms_eps;
 
-    float rope_freq_base_train;
-    float rope_freq_scale_train;
+    float    rope_freq_base_train;
+    float    rope_freq_scale_train;
+    uint32_t n_yarn_orig_ctx;
+    int8_t   rope_scaling_type_train : 3;
+    bool     rope_finetuned : 1;
 
     float f_clamp_kqv;
     float f_max_alibi_bias;
@@ -1051,6 +1079,8 @@ struct llama_hparams {
         if (this->n_layer     != other.n_layer)     return true;
         if (this->n_rot       != other.n_rot)       return true;
         if (this->n_ff        != other.n_ff)        return true;
+        if (this->rope_finetuned  != other.rope_finetuned)  return true;
+        if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
 
         const float EPSILON = 1e-9;
 
@@ -1081,8 +1111,16 @@ struct llama_cparams {
     uint32_t n_threads;       // number of threads to use for generation
     uint32_t n_threads_batch; // number of threads to use for batch processing
 
-    float rope_freq_base;
-    float rope_freq_scale;
+    float    rope_freq_base;
+    float    rope_freq_scale;
+
+    uint32_t n_yarn_orig_ctx;
+    // These hyperparameters are not exposed in GGUF, because all
+    // existing YaRN models use the same values for them.
+    float yarn_ext_factor;
+    float yarn_attn_factor;
+    float yarn_beta_fast;
+    float yarn_beta_slow;
 
     bool mul_mat_q;
 };
@@ -2014,14 +2052,30 @@ static void llm_load_hparams(
     hparams.n_head_kv = hparams.n_head;
     GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
 
+    hparams.rope_finetuned = false;
+    GGUF_GET_KEY(ctx, hparams.rope_finetuned, gguf_get_val_bool, GGUF_TYPE_BOOL, false,
+                 kv(LLM_KV_ROPE_SCALING_FINETUNED));
+
+    hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
+    GGUF_GET_KEY(ctx, hparams.n_yarn_orig_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false,
+                 kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN));
+
     // rope_freq_base (optional)
     hparams.rope_freq_base_train = 10000.0f;
     GGUF_GET_KEY(ctx, hparams.rope_freq_base_train, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
 
+    std::string rope_scaling("linear");
+    GGUF_GET_KEY(ctx, rope_scaling, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_ROPE_SCALING_TYPE));
+    hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
+    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
+
     // rope_freq_scale (inverse of the kv) is optional
-    float ropescale = 1.0f;
-    GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
-    hparams.rope_freq_scale_train = 1.0f/ropescale;
+    float ropescale = 0.0f;
+    GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALING_FACTOR));
+    if (ropescale == 0.0f) { // try the old key name
+        GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
+    }
+    hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
 
     // sanity check for n_rot (optional)
     {
@@ -2371,6 +2425,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
+    const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+
     // hparams
     LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
     LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
@@ -2389,8 +2445,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
     LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
     LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
+    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type.c_str());
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
+    LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
+    LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
     LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
     LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
@@ -3047,21 +3106,11 @@ static void llm_load_tensors(
     model.t_load_us = ggml_time_us() - model.t_start_us;
 }
 
-static bool llama_model_load(
-        const std::string & fname,
-        llama_model & model,
-        int n_gpu_layers,
-        int main_gpu,
-        const float * tensor_split,
-        bool use_mmap,
-        bool use_mlock,
-        bool vocab_only,
-        llama_progress_callback progress_callback,
-        void *progress_callback_user_data) {
+static bool llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) {
     try {
-        llama_model_loader ml(fname, use_mmap);
+        llama_model_loader ml(fname, params.use_mmap);
 
-        model.hparams.vocab_only = vocab_only;
+        model.hparams.vocab_only = params.vocab_only;
 
         llm_load_arch   (ml, model);
         llm_load_hparams(ml, model);
@@ -3073,15 +3122,15 @@ static bool llama_model_load(
             throw std::runtime_error("vocab size mismatch");
         }
 
-        if (vocab_only) {
+        if (params.vocab_only) {
             LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
             return true;
         }
 
         llm_load_tensors(
-                ml, model, n_gpu_layers,
-                main_gpu, tensor_split,
-                use_mlock, progress_callback, progress_callback_user_data);
+            ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock,
+            params.progress_callback, params.progress_callback_user_data
+        );
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
         return false;
@@ -3150,6 +3199,7 @@ static struct ggml_tensor * llm_build_inp_embd(
 static void llm_build_k_shift(
       struct ggml_context * ctx,
       const llama_hparams & hparams,
+      const llama_cparams & cparams,
      const llama_kv_cache & kv,
        struct ggml_cgraph * graph,
             llm_rope_type   type,
@@ -3162,6 +3212,11 @@ static void llm_build_k_shift(
     const int64_t n_head_kv   = hparams.n_head_kv;
     const int64_t n_embd_gqa  = hparams.n_embd_gqa();
     const int64_t n_embd_head = hparams.n_embd_head();
+    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
+    const float   ext_factor  = cparams.yarn_ext_factor;
+    const float   attn_factor = cparams.yarn_attn_factor;
+    const float   beta_fast   = cparams.yarn_beta_fast;
+    const float   beta_slow   = cparams.yarn_beta_slow;
 
     GGML_ASSERT(n_embd_head % n_rot == 0);
 
@@ -3185,7 +3240,8 @@ static void llm_build_k_shift(
                         ggml_element_size(kv.k)*n_embd_head,
                         ggml_element_size(kv.k)*n_embd_gqa,
                         ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il),
-                    K_shift, n_rot, rope_type, 0, freq_base, freq_scale);
+                    K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow);
         cb(tmp, "K_shifted", il);
         ggml_build_forward_expand(graph, tmp);
     }
@@ -3442,12 +3498,17 @@ struct llm_build_context {
 
     const float freq_base;
     const float freq_scale;
+    const float ext_factor;
+    const float attn_factor;
+    const float beta_fast;
+    const float beta_slow;
     const float norm_eps;
     const float norm_rms_eps;
 
     const int32_t n_tokens;
     const int32_t n_kv;     // size of KV cache to consider (n_kv <= n_ctx)
     const int32_t kv_head;  // index of where we store new KV data in the cache
+    const int32_t n_orig_ctx;
 
     const bool do_rope_shift;
 
@@ -3477,11 +3538,16 @@ struct llm_build_context {
         n_embd_gqa    (hparams.n_embd_gqa()),
         freq_base     (cparams.rope_freq_base),
         freq_scale    (cparams.rope_freq_scale),
+        ext_factor    (cparams.yarn_ext_factor),
+        attn_factor   (cparams.yarn_attn_factor),
+        beta_fast     (cparams.yarn_beta_fast),
+        beta_slow     (cparams.yarn_beta_slow),
         norm_eps      (hparams.f_norm_eps),
         norm_rms_eps  (hparams.f_norm_rms_eps),
         n_tokens      (batch.n_tokens),
         n_kv          (worst_case ? n_ctx            : kv_self.n),
         kv_head       (worst_case ? n_ctx - n_tokens : kv_self.head),
+        n_orig_ctx    (cparams.n_yarn_orig_ctx),
         do_rope_shift (worst_case || kv_self.has_shift),
         cb            (cb),
         buf_compute   (lctx.buf_compute) {
@@ -3532,7 +3598,7 @@ struct llm_build_context {
 
         // shift the entire K-cache if needed
         if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
         }
 
         for (int il = 0; il < n_layer; ++il) {
@@ -3556,10 +3622,18 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
                 cb(Kcur, "Kcur", il);
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
@@ -3634,7 +3708,7 @@ struct llm_build_context {
 
         // shift the entire K-cache if needed
         if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
         }
 
         for (int il = 0; il < n_layer; ++il) {
@@ -3658,8 +3732,16 @@ struct llm_build_context {
 
                 switch (model.type) {
                     case MODEL_7B:
-                        Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens),    inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
-                        Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, n_embd_head, 0, 0, freq_base, freq_scale);
+                        Qcur = ggml_rope_custom(
+                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                            n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                        Kcur = ggml_rope_custom(
+                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                            n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow
+                        );
                         break;
                     case MODEL_13B:
                         Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
@@ -3746,7 +3828,7 @@ struct llm_build_context {
 
         // shift the entire K-cache if needed
         if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
         }
 
         for (int il = 0; il < n_layer; ++il) {
@@ -3786,10 +3868,16 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(ctx0, Qcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+                Qcur = ggml_rope_custom(
+                    ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(ctx0, Kcur, inp_pos, n_embd_head, 2, 0, freq_base, freq_scale);
+                Kcur = ggml_rope_custom(
+                    ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
                 cb(Kcur, "Kcur", il);
 
                 llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
@@ -3960,7 +4048,7 @@ struct llm_build_context {
         cb(KQ_mask, "KQ_mask", -1);
 
         if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
+            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
         }
 
         for (int il = 0; il < n_layer; ++il) {
@@ -4053,13 +4141,15 @@ struct llm_build_context {
                 cb(kpass, "kpass", il);
 
                 struct ggml_tensor * qrotated = ggml_rope_custom(
-                        ctx0, qrot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
-                        );
+                    ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
                 cb(qrotated, "qrotated", il);
 
                 struct ggml_tensor * krotated = ggml_rope_custom(
-                        ctx0, krot, inp_pos, n_rot, 2, 0, freq_base, freq_scale
-                        );
+                    ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
                 cb(krotated, "krotated", il);
 
                 // ggml currently only supports concatenation on dim=2
@@ -7883,8 +7973,13 @@ struct llama_context_params llama_context_default_params() {
         /*.n_batch                     =*/ 512,
         /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
+        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
+        /*.yarn_ext_factor             =*/ NAN,
+        /*.yarn_attn_factor            =*/ 1.0f,
+        /*.yarn_beta_fast              =*/ 32.0f,
+        /*.yarn_beta_slow              =*/ 1.0f,
         /*.mul_mat_q                   =*/ true,
         /*.f16_kv                      =*/ true,
         /*.logits_all                  =*/ false,
@@ -7971,10 +8066,7 @@ struct llama_model * llama_load_model_from_file(
         };
     }
 
-    if (!llama_model_load(path_model, *model, params.n_gpu_layers,
-                params.main_gpu, params.tensor_split,
-                params.use_mmap, params.use_mlock, params.vocab_only,
-                params.progress_callback, params.progress_callback_user_data)) {
+    if (!llama_model_load(path_model, *model, params)) {
         LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
         delete model;
         return nullptr;
@@ -8000,13 +8092,35 @@ struct llama_context * llama_new_context_with_model(
     const auto & hparams = model->hparams;
     auto       & cparams = ctx->cparams;
 
-    cparams.n_batch         = params.n_batch;
-    cparams.n_ctx           = params.n_ctx == 0           ? hparams.n_ctx_train           : params.n_ctx;
-    cparams.rope_freq_base  = params.rope_freq_base == 0  ? hparams.rope_freq_base_train  : params.rope_freq_base;
-    cparams.rope_freq_scale = params.rope_freq_scale == 0 ? hparams.rope_freq_scale_train : params.rope_freq_scale;
-    cparams.n_threads       = params.n_threads;
-    cparams.n_threads_batch = params.n_threads_batch;
-    cparams.mul_mat_q       = params.mul_mat_q;
+    cparams.n_batch          = params.n_batch;
+    cparams.n_threads        = params.n_threads;
+    cparams.n_threads_batch  = params.n_threads_batch;
+    cparams.yarn_ext_factor  = params.yarn_ext_factor;
+    cparams.yarn_attn_factor = params.yarn_attn_factor;
+    cparams.yarn_beta_fast   = params.yarn_beta_fast;
+    cparams.yarn_beta_slow   = params.yarn_beta_slow;
+    cparams.mul_mat_q        = params.mul_mat_q;
+
+    cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
+    cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
+    cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
+
+    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
+                               hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
+                                                              hparams.n_ctx_train;
+
+    auto rope_scaling_type = params.rope_scaling_type;
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
+        rope_scaling_type = hparams.rope_scaling_type_train;
+    }
+
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
+        cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
+    }
+
+    if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set'
+        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
+    }
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
diff --git a/llama.h b/llama.h
index 75fe391ef2e733..3f1becd7616885 100644
--- a/llama.h
+++ b/llama.h
@@ -106,6 +106,14 @@ extern "C" {
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
 
+    enum llama_rope_scaling_type {
+        LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
+        LLAMA_ROPE_SCALING_NONE        = 0,
+        LLAMA_ROPE_SCALING_LINEAR      = 1,
+        LLAMA_ROPE_SCALING_YARN        = 2,
+        LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
+    };
+
     typedef struct llama_token_data {
         llama_token id; // token id
         float logit;    // log-odds of the token
@@ -172,10 +180,16 @@ extern "C" {
         uint32_t n_batch;         // prompt processing maximum batch size
         uint32_t n_threads;       // number of threads to use for generation
         uint32_t n_threads_batch; // number of threads to use for batch processing
+        int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
-        float rope_freq_base;  // RoPE base frequency, 0 = from model
-        float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
+        float    rope_freq_base;   // RoPE base frequency, 0 = from model
+        float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
+        float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
+        float    yarn_attn_factor; // YaRN magnitude scaling factor
+        float    yarn_beta_fast;   // YaRN low correction dim
+        float    yarn_beta_slow;   // YaRN high correction dim
+        uint32_t yarn_orig_ctx;    // YaRN original context size
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool mul_mat_q;  // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)

From d02e98cde035d91ed8032ab943d1d504fe9da394 Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Wed, 1 Nov 2023 23:10:09 +0100
Subject: [PATCH 49/79] ggml-cuda : compute ptrs for cublasGemmBatchedEx in a
 kernel (#3891)

* ggml-cuda : compute ptrs for cublasGemmBatchedEx in a kernel

* fix warnings
---
 ggml-cuda.cu | 80 ++++++++++++++++++++++++++++++----------------------
 1 file changed, 46 insertions(+), 34 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 12ee10e3d9bdcd..61cd1747cac4fc 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -6696,8 +6696,10 @@ inline void ggml_cuda_op_clamp(
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    const float min = ((float *) dst->op_params)[0];
-    const float max = ((float *) dst->op_params)[1];
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
 
     clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
     CUDA_CHECK(cudaGetLastError());
@@ -7221,6 +7223,30 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
     ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
 }
 
+__global__ void k_compute_batched_ptrs(
+        const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
+        void ** ptrs,
+        int ne12, int ne13,
+        int ne23,
+        int nb02, int nb03,
+        int nb12, int nb13,
+        int nb2, int nb3,
+        int r2, int r3) {
+    int i13 = blockIdx.x * blockDim.x + threadIdx.x;
+    int i12 = blockIdx.y * blockDim.y + threadIdx.y;
+
+    if (i13 >= ne13 || i12 >= ne12) {
+        return;
+    }
+
+    int i03 = i13 / r3;
+    int i02 = i12 / r2;
+
+    ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02   + i03*nb03;
+    ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
+    ptrs[2*ne23 + i12 + i13*ne12] = (char *)     dst_f16 + i12* nb2/2 + i13* nb3/2;
+}
+
 static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
@@ -7322,49 +7348,35 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
     } else {
         // use cublasGemmBatchedEx
-        // TODO: https://github.com/ggerganov/llama.cpp/pull/3749#discussion_r1369997000
         const int ne23 = ne12*ne13;
 
-        // TODO: avoid this alloc
-        void ** ptrs = (void **) malloc(3*ne23*sizeof(void *));
-
-        for (int i13 = 0; i13 < ne13; ++i13) {
-            for (int i12 = 0; i12 < ne12; ++i12) {
-                int i03 = i13 / r3;
-                int i02 = i12 / r2;
-
-                ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3];
-                ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2;
-                ptrs[2*ne23 + i12 + i13*ne12] = (char *)     dst_f16 + i12* dst->nb[2]/2 + i13* dst->nb[3]/2;
-            }
-        }
-
-        // allocate device memory for pointers
         void ** ptrs_as = nullptr;
-        CUDA_CHECK(cudaMalloc(&ptrs_as, 3*ne23*sizeof(void *)));
-
-        // TODO: this does not work for some reason -- not sure why?
-        //size_t ptrs_s = 0;
-        //ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s);
-
-        // copy pointers to device
-        CUDA_CHECK(cudaMemcpy(ptrs_as, ptrs, 3*ne23*sizeof(void *), cudaMemcpyHostToDevice));
-
-        free(ptrs);
+        size_t ptrs_s = 0;
+        ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s);
+
+        dim3 block_dims(ne13, ne12);
+        k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
+                src0_as_f16, src1_as_f16, dst_f16,
+                ptrs_as,
+                ne12, ne13,
+                ne23,
+                nb02, nb03,
+                nb12, nb13,
+                dst->nb[2], dst->nb[3],
+                r2, r3);
+        CUDA_CHECK(cudaGetLastError());
 
         CUBLAS_CHECK(
         cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
-                &alpha_f16, (const void **) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
-                            (const void **) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
-                &beta_f16,  (      void **) (ptrs_as + 2*ne23), CUDA_R_16F, ne01,
+                &alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
+                            (const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
+                &beta_f16,  (      void **       ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01,
                 ne23,
                 CUBLAS_COMPUTE_16F,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 
-        // free device memory for pointers
-        CUDA_CHECK(cudaFree(ptrs_as));
-        //ggml_cuda_pool_free(ptrs_as, ptrs_s);
+        ggml_cuda_pool_free(ptrs_as, ptrs_s);
     }
 #endif
 

From 0eb332a10f3f14a3746c391bf80ff5e7bdf29d5d Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Wed, 1 Nov 2023 19:29:14 -0400
Subject: [PATCH 50/79] llama : fix llama_context_default_params after #2268
 (#3893)

---
 llama.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/llama.cpp b/llama.cpp
index 685882c201921d..32d7d23de6afb1 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -7980,6 +7980,7 @@ struct llama_context_params llama_context_default_params() {
         /*.yarn_attn_factor            =*/ 1.0f,
         /*.yarn_beta_fast              =*/ 32.0f,
         /*.yarn_beta_slow              =*/ 1.0f,
+        /*.yarn_orig_ctx               =*/ 0,
         /*.mul_mat_q                   =*/ true,
         /*.f16_kv                      =*/ true,
         /*.logits_all                  =*/ false,

From 2fffa0d61fa10e4b466e78cabcc6a4e16717b580 Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Thu, 2 Nov 2023 01:49:44 -0400
Subject: [PATCH 51/79] cuda : fix RoPE after #2268 (#3897)

---
 ggml-cuda.cu | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 61cd1747cac4fc..57a528ede23ed2 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -4539,7 +4539,7 @@ static __global__ void rope(
     const int i2 = row/p_delta_rows;
 
     const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*powf(freq_base, -col/ncols);
+    const float theta_base = p*powf(freq_base, -float(col)/ncols);
 
     float cos_theta, sin_theta;
     rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
@@ -4566,8 +4566,8 @@ static __global__ void rope_neox(
     const int i = row*ncols + col/2;
     const int i2 = row/p_delta_rows;
 
-    // simplified from `(row * ncols + col) * (-1 / ncols)`
-    const float cur_rot = -col/ncols - row;
+    // simplified from `(ib * ncols + col) * (-1 / ncols)`, where ib is assumed to be zero
+    const float cur_rot = -float(col)/ncols;
 
     const int p = has_pos ? pos[i2] : 0;
     const float theta_base = p*powf(freq_base, cur_rot);

From 183b3fac6c28e65d23ac0230c1dd6fb84bf0154d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 08:33:37 +0200
Subject: [PATCH 52/79] metal : fix build errors and kernel sig after #2268
 (#3898)

---
 ggml-metal.m     | 57 ++++++++++++++++++++++++------------------------
 ggml-metal.metal | 16 +++++++++-----
 2 files changed, 40 insertions(+), 33 deletions(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index 611d5e173681eb..b33a3cb8fd0128 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1419,34 +1419,35 @@ void ggml_metal_graph_compute(
                                 default: GGML_ASSERT(false);
                             };
 
-                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_src1 offset:offs_src1        atIndex:1];
-                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:2];
-                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:6];
-                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:10];
-                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:14];
-                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:17];
-                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:18];
-                            [encoder setBytes:&n_past  length:sizeof(     int) atIndex:19];
-                            [encoder setBytes:&n_dims  length:sizeof(     int) atIndex:20];
-                            [encoder setBytes:&mode    length:sizeof(     int) atIndex:21];
-                            [encoder setBytes:&freq_base  length:sizeof(float) atIndex:22];
-                            [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23];
-                            [encoder setBytes:&ext_factor  length:sizeof(float) atIndex:24];
-                            [encoder setBytes:&attn_factor length:sizeof(float) atIndex:25];
-                            [encoder setBytes:&beta_fast   length:sizeof(float) atIndex:26];
-                            [encoder setBytes:&beta_slow   length:sizeof(float) atIndex:27];
+                            [encoder setBuffer:id_src0     offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_src1     offset:offs_src1        atIndex:1];
+                            [encoder setBuffer:id_dst      offset:offs_dst         atIndex:2];
+                            [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne01        length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne02        length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&ne03        length:sizeof( int64_t) atIndex:6];
+                            [encoder setBytes:&nb00        length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb01        length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb02        length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&nb03        length:sizeof(uint64_t) atIndex:10];
+                            [encoder setBytes:&ne0         length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne1         length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne2         length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&ne3         length:sizeof( int64_t) atIndex:14];
+                            [encoder setBytes:&nb0         length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb1         length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb2         length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBytes:&nb3         length:sizeof(uint64_t) atIndex:18];
+                            [encoder setBytes:&n_past      length:sizeof(     int) atIndex:19];
+                            [encoder setBytes:&n_dims      length:sizeof(     int) atIndex:20];
+                            [encoder setBytes:&mode        length:sizeof(     int) atIndex:21];
+                            [encoder setBytes:&n_orig_ctx  length:sizeof(     int) atIndex:22];
+                            [encoder setBytes:&freq_base   length:sizeof(   float) atIndex:23];
+                            [encoder setBytes:&freq_scale  length:sizeof(   float) atIndex:24];
+                            [encoder setBytes:&ext_factor  length:sizeof(   float) atIndex:25];
+                            [encoder setBytes:&attn_factor length:sizeof(   float) atIndex:26];
+                            [encoder setBytes:&beta_fast   length:sizeof(   float) atIndex:27];
+                            [encoder setBytes:&beta_slow   length:sizeof(   float) atIndex:28];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 471d7d390f8138..7c35f23a7612fd 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -1070,20 +1070,20 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
 // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
 static void rope_yarn(
     float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
+    thread float * cos_theta, thread float * sin_theta
 ) {
     // Get n-d rotational scaling corrected for extrapolation
     float theta_interp = freq_scale * theta_extrap;
     float theta = theta_interp;
     if (ext_factor != 0.0f) {
-        ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
+        float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
         theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
 
         // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+        mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
     }
-    *cos_theta = cosf(theta) * mscale;
-    *sin_theta = sinf(theta) * mscale;
+    *cos_theta = cos(theta) * mscale;
+    *sin_theta = sin(theta) * mscale;
 }
 
 // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
@@ -1123,8 +1123,13 @@ typedef void (rope_t)(
         constant         int & n_past,
         constant         int & n_dims,
         constant         int & mode,
+        constant         int & n_orig_ctx,
         constant       float & freq_base,
         constant       float & freq_scale,
+        constant       float & ext_factor,
+        constant       float & attn_factor,
+        constant       float & beta_fast,
+        constant       float & beta_slow,
         uint  tiitg[[thread_index_in_threadgroup]],
         uint3 tptg[[threads_per_threadgroup]],
         uint3 tgpig[[threadgroup_position_in_grid]]);
@@ -1153,6 +1158,7 @@ kernel void kernel_rope(
         constant         int & n_past,
         constant         int & n_dims,
         constant         int & mode,
+        constant         int & n_orig_ctx,
         constant       float & freq_base,
         constant       float & freq_scale,
         constant       float & ext_factor,

From 4d719a6d4e74b9a98e75f826f865f3153717d54b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 08:35:10 +0200
Subject: [PATCH 53/79] cuda : check if this fixes Pascal card regression
 (#3882)

---
 ggml-cuda.cu | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 57a528ede23ed2..e4629512611b6c 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -7420,7 +7420,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (all_on_device && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+    } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
         // KQ + KQV multi-batch
         ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {

From b12fa0d1c13596869c512f49a526b979c94787cc Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Thu, 2 Nov 2023 02:50:16 -0400
Subject: [PATCH 54/79] build : link against build info instead of compiling
 against it (#3879)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* cmake : fix build when .git does not exist

* cmake : simplify BUILD_INFO target

* cmake : add missing dependencies on BUILD_INFO

* build : link against build info instead of compiling against it

* zig : make build info a .cpp source instead of a header

Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>

* cmake : revert change to CMP0115

---------

Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>
---
 .gitignore                                   |  2 +-
 CMakeLists.txt                               | 33 ---------
 Makefile                                     | 71 ++++++++++----------
 build.zig                                    | 38 +++++------
 common/CMakeLists.txt                        | 42 +++++++++++-
 common/build-info.cpp.in                     |  4 ++
 common/common.cpp                            |  5 +-
 common/common.h                              | 12 +++-
 examples/benchmark/CMakeLists.txt            |  5 +-
 examples/benchmark/benchmark-matmult.cpp     |  1 -
 examples/embedding/CMakeLists.txt            |  3 -
 examples/embedding/embedding.cpp             |  1 -
 examples/infill/CMakeLists.txt               |  3 -
 examples/infill/infill.cpp                   |  5 +-
 examples/llama-bench/CMakeLists.txt          |  3 -
 examples/llama-bench/llama-bench.cpp         |  5 +-
 examples/llava/CMakeLists.txt                |  6 --
 examples/main/CMakeLists.txt                 |  3 -
 examples/main/main.cpp                       |  5 +-
 examples/parallel/CMakeLists.txt             |  3 -
 examples/parallel/parallel.cpp               |  2 -
 examples/perplexity/CMakeLists.txt           |  3 -
 examples/perplexity/perplexity.cpp           |  1 -
 examples/quantize-stats/CMakeLists.txt       |  2 +-
 examples/quantize-stats/quantize-stats.cpp   |  1 -
 examples/quantize/CMakeLists.txt             |  5 +-
 examples/quantize/quantize.cpp               |  1 -
 examples/save-load-state/CMakeLists.txt      |  3 -
 examples/save-load-state/save-load-state.cpp |  1 -
 examples/server/CMakeLists.txt               |  3 -
 examples/server/server.cpp                   |  5 +-
 examples/speculative/CMakeLists.txt          |  3 -
 examples/speculative/speculative.cpp         |  2 -
 scripts/build-info.cmake                     | 30 +++++----
 scripts/build-info.h.in                      |  9 ---
 scripts/build-info.sh                        | 13 ++--
 36 files changed, 143 insertions(+), 191 deletions(-)
 create mode 100644 common/build-info.cpp.in
 delete mode 100644 scripts/build-info.h.in

diff --git a/.gitignore b/.gitignore
index 5d7c5479ef67ae..50cbd0b47cae36 100644
--- a/.gitignore
+++ b/.gitignore
@@ -65,7 +65,7 @@ models-mnt
 /parallel
 /train-text-from-scratch
 /vdot
-build-info.h
+/common/build-info.cpp
 arm_neon.h
 compile_commands.json
 CMakeSettings.json
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3659279e2d7d09..611ed3f4d2a64d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -100,39 +100,6 @@ option(LLAMA_BUILD_TESTS                "llama: build tests"    ${LLAMA_STANDALO
 option(LLAMA_BUILD_EXAMPLES             "llama: build examples" ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER               "llama: build server example"                           ON)
 
-#
-# Build info header
-#
-
-# Generate initial build-info.h
-include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
-
-if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.git")
-    set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/.git")
-
-    # Is git submodule
-    if(NOT IS_DIRECTORY "${GIT_DIR}")
-        file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
-        string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
-        set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}")
-    endif()
-
-    # Add a custom target for build-info.h
-    add_custom_target(BUILD_INFO ALL DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h")
-
-    # Add a custom command to rebuild build-info.h when .git/index changes
-    add_custom_command(
-        OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h"
-        COMMENT "Generating build details from Git"
-        COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake"
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-        DEPENDS "${GIT_DIR}/index"
-        VERBATIM
-    )
-else()
-    message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
-endif()
-
 #
 # Compile flags
 #
diff --git a/Makefile b/Makefile
index c53c1e7260fef4..300c1e6c7e1276 100644
--- a/Makefile
+++ b/Makefile
@@ -542,9 +542,9 @@ llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h l
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
-COMMON_DEPS   = common.o sampling.o grammar-parser.o
+COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o
 
-common.o: common/common.cpp build-info.h $(COMMON_H_DEPS)
+common.o: common/common.cpp $(COMMON_H_DEPS)
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 sampling.o: common/sampling.cpp $(COMMON_H_DEPS)
@@ -563,46 +563,46 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
 
 #
 # Examples
 #
 
-main: examples/main/main.cpp                                  build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
+main: examples/main/main.cpp                                  ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 	@echo
 	@echo '====  Run ./main -h for help.  ===='
 	@echo
 
-infill: examples/infill/infill.cpp                            build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
+infill: examples/infill/infill.cpp                            ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-simple: examples/simple/simple.cpp                            build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+simple: examples/simple/simple.cpp                            ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-batched: examples/batched/batched.cpp                         build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+batched: examples/batched/batched.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-batched-bench: examples/batched-bench/batched-bench.cpp       build-info.h ggml.o llama.o common.o $(OBJS)
+batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-quantize: examples/quantize/quantize.cpp                      build-info.h ggml.o llama.o $(OBJS)
+quantize: examples/quantize/quantize.cpp                      build-info.o ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-quantize-stats: examples/quantize-stats/quantize-stats.cpp    build-info.h ggml.o llama.o $(OBJS)
+quantize-stats: examples/quantize-stats/quantize-stats.cpp    build-info.o ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-perplexity: examples/perplexity/perplexity.cpp                build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+perplexity: examples/perplexity/perplexity.cpp                ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-embedding: examples/embedding/embedding.cpp                   build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+embedding: examples/embedding/embedding.cpp                   ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual
 
 gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS)
@@ -614,7 +614,7 @@ train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratc
 convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
@@ -623,19 +623,19 @@ llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip
 baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-finetune: examples/finetune/finetune.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
+finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-export-lora: examples/export-lora/export-lora.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-parallel: examples/parallel/parallel.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 ifdef LLAMA_METAL
@@ -648,7 +648,7 @@ swift: examples/batched.swift
 	(cd examples/batched.swift; make build)
 endif
 
-build-info.h: $(wildcard .git/index) scripts/build-info.sh
+common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
 	@sh scripts/build-info.sh $(CC) > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \
 		mv $@.tmp $@; \
@@ -656,13 +656,16 @@ build-info.h: $(wildcard .git/index) scripts/build-info.sh
 		rm $@.tmp; \
 	fi
 
+build-info.o: common/build-info.cpp
+	$(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@
+
 #
 # Tests
 #
 
 tests: $(TEST_TARGETS)
 
-benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(OBJS)
+benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 run-benchmark-matmult: benchmark-matmult
@@ -676,40 +679,40 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
-tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-c.o: tests/test-c.c llama.h
diff --git a/build.zig b/build.zig
index 9b58b74ca858b6..699738f3dd5092 100644
--- a/build.zig
+++ b/build.zig
@@ -10,7 +10,6 @@ const Maker = struct {
     builder: *std.build.Builder,
     target: CrossTarget,
     optimize: Mode,
-    config_header: *ConfigHeader,
     enable_lto: bool,
 
     include_dirs: ArrayList([]const u8),
@@ -41,26 +40,24 @@ const Maker = struct {
         const commit_hash = try std.ChildProcess.exec(
             .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } },
         );
-        const config_header = builder.addConfigHeader(
-            .{ .style = .blank, .include_path = "build-info.h" },
-            .{
-                .BUILD_NUMBER = 0,
-                .BUILD_COMMIT = commit_hash.stdout[0 .. commit_hash.stdout.len - 1], // omit newline
-                .BUILD_COMPILER = builder.fmt("Zig {s}", .{zig_version}),
-                .BUILD_TARGET = try target.allocDescription(builder.allocator),
-            },
-        );
+        try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt(
+            \\int LLAMA_BUILD_NUMBER = {};
+            \\char const *LLAMA_COMMIT = "{s}";
+            \\char const *LLAMA_COMPILER = "Zig {s}";
+            \\char const *LLAMA_BUILD_TARGET = "{s}";
+            \\
+        , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) }));
         var m = Maker{
             .builder = builder,
             .target = target,
             .optimize = builder.standardOptimizeOption(.{}),
-            .config_header = config_header,
             .enable_lto = false,
             .include_dirs = ArrayList([]const u8).init(builder.allocator),
             .cflags = ArrayList([]const u8).init(builder.allocator),
             .cxxflags = ArrayList([]const u8).init(builder.allocator),
             .objs = ArrayList(*Compile).init(builder.allocator),
         };
+
         try m.addCFlag("-std=c11");
         try m.addCxxFlag("-std=c++11");
         try m.addProjectInclude(&.{});
@@ -72,7 +69,7 @@ const Maker = struct {
         const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize });
         if (o.target.getAbi() != .msvc)
             o.defineCMacro("_GNU_SOURCE", null);
-        o.addConfigHeader(m.config_header);
+
         if (std.mem.endsWith(u8, src, ".c")) {
             o.addCSourceFiles(&.{src}, m.cflags.items);
             o.linkLibC();
@@ -85,7 +82,6 @@ const Maker = struct {
                 o.linkLibCpp();
             }
         }
-        o.addConfigHeader(m.config_header);
         for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i });
         o.want_lto = m.enable_lto;
         return o;
@@ -105,7 +101,6 @@ const Maker = struct {
             // linkLibCpp already add (libc++ + libunwind + libc)
             e.linkLibCpp();
         }
-        e.addConfigHeader(m.config_header);
         m.builder.installArtifact(e);
         e.want_lto = m.enable_lto;
         return e;
@@ -121,6 +116,7 @@ pub fn build(b: *std.build.Builder) !void {
     const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
     const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
     const llama = make.obj("llama", "llama.cpp");
+    const buildinfo = make.obj("common", "common/build-info.cpp");
     const common = make.obj("common", "common/common.cpp");
     const console = make.obj("console", "common/console.cpp");
     const sampling = make.obj("sampling", "common/sampling.cpp");
@@ -128,14 +124,14 @@ pub fn build(b: *std.build.Builder) !void {
     const train = make.obj("train", "common/train.cpp");
     const clip = make.obj("clip", "examples/llava/clip.cpp");
 
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train });
-    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, train });
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });
+    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
+    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
+    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
+    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
+    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
 
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, sampling, grammar_parser, clip });
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip });
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index fbb0ff0952ac7a..0150114e3bd2ce 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -1,8 +1,46 @@
 # common
 
+
+# Build info header
+#
+
+if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
+    set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
+
+    # Is git submodule
+    if(NOT IS_DIRECTORY "${GIT_DIR}")
+        file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
+        string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
+        set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}")
+    endif()
+
+    set(GIT_INDEX "${GIT_DIR}/index")
+else()
+    message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
+    set(GIT_INDEX "")
+endif()
+
+# Add a custom command to rebuild build-info.cpp when .git/index changes
+add_custom_command(
+    OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp"
+    COMMENT "Generating build details from Git"
+    COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
+            -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake"
+    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
+    DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
+    VERBATIM
+)
+set(TARGET build_info)
+add_library(${TARGET} OBJECT build-info.cpp)
+if (BUILD_SHARED_LIBS)
+    set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+endif()
+
+
 set(TARGET common)
 
-add_library(${TARGET} OBJECT
+add_library(${TARGET} STATIC
     common.h
     common.cpp
     sampling.h
@@ -21,4 +59,4 @@ endif()
 
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features(${TARGET} PUBLIC cxx_std_11)
-target_link_libraries(${TARGET} PRIVATE llama)
+target_link_libraries(${TARGET} PRIVATE llama build_info)
diff --git a/common/build-info.cpp.in b/common/build-info.cpp.in
new file mode 100644
index 00000000000000..0b945aa68fff3e
--- /dev/null
+++ b/common/build-info.cpp.in
@@ -0,0 +1,4 @@
+int LLAMA_BUILD_NUMBER = @BUILD_NUMBER@;
+char const *LLAMA_COMMIT = "@BUILD_COMMIT@";
+char const *LLAMA_COMPILER = "@BUILD_COMPILER@";
+char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@";
diff --git a/common/common.cpp b/common/common.cpp
index b182ffaaef48ec..e938dee165d9da 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1,5 +1,4 @@
 #include "common.h"
-#include "build-info.h"
 #include "llama.h"
 
 #include <algorithm>
@@ -1199,8 +1198,8 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
                                const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
     const llama_sampling_params & sparams = params.sparams;
 
-    fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
-    fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
+    fprintf(stream, "build_commit: %s\n",        LLAMA_COMMIT);
+    fprintf(stream, "build_number: %d\n",        LLAMA_BUILD_NUMBER);
     fprintf(stream, "cpu_has_arm_fma: %s\n",     ggml_cpu_has_arm_fma()     ? "true" : "false");
     fprintf(stream, "cpu_has_avx: %s\n",         ggml_cpu_has_avx()         ? "true" : "false");
     fprintf(stream, "cpu_has_avx2: %s\n",        ggml_cpu_has_avx2()        ? "true" : "false");
diff --git a/common/common.h b/common/common.h
index 7be69f925bc2be..72a49b8901f26e 100644
--- a/common/common.h
+++ b/common/common.h
@@ -26,11 +26,17 @@
 #define die(msg)          do { fputs("error: " msg "\n", stderr);                exit(1); } while (0)
 #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
 
-#define print_build_info() do {                                                             \
-    fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);         \
-    fprintf(stderr, "%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET);  \
+#define print_build_info() do {                                                                     \
+    fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);           \
+    fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
 } while(0)
 
+// build info
+extern int LLAMA_BUILD_NUMBER;
+extern char const *LLAMA_COMMIT;
+extern char const *LLAMA_COMPILER;
+extern char const *LLAMA_BUILD_TARGET;
+
 //
 // CLI argument parsing
 //
diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt
index 14916d83134633..2bb47bab5a868b 100644
--- a/examples/benchmark/CMakeLists.txt
+++ b/examples/benchmark/CMakeLists.txt
@@ -1,9 +1,6 @@
 set(TARGET benchmark)
 add_executable(${TARGET} benchmark-matmult.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp
index f1c382aa9b9557..76e3f57ccce8e0 100644
--- a/examples/benchmark/benchmark-matmult.cpp
+++ b/examples/benchmark/benchmark-matmult.cpp
@@ -1,4 +1,3 @@
-#include "build-info.h"
 #include "common.h"
 #include "ggml.h"
 
diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt
index 0c752c7bbb59f8..8ffc33868401f8 100644
--- a/examples/embedding/CMakeLists.txt
+++ b/examples/embedding/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} embedding.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 14075609ebfd91..3295cd2400ac3d 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -1,4 +1,3 @@
-#include "build-info.h"
 #include "common.h"
 #include "llama.h"
 
diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt
index 57d01cb0b5a8fd..e4e8028da09da6 100644
--- a/examples/infill/CMakeLists.txt
+++ b/examples/infill/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} infill.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-    add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 9c52b7bbad1dba..62f5ce3c16a322 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -2,7 +2,6 @@
 
 #include "console.h"
 #include "llama.h"
-#include "build-info.h"
 #include "grammar-parser.h"
 
 #include <cassert>
@@ -184,8 +183,8 @@ int main(int argc, char ** argv) {
         LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
     }
 
-    LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
-    LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET);
+    LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+    LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
diff --git a/examples/llama-bench/CMakeLists.txt b/examples/llama-bench/CMakeLists.txt
index 7e395afd05f755..5bdbea4e28187e 100644
--- a/examples/llama-bench/CMakeLists.txt
+++ b/examples/llama-bench/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} llama-bench.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 780398184d2215..9bd82d565834a9 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -19,7 +19,6 @@
 #include "ggml.h"
 #include "llama.h"
 #include "common.h"
-#include "build-info.h"
 #include "ggml-cuda.h"
 
 // utils
@@ -641,8 +640,8 @@ struct test {
     }
 };
 
-const std::string test::build_commit = BUILD_COMMIT;
-const int         test::build_number = BUILD_NUMBER;
+const std::string test::build_commit = LLAMA_COMMIT;
+const int         test::build_number = LLAMA_BUILD_NUMBER;
 const bool        test::cuda         = !!ggml_cpu_has_cublas();
 const bool        test::opencl       = !!ggml_cpu_has_clblast();
 const bool        test::metal        = !!ggml_cpu_has_metal();
diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt
index 2d7979ecd89527..03d32c26efaddd 100644
--- a/examples/llava/CMakeLists.txt
+++ b/examples/llava/CMakeLists.txt
@@ -5,9 +5,6 @@ target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if (NOT MSVC)
     target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h
-    endif()
-if(TARGET BUILD_INFO)
-    add_dependencies(${TARGET} BUILD_INFO)
 endif()
 
 set(TARGET llava)
@@ -15,6 +12,3 @@ add_executable(${TARGET} llava.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-    add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt
index cc188894804bab..d532980b76da83 100644
--- a/examples/main/CMakeLists.txt
+++ b/examples/main/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} main.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 8a43b6ab878a5f..8d985c82ac21a9 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -2,7 +2,6 @@
 
 #include "console.h"
 #include "llama.h"
-#include "build-info.h"
 
 #include <cassert>
 #include <cinttypes>
@@ -153,8 +152,8 @@ int main(int argc, char ** argv) {
         LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
     }
 
-    LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
-    LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET);
+    LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+    LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt
index 0bbf89eaefce6f..319535a6e9054d 100644
--- a/examples/parallel/CMakeLists.txt
+++ b/examples/parallel/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} parallel.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index 9a0b9c183d1070..a78df305f415c3 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -1,8 +1,6 @@
 // A basic application simulating a server with multiple clients.
 // The clients submite requests to the server and they are processed in parallel.
 
-#include "build-info.h"
-
 #include "common.h"
 #include "llama.h"
 
diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt
index af00b4e1650162..3c76d3221416b7 100644
--- a/examples/perplexity/CMakeLists.txt
+++ b/examples/perplexity/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} perplexity.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index bd2c73d87875fe..de60c5227f7c17 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -1,4 +1,3 @@
-#include "build-info.h"
 #include "common.h"
 #include "llama.h"
 
diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt
index db182e2633f1fb..e31cf5e3809c1e 100644
--- a/examples/quantize-stats/CMakeLists.txt
+++ b/examples/quantize-stats/CMakeLists.txt
@@ -1,6 +1,6 @@
 set(TARGET quantize-stats)
 add_executable(${TARGET} quantize-stats.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index dd76b1ceef134d..2712824774ae74 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -1,5 +1,4 @@
 #define LLAMA_API_INTERNAL
-#include "build-info.h"
 #include "common.h"
 #include "ggml.h"
 #include "llama.h"
diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt
index 4a8eed544cb04a..6f374a2bd3b467 100644
--- a/examples/quantize/CMakeLists.txt
+++ b/examples/quantize/CMakeLists.txt
@@ -1,9 +1,6 @@
 set(TARGET quantize)
 add_executable(${TARGET} quantize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index be0b2fe1eb963f..d27ea5e9132fdc 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -1,4 +1,3 @@
-#include "build-info.h"
 #include "common.h"
 #include "llama.h"
 
diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt
index eadd13cdf7930a..cc6ed8554a6e34 100644
--- a/examples/save-load-state/CMakeLists.txt
+++ b/examples/save-load-state/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} save-load-state.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 38d05f4d328e7a..48d80111010dfa 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -1,4 +1,3 @@
-#include "build-info.h"
 #include "common.h"
 #include "llama.h"
 
diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index a23ddcc550274f..1f0d26f7776894 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -11,6 +11,3 @@ if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 84b04d5a0493a6..fd755327a511db 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1,6 +1,5 @@
 #include "common.h"
 #include "llama.h"
-#include "build-info.h"
 #include "grammar-parser.h"
 
 #include "../llava/clip.h"
@@ -2264,8 +2263,8 @@ int main(int argc, char **argv)
 
     llama_backend_init(params.numa);
 
-    LOG_INFO("build info", {{"build", BUILD_NUMBER},
-                            {"commit", BUILD_COMMIT}});
+    LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER},
+                            {"commit", LLAMA_COMMIT}});
 
     LOG_INFO("system info", {
                                 {"n_threads", params.n_threads},
diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt
index 6c5c9456e62344..810f3c46ac4aab 100644
--- a/examples/speculative/CMakeLists.txt
+++ b/examples/speculative/CMakeLists.txt
@@ -3,6 +3,3 @@ add_executable(${TARGET} speculative.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 323c74652c9a69..798684f66678e2 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -1,5 +1,3 @@
-#include "build-info.h"
-
 #include "common.h"
 #include "llama.h"
 
diff --git a/scripts/build-info.cmake b/scripts/build-info.cmake
index c86ab43790c753..73853dfa47f41e 100644
--- a/scripts/build-info.cmake
+++ b/scripts/build-info.cmake
@@ -1,5 +1,5 @@
-set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.h.in")
-set(HEADER_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.h")
+set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
+set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
 set(BUILD_NUMBER 0)
 set(BUILD_COMMIT "unknown")
 set(BUILD_COMPILER "unknown")
@@ -24,15 +24,21 @@ if(Git_FOUND)
         WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
         OUTPUT_VARIABLE HEAD
         OUTPUT_STRIP_TRAILING_WHITESPACE
+        RESULT_VARIABLE RES
     )
+    if (RES EQUAL 0)
+        set(BUILD_COMMIT ${HEAD})
+    endif()
     execute_process(
         COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
         WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
         OUTPUT_VARIABLE COUNT
         OUTPUT_STRIP_TRAILING_WHITESPACE
+        RESULT_VARIABLE RES
     )
-    set(BUILD_COMMIT ${HEAD})
-    set(BUILD_NUMBER ${COUNT})
+    if (RES EQUAL 0)
+        set(BUILD_NUMBER ${COUNT})
+    endif()
 endif()
 
 if(MSVC)
@@ -53,22 +59,22 @@ else()
     set(BUILD_TARGET ${OUT})
 endif()
 
-# Only write the header if it's changed to prevent unnecessary recompilation
-if(EXISTS ${HEADER_FILE})
-    file(READ ${HEADER_FILE} CONTENTS)
-    string(REGEX MATCH "BUILD_COMMIT \"([^\"]*)\"" _ ${CONTENTS})
+# Only write the build info if it changed
+if(EXISTS ${OUTPUT_FILE})
+    file(READ ${OUTPUT_FILE} CONTENTS)
+    string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
     set(OLD_COMMIT ${CMAKE_MATCH_1})
-    string(REGEX MATCH "BUILD_COMPILER \"([^\"]*)\"" _ ${CONTENTS})
+    string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
     set(OLD_COMPILER ${CMAKE_MATCH_1})
-    string(REGEX MATCH "BUILD_TARGET \"([^\"]*)\"" _ ${CONTENTS})
+    string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
     set(OLD_TARGET ${CMAKE_MATCH_1})
     if (
         NOT OLD_COMMIT   STREQUAL BUILD_COMMIT   OR
         NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
         NOT OLD_TARGET   STREQUAL BUILD_TARGET
     )
-        configure_file(${TEMPLATE_FILE} ${HEADER_FILE})
+        configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
     endif()
 else()
-    configure_file(${TEMPLATE_FILE} ${HEADER_FILE})
+    configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
 endif()
diff --git a/scripts/build-info.h.in b/scripts/build-info.h.in
deleted file mode 100644
index e996faef039748..00000000000000
--- a/scripts/build-info.h.in
+++ /dev/null
@@ -1,9 +0,0 @@
-#ifndef BUILD_INFO_H
-#define BUILD_INFO_H
-
-#define BUILD_NUMBER @BUILD_NUMBER@
-#define BUILD_COMMIT "@BUILD_COMMIT@"
-#define BUILD_COMPILER "@BUILD_COMPILER@"
-#define BUILD_TARGET "@BUILD_TARGET@"
-
-#endif // BUILD_INFO_H
diff --git a/scripts/build-info.sh b/scripts/build-info.sh
index 3c8b1fb850eef0..32682afbdbd520 100755
--- a/scripts/build-info.sh
+++ b/scripts/build-info.sh
@@ -24,12 +24,7 @@ if out=$($CC -dumpmachine); then
   build_target=$out
 fi
 
-echo "#ifndef BUILD_INFO_H"
-echo "#define BUILD_INFO_H"
-echo
-echo "#define BUILD_NUMBER $build_number"
-echo "#define BUILD_COMMIT \"$build_commit\""
-echo "#define BUILD_COMPILER \"$build_compiler\""
-echo "#define BUILD_TARGET \"$build_target\""
-echo
-echo "#endif // BUILD_INFO_H"
+echo "int LLAMA_BUILD_NUMBER = ${build_number};"
+echo "char const *LLAMA_COMMIT = \"${build_commit}\";"
+echo "char const *LLAMA_COMPILER = \"${build_compiler}\";"
+echo "char const *LLAMA_BUILD_TARGET = \"${build_target}\";"

From 1efae9b7dca2a5cc5aa21c1997b538022964ea19 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 09:54:18 +0200
Subject: [PATCH 55/79] llm : prevent from 1-D tensors being GPU split (#3697)

---
 llama.cpp | 28 +++++++++++++++++-----------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index 32d7d23de6afb1..bb60044b4707f7 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1837,6 +1837,12 @@ struct llama_model_loader {
             throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
         }
 
+        if (backend == GGML_BACKEND_GPU_SPLIT) {
+            if (ne.size() == 1) {
+                throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str()));
+            }
+        }
+
         {
             bool is_ok = true;
             for (size_t i = 0; i < ne.size(); ++i) {
@@ -2817,8 +2823,8 @@ static void llm_load_tensors(
                         layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
                         layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
 
-                        layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);
+                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, backend_split);
+                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),           {n_ff}, backend);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=
@@ -2877,13 +2883,13 @@ static void llm_load_tensors(
                         layer.attn_norm     = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
                         layer.attn_norm_b   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd}, backend);
                         layer.wqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa},         backend_split);
+                        layer.bqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa},         backend);
                         layer.wo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd},   backend_split);
-                        layer.bo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd},           backend_split);
+                        layer.bo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd},           backend);
                         layer.ffn_down      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd},       backend_split);
+                        layer.ffn_down_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd},       backend);
                         layer.ffn_up        = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_up_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff},           backend_split);
+                        layer.ffn_up_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff},           backend);
                         layer.ffn_norm      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, backend);
                         layer.ffn_norm_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd}, backend);
                         layer.attn_q_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
@@ -2949,19 +2955,19 @@ static void llm_load_tensors(
                         layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
 
                         layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend_split);
+                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);
 
                         layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},                        backend_split);
+                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},                        backend);
 
                         layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                         layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
 
                         layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend_split);
+                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
 
-                        layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend_split);
+                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);
 
                         if (backend == GGML_BACKEND_GPU) {
                             vram_weights +=

From 2756c4fbffab097736d5116007872d86456a544a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 11:20:21 +0200
Subject: [PATCH 56/79] gguf : remove special-case code for GGUFv1 (#3901)

ggml-ci
---
 ggml.c                       |  58 +++--------------------------------
 models/ggml-vocab-llama.gguf | Bin 595423 -> 723676 bytes
 2 files changed, 5 insertions(+), 53 deletions(-)

diff --git a/ggml.c b/ggml.c
index 2c7fe476b176d5..d5a49d8e4f3148 100644
--- a/ggml.c
+++ b/ggml.c
@@ -18811,8 +18811,7 @@ static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset)
     return n == size;
 }
 
-// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
-static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) {
+static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) {
     p->n    = 0;
     p->data = NULL;
 
@@ -18824,19 +18823,6 @@ static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset
     return ok;
 }
 
-static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) {
-    p->n    = 0;
-    p->data = NULL;
-
-    bool ok = true;
-
-    uint32_t n = 0;
-    ok = ok && gguf_fread_el(file, &n,       sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n;
-    ok = ok && gguf_fread_el(file,  p->data, p->n,      offset);
-
-    return ok;
-}
-
 struct gguf_context * gguf_init_empty(void) {
     struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
 
@@ -18895,21 +18881,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         ctx->data  = NULL;
 
         ok = ok && gguf_fread_el(file, &ctx->header.version,   sizeof(ctx->header.version),   &offset);
-
-        if (ctx->header.version == 1) {
-            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
-            uint32_t n_tensors = 0;
-            uint32_t n_kv      = 0;
-
-            ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset);
-            ok = ok && gguf_fread_el(file, &n_kv,      sizeof(n_kv),      &offset);
-
-            ctx->header.n_tensors = n_tensors;
-            ctx->header.n_kv      = n_kv;
-        } else {
-            ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
-            ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
-        }
+        ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
+        ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
 
         if (!ok) {
             fprintf(stderr, "%s: failed to read header\n", __func__);
@@ -18919,12 +18892,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         }
     }
 
-    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
-    bool (* gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur;
-    if (ctx->header.version == 1) {
-        gguf_fread_str = gguf_fread_str_v1;
-    }
-
     // read the kv pairs
     {
         ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv));
@@ -18955,15 +18922,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 case GGUF_TYPE_ARRAY:
                     {
                         ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset);
-
-                        if (ctx->header.version == 1) {
-                            // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
-                            uint32_t n = 0;
-                            ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset);
-                            kv->value.arr.n = n;
-                        } else {
-                            ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset);
-                        }
+                        ok = ok && gguf_fread_el(file, &kv->value.arr.n,    sizeof(kv->value.arr.n), &offset);
 
                         switch (kv->value.arr.type) {
                             case GGUF_TYPE_UINT8:
@@ -19022,14 +18981,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             ok = ok && gguf_fread_str(file, &info->name,                          &offset);
             ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims),  &offset);
             for (uint32_t j = 0; j < info->n_dims; ++j) {
-                if (ctx->header.version == 1) {
-                    // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023
-                    uint32_t t = 0;
-                    ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset);
-                    info->ne[j] = t;
-                } else {
-                    ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
-                }
+                ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
             }
             ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
             ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
diff --git a/models/ggml-vocab-llama.gguf b/models/ggml-vocab-llama.gguf
index 63bfaf672f382c0f5bbcffe54736e2698ef3ac55..549eed8c53f438a61f1b00c9bd3b7d02325f2479 100644
GIT binary patch
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From 21958bb393a654591ed26f339791b752d58f5c8b Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Thu, 2 Nov 2023 13:10:33 +0100
Subject: [PATCH 57/79] cmake : disable LLAMA_NATIVE by default (#3906)

---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 611ed3f4d2a64d..3c49d645c3196f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -44,7 +44,7 @@ endif()
 
 # general
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
-option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
+option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      OFF)
 option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
 
 # debug

From 4ff1046d75e64f0e556d8dcd930ea25c23eb8b18 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 16:22:30 +0200
Subject: [PATCH 58/79] gguf : print error for GGUFv1 files (#3908)

---
 ggml.c | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/ggml.c b/ggml.c
index d5a49d8e4f3148..605a27940fc81f 100644
--- a/ggml.c
+++ b/ggml.c
@@ -18884,6 +18884,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset);
         ok = ok && gguf_fread_el(file, &ctx->header.n_kv,      sizeof(ctx->header.n_kv),      &offset);
 
+        if (ctx->header.version == 1) {
+            fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__);
+            fclose(file);
+            gguf_free(ctx);
+            return NULL;
+        }
+
         if (!ok) {
             fprintf(stderr, "%s: failed to read header\n", __func__);
             fclose(file);

From d6069051de7165a4e06662c89257f5d2905bb156 Mon Sep 17 00:00:00 2001
From: Oleksii Maryshchenko <oleksii.maryshchenko@gmail.com>
Date: Thu, 2 Nov 2023 18:10:39 +0100
Subject: [PATCH 59/79] cuda : use CUDA memory pool with async memory
 allocation/deallocation when available (#3903)

* Using cuda memory pools for async alloc/dealloc.

* If cuda device doesnt support memory pool than use old implementation.

* Removed redundant cublasSetStream

---------

Co-authored-by: Oleksii Maryshchenko <omaryshchenko@dtis.com>
---
 ggml-cuda.cu | 128 +++++++++++++++++++++++++++++++--------------------
 1 file changed, 77 insertions(+), 51 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index e4629512611b6c..58b58f33154286 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -181,11 +181,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
     do {                                                                                \
         cudaError_t err_ = (err);                                                       \
         if (err_ != cudaSuccess) {                                                      \
-            int id;                                                                     \
-            cudaGetDevice(&id);                                                         \
+            int dev_id;                                                                     \
+            cudaGetDevice(&dev_id);                                                         \
             fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
                 cudaGetErrorString(err_));                                              \
-            fprintf(stderr, "current device: %d\n", id);                                \
+            fprintf(stderr, "current device: %d\n", dev_id);                                \
             exit(1);                                                                    \
         }                                                                               \
     } while (0)
@@ -195,11 +195,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
     do {                                                                                \
         cublasStatus_t err_ = (err);                                                    \
         if (err_ != CUBLAS_STATUS_SUCCESS) {                                            \
-            int id;                                                                     \
-            cudaGetDevice(&id);                                                         \
+            int dev_id;                                                                     \
+            cudaGetDevice(&dev_id);                                                         \
             fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n",                         \
                     err_, __FILE__, __LINE__, cublasGetStatusString(err_));             \
-            fprintf(stderr, "current device: %d\n", id);                                \
+            fprintf(stderr, "current device: %d\n", dev_id);                                \
             exit(1);                                                                    \
         }                                                                               \
     } while (0)
@@ -465,6 +465,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA
 
 #define MAX_STREAMS 8
 static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr };
+static cudaMemPool_t g_cudaMemPools[GGML_CUDA_MAX_DEVICES] = { nullptr };
 
 struct ggml_tensor_extra_gpu {
     void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
@@ -5772,6 +5773,16 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
     return ptr;
 }
 
+static void * ggml_cuda_pool_malloc_async(size_t size, size_t * actual_size, int id, cudaStream_t stream) {
+    if (g_cudaMemPools[id] == nullptr) {
+        return ggml_cuda_pool_malloc(size, actual_size);
+    }
+    void *ptr;
+    CUDA_CHECK(cudaMallocFromPoolAsync(&ptr, size, g_cudaMemPools[id], stream));
+    *actual_size = size;
+    return ptr;
+}
+
 static void ggml_cuda_pool_free(void * ptr, size_t size) {
     scoped_spin_lock lock(g_cuda_pool_lock);
     int id;
@@ -5790,6 +5801,13 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) {
 }
 
 
+static void ggml_cuda_pool_free_async(void * ptr, size_t actual_size, int id, cudaStream_t stream) {
+    if (g_cudaMemPools[id] == nullptr) {
+        return ggml_cuda_pool_free(ptr, actual_size);
+    }
+    CUDA_CHECK(cudaFreeAsync(ptr, stream));
+}
+
 void ggml_init_cublas() {
     static bool initialized = false;
 
@@ -5844,6 +5862,13 @@ void ggml_init_cublas() {
             // create cublas handle
             CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
             CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH));
+
+            // configure memory pool
+            cudaError_t err = cudaDeviceGetMemPool(&g_cudaMemPools[id], id);
+            if (err == cudaSuccess) {
+                size_t treshold = UINT64_MAX;
+                CUDA_CHECK(cudaMemPoolSetAttribute(g_cudaMemPools[id], cudaMemPoolAttrReleaseThreshold, &treshold));
+            }
         }
 
         // configure logging to stdout
@@ -6437,7 +6462,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
             size_t ne = row_diff*ne00;
-            src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as);
+            src0_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src0_as, id, stream);
             to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream);
         }
         const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16;
@@ -6448,13 +6473,12 @@ inline void ggml_cuda_op_mul_mat_cublas(
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
             size_t ne = src1_ncols*ne10;
-            src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as);
+            src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src1_as, id, stream);
             to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
         }
         const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16;
-
-        size_t dst_as = 0;
-        half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
+        size_t dst_f16_as = 0;
+        half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(row_diff*src1_ncols * sizeof(half), &dst_f16_as, id, stream);
 
         const half alpha_f16 = 1.0f;
         const half beta_f16 = 0.0f;
@@ -6472,14 +6496,15 @@ inline void ggml_cuda_op_mul_mat_cublas(
         const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
         to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
 
-        ggml_cuda_pool_free(dst_f16, dst_as);
+        if (dst_f16_as != 0) {
+            ggml_cuda_pool_free_async(dst_f16, dst_f16_as, id, stream);
+        }
 
         if (src0_as != 0) {
-            ggml_cuda_pool_free(src0_as_f16, src0_as);
+            ggml_cuda_pool_free_async(src0_as_f16, src0_as, id, stream);
         }
-
         if (src1_as != 0) {
-            ggml_cuda_pool_free(src1_as_f16, src1_as);
+            ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, stream);
         }
     }
     else {
@@ -6489,7 +6514,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
         if (src0->type != GGML_TYPE_F32) {
             const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
             GGML_ASSERT(to_fp32_cuda != nullptr);
-            src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT
+            src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc_async(row_diff*ne00 * sizeof(float), &src0_as, id, stream); // NOLINT
             to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream);
         }
         const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32;
@@ -6506,7 +6531,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
                     &beta,  dst_dd_i,   ldc));
 
         if (src0_as != 0) {
-            ggml_cuda_pool_free(src0_ddq_as_f32, src0_as);
+            ggml_cuda_pool_free_async(src0_ddq_as_f32, src0_as, id, stream);
         }
     }
 
@@ -6929,21 +6954,22 @@ static void ggml_cuda_op_mul_mat(
             src0_dd[id] = (char *) src0_extra->data_device[id];
         } else {
             const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0);
-            src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]);
+            src0_dd[id] = (char *) ggml_cuda_pool_malloc_async(ggml_nbytes(src0), &src0_as[id], id, stream);
         }
 
         if (src1_on_device && src1_is_contiguous) {
             src1_ddf[id] = (float *) src1_extra->data_device[id];
         } else {
-            src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]);
+            src1_ddf[id] = (float *) ggml_cuda_pool_malloc_async(ggml_nbytes(src1), &src1_asf[id], id, stream);
         }
 
         if (convert_src1_to_q8_1) {
-            src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]);
+            const size_t size_dst_ddq = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs;
+            src1_ddq[id] = (char *) ggml_cuda_pool_malloc_async(size_dst_ddq, &src1_asq[id], id, stream);
 
             if (src1_on_device && src1_is_contiguous) {
                 quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream);
-                CUDA_CHECK(cudaGetLastError());
+                // CUDA_CHECK(cudaGetLastError());
             }
         }
 
@@ -6951,7 +6977,7 @@ static void ggml_cuda_op_mul_mat(
             dst_dd[id] = (float *) dst_extra->data_device[id];
         } else {
             const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst);
-            dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]);
+            dst_dd[id] = (float *) ggml_cuda_pool_malloc_async(size_dst_ddf, &dst_as[id], id,  stream);
         }
     }
 
@@ -7077,24 +7103,6 @@ static void ggml_cuda_op_mul_mat(
         }
     }
 
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        CUDA_CHECK(ggml_cuda_set_device(id));
-
-        // free buffers again when done
-        if (src0_as[id] > 0) {
-            ggml_cuda_pool_free(src0_dd[id], src0_as[id]);
-        }
-        if (src1_asf[id] > 0) {
-            ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]);
-        }
-        if (src1_asq[id] > 0) {
-            ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]);
-        }
-        if (dst_as[id] > 0) {
-            ggml_cuda_pool_free(dst_dd[id], dst_as[id]);
-        }
-    }
-
     // main device waits for all other devices to be finished
     if (split && g_device_count > 1) {
         int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
@@ -7112,6 +7120,21 @@ static void ggml_cuda_op_mul_mat(
         CUDA_CHECK(ggml_cuda_set_device(g_main_device));
         CUDA_CHECK(cudaDeviceSynchronize());
     }
+
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        if (src0_as[id] > 0) {
+            ggml_cuda_pool_free_async(src0_dd[id], src0_as[id], id, g_cudaStreams[id][0]);
+        }
+        if (src1_asf[id] > 0) {
+            ggml_cuda_pool_free_async(src1_ddf[id], src1_asf[id], id, g_cudaStreams[id][0]);
+        }
+        if (src1_asq[id] > 0) {
+            ggml_cuda_pool_free_async(src1_ddq[id], src1_asq[id], id, g_cudaStreams[id][0]);
+        }
+        if (dst_as[id] > 0) {
+            ggml_cuda_pool_free_async(dst_dd[id], dst_as[id], id, g_cudaStreams[id][0]);
+        }
+    }
 }
 
 static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -7298,11 +7321,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     GGML_ASSERT(to_fp16_cuda != nullptr);
 
     size_t src1_as = 0;
-    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
+    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne1 * sizeof(half), &src1_as, id, main_stream);
     to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
 
     size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
+    half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &dst_as, id, main_stream);
 
     GGML_ASSERT(ne12 % ne02 == 0);
     GGML_ASSERT(ne13 % ne03 == 0);
@@ -7349,10 +7372,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     } else {
         // use cublasGemmBatchedEx
         const int ne23 = ne12*ne13;
-
-        void ** ptrs_as = nullptr;
+        // allocate device memory for pointers
         size_t ptrs_s = 0;
-        ptrs_as = (void **) ggml_cuda_pool_malloc(3*ne23*sizeof(void *), &ptrs_s);
+        void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream);
 
         dim3 block_dims(ne13, ne12);
         k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
@@ -7365,7 +7387,6 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
                 dst->nb[2], dst->nb[3],
                 r2, r3);
         CUDA_CHECK(cudaGetLastError());
-
         CUBLAS_CHECK(
         cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
@@ -7375,16 +7396,21 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
                 ne23,
                 CUBLAS_COMPUTE_16F,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
-
-        ggml_cuda_pool_free(ptrs_as, ptrs_s);
+        // free device memory for pointers
+        if (ptrs_s != 0) {
+            ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream);
+        }
     }
 #endif
 
     const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
     to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
-
-    ggml_cuda_pool_free(src1_as_f16, src1_as);
-    ggml_cuda_pool_free(dst_f16, dst_as);
+    if (src1_as != 0) {
+        ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, main_stream);
+    }
+    if (dst_as != 0) {
+        ggml_cuda_pool_free_async(dst_f16, dst_as, id, main_stream);
+    }
 }
 
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {

From c7743fe1c1cbda5a886362aa371480360580fdf0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 20:32:11 +0200
Subject: [PATCH 60/79] cuda : fix const ptrs warning causing ROCm build issues
 (#3913)

---
 ggml-cuda.cu | 37 +++++++++++++++++++++++--------------
 1 file changed, 23 insertions(+), 14 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 58b58f33154286..06c28f5651b72f 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -7248,7 +7248,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
 
 __global__ void k_compute_batched_ptrs(
         const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
-        void ** ptrs,
+        const void ** ptrs_src, void ** ptrs_dst,
         int ne12, int ne13,
         int ne23,
         int nb02, int nb03,
@@ -7265,9 +7265,9 @@ __global__ void k_compute_batched_ptrs(
     int i03 = i13 / r3;
     int i02 = i12 / r2;
 
-    ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02   + i03*nb03;
-    ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
-    ptrs[2*ne23 + i12 + i13*ne12] = (char *)     dst_f16 + i12* nb2/2 + i13* nb3/2;
+    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02   + i03*nb03;
+    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
+    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)     dst_f16 + i12* nb2/2 + i13* nb3/2;
 }
 
 static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -7372,14 +7372,20 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     } else {
         // use cublasGemmBatchedEx
         const int ne23 = ne12*ne13;
-        // allocate device memory for pointers
-        size_t ptrs_s = 0;
-        void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream);
+
+        const void ** ptrs_src = nullptr;
+              void ** ptrs_dst = nullptr;
+
+        size_t ptrs_src_s = 0;
+        size_t ptrs_dst_s = 0;
+
+        ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream);
+        ptrs_dst = (      void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream);
 
         dim3 block_dims(ne13, ne12);
         k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
                 src0_as_f16, src1_as_f16, dst_f16,
-                ptrs_as,
+                ptrs_src, ptrs_dst,
                 ne12, ne13,
                 ne23,
                 nb02, nb03,
@@ -7390,15 +7396,18 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         CUBLAS_CHECK(
         cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
-                &alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
-                            (const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
-                &beta_f16,  (      void **       ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01,
+                &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
+                            (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
+                &beta_f16,  (      void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
                 ne23,
                 CUBLAS_COMPUTE_16F,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
-        // free device memory for pointers
-        if (ptrs_s != 0) {
-            ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream);
+
+        if (ptrs_src_s != 0) {
+            ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream);
+        }
+        if (ptrs_dst_s != 0) {
+            ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream);
         }
     }
 #endif

From 224e7d5b14cbabab7ae45c64db2cfde979c8455d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 2 Nov 2023 20:44:12 +0200
Subject: [PATCH 61/79] readme : add notice about #3912

---
 README.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/README.md b/README.md
index b56ecaec74b434..9c9e36ad07accf 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,6 @@
 
 ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
 
-[![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
@@ -11,8 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
-- LLaVA support: https://github.com/ggerganov/llama.cpp/pull/3436
-- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252)
+- ⚠️ **Upcoming change that might break functionality. Help with testing is needed:** https://github.com/ggerganov/llama.cpp/pull/3912
 
 ----
 

From 51b2fc11f7f605fff49725a4540e9a6ef7b51b70 Mon Sep 17 00:00:00 2001
From: Andrei <abetlen@gmail.com>
Date: Thu, 2 Nov 2023 15:40:31 -0400
Subject: [PATCH 62/79] cmake : fix relative path to git submodule index
 (#3915)

---
 common/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 0150114e3bd2ce..ac594b2ca84ea8 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -11,7 +11,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
     if(NOT IS_DIRECTORY "${GIT_DIR}")
         file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
         string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
-        set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/${REAL_GIT_DIR}")
+        set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
     endif()
 
     set(GIT_INDEX "${GIT_DIR}/index")

From 629f917cd6b96ba1274c49a8aab163b1b189229d Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Thu, 2 Nov 2023 13:58:22 -0600
Subject: [PATCH 63/79] cuda : add ROCM aliases for CUDA pool stuff (#3918)

---
 ggml-cuda.cu | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 06c28f5651b72f..baf02df2b22948 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -39,6 +39,10 @@
 #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
 #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
 #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
+#define cudaDeviceGetMemPool hipDeviceGetMemPool
+#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold
+#define cudaMemPoolSetAttribute hipMemPoolSetAttribute
+#define cudaMemPool_t hipMemPool_t
 #define cudaDeviceProp hipDeviceProp_t
 #define cudaDeviceSynchronize hipDeviceSynchronize
 #define cudaError_t hipError_t
@@ -48,6 +52,7 @@
 #define cudaEvent_t hipEvent_t
 #define cudaEventDestroy hipEventDestroy
 #define cudaFree hipFree
+#define cudaFreeAsync hipFreeAsync
 #define cudaFreeHost hipHostFree
 #define cudaGetDevice hipGetDevice
 #define cudaGetDeviceCount hipGetDeviceCount
@@ -55,6 +60,7 @@
 #define cudaGetErrorString hipGetErrorString
 #define cudaGetLastError hipGetLastError
 #define cudaMalloc hipMalloc
+#define cudaMallocFromPoolAsync hipMallocFromPoolAsync
 #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
 #define cudaMemcpy hipMemcpy
 #define cudaMemcpy2DAsync hipMemcpy2DAsync

From 3fdbe6b66b7b5c6ad3b2f245cbad1517c27ff776 Mon Sep 17 00:00:00 2001
From: cebtenzzre <cebtenzzre@gmail.com>
Date: Fri, 3 Nov 2023 02:31:58 -0400
Subject: [PATCH 64/79] llama : change yarn_ext_factor placeholder to -1
 (#3922)

---
 llama.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index bb60044b4707f7..cc0211ceb02113 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -7982,7 +7982,7 @@ struct llama_context_params llama_context_default_params() {
         /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
-        /*.yarn_ext_factor             =*/ NAN,
+        /*.yarn_ext_factor             =*/ -1.0f,
         /*.yarn_attn_factor            =*/ 1.0f,
         /*.yarn_beta_fast              =*/ 32.0f,
         /*.yarn_beta_slow              =*/ 1.0f,
@@ -8125,7 +8125,7 @@ struct llama_context * llama_new_context_with_model(
         cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
     }
 
-    if (std::isnan(cparams.yarn_ext_factor)) { // NaN indicates 'not set'
+    if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
         cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
     }
 

From 05816027d649f977468fc804cdb54e99eac246d1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 3 Nov 2023 09:24:00 +0200
Subject: [PATCH 65/79] common : YAYF (yet another YARN fix) (#3925)

ggml-ci
---
 common/common.h | 44 ++++++++++++++++++++++----------------------
 llama.h         | 10 +++++-----
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/common/common.h b/common/common.h
index 72a49b8901f26e..9ad62563302872 100644
--- a/common/common.h
+++ b/common/common.h
@@ -43,29 +43,29 @@ extern char const *LLAMA_BUILD_TARGET;
 int32_t get_num_physical_cores();
 
 struct gpt_params {
-    uint32_t seed                           = -1;   // RNG seed
+    uint32_t seed                           = -1;    // RNG seed
     int32_t n_threads                       = get_num_physical_cores();
-    int32_t n_threads_batch                 = -1;   // number of threads to use for batch processing (-1 = use n_threads)
-    int32_t n_predict                       = -1;   // new tokens to predict
-    int32_t n_ctx                           = 512;  // context size
-    int32_t n_batch                         = 512;  // batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_keep                          = 0;    // number of tokens to keep from initial prompt
-    int32_t n_draft                         = 16;   // number of tokens to draft during speculative decoding
-    int32_t n_chunks                        = -1;   // max number of chunks to process (-1 = unlimited)
-    int32_t n_parallel                      = 1;    // number of parallel sequences to decode
-    int32_t n_sequences                     = 1;    // number of sequences to decode
-    int32_t n_gpu_layers                    = -1;   // number of layers to store in VRAM (-1 - use default)
-    int32_t n_gpu_layers_draft              = -1;   // number of layers to store in VRAM for the draft model (-1 - use default)
-    int32_t main_gpu                        = 0;    // the GPU that is used for scratch and small tensors
-    float   tensor_split[LLAMA_MAX_DEVICES] = {0};  // how split tensors should be distributed across GPUs
-    int32_t n_beams                         = 0;    // if non-zero then use beam search of given width.
-    float   rope_freq_base                  = 0.0f; // RoPE base frequency
-    float   rope_freq_scale                 = 0.0f; // RoPE frequency scaling factor
-    float   yarn_ext_factor                 = NAN;  // YaRN extrapolation mix factor
-    float   yarn_attn_factor                = 1.0f; // YaRN magnitude scaling factor
-    float   yarn_beta_fast                  = 32.0f;// YaRN low correction dim
-    float   yarn_beta_slow                  = 1.0f; // YaRN high correction dim
-    int32_t yarn_orig_ctx                   = 0;    // YaRN original context length
+    int32_t n_threads_batch                 = -1;    // number of threads to use for batch processing (-1 = use n_threads)
+    int32_t n_predict                       = -1;    // new tokens to predict
+    int32_t n_ctx                           = 512;   // context size
+    int32_t n_batch                         = 512;   // batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_keep                          = 0;     // number of tokens to keep from initial prompt
+    int32_t n_draft                         = 16;    // number of tokens to draft during speculative decoding
+    int32_t n_chunks                        = -1;    // max number of chunks to process (-1 = unlimited)
+    int32_t n_parallel                      = 1;     // number of parallel sequences to decode
+    int32_t n_sequences                     = 1;     // number of sequences to decode
+    int32_t n_gpu_layers                    = -1;    // number of layers to store in VRAM (-1 - use default)
+    int32_t n_gpu_layers_draft              = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
+    int32_t main_gpu                        = 0;     // the GPU that is used for scratch and small tensors
+    float   tensor_split[LLAMA_MAX_DEVICES] = {0};   // how split tensors should be distributed across GPUs
+    int32_t n_beams                         = 0;     // if non-zero then use beam search of given width.
+    float   rope_freq_base                  = 0.0f;  // RoPE base frequency
+    float   rope_freq_scale                 = 0.0f;  // RoPE frequency scaling factor
+    float   yarn_ext_factor                 = -1.0f; // YaRN extrapolation mix factor
+    float   yarn_attn_factor                = 1.0f;  // YaRN magnitude scaling factor
+    float   yarn_beta_fast                  = 32.0f; // YaRN low correction dim
+    float   yarn_beta_slow                  = 1.0f;  // YaRN high correction dim
+    int32_t yarn_orig_ctx                   = 0;     // YaRN original context length
     int8_t  rope_scaling_type               = LLAMA_ROPE_SCALING_UNSPECIFIED;
 
     // // sampling parameters
diff --git a/llama.h b/llama.h
index 3f1becd7616885..e8dc04bb54b81c 100644
--- a/llama.h
+++ b/llama.h
@@ -175,11 +175,11 @@ extern "C" {
     };
 
     struct llama_context_params {
-        uint32_t seed;            // RNG seed, -1 for random
-        uint32_t n_ctx;           // text context, 0 = from model
-        uint32_t n_batch;         // prompt processing maximum batch size
-        uint32_t n_threads;       // number of threads to use for generation
-        uint32_t n_threads_batch; // number of threads to use for batch processing
+        uint32_t seed;              // RNG seed, -1 for random
+        uint32_t n_ctx;             // text context, 0 = from model
+        uint32_t n_batch;           // prompt processing maximum batch size
+        uint32_t n_threads;         // number of threads to use for generation
+        uint32_t n_threads_batch;   // number of threads to use for batch processing
         int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054

From 8f961abdc4e134c83bf8c2ad618ab256b4cae0f9 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 3 Nov 2023 09:41:17 +0200
Subject: [PATCH 66/79] speculative : change default p_accept to 0.5 + CLI args
 (#3919)

ggml-ci
---
 common/common.cpp                    | 14 ++++++++++++++
 common/common.h                      |  8 ++++++--
 examples/speculative/speculative.cpp |  8 +++++---
 3 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index e938dee165d9da..20cc4a081b2225 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -403,6 +403,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_sequences = std::stoi(argv[i]);
+        } else if (arg == "--p-accept" || arg == "-pa") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.p_accept = std::stof(argv[i]);
+        } else if (arg == "--p-split" || arg == "-ps") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.p_split = std::stof(argv[i]);
         } else if (arg == "-m" || arg == "--model") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -778,6 +790,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     printf("  -np N, --parallel N   number of parallel sequences to decode (default: %d)\n", params.n_parallel);
     printf("  -ns N, --sequences N  number of sequences to decode (default: %d)\n", params.n_sequences);
+    printf("  -pa N, --p-accept N   speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept);
+    printf("  -ps N, --p-split N    speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
     printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
     printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
     printf("  --image IMAGE_FILE    path to an image file. use with multimodal models\n");
diff --git a/common/common.h b/common/common.h
index 9ad62563302872..dd6b002eb94ba2 100644
--- a/common/common.h
+++ b/common/common.h
@@ -44,6 +44,7 @@ int32_t get_num_physical_cores();
 
 struct gpt_params {
     uint32_t seed                           = -1;    // RNG seed
+
     int32_t n_threads                       = get_num_physical_cores();
     int32_t n_threads_batch                 = -1;    // number of threads to use for batch processing (-1 = use n_threads)
     int32_t n_predict                       = -1;    // new tokens to predict
@@ -54,6 +55,8 @@ struct gpt_params {
     int32_t n_chunks                        = -1;    // max number of chunks to process (-1 = unlimited)
     int32_t n_parallel                      = 1;     // number of parallel sequences to decode
     int32_t n_sequences                     = 1;     // number of sequences to decode
+    float   p_accept                        = 0.5f;  // speculative decoding accept probability
+    float   p_split                         = 0.1f;  // speculative decoding split probability
     int32_t n_gpu_layers                    = -1;    // number of layers to store in VRAM (-1 - use default)
     int32_t n_gpu_layers_draft              = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
     int32_t main_gpu                        = 0;     // the GPU that is used for scratch and small tensors
@@ -66,7 +69,8 @@ struct gpt_params {
     float   yarn_beta_fast                  = 32.0f; // YaRN low correction dim
     float   yarn_beta_slow                  = 1.0f;  // YaRN high correction dim
     int32_t yarn_orig_ctx                   = 0;     // YaRN original context length
-    int8_t  rope_scaling_type               = LLAMA_ROPE_SCALING_UNSPECIFIED;
+    int8_t  rope_scaling_type               = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
+                                                                              //       pinging @cebtenzzre
 
     // // sampling parameters
     struct llama_sampling_params sparams;
@@ -90,7 +94,7 @@ struct gpt_params {
     int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
                                     //                                       (which is more convenient to use for plotting)
                                     //
-    bool hellaswag         = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
+    bool   hellaswag       = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
     size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
 
     bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 798684f66678e2..3a8e278110c20e 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -37,9 +37,11 @@ int main(int argc, char ** argv) {
     // max number of parallel drafting sequences (i.e. tree branches)
     const int n_seq_dft = params.n_parallel;
 
-    // TODO: make this configurable
-    const float p_accept = 0.80f;
-    const float p_split  = 0.10f;
+    // probability threshold for accepting a token from the draft model
+    const float p_accept = params.p_accept;
+
+    // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
+    const float p_split  = params.p_split;
 
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("speculative", "log"));

From abb77e7319aabc0b5cfb7c22da690a692489b6b7 Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Fri, 3 Nov 2023 12:13:09 +0100
Subject: [PATCH 67/79] ggml-cuda : move row numbers to x grid dim in mmv
 kernels (#3921)

---
 ggml-cuda.cu | 53 ++++++++++++++++++++++++++--------------------------
 1 file changed, 27 insertions(+), 26 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index baf02df2b22948..bdbcca0cabb88e 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -989,7 +989,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
 
     static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
 
-    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
     if (row > nrows) return;
 
     const int num_blocks_per_row = ncols / QK_K;
@@ -1093,7 +1093,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
 
 static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
 
-    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
     if (row > nrows) return;
 
     const int num_blocks_per_row = ncols / QK_K;
@@ -1197,7 +1197,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx,
 
 static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
 
-    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
     if (row > nrows) return;
     const int num_blocks_per_row = ncols / QK_K;
     const int ib0 = row*num_blocks_per_row;
@@ -1451,7 +1451,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
 
     static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
 
-    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
     if (row > nrows) return;
 
     const int num_blocks_per_row = ncols / QK_K;
@@ -4261,7 +4261,7 @@ template <bool need_check> static __global__ void
 
 template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
 static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
-    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
 
     if (row >= nrows) {
         return;
@@ -4301,7 +4301,7 @@ template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
     // qk = quantized weights per x block
     // qr = number of quantized weights per data value in x block
-    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
 
     if (row >= nrows) {
         return;
@@ -4874,7 +4874,8 @@ static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cu
 static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
@@ -4883,7 +4884,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y,
 static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
@@ -4892,7 +4893,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y,
 static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
@@ -4901,7 +4902,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y,
 static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
@@ -4910,7 +4911,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y,
 static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
@@ -4920,7 +4921,7 @@ static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, f
     GGML_ASSERT(ncols % QK_K == 0);
     const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
     const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(32, ny, 1);
     dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -4929,7 +4930,7 @@ static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, f
     GGML_ASSERT(ncols % QK_K == 0);
     const int ny = 2 / K_QUANTS_PER_ITERATION;
     const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(32, ny, 1);
     dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -4938,7 +4939,7 @@ static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, f
     GGML_ASSERT(ncols % QK_K == 0);
     const int ny = 2 / K_QUANTS_PER_ITERATION;
     const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(32, ny, 1);
     dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -4953,7 +4954,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
     GGML_ASSERT(ncols % QK_K == 0);
     const int ny = 2 / K_QUANTS_PER_ITERATION;
     const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(32, ny, 1);
     dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -4961,7 +4962,7 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
 static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK4_0 == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -4970,7 +4971,7 @@ static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK4_1 == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -4979,7 +4980,7 @@ static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK5_0 == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -4988,7 +4989,7 @@ static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK5_1 == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -4997,7 +4998,7 @@ static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK8_0 == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -5006,7 +5007,7 @@ static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK_K == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -5015,7 +5016,7 @@ static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK_K == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -5024,7 +5025,7 @@ static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK_K == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -5033,7 +5034,7 @@ static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK_K == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -5042,7 +5043,7 @@ static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float *
 static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % QK_K == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
@@ -5061,7 +5062,7 @@ static void convert_fp32_to_fp16_cuda(const void * vx, half * y, const int k, cu
 static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<1, 1, convert_f16>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);

From 5ba37461711095c0284233dbd14f0d9010cdbf56 Mon Sep 17 00:00:00 2001
From: Xiao-Yong Jin <jinxiaoyong@gmail.com>
Date: Fri, 3 Nov 2023 13:00:31 -0500
Subject: [PATCH 68/79] ggml-metal: fix yarn rope (#3937)

---
 ggml-metal.m | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index b33a3cb8fd0128..acdb8384316862 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1403,7 +1403,8 @@ void ggml_metal_graph_compute(
                             const int n_past     = ((int32_t *) dst->op_params)[0];
                             const int n_dims     = ((int32_t *) dst->op_params)[1];
                             const int mode       = ((int32_t *) dst->op_params)[2];
-                            const int n_orig_ctx = ((int32_t *) dst->op_params)[3];
+                            // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
+                            const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
 
                             float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
                             memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));

From d9b33fe95bd257b36c84ee5769cc048230067d6f Mon Sep 17 00:00:00 2001
From: Peter Sugihara <peter@campsh.com>
Date: Fri, 3 Nov 2023 12:18:18 -0700
Subject: [PATCH 69/79] metal : round up to 16 to fix
 MTLDebugComputeCommandEncoder assertion (#3938)

---
 ggml-metal.m | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml-metal.m b/ggml-metal.m
index acdb8384316862..78ae4485da8e27 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1017,7 +1017,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
                             [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
                             [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0];
+                            [encoder setThreadgroupMemoryLength:MAX(16, nth/32*sizeof(float)) atIndex:0];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
@@ -1348,7 +1348,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
                             [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
                             [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
+                            [encoder setThreadgroupMemoryLength:MAX(16, nth*sizeof(float)) atIndex:0];
 
                             const int64_t nrows = ggml_nrows(src0);
 

From f28af0d81aa1010afa5de74cf627dcb04bea3157 Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Sat, 4 Nov 2023 16:20:34 -0600
Subject: [PATCH 70/79] gguf-py: Support 01.AI Yi models (#3943)

---
 gguf-py/gguf/gguf.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py
index 727b4e55495a76..a2271d225d0012 100644
--- a/gguf-py/gguf/gguf.py
+++ b/gguf-py/gguf/gguf.py
@@ -393,6 +393,7 @@ class TensorNameMap:
             "layers.{bid}.attention_norm",                         # llama-pth
             "encoder.layer.{bid}.attention.output.LayerNorm",      # bert
             "language_model.encoder.layers.{bid}.input_layernorm", # persimmon
+            "model.layers.{bid}.ln1",                              # yi
         ),
 
         # Attention norm 2
@@ -464,6 +465,7 @@ class TensorNameMap:
             "layers.{bid}.ffn_norm",                                        # llama-pth
             "encoder.layer.{bid}.output.LayerNorm",                         # bert
             "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
+            "model.layers.{bid}.ln2",                                       # yi
         ),
 
         # Feed-forward up

From 48ade94538fa509465d71023e49d07aab0ec8cd5 Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Sun, 5 Nov 2023 08:12:13 +0100
Subject: [PATCH 71/79] cuda : revert CUDA pool stuff (#3944)

* Revert "cuda : add ROCM aliases for CUDA pool stuff (#3918)"

This reverts commit 629f917cd6b96ba1274c49a8aab163b1b189229d.

* Revert "cuda : use CUDA memory pool with async memory allocation/deallocation when available (#3903)"

This reverts commit d6069051de7165a4e06662c89257f5d2905bb156.

ggml-ci
---
 ggml-cuda.cu | 131 ++++++++++++++++++++-------------------------------
 1 file changed, 50 insertions(+), 81 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index bdbcca0cabb88e..dc14f2f5d76c66 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -39,10 +39,6 @@
 #define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
 #define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
 #define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
-#define cudaDeviceGetMemPool hipDeviceGetMemPool
-#define cudaMemPoolAttrReleaseThreshold hipMemPoolAttrReleaseThreshold
-#define cudaMemPoolSetAttribute hipMemPoolSetAttribute
-#define cudaMemPool_t hipMemPool_t
 #define cudaDeviceProp hipDeviceProp_t
 #define cudaDeviceSynchronize hipDeviceSynchronize
 #define cudaError_t hipError_t
@@ -52,7 +48,6 @@
 #define cudaEvent_t hipEvent_t
 #define cudaEventDestroy hipEventDestroy
 #define cudaFree hipFree
-#define cudaFreeAsync hipFreeAsync
 #define cudaFreeHost hipHostFree
 #define cudaGetDevice hipGetDevice
 #define cudaGetDeviceCount hipGetDeviceCount
@@ -60,7 +55,6 @@
 #define cudaGetErrorString hipGetErrorString
 #define cudaGetLastError hipGetLastError
 #define cudaMalloc hipMalloc
-#define cudaMallocFromPoolAsync hipMallocFromPoolAsync
 #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
 #define cudaMemcpy hipMemcpy
 #define cudaMemcpy2DAsync hipMemcpy2DAsync
@@ -187,11 +181,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
     do {                                                                                \
         cudaError_t err_ = (err);                                                       \
         if (err_ != cudaSuccess) {                                                      \
-            int dev_id;                                                                     \
-            cudaGetDevice(&dev_id);                                                         \
+            int id;                                                                     \
+            cudaGetDevice(&id);                                                         \
             fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
                 cudaGetErrorString(err_));                                              \
-            fprintf(stderr, "current device: %d\n", dev_id);                                \
+            fprintf(stderr, "current device: %d\n", id);                                \
             exit(1);                                                                    \
         }                                                                               \
     } while (0)
@@ -201,11 +195,11 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
     do {                                                                                \
         cublasStatus_t err_ = (err);                                                    \
         if (err_ != CUBLAS_STATUS_SUCCESS) {                                            \
-            int dev_id;                                                                     \
-            cudaGetDevice(&dev_id);                                                         \
+            int id;                                                                     \
+            cudaGetDevice(&id);                                                         \
             fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n",                         \
                     err_, __FILE__, __LINE__, cublasGetStatusString(err_));             \
-            fprintf(stderr, "current device: %d\n", dev_id);                                \
+            fprintf(stderr, "current device: %d\n", id);                                \
             exit(1);                                                                    \
         }                                                                               \
     } while (0)
@@ -471,7 +465,6 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA
 
 #define MAX_STREAMS 8
 static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { nullptr };
-static cudaMemPool_t g_cudaMemPools[GGML_CUDA_MAX_DEVICES] = { nullptr };
 
 struct ggml_tensor_extra_gpu {
     void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
@@ -5780,16 +5773,6 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
     return ptr;
 }
 
-static void * ggml_cuda_pool_malloc_async(size_t size, size_t * actual_size, int id, cudaStream_t stream) {
-    if (g_cudaMemPools[id] == nullptr) {
-        return ggml_cuda_pool_malloc(size, actual_size);
-    }
-    void *ptr;
-    CUDA_CHECK(cudaMallocFromPoolAsync(&ptr, size, g_cudaMemPools[id], stream));
-    *actual_size = size;
-    return ptr;
-}
-
 static void ggml_cuda_pool_free(void * ptr, size_t size) {
     scoped_spin_lock lock(g_cuda_pool_lock);
     int id;
@@ -5808,13 +5791,6 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) {
 }
 
 
-static void ggml_cuda_pool_free_async(void * ptr, size_t actual_size, int id, cudaStream_t stream) {
-    if (g_cudaMemPools[id] == nullptr) {
-        return ggml_cuda_pool_free(ptr, actual_size);
-    }
-    CUDA_CHECK(cudaFreeAsync(ptr, stream));
-}
-
 void ggml_init_cublas() {
     static bool initialized = false;
 
@@ -5869,13 +5845,6 @@ void ggml_init_cublas() {
             // create cublas handle
             CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
             CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH));
-
-            // configure memory pool
-            cudaError_t err = cudaDeviceGetMemPool(&g_cudaMemPools[id], id);
-            if (err == cudaSuccess) {
-                size_t treshold = UINT64_MAX;
-                CUDA_CHECK(cudaMemPoolSetAttribute(g_cudaMemPools[id], cudaMemPoolAttrReleaseThreshold, &treshold));
-            }
         }
 
         // configure logging to stdout
@@ -6469,7 +6438,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
             size_t ne = row_diff*ne00;
-            src0_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src0_as, id, stream);
+            src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as);
             to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream);
         }
         const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16;
@@ -6480,12 +6449,13 @@ inline void ggml_cuda_op_mul_mat_cublas(
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
             size_t ne = src1_ncols*ne10;
-            src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &src1_as, id, stream);
+            src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as);
             to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
         }
         const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16;
-        size_t dst_f16_as = 0;
-        half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(row_diff*src1_ncols * sizeof(half), &dst_f16_as, id, stream);
+
+        size_t dst_as = 0;
+        half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
 
         const half alpha_f16 = 1.0f;
         const half beta_f16 = 0.0f;
@@ -6503,15 +6473,14 @@ inline void ggml_cuda_op_mul_mat_cublas(
         const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
         to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
 
-        if (dst_f16_as != 0) {
-            ggml_cuda_pool_free_async(dst_f16, dst_f16_as, id, stream);
-        }
+        ggml_cuda_pool_free(dst_f16, dst_as);
 
         if (src0_as != 0) {
-            ggml_cuda_pool_free_async(src0_as_f16, src0_as, id, stream);
+            ggml_cuda_pool_free(src0_as_f16, src0_as);
         }
+
         if (src1_as != 0) {
-            ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, stream);
+            ggml_cuda_pool_free(src1_as_f16, src1_as);
         }
     }
     else {
@@ -6521,7 +6490,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
         if (src0->type != GGML_TYPE_F32) {
             const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
             GGML_ASSERT(to_fp32_cuda != nullptr);
-            src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc_async(row_diff*ne00 * sizeof(float), &src0_as, id, stream); // NOLINT
+            src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT
             to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream);
         }
         const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32;
@@ -6538,7 +6507,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
                     &beta,  dst_dd_i,   ldc));
 
         if (src0_as != 0) {
-            ggml_cuda_pool_free_async(src0_ddq_as_f32, src0_as, id, stream);
+            ggml_cuda_pool_free(src0_ddq_as_f32, src0_as);
         }
     }
 
@@ -6961,22 +6930,21 @@ static void ggml_cuda_op_mul_mat(
             src0_dd[id] = (char *) src0_extra->data_device[id];
         } else {
             const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0);
-            src0_dd[id] = (char *) ggml_cuda_pool_malloc_async(ggml_nbytes(src0), &src0_as[id], id, stream);
+            src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]);
         }
 
         if (src1_on_device && src1_is_contiguous) {
             src1_ddf[id] = (float *) src1_extra->data_device[id];
         } else {
-            src1_ddf[id] = (float *) ggml_cuda_pool_malloc_async(ggml_nbytes(src1), &src1_asf[id], id, stream);
+            src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]);
         }
 
         if (convert_src1_to_q8_1) {
-            const size_t size_dst_ddq = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs;
-            src1_ddq[id] = (char *) ggml_cuda_pool_malloc_async(size_dst_ddq, &src1_asq[id], id, stream);
+            src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]);
 
             if (src1_on_device && src1_is_contiguous) {
                 quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream);
-                // CUDA_CHECK(cudaGetLastError());
+                CUDA_CHECK(cudaGetLastError());
             }
         }
 
@@ -6984,7 +6952,7 @@ static void ggml_cuda_op_mul_mat(
             dst_dd[id] = (float *) dst_extra->data_device[id];
         } else {
             const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst);
-            dst_dd[id] = (float *) ggml_cuda_pool_malloc_async(size_dst_ddf, &dst_as[id], id,  stream);
+            dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]);
         }
     }
 
@@ -7110,6 +7078,24 @@ static void ggml_cuda_op_mul_mat(
         }
     }
 
+    for (int64_t id = 0; id < g_device_count; ++id) {
+        CUDA_CHECK(ggml_cuda_set_device(id));
+
+        // free buffers again when done
+        if (src0_as[id] > 0) {
+            ggml_cuda_pool_free(src0_dd[id], src0_as[id]);
+        }
+        if (src1_asf[id] > 0) {
+            ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]);
+        }
+        if (src1_asq[id] > 0) {
+            ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]);
+        }
+        if (dst_as[id] > 0) {
+            ggml_cuda_pool_free(dst_dd[id], dst_as[id]);
+        }
+    }
+
     // main device waits for all other devices to be finished
     if (split && g_device_count > 1) {
         int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
@@ -7127,21 +7113,6 @@ static void ggml_cuda_op_mul_mat(
         CUDA_CHECK(ggml_cuda_set_device(g_main_device));
         CUDA_CHECK(cudaDeviceSynchronize());
     }
-
-    for (int64_t id = 0; id < g_device_count; ++id) {
-        if (src0_as[id] > 0) {
-            ggml_cuda_pool_free_async(src0_dd[id], src0_as[id], id, g_cudaStreams[id][0]);
-        }
-        if (src1_asf[id] > 0) {
-            ggml_cuda_pool_free_async(src1_ddf[id], src1_asf[id], id, g_cudaStreams[id][0]);
-        }
-        if (src1_asq[id] > 0) {
-            ggml_cuda_pool_free_async(src1_ddq[id], src1_asq[id], id, g_cudaStreams[id][0]);
-        }
-        if (dst_as[id] > 0) {
-            ggml_cuda_pool_free_async(dst_dd[id], dst_as[id], id, g_cudaStreams[id][0]);
-        }
-    }
 }
 
 static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -7328,11 +7299,11 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     GGML_ASSERT(to_fp16_cuda != nullptr);
 
     size_t src1_as = 0;
-    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc_async(ne1 * sizeof(half), &src1_as, id, main_stream);
+    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
     to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
 
     size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_cuda_pool_malloc_async(ne * sizeof(half), &dst_as, id, main_stream);
+    half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
 
     GGML_ASSERT(ne12 % ne02 == 0);
     GGML_ASSERT(ne13 % ne03 == 0);
@@ -7386,8 +7357,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         size_t ptrs_src_s = 0;
         size_t ptrs_dst_s = 0;
 
-        ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream);
-        ptrs_dst = (      void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream);
+        ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
+        ptrs_dst = (      void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
 
         dim3 block_dims(ne13, ne12);
         k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
@@ -7400,6 +7371,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
                 dst->nb[2], dst->nb[3],
                 r2, r3);
         CUDA_CHECK(cudaGetLastError());
+
         CUBLAS_CHECK(
         cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
@@ -7411,22 +7383,19 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
 
         if (ptrs_src_s != 0) {
-            ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream);
+            ggml_cuda_pool_free(ptrs_src, ptrs_src_s);
         }
         if (ptrs_dst_s != 0) {
-            ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream);
+            ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s);
         }
     }
 #endif
 
     const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
     to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
-    if (src1_as != 0) {
-        ggml_cuda_pool_free_async(src1_as_f16, src1_as, id, main_stream);
-    }
-    if (dst_as != 0) {
-        ggml_cuda_pool_free_async(dst_f16, dst_as, id, main_stream);
-    }
+
+    ggml_cuda_pool_free(src1_as_f16, src1_as);
+    ggml_cuda_pool_free(dst_f16, dst_as);
 }
 
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {

From a7fac013cf1cc7bbc0160a226aa2412e9f22e78a Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Sun, 5 Nov 2023 07:46:44 +0000
Subject: [PATCH 72/79] ci : use intel sde when ci cpu doesn't support avx512
 (#3949)

---
 .github/workflows/build.yml | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 5af497a3ce3214..bc295d52d2d5d2 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -288,6 +288,7 @@ jobs:
       OPENBLAS_VERSION: 0.3.23
       OPENCL_VERSION: 2023.04.17
       CLBLAST_VERSION: 1.6.0
+      SDE_VERSION: 9.21.1-2023-04-24
 
     strategy:
       matrix:
@@ -383,11 +384,23 @@ jobs:
 
       - name: Test
         id: cmake_test
-        if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # Test AVX-512 only when possible
+        if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512
         run: |
           cd build
           ctest -C Release --verbose --timeout 900
 
+      - name: Test (Intel SDE)
+        id: cmake_test_sde
+        if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/777395/sde-external-${env:SDE_VERSION}-win.tar.xz"
+          # for some weird reason windows tar doesn't like sde tar.xz
+          7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz
+          7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
+          $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)
+          cd build
+          & $sde -future -- ctest -C Release --verbose --timeout 900
+
       - name: Determine tag name
         id: tag
         shell: bash

From c41ea36eaa3548776de4cb3d5d49b925cd3fc0f2 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Sun, 5 Nov 2023 08:03:09 +0000
Subject: [PATCH 73/79] cmake : MSVC instruction detection (fixed up #809)
 (#3923)

* Add detection code for avx

* Only check hardware when option is ON

* Modify per code review sugguestions

* Build locally will detect CPU

* Fixes CMake style to use lowercase like everywhere else

* cleanup

* fix merge

* linux/gcc version for testing

* msvc combines avx2 and fma into /arch:AVX2 so check for both

* cleanup

* msvc only version

* style

* Update FindSIMD.cmake

---------

Co-authored-by: Howard Su <howard0su@gmail.com>
Co-authored-by: Jeremy Dunn <jeremydunn123@gmail.com>
---
 CMakeLists.txt       |   8 +++-
 cmake/FindSIMD.cmake | 100 +++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 106 insertions(+), 2 deletions(-)
 create mode 100644 cmake/FindSIMD.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3c49d645c3196f..7b4eb18403c0bf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,7 +10,7 @@ endif()
 
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 
-if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
+if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(LLAMA_STANDALONE ON)
 
     # configure project version
@@ -44,7 +44,7 @@ endif()
 
 # general
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
-option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      OFF)
+option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
 option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
 
 # debug
@@ -510,6 +510,10 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC
 elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" )
     message(STATUS "x86 detected")
     if (MSVC)
+        # instruction set detection for MSVC only
+        if (LLAMA_NATIVE)
+            include(cmake/FindSIMD.cmake)
+        endif ()
         if (LLAMA_AVX512)
             add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX512>)
             add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX512>)
diff --git a/cmake/FindSIMD.cmake b/cmake/FindSIMD.cmake
new file mode 100644
index 00000000000000..33377ec44de12c
--- /dev/null
+++ b/cmake/FindSIMD.cmake
@@ -0,0 +1,100 @@
+include(CheckCSourceRuns)
+
+set(AVX_CODE "
+    #include <immintrin.h>
+    int main()
+    {
+        __m256 a;
+        a = _mm256_set1_ps(0);
+        return 0;
+    }
+")
+
+set(AVX512_CODE "
+    #include <immintrin.h>
+    int main()
+    {
+        __m512i a = _mm512_set_epi8(0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0);
+        __m512i b = a;
+        __mmask64 equality_mask = _mm512_cmp_epi8_mask(a, b, _MM_CMPINT_EQ);
+        return 0;
+    }
+")
+
+set(AVX2_CODE "
+    #include <immintrin.h>
+    int main()
+    {
+        __m256i a = {0};
+        a = _mm256_abs_epi16(a);
+        __m256i x;
+        _mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code
+        return 0;
+    }
+")
+
+set(FMA_CODE "
+    #include <immintrin.h>
+    int main()
+    {
+        __m256 acc = _mm256_setzero_ps();
+        const __m256 d = _mm256_setzero_ps();
+        const __m256 p = _mm256_setzero_ps();
+        acc = _mm256_fmadd_ps( d, p, acc );
+        return 0;
+    }
+")
+
+macro(check_sse type flags)
+    set(__FLAG_I 1)
+    set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
+    foreach (__FLAG ${flags})
+        if (NOT ${type}_FOUND)
+            set(CMAKE_REQUIRED_FLAGS ${__FLAG})
+            check_c_source_runs("${${type}_CODE}" HAS_${type}_${__FLAG_I})
+            if (HAS_${type}_${__FLAG_I})
+                set(${type}_FOUND TRUE CACHE BOOL "${type} support")
+                set(${type}_FLAGS "${__FLAG}" CACHE STRING "${type} flags")
+            endif()
+            math(EXPR __FLAG_I "${__FLAG_I}+1")
+        endif()
+    endforeach()
+    set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
+
+    if (NOT ${type}_FOUND)
+        set(${type}_FOUND FALSE CACHE BOOL "${type} support")
+        set(${type}_FLAGS "" CACHE STRING "${type} flags")
+    endif()
+
+    mark_as_advanced(${type}_FOUND ${type}_FLAGS)
+endmacro()
+
+# flags are for MSVC only!
+check_sse("AVX" " ;/arch:AVX")
+if (NOT ${AVX_FOUND})
+    set(LLAMA_AVX OFF)
+else()
+    set(LLAMA_AVX ON)
+endif()
+
+check_sse("AVX2" " ;/arch:AVX2")
+check_sse("FMA" " ;/arch:AVX2")
+if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))
+    set(LLAMA_AVX2 OFF)
+else()
+    set(LLAMA_AVX2 ON)
+endif()
+
+check_sse("AVX512" " ;/arch:AVX512")
+if (NOT ${AVX512_FOUND})
+    set(LLAMA_AVX512 OFF)
+else()
+    set(LLAMA_AVX512 ON)
+endif()

From 3d48f42efcd05381221654376e9f6f69d76af739 Mon Sep 17 00:00:00 2001
From: Meng Zhang <meng@tabbyml.com>
Date: Sun, 5 Nov 2023 04:40:08 -0800
Subject: [PATCH 74/79] llama : mark LLM_ARCH_STARCODER as full offload
 supported (#3945)

as done in https://github.com/ggerganov/llama.cpp/pull/3827
---
 llama.cpp | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/llama.cpp b/llama.cpp
index cc0211ceb02113..e165390005c850 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -5164,11 +5164,12 @@ static int llama_decode_internal(
 
     // If all tensors can be run on the GPU then using more than 1 thread is detrimental.
     const bool full_offload_supported =
-        model.arch == LLM_ARCH_LLAMA    ||
-        model.arch == LLM_ARCH_BAICHUAN ||
-        model.arch == LLM_ARCH_FALCON   ||
-        model.arch == LLM_ARCH_REFACT   ||
-        model.arch == LLM_ARCH_MPT;
+        model.arch == LLM_ARCH_LLAMA      ||
+        model.arch == LLM_ARCH_BAICHUAN   ||
+        model.arch == LLM_ARCH_FALCON     ||
+        model.arch == LLM_ARCH_REFACT     ||
+        model.arch == LLM_ARCH_MPT        ||
+        model.arch == LLM_ARCH_STARCODER;
 
     const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
     if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {

From 132d25b8a62ea084447e0014a0112c1b371fb3f8 Mon Sep 17 00:00:00 2001
From: Jared Van Bortel <cebtenzzre@gmail.com>
Date: Sun, 5 Nov 2023 10:08:57 -0500
Subject: [PATCH 75/79] cuda : fix disabling device with --tensor-split 1,0
 (#3951)

Co-authored-by: slaren <slarengh@gmail.com>
---
 ggml-cuda.cu | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index dc14f2f5d76c66..9f873035ad0c05 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -6893,6 +6893,8 @@ static void ggml_cuda_op_mul_mat(
     int64_t  row_low[GGML_CUDA_MAX_DEVICES];
     int64_t row_high[GGML_CUDA_MAX_DEVICES];
 
+    int used_devices = 0;
+
     for (int64_t id = 0; id < g_device_count; ++id) {
         // by default, use all rows
         row_low[id]  = 0;
@@ -6920,6 +6922,8 @@ static void ggml_cuda_op_mul_mat(
             continue;
         }
 
+        used_devices++;
+
         const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
         const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
 
@@ -6958,12 +6962,12 @@ static void ggml_cuda_op_mul_mat(
 
     // if multiple devices are used they need to wait for the main device
     // here an event is recorded that signals that the main device has finished calculating the input data
-    if (split && g_device_count > 1) {
+    if (split && used_devices > 1) {
         CUDA_CHECK(ggml_cuda_set_device(g_main_device));
         CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0]));
     }
 
-    const int64_t src1_col_stride = split && g_device_count > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
+    const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
     for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
         const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
         const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
@@ -7079,6 +7083,9 @@ static void ggml_cuda_op_mul_mat(
     }
 
     for (int64_t id = 0; id < g_device_count; ++id) {
+        if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
+            continue;
+        }
         CUDA_CHECK(ggml_cuda_set_device(id));
 
         // free buffers again when done
@@ -7103,6 +7110,9 @@ static void ggml_cuda_op_mul_mat(
 
         CUDA_CHECK(ggml_cuda_set_device(g_main_device));
         for (int64_t id = 0; id < g_device_count; ++id) {
+            if (row_low[id] == row_high[id]) {
+                continue;
+            }
             for (int64_t is = 0; is < is_max; ++is) {
                 CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
             }
@@ -7400,7 +7410,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
 
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool all_on_device =
-        (src0->backend == GGML_BACKEND_GPU) &&
+        (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
         (src1->backend == GGML_BACKEND_GPU) &&
         ( dst->backend == GGML_BACKEND_GPU);
 

From bb60fd0bf6bb270744d86dd45b3a95af01b7de45 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Th=C3=A1i=20Ho=C3=A0ng=20T=C3=A2m?=
 <75922889+RoyalHeart@users.noreply.github.com>
Date: Sun, 5 Nov 2023 23:15:27 +0700
Subject: [PATCH 76/79] server : fix typo for --alias shortcut from -m to -a
 (#3958)

---
 examples/server/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 715007735c122f..089ebe2d1533f8 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -7,7 +7,7 @@ Command line options:
 -   `--threads N`, `-t N`: Set the number of threads to use during generation.
 -   `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
 -   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
--   `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
+-   `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
 -   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
 -   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.

From d9ccce2e339ca0396560d18b8637f2c848d72a08 Mon Sep 17 00:00:00 2001
From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Date: Sun, 5 Nov 2023 10:06:06 -0700
Subject: [PATCH 77/79] Allow common process_escapes to handle \x sequences
 (#3928)

* Allow common process_escapes to handle \x sequences

* Fix edge case when second hex digit is NUL
---
 common/common.cpp | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/common/common.cpp b/common/common.cpp
index 20cc4a081b2225..37e3ace8ac5d92 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -90,6 +90,19 @@ void process_escapes(std::string& input) {
                 case '\'': input[output_idx++] = '\''; break;
                 case '\"': input[output_idx++] = '\"'; break;
                 case '\\': input[output_idx++] = '\\'; break;
+                case 'x':
+                    // Handle \x12, etc
+                    if (input_idx + 2 < input_len) {
+                        const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
+                        char *err_p = nullptr;
+                        const long val = std::strtol(x, &err_p, 16);
+                        if (err_p == x + 2) {
+                            input_idx += 2;
+                            input[output_idx++] = char(val);
+                            break;
+                        }
+                        // Intentionally fall through to default.
+                    }
                 default:   input[output_idx++] = '\\';
                            input[output_idx++] = input[input_idx]; break;
             }

From 2833a6f63c1b87c7f4ac574bcf7a15a2f3bf3ede Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Sun, 5 Nov 2023 18:45:16 +0100
Subject: [PATCH 78/79] ggml-cuda : fix f16 mul mat (#3961)

* ggml-cuda : fix f16 mul mat

ggml-ci

* silence common.cpp warning (bonus)
---
 common/common.cpp | 2 +-
 ggml-cuda.cu      | 8 +++++---
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 37e3ace8ac5d92..6a711420004b48 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -101,8 +101,8 @@ void process_escapes(std::string& input) {
                             input[output_idx++] = char(val);
                             break;
                         }
-                        // Intentionally fall through to default.
                     }
+                    // fall through
                 default:   input[output_idx++] = '\\';
                            input[output_idx++] = input[input_idx]; break;
             }
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 9f873035ad0c05..2d9ffffbf74966 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -7414,6 +7414,8 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
         (src1->backend == GGML_BACKEND_GPU) &&
         ( dst->backend == GGML_BACKEND_GPU);
 
+    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+
     int64_t min_compute_capability = INT_MAX;
     for (int64_t id = 0; id < g_device_count; ++id) {
         if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
@@ -7435,13 +7437,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+    if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
         ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+    } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+    } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
         // KQ + KQV multi-batch
         ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {

From fb8e2fe606f1e1013c7e6b315db433e9f389c16f Mon Sep 17 00:00:00 2001
From: KerfuffleV2 <kerfliffle@keemail.me>
Date: Mon, 23 Oct 2023 06:27:28 -0600
Subject: [PATCH 79/79] Add support for loading merges.txt

Add --padvocab option to convert.py

Other minor cleanups
---
 convert.py             | 36 +++++++++++++++++++++++++++++-------
 gguf-py/gguf/gguf.py   | 32 ++++++++++++++++++++++++++++++++
 gguf-py/pyproject.toml |  2 +-
 3 files changed, 62 insertions(+), 8 deletions(-)

diff --git a/convert.py b/convert.py
index 9110f15806c6bc..433dbe95eb1fde 100755
--- a/convert.py
+++ b/convert.py
@@ -779,7 +779,7 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
                     break
             yield result
 
-def check_vocab_size(params: Params, vocab: Vocab) -> None:
+def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
     if params.n_vocab != vocab.vocab_size:
         assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
         if params.n_vocab == vocab.vocab_size_base:
@@ -787,12 +787,21 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
             vocab.added_tokens_list = []
             vocab.vocab_size = vocab.vocab_size_base
             return
+        if pad_vocab and params.n_vocab > vocab.vocab_size:
+            pad_count = params.n_vocab - vocab.vocab_size
+            print(f'Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>')
+            for i in range(1, (params.n_vocab - vocab.vocab_size) + 1):
+                vocab.added_tokens_list.append(f'<dummy{i:05}>')
+            vocab.vocab_size = params.n_vocab
+            return
         msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}"
         if vocab.fname_added_tokens is not None:
             msg += f" combined with {vocab.fname_added_tokens}"
         msg += f" has {vocab.vocab_size})."
         if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None:
             msg += f"  Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})."
+        if vocab.vocab_size < params.n_vocab:
+            msg += " Possibly try using the --padvocab option."
         raise Exception(msg)
 
 
@@ -877,8 +886,12 @@ def close(self) -> None:
         self.gguf.close()
 
     @staticmethod
-    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
-        check_vocab_size(params, vocab)
+    def write_vocab_only(
+        fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
+        pad_vocab: bool            = False,
+        ) -> None:
+        check_vocab_size(params, vocab, pad_vocab = pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
@@ -905,8 +918,14 @@ def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray:
         return dt.quantize(arr)
 
     @staticmethod
-    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None:
-        check_vocab_size(params, vocab)
+    def write_all(
+        fname_out  : Path, ftype: GGMLFileType, params: Params,
+        model      : LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab,
+        concurrency: int             = DEFAULT_CONCURRENCY,
+        endianess  : gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
+        pad_vocab  : bool            = False,
+        ) -> None:
+        check_vocab_size(params, vocab, pad_vocab = pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
@@ -1126,6 +1145,7 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
     parser.add_argument("--concurrency", type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
     parser.add_argument("--bigendian",   action="store_true",    help="model is executed on big endian machine")
+    parser.add_argument("--padvocab",    action="store_true",    help="add pad tokens when model vocab expects more than tokenizer metadata provides")
 
     args = parser.parse_args(args_in)
     if args.dump_single:
@@ -1173,7 +1193,8 @@ def main(args_in: list[str] | None = None) -> None:
             load_merges = args.vocabtype == 'bpe',
             n_vocab = vocab.vocab_size)
         outfile = args.outfile
-        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
+        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
+            endianess = endianess, pad_vocab = args.padvocab)
         print(f"Wrote {outfile}")
         return
 
@@ -1196,7 +1217,8 @@ def main(args_in: list[str] | None = None) -> None:
     params.ftype = ftype
     print(f"Writing {outfile}, format {ftype}")
 
-    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)
+    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
+        concurrency = args.concurrency, endianess = endianess, pad_vocab = args.padvocab)
     print(f"Wrote {outfile}")
 
 
diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py
index a2271d225d0012..36d8a2ef84e7ac 100644
--- a/gguf-py/gguf/gguf.py
+++ b/gguf-py/gguf/gguf.py
@@ -1023,6 +1023,35 @@ def __init__(
     def _load(self, path: Path) -> None:
         if not self._try_load_from_tokenizer_json(path):
             self._try_load_from_config_json(path)
+        if self.load_merges and len(self.merges) == 0:
+            self._try_load_merges_txt(path)
+
+    def _try_load_merges_txt(self, path: Path) -> bool:
+        merges_file = path / 'merges.txt'
+        if not merges_file.is_file():
+            return False
+        with open(merges_file, 'r') as fp:
+            first_line = next(fp, '').strip()
+            if not first_line.startswith('#'):
+                fp.seek(0)
+                line_num = 0
+            else:
+                line_num = 1
+            merges = []
+            for line in fp:
+                line_num += 1
+                line = line.strip()
+                if len(line) == 0:
+                    continue
+                parts = line.split(None, 3)
+                if len(parts) != 2:
+                    print(f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring',
+                        file = sys.stderr)
+                    continue
+                merges.append(f'{parts[0]} {parts[1]}')
+        self.merges = merges
+        return True
+
 
     def _set_special_token(self, typ: str, tid: Any):
         if not isinstance(tid, int) or tid < 0:
@@ -1083,6 +1112,9 @@ def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
             if not quiet:
                 print(f'gguf: Adding {len(self.merges)} merge(s).')
             gw.add_token_merges(self.merges)
+        elif self.load_merges:
+            print('gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.',
+                file = sys.stderr)
         for typ, tokid in self.special_token_ids.items():
             handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
             if handler is None:
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index f0741a7c23e034..c6cb2c37a0e0a1 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.4.5"
+version = "0.4.6"
 description = "Write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [