ggml : remove bit shuffling (ggerganov#1405)

* ggml : remove Q4_0 bit shufling (ARM NEON) * ggml : remove Q4_1 bit shuffling (ARM NEON + reference) * ggml : nibbles_from_floats() + bytes_from_nibbles() (ARM NEON) * ggml : remove Q4_2 bit shuffling (WIP, BROKEN) * ggml : remove Q5_0 bit shuffling (ARM NEON) * ggml : 2x faster scalar implementations * ggml : remove Q5_1 bit shuffling (ARM NEON + scalar) * ggml : simplify scalar dot * ggml : remove WASM SIMD bit shuffling + remove vzip for ARM 32-bit * ggml : fix Q4_1 quantization * ggml : update cuBLAS + normalize variable names * ggml : remove Q4_2 mode * ggml : minor formatting * ggml : fix Q5_0 quantization * scripts : add script for measuring the time per token * AVX implementations (ggerganov#1370) * ggml : uniform 5th bit extraction * llama : produce error upon loading old model files * llama : fix model magic/version write * ggml : speed-up Q5_0 + Q5_1 at 4 threads * ggml : preserve old Q4 and Q5 formats * ggml : simplify Q8_1 - no need for low / high sums anymore * ggml : fix Q8_0 and Q8_1 rounding * Revert "AVX implementations (ggerganov#1370)" This reverts commit 948d124. * ggml : fix AVX2 implementation * sha : update hashes for 7B and 13B * readme : update timings + remove warning banner * llama : update v2 PR number to 1405 * ggml : fix WASM comments * ggml : back to original bit order * readme : add note that Q4 and Q5 have been changed * llama : fix return for unknown version --------- Co-authored-by: Stephan Walter <stephan@walter.name>
heiner · May 11, 2023 · b9fd7ee · b9fd7ee
1 parent b608b55
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diff --git a/.gitignore b/.gitignore
@@ -44,5 +44,6 @@ zig-cache/
 
 ppl-*.txt
 qnt-*.txt
+perf-*.txt
 
 examples/jeopardy/results.txt
diff --git a/README.md b/README.md
@@ -7,18 +7,10 @@
 
 Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
-## ⚠️ TEMPORARY NOTICE ABOUT UPCOMING BREAKING CHANGE ⚠️
-
-**The quantization formats will soon be updated: https://github.com/ggerganov/llama.cpp/pull/1305**
-
-**All `ggml` model files using the old format will not work with the latest `llama.cpp` code after that change is merged**
-
----
-
 **Hot topics:**
 
+- Qauntization formats `Q4` and `Q5` have changed - requantize any old models [(info)](https://github.com/ggerganov/llama.cpp/pull/1405)
 - [Roadmap May 2023](https://github.com/ggerganov/llama.cpp/discussions/1220)
-- [New quantization methods](https://github.com/ggerganov/llama.cpp#quantization)
 
 <details>
   <summary>Table of Contents</summary>
@@ -338,18 +330,18 @@ As the models are currently fully loaded into memory, you will need adequate dis
 
 Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
 
-| Model | Measure      | F16    | Q4_0   | Q4_1   | Q4_2   | Q5_0   | Q5_1   | Q8_0   |
-|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|-------:|
-|    7B | perplexity   | 5.9066 | 6.1620 | 6.0910 | 6.1466 | 5.9862 | 5.9481 | 5.9069 |
-|    7B | file size    |  13.0G |   4.0G |   4.8G |   4.0G |   4.4G |   4.8G |   7.1G |
-|    7B | ms/tok @ 4th |    128 |     56 |     61 |     84 |     91 |     95 |     75 |
-|    7B | ms/tok @ 8th |    128 |     47 |     55 |     48 |     53 |     59 |     75 |
-|    7B | bits/weight  |   16.0 |    5.0 |    6.0 |    5.0 |    5.5 |    6.0 |    9.0 |
-|   13B | perplexity   | 5.2543 | 5.3863 | 5.3607 | 5.3513 | 5.2856 | 5.2706 | 5.2548 |
-|   13B | file size    |  25.0G |   7.6G |   9.1G |   7.6G |   8.4G |   9.1G |    14G |
-|   13B | ms/tok @ 4th |    239 |    104 |    113 |    160 |    176 |    185 |    141 |
-|   13B | ms/tok @ 8th |    240 |     85 |     99 |     97 |    108 |    117 |    147 |
-|   13B | bits/weight  |   16.0 |    5.0 |    6.0 |    5.0 |    5.5 |    6.0 |    9.0 |
+| Model | Measure      | F16    | Q4_0   | Q4_1   | Q5_0   | Q5_1   | Q8_0   |
+|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
+|    7B | perplexity   | 5.9066 | 6.1620 | 6.0910 | 5.9862 | 5.9481 | 5.9069 |
+|    7B | file size    |  13.0G |   4.0G |   4.8G |   4.4G |   4.8G |   7.1G |
+|    7B | ms/tok @ 4th |    128 |     50 |     54 |     75 |     83 |     75 |
+|    7B | ms/tok @ 8th |    123 |     44 |     52 |     53 |     58 |     72 |
+|    7B | bits/weight  |   16.0 |    5.0 |    6.0 |    5.5 |    6.0 |    9.0 |
+|   13B | perplexity   | 5.2543 | 5.3863 | 5.3607 | 5.2856 | 5.2706 | 5.2548 |
+|   13B | file size    |  25.0G |   7.6G |   9.1G |   8.4G |   9.1G |    14G |
+|   13B | ms/tok @ 4th |    239 |     93 |    101 |    150 |    164 |    141 |
+|   13B | ms/tok @ 8th |    240 |     81 |     96 |     96 |    104 |    136 |
+|   13B | bits/weight  |   16.0 |    5.0 |    6.0 |    5.5 |    6.0 |    9.0 |
 
 ### Perplexity (measuring model quality)
 

diff --git a/SHA256SUMS b/SHA256SUMS
@@ -1,24 +1,27 @@
 700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d  models/7B/consolidated.00.pth
 666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847  models/7B/ggml-model-f16.bin
-99aeb35f26b577fa2732716cca4d8b5ada39a78ea9b2dca2651fc632b5d101b6  models/7B/ggml-model-q4_0.bin
-cc061458339a3eb8bcecbf0a825e9924fb7d1a8150f63cd5d091caa99215aafe  models/7B/ggml-model-q4_1.bin
-25b050337a87344da687a7f2adddc03bd99b7f6c140450e836649f3585fb6496  models/7B/ggml-model-q4_2.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q4_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q4_1.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q5_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q5_1.bin
 7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265  models/7B/params.json
 745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08  models/13B/consolidated.00.pth
 d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085  models/13B/consolidated.01.pth
 2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808  models/13B/ggml-model-f16.bin
-eecb575d325d935157761172e2bf05984dad216eb2b06777b73463cf9b818bab  models/13B/ggml-model-q4_0.bin
-d9581b5b88e5622532fe897c9f9b0e67a317d22dd27a6f90fa4ab8c6d23ccdbb  models/13B/ggml-model-q4_1.bin
-75a218a47df03f5f96354656329864613abcb67779412b9bc2282b28c1c3cbaa  models/13B/ggml-model-q4_2.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q4_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q4_1.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q5_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q5_1.bin
 4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f  models/13B/params.json
 e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067  models/30B/consolidated.00.pth
 4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff  models/30B/consolidated.01.pth
 24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378  models/30B/consolidated.02.pth
 1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b  models/30B/consolidated.03.pth
 7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37  models/30B/ggml-model-f16.bin
-517b9e525742c42b5478a6280a4b41ec66f46298c57aba7f0453d491682fe42d  models/30B/ggml-model-q4_0.bin
-7b75ac615fa369ee593493a7e6ef87542bf0350255db928b22c5a24f6d598bcd  models/30B/ggml-model-q4_1.bin
-aadbc9cf806313a55be570f62884eed289d30c313fac3b7838717e01bd553204  models/30B/ggml-model-q4_2.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q4_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q4_1.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q5_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q5_1.bin
 2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb  models/30B/params.json
 135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe  models/65B/consolidated.00.pth
 9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde  models/65B/consolidated.01.pth
@@ -29,8 +32,9 @@ a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78  models/65B/con
 72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b  models/65B/consolidated.06.pth
 d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638  models/65B/consolidated.07.pth
 60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0  models/65B/ggml-model-f16.bin
-01672072136f8be6ca9d7cebe5f86ed316e8b85851b9fe3de951809233cea4f2  models/65B/ggml-model-q4_0.bin
-4743a28aac3e5f32a6e838a815f51d3779de44fbbe251d745251e66c23c5950f  models/65B/ggml-model-q4_1.bin
-1b6f6588d0e2ecfe6c4d849088e48e5e3083466b962daa32e3261363e21fc5e9  models/65B/ggml-model-q4_2.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q4_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q4_1.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q5_0.bin
+ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q5_1.bin
 999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b  models/65B/params.json
 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347  models/tokenizer.model
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
@@ -7,12 +7,11 @@
 #include <string>
 
 static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
-    {"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
-    {"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
-    {"q4_2", LLAMA_FTYPE_MOSTLY_Q4_2},
-    {"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
-    {"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
-    {"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
+  {"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
+  {"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
+  {"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
+  {"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
+  {"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
 };
 
 bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
@@ -49,13 +49,6 @@ typedef struct {
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
 
-#define QK4_2 16
-typedef struct {
-    half  d;                // delta
-    uint8_t qs[QK4_2 / 2];  // nibbles / quants
-} block_q4_2;
-static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
-
 #define QK5_0 32
 typedef struct {
     half d;                 // delta
@@ -81,147 +74,102 @@ typedef struct {
 static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
 
 static __global__ void dequantize_block_q4_0(const void * vx, float * y) {
+    static const int qk = QK4_0;
+
     const block_q4_0 * x = (const block_q4_0 *) vx;
 
     const int i = blockIdx.x;
 
     const float d = x[i].d;
 
-    const uint8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK4_0; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vi0 = vi & 0xf;
-        const int8_t vi1 = vi >> 4;
+    for (int j = 0; j < qk/2; ++j) {
+        const int x0 = (x[i].qs[j] & 0xf) - 8;
+        const int x1 = (x[i].qs[j] >>  4) - 8;
 
-        const float v0 = (vi0 - 8)*d;
-        const float v1 = (vi1 - 8)*d;
-
-        y[i*QK4_0 + l + 0] = v0;
-        y[i*QK4_0 + l + 1] = v1;
+        y[i*qk + j + 0   ] = x0*d;
+        y[i*qk + j + qk/2] = x1*d;
     }
 }
 
 static __global__ void dequantize_block_q4_1(const void * vx, float * y) {
+    static const int qk = QK4_1;
+
     const block_q4_1 * x = (const block_q4_1 *) vx;
 
     const int i = blockIdx.x;
 
     const float d = x[i].d;
     const float m = x[i].m;
 
-    const uint8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK4_1; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vi0 = vi & 0xf;
-        const int8_t vi1 = vi >> 4;
+    for (int j = 0; j < qk/2; ++j) {
+        const int x0 = (x[i].qs[j] & 0xf);
+        const int x1 = (x[i].qs[j] >>  4);
 
-        const float v0 = vi0*d + m;
-        const float v1 = vi1*d + m;
-
-        y[i*QK4_1 + l + 0] = v0;
-        y[i*QK4_1 + l + 1] = v1;
-    }
-}
-
-static __global__ void dequantize_block_q4_2(const void * vx, float * y) {
-    const block_q4_2 * x = (const block_q4_2 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-
-    const uint8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK4_2; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vi0 = vi & 0xf;
-        const int8_t vi1 = vi >> 4;
-
-        const float v0 = (vi0 - 8)*d;
-        const float v1 = (vi1 - 8)*d;
-
-        y[i*QK4_2 + l + 0] = v0;
-        y[i*QK4_2 + l + 1] = v1;
+        y[i*qk + j + 0   ] = x0*d + m;
+        y[i*qk + j + qk/2] = x1*d + m;
     }
 }
 
 static __global__ void dequantize_block_q5_0(const void * vx, float * y) {
+    static const int qk = QK5_0;
+
     const block_q5_0 * x = (const block_q5_0 *) vx;
 
     const int i = blockIdx.x;
 
     const float d = x[i].d;
 
-    const uint8_t * pp = x[i].qs;
-
     uint32_t qh;
     memcpy(&qh, x[i].qh, sizeof(qh));
 
-    for (int l = 0; l < QK5_0; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
-        const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
+    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 int8_t vi0 = ((vi & 0xf) | vh0);
-        const int8_t vi1 = ((vi >>  4) | vh1);
+        const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
+        const int32_t x1 = ((x[i].qs[j] >>  4) | xh_1) - 16;
 
-        const float v0 = (vi0 - 16)*d;
-        const float v1 = (vi1 - 16)*d;
-
-        y[i*QK5_0 + l + 0] = v0;
-        y[i*QK5_0 + l + 1] = v1;
+        y[i*qk + j + 0   ] = x0*d;
+        y[i*qk + j + qk/2] = x1*d;
     }
 }
 
 static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
+    static const int qk = QK5_1;
+
     const block_q5_1 * x = (const block_q5_1 *) vx;
 
     const int i = blockIdx.x;
 
     const float d = x[i].d;
     const float m = x[i].m;
 
-    const uint8_t * pp = x[i].qs;
-
     uint32_t qh;
     memcpy(&qh, x[i].qh, sizeof(qh));
 
-    for (int l = 0; l < QK5_1; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
-        const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
+    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 int8_t vi0 = (vi & 0xf) | vh0;
-        const int8_t vi1 = (vi >>  4) | vh1;
+        const int x0 = (x[i].qs[j] & 0xf) | xh_0;
+        const int x1 = (x[i].qs[j] >>  4) | xh_1;
 
-        const float v0 = vi0*d + m;
-        const float v1 = vi1*d + m;
-
-        y[i*QK5_1 + l + 0] = v0;
-        y[i*QK5_1 + l + 1] = v1;
+        y[i*qk + j + 0   ] = x0*d + m;
+        y[i*qk + j + qk/2] = x1*d + m;
     }
 }
 
 static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
+    static const int qk = QK8_0;
+
     const block_q8_0 * x = (const block_q8_0 *) vx;
 
     const int i = blockIdx.x;
 
     const float d = x[i].d;
 
-    const int8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK8_0; l++) {
-        const int8_t vi = pp[l];
-
-        y[i*QK8_0 + l] = vi*d;
+    for (int j = 0; j < qk; ++j) {
+        y[i*qk + j] = x[i].qs[j]*d;
     }
 }
 
@@ -235,11 +183,6 @@ static void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStre
     dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
 }
 
-static void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK4_2;
-    dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
-}
-
 static void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
     const int nb = k / QK5_0;
     dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
@@ -274,8 +217,6 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
             return dequantize_row_q4_0_cuda;
         case GGML_TYPE_Q4_1:
             return dequantize_row_q4_1_cuda;
-        case GGML_TYPE_Q4_2:
-            return dequantize_row_q4_2_cuda;
         case GGML_TYPE_Q5_0:
             return dequantize_row_q5_0_cuda;
         case GGML_TYPE_Q5_1: