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llama : fix data units #4101

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Nov 17, 2023
Merged

llama : fix data units #4101

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f5feac8
llama : fix data units
ggerganov Nov 16, 2023
82d9749
Revert "llama : fix data units"
ggerganov Nov 16, 2023
63d85d1
llama : disambiguate data units
ggerganov Nov 16, 2023
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4 changes: 2 additions & 2 deletions ggml-cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -5840,7 +5840,7 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
return ptr;
}
#ifdef DEBUG_CUDA_MALLOC
fprintf(stderr, "%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz,
fprintf(stderr, "%s: %d buffers, max_size = %u MiB, tot_size = %u MiB, requested %u MiB\n", __func__, nnz,
(uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024));
#endif
void * ptr;
Expand Down Expand Up @@ -5978,7 +5978,7 @@ void * ggml_cuda_host_malloc(size_t size) {
// The allocation error can be bypassed. A null ptr will assigned out of this function.
// This can fixed the OOM error in WSL.
cudaGetLastError();
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
fprintf(stderr, "WARNING: failed to allocate %.2f MiB of pinned memory: %s\n",
size/1024.0/1024.0, cudaGetErrorString(err));
return nullptr;
}
Expand Down
14 changes: 7 additions & 7 deletions ggml-metal.m
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Original file line number Diff line number Diff line change
Expand Up @@ -345,10 +345,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
}
}

GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MiB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.maxTransferRate != 0) {
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MiB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
} else {
GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
}
Expand Down Expand Up @@ -541,11 +541,11 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];

if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
return false;
}

GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0);

++ctx->n_buffers;
} else {
Expand All @@ -565,11 +565,11 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];

if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
return false;
}

GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
if (i + size_step < size) {
GGML_METAL_LOG_INFO("\n");
}
Expand Down
32 changes: 16 additions & 16 deletions llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1083,9 +1083,9 @@ enum e_model {
MODEL_70B,
};

static const size_t kB = 1024;
static const size_t MB = 1024*kB;
static const size_t GB = 1024*MB;
static const size_t kiB = 1024;
static const size_t MiB = 1024*kiB;
static const size_t GiB = 1024*MiB;

struct llama_hparams {
bool vocab_only;
Expand Down Expand Up @@ -1481,7 +1481,7 @@ static bool llama_kv_cache_init(
vram_kv_cache += ggml_nbytes(cache.k);
}
if (vram_kv_cache > 0) {
LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MiB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
}
}
#endif
Expand Down Expand Up @@ -2519,8 +2519,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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);
if (ml.n_bytes < GB) {
LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
if (ml.n_bytes < GiB) {
LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
} else {
LLAMA_LOG_INFO("%s: model size = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
}
Expand Down Expand Up @@ -2558,7 +2558,7 @@ static void llm_load_tensors(

ml.calc_sizes(ctx_size, mmapped_size);

LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0);

// create the ggml context
{
Expand Down Expand Up @@ -3207,7 +3207,7 @@ static void llm_load_tensors(
ctx_size +
mmapped_size - vram_weights; // weights in VRAM not in memory

LLAMA_LOG_INFO("%s: mem required = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: mem required = %7.2f MiB\n", __func__, mem_required / 1024.0 / 1024.0);

#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
Expand All @@ -3226,7 +3226,7 @@ static void llm_load_tensors(
#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);
LLAMA_LOG_INFO("%s: VRAM used: %.2f MB\n", __func__, vram_weights / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: VRAM used: %.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0);
#else
(void) n_gpu_layers;
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
Expand Down Expand Up @@ -7938,7 +7938,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
workers.clear();
}

LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i];
Expand Down Expand Up @@ -8478,7 +8478,7 @@ struct llama_context * llama_new_context_with_model(

{
const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: kv self size = %7.2f MiB\n", __func__, memory_size / 1024.0 / 1024.0);
}

// resized during inference
Expand Down Expand Up @@ -8523,7 +8523,7 @@ struct llama_context * llama_new_context_with_model(
// measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;

LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);

// recreate allocator with exact memory requirements
ggml_allocr_free(ctx->alloc);
Expand All @@ -8537,7 +8537,7 @@ struct llama_context * llama_new_context_with_model(
#endif
#ifdef GGML_USE_CUBLAS
ggml_cuda_set_scratch_size(alloc_size);
LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MB\n", __func__, alloc_size / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0);

// calculate total VRAM usage
auto add_tensor = [](const ggml_tensor * t, size_t & size) {
Expand All @@ -8557,10 +8557,10 @@ struct llama_context * llama_new_context_with_model(
size_t ctx_vram_size = alloc_size + kv_vram_size;
size_t total_vram_size = model_vram_size + ctx_vram_size;

LLAMA_LOG_INFO("%s: total VRAM used: %.2f MB (model: %.2f MB, context: %.2f MB)\n", __func__,
LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__,
total_vram_size / 1024.0 / 1024.0,
model_vram_size / 1024.0 / 1024.0,
ctx_vram_size / 1024.0 / 1024.0);
ctx_vram_size / 1024.0 / 1024.0);
#endif
}

Expand All @@ -8581,7 +8581,7 @@ struct llama_context * llama_new_context_with_model(

const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);

LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MiB\n", __func__, max_size/1024.0/1024.0);

#define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \
Expand Down
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