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llama : fix data units

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ggml-ci
This commit is contained in:
Georgi Gerganov 2023-11-16 17:19:35 +02:00
parent 8da46278e1
commit f5feac831f
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GPG Key ID: 449E073F9DC10735
3 changed files with 31 additions and 31 deletions
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4
ggml-cuda.cu
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@ -5841,7 +5841,7 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
} }
#ifdef DEBUG_CUDA_MALLOC #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 MB, tot_size = %u MB, requested %u MB\n", __func__, nnz,
(uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024)); (uint32_t)(max_size/1e6), (uint32_t)(tot_size/1e6), (uint32_t)(size/1e6));
#endif #endif
void * ptr; void * ptr;
size_t look_ahead_size = (size_t) (1.05 * size); size_t look_ahead_size = (size_t) (1.05 * size);
@ -5979,7 +5979,7 @@ void * ggml_cuda_host_malloc(size_t size) {
// This can fixed the OOM error in WSL. // This can fixed the OOM error in WSL.
cudaGetLastError(); cudaGetLastError();
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
size/1024.0/1024.0, cudaGetErrorString(err)); size/1e6, cudaGetErrorString(err));
return nullptr; return nullptr;
} }

18
ggml-metal.m
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@ -346,9 +346,9 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
} }
GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); 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: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
if (ctx->device.maxTransferRate != 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 MB/s\n", __func__, ctx->device.maxTransferRate / 1e6);
} else { } else {
GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__); GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
} }
@ -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]; 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) { 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 MB\n", __func__, name, size_aligned / 1e6);
return false; 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 MB", __func__, name, size_aligned / 1e6);
++ctx->n_buffers; ++ctx->n_buffers;
} else { } else {
@ -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]; 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) { 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 MB\n", __func__, name, size_step_aligned / 1e6);
return false; 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 MB, offs = %12ld", __func__, name, size_step_aligned / 1e6, i);
if (i + size_step < size) { if (i + size_step < size) {
GGML_METAL_LOG_INFO("\n"); GGML_METAL_LOG_INFO("\n");
} }
@ -580,8 +580,8 @@ bool ggml_metal_add_buffer(
#if TARGET_OS_OSX #if TARGET_OS_OSX
GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0, ctx->device.currentAllocatedSize / 1e6,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); ctx->device.recommendedMaxWorkingSetSize / 1e6);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
@ -589,7 +589,7 @@ bool ggml_metal_add_buffer(
GGML_METAL_LOG_INFO("\n"); GGML_METAL_LOG_INFO("\n");
} }
#else #else
GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0); GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1e6);
#endif #endif
} }

40
llama.cpp
View File

@ -1083,9 +1083,9 @@ enum e_model {
MODEL_70B, MODEL_70B,
}; };
static const size_t kB = 1024; static const size_t kB = 1000;
static const size_t MB = 1024*kB; static const size_t MB = 1000*kB;
static const size_t GB = 1024*MB; static const size_t GB = 1000*MB;
struct llama_hparams { struct llama_hparams {
bool vocab_only; bool vocab_only;
@ -1481,7 +1481,7 @@ static bool llama_kv_cache_init(
vram_kv_cache += ggml_nbytes(cache.k); vram_kv_cache += ggml_nbytes(cache.k);
} }
if (vram_kv_cache > 0) { 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 MB\n", __func__, vram_kv_cache / 1e6);
} }
} }
#endif #endif
@ -2520,9 +2520,9 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); 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); LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9);
if (ml.n_bytes < GB) { 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); LLAMA_LOG_INFO("%s: model size = %.2f MB (%.2f BPW) \n", __func__, ml.n_bytes/1e6, ml.n_bytes*8.0/ml.n_elements);
} else { } 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); LLAMA_LOG_INFO("%s: model size = %.2f GB (%.2f BPW) \n", __func__, ml.n_bytes/1e9, ml.n_bytes*8.0/ml.n_elements);
} }
// general kv // general kv
@ -2558,7 +2558,7 @@ static void llm_load_tensors(
ml.calc_sizes(ctx_size, mmapped_size); 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 MB\n", __func__, ctx_size/1e6);
// create the ggml context // create the ggml context
{ {
@ -3207,7 +3207,7 @@ static void llm_load_tensors(
ctx_size + ctx_size +
mmapped_size - vram_weights; // weights in VRAM not in memory 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 MB\n", __func__, mem_required / 1e6);
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
@ -3226,7 +3226,7 @@ static void llm_load_tensors(
#endif // GGML_USE_CUBLAS #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: 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 MB\n", __func__, vram_weights / 1e6);
#else #else
(void) n_gpu_layers; (void) n_gpu_layers;
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) #endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
@ -7878,7 +7878,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type = tensor->type; new_type = tensor->type;
new_data = tensor->data; new_data = tensor->data;
new_size = ggml_nbytes(tensor); new_size = ggml_nbytes(tensor);
LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0); LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1e6);
} else { } else {
const size_t nelements = ggml_nelements(tensor); const size_t nelements = ggml_nelements(tensor);
@ -7938,7 +7938,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
workers.clear(); 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 MB -> %8.2f MB | hist: ", ggml_nbytes(tensor)/1e6, new_size/1e6);
int64_t tot_count = 0; int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) { for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i]; hist_all[i] += hist_cur[i];
@ -7976,8 +7976,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
gguf_free(ctx_out); gguf_free(ctx_out);
LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1e6);
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1e6);
// print histogram for all tensors // print histogram for all tensors
{ {
@ -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); 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 MB\n", __func__, memory_size / 1e6);
} }
// resized during inference // resized during inference
@ -8523,7 +8523,7 @@ struct llama_context * llama_new_context_with_model(
// measure memory requirements for the graph // measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment; 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 MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1e6);
// recreate allocator with exact memory requirements // recreate allocator with exact memory requirements
ggml_allocr_free(ctx->alloc); ggml_allocr_free(ctx->alloc);
@ -8537,7 +8537,7 @@ struct llama_context * llama_new_context_with_model(
#endif #endif
#ifdef GGML_USE_CUBLAS #ifdef GGML_USE_CUBLAS
ggml_cuda_set_scratch_size(alloc_size); 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 MB\n", __func__, alloc_size / 1e6);
// calculate total VRAM usage // calculate total VRAM usage
auto add_tensor = [](const ggml_tensor * t, size_t & size) { auto add_tensor = [](const ggml_tensor * t, size_t & size) {
@ -8558,9 +8558,9 @@ struct llama_context * llama_new_context_with_model(
size_t total_vram_size = model_vram_size + ctx_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 MB (model: %.2f MB, context: %.2f MB)\n", __func__,
total_vram_size / 1024.0 / 1024.0, total_vram_size / 1e6,
model_vram_size / 1024.0 / 1024.0, model_vram_size / 1e6,
ctx_vram_size / 1024.0 / 1024.0); ctx_vram_size / 1e6);
#endif #endif
} }
@ -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); 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 MB\n", __func__, max_size/1e6);
#define LLAMA_METAL_CHECK_BUF(result) \ #define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \ if (!(result)) { \