llama : fix data units (#4101)

* llama : fix data units

ggml-ci

* Revert "llama : fix data units"

This reverts commit f5feac831fe225ed7f3db938d115732a49dccfc4.

* llama : disambiguate data units

ggml-ci

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index c0c9edd56dbc232b060afd62304d04bf4df45be3..9aa61fe4da7b04cce8d2fb0bd7e0c14b5e58af36 100644 (file)
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -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;
@@ -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;
     }

diff --git a/ggml-metal.m b/ggml-metal.m
index 3d22b0b27e444f2a1995a7e729ee7171faa189a3..a9fdd39035aa319f9648d81b8d5fb698247590a9 100644 (file)
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -345,10 +345,10 @@ 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: 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__);
     }
@@ -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 {
@@ -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");
                 }

diff --git a/llama.cpp b/llama.cpp
index 3f6b7fe761b6467c868fa010b0d4b9ee11e961ed..8500b20cbaf09f567925f6825b3710268c1f2eb8 100644 (file)
--- a/llama.cpp
+++ b/llama.cpp
@@ -1087,9 +1087,9 @@ 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;
@@ -1488,7 +1488,7 @@ 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
@@ -2543,8 +2543,8 @@ 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);
     }
@@ -2582,7 +2582,7 @@ 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
     {
@@ -3231,7 +3231,7 @@ 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));
@@ -3250,7 +3250,7 @@ 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)
@@ -7962,7 +7962,7 @@ 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];
@@ -8502,7 +8502,7 @@ 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
@@ -8547,7 +8547,7 @@ 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);
@@ -8561,7 +8561,7 @@ 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) {
@@ -8581,10 +8581,10 @@ 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
         }
 
@@ -8605,7 +8605,7 @@ 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)) {                                             \