haraldwolff/llama.cpp
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llama : fix compile warnings

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Georgi Gerganov 2023-06-06 22:41:53 +03:00
parent 17366df842
commit 2a4e41a086
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GPG key ID: 449E073F9DC10735
2 changed files with 19 additions and 18 deletions
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22
ggml.c
View file

@ -14720,12 +14720,12 @@ static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fou
const int64_t * ne = tensor->ne;
const size_t * nb = tensor->nb;
fprintf(fout, "%-6s %-12s %8d %8jd %jd %jd %jd %16zu %16zu %16zu %16zu %16p %32s\n",
fprintf(fout, "%-6s %-12s %8d %8d %d %d %d %16zu %16zu %16zu %16zu %16p %32s\n",
ggml_type_name(tensor->type),
ggml_op_name (tensor->op),
tensor->n_dims,
ne[0], ne[1], ne[2], ne[3],
nb[0], nb[1], nb[2], nb[3],
(int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3],
nb[0], nb[1], nb[2], nb[3],
tensor->data,
tensor->name);
}
@ -14734,13 +14734,13 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
const int64_t * ne = tensor->ne;
const size_t * nb = tensor->nb;
fprintf(fout, "%-6s %-6s %-12s %8d %jd %jd %jd %jd %16zu %16zu %16zu %16zu %8d %16p %32s\n",
fprintf(fout, "%-6s %-6s %-12s %8d %d %d %d %d %16zu %16zu %16zu %16zu %8d %16p %32s\n",
arg,
ggml_type_name(tensor->type),
ggml_op_name (tensor->op),
tensor->n_dims,
ne[0], ne[1], ne[2], ne[3],
nb[0], nb[1], nb[2], nb[3],
(int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3],
nb[0], nb[1], nb[2], nb[3],
tensor->n_tasks,
tensor->data,
tensor->name);
@ -14763,11 +14763,11 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
FILE * fout = stdout;
fprintf(fout, "\n");
fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC);
fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION);
fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs);
fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes);
fprintf(fout, "%-16s %8ju\n", "eval", size_eval);
fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC);
fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION);
fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs);
fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes);
fprintf(fout, "%-16s %8d\n", "eval", (int) size_eval);
// header
fprintf(fout, "\n");

15
llama.cpp
View file

@ -1059,23 +1059,23 @@ static void llama_model_load_internal(
}
}
(void) main_gpu;
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
#elif defined(GGML_USE_CLBLAST)
fprintf(stderr, "%s: using OpenCL for GPU acceleration\n", __func__);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
#else
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
#endif
// prepare memory for the weights
size_t vram_weights = 0;
size_t vram_scratch = 0;
{
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
@ -1152,10 +1152,8 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
#ifdef GGML_USE_CUBLAS
vram_scratch = n_batch * MB;
const size_t vram_scratch = n_batch * MB;
ggml_cuda_set_scratch_size(vram_scratch);
if (n_gpu_layers > 0) {
fprintf(stderr, "%s: allocating batch_size x 1 MB = %ld MB VRAM for the scratch buffer\n",
@ -1163,6 +1161,8 @@ static void llama_model_load_internal(
}
#endif // GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: offloading %d layers to GPU\n", __func__, n_gpu);
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: offloading output layer to GPU\n", __func__);
@ -1331,6 +1331,7 @@ static bool llama_eval_internal(
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start;
for (int il = 0; il < n_layer; ++il) {
offload_func_t offload_func = llama_nop;