vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

llama : init metal backend as CPU backend for now

Browse source
This commit is contained in:
Georgi Gerganov 2023-07-18 17:52:13 +03:00
parent 0a3861c47b
commit 90503f150d
No known key found for this signature in database
GPG key ID: 449E073F9DC10735
2 changed files with 48 additions and 27 deletions
Download patch file Download diff file Expand all files Collapse all files

2
ggml-backend.h
View file

@ -78,7 +78,7 @@ extern "C" {
static inline void ggml_backend_graph_compute(struct ggml_backend * backend, struct ggml_cgraph * cgraph) { backend->interface->graph_compute(backend->context, cgraph); } static inline void ggml_backend_graph_compute(struct ggml_backend * backend, struct ggml_cgraph * cgraph) { backend->interface->graph_compute(backend->context, cgraph); }
// buffer and tensor allocation // buffer and tensor allocation
GGML_API struct ggml_buffer ggml_backend_alloc_buffer(struct ggml_backend * backend, size_t size, size_t max_tensors); GGML_API struct ggml_buffer ggml_backend_alloc_buffer(struct ggml_backend * backend, size_t size, size_t max_tensors); // GG: probably return ptr
GGML_API void ggml_backend_free_buffer(struct ggml_buffer * buffer); GGML_API void ggml_backend_free_buffer(struct ggml_buffer * buffer);
static inline void ggml_backend_reset_buffer(struct ggml_buffer * buffer) { buffer->backend->interface->reset_buffer(buffer->backend->context, buffer->backend_buffer); } static inline void ggml_backend_reset_buffer(struct ggml_buffer * buffer) { buffer->backend->interface->reset_buffer(buffer->backend->context, buffer->backend_buffer); }
static inline void ggml_backend_alloc_tensor(struct ggml_buffer * buffer, struct ggml_tensor * tensor) { buffer->backend->interface->alloc_tensor(buffer->backend->context, buffer->backend_buffer, tensor); } static inline void ggml_backend_alloc_tensor(struct ggml_buffer * buffer, struct ggml_tensor * tensor) { buffer->backend->interface->alloc_tensor(buffer->backend->context, buffer->backend_buffer, tensor); }

63
llama.cpp
View file

@ -240,8 +240,8 @@ struct llama_model {
#endif #endif
// backend assigned to each layer // backend assigned to each layer
ggml_backend * backend_input = NULL; ggml_backend * backend_inp = NULL;
ggml_backend * backend_output = NULL; ggml_backend * backend_out = NULL;
std::vector<ggml_backend *> backend_layers; std::vector<ggml_backend *> backend_layers;
~llama_model() { ~llama_model() {
@ -965,15 +965,15 @@ static void llama_model_load_internal(
#endif #endif
#ifdef GGML_USE_METAL #ifdef GGML_USE_METAL
if (n_gpu_layers > 0) { if (n_gpu_layers > 0) {
model.backend_metal = ggml_backend_metal_init(); model.backend_metal = ggml_backend_cpu_init();
backend_gpu = &model.backend_metal; backend_gpu = &model.backend_metal;
} }
#endif #endif
// assign splits to the backends // assign splits to the backends
const int i_gpu_start = std::max(0, (int)n_layer - n_gpu_layers); const int i_gpu_start = std::max(0, (int)n_layer - n_gpu_layers);
model.backend_input = n_gpu_layers > (int)n_layer ? backend_gpu : backend_cpu; model.backend_inp = n_gpu_layers > (int)n_layer ? backend_gpu : backend_cpu;
model.backend_output = n_gpu_layers > 0 ? backend_gpu : backend_cpu; model.backend_out = n_gpu_layers > 0 ? backend_gpu : backend_cpu;
model.backend_layers.resize(n_layer); model.backend_layers.resize(n_layer);
std::fill(model.backend_layers.begin(), model.backend_layers.begin() + i_gpu_start, backend_cpu); std::fill(model.backend_layers.begin(), model.backend_layers.begin() + i_gpu_start, backend_cpu);
@ -983,10 +983,10 @@ static void llama_model_load_internal(
std::unordered_map<struct ggml_backend *, size_t> ctx_sizes; std::unordered_map<struct ggml_backend *, size_t> ctx_sizes;
for (const llama_load_tensor & lt : ml->tensors_map.tensors) { for (const llama_load_tensor & lt : ml->tensors_map.tensors) {
if (lt.name == "tok_embeddings.weight") { if (lt.name == "tok_embeddings.weight") {
ctx_sizes[model.backend_input] += lt.size; ctx_sizes[model.backend_inp] += lt.size;
} }
else if (lt.name == "norm.weight" || lt.name == "output.weight") { else if (lt.name == "norm.weight" || lt.name == "output.weight") {
ctx_sizes[model.backend_output] += lt.size; ctx_sizes[model.backend_out] += lt.size;
} }
else { else {
// parse layer number from name // parse layer number from name
@ -1032,6 +1032,7 @@ static void llama_model_load_internal(
} }
ggml_context * ctx_gpu = model.ctx_cpu; ggml_context * ctx_gpu = model.ctx_cpu;
#ifdef GGML_USE_CUDA #ifdef GGML_USE_CUDA
if (n_gpu_layers > 0) { if (n_gpu_layers > 0) {
size_t gpu_num_tensors = ml->tensors_map.tensors.size(); size_t gpu_num_tensors = ml->tensors_map.tensors.size();
@ -1043,15 +1044,35 @@ static void llama_model_load_internal(
if (!model.ctx_cuda) { if (!model.ctx_cuda) {
throw std::runtime_error(format("ggml_init() failed for CUDA backend")); throw std::runtime_error(format("ggml_init() failed for CUDA backend"));
} }
ctx_gpu = model.ctx_cuda; ctx_gpu = model.ctx_cuda;
} }
#endif #endif
#ifdef GGML_USE_METAL
if (n_gpu_layers > 0) {
// the metal context is actually a CPU context because we have unified memory
const size_t ctx_size = ctx_sizes[&model.backend_metal];
const size_t n_tensors = ml->tensors_map.tensors.size();
model.buf_metal = ggml_backend_alloc_buffer(&model.backend_metal, ctx_size, n_tensors);
struct ggml_init_params params = ggml_init_params_default();
params.buffer = &model.buf_metal;
params.no_alloc = ml->use_mmap;
model.ctx_metal = ggml_init(params);
if (!model.ctx_metal) {
throw std::runtime_error(format("ggml_init() failed for CPU backend"));
}
ctx_gpu = model.ctx_metal;
}
#endif
// TODO: clean this // TODO: clean this
ggml_context * ctx_input = model.ctx_cpu; ggml_context * ctx_input = (model.backend_inp == backend_gpu) ? ctx_gpu : model.ctx_cpu;
if (model.backend_input == backend_gpu) ctx_input = ctx_gpu; ggml_context * ctx_output = (model.backend_out == backend_gpu) ? ctx_gpu : model.ctx_cpu;
ggml_context * ctx_output = model.ctx_cpu;
if (model.backend_output == backend_gpu) ctx_output = ctx_gpu;
std::vector<ggml_context *> ctx_layers(n_layer, model.ctx_cpu); std::vector<ggml_context *> ctx_layers(n_layer, model.ctx_cpu);
for (uint32_t i = 0; i < n_layer; ++i) { for (uint32_t i = 0; i < n_layer; ++i) {
@ -1102,7 +1123,6 @@ static void llama_model_load_internal(
(void) low_vram; (void) low_vram;
(void) n_batch; (void) n_batch;
// print memory requirements // print memory requirements
{ {
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1; const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
@ -1229,24 +1249,25 @@ static ggml_graph_splits llama_build_graph(
struct ggml_context * ctx_o = nullptr; struct ggml_context * ctx_o = nullptr;
struct ggml_context * ctx_kv = nullptr; struct ggml_context * ctx_kv = nullptr;
if (lctx.model.backend_input == &lctx.model.backend_cpu) ctx_i = ctx_cpu; if (lctx.model.backend_inp == &lctx.model.backend_cpu) ctx_i = ctx_cpu;
if (lctx.model.backend_output == &lctx.model.backend_cpu) ctx_o = ctx_cpu; if (lctx.model.backend_out == &lctx.model.backend_cpu) ctx_o = ctx_cpu;
#ifdef GGML_USE_CUDA #ifdef GGML_USE_CUDA
if (lctx.model.backend_input == &lctx.model.backend_cuda) ctx_i = ctx_cuda; if (lctx.model.backend_inp == &lctx.model.backend_cuda) ctx_i = ctx_cuda;
if (lctx.model.backend_output == &lctx.model.backend_cuda) ctx_o = ctx_cuda; if (lctx.model.backend_out == &lctx.model.backend_cuda) ctx_o = ctx_cuda;
#endif #endif
for (int il = 0; il < n_layer; il++) { for (int il = 0; il < n_layer; il++) {
if (lctx.model.backend_layers[il] == &lctx.model.backend_cpu) ctx_ls[il] = ctx_cpu; if (lctx.model.backend_layers[il] == &lctx.model.backend_cpu) ctx_ls[il] = ctx_cpu;
#ifdef GGML_USE_CUDA #ifdef GGML_USE_CUDA
if (lctx.model.backend_layers[il] == &lctx.model.backend_cuda) ctx_ls[il] = ctx_cuda; if (lctx.model.backend_layers[il] == &lctx.model.backend_cuda) ctx_ls[il] = ctx_cuda;
#endif #endif
} }
if (lctx.backend_kv == &lctx.model.backend_cpu) ctx_kv = ctx_cpu; if (lctx.backend_kv == &lctx.model.backend_cpu) ctx_kv = ctx_cpu;
#ifdef GGML_USE_CUDA #ifdef GGML_USE_CUDA
if (lctx.backend_kv == &lctx.model.backend_cuda) ctx_kv = ctx_cuda; if (lctx.backend_kv == &lctx.model.backend_cuda) ctx_kv = ctx_cuda;
#endif #endif
struct ggml_tensor * inpL; struct ggml_tensor * inpL;
if (embeddings_input) { if (embeddings_input) {
@ -2678,7 +2699,7 @@ struct llama_context * llama_new_context_with_model(
buf_input_size += hparams.n_ctx * ggml_type_size(GGML_TYPE_F32); // input tokens buf_input_size += hparams.n_ctx * ggml_type_size(GGML_TYPE_F32); // input tokens
// TODO: input embeddings should be optional to save memory // TODO: input embeddings should be optional to save memory
buf_input_size += hparams.n_embd * hparams.n_ctx * ggml_type_size(GGML_TYPE_F32); // input embeddings buf_input_size += hparams.n_embd * hparams.n_ctx * ggml_type_size(GGML_TYPE_F32); // input embeddings
ctx->buf_input = ggml_backend_alloc_buffer(model->backend_input, buf_input_size, 2); ctx->buf_input = ggml_backend_alloc_buffer(model->backend_inp, buf_input_size, 2);
struct ggml_init_params ggml_params = ggml_init_params_default(); struct ggml_init_params ggml_params = ggml_init_params_default();
ggml_params.buffer = &ctx->buf_input; ggml_params.buffer = &ctx->buf_input;
@ -2702,7 +2723,7 @@ struct llama_context * llama_new_context_with_model(
if (params.embedding) { if (params.embedding) {
buf_output_size += hparams.n_embd * ggml_type_size(GGML_TYPE_F32); buf_output_size += hparams.n_embd * ggml_type_size(GGML_TYPE_F32);
} }
ctx->buf_output = ggml_backend_alloc_buffer(model->backend_output, buf_output_size, 2); ctx->buf_output = ggml_backend_alloc_buffer(model->backend_out, buf_output_size, 2);
struct ggml_init_params ggml_params = ggml_init_params_default(); struct ggml_init_params ggml_params = ggml_init_params_default();
ggml_params.buffer = &ctx->buf_output; ggml_params.buffer = &ctx->buf_output;
@ -2731,7 +2752,7 @@ struct llama_context * llama_new_context_with_model(
} }
fprintf(stderr, "%s: layer backends: ", __func__); fprintf(stderr, "%s: layer backends: ", __func__);
fprintf(stderr, "input: %s, ", ggml_backend_name(ctx->model.backend_input)); fprintf(stderr, "input: %s, ", ggml_backend_name(ctx->model.backend_inp));
int start = 0; int start = 0;
struct ggml_backend * prev_backend = ctx->model.backend_layers[0]; struct ggml_backend * prev_backend = ctx->model.backend_layers[0];
@ -2746,7 +2767,7 @@ struct llama_context * llama_new_context_with_model(
prev_backend = ctx->model.backend_layers[i]; prev_backend = ctx->model.backend_layers[i];
} }
} }
fprintf(stderr, "output: %s, ", ggml_backend_name(ctx->model.backend_output)); fprintf(stderr, "output: %s, ", ggml_backend_name(ctx->model.backend_out));
fprintf(stderr, "kv: %s\n", ggml_backend_name(ctx->backend_kv)); fprintf(stderr, "kv: %s\n", ggml_backend_name(ctx->backend_kv));
#ifdef GGML_USE_MPI #ifdef GGML_USE_MPI