diff options
| -rw-r--r-- | main.cpp | 13 | ||||
| -rw-r--r-- | utils.cpp | 3 | ||||
| -rw-r--r-- | utils.h | 1 |
3 files changed, 11 insertions, 6 deletions
@@ -86,7 +86,7 @@ struct llama_model { }; // load the model's weights from a file -bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) { +bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) { fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str()); std::vector<char> f_buf(1024*1024); @@ -207,8 +207,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2 ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3 - ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k - ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v + ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k + ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v ctx_size += (5 + 10*n_layer)*256; // object overhead @@ -293,8 +293,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab const int n_mem = n_layer*n_ctx; const int n_elements = n_embd*n_mem; - model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements); - model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements); + model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements); + model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements); const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v); @@ -814,8 +814,9 @@ int main(int argc, char ** argv) { // load the model { + const ggml_type memory_type = params.memory_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32; const int64_t t_start_us = ggml_time_us(); - if (!llama_model_load(params.model, model, vocab, params.n_ctx)) { + if (!llama_model_load(params.model, model, vocab, params.n_ctx, memory_type)) { fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); return 1; } @@ -49,6 +49,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.top_k = std::stoi(argv[++i]); } else if (arg == "-c" || arg == "--ctx_size") { params.n_ctx = std::stoi(argv[++i]); + } else if (arg == "--memory_f16") { + params.memory_f16 = true; } else if (arg == "--top_p") { params.top_p = std::stof(argv[++i]); } else if (arg == "--temp") { @@ -104,6 +106,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n); fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty); fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx); + fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n"); fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp); fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch); fprintf(stderr, " -m FNAME, --model FNAME\n"); @@ -18,6 +18,7 @@ struct gpt_params { int32_t n_predict = 128; // new tokens to predict int32_t repeat_last_n = 64; // last n tokens to penalize int32_t n_ctx = 512; //context size + bool memory_f16 = false; // use f16 instead of f32 for memory kv // sampling parameters int32_t top_k = 40; |