From 905d87b70aa189623d500a28602d7a3a755a4769 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 13 May 2023 15:38:36 +0200 Subject: ggml : GPU-accelerated token generation (#1412) * CUDA kernel for q4_0 dequant. + mat. vec. mult. * Added q4_1 via template * Added missing __syncthreads(); * --gpu_layers -> --gpu-layers * Shorter dequantize_mul_mat_vec line * q5_0 dequantize_mul_mat kernel * More readable dequantize_mul_mat_vec logic * dequantize_mul_mat_vec kernels for q5_1, q8_0, f16 * llama : offload "output" tensor to GPU too + coding style fixes --------- Co-authored-by: Georgi Gerganov --- examples/common.cpp | 25 +++++++++++++++++-------- examples/common.h | 11 ++++++----- 2 files changed, 23 insertions(+), 13 deletions(-) (limited to 'examples') diff --git a/examples/common.cpp b/examples/common.cpp index 80e35d2..86c1eef 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -277,6 +277,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.use_color = true; } else if (arg == "--mlock") { params.use_mlock = true; + } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_gpu_layers = std::stoi(argv[i]); } else if (arg == "--no-mmap") { params.use_mmap = false; } else if (arg == "--mtest") { @@ -421,6 +427,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { if (llama_mmap_supported()) { fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } + fprintf(stderr, " -ngl N, --n-gpu-layers N\n"); + fprintf(stderr, " number of layers to store in VRAM\n"); fprintf(stderr, " --mtest compute maximum memory usage\n"); fprintf(stderr, " --verbose-prompt print prompt before generation\n"); fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); @@ -463,14 +471,15 @@ std::vector llama_tokenize(struct llama_context * ctx, const std::s struct llama_context * llama_init_from_gpt_params(const gpt_params & params) { auto lparams = llama_context_default_params(); - lparams.n_ctx = params.n_ctx; - lparams.n_parts = params.n_parts; - lparams.seed = params.seed; - lparams.f16_kv = params.memory_f16; - lparams.use_mmap = params.use_mmap; - lparams.use_mlock = params.use_mlock; - lparams.logits_all = params.perplexity; - lparams.embedding = params.embedding; + lparams.n_ctx = params.n_ctx; + lparams.n_parts = params.n_parts; + lparams.n_gpu_layers = params.n_gpu_layers; + lparams.seed = params.seed; + lparams.f16_kv = params.memory_f16; + lparams.use_mmap = params.use_mmap; + lparams.use_mlock = params.use_mlock; + lparams.logits_all = params.perplexity; + lparams.embedding = params.embedding; llama_context * lctx = llama_init_from_file(params.model.c_str(), lparams); diff --git a/examples/common.h b/examples/common.h index 499671b..717838f 100644 --- a/examples/common.h +++ b/examples/common.h @@ -21,13 +21,14 @@ int32_t get_num_physical_cores(); struct gpt_params { - int32_t seed = -1; // RNG seed + int32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); int32_t n_predict = -1; // new tokens to predict - int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) + int32_t n_ctx = 512; // context size + int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_gpu_layers = 0; // number of layers to store in VRAM // sampling parameters std::unordered_map logit_bias; // logit bias for specific tokens -- cgit v1.2.3