diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/common.cpp | 12 | ||||
| -rw-r--r-- | examples/common.h | 3 | ||||
| -rw-r--r-- | examples/main/main.cpp | 4 |
3 files changed, 14 insertions, 5 deletions
diff --git a/examples/common.cpp b/examples/common.cpp index 6610397..5608ca8 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -168,6 +168,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_ctx = std::stoi(argv[i]); + } else if (arg == "-gqa" || arg == "--gqa") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_gqa = std::stoi(argv[i]); } else if (arg == "--rope-freq-base") { if (++i >= argc) { invalid_param = true; @@ -485,6 +491,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " -f FNAME, --file FNAME\n"); fprintf(stdout, " prompt file to start generation.\n"); fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); + fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa); fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); @@ -505,7 +514,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --cfg-negative-prompt PROMPT \n"); fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n"); fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); - fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base); fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale); fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); @@ -513,7 +521,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n"); fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp); - fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n"); fprintf(stdout, " --perplexity-lines compute perplexity over each line of the prompt\n"); fprintf(stdout, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); @@ -580,6 +587,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param lparams.n_ctx = params.n_ctx; lparams.n_batch = params.n_batch; + lparams.n_gqa = params.n_gqa; lparams.n_gpu_layers = params.n_gpu_layers; lparams.main_gpu = params.main_gpu; lparams.tensor_split = params.tensor_split; diff --git a/examples/common.h b/examples/common.h index c936de6..fb8f6d6 100644 --- a/examples/common.h +++ b/examples/common.h @@ -27,6 +27,7 @@ struct gpt_params { int32_t n_predict = -1; // new tokens to predict 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_gqa = 1; // grouped-query attention factor (TODO: move to hparams) int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_gpu_layers = 0; // number of layers to store in VRAM @@ -47,7 +48,7 @@ struct gpt_params { int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) float frequency_penalty = 0.00f; // 0.0 = disabled float presence_penalty = 0.00f; // 0.0 = disabled - int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 4b4cd1d..3bd8ba2 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -93,8 +93,8 @@ int main(int argc, char ** argv) { } if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified);" - " you are on your own\n", __func__, params.n_ctx); + // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048 + fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); } else if (params.n_ctx < 8) { fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; |