speculative : change default p_accept to 0.5 + CLI args (#3919)
ggml-ci
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3 changed files with 25 additions and 5 deletions
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@ -44,6 +44,7 @@ int32_t get_num_physical_cores();
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struct gpt_params {
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uint32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
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int32_t n_predict = -1; // new tokens to predict
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@ -54,6 +55,8 @@ struct gpt_params {
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int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
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int32_t n_parallel = 1; // number of parallel sequences to decode
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int32_t n_sequences = 1; // number of sequences to decode
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float p_accept = 0.5f; // speculative decoding accept probability
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float p_split = 0.1f; // speculative decoding split probability
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int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
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int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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@ -66,7 +69,8 @@ struct gpt_params {
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float yarn_beta_fast = 32.0f; // YaRN low correction dim
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float yarn_beta_slow = 1.0f; // YaRN high correction dim
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED;
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int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
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// pinging @cebtenzzre
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// // sampling parameters
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struct llama_sampling_params sparams;
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@ -90,7 +94,7 @@ struct gpt_params {
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int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
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// (which is more convenient to use for plotting)
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//
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bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
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bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
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