#pragma once #include "llama.h" #include #include #include // sampling parameters typedef struct llama_sampling_params { int32_t top_k = 40; // <= 0 to use vocab size float top_p = 0.95f; // 1.0 = disabled float tfs_z = 1.00f; // 1.0 = disabled float typical_p = 1.00f; // 1.0 = disabled float temp = 0.80f; // 1.0 = disabled float repeat_penalty = 1.10f; // 1.0 = disabled 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 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 bool penalize_nl = true; // consider newlines as a repeatable token int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. // Classifier-Free Guidance // https://arxiv.org/abs/2306.17806 std::string cfg_negative_prompt; // string to help guidance float cfg_scale = 1.f; // How strong is guidance std::unordered_map logit_bias; // logit bias for specific tokens } llama_sampling_params; // per-sequence sampler context typedef struct llama_sampler_sequence_context { float mirostat_mu; // mirostat sampler state llama_grammar * grammar; } llama_sampler_sequence_context; // general sampler context typedef struct llama_sampling_context { ~llama_sampling_context(); // parameters that will be used for sampling and when creating // new llama_sampler_sequence_context instances llama_sampling_params params; // map of sequence ids to sampler contexts std::unordered_map sequence_contexts; // when non-NULL, new instances of llama_sampler_sequence_context // will get a copy of the grammar here // note: only the pointer is stored here, it is not a copy of // the grammar and shouldn't be freed llama_grammar * grammar; } llama_sampling_context; #include "common.h" // Create a new sampling context instance. llama_sampling_context llama_sampling_context_init( const struct gpt_params & params, llama_grammar * grammar = NULL); // Fetches the sampler context for the specified sequence id (defaults to 0). // If the context for that sequence id doesn't already exist, it will be created with // default values based on the parameters in the ctx_sampling argument. llama_sampler_sequence_context & llama_sampling_get_sequence_context( llama_sampling_context & ctx_sampling, const llama_seq_id seq = 0); // Reset the sampler context for the supplied sequence id (defaults to 0). // This is necessary to reuse a sequence id or free memory used by sequences // that are no longer required. bool llama_sampling_context_reset( llama_sampling_context & ctx_sampling, const llama_seq_id seq = 0); // this is a common sampling function used across the examples for convenience // it can serve as a starting point for implementing your own sampling function // Note: When using multiple sequences, it is the caller's responsibility to call // llama_sampling_context_reset when a sequence ends // // required: // - ctx: context to use for sampling // - ctx_sampling: sampling-specific context // // optional: // - ctx_guidance: context to use for classifier-free guidance, ignore if NULL // - last_tokens: needed for repetition penalty, ignore if empty // - idx: sample from llama_get_logits_ith(ctx, idx) // - seq: sequence id to associate sampler state with // // returns: // - token: sampled token // - candidates: vector of candidate tokens // llama_token llama_sampling_sample( struct llama_context * ctx, struct llama_context * ctx_guidance, struct llama_sampling_context & ctx_sampling, const std::vector & last_tokens, std::vector & candidates, const int idx = 0, llama_seq_id seq = 0);