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238657db23
* Introduce the new Min-P sampler by @kalomaze The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. * Min-P enabled and set to 0.05 default --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
111 lines
3.9 KiB
C++
111 lines
3.9 KiB
C++
#pragma once
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#include "llama.h"
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#include "grammar-parser.h"
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#include <string>
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#include <vector>
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#include <unordered_map>
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// sampling parameters
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typedef struct llama_sampling_params {
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int32_t n_prev = 64; // number of previous tokens to remember
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int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
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int32_t top_k = 40; // <= 0 to use vocab size
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float top_p = 0.95f; // 1.0 = disabled
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float min_p = 0.05f; // 0.0 = disabled
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float tfs_z = 1.00f; // 1.0 = disabled
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float typical_p = 1.00f; // 1.0 = disabled
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float temp = 0.80f; // 1.0 = disabled
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int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
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float penalty_repeat = 1.10f; // 1.0 = disabled
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float penalty_freq = 0.00f; // 0.0 = disabled
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float penalty_present = 0.00f; // 0.0 = disabled
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int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
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float mirostat_tau = 5.00f; // target entropy
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float mirostat_eta = 0.10f; // learning rate
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bool penalize_nl = true; // consider newlines as a repeatable token
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std::string grammar; // optional BNF-like grammar to constrain sampling
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// Classifier-Free Guidance
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// https://arxiv.org/abs/2306.17806
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std::string cfg_negative_prompt; // string to help guidance
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float cfg_scale = 1.f; // how strong is guidance
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std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
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} llama_sampling_params;
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// general sampler context
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// TODO: move to llama.h
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struct llama_sampling_context {
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// parameters that will be used for sampling
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llama_sampling_params params;
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// mirostat sampler state
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float mirostat_mu;
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llama_grammar * grammar;
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// internal
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grammar_parser::parse_state parsed_grammar;
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// TODO: replace with ring-buffer
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std::vector<llama_token> prev;
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std::vector<llama_token_data> cur;
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};
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#include "common.h"
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// Create a new sampling context instance.
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struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params);
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void llama_sampling_free(struct llama_sampling_context * ctx);
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// Reset the sampler context
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// - clear prev tokens
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// - reset grammar
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void llama_sampling_reset(llama_sampling_context * ctx);
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// Copy the sampler context
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void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
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// Get the last sampled token
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llama_token llama_sampling_last(llama_sampling_context * ctx);
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// Get a string representation of the last sampled tokens
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std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n);
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// Print sampling parameters into a string
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std::string llama_sampling_print(const llama_sampling_params & params);
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// this is a common sampling function used across the examples for convenience
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// it can serve as a starting point for implementing your own sampling function
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// Note: When using multiple sequences, it is the caller's responsibility to call
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// llama_sampling_reset when a sequence ends
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//
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// required:
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// - ctx_main: context to use for sampling
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// - ctx_sampling: sampling-specific context
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//
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// optional:
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// - ctx_cfg: context to use for classifier-free guidance
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// - idx: sample from llama_get_logits_ith(ctx, idx)
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//
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// returns:
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// - token: sampled token
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// - candidates: vector of candidate tokens
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//
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llama_token llama_sampling_sample(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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struct llama_context * ctx_cfg,
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int idx = 0);
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void llama_sampling_accept(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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llama_token id,
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bool apply_grammar);
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