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https://github.com/ggerganov/llama.cpp.git
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84 lines
3.4 KiB
C++
84 lines
3.4 KiB
C++
#pragma once
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#include "llama.h"
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#include "common.h"
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#include <string>
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#include <vector>
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// gpt_sampler extends llama_sampler with additional functionality:
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//
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// - grammar support
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// - custom sampler logic based on the parameters
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// - history of the last accepted tokens
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// - performance metrics
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//
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// This goal is to have a common implementation of the sampling logic shared across the examples.
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// For example, depending on the temperature, the sampling chain can be very simple (greedy) or more
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// complex (top-k, top-p, etc).
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//
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// Another example is related to the grammar. In general, the grammar constraints applied on the full
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// vocabulary can be very taxing. To improve performance, the grammar can be applied only to the sampled
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// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the
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// grammar constraints are applied to the full vocabulary and the token is resampled.
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//
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// The gpt_sampler also maintains a container with the last accepted tokens. In the future, this can
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// be moved into the core llama library.
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//
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// For convenience, the gpt_sampler also maintains a container with the current candidate tokens.
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// This can be used to access the probabilities of the rest of the non-sampled tokens.
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//
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// TODO: measure grammar performance
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//
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struct gpt_sampler;
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// llama_sampler API overloads
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struct gpt_sampler * gpt_sampler_init(const struct llama_model * model, const struct gpt_sampler_params & params);
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void gpt_sampler_free(struct gpt_sampler * gsmpl);
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// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
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void gpt_sampler_accept(struct gpt_sampler * gsmpl, llama_token token, bool accept_grammar);
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void gpt_sampler_reset (struct gpt_sampler * gsmpl);
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struct gpt_sampler * gpt_sampler_clone (struct gpt_sampler * gsmpl);
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// arguments can be nullptr to skip printing
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void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler * gsmpl);
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// extended sampling implementation:
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//
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// - set logits
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// - apply the configured sampler chain
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// - check if the token fits the grammar (if any)
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// - if not: resample by first applying the grammar constraints and then sampling again (slower path)
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//
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// if grammar_first is true, the grammar is applied before the samplers (slower)
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// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
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//
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llama_token gpt_sampler_sample(struct gpt_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
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uint32_t gpt_sampler_get_seed(const struct gpt_sampler * gsmpl);
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// helpers
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// access the internal list of current candidate tokens
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llama_token_data_array * gpt_sampler_get_candidates(struct gpt_sampler * gsmpl);
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// get the last accepted token
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llama_token gpt_sampler_last(const struct gpt_sampler * gsmpl);
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// print the sampler chain into a string
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std::string gpt_sampler_print(const struct gpt_sampler * gsmpl);
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// get a string representation of the last accepted tokens
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std::string gpt_sampler_prev_str(gpt_sampler * gsmpl, llama_context * ctx, int n);
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char gpt_sampler_type_to_chr(enum gpt_sampler_type cnstr);
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std::string gpt_sampler_type_to_str(enum gpt_sampler_type cnstr);
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std::vector<enum gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
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std::vector<enum gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars);
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