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llama: update vendored code to commit 40c6d79f (#7875)
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63
llama/sampling.h
vendored
63
llama/sampling.h
vendored
@@ -1,5 +1,5 @@
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/**
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* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
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* llama.cpp - commit 40c6d79fb52f995f47507fedfeaae2ac05d9b35c - do not edit this file
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*
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* MIT License
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*
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@@ -33,7 +33,7 @@
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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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// common_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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@@ -49,30 +49,30 @@
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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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// The common_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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// For convenience, the common_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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struct common_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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struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
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void gpt_sampler_free(struct gpt_sampler * gsmpl);
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void common_sampler_free(struct common_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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void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
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void common_sampler_reset (struct common_sampler * gsmpl);
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struct common_sampler * common_sampler_clone (struct common_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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void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
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// extended sampling implementation:
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//
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@@ -84,26 +84,47 @@ void gpt_perf_print(const struct llama_context * ctx, const struct gpt_sampler *
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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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llama_token common_sampler_sample(struct common_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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// generalized version of common_sampler_sample
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//
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// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match
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// if the sampler disagrees at some point, we stop and return the accepted tokens up to now
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//
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// common_sampler_sample_n(gsmpl, ctx, { idx }, {});
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//
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// is equivalent to
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//
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// common_sampler_sample(gsmpl, ctx, idx);
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// common_sampler_accept(gsmpl, token, true);
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//
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// requires: idxs.size() == draft.size() + 1
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//
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// returns at least 1 token, up to idxs.size()
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//
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std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
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// assume idxs == [ 0, 1, 2, ..., draft.size() ]
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std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
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uint32_t common_sampler_get_seed(const struct common_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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llama_token_data_array * common_sampler_get_candidates(struct common_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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llama_token common_sampler_last(const struct common_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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std::string common_sampler_print(const struct common_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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std::string common_sampler_prev_str(common_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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char common_sampler_type_to_chr(enum common_sampler_type cnstr);
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std::string common_sampler_type_to_str(enum common_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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std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
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std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
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