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sampling : optimize samplers by reusing bucket sort (#15665)
* sampling : optimize sorting using bucket sort in more places ggml-ci * sampling : do not sort in dist sampler ggml-ci * sampling : avoid heap allocations for sort buffers ggml-ci * common : add option to sort sampling candidates by probability ggml-ci * sampling : revert the change for preserving sort buffers * sampling : use std::copy instead of memcpy * sampling : clarify purpose of partial sort helpers ggml-ci * cont : remove wrong comment [no ci] * common : update comment Co-authored-by: Johannes Gäßler <johannesg@5d6.de> --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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@@ -206,7 +206,7 @@ extern "C" {
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llama_token_data * data;
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size_t size;
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int64_t selected; // this is the index in the data array (i.e. not the token id)
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bool sorted;
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bool sorted; // note: do not assume the data is sorted - always check this flag
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} llama_token_data_array;
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typedef bool (*llama_progress_callback)(float progress, void * user_data);
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@@ -1156,11 +1156,6 @@ extern "C" {
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LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void);
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LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed);
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/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
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/// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
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DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void),
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"will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
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/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
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/// Setting k <= 0 makes this a noop
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LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k);
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