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llama : refactor samplers internal implementation (#9370)
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@ -101,6 +101,10 @@ struct ring_buffer {
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}
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void push_back(const T & value) {
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if (capacity == 0) {
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throw std::runtime_error("ring buffer: capacity is zero");
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}
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if (sz == capacity) {
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// advance the start when buffer is full
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first = (first + 1) % capacity;
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File diff suppressed because it is too large
Load Diff
@ -23,16 +23,6 @@ struct llama_sampler_chain {
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mutable int32_t n_sample;
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};
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using llama_token_cnt = std::unordered_map<llama_token, int>;
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// TODO: tmp exposed until test-sampling is fixed
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void llama_sampler_penalties_impl(
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llama_token_data_array * cur_p,
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const llama_token_cnt & token_count,
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float penalty_repeat,
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float penalty_freq,
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float penalty_present);
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struct llama_sampler * llama_sampler_init_grammar_impl(
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const struct llama_vocab & vocab,
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const char * grammar_str,
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@ -148,15 +148,17 @@ static void test_penalties(
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cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
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}
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llama_token_cnt token_count;
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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auto * sampler = llama_sampler_init_penalties(n_vocab, LLAMA_TOKEN_NULL, LLAMA_TOKEN_NULL, last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence, false, false);
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for (size_t i = 0; i < last_tokens.size(); i++) {
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token_count[last_tokens[i]]++;
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llama_sampler_accept(sampler, last_tokens[i]);
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}
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llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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llama_sampler_penalties_impl(&cur_p, token_count, repeat_penalty, alpha_frequency, alpha_presence); // TODO: avoid
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APPLY(sampler, &cur_p);
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APPLY(llama_sampler_init_softmax(), &cur_p);
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DUMP(&cur_p);
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