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https://github.com/ikawrakow/ik_llama.cpp.git
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Merge mainline llama.cpp (#3)
* Merging mainline - WIP * Merging mainline - WIP AVX2 and CUDA appear to work. CUDA performance seems slightly (~1-2%) lower as it is so often the case with llama.cpp/ggml after some "improvements" have been made. * Merging mainline - fix Metal * Remove check --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@@ -28,9 +28,13 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
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std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
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result->grammar = llama_grammar_init(
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struct llama_grammar * grammar = llama_grammar_init(
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grammar_rules.data(),
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grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
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if (grammar == nullptr) {
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throw std::runtime_error("Failed to initialize llama_grammar");
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}
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result->grammar = grammar;
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}
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result->prev.resize(params.n_prev);
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@@ -59,9 +63,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
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if (!ctx->parsed_grammar.rules.empty()) {
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std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
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ctx->grammar = llama_grammar_init(
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struct llama_grammar * grammar = llama_grammar_init(
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grammar_rules.data(),
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grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
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if (grammar == nullptr) {
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throw std::runtime_error("Failed to initialize llama_grammar");
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}
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ctx->grammar = grammar;
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}
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std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
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@@ -274,8 +282,6 @@ static llama_token llama_sampling_sample_impl(
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GGML_ASSERT(!original_logits.empty());
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}
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llama_token id = 0;
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// Get a pointer to the logits
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float * logits = llama_get_logits_ith(ctx_main, idx);
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if (temp < 0.0) {
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// greedy sampling, with probs
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@@ -316,12 +322,15 @@ static llama_token llama_sampling_sample_impl(
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}
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if (ctx_sampling->grammar != NULL && !is_resampling) {
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// Get a pointer to the logits
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float * logits = llama_get_logits_ith(ctx_main, idx);
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// Create an array with a single token data element for the sampled id
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llama_token_data single_token_data = {id, logits[id], 0.0f};
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llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
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// Apply grammar constraints to the single token
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llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
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llama_grammar_sample(ctx_sampling->grammar, ctx_main, &single_token_data_array);
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// Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
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bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
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@@ -369,7 +378,7 @@ static llama_token_data_array llama_sampling_prepare_impl(
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if (ctx_sampling->grammar != NULL && !apply_grammar) {
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GGML_ASSERT(original_logits != NULL);
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// Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
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*original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))};
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*original_logits = {logits, logits + n_vocab};
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}
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// apply params.logit_bias map
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@@ -382,10 +391,10 @@ static llama_token_data_array llama_sampling_prepare_impl(
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llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
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}
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cur.clear();
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cur.resize(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
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}
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llama_token_data_array cur_p = { cur.data(), cur.size(), false };
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@@ -412,7 +421,7 @@ static llama_token_data_array llama_sampling_prepare_impl(
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// apply grammar checks before sampling logic
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if (apply_grammar && ctx_sampling->grammar != NULL) {
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llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
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llama_grammar_sample(ctx_sampling->grammar, ctx_main, &cur_p);
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}
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return cur_p;
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@@ -446,6 +455,6 @@ void llama_sampling_accept(
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ctx_sampling->prev.push_back(id);
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if (ctx_sampling->grammar != NULL && apply_grammar) {
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llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
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llama_grammar_accept_token(ctx_sampling->grammar, ctx_main, id);
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}
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}
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