common : add llama_batch_add() and llama_batch_clear() helpers
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10 changed files with 98 additions and 122 deletions
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@ -820,6 +820,27 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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return cparams;
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}
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void llama_batch_clear(struct llama_batch & batch) {
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batch.n_tokens = 0;
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}
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void llama_batch_add(
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struct llama_batch & batch,
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llama_token id,
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llama_pos pos,
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const std::vector<llama_seq_id> & seq_ids,
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bool logits) {
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batch.token [batch.n_tokens] = id;
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batch.pos [batch.n_tokens] = pos,
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batch.n_seq_id[batch.n_tokens] = seq_ids.size();
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for (size_t i = 0; i < seq_ids.size(); ++i) {
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batch.seq_id[batch.n_tokens][i] = seq_ids[i];
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}
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batch.logits [batch.n_tokens] = logits;
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batch.n_tokens++;
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}
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std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
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auto mparams = llama_model_params_from_gpt_params(params);
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@ -70,6 +70,7 @@ struct gpt_params {
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std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
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std::string logdir = ""; // directory in which to save YAML log files
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// TODO: avoid tuple, use struct
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std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
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std::string lora_base = ""; // base model path for the lora adapter
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@ -124,10 +125,23 @@ void process_escapes(std::string& input);
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// Model utils
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//
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// TODO: avoid tuplue, use struct
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std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
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struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params);
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struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);
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struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
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// Batch utils
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void llama_batch_clear(struct llama_batch & batch);
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void llama_batch_add(
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struct llama_batch & batch,
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llama_token id,
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llama_pos pos,
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const std::vector<llama_seq_id> & seq_ids,
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bool logits);
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//
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// Vocab utils
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//
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@ -53,6 +53,19 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
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ctx->cur.clear();
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}
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void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
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if (dst->grammar) {
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llama_grammar_free(dst->grammar);
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dst->grammar = nullptr;
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}
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if (src->grammar) {
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dst->grammar = llama_grammar_copy(src->grammar);
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}
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dst->prev = src->prev;
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}
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llama_token llama_sampling_sample(
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struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_main,
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@ -67,6 +67,9 @@ void llama_sampling_free(struct llama_sampling_context * ctx);
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// - reset grammar
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void llama_sampling_reset(llama_sampling_context * ctx);
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// Copy the sampler context
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void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
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// this is a common sampling function used across the examples for convenience
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// it can serve as a starting point for implementing your own sampling function
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// Note: When using multiple sequences, it is the caller's responsibility to call
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