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https://github.com/ggerganov/whisper.cpp.git
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talk-llama : sync llama.cpp
ggml-ci
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@ -4,6 +4,7 @@
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#include "ggml.h"
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#include "ggml-cpu.h"
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#include "ggml-backend.h"
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#include "ggml-opt.h"
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#include <stddef.h>
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#include <stdint.h>
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@ -112,6 +113,7 @@ extern "C" {
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LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
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LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33,
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LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34,
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LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
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};
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enum llama_rope_type {
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@ -343,7 +345,7 @@ extern "C" {
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float yarn_beta_fast; // YaRN low correction dim
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float yarn_beta_slow; // YaRN high correction dim
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uint32_t yarn_orig_ctx; // YaRN original context size
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float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default)
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float defrag_thold; // defragment the KV cache if holes/size > thold, <= 0 disabled (default)
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ggml_backend_sched_eval_callback cb_eval;
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void * cb_eval_user_data;
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@ -351,19 +353,18 @@ extern "C" {
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enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
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enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
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// Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
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// TODO: move at the end of the struct
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bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
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bool embeddings; // if true, extract embeddings (together with logits)
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bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
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bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
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bool no_perf; // whether to measure performance timings
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// Abort callback
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// if it returns true, execution of llama_decode() will be aborted
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// currently works only with CPU execution
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ggml_abort_callback abort_callback;
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void * abort_callback_data;
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// Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
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bool embeddings; // if true, extract embeddings (together with logits)
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bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
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bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
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bool no_perf; // whether to measure performance timings
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bool op_offload; // whether to offload host tensor operations to device
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};
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// model quantization parameters
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@ -445,6 +446,10 @@ extern "C" {
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size_t n_paths,
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struct llama_model_params params);
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LLAMA_API void llama_model_save_to_file(
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const struct llama_model * model,
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const char * path_model);
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DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
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"use llama_model_free instead");
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@ -924,14 +929,19 @@ extern "C" {
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// Frees a batch of tokens allocated with llama_batch_init()
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LLAMA_API void llama_batch_free(struct llama_batch batch);
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// Processes a batch of tokens with the ecoder part of the encoder-decoder model.
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// Stores the encoder output internally for later use by the decoder cross-attention layers.
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// Process a batch of tokens.
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// In contrast to llama_decode() - this call does not use KV cache.
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// For encode-decoder contexts, processes the batch using the encoder.
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// Can store the encoder output internally for later use by the decoder's cross-attention layers.
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// 0 - success
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// < 0 - error. the KV cache state is restored to the state before this call
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LLAMA_API int32_t llama_encode(
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struct llama_context * ctx,
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struct llama_batch batch);
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// Process a batch of tokens.
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// Requires KV cache.
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// For encode-decoder contexts, processes the batch using the decoder.
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// Positive return values does not mean a fatal error, but rather a warning.
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// 0 - success
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// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
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@ -1428,6 +1438,37 @@ extern "C" {
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LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain);
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LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain);
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//
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// training
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//
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// function that returns whether or not a given tensor contains trainable parameters
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typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata);
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// always returns true
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LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata);
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struct llama_opt_params {
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uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0
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llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters
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void * param_filter_ud; // userdata for determining which tensors contain trainable parameters
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ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
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void * get_opt_pars_ud; // userdata for calculating optimizer parameters
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};
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LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
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LLAMA_API void llama_opt_epoch(
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struct llama_context * lctx,
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ggml_opt_dataset_t dataset,
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ggml_opt_result_t result_train,
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ggml_opt_result_t result_eval,
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int64_t idata_split,
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ggml_opt_epoch_callback callback_train,
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ggml_opt_epoch_callback callback_eval);
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#ifdef __cplusplus
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}
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#endif
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