extern/whisper.cpp
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talk-llama : sync llama.cpp

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-05-13 13:20:19 +03:00
parent a14c89aefa
commit f890560575
25 changed files with 2847 additions and 1125 deletions
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78
examples/talk-llama/llama-context.h
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@ -7,6 +7,7 @@
#include "llama-adapter.h"
#include "ggml-cpp.h"
#include "ggml-opt.h"
#include <map>
#include <vector>
@ -27,7 +28,12 @@ struct llama_context {
void synchronize();
const llama_model & get_model() const;
const llama_model & get_model() const;
const llama_cparams & get_cparams() const;
ggml_backend_sched_t get_sched() const;
ggml_context * get_ctx_compute() const;
uint32_t n_ctx() const;
uint32_t n_ctx_per_seq() const;
@ -128,6 +134,32 @@ struct llama_context {
llama_perf_context_data perf_get_data() const;
void perf_reset();
//
// training
//
void opt_init(struct llama_model * model, struct llama_opt_params lopt_params);
void opt_epoch(
ggml_opt_dataset_t dataset,
ggml_opt_result_t result_train,
ggml_opt_result_t result_eval,
int64_t idata_split,
ggml_opt_epoch_callback callback_train,
ggml_opt_epoch_callback callback_eval);
void opt_epoch_iter(
ggml_opt_dataset_t dataset,
ggml_opt_result_t result,
const std::vector<llama_token> & tokens,
const std::vector<llama_token> & labels_sparse,
llama_batch & batch,
ggml_opt_epoch_callback callback,
bool train,
int64_t idata_in_loop,
int64_t ndata_in_loop,
int64_t t_loop_start);
private:
//
// output
@ -137,49 +169,30 @@ private:
// Returns max number of outputs for which space was reserved.
int32_t output_reserve(int32_t n_outputs);
// make the outputs have the same order they had in the user-provided batch
// TODO: maybe remove this
void output_reorder();
//
// graph
//
public:
int32_t graph_max_nodes() const;
// zero-out inputs and create the ctx_compute for the compute graph
ggml_cgraph * graph_init();
llm_graph_result_ptr graph_build(
ggml_context * ctx,
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype);
// returns the result of ggml_backend_sched_graph_compute_async execution
ggml_status graph_compute(
ggml_cgraph * gf,
bool batched);
private:
llm_graph_result_ptr graph_build(
ggml_context * ctx,
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype);
llm_graph_cb graph_get_cb() const;
// used by kv_self_update()
ggml_tensor * build_rope_shift(
ggml_context * ctx0,
ggml_tensor * cur,
ggml_tensor * shift,
ggml_tensor * factors,
float freq_base,
float freq_scale) const;
llm_graph_result_ptr build_kv_self_shift(
ggml_context * ctx0,
ggml_cgraph * gf) const;
llm_graph_result_ptr build_kv_self_defrag(
ggml_context * ctx0,
ggml_cgraph * gf) const;
// TODO: read/write lora adapters and cvec
size_t state_write_data(llama_io_write_i & io);
size_t state_read_data (llama_io_read_i & io);
@ -196,14 +209,10 @@ private:
llama_cparams cparams;
llama_adapter_cvec cvec;
llama_adapter_loras loras;
llama_sbatch sbatch;
llama_cross cross; // TODO: tmp for handling cross-attention - need something better probably
std::unique_ptr<llama_kv_cache_unified> kv_self;
// TODO: remove
bool logits_all = false;
std::unique_ptr<llama_memory_i> memory;
// decode output (2-dimensional array: [n_outputs][n_vocab])
size_t logits_size = 0; // capacity (of floats) for logits
@ -230,6 +239,9 @@ private:
ggml_context_ptr ctx_compute;
// training
ggml_opt_context_t opt_ctx = nullptr;
ggml_threadpool_t threadpool = nullptr;
ggml_threadpool_t threadpool_batch = nullptr;