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llama : reuse compute graphs (#14482)

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* llama : reuse compute graphs

ggml-ci

* llama-bench : add graph reuse parameter

ggml-ci

* cont : remove the parameter and the sched resets

ggml-ci

* graph : rename update() to can_reuse()

ggml-ci

* params : remove is_same()

ggml-ci

* graph : set res->params in llm_graph_context constructor

ggml-ci

* graph : avoid set_max_nodes in llm_graph_result

ggml-ci

* kv-cache : reuse llama_context's graph result instance

ggml-ci

* context : reset the previous graph result upon memory updates

ggml-ci

* batch : llama_ubatch now carries its data instead of pointing to balloc

ggml-ci

* merge : fix build

ggml-ci

* graph : fix can_reuse() checks when flash-attention is disabled

* graph : move llm_graph_result impl in source file + debug env

ggml-ci
This commit is contained in:
Georgi Gerganov
2025-07-17 19:08:33 +03:00
committed by GitHub
parent 086cf81e88
commit 01612b7409
12 changed files with 548 additions and 289 deletions
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29
src/llama-context.h
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@@ -35,8 +35,6 @@ struct llama_context {
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;
uint32_t n_batch() const;
@@ -96,7 +94,7 @@ struct llama_context {
// if memory_context is provided, it will be applied first to the context's memory
// ret contains the status of the graph computation
// returns nullptr only if ret != GGML_STATUS_SUCCESS
llm_graph_result_ptr process_ubatch(
llm_graph_result_i * process_ubatch(
const llama_ubatch & ubatch,
llm_graph_type gtype,
llama_memory_context_i * mctx,
@@ -188,10 +186,10 @@ private:
//
public:
int32_t graph_max_nodes() const;
uint32_t graph_max_nodes() const;
// zero-out inputs and create the ctx_compute for the compute graph
ggml_cgraph * graph_init();
// can reuse the llm_graph_result instance of the context (for example to update a memory module)
llm_graph_result * get_gf_res_reserve() const;
// returns the result of ggml_backend_sched_graph_compute_async execution
ggml_status graph_compute(ggml_cgraph * gf, bool batched);
@@ -200,12 +198,11 @@ public:
ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx);
private:
llm_graph_result_ptr graph_build(
ggml_context * ctx,
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype,
const llama_memory_context_i * mctx);
llm_graph_params graph_params(
llm_graph_result_i * res,
const llama_ubatch & ubatch,
const llama_memory_context_i * mctx,
llm_graph_type gtype) const;
llm_graph_cb graph_get_cb() const;
@@ -258,8 +255,6 @@ private:
ggml_backend_t backend_cpu = nullptr;
std::vector<ggml_backend_ptr> backends;
ggml_context_ptr ctx_compute;
// training
ggml_opt_context_t opt_ctx = nullptr;
@@ -275,8 +270,8 @@ private:
std::vector<ggml_backend_t> backend_ptrs;
std::vector<ggml_backend_buffer_type_t> backend_buft;
// memory buffers used to evaluate the model
std::vector<uint8_t> buf_compute_meta;
llm_graph_result_ptr gf_res_prev;
llm_graph_result_ptr gf_res_reserve;
// host buffer for the model output (logits and embeddings)
ggml_backend_buffer_ptr buf_output;
@@ -294,4 +289,6 @@ private:
mutable int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
mutable int32_t n_eval = 0; // number of eval calls
mutable int32_t n_reused = 0; // number of times the previous graph was reused
};