kv-cache : separate recurrent vs non-recurrent impl (#12799)

* kv-cache : serparate recurrent vs non-recurrent impl (wip)

ggml-ci

* kv-cache : init -> contructor + add llama_memory_params

ggml-ci

* kv-cache : fix callback reference

ggml-ci

* context : llama_kv_cache -> llama_memory_i

ggml-ci

* context : move memory creation logic to model

ggml-ci

* llama : remove reference of memory during encode

ggml-ci

* kv-cache : hide padding details in the implementation

ggml-ci

* kv-cache : add ubatch_next()

ggml-ci

* context : simplify sbatch logic

ggml-ci

* kv-cache : hide defrag logic in the implementation

ggml-ci

* context : hide kv cache details in implementation

ggml-ci

* build : fix

ggml-ci

* cont : another fix

ggml-ci

* kv-cache : simplify interface (wip)

ggml-ci

* kv-cache : use separate KV cell structs for unified/recurrent

ggml-ci

* kv-cache : clean-up

ggml-ci

* model : better llama_model::create_model() signature

ggml-ci

* kv-cache : fix recurrent seq_rm()

ggml-ci

* kv-cache : replace `struct callbacks` with `llama_model &`

ggml-ci

* kv-cache : replace `struct graph_params` with `llama_context &`

ggml-ci

* kv-cache : fix offload check

ggml-ci

* context : avoid passing unique_ptr

ggml-ci

* kv-cache : avoid using the backends from the llama_context

ref #13113

ggml-ci

* kv-cache : more consistent debug logs [no ci]

* kv-cache : do not pass the full llama_context for kv graphs

ggml-ci

* kv-cache : remove comment

* kv-cache : ggml_rope_ext_inplace -> ggml_rope_ext

ggml-ci

* kv-cache : fix recurrent multi-user case

ggml-ci

* memory : remove comments [no ci]
This commit is contained in:
Georgi Gerganov
2025-05-02 17:48:36 +03:00
committed by GitHub
parent cb06a3c363
commit c642bc014c
11 changed files with 1960 additions and 1048 deletions

View File

@ -19,6 +19,7 @@ struct llama_cparams;
class llama_memory_i;
class llama_kv_cache_unified;
class llama_kv_cache_recurrent;
// certain models (typically multi-modal) can produce different types of graphs
enum llm_graph_type {
@ -186,26 +187,26 @@ public:
class llm_graph_input_s_copy : public llm_graph_input_i {
public:
llm_graph_input_s_copy(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
llm_graph_input_s_copy(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {}
virtual ~llm_graph_input_s_copy() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_copy; // I32 [kv_size]
const llama_kv_cache_unified * kv_self;
const llama_kv_cache_recurrent * kv_self;
};
class llm_graph_input_s_mask : public llm_graph_input_i {
public:
llm_graph_input_s_mask(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
llm_graph_input_s_mask(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {}
virtual ~llm_graph_input_s_mask() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_mask; // F32 [1, n_kv]
const llama_kv_cache_unified * kv_self;
const llama_kv_cache_recurrent * kv_self;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
@ -350,8 +351,8 @@ struct llm_graph_params {
const llama_cparams & cparams;
const llama_ubatch & ubatch;
ggml_backend_sched * sched;
ggml_backend * backend_cpu;
ggml_backend_sched_t sched;
ggml_backend_t backend_cpu;
const llama_adapter_cvec * cvec;
const llama_adapter_loras * loras;
@ -402,9 +403,9 @@ struct llm_graph_context {
ggml_context * ctx0 = nullptr;
ggml_backend_sched * sched;
ggml_backend_sched_t sched;
ggml_backend * backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
const llama_adapter_cvec * cvec;
const llama_adapter_loras * loras;