Files
llama_cpp/src/llama-kv-cache-iswa.h
T
8c146a8366 DeepSeek V4 (#24162)
* convert: add dsv4 conversion

* add basic setup

* add llm_graph_input_dsv4

* add save-load state

* add sinkhorn eps - correction by @fairydreaming

* add rope fix

* cleanup dead code

* fix bugs

* support pro model: added by @fairydreaming

* remove redundant V cache

* Chat template

* remove debugging leftovers

* Add mechanism for inlining templates based on architecture

* s/deepseek-v4-flash/deepseek4/g

* s/deepseek-v4-flash/deepseek4/g continued

* enable graph reuse

* enable FA

* fix test llama archs

* rename

* compatibility with antirez ds4 GGUFs

* simplified set_gguf_parameters() by calling super class method, replaced moe.score_func with expert_gating_func.

* reserve worst-case kv-cache

* revert max split inputs

* address review comments

* add padding to enable FA

* pad only the final value of plan.n_kv to 256

* remove built-in cpp chat template

* cont: remove cpp built-in template

* rm outdated test

* replace ggml_view_3d() with ggml_reshape_3d()

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* only support n_seq=1 for now

* remove unused var

* cont: remove unused var

* use scale bias

* use correct ptr for can_reuse

* remove gen-chat-inline-templates.py

* simplify graph reuse

* cont: cleanup

* remove unused inputs

* enable partial checkpointing

* add correct shape for kq_mask + set llama_model_n_swa to 0 for dsv4

* precompute source_idx + add comment about dummy write

* support multi-seq

* remove restored_trim_pos

* use split_equal when possible

* fix indent

* address review comments

* use LLM_KV

* fix ci

---------

Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-06-29 16:58:51 +08:00

156 lines
4.7 KiB
C++

#pragma once
#include "llama-kv-cache.h"
#include <vector>
//
// llama_kv_cache_iswa
//
// utilizes two instances of llama_kv_cache
// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
class llama_kv_cache_iswa : public llama_memory_i {
public:
llama_kv_cache_iswa(
const llama_model & model,
ggml_type type_k,
ggml_type type_v,
bool v_trans,
bool offload,
bool swa_full,
bool unified,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_ubatch,
uint32_t n_pad,
llama_memory_t mem_other,
const layer_filter_cb & filter,
const layer_reuse_cb & reuse,
const layer_share_cb & share);
llama_kv_cache_iswa(
const llama_model & model,
const llama_hparams & hparams,
ggml_type type_k,
ggml_type type_v,
bool v_trans,
bool offload,
bool swa_full,
bool unified,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_ubatch,
uint32_t n_pad,
llama_memory_t mem_other,
const layer_filter_cb & filter,
const layer_reuse_cb & reuse,
const layer_share_cb & share);
~llama_kv_cache_iswa() = default;
//
// llama_memory_i
//
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_context_ptr init_full() override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
void clear(bool data) override;
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
void seq_keep(llama_seq_id seq_id) override;
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
// state write/load
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
//
// llama_kv_cache_iswa specific API
//
llama_kv_cache * get_base() const;
llama_kv_cache * get_swa () const;
private:
const bool unified;
std::unique_ptr<llama_kv_cache> kv_base;
std::unique_ptr<llama_kv_cache> kv_swa;
};
class llama_kv_cache_iswa_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
// used for errors
llama_kv_cache_iswa_context(llama_memory_status status);
// used to create a full-cache context
llama_kv_cache_iswa_context(
llama_kv_cache_iswa * kv);
// used to create an update context
llama_kv_cache_iswa_context(
llama_kv_cache_iswa * kv,
llama_context * lctx,
bool optimize);
// used to create a batch processing context from a batch
llama_kv_cache_iswa_context(
llama_kv_cache_iswa * kv,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_swa,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_iswa_context();
//
// llama_memory_context_i
//
bool next() override;
bool apply() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_kv_cache_iswa_context specific API
//
const llama_kv_cache_context * get_base() const;
const llama_kv_cache_context * get_swa() const;
private:
//llama_kv_cache_iswa * kv;
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_context_ptr ctx_base;
const llama_memory_context_ptr ctx_swa;
const llama_memory_status status;
};