vllm/csrc/cache.h
Lucas Wilkinson 288cc6c234
[Attention] MLA with chunked prefill (#12639)
Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>
Co-authored-by: Patrick Horn <patrick.horn@gmail.com>
Co-authored-by: simon-mo <xmo@berkeley.edu>
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
2025-02-21 15:30:12 -08:00

48 lines
2.1 KiB
C++

#pragma once
#include <torch/all.h>
#include <map>
#include <vector>
void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
const torch::Tensor& block_mapping);
// Note: the key_caches and value_caches vectors are constant but
// not the Tensors they contain. The vectors need to be const refs
// in order to satisfy pytorch's C++ operator registration code.
void copy_blocks(std::vector<torch::Tensor> const& key_caches,
std::vector<torch::Tensor> const& value_caches,
const torch::Tensor& block_mapping);
void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
const torch::Tensor& block_mapping);
void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
torch::Tensor& key_cache, torch::Tensor& value_cache,
torch::Tensor& slot_mapping,
const std::string& kv_cache_dtype,
torch::Tensor& k_scale, torch::Tensor& v_scale);
void reshape_and_cache_flash(torch::Tensor& key, torch::Tensor& value,
torch::Tensor& key_cache,
torch::Tensor& value_cache,
torch::Tensor& slot_mapping,
const std::string& kv_cache_dtype,
torch::Tensor& k_scale, torch::Tensor& v_scale);
void concat_and_cache_mla(torch::Tensor& kv_c, torch::Tensor& k_pe,
torch::Tensor& kv_cache, torch::Tensor& slot_mapping,
const std::string& kv_cache_dtype,
torch::Tensor& scale);
// Just for unittest
void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
const double scale, const std::string& kv_cache_dtype);
void gather_cache(
torch::Tensor const& src_cache, // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
torch::Tensor const& dst, // [TOT_TOKENS, ENTRIES...]
torch::Tensor const& block_table, // [BATCH, BLOCK_INDICES]
torch::Tensor const& cu_seq_lens, // [BATCH+1]
int64_t batch_size, std::optional<torch::Tensor> seq_starts = std::nullopt);