Implement cache ops
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@ -1,6 +1,7 @@
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from typing import Dict, List, Tuple
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import torch
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from cacheflow import ops
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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@ -92,14 +93,30 @@ class CacheEngine:
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cpu_cache.append((key_blocks, value_blocks))
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return cpu_cache
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def _copy_blocks(
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self,
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src: List[KVCache],
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dst: List[KVCache],
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src_to_dst: Dict[int, int],
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) -> None:
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with torch.cuda.stream(self.cache_stream):
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for i in range(self.num_layers):
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src_key_cache, src_value_cache = src[i]
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dst_key_cache, dst_value_cache = dst[i]
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# Copy the key blocks.
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ops.copy_cache_blocks(
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src_key_cache, dst_key_cache, src_to_dst)
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# Copy the value blocks.
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ops.copy_cache_blocks(
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src_value_cache, dst_value_cache, src_to_dst)
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event = self.events[i]
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event.record(stream=self.cache_stream)
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def copy(self, src_to_dst: Dict[int, int]) -> None:
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for event in self.events:
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pass
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self._copy_blocks(self.gpu_cache, self.gpu_cache, src_to_dst)
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def swap_in(self, src_to_dst: Dict[int, int]) -> None:
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for event in self.events:
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pass
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self._copy_blocks(self.cpu_cache, self.gpu_cache, src_to_dst)
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def swap_out(self, src_to_dst: Dict[int, int]) -> None:
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for event in self.events:
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pass
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self._copy_blocks(self.gpu_cache, self.cpu_cache, src_to_dst)
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20
csrc/cache.cpp
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20
csrc/cache.cpp
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@ -0,0 +1,20 @@
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#include <torch/extension.h>
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void copy_blocks(
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torch::Tensor& src,
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torch::Tensor& dst,
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const std::map<int64_t, int64_t>& block_mapping);
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void copy_cache_blocks(
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torch::Tensor& src,
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torch::Tensor& dst,
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const std::map<int64_t, int64_t>& block_mapping) {
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copy_blocks(src, dst, block_mapping);
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def(
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"copy_cache_blocks",
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©_cache_blocks,
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"Copy the cache blocks from src to dst");
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}
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43
csrc/cache_kernel.cu
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43
csrc/cache_kernel.cu
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@ -0,0 +1,43 @@
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#include <torch/extension.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <cassert>
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#include <map>
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void copy_blocks(
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torch::Tensor& src,
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torch::Tensor& dst,
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const std::map<int64_t, int64_t>& block_mapping) {
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torch::Device src_device = src.device();
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torch::Device dst_device = dst.device();
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cudaMemcpyKind memcpy_type;
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if (src_device.is_cuda() && dst_device.is_cuda()) {
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assert(src_device.index() == dst_device.index());
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memcpy_type = cudaMemcpyDeviceToDevice;
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} else if (src_device.is_cuda() && dst_device.is_cpu()) {
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memcpy_type = cudaMemcpyDeviceToHost;
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} else if (src_device.is_cpu() && dst_device.is_cuda()) {
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memcpy_type = cudaMemcpyHostToDevice;
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} else {
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assert(false);
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}
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void *src_ptr = src.data_ptr();
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void *dst_ptr = dst.data_ptr();
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const int64_t block_size_in_bytes = src.element_size() * src[0].numel();
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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for (const auto& pair : block_mapping) {
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int64_t src_block_number = pair.first;
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int64_t dst_block_number = pair.second;
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int64_t src_offset = src_block_number * block_size_in_bytes;
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int64_t dst_offset = dst_block_number * block_size_in_bytes;
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cudaMemcpyAsync(
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dst_ptr + dst_offset,
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src_ptr + src_offset,
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block_size_in_bytes,
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memcpy_type,
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stream);
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}
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}
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23
setup.py
23
setup.py
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import setuptools
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from torch.utils import cpp_extension
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CXX_FLAGS = ['-g']
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NVCC_FLAGS = ['-O2']
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ext_modules = []
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# Cache operations.
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cache_extension = cpp_extension.CUDAExtension(
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name='cacheflow.ops',
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sources=['csrc/cache.cpp', 'csrc/cache_kernel.cu'],
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extra_compile_args={'cxx': CXX_FLAGS, 'nvcc': NVCC_FLAGS},
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)
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ext_modules.append(cache_extension)
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setuptools.setup(
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name='cacheflow',
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requires_python='>=3.9',
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ext_modules=ext_modules,
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cmdclass={'build_ext': cpp_extension.BuildExtension},
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)
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