387 lines
16 KiB
Plaintext
387 lines
16 KiB
Plaintext
#include <torch/extension.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include "cuda_compat.h"
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#include "dispatch_utils.h"
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#ifdef USE_ROCM
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#include "quantization/fp8/amd/quant_utils.cuh"
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#else
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#include "quantization/fp8/nvidia/quant_utils.cuh"
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#endif
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#include <algorithm>
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#include <cassert>
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#include <map>
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#include <vector>
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#ifdef USE_ROCM
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#include <hip/hip_bf16.h>
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typedef __hip_bfloat16 __nv_bfloat16;
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#endif
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void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
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const torch::Tensor& 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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TORCH_CHECK(src_device.index() == dst_device.index(),
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"src and dst must be on the same GPU");
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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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TORCH_CHECK(false, "Invalid device combination");
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}
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// NOTE(youkaichao): keep in mind that `block_mapping` should be
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// a cpu tensor, otherwise every `item` call will require a gpu-cpu
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// synchronization.
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TORCH_CHECK(block_mapping.device().is_cpu(), "block_mapping must be on CPU");
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char* src_ptr = static_cast<char*>(src.data_ptr());
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char* dst_ptr = static_cast<char*>(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 at::cuda::OptionalCUDAGuard device_guard(
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src_device.is_cuda() ? src_device : dst_device);
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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// NOTE(woosuk): This can be slow if the number of blocks is large.
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const int64_t num_blocks = block_mapping.size(0);
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for (size_t i = 0; i < num_blocks; i++) {
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int64_t src_block_number = block_mapping[i][0].item<int64_t>();
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int64_t dst_block_number = block_mapping[i][1].item<int64_t>();
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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(dst_ptr + dst_offset, src_ptr + src_offset,
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block_size_in_bytes, memcpy_type, stream);
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}
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}
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namespace vllm {
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// Grid: (num_layers, num_pairs)
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template <typename scalar_t>
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__global__ void copy_blocks_kernel(int64_t* key_cache_ptrs,
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int64_t* value_cache_ptrs,
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const int64_t* __restrict__ block_mapping,
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const int numel_per_block) {
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const int layer_idx = blockIdx.x;
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const int pair_idx = blockIdx.y;
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scalar_t* key_cache = reinterpret_cast<scalar_t*>(key_cache_ptrs[layer_idx]);
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scalar_t* value_cache =
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reinterpret_cast<scalar_t*>(value_cache_ptrs[layer_idx]);
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int64_t src_block_number = block_mapping[2 * pair_idx];
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int64_t dst_block_number = block_mapping[2 * pair_idx + 1];
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const int64_t src_block_offset = src_block_number * numel_per_block;
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const int64_t dst_block_offset = dst_block_number * numel_per_block;
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for (int i = threadIdx.x; i < numel_per_block; i += blockDim.x) {
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int64_t src_offset = src_block_offset + i;
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int64_t dst_offset = dst_block_offset + i;
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key_cache[dst_offset] = key_cache[src_offset];
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}
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for (int i = threadIdx.x; i < numel_per_block; i += blockDim.x) {
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int64_t src_offset = src_block_offset + i;
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int64_t dst_offset = dst_block_offset + i;
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value_cache[dst_offset] = value_cache[src_offset];
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}
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}
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} // namespace vllm
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void copy_blocks(std::vector<torch::Tensor>& key_caches,
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std::vector<torch::Tensor>& value_caches,
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const torch::Tensor& block_mapping) {
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int num_layers = key_caches.size();
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TORCH_CHECK(num_layers == value_caches.size());
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if (num_layers == 0) {
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return;
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}
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torch::Device cache_device = key_caches[0].device();
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TORCH_CHECK(cache_device.is_cuda());
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// Create data structures for the kernel.
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// Create an array of pointers to the key and value caches.
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int64_t key_cache_ptrs[num_layers];
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int64_t value_cache_ptrs[num_layers];
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for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
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key_cache_ptrs[layer_idx] =
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reinterpret_cast<int64_t>(key_caches[layer_idx].data_ptr());
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value_cache_ptrs[layer_idx] =
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reinterpret_cast<int64_t>(value_caches[layer_idx].data_ptr());
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}
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// block_mapping is a 2D tensor with shape (num_pairs, 2).
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int num_pairs = block_mapping.size(0);
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// Move the data structures to the GPU.
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// NOTE: This synchronizes the CPU and GPU.
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torch::Tensor key_cache_ptrs_tensor =
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torch::from_blob(key_cache_ptrs, {num_layers}, torch::kInt64)
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.to(cache_device);
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torch::Tensor value_cache_ptrs_tensor =
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torch::from_blob(value_cache_ptrs, {num_layers}, torch::kInt64)
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.to(cache_device);
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// Launch the kernel.
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const int numel_per_block = key_caches[0][0].numel();
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dim3 grid(num_layers, num_pairs);
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dim3 block(std::min(1024, numel_per_block));
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const at::cuda::OptionalCUDAGuard device_guard(cache_device);
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
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key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
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vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
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key_cache_ptrs_tensor.data_ptr<int64_t>(),
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value_cache_ptrs_tensor.data_ptr<int64_t>(),
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block_mapping.data_ptr<int64_t>(), numel_per_block);
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}));
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}
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namespace vllm {
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template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
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__global__ void reshape_and_cache_kernel(
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const scalar_t* __restrict__ key, // [num_tokens, num_heads, head_size]
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const scalar_t* __restrict__ value, // [num_tokens, num_heads, head_size]
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cache_t* __restrict__ key_cache, // [num_blocks, num_heads, head_size/x,
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// block_size, x]
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cache_t* __restrict__ value_cache, // [num_blocks, num_heads, head_size,
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// block_size]
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const int64_t* __restrict__ slot_mapping, // [num_tokens]
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const int key_stride, const int value_stride, const int num_heads,
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const int head_size, const int block_size, const int x,
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const float kv_scale) {
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const int64_t token_idx = blockIdx.x;
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const int64_t slot_idx = slot_mapping[token_idx];
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if (slot_idx < 0) {
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// Padding token that should be ignored.
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return;
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}
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const int64_t block_idx = slot_idx / block_size;
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const int64_t block_offset = slot_idx % block_size;
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const int n = num_heads * head_size;
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for (int i = threadIdx.x; i < n; i += blockDim.x) {
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const int64_t src_key_idx = token_idx * key_stride + i;
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const int64_t src_value_idx = token_idx * value_stride + i;
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const int head_idx = i / head_size;
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const int head_offset = i % head_size;
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const int x_idx = head_offset / x;
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const int x_offset = head_offset % x;
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const int64_t tgt_key_idx =
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block_idx * num_heads * (head_size / x) * block_size * x +
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head_idx * (head_size / x) * block_size * x + x_idx * block_size * x +
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block_offset * x + x_offset;
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const int64_t tgt_value_idx =
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block_idx * num_heads * head_size * block_size +
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head_idx * head_size * block_size + head_offset * block_size +
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block_offset;
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scalar_t tgt_key = key[src_key_idx];
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scalar_t tgt_value = value[src_value_idx];
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if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
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key_cache[tgt_key_idx] = tgt_key;
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value_cache[tgt_value_idx] = tgt_value;
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} else {
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key_cache[tgt_key_idx] =
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fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_key, kv_scale);
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value_cache[tgt_value_idx] =
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fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, kv_scale);
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}
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}
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}
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template <typename scalar_t>
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__global__ void reshape_and_cache_flash_kernel(
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const scalar_t* __restrict__ key, // [num_tokens, num_heads, head_size]
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const scalar_t* __restrict__ value, // [num_tokens, num_heads, head_size]
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scalar_t* __restrict__ k_cache, // [num_blocks, block_size, num_heads,
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// head_size]
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scalar_t* __restrict__ v_cache, // [num_blocks, block_size, num_heads,
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// head_size]
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const int64_t* __restrict__ slot_mapping, // [num_tokens]
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const int block_stride, const int key_stride, const int value_stride,
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const int num_heads, const int head_size, const int block_size) {
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const int64_t token_idx = blockIdx.x;
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const int64_t slot_idx = slot_mapping[token_idx];
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// NOTE: slot_idx can be -1 if the token is padded
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if (slot_idx < 0) {
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return;
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}
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const int64_t block_idx = slot_idx / block_size;
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const int64_t block_offset = slot_idx % block_size;
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const int n = num_heads * head_size;
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for (int i = threadIdx.x; i < n; i += blockDim.x) {
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const int64_t src_key_idx = token_idx * key_stride + i;
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const int64_t src_value_idx = token_idx * value_stride + i;
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const int head_idx = i / head_size;
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const int head_offset = i % head_size;
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const int64_t tgt_value_idx = block_idx * block_stride +
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block_offset * num_heads * head_size +
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head_idx * head_size + head_offset;
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k_cache[tgt_value_idx] = key[src_key_idx];
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v_cache[tgt_value_idx] = value[src_value_idx];
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}
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}
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} // namespace vllm
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// KV_T is the stored data type of kv-cache.
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// CACHE_T is the data type of key and value tensors.
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// KV_DTYPE is the real data type of kv-cache.
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#define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, KV_DTYPE) \
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vllm::reshape_and_cache_kernel<KV_T, CACHE_T, KV_DTYPE> \
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<<<grid, block, 0, stream>>>( \
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reinterpret_cast<KV_T*>(key.data_ptr()), \
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reinterpret_cast<KV_T*>(value.data_ptr()), \
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reinterpret_cast<CACHE_T*>(key_cache.data_ptr()), \
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reinterpret_cast<CACHE_T*>(value_cache.data_ptr()), \
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slot_mapping.data_ptr<int64_t>(), key_stride, value_stride, \
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num_heads, head_size, block_size, x, kv_scale);
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void reshape_and_cache(
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torch::Tensor& key, // [num_tokens, num_heads, head_size]
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torch::Tensor& value, // [num_tokens, num_heads, head_size]
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torch::Tensor&
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key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
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torch::Tensor&
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value_cache, // [num_blocks, num_heads, head_size, block_size]
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torch::Tensor& slot_mapping, // [num_tokens]
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const std::string& kv_cache_dtype, const float kv_scale) {
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int num_tokens = key.size(0);
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int num_heads = key.size(1);
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int head_size = key.size(2);
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int block_size = key_cache.size(3);
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int x = key_cache.size(4);
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int key_stride = key.stride(0);
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int value_stride = value.stride(0);
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dim3 grid(num_tokens);
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dim3 block(std::min(num_heads * head_size, 512));
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const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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DISPATCH_BY_KV_CACHE_DTYPE(key.dtype(), kv_cache_dtype,
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CALL_RESHAPE_AND_CACHE)
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}
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void reshape_and_cache_flash(
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torch::Tensor& key, // [num_tokens, num_heads, head_size]
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torch::Tensor& value, // [num_tokens, num_heads, head_size]
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torch::Tensor& k_cache, // [num_blocks, block_size, num_heads, head_size]
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torch::Tensor& v_cache, // [num_blocks, block_size, num_heads, head_size]
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torch::Tensor& slot_mapping, // [num_tokens]
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const std::string& kv_cache_dtype) {
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// FIXME: only support auto datatype, does not support fp8
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if (kv_cache_dtype != "auto") {
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TORCH_CHECK(false, "Unsupported data type of kv cache: ", kv_cache_dtype);
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}
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int num_tokens = key.size(0);
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int num_heads = key.size(1);
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int head_size = key.size(2);
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int block_size = k_cache.size(1);
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int key_stride = key.stride(0);
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int value_stride = value.stride(0);
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int block_stride = k_cache.stride(0);
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TORCH_CHECK(k_cache.stride(0) == v_cache.stride(0));
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dim3 grid(num_tokens);
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dim3 block(std::min(num_heads * head_size, 512));
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const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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VLLM_DISPATCH_FLOATING_TYPES(
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key.scalar_type(), "reshape_and_cache_flash", [&] {
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vllm::reshape_and_cache_flash_kernel<scalar_t>
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<<<grid, block, 0, stream>>>(
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key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
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k_cache.data_ptr<scalar_t>(), v_cache.data_ptr<scalar_t>(),
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slot_mapping.data_ptr<int64_t>(), block_stride, key_stride,
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value_stride, num_heads, head_size, block_size);
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});
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}
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namespace vllm {
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template <typename Tout, typename Tin, Fp8KVCacheDataType kv_dt>
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__global__ void convert_fp8_kernel(const Tin* __restrict__ src_cache,
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Tout* __restrict__ dst_cache,
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const float kv_scale,
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const int64_t block_stride) {
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const int64_t block_idx = blockIdx.x;
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for (int i = threadIdx.x; i < block_stride; i += blockDim.x) {
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int64_t idx = block_idx * block_stride + i;
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dst_cache[idx] =
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fp8::scaled_convert<Tout, Tin, kv_dt>(src_cache[idx], kv_scale);
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}
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}
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} // namespace vllm
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#define CALL_CONVERT_FP8(Tout, Tin, KV_DTYPE) \
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vllm::convert_fp8_kernel<Tout, Tin, KV_DTYPE><<<grid, block, 0, stream>>>( \
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reinterpret_cast<Tin*>(src_cache.data_ptr()), \
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reinterpret_cast<Tout*>(dst_cache.data_ptr()), kv_scale, block_stride);
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// Only for testing.
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void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
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const float kv_scale, const std::string& kv_cache_dtype) {
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torch::Device src_device = src_cache.device();
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torch::Device dst_device = dst_cache.device();
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TORCH_CHECK(src_device.is_cuda(), "src must be on a GPU")
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TORCH_CHECK(dst_device.is_cuda(), "dst must be on a GPU")
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TORCH_CHECK(src_device.index() == dst_device.index(),
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"src and dst must be on the same GPU");
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at::cuda::OptionalCUDAGuard device_guard(src_device);
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int64_t num_blocks = src_cache.size(0);
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int64_t block_stride = src_cache.stride(0);
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dim3 grid(num_blocks);
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dim3 block(std::min(block_stride, int64_t(512)));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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if (kv_cache_dtype == "auto") {
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if (src_cache.dtype() == at::ScalarType::Float) {
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CALL_CONVERT_FP8(uint8_t, float, vllm::Fp8KVCacheDataType::kAuto);
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} else if (src_cache.dtype() == at::ScalarType::Half) {
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CALL_CONVERT_FP8(uint8_t, uint16_t, vllm::Fp8KVCacheDataType::kAuto);
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} else if (src_cache.dtype() == at::ScalarType::BFloat16) {
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CALL_CONVERT_FP8(uint8_t, __nv_bfloat16, vllm::Fp8KVCacheDataType::kAuto);
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} else if (dst_cache.dtype() == at::ScalarType::Float) {
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CALL_CONVERT_FP8(float, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
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} else if (dst_cache.dtype() == at::ScalarType::Half) {
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CALL_CONVERT_FP8(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
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} else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
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CALL_CONVERT_FP8(__nv_bfloat16, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
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}
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} else if (kv_cache_dtype == "fp8" || kv_cache_dtype == "fp8_e4m3") {
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if (src_cache.dtype() == at::ScalarType::Float) {
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CALL_CONVERT_FP8(uint8_t, float, vllm::Fp8KVCacheDataType::kFp8E4M3);
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} else if (src_cache.dtype() == at::ScalarType::Half) {
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CALL_CONVERT_FP8(uint8_t, uint16_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
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} else if (src_cache.dtype() == at::ScalarType::BFloat16) {
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CALL_CONVERT_FP8(uint8_t, __nv_bfloat16,
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vllm::Fp8KVCacheDataType::kFp8E4M3);
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} else if (dst_cache.dtype() == at::ScalarType::Float) {
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CALL_CONVERT_FP8(float, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
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} else if (dst_cache.dtype() == at::ScalarType::Half) {
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CALL_CONVERT_FP8(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
|
|
} else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
|
|
CALL_CONVERT_FP8(__nv_bfloat16, uint8_t,
|
|
vllm::Fp8KVCacheDataType::kFp8E4M3);
|
|
}
|
|
} else {
|
|
TORCH_CHECK(false, "Unsupported data type: ", kv_cache_dtype);
|
|
}
|
|
}
|