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sglang_v0.5.2/flashinfer_0.3.1/csrc/batch_prefill.cu

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/*
* Copyright (c) 2023 by FlashInfer team.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <flashinfer/attention/mask.cuh>
#include <flashinfer/attention/scheduler.cuh>
#include <flashinfer/pos_enc.cuh>
#include <optional>
#include "batch_prefill_config.inc"
#include "pytorch_conversion_utils.h"
#include "pytorch_extension_utils.h"
namespace flashinfer {
template <uint32_t CTA_TILE_Q, uint32_t HEAD_DIM_QK, uint32_t HEAD_DIM_VO,
PosEncodingMode POS_ENCODING_MODE, bool USE_FP16_QK_REDUCTION, MaskMode MASK_MODE,
typename AttentionVariant, typename Params>
cudaError_t BatchPrefillWithPagedKVCacheDispatched(Params params, typename Params::DTypeO* tmp_v,
float* tmp_s, bool enable_pdl,
cudaStream_t stream);
template <uint32_t CTA_TILE_Q, uint32_t HEAD_DIM_QK, uint32_t HEAD_DIM_VO,
PosEncodingMode POS_ENCODING_MODE, bool USE_FP16_QK_REDUCTION, MaskMode MASK_MODE,
typename AttentionVariant, typename Params>
cudaError_t BatchPrefillWithRaggedKVCacheDispatched(Params params, typename Params::DTypeO* tmp_v,
float* tmp_s, bool enable_pdl,
cudaStream_t stream);
} // namespace flashinfer
using namespace flashinfer;
at::Tensor BatchPrefillWithKVCachePlan(
at::Tensor float_workspace_buffer, at::Tensor int_workspace_buffer,
at::Tensor page_locked_int_workspace_buffer, at::Tensor qo_indptr, at::Tensor kv_indptr,
at::Tensor kv_len_arr, int64_t total_num_rows, int64_t batch_size, int64_t num_qo_heads,
int64_t num_kv_heads, int64_t page_size, bool enable_cuda_graph, int64_t head_dim_qk,
int64_t head_dim_vo, bool causal) {
size_t float_workspace_size_in_bytes =
float_workspace_buffer.size(0) * float_workspace_buffer.element_size();
size_t int_workspace_size_in_bytes =
int_workspace_buffer.size(0) * int_workspace_buffer.element_size();
PrefillPlanInfo plan_info;
const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer.device());
const cudaStream_t stream = c10::cuda::getCurrentCUDAStream();
cudaError_t status = PrefillPlan<IdType>(
float_workspace_buffer.data_ptr(), float_workspace_size_in_bytes,
int_workspace_buffer.data_ptr(), page_locked_int_workspace_buffer.data_ptr(),
int_workspace_size_in_bytes, plan_info, qo_indptr.data_ptr<IdType>(),
kv_indptr.data_ptr<IdType>(), total_num_rows, batch_size, num_qo_heads, num_kv_heads,
head_dim_qk, head_dim_vo, page_size, enable_cuda_graph, /*sizeof_dtype_o=*/2, stream);
TORCH_CHECK(status == cudaSuccess,
"Failed to plan prefill with error: ", cudaGetErrorString(status));
return vec_to_tensor(plan_info.ToVector());
}
void BatchPrefillWithRaggedKVCacheRun(at::Tensor float_workspace_buffer,
at::Tensor int_workspace_buffer, at::Tensor plan_info_vec,
at::Tensor q, at::Tensor k, at::Tensor v,
at::Tensor qo_indptr, at::Tensor kv_indptr, at::Tensor o,
std::optional<at::Tensor> maybe_lse, int64_t mask_mode_code,
int64_t layout, int64_t window_left,
bool enable_pdl ADDITIONAL_FUNC_PARAMS) {
PrefillPlanInfo plan_info;
plan_info.FromVector(tensor_to_vec(plan_info_vec));
QKVLayout kv_layout = static_cast<QKVLayout>(layout);
int64_t num_qo_heads = q.size(1);
int64_t head_dim_qk = q.size(2);
int64_t num_kv_heads = (kv_layout == QKVLayout::kNHD) ? k.size(1) : k.size(0);
uint32_t q_stride_n = q.stride(0), q_stride_h = q.stride(1), k_stride_n, k_stride_h, v_stride_n,
v_stride_h;
if (kv_layout == QKVLayout::kNHD) {
k_stride_n = k.stride(0);
k_stride_h = k.stride(1);
v_stride_n = v.stride(0);
v_stride_h = v.stride(1);
} else {
k_stride_h = k.stride(0);
k_stride_n = k.stride(1);
v_stride_h = v.stride(0);
v_stride_n = v.stride(1);
}
if (maybe_lse) {
const auto& lse = *maybe_lse;
TORCH_CHECK(lse.size(0) == q.size(0), lse.size(0), q.size(0));
TORCH_CHECK(lse.size(1) == q.size(1), lse.size(1), q.size(1));
}
void* float_buffer_ptr = float_workspace_buffer.data_ptr();
void* int_buffer_ptr = int_workspace_buffer.data_ptr();
const MaskMode mask_mode = static_cast<MaskMode>(mask_mode_code);
auto q_scalar_type = q.scalar_type();
auto kv_scalar_type = k.scalar_type();
const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer.device());
const cudaStream_t stream = c10::cuda::getCurrentCUDAStream();
DISPATCH_context(
DTypeQ, DTypeKV, DTypeO, IdType, MASK_MODE, HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE,
USE_SLIDING_WINDOW, USE_LOGITS_SOFT_CAP, USE_FP16_QK_REDUCTION, AttentionVariant,
RaggedParams, PagedParams, [&] {
RaggedParams params;
params.q = static_cast<DTypeQ*>(q.data_ptr());
params.k = static_cast<DTypeKV*>(k.data_ptr());
params.v = static_cast<DTypeKV*>(v.data_ptr());
params.o = static_cast<DTypeO*>(o.data_ptr());
params.lse = maybe_lse ? static_cast<float*>(maybe_lse->data_ptr()) : nullptr;
params.q_indptr = static_cast<IdType*>(qo_indptr.data_ptr());
params.kv_indptr = static_cast<IdType*>(kv_indptr.data_ptr());
params.num_qo_heads = num_qo_heads;
params.num_kv_heads = num_kv_heads;
params.group_size = uint_fastdiv(num_qo_heads / num_kv_heads);
params.q_stride_n = q_stride_n;
params.q_stride_h = q_stride_h;
params.k_stride_n = k_stride_n;
params.k_stride_h = k_stride_h;
params.v_stride_n = v_stride_n;
params.v_stride_h = v_stride_h;
params.window_left = window_left;
params.request_indices = nullptr;
params.qo_tile_indices = nullptr;
params.kv_tile_indices = nullptr;
params.merge_indptr = nullptr;
params.o_indptr = nullptr;
params.kv_chunk_size_ptr = nullptr;
params.block_valid_mask = nullptr;
params.total_num_rows = nullptr;
params.max_total_num_rows = 0;
params.padded_batch_size = 0;
params.partition_kv = false;
ADDITIONAL_PARAMS_SETTER
DTypeO* tmp_v = nullptr;
float* tmp_s = nullptr;
params.request_indices =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.request_indices_offset);
params.qo_tile_indices =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.qo_tile_indices_offset);
params.kv_tile_indices =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_tile_indices_offset);
params.o_indptr = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.o_indptr_offset);
params.kv_chunk_size_ptr =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_chunk_size_ptr_offset);
if (plan_info.split_kv) {
params.merge_indptr =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.merge_indptr_offset);
tmp_v = GetPtrFromBaseOffset<DTypeO>(float_buffer_ptr, plan_info.v_offset);
tmp_s = GetPtrFromBaseOffset<float>(float_buffer_ptr, plan_info.s_offset);
if (plan_info.enable_cuda_graph) {
params.block_valid_mask =
GetPtrFromBaseOffset<bool>(int_buffer_ptr, plan_info.block_valid_mask_offset);
}
}
params.padded_batch_size = plan_info.padded_batch_size;
params.max_total_num_rows = plan_info.total_num_rows;
if (plan_info.enable_cuda_graph) {
params.total_num_rows =
GetPtrFromBaseOffset<uint32_t>(int_buffer_ptr, plan_info.total_num_rows_offset);
}
cudaError_t status = cudaSuccess;
DISPATCH_CTA_TILE_Q(plan_info.cta_tile_q, CTA_TILE_Q, {
status = flashinfer::BatchPrefillWithRaggedKVCacheDispatched<
CTA_TILE_Q, HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE,
/*use_fp16_qk_reduction=*/USE_FP16_QK_REDUCTION, MASK_MODE, AttentionVariant,
RaggedParams>(params, tmp_v, tmp_s, enable_pdl, stream);
});
TORCH_CHECK(status == cudaSuccess, "BatchPrefillWithRaggedKVCache failed with error ",
cudaGetErrorString(status));
return true;
});
}
void BatchPrefillWithPagedKVCacheRun(
at::Tensor float_workspace_buffer, at::Tensor int_workspace_buffer, at::Tensor plan_info_vec,
at::Tensor q, at::Tensor paged_k_cache, at::Tensor paged_v_cache, at::Tensor qo_indptr,
at::Tensor paged_kv_indptr, at::Tensor paged_kv_indices, at::Tensor paged_kv_last_page_len,
at::Tensor o, std::optional<at::Tensor> maybe_lse, int64_t mask_mode_code, int64_t layout,
int64_t window_left, bool enable_pdl ADDITIONAL_FUNC_PARAMS) {
PrefillPlanInfo plan_info;
plan_info.FromVector(tensor_to_vec(plan_info_vec));
QKVLayout kv_layout = static_cast<QKVLayout>(layout);
auto device = q.device();
int64_t batch_size = paged_kv_indptr.size(0) - 1;
int64_t num_qo_heads = q.size(1);
int64_t num_kv_heads, page_size;
uint32_t head_dim_qk = q.size(2);
if (kv_layout == QKVLayout::kHND) {
num_kv_heads = paged_k_cache.size(1);
page_size = paged_k_cache.size(2);
} else {
page_size = paged_k_cache.size(1);
num_kv_heads = paged_k_cache.size(2);
}
if (maybe_lse) {
const auto& lse = *maybe_lse;
TORCH_CHECK(lse.size(0) == q.size(0), lse.size(0), q.size(0));
TORCH_CHECK(lse.size(1) == q.size(1), lse.size(1), q.size(1));
}
void* float_buffer_ptr = static_cast<void*>(float_workspace_buffer.data_ptr());
void* int_buffer_ptr = static_cast<void*>(int_workspace_buffer.data_ptr());
const MaskMode mask_mode = static_cast<MaskMode>(mask_mode_code);
auto q_scalar_type = q.scalar_type();
auto kv_scalar_type = paged_k_cache.scalar_type();
// get q_stride_n and q_stride_h
const auto q_stride_n = q.stride(0);
const auto q_stride_h = q.stride(1);
// get kv_cache_strides
const int64_t* kv_cache_strides = nullptr;
auto k_strides = paged_k_cache.strides();
auto v_strides = paged_v_cache.strides();
TORCH_CHECK(k_strides == v_strides, "k/v strides must be identical");
kv_cache_strides = k_strides.data();
const c10::cuda::OptionalCUDAGuard device_guard(float_workspace_buffer.device());
const cudaStream_t stream = c10::cuda::getCurrentCUDAStream();
DISPATCH_context(
DTypeQ, DTypeKV, DTypeO, IdType, MASK_MODE, HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE,
USE_SLIDING_WINDOW, USE_LOGITS_SOFT_CAP, USE_FP16_QK_REDUCTION, AttentionVariant,
RaggedParams, PagedParams, [&] {
PagedParams params;
params.q = static_cast<DTypeQ*>(q.data_ptr());
paged_kv_t<DTypeKV, IdType> paged_kv(
num_kv_heads, page_size, HEAD_DIM_VO, batch_size, kv_layout,
static_cast<DTypeKV*>(paged_k_cache.data_ptr()),
static_cast<DTypeKV*>(paged_v_cache.data_ptr()), kv_cache_strides,
static_cast<IdType*>(paged_kv_indices.data_ptr()),
static_cast<IdType*>(paged_kv_indptr.data_ptr()),
static_cast<IdType*>(paged_kv_last_page_len.data_ptr()));
params.paged_kv = paged_kv;
params.q_indptr = static_cast<IdType*>(qo_indptr.data_ptr());
params.o = static_cast<DTypeO*>(o.data_ptr());
params.lse = maybe_lse ? static_cast<float*>(maybe_lse->data_ptr()) : nullptr;
params.num_qo_heads = num_qo_heads;
params.group_size = uint_fastdiv(num_qo_heads / paged_kv.num_heads);
params.q_stride_n = q_stride_n;
params.q_stride_h = q_stride_h;
params.window_left = window_left;
params.request_indices = nullptr;
params.qo_tile_indices = nullptr;
params.kv_tile_indices = nullptr;
params.merge_indptr = nullptr;
params.o_indptr = nullptr;
params.kv_chunk_size_ptr = nullptr;
params.block_valid_mask = nullptr;
params.total_num_rows = nullptr;
params.max_total_num_rows = 0;
params.padded_batch_size = 0;
params.partition_kv = false;
ADDITIONAL_PARAMS_SETTER
DTypeO* tmp_v = nullptr;
float* tmp_s = nullptr;
params.request_indices =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.request_indices_offset);
params.qo_tile_indices =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.qo_tile_indices_offset);
params.kv_tile_indices =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_tile_indices_offset);
params.o_indptr = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.o_indptr_offset);
params.kv_chunk_size_ptr =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_chunk_size_ptr_offset);
if (plan_info.split_kv) {
params.merge_indptr =
GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.merge_indptr_offset);
tmp_v = GetPtrFromBaseOffset<DTypeO>(float_buffer_ptr, plan_info.v_offset);
tmp_s = GetPtrFromBaseOffset<float>(float_buffer_ptr, plan_info.s_offset);
if (plan_info.enable_cuda_graph) {
params.block_valid_mask =
GetPtrFromBaseOffset<bool>(int_buffer_ptr, plan_info.block_valid_mask_offset);
}
}
params.padded_batch_size = plan_info.padded_batch_size;
params.max_total_num_rows = plan_info.total_num_rows;
if (plan_info.enable_cuda_graph) {
params.total_num_rows =
GetPtrFromBaseOffset<uint32_t>(int_buffer_ptr, plan_info.total_num_rows_offset);
}
cudaError_t status = cudaSuccess;
DISPATCH_CTA_TILE_Q(plan_info.cta_tile_q, CTA_TILE_Q, {
status = flashinfer::BatchPrefillWithPagedKVCacheDispatched<
CTA_TILE_Q, HEAD_DIM_QK, HEAD_DIM_VO, POS_ENCODING_MODE,
/*use_fp16_qk_reduction=*/USE_FP16_QK_REDUCTION, MASK_MODE, AttentionVariant,
PagedParams>(params, tmp_v, tmp_s, enable_pdl, stream);
});
TORCH_CHECK(status == cudaSuccess, "BatchPrefillWithPagedKVCache failed with error ",
cudaGetErrorString(status));
return true;
});
}
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