126 lines
5.9 KiB
Plaintext
126 lines
5.9 KiB
Plaintext
/*
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* Copyright (c) 2025 by FlashInfer team.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <flashinfer/attention/mla.cuh>
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#include <flashinfer/attention/scheduler.cuh>
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#include <flashinfer/fastdiv.cuh>
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#include <optional>
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#include "batch_mla_config.inc"
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#include "pytorch_conversion_utils.h"
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#include "pytorch_extension_utils.h"
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using namespace flashinfer;
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void BatchMLAPagedAttentionRun(at::Tensor float_workspace_buffer, at::Tensor int_workspace_buffer,
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at::Tensor plan_info_vec, at::Tensor q_nope, at::Tensor q_pe,
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at::Tensor ckv_cache, at::Tensor kpe_cache, at::Tensor kv_indices,
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at::Tensor o, std::optional<at::Tensor> maybe_lse,
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int64_t mask_mode_code, int64_t num_heads, int64_t page_size,
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double sm_scale) {
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// q_nope: [n, num_heads, head_dim_ckv]
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// q_pe: [n, num_heads, head_dim_kpe]
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// ckv_cache: [num_pages, page_size, head_dim_ckv]
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// kpe_cache: [num_pages, page_size, head_dim_kpe]
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MLAPlanInfo plan_info;
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plan_info.FromVector(tensor_to_vec(plan_info_vec));
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auto device = q_nope.device();
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void* float_buffer_ptr = float_workspace_buffer.data_ptr();
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void* int_buffer_ptr = int_workspace_buffer.data_ptr();
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const MaskMode mask_mode = static_cast<MaskMode>(mask_mode_code);
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auto q_scalar_type = q_nope.scalar_type();
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auto kv_scalar_type = ckv_cache.scalar_type();
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unsigned int q_nope_stride_n = q_nope.stride(0);
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unsigned int q_nope_stride_h = q_nope.stride(1);
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unsigned int q_pe_stride_n = q_pe.stride(0);
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unsigned int q_pe_stride_h = q_pe.stride(1);
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unsigned int ckv_stride_page = ckv_cache.stride(0);
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unsigned int ckv_stride_n = ckv_cache.stride(1);
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unsigned int kpe_stride_page = kpe_cache.stride(0);
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unsigned int kpe_stride_n = kpe_cache.stride(1);
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unsigned int o_stride_n = o.stride(0);
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unsigned int o_stride_h = o.stride(1);
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const c10::cuda::OptionalCUDAGuard device_guard(device);
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const cudaStream_t stream = c10::cuda::getCurrentCUDAStream();
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DISPATCH_context(
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DTypeQ, DTypeKV, DTypeO, IdType, MASK_MODE, HEAD_DIM_CKV, HEAD_DIM_KPE, Params, [&] {
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Params params;
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params.q_nope = static_cast<DTypeQ*>(q_nope.data_ptr());
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params.q_pe = static_cast<DTypeQ*>(q_pe.data_ptr());
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params.ckv = static_cast<DTypeKV*>(ckv_cache.data_ptr());
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params.kpe = static_cast<DTypeKV*>(kpe_cache.data_ptr());
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params.q_indptr = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.q_indptr_offset);
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params.kv_indptr = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_indptr_offset);
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params.partial_indptr =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.partial_indptr_offset);
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params.kv_indices = static_cast<IdType*>(kv_indices.data_ptr());
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params.q_len = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.q_len_offset);
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params.kv_len = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_len_offset);
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params.q_start = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.q_start_offset);
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params.kv_start = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_start_offset);
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params.kv_end = GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.kv_end_offset);
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params.work_indptr =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.work_indptr_offset);
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params.merge_packed_offset_start = GetPtrFromBaseOffset<IdType>(
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int_buffer_ptr, plan_info.merge_packed_offset_start_offset);
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params.merge_packed_offset_end =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.merge_packed_offset_end_offset);
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params.merge_partial_packed_offset_start = GetPtrFromBaseOffset<IdType>(
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int_buffer_ptr, plan_info.merge_partial_packed_offset_start_offset);
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params.merge_partial_packed_offset_end = GetPtrFromBaseOffset<IdType>(
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int_buffer_ptr, plan_info.merge_partial_packed_offset_end_offset);
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params.merge_partial_stride =
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GetPtrFromBaseOffset<IdType>(int_buffer_ptr, plan_info.merge_partial_stride_offset);
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params.final_o = static_cast<DTypeO*>(o.data_ptr());
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params.final_lse =
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maybe_lse.has_value() ? static_cast<float*>(maybe_lse->data_ptr()) : nullptr;
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params.partial_o =
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GetPtrFromBaseOffset<DTypeO>(float_buffer_ptr, plan_info.partial_o_offset);
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params.partial_lse =
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GetPtrFromBaseOffset<float>(float_buffer_ptr, plan_info.partial_lse_offset);
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params.num_heads = uint_fastdiv(num_heads);
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params.block_size = uint_fastdiv(page_size);
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params.q_nope_stride_n = q_nope_stride_n;
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params.q_nope_stride_h = q_nope_stride_h;
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params.q_pe_stride_n = q_pe_stride_n;
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params.q_pe_stride_h = q_pe_stride_h;
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params.ckv_stride_page = ckv_stride_page;
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params.ckv_stride_n = ckv_stride_n;
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params.kpe_stride_page = kpe_stride_page;
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params.kpe_stride_n = kpe_stride_n;
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params.o_stride_n = o_stride_n;
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params.o_stride_h = o_stride_h;
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params.sm_scale = sm_scale;
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cudaError_t status = mla::BatchMLAPagedAttention<MASK_MODE, HEAD_DIM_CKV, HEAD_DIM_KPE>(
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params, plan_info.num_blks_x, plan_info.num_blks_y, stream);
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TORCH_CHECK(status == cudaSuccess,
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"Failed to run MLA, error: ", cudaGetErrorString(status));
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});
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}
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