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perf: use packed bit array for attention mask (flashinfer-ai#308)
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| Original file line number | Diff line number | Diff line change |
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| .. _apiquantization: | ||
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| flashinfer.quantization | ||
| ======================= | ||
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| Quantization related kernels. | ||
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| .. currentmodule:: flashinfer.quantization | ||
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| .. autosummary:: | ||
| :toctree: _generate | ||
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| packbits | ||
| segment_packbits |
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| Original file line number | Diff line number | Diff line change |
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| /* | ||
| * Copyright (c) 2024 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. | ||
| */ | ||
| #ifndef FLASHINFER_QUANTIZATION_CUH_ | ||
| #define FLASHINFER_QUANTIZATION_CUH_ | ||
| #include <cuda_runtime.h> | ||
| #include <cuda_runtime_api.h> | ||
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| #include <cub/cub.cuh> | ||
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| #include "utils.cuh" | ||
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| namespace flashinfer { | ||
| namespace quantization { | ||
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| enum class BitOrder { kBig = 0U, kLittle = 1U }; | ||
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| #define DISPATCH_BITORDER(bitorder, BITORDER, ...) \ | ||
| if (bitorder == BitOrder::kBig) { \ | ||
| constexpr BitOrder BITORDER = BitOrder::kBig; \ | ||
| __VA_ARGS__ \ | ||
| } else { \ | ||
| constexpr BitOrder BITORDER = BitOrder::kLittle; \ | ||
| __VA_ARGS__ \ | ||
| } | ||
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| template <BitOrder BITORDER> | ||
| __global__ void PackBitsKernel(bool* input, uint8_t* output, int64_t num_elements) { | ||
| int64_t start_offset = blockIdx.x * blockDim.x * 8, tx = threadIdx.x; | ||
| uint8_t ret = 0; | ||
| bool input_vec[8]; | ||
| typedef cub::BlockLoad<bool, 256, 8, cub::BLOCK_LOAD_VECTORIZE> BlockLoad; | ||
| __shared__ typename BlockLoad::TempStorage temp_storage; | ||
| BlockLoad(temp_storage) | ||
| .Load(input + start_offset, input_vec, num_elements - start_offset, /*default=*/0); | ||
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| if constexpr (BITORDER == BitOrder::kBig) { | ||
| ret = (input_vec[0] << 7) | (input_vec[1] << 6) | (input_vec[2] << 5) | (input_vec[3] << 4) | | ||
| (input_vec[4] << 3) | (input_vec[5] << 2) | (input_vec[6] << 1) | input_vec[7]; | ||
| } else { | ||
| ret = (input_vec[7] << 7) | (input_vec[6] << 6) | (input_vec[5] << 5) | (input_vec[4] << 4) | | ||
| (input_vec[3] << 3) | (input_vec[2] << 2) | (input_vec[1] << 1) | input_vec[0]; | ||
| } | ||
| if (start_offset + tx * 8 < num_elements) output[start_offset / 8 + tx] = ret; | ||
| } | ||
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| template <BitOrder BITORDER, typename IdType> | ||
| __global__ void SegmentPackBitsKernel(bool* input, uint8_t* output, IdType* input_indptr, | ||
| IdType* output_indptr) { | ||
| int64_t bx = blockIdx.x, tx = threadIdx.x; | ||
| bool input_vec[8]; | ||
| typedef cub::BlockLoad<bool, 256, 8, cub::BLOCK_LOAD_VECTORIZE> BlockLoad; | ||
| __shared__ typename BlockLoad::TempStorage temp_storage; | ||
| int64_t num_elements = input_indptr[bx + 1] - input_indptr[bx]; | ||
| for (uint32_t start_offset = 0; start_offset < num_elements; start_offset += 8 * blockDim.x) { | ||
| uint8_t ret = 0; | ||
| BlockLoad(temp_storage) | ||
| .Load(input + input_indptr[bx] + start_offset, input_vec, num_elements - start_offset, | ||
| /*default=*/0); | ||
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| if constexpr (BITORDER == BitOrder::kBig) { | ||
| ret = (input_vec[0] << 7) | (input_vec[1] << 6) | (input_vec[2] << 5) | (input_vec[3] << 4) | | ||
| (input_vec[4] << 3) | (input_vec[5] << 2) | (input_vec[6] << 1) | input_vec[7]; | ||
| } else { | ||
| ret = (input_vec[7] << 7) | (input_vec[6] << 6) | (input_vec[5] << 5) | (input_vec[4] << 4) | | ||
| (input_vec[3] << 3) | (input_vec[2] << 2) | (input_vec[1] << 1) | input_vec[0]; | ||
| } | ||
| if (start_offset + tx * 8 < num_elements) | ||
| output[output_indptr[bx] + start_offset / 8 + tx] = ret; | ||
| } | ||
| } | ||
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| cudaError_t PackBits(bool* input, uint8_t* output, int64_t num_elements, BitOrder bitorder, | ||
| cudaStream_t stream) { | ||
| DISPATCH_BITORDER(bitorder, BITORDER, { | ||
| auto kernel = PackBitsKernel<BITORDER>; | ||
| const dim3 nthrs(256); | ||
| const dim3 nblks(ceil_div(num_elements, nthrs.x * 8)); | ||
| void* args[] = {&input, &output, &num_elements}; | ||
| FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, nblks, nthrs, args, 0, stream)); | ||
| }); | ||
| return cudaSuccess; | ||
| } | ||
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| template <typename IdType> | ||
| cudaError_t SegmentPackBits(bool* input, uint8_t* output, IdType* input_indptr, | ||
| IdType* output_indptr, uint32_t batch_size, BitOrder bitorder, | ||
| cudaStream_t stream) { | ||
| DISPATCH_BITORDER(bitorder, BITORDER, { | ||
| auto kernel = SegmentPackBitsKernel<BITORDER, IdType>; | ||
| const dim3 nthrs(256); | ||
| const dim3 nblks(batch_size); | ||
| void* args[] = {&input, &output, &input_indptr, &output_indptr}; | ||
| FLASHINFER_CUDA_CALL(cudaLaunchKernel((void*)kernel, nblks, nthrs, args, 0, stream)); | ||
| }); | ||
| return cudaSuccess; | ||
| } | ||
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| } // namespace quantization | ||
| } // namespace flashinfer | ||
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| #endif // FLASHINFER_QUANTIZATION_CUH_ |
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