### What this PR does / why we need it? This PR introduces support for adding custom CANN `aclnn` ops to `vllm-ascend`, allowing users to define and use their own custom operators. Key changes include: - Building and installing custom ops into the `vllm-ascend`-specified directory - Binding the `aclnn` op interface to the `torch.ops._C_ascend` module - Enabling invocation of these ops within `vllm-ascend` This PR includes a sample custom op: `aclnnGroupedMatmulSwigluQuantWeightNzTensorList`, which is adapted from the CANN operator [`aclnnGroupedMatmulSwigluQuantWeightNZ`](https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/API/aolapi/context/aclnnGroupedMatmulSwigluQuantWeightNZ.md). Its input parameters `weight` and `weight_scale` now accept `list[torch.Tensor]` (i.e., `at::TensorList`). ### Does this PR introduce _any_ user-facing change? No. - vLLM version: v0.11.2 --------- Signed-off-by: QianChenxi <chenxi.qian.cq@outlook.com>
145 lines
5.3 KiB
C++
145 lines
5.3 KiB
C++
/**
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* Copyright (c) 2024 Huawei Technologies Co., Ltd.
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* This file is a part of the CANN Open Software.
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* Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
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* Please refer to the License for details. You may not use this file except in compliance with the License.
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* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
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* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
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* See LICENSE in the root of the software repository for the full text of the License.
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*/
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/*!
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* \file util.h
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* \brief
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*/
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#ifndef FLASH_ATTENTION_UTIL_H
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#define FLASH_ATTENTION_UTIL_H
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constexpr int32_t blockBytes = 32;
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constexpr int32_t byteBitRatio = 8;
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constexpr int64_t prefixAttenMaskDownHeight = 1024;
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constexpr static int32_t blockSize = blockBytes / 4; // 4 means sizeof(T)
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constexpr static int32_t repeatMaxBytes = 256;
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constexpr static int32_t repeatMaxTimes = 255;
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constexpr static int32_t repeatMaxSize = repeatMaxBytes / 4; // 4 means sizeof(T)
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using AscendC::LocalTensor;
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using AscendC::GlobalTensor;
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using AscendC::DataFormat;
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using AscendC::ShapeInfo;
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using AscendC::DataCopyParams;
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using AscendC::DataCopyPadParams;
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using AscendC::BinaryRepeatParams;
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using AscendC::IsSameType;
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using AscendC::HardEvent;
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using AscendC::SetFlag;
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using AscendC::WaitFlag;
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enum class LayOutTypeEnum { None = 0, LAYOUT_BSH = 1, LAYOUT_SBH = 2, LAYOUT_BNSD = 3, LAYOUT_TND = 4, LAYOUT_NTD_TND = 5};
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namespace math {
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template <typename T> __aicore__ inline T Ceil(T a, T b)
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{
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if (b == 0) {
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return 0;
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}
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return (a + b - 1) / b;
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}
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template <typename T> __aicore__ inline T Align(T a, T b)
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{
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if (b == 0) {
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return 0;
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}
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return (a + b - 1) / b * b;
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}
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}
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template <typename T1, typename T2>
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__aicore__ inline T1 CeilDiv(T1 a, T2 b)
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{
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if (b == 0) {
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return 0;
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}
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return (a + b - 1) / b;
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}
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template <typename T1, typename T2>
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__aicore__ inline T1 Max(T1 a, T2 b)
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{
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return (a > b) ? (a) : (b);
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}
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template <typename T1, typename T2>
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__aicore__ inline T1 Min(T1 a, T2 b)
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{
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return (a > b) ? (b) : (a);
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}
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__aicore__ inline void BoolCopyIn(LocalTensor<uint8_t> &dstTensor, GlobalTensor<uint8_t> &srcTensor,
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int64_t srcOffset, uint32_t s1Size, uint32_t s2Size, int64_t totalS2Size, int64_t alignedSize = blockBytes)
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{
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uint32_t alignedS2Size = CeilDiv(s2Size, alignedSize) * alignedSize;
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uint32_t shapeArray[] = {s1Size, alignedS2Size};
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dstTensor.SetShapeInfo(ShapeInfo(2, shapeArray, DataFormat::ND));
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dstTensor.SetSize(s1Size * alignedS2Size);
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DataCopyParams dataCopyParams;
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dataCopyParams.blockCount = s1Size;
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dataCopyParams.dstStride = 0;
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if (totalS2Size == blockBytes && alignedSize == 64) { // totalS2Size < 64 && totalS2Size % blockBytes == 0
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dataCopyParams.dstStride = 1;
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alignedSize = blockBytes;
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alignedS2Size = CeilDiv(s2Size, blockBytes) * blockBytes;
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}
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if (totalS2Size % alignedSize == 0) {
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dataCopyParams.blockLen = alignedS2Size / blockBytes;
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dataCopyParams.srcStride = (totalS2Size - alignedS2Size) / blockBytes;
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DataCopy(dstTensor, srcTensor[srcOffset], dataCopyParams);
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} else {
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dataCopyParams.blockLen = s2Size;
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dataCopyParams.srcStride = totalS2Size - s2Size;
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DataCopyPadParams dataCopyPadParams;
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dataCopyPadParams.isPad = true;
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dataCopyPadParams.rightPadding = Min(alignedS2Size - s2Size, blockBytes);
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dataCopyPadParams.paddingValue = 1;
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DataCopyPad(dstTensor, srcTensor[srcOffset], dataCopyParams, dataCopyPadParams);
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}
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}
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__aicore__ inline void Bit2Int8CopyIn(LocalTensor<uint8_t> &dstTensor, GlobalTensor<uint8_t> &srcTensor,
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int64_t srcOffset, uint32_t batchSize, uint32_t s1BaseSize, uint32_t s2BaseSize, int64_t s2TotalSize,
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int64_t alignedSize = blockBytes)
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{
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uint32_t alignedS2Size = CeilDiv(s2BaseSize / byteBitRatio, alignedSize) * alignedSize;
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uint32_t shapeArray[] = {batchSize * s1BaseSize, alignedS2Size};
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dstTensor.SetShapeInfo(ShapeInfo(2, shapeArray, DataFormat::ND));
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dstTensor.SetSize(batchSize * s1BaseSize * alignedS2Size);
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DataCopyParams dataCopyParams;
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dataCopyParams.blockCount = batchSize * s1BaseSize;
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dataCopyParams.blockLen = CeilDiv(s2BaseSize / byteBitRatio, blockBytes);
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dataCopyParams.dstStride = 0;
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if (s2TotalSize / byteBitRatio % alignedSize == 0 && s2BaseSize / byteBitRatio % alignedSize == 0) {
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dataCopyParams.srcStride =
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(s2TotalSize / byteBitRatio - dataCopyParams.blockLen * blockBytes) / blockBytes;
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DataCopy(dstTensor, srcTensor[srcOffset / byteBitRatio], dataCopyParams);
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} else {
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dataCopyParams.blockLen = CeilDiv(s2BaseSize , byteBitRatio);
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dataCopyParams.srcStride = (s2TotalSize - s2BaseSize) / byteBitRatio;
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DataCopyPadParams dataCopyPadParams;
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dataCopyPadParams.isPad = true;
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dataCopyPadParams.rightPadding = 0;
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dataCopyPadParams.paddingValue = 0;
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DataCopyPad(dstTensor, srcTensor[srcOffset / byteBitRatio], dataCopyParams, dataCopyPadParams);
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}
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}
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__aicore__ inline int32_t Align(int32_t shape)
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{
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int32_t alignFactor = 16;
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int32_t alignedSize = CeilDiv<int32_t, int32_t>(shape, alignFactor) * alignFactor;
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return alignedSize;
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}
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#endif // FLASH_ATTENTION_UTIL_H
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