358 lines
13 KiB
C++
358 lines
13 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2025 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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#pragma once
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#include "cute/tensor.hpp"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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class TensorDQ, /* class TensorDK, class TensorDV, */
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class Fusion
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>
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void __global__ fmha_bwd_reference_dQ_kernel(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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TensorDQ mDQ, /* TensorDK mDK, TensorDV mDV, */
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Fusion fusion
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) {
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using namespace cute;
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using Element = typename TensorO::value_type;
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using ElementAccumulator = typename TensorLSE::value_type;
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extern __shared__ char mS_mem[];
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Element* mS = reinterpret_cast<Element*>(mS_mem);
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Element softmax_scale = static_cast<Element>(1.0 / sqrt(1.0 * size<1>(mO)));
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for (int idx_L = blockIdx.y; idx_L < size<2>(mDQ); idx_L += gridDim.y) {
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for (int idx_Q = blockIdx.x; idx_Q < size<0>(mDQ); idx_Q += gridDim.x) {
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for (int idx_K = threadIdx.x; idx_K < size<0>(mK); idx_K += blockDim.x) {
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ElementAccumulator acc_qk = 0;
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ElementAccumulator acc_dov = 0;
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ElementAccumulator acc_doo = 0;
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for (int idx_D0 = 0; idx_D0 < size<1>(mK); idx_D0++) {
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acc_qk += mQ(idx_Q, idx_D0, idx_L) * mK(idx_K, idx_D0, idx_L);
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acc_dov += mDO(idx_Q, idx_D0, idx_L) * mV(idx_K, idx_D0, idx_L);
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acc_doo += mDO(idx_Q, idx_D0, idx_L) * mO(idx_Q, idx_D0, idx_L);
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}
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auto id = make_identity_tensor(make_shape(1, 1));
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auto frag = make_tensor<ElementAccumulator>(Shape<_1, _1>{});
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frag(0) = acc_qk;
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fusion.before_softmax(frag, make_tensor(id.data() + make_arithmetic_tuple(idx_Q, idx_K), id.layout()), problem_shape);
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acc_qk = frag(0);
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mS[idx_K] = static_cast<Element>(exp(softmax_scale * acc_qk - mLSE(idx_Q, idx_L)) * softmax_scale * (acc_dov - acc_doo));
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}
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__syncthreads();
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for (int idx_D = threadIdx.x; idx_D < size<1>(mDQ); idx_D += blockDim.x) {
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ElementAccumulator acc = 0;
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for (int idx_K = 0; idx_K < size<0>(mK); idx_K++) {
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acc += mS[idx_K] * mK(idx_K, idx_D, idx_L);
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}
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mDQ(idx_Q, idx_D, idx_L) = acc;
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}
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}
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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/* class TensorDQ, */ class TensorDK, /* class TensorDV, */
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class Fusion
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>
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void __global__ fmha_bwd_reference_dK_kernel(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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/* TensorDQ mDQ, */ TensorDK mDK, /* TensorDV mDV, */
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Fusion fusion
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) {
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using namespace cute;
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using Element = typename TensorO::value_type;
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using ElementAccumulator = typename TensorLSE::value_type;
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extern __shared__ char mS_mem[];
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Element* mS = reinterpret_cast<Element*>(mS_mem);
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Element softmax_scale = static_cast<Element>(1.0 / sqrt(1.0 * size<1>(mO)));
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for (int idx_L = blockIdx.y; idx_L < size<2>(mDK); idx_L += gridDim.y) {
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for (int idx_K = blockIdx.x; idx_K < size<0>(mDK); idx_K += gridDim.x) {
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for (int idx_Q = threadIdx.x; idx_Q < size<0>(mDO); idx_Q += blockDim.x) {
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ElementAccumulator acc_qk = 0;
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ElementAccumulator acc_dov = 0;
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ElementAccumulator acc_doo = 0;
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for (int idx_D0 = 0; idx_D0 < size<1>(mK); idx_D0++) {
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acc_qk += mQ(idx_Q, idx_D0, idx_L) * mK(idx_K, idx_D0, idx_L);
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acc_dov += mDO(idx_Q, idx_D0, idx_L) * mV(idx_K, idx_D0, idx_L);
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acc_doo += mDO(idx_Q, idx_D0, idx_L) * mO(idx_Q, idx_D0, idx_L);
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}
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auto id = make_identity_tensor(make_shape(1, 1));
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auto frag = make_tensor<ElementAccumulator>(Shape<_1, _1>{});
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frag(0) = acc_qk;
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fusion.before_softmax(frag, make_tensor(id.data() + make_arithmetic_tuple(idx_Q, idx_K), id.layout()), problem_shape);
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acc_qk = frag(0);
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mS[idx_Q] = static_cast<Element>(exp(softmax_scale * acc_qk - mLSE(idx_Q, idx_L)) * softmax_scale * (acc_dov - acc_doo));
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}
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__syncthreads();
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for (int idx_D = threadIdx.x; idx_D < size<1>(mDK); idx_D += blockDim.x) {
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ElementAccumulator acc = 0;
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for (int idx_Q = 0; idx_Q < size<0>(mDO); idx_Q++) {
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acc += mS[idx_Q] * mQ(idx_Q, idx_D, idx_L);
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}
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mDK(idx_K, idx_D, idx_L) = acc;
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}
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}
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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/* class TensorDQ, class TensorDK, */ class TensorDV,
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class Fusion
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>
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void __global__ fmha_bwd_reference_dV_kernel(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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/* TensorDQ mDQ, TensorDK mDK, */ TensorDV mDV,
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Fusion fusion
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) {
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using namespace cute;
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using Element = typename TensorO::value_type;
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using ElementAccumulator = typename TensorLSE::value_type;
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extern __shared__ char mS_mem[];
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Element* mS = reinterpret_cast<Element*>(mS_mem);
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Element softmax_scale = static_cast<Element>(1.0 / sqrt(1.0 * size<1>(mO)));
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for (int idx_L = blockIdx.y; idx_L < size<2>(mDV); idx_L += gridDim.y) {
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for (int idx_K = blockIdx.x; idx_K < size<0>(mDV); idx_K += gridDim.x) {
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for (int idx_Q = threadIdx.x; idx_Q < size<0>(mDO); idx_Q += blockDim.x) {
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ElementAccumulator acc_qk = 0;
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for (int idx_D0 = 0; idx_D0 < size<1>(mK); idx_D0++) {
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acc_qk += mQ(idx_Q, idx_D0, idx_L) * mK(idx_K, idx_D0, idx_L);
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}
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auto id = make_identity_tensor(make_shape(1, 1));
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auto frag = make_tensor<ElementAccumulator>(Shape<_1, _1>{});
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frag(0) = acc_qk;
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fusion.before_softmax(frag, make_tensor(id.data() + make_arithmetic_tuple(idx_Q, idx_K), id.layout()), problem_shape);
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acc_qk = frag(0);
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mS[idx_Q] = static_cast<Element>(exp(softmax_scale * acc_qk - mLSE(idx_Q, idx_L)));
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}
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__syncthreads();
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for (int idx_D = threadIdx.x; idx_D < size<1>(mDV); idx_D += blockDim.x) {
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ElementAccumulator acc = 0;
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for (int idx_Q = 0; idx_Q < size<0>(mDO); idx_Q++) {
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acc += mS[idx_Q] * mDO(idx_Q, idx_D, idx_L);
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}
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mDV(idx_K, idx_D, idx_L) = acc;
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}
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}
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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/**/ class TensorDQ, /** / class TensorDK, / ** / class TensorDV, / **/
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class Fusion
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>
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void fmha_bwd_reference_dQ(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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/**/ TensorDQ mDQ, /** / TensorDK mDK, / ** / TensorDV mDV, / **/
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Fusion fusion
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) {
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using namespace cute;
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dim3 grid(size<0>(mDQ), size<2>(mDQ), 1);
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dim3 block(256);
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int shared_mem = size<0>(mK) * sizeof(typename TensorO::value_type);
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if (shared_mem >= (48 << 10)) {
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CUTLASS_TRACE_HOST(" Setting smem size to " << shared_mem);
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auto result = cudaFuncSetAttribute(
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fmha_bwd_reference_dQ_kernel<ProblemShape, TensorQ, TensorK, TensorV, TensorO, TensorLSE, TensorDO, TensorDQ, Fusion>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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shared_mem);
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if (cudaSuccess != result) {
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result = cudaGetLastError(); // to clear the error bit
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CUTLASS_TRACE_HOST(
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" cudaFuncSetAttribute() returned error: "
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<< cudaGetErrorString(result));
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return;
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}
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}
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fmha_bwd_reference_dQ_kernel<<<grid, block, shared_mem>>>(problem_shape, mQ, mK, mV, mO, mLSE, mDO, mDQ, fusion);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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/** / class TensorDQ, / **/ class TensorDK, /** / class TensorDV, / **/
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class Fusion
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>
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void fmha_bwd_reference_dK(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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/** / TensorDQ mDQ, / **/ TensorDK mDK, /** / TensorDV mDV, / **/
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Fusion fusion
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) {
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using namespace cute;
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dim3 grid(size<0>(mDK), size<2>(mDK), 1);
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dim3 block(256);
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int shared_mem = size<0>(mDO) * sizeof(typename TensorO::value_type);
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if (shared_mem >= (48 << 10)) {
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CUTLASS_TRACE_HOST(" Setting smem size to " << shared_mem);
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auto result = cudaFuncSetAttribute(
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fmha_bwd_reference_dK_kernel<ProblemShape, TensorQ, TensorK, TensorV, TensorO, TensorLSE, TensorDO, TensorDK, Fusion>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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shared_mem);
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if (cudaSuccess != result) {
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result = cudaGetLastError(); // to clear the error bit
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CUTLASS_TRACE_HOST(
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" cudaFuncSetAttribute() returned error: "
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<< cudaGetErrorString(result));
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return;
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}
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}
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fmha_bwd_reference_dK_kernel<<<grid, block, shared_mem>>>(problem_shape, mQ, mK, mV, mO, mLSE, mDO, mDK, fusion);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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/** / class TensorDQ, / ** / class TensorDK, / **/ class TensorDV, /**/
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class Fusion
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>
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void fmha_bwd_reference_dV(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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/** / TensorDQ mDQ, / ** / TensorDK mDK, / **/ TensorDV mDV, /**/
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Fusion fusion
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) {
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using namespace cute;
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dim3 grid(size<0>(mDV), size<2>(mDV), 1);
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dim3 block(256);
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int shared_mem = size<0>(mDO) * sizeof(typename TensorO::value_type);
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if (shared_mem >= (48 << 10)) {
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CUTLASS_TRACE_HOST(" Setting smem size to " << shared_mem);
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auto result = cudaFuncSetAttribute(
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fmha_bwd_reference_dV_kernel<ProblemShape, TensorQ, TensorK, TensorV, TensorO, TensorLSE, TensorDO, TensorDV, Fusion>,
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cudaFuncAttributeMaxDynamicSharedMemorySize,
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shared_mem);
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if (cudaSuccess != result) {
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result = cudaGetLastError(); // to clear the error bit
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CUTLASS_TRACE_HOST(
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" cudaFuncSetAttribute() returned error: "
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<< cudaGetErrorString(result));
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return;
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}
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}
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fmha_bwd_reference_dV_kernel<<<grid, block, shared_mem>>>(problem_shape, mQ, mK, mV, mO, mLSE, mDO, mDV, fusion);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template<
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class ProblemShape,
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class TensorQ, class TensorK, class TensorV,
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class TensorO, class TensorLSE, class TensorDO,
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class TensorDQ, class TensorDK, class TensorDV,
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class Fusion
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>
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void fmha_bwd_reference(
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ProblemShape problem_shape,
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TensorQ mQ, TensorK mK, TensorV mV,
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TensorO mO, TensorLSE mLSE, TensorDO mDO,
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TensorDQ mDQ, TensorDK mDK, TensorDV mDV,
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Fusion fusion
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) {
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fmha_bwd_reference_dQ(problem_shape, mQ, mK, mV, mO, mLSE, mDO, mDQ, fusion);
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fmha_bwd_reference_dK(problem_shape, mQ, mK, mV, mO, mLSE, mDO, mDK, fusion);
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fmha_bwd_reference_dV(problem_shape, mQ, mK, mV, mO, mLSE, mDO, mDV, fusion);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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