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* [Sync] Update to latest code * Add new code files * Add new code files * update code * Try to fix build.sh * Try to fix build.sh * Update code * Update requirements.txt * Update code --------- Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com>
256 lines
9.5 KiB
Plaintext
256 lines
9.5 KiB
Plaintext
// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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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* Copyright (c) 2024, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri
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* Dao. Licensed under the BSD 3-Clause.
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*
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* Modified by the FlashInfer team.
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*/
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#include <cute/tensor.hpp>
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#include <cutlass/detail/helper_macros.hpp>
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#include "utils.cuh"
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namespace mla_attn {
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using namespace cute;
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template <typename T>
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struct MaxOp {
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__device__ __forceinline__ T operator()(T const& x, T const& y) { return x > y ? x : y; }
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};
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template <>
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struct MaxOp<float> {
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// This is slightly faster
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__device__ __forceinline__ float operator()(float const& x, float const& y) { return max(x, y); }
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};
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template <typename T>
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struct SumOp {
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__device__ __forceinline__ T operator()(T const& x, T const& y) { return x + y; }
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};
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template <int THREADS>
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struct Allreduce {
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static_assert(THREADS == 32 || THREADS == 16 || THREADS == 8 || THREADS == 4);
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template <typename T, typename Operator>
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static __device__ __forceinline__ T run(T x, Operator& op) {
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constexpr int OFFSET = THREADS / 2;
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x = op(x, __shfl_xor_sync(uint32_t(-1), x, OFFSET));
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return Allreduce<OFFSET>::run(x, op);
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}
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};
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template <>
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struct Allreduce<2> {
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template <typename T, typename Operator>
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static __device__ __forceinline__ T run(T x, Operator& op) {
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x = op(x, __shfl_xor_sync(uint32_t(-1), x, 1));
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return x;
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}
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};
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template <bool init, typename Engine0, typename Layout0, typename Engine1, typename Layout1,
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typename Operator>
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__device__ __forceinline__ void thread_reduce_(Tensor<Engine0, Layout0> const& tensor,
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Tensor<Engine1, Layout1>& summary, Operator& op) {
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static_assert(Layout0::rank == 2, "Only support 2D Tensor");
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static_assert(Layout1::rank == 1, "Only support 1D Tensor");
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CUTE_STATIC_ASSERT_V(size<0>(summary) == size<0>(tensor));
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#pragma unroll
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for (int mi = 0; mi < size<0>(tensor); mi++) {
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summary(mi) = init ? tensor(mi, 0) : op(summary(mi), tensor(mi, 0));
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#pragma unroll
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for (int ni = 1; ni < size<1>(tensor); ni++) {
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summary(mi) = op(summary(mi), tensor(mi, ni));
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}
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}
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}
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template <typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
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__device__ __forceinline__ void quad_allreduce_(Tensor<Engine0, Layout0>& dst,
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Tensor<Engine1, Layout1>& src, Operator& op) {
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CUTE_STATIC_ASSERT_V(size(dst) == size(src));
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#pragma unroll
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for (int i = 0; i < size(dst); i++) {
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dst(i) = Allreduce<4>::run(src(i), op);
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}
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}
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template <bool init, typename Engine0, typename Layout0, typename Engine1, typename Layout1,
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typename Operator>
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__device__ __forceinline__ void reduce_(Tensor<Engine0, Layout0> const& tensor,
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Tensor<Engine1, Layout1>& summary, Operator& op) {
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thread_reduce_<init>(tensor, summary, op);
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quad_allreduce_(summary, summary, op);
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}
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template <bool init, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
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__device__ __forceinline__ void reduce_max(Tensor<Engine0, Layout0> const& tensor,
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Tensor<Engine1, Layout1>& max) {
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MaxOp<float> max_op;
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reduce_<init>(tensor, max, max_op);
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}
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template <bool init, bool warp_reduce = true, typename Engine0, typename Layout0, typename Engine1,
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typename Layout1>
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__device__ __forceinline__ void reduce_sum(Tensor<Engine0, Layout0> const& tensor,
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Tensor<Engine1, Layout1>& sum) {
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SumOp<float> sum_op;
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thread_reduce_<init>(tensor, sum, sum_op);
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if constexpr (warp_reduce) {
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quad_allreduce_(sum, sum, sum_op);
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}
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}
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template <typename Engine0, typename Layout0, typename Engine1, typename Layout1>
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__forceinline__ __device__ void apply_exp2(Tensor<Engine0, Layout0>& tensor,
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Tensor<Engine1, Layout1> const& max) {
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static_assert(Layout0::rank == 2, "Only support 2D Tensor");
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static_assert(Layout1::rank == 1, "Only support 1D Tensor");
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CUTE_STATIC_ASSERT_V(size<0>(max) == size<0>(tensor));
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#pragma unroll
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for (int mi = 0; mi < size<0>(tensor); ++mi) {
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auto row_max = max(mi);
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#pragma unroll
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for (int ni = 0; ni < size<1>(tensor); ++ni) {
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tensor(mi, ni) = __expf(tensor(mi, ni) - row_max);
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}
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}
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}
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template <typename Engine0, typename Layout0, typename Engine1, typename Layout1>
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__forceinline__ __device__ void scale_apply_exp2(Tensor<Engine0, Layout0>& tensor,
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Tensor<Engine1, Layout1> const& max,
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const float scale) {
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static_assert(Layout0::rank == 2, "Only support 2D Tensor");
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static_assert(Layout1::rank == 1, "Only support 1D Tensor");
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CUTE_STATIC_ASSERT_V(size<0>(max) == size<0>(tensor));
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#pragma unroll
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for (int mi = 0; mi < size<0>(tensor); ++mi) {
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auto row_max = max(mi);
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#pragma unroll
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for (int ni = 0; ni < size<1>(tensor); ++ni) {
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// row_max * scale is a constant for each row, so we can use fma here
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tensor(mi, ni) = __expf(tensor(mi, ni) * scale - row_max * scale);
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}
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}
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}
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template <int NUM_ROWS_PER_THREAD, bool WITH_SCALE>
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struct OnlineSoftmax {
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constexpr static float fill_value = -5e4;
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using TensorT = decltype(make_tensor<float>(Shape<Int<NUM_ROWS_PER_THREAD>>{}));
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TensorT row_max, row_sum, scores_scale;
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float sm_scale_log2;
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CUTLASS_DEVICE OnlineSoftmax(float sm_scale_log2) : sm_scale_log2(sm_scale_log2) {
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clear(scores_scale);
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};
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__forceinline__ __device__ TensorT get_lse() const { return row_sum; }
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template <bool init, typename Tensor0>
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__forceinline__ __device__ TensorT update(Tensor0& acc_s) {
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// Reshape acc_s from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
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Tensor scores = make_tensor(acc_s.data(), convert_layout_acc_rowcol(acc_s.layout()));
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static_assert(decltype(size<0>(scores))::value == NUM_ROWS_PER_THREAD);
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if constexpr (init) {
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reduce_max</*init=*/true>(scores, row_max);
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if constexpr (WITH_SCALE) {
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scale_apply_exp2(scores, row_max, sm_scale_log2);
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} else {
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apply_exp2(scores, row_max);
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}
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reduce_sum</*init=*/true, /*warp_reduce=*/false>(scores, row_sum);
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} else {
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// update row_max
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Tensor scores_max_prev = make_fragment_like(row_max);
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cute::copy(row_max, scores_max_prev);
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reduce_max</*init=*/false>(scores, row_max);
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// update scores_scale and scale row_sum
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#pragma unroll
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for (int mi = 0; mi < size(row_max); ++mi) {
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float scores_max_cur = row_max(mi);
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if constexpr (WITH_SCALE) {
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scores_scale(mi) = __expf((scores_max_prev(mi) - scores_max_cur) * sm_scale_log2);
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} else {
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scores_scale(mi) = __expf(scores_max_prev(mi) - scores_max_cur);
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}
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row_sum(mi) *= scores_scale(mi);
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}
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// perform exp2 on scores
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if constexpr (WITH_SCALE) {
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scale_apply_exp2(scores, row_max, sm_scale_log2);
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} else {
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apply_exp2(scores, row_max);
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}
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// update row_sum
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reduce_sum</*init=*/false, /*warp_reduce=*/false>(scores, row_sum);
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return scores_scale;
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}
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};
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template <typename Tensor0>
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__forceinline__ __device__ TensorT finalize(Tensor0& acc_s) {
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// Reshape acc_s from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
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Tensor scores = make_tensor(acc_s.data(), convert_layout_acc_rowcol(acc_s.layout()));
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static_assert(decltype(size<0>(scores))::value == NUM_ROWS_PER_THREAD);
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SumOp<float> sum_op;
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quad_allreduce_(row_sum, row_sum, sum_op);
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#pragma unroll
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for (int mi = 0; mi < size(row_max); ++mi) {
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float sum = row_sum(mi);
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float inv_sum = 1.f / sum;
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scores_scale(mi) = inv_sum;
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row_max(mi) *= sm_scale_log2;
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}
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return scores_scale;
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};
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template <typename Tensor1>
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__forceinline__ __device__ void rescale_o(Tensor1& acc_o) {
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// Reshape acc_o from (MMA=4, MMA_M, MMA_K) to (nrow=(2, MMA_M), ncol=(2, MMA_K))
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Tensor acc_o_rowcol = make_tensor(acc_o.data(), convert_layout_acc_rowcol(acc_o.layout()));
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static_assert(decltype(size<0>(acc_o_rowcol))::value == NUM_ROWS_PER_THREAD);
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#pragma unroll
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for (int mi = 0; mi < size(row_max); ++mi) {
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#pragma unroll
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for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) {
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acc_o_rowcol(mi, ni) *= scores_scale(mi);
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}
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}
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};
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template <typename Tensor1, typename Tensor2>
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__forceinline__ __device__ void rescale_o(Tensor1& acc_o, Tensor2& scores_scale_input) {
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// Reshape acc_o from (MMA=4, MMA_M, MMA_K) to (nrow=(2, MMA_M), ncol=(2, MMA_K))
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Tensor acc_o_rowcol = make_tensor(acc_o.data(), convert_layout_acc_rowcol(acc_o.layout()));
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static_assert(decltype(size<0>(acc_o_rowcol))::value == NUM_ROWS_PER_THREAD);
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#pragma unroll
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for (int mi = 0; mi < size(row_max); ++mi) {
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#pragma unroll
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for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) {
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acc_o_rowcol(mi, ni) *= scores_scale_input(mi);
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}
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}
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};
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};
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} // namespace mla_attn
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