fmha_ref.h 24.3 KB
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/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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
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    http://www.apache.org/licenses/LICENSE-2.0
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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. */

#pragma once

#include "paddle/fluid/operators/dropout_impl.cu.h"
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#include "paddle/fluid/operators/fused/fused_softmax_mask.cu.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/funcs/elementwise_functor.h"
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#include "paddle/phi/kernels/funcs/functors.h"
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#include "paddle/phi/kernels/funcs/transpose_function.cu.h"
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#include "paddle/phi/kernels/gpudnn/softmax_gpudnn.h"
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namespace paddle {
namespace operators {

class AttnDropoutParam {
 public:
  AttnDropoutParam() {
    is_test_ = false;
    dropout_implementation_ = "downgrade_in_infer";
    dropout_prob_ = 0.5;
    is_upscale_in_train_ = false;
    is_fix_seed_ = false;
    seed_val_ = 0;
    seed_ = nullptr;
  }
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  AttnDropoutParam(bool is_test,
                   const std::string dropout_implementation,
                   float dropout_prob,
                   bool is_upscale_in_train,
                   bool is_fix_seed,
                   int seed_val,
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                   const phi::DenseTensor* seed) {
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    is_test_ = is_test;
    dropout_implementation_ = dropout_implementation;
    dropout_prob_ = dropout_prob;
    is_upscale_in_train_ = is_upscale_in_train;
    is_fix_seed_ = is_fix_seed;
    seed_val_ = seed_val;
    seed_ = seed;
  }
  bool is_test_;
  std::string dropout_implementation_;
  float dropout_prob_;
  bool is_upscale_in_train_;
  bool is_fix_seed_;
  int seed_val_;
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  const phi::DenseTensor* seed_;
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};

template <typename T>
class FMHARef {
 public:
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  FMHARef(const phi::GPUContext& dev_ctx,
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          int64_t batch_size,
          int64_t seq_len,
          int64_t num_head,
          int64_t head_dim,
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          AttnDropoutParam param)
      : dev_ctx_(dev_ctx),
        batch_size_(batch_size),
        seq_len_(seq_len),
        num_head_(num_head),
        head_dim_(head_dim),
        dropout_param_(param) {}

  ~FMHARef() {}

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  void ComputeForward(const phi::DenseTensor& qkv_input_tensor,
                      const phi::DenseTensor* cache_kv_tensor,
                      const phi::DenseTensor* src_mask_tensor,
                      phi::DenseTensor* transpose_2_out_tensor,
                      phi::DenseTensor* cache_kv_out_tensor,
                      phi::DenseTensor* qk_out_tensor,
                      phi::DenseTensor* src_mask_out_tensor,
                      phi::DenseTensor* softmax_out_tensor,
                      phi::DenseTensor* dropout_mask_out_tensor,
                      phi::DenseTensor* dropout_out_tensor,
                      phi::DenseTensor* qktv_out_tensor,
                      phi::DenseTensor* fmha_out_tensor) {
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    // input shape: [bs, seq_len, 3, num_head, head_dim]
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    // transpose with perm [2, 0, 3, 1, 4],
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    // output_shape: [3, bs, num_head, seq_len, head_dim]
    std::vector<int> perm_1 = {2, 0, 3, 1, 4};
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    phi::funcs::TransposeGPUKernelDriver<T>(
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        dev_ctx_, qkv_input_tensor, perm_1, transpose_2_out_tensor);
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    T* qkv_data = transpose_2_out_tensor->data<T>();
    T* qk_out_data = qk_out_tensor->data<T>();
    T* qktv_out_data = qktv_out_tensor->data<T>();
    T* softmax_out_data = softmax_out_tensor->data<T>();
    T* fmha_out_data = fmha_out_tensor->data<T>();

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    auto out_seq_len = seq_len_;
    if (cache_kv_tensor) {
      // kv [2, bs, num_head, seq_len, head_dim]
      auto kv_tensor = transpose_2_out_tensor->Slice(1, 3);
      phi::funcs::ConcatFunctor<phi::GPUContext, T> concat;
      // out [2, bs, num_head, cache_seq_len + seq_len, head_dim]
      concat(dev_ctx_, {*cache_kv_tensor, kv_tensor}, 3, cache_kv_out_tensor);
      out_seq_len = cache_kv_out_tensor->dims()[3];
    }

    int64_t q_size = batch_size_ * seq_len_ * num_head_ * head_dim_;
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    T* q_ptr = qkv_data;
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    T* k_ptr = nullptr;
    T* v_ptr = nullptr;

    if (cache_kv_tensor) {
      int64_t k_size = cache_kv_out_tensor->numel() / 2;
      k_ptr = cache_kv_out_tensor->data<T>();
      v_ptr = k_ptr + k_size;
    } else {
      int64_t k_size = q_size;
      k_ptr = q_ptr + q_size;
      v_ptr = k_ptr + k_size;
    }
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    {
      // NOTE(wangxi): We scale Q with 1/sqrt(Dh) before QK^T, because for
      // float16 calculation, INF may appear in QK^T if we do not scale before.
      float alpha = 1.0 / sqrt(head_dim_);
      auto q_tensor = transpose_2_out_tensor->Slice(0, 1);
      auto functor = phi::funcs::ScaleFunctor<T>(alpha);
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      std::vector<const phi::DenseTensor*> ins = {&q_tensor};
      std::vector<phi::DenseTensor*> outs = {&q_tensor};
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      phi::funcs::ElementwiseKernel<T>(dev_ctx_, ins, &outs, functor);
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    }

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    // q*k^t, batched_gemm
    CBLAS_TRANSPOSE transA = CblasNoTrans;
    CBLAS_TRANSPOSE transB = CblasTrans;
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    auto blas = phi::funcs::GetBlas<phi::GPUContext, T>(dev_ctx_);
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    int gemm_batch_size = batch_size_ * num_head_;
    int gemm_m = seq_len_;
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    int gemm_n = out_seq_len;
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    int gemm_k = head_dim_;
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    T alpha = static_cast<T>(1.0);
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    T beta = static_cast<T>(0.0);
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    int64_t stride_a = gemm_m * gemm_k;
    int64_t stride_b = gemm_k * gemm_n;
    blas.BatchedGEMM(transA,
                     transB,
                     gemm_m,
                     gemm_n,
                     gemm_k,
                     alpha,
                     q_ptr,
                     k_ptr,
                     beta,
                     qk_out_data,
                     gemm_batch_size,
                     stride_a,
                     stride_b);
    int softmax_axis = -1;
    if (src_mask_tensor != nullptr) {
      if (src_mask_out_tensor == nullptr && seq_len_ == out_seq_len) {
        LaunchFusedSoftmaxMaskKernel<T>(qk_out_data,
                                        src_mask_tensor->data<T>(),
                                        softmax_out_data,
                                        batch_size_,
                                        num_head_,
                                        seq_len_,
                                        dev_ctx_.stream());
      } else {
        std::vector<const phi::DenseTensor*> ins;
        std::vector<phi::DenseTensor*> outs;
        ins.emplace_back(qk_out_tensor);
        ins.emplace_back(src_mask_tensor);
        outs.emplace_back(src_mask_out_tensor);
        int elewise_add_axis = -1;
        phi::funcs::BroadcastKernel<phi::ElementwiseType::kBinary, T, T>(
            dev_ctx_,
            ins,
            &outs,
            elewise_add_axis,
            phi::funcs::AddFunctor<T>());

        phi::SoftmaxForwardCUDAKernelDriver<T>(
            dev_ctx_, *src_mask_out_tensor, softmax_axis, softmax_out_tensor);
      }
    } else {
      phi::SoftmaxForwardCUDAKernelDriver<T>(
          dev_ctx_, *qk_out_tensor, softmax_axis, softmax_out_tensor);
    }

    transB = CblasNoTrans;
    gemm_m = seq_len_;
    gemm_n = head_dim_;
    gemm_k = out_seq_len;
    alpha = static_cast<T>(1.0);
    stride_a = gemm_m * gemm_k;
    stride_b = gemm_k * gemm_n;

    if (dropout_param_.dropout_prob_) {
      DropoutFwGPUKernelDriver<T>(
          static_cast<const phi::GPUContext&>(dev_ctx_),
          dropout_param_.is_test_,
          dropout_param_.dropout_prob_,
          dropout_param_.is_upscale_in_train_,
          dropout_param_.is_fix_seed_,
          dropout_param_.seed_val_,
          static_cast<const phi::DenseTensor&>(*softmax_out_tensor),
          dropout_param_.seed_,
          dropout_mask_out_tensor,
          dropout_out_tensor,
          false);
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      T* dropout_out_data = dropout_out_tensor->data<T>();
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      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       dropout_out_data,
                       v_ptr,
                       beta,
                       qktv_out_data,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
    } else {
      // softmax_out * v, batched_gemm
      // output shape: [batch_size, num_heads, seq_len, head_dim]
      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       softmax_out_data,
                       v_ptr,
                       beta,
                       qktv_out_data,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
    }
    // transpose: [0, 2, 1, 3]
    // output shape: [batch_size, seq_len, num_heads, head_dim]
    std::vector<int> perm_3 = {0, 2, 1, 3};
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    phi::funcs::TransposeGPUKernelDriver<T>(
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        dev_ctx_, *qktv_out_tensor, perm_3, fmha_out_tensor);
  }

  void ComputeForwardWithoutTranspose(const phi::DenseTensor& qkv_input_tensor,
                                      const phi::DenseTensor* cache_kv_tensor,
                                      const phi::DenseTensor* src_mask_tensor,
                                      phi::DenseTensor* q_transpose_out_tensor,
                                      phi::DenseTensor* kv_transpose_out_tensor,
                                      phi::DenseTensor* cache_kv_out_tensor,
                                      phi::DenseTensor* qk_out_tensor,
                                      phi::DenseTensor* src_mask_out_tensor,
                                      phi::DenseTensor* softmax_out_tensor,
                                      phi::DenseTensor* dropout_mask_out_tensor,
                                      phi::DenseTensor* dropout_out_tensor,
                                      phi::DenseTensor* qktv_out_tensor,
                                      phi::DenseTensor* fmha_out_tensor) {
    // input shape: [bs, seq_len, 3, num_head, head_dim]
    // transpose with perm [2, 0, 3, 1, 4],
    // output_shape: [3, bs, num_head, seq_len, head_dim]
    T* qk_out_data = qk_out_tensor->data<T>();
    T* qktv_out_data = qktv_out_tensor->data<T>();
    T* softmax_out_data = softmax_out_tensor->data<T>();
    T* dropout_out_data = dropout_out_tensor->data<T>();
    T* fmha_out_data = fmha_out_tensor->data<T>();

    auto out_seq_len = seq_len_;
    if (cache_kv_tensor) {
      // kv [2, bs, num_head, seq_len, head_dim]
      phi::funcs::ConcatFunctor<phi::GPUContext, T> concat;
      // out [2, bs, num_head, cache_seq_len + seq_len, head_dim]
      concat(dev_ctx_,
             {*cache_kv_tensor, *kv_transpose_out_tensor},
             3,
             cache_kv_out_tensor);
      out_seq_len = cache_kv_out_tensor->dims()[3];
    }

    int64_t q_size = batch_size_ * seq_len_ * num_head_ * head_dim_;
    T* q_ptr = q_transpose_out_tensor->data<T>();
    T* k_ptr = nullptr;
    T* v_ptr = nullptr;

    if (cache_kv_tensor) {
      int64_t k_size = cache_kv_out_tensor->numel() / 2;
      k_ptr = cache_kv_out_tensor->data<T>();
      v_ptr = k_ptr + k_size;
    } else {
      int64_t k_size = q_size;
      k_ptr = kv_transpose_out_tensor->data<T>();
      v_ptr = k_ptr + k_size;
    }

    {
      // NOTE(wangxi): We scale Q with 1/sqrt(Dh) before QK^T, because for
      // float16 calculation, INF may appear in QK^T if we do not scale before.
      float alpha = 1.0 / sqrt(head_dim_);
      auto functor = phi::funcs::ScaleFunctor<T>(alpha);
      std::vector<const phi::DenseTensor*> ins = {q_transpose_out_tensor};
      std::vector<phi::DenseTensor*> outs = {q_transpose_out_tensor};
      phi::funcs::ElementwiseKernel<T>(dev_ctx_, ins, &outs, functor);
    }

    // q*k^t, batched_gemm
    CBLAS_TRANSPOSE transA = CblasNoTrans;
    CBLAS_TRANSPOSE transB = CblasTrans;
    auto blas = phi::funcs::GetBlas<phi::GPUContext, T>(dev_ctx_);
    int gemm_batch_size = batch_size_ * num_head_;
    int gemm_m = seq_len_;
    int gemm_n = out_seq_len;
    int gemm_k = head_dim_;
    T alpha = static_cast<T>(1.0);
    T beta = static_cast<T>(0.0);
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    int64_t stride_a = gemm_m * gemm_k;
    int64_t stride_b = gemm_k * gemm_n;
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    blas.BatchedGEMM(transA,
                     transB,
                     gemm_m,
                     gemm_n,
                     gemm_k,
                     alpha,
                     q_ptr,
                     k_ptr,
                     beta,
                     qk_out_data,
                     gemm_batch_size,
                     stride_a,
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                     stride_b);
    int softmax_axis = -1;
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    if (src_mask_tensor != nullptr) {
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      if (src_mask_out_tensor == nullptr && seq_len_ == out_seq_len) {
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        LaunchFusedSoftmaxMaskKernel<T>(qk_out_data,
                                        src_mask_tensor->data<T>(),
                                        softmax_out_data,
                                        batch_size_,
                                        num_head_,
                                        seq_len_,
                                        dev_ctx_.stream());
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      } else {
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        std::vector<const phi::DenseTensor*> ins;
        std::vector<phi::DenseTensor*> outs;
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        ins.emplace_back(qk_out_tensor);
        ins.emplace_back(src_mask_tensor);
        outs.emplace_back(src_mask_out_tensor);
        int elewise_add_axis = -1;
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        phi::funcs::BroadcastKernel<phi::ElementwiseType::kBinary, T, T>(
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            dev_ctx_,
            ins,
            &outs,
            elewise_add_axis,
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            phi::funcs::AddFunctor<T>());
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        phi::SoftmaxForwardCUDAKernelDriver<T>(
            dev_ctx_, *src_mask_out_tensor, softmax_axis, softmax_out_tensor);
      }
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    } else {
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      phi::SoftmaxForwardCUDAKernelDriver<T>(
          dev_ctx_, *qk_out_tensor, softmax_axis, softmax_out_tensor);
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    }

    transB = CblasNoTrans;
    gemm_m = seq_len_;
    gemm_n = head_dim_;
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    gemm_k = out_seq_len;
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    alpha = static_cast<T>(1.0);
    stride_a = gemm_m * gemm_k;
    stride_b = gemm_k * gemm_n;

    if (dropout_param_.dropout_prob_) {
      DropoutFwGPUKernelDriver<T>(
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          static_cast<const phi::GPUContext&>(dev_ctx_),
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          dropout_param_.is_test_,
          dropout_param_.dropout_prob_,
          dropout_param_.is_upscale_in_train_,
          dropout_param_.is_fix_seed_,
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          dropout_param_.seed_val_,
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          static_cast<const phi::DenseTensor&>(*softmax_out_tensor),
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          dropout_param_.seed_,
          dropout_mask_out_tensor,
          dropout_out_tensor,
          false);
      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       dropout_out_data,
                       v_ptr,
                       beta,
                       qktv_out_data,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
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    } else {
      // softmax_out * v, batched_gemm
      // output shape: [batch_size, num_heads, seq_len, head_dim]
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      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       softmax_out_data,
                       v_ptr,
                       beta,
                       qktv_out_data,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
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    }
    // transpose: [0, 2, 1, 3]
    // output shape: [batch_size, seq_len, num_heads, head_dim]
    std::vector<int> perm_3 = {0, 2, 1, 3};
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    phi::funcs::TransposeGPUKernelDriver<T>(
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        dev_ctx_, *qktv_out_tensor, perm_3, fmha_out_tensor);
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  }

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  void ComputeBackward(const phi::DenseTensor& transpose_2_out_tensor,
                       const phi::DenseTensor* src_mask_tensor,
                       const phi::DenseTensor& softmax_out_tensor,
                       const phi::DenseTensor& dropout_mask_out_tensor,
                       const phi::DenseTensor& dropout_out_tensor,
                       const phi::DenseTensor& qk_out_tensor,
                       const phi::DenseTensor& src_mask_out_tensor,
                       const phi::DenseTensor& fmha_out_grad_tensor,
                       phi::DenseTensor* qktv_out_grad_tensor,
                       phi::DenseTensor* dropout_out_grad_tensor,
                       phi::DenseTensor* softmax_out_grad_tensor,
                       phi::DenseTensor* src_mask_out_grad_tensor,
                       phi::DenseTensor* qk_out_grad_tensor,
                       phi::DenseTensor* transpose_2_out_grad_tensor,
                       phi::DenseTensor* src_mask_grad_tensor,
                       phi::DenseTensor* qkv_input_grad_tensor) {
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    auto blas = phi::funcs::GetBlas<phi::GPUContext, T>(dev_ctx_);
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    int q_size = batch_size_ * seq_len_ * num_head_ * head_dim_;
    int k_size = q_size;
    int softmax_axis = -1;

    T* qkv_grad_data = transpose_2_out_grad_tensor->data<T>();
    T* q_grad_ptr = qkv_grad_data;
    T* k_grad_ptr = q_grad_ptr + q_size;
    T* v_grad_ptr = k_grad_ptr + k_size;
    const T* qkv_data = transpose_2_out_tensor.data<T>();
    const T* q_ptr = qkv_data;
    const T* k_ptr = q_ptr + q_size;
    const T* v_ptr = k_ptr + k_size;

    const T* softmax_out_data = softmax_out_tensor.data<T>();
    T* softmax_out_grad_data = softmax_out_grad_tensor->data<T>();
    T* qktv_out_grad_data = qktv_out_grad_tensor->data<T>();

    // transpose bw
    std::vector<int> perm_3 = {0, 2, 1, 3};
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    phi::funcs::TransposeGPUKernelDriver<T>(
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        dev_ctx_, fmha_out_grad_tensor, perm_3, qktv_out_grad_tensor);
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    // recall batchedgemm(nn) fw: softmax_out_data(x) * v_ptr(y) =
    // qktv_out_data(out)
    CBLAS_TRANSPOSE transA = CblasTrans;
    CBLAS_TRANSPOSE transB = CblasNoTrans;
    int gemm_batch_size = batch_size_ * num_head_;
    int gemm_m = seq_len_;
    int gemm_n = head_dim_;
    int gemm_k = seq_len_;
    T alpha = static_cast<T>(1.0);
    T beta = static_cast<T>(0.0);
    int64_t stride_a = gemm_m * gemm_k;
    int64_t stride_b = gemm_k * gemm_n;
    // bw: dy = x^t * dout
    if (dropout_param_.dropout_prob_) {
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      const T* dropout_out_data = dropout_out_tensor.data<T>();
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      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       dropout_out_data,
                       qktv_out_grad_data,
                       beta,
                       v_grad_ptr,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
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    } else {
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      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       softmax_out_data,
                       qktv_out_grad_data,
                       beta,
                       v_grad_ptr,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
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    }
    // bw: dx = dout * y^t
    transA = CblasNoTrans;
    transB = CblasTrans;
    gemm_m = seq_len_;
    gemm_n = seq_len_;
    gemm_k = head_dim_;
    stride_a = gemm_m * gemm_k;
    stride_b = gemm_k * gemm_n;
    if (dropout_param_.dropout_prob_) {
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      T* dropout_out_grad_data = dropout_out_grad_tensor->data<T>();
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      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       qktv_out_grad_data,
                       v_ptr,
                       beta,
                       dropout_out_grad_data,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
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    } else {
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      blas.BatchedGEMM(transA,
                       transB,
                       gemm_m,
                       gemm_n,
                       gemm_k,
                       alpha,
                       qktv_out_grad_data,
                       v_ptr,
                       beta,
                       softmax_out_grad_data,
                       gemm_batch_size,
                       stride_a,
                       stride_b);
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    }
    // dropout bw
    if (dropout_param_.dropout_prob_) {
      DropoutGradGPUKernelDriver<T>(
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          static_cast<const phi::GPUContext&>(dev_ctx_),
          false,
          dropout_param_.dropout_prob_,
          dropout_param_.is_upscale_in_train_,
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          static_cast<const phi::DenseTensor&>(*dropout_out_grad_tensor),
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          dropout_mask_out_tensor,
          softmax_out_grad_tensor,
          false);
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    }

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    if (src_mask_tensor != nullptr) {
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      phi::SoftmaxBackwardCUDAKernelDriver<T>(dev_ctx_,
                                              softmax_out_tensor,
                                              *softmax_out_grad_tensor,
                                              softmax_axis,
                                              src_mask_out_grad_tensor);
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      // recall LaunchElementwiseCudaKernel fw:  src_mask_out = qk_out +
      // src_mask
      // Special case when dy is not needed and dx doesn't reduce
      if (qk_out_grad_tensor != nullptr && src_mask_grad_tensor == nullptr &&
          qk_out_tensor.dims() == src_mask_out_tensor.dims()) {
        VLOG(4) << "Special case when dy is not needed and dx doesn't "
                   "reduce";
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        framework::TensorCopy(*src_mask_out_grad_tensor,
                              dev_ctx_.GetPlace(),
                              dev_ctx_,
                              qk_out_grad_tensor);
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      } else {
        PADDLE_THROW(platform::errors::InvalidArgument(
            "Only used for the backward elementwise_add op when"
            "dy is not needed and dx is not reduce"));
        return;
      }

    } else {
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      phi::SoftmaxBackwardCUDAKernelDriver<T>(dev_ctx_,
                                              softmax_out_tensor,
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                                              *softmax_out_grad_tensor,
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                                              softmax_axis,
                                              qk_out_grad_tensor);
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    }

    T* qk_out_grad_data = qk_out_grad_tensor->data<T>();
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    // NOTE(wangxi): For we scale Q with 1/sqrt(Dh) in forward, so we set
    //   alpha = 1.0 in backward.
    alpha = static_cast<T>(1.0);
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    // recall batchedgemm(nt) fw:  q_ptr * (k_ptr)^t = qk_out
    // bw: dy (seq_len * head_dim) = (dout)^t * x
    transA = CblasTrans;
    transB = CblasNoTrans;
    gemm_m = seq_len_;
    gemm_n = head_dim_;
    gemm_k = seq_len_;
    stride_a = gemm_m * gemm_k;
    stride_b = gemm_k * gemm_n;
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    blas.BatchedGEMM(transA,
                     transB,
                     gemm_m,
                     gemm_n,
                     gemm_k,
                     alpha,
                     qk_out_grad_data,
                     q_ptr,
                     beta,
                     k_grad_ptr,
                     gemm_batch_size,
                     stride_a,
                     stride_b);
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    // dx (seq_len * head_dim) = dout * y
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    alpha = static_cast<T>(1.0 / sqrt(head_dim_));
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    transA = CblasNoTrans;
    transB = CblasNoTrans;
    gemm_m = seq_len_;
    gemm_n = head_dim_;
    gemm_k = seq_len_;
    stride_a = gemm_m * gemm_k;
    stride_b = gemm_k * gemm_n;
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    blas.BatchedGEMM(transA,
                     transB,
                     gemm_m,
                     gemm_n,
                     gemm_k,
                     alpha,
                     qk_out_grad_data,
                     k_ptr,
                     beta,
                     q_grad_ptr,
                     gemm_batch_size,
                     stride_a,
                     stride_b);
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    // transpose bw
    std::vector<int> perm_1 = {1, 3, 0, 2, 4};
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    phi::funcs::TransposeGPUKernelDriver<T>(
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        dev_ctx_, *transpose_2_out_grad_tensor, perm_1, qkv_input_grad_tensor);
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  }

 private:
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  const phi::GPUContext& dev_ctx_;
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  int64_t batch_size_;
  int64_t seq_len_;
  int64_t num_head_;
  int64_t head_dim_;

  AttnDropoutParam dropout_param_;
};

}  // namespace operators
}  // namespace paddle