fused_attention_op.cu

/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include <cuda_fp16.h>

#include <cub/cub.cuh>

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/fused/attention_layer_norm.h"
#include "paddle/fluid/operators/fused/attn_gemm.h"
#include "paddle/fluid/operators/fused/fmha_ref.h"
#include "paddle/fluid/operators/fused/fused_dropout_helper.h"
#include "paddle/fluid/platform/device/gpu/gpu_device_function.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
#include "paddle/phi/api/include/tensor.h"
#include "paddle/phi/kernels/funcs/broadcast_function.h"
#include "paddle/phi/kernels/funcs/elementwise_functor.h"
#include "paddle/phi/kernels/funcs/math_function.h"

#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
#include "paddle/fluid/distributed/collective/ProcessGroup.h"
#include "paddle/fluid/platform/collective_helper.h"
#include "paddle/fluid/platform/device/gpu/nccl_helper.h"
#endif

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename T>
static void AllReduce(framework::Tensor &tensor,  // NOLINT
                      const int ring_id,
                      const phi::GPUContext &ctx) {
  if (ring_id == -1) return;
#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
  auto map = paddle::distributed::ProcessGroupMapFromGid::getInstance();

  if (map->has(ring_id)) {
    paddle::distributed::ProcessGroup *pg = map->get(ring_id);
    std::vector<phi::DenseTensor> in_tensor;
    std::vector<phi::DenseTensor> out_tensor;
    in_tensor.push_back(tensor);
    out_tensor.push_back(tensor);
    paddle::distributed::AllreduceOptions opts;
    opts.reduce_op = distributed::ReduceOp::SUM;
    auto task = pg->AllReduce(in_tensor, out_tensor, opts);
    task->Wait();
  } else {
    auto dtype = platform::ToNCCLDataType(
        framework::TransToProtoVarType(tensor.dtype()));
    int64_t numel = tensor.numel();
    const void *sendbuff = tensor.data<T>();
    auto place = ctx.GetPlace();
    void *recvbuff = tensor.mutable_data<T>(place);
    auto comm = platform::NCCLCommContext::Instance().Get(ring_id, place);
    auto stream = ctx.stream();
    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::ncclAllReduce(
        sendbuff, recvbuff, numel, dtype, ncclSum, comm->comm(), stream));
  }
#else
  PADDLE_THROW(platform::errors::Unimplemented(
      "PaddlePaddle should compile with NCCL or RCCL when used tensor model "
      "parallel op."));
#endif
}

template <typename T>
class FusedAttentionOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    using U = LayerNormParamType<T>;
    auto *input_x = ctx.Input<Tensor>("X");

    const auto pre_layer_norm = ctx.Attr<bool>("pre_layer_norm");
    const float epsilon = ctx.Attr<float>("epsilon");
    auto *ln_scale = ctx.Input<Tensor>("LnScale");
    auto *ln_bias = ctx.Input<Tensor>("LnBias");
    auto *ln_mean = ctx.Output<Tensor>("LnMean");
    auto *ln_var = ctx.Output<Tensor>("LnVariance");
    auto *ln_out = ctx.Output<Tensor>("LnOut");

    // x: qkv's input [batch_size, seq_len, dim_embed]
    // y: qkv's weight: [3, num_head, dim_head, dim_embed]
    auto *qkv_weight = ctx.Input<Tensor>("QKVW");
    auto *qkv_bias = ctx.Input<Tensor>("QKVBias");
    auto *qkv_out = ctx.Output<Tensor>("QKVOut");
    auto *qkv_bias_out = ctx.Output<Tensor>("QKVBiasOut");

    auto *src_mask = ctx.Input<Tensor>("SrcMask");
    auto *transpose_out_2 = ctx.Output<Tensor>("TransposeOut2");
    auto *cache_kv = ctx.Input<Tensor>("CacheKV");
    auto *cache_kv_out = ctx.Output<Tensor>("CacheKVOut");
    auto *qk_out = ctx.Output<Tensor>("QKOut");
    auto *qktv_out = ctx.Output<Tensor>("QKTVOut");
    auto *softmax_out = ctx.Output<Tensor>("SoftmaxOut");
    auto *attn_dropout_mask_out = ctx.Output<Tensor>("AttnDropoutMaskOut");
    auto *attn_dropout_out = ctx.Output<Tensor>("AttnDropoutOut");
    auto *src_mask_out = ctx.Output<Tensor>("SrcMaskOut");
    auto *fmha_out = ctx.Output<Tensor>("FMHAOut");

    auto *out_linear_weight = ctx.Input<Tensor>("OutLinearW");
    auto *out_linear_bias = ctx.Input<Tensor>("OutLinearBias");
    auto *out_linear_out = ctx.Output<Tensor>("OutLinearOut");

    auto *ln_scale_2 = ctx.Input<Tensor>("Ln2Scale");
    auto *ln_bias_2 = ctx.Input<Tensor>("Ln2Bias");
    auto *dropout_mask_out = ctx.Output<Tensor>("DropoutMaskOut");
    auto *bias_dropout_residual_out =
        ctx.Output<Tensor>("BiasDropoutResidualOut");
    auto *ln_mean_2 = ctx.Output<Tensor>("Ln2Mean");
    auto *ln_var_2 = ctx.Output<Tensor>("Ln2Variance");
    const float ln_epsilon = ctx.Attr<float>("ln_epsilon");

    float attn_dropout_rate = ctx.Attr<float>("attn_dropout_rate");
    bool is_test_1 = ctx.Attr<bool>("is_test");
    auto &dropout_implementation_1 =
        ctx.Attr<std::string>("attn_dropout_implementation");
    bool is_upscale_in_train_1 =
        (dropout_implementation_1 == "upscale_in_train");
    auto *seed_1 = ctx.HasInput("Seed1") ? ctx.Input<Tensor>("Seed1") : nullptr;
    bool is_fix_seed_1 = ctx.Attr<bool>("attn_dropout_fix_seed");
    int seed_val_1 = ctx.Attr<int>("attn_dropout_seed");
    int ring_id = ctx.Attr<int>("ring_id");

    // final output.
    auto *out = ctx.Output<Tensor>("Y");

    // get data ptr for qkv part.
    const auto input_x_dims = input_x->dims();
    const auto qkv_w_dims = qkv_weight->dims();

    auto *x_data = input_x->data<T>();
    auto *qkv_weight_data = qkv_weight->data<T>();
    auto *qkv_bias_data = (qkv_bias == nullptr) ? nullptr : qkv_bias->data<T>();
    auto *qkv_out_data = qkv_out->mutable_data<T>(ctx.GetPlace());
    auto *qkv_bias_out_data =
        (qkv_bias == nullptr) ? nullptr
                              : qkv_bias_out->mutable_data<T>(ctx.GetPlace());

    // get data ptr for FMHA.
    auto *transpose_out_2_data =
        transpose_out_2->mutable_data<T>(ctx.GetPlace());
    auto *cache_kv_out_data =
        (cache_kv_out == nullptr)
            ? nullptr
            : cache_kv_out->mutable_data<T>(ctx.GetPlace());
    auto *qk_out_data = qk_out->mutable_data<T>(ctx.GetPlace());
    auto *qktv_out_data = qktv_out->mutable_data<T>(ctx.GetPlace());
    auto *src_mask_out_data =
        (src_mask == nullptr) ? nullptr
                              : src_mask_out->mutable_data<T>(ctx.GetPlace());
    auto *softmax_out_data = softmax_out->mutable_data<T>(ctx.GetPlace());
    auto *attn_dropout_mask_out_data =
        attn_dropout_mask_out->mutable_data<uint8_t>(ctx.GetPlace());
    auto *attn_dropout_out_data =
        attn_dropout_out->mutable_data<T>(ctx.GetPlace());
    auto *fmha_out_data = fmha_out->mutable_data<T>(ctx.GetPlace());

    // get data ptr for out_linear.
    auto *out_linear_weight_data = out_linear_weight->data<T>();
    auto *out_linear_bias_data =
        (out_linear_bias == nullptr) ? nullptr : out_linear_bias->data<T>();
    auto *out_linear_out_data = out_linear_out->mutable_data<T>(ctx.GetPlace());

    // get data ptr for bias+dropout+residual+layernorm
    auto *dropout_mask_out_data =
        dropout_mask_out->mutable_data<uint8_t>(ctx.GetPlace());
    auto *final_out_data = out->mutable_data<T>(ctx.GetPlace());

    int batch_size = input_x_dims[0];
    int max_seq_len = input_x_dims[1];
    int dim_embed = input_x_dims[2];

    int num_head = qkv_w_dims[1];
    int dim_head = qkv_w_dims[2];

    int bsz_seq = batch_size * max_seq_len;
    int hidden_size = num_head * dim_head;
    int output_size = 3 * hidden_size;
    int input_size = dim_embed;

    auto layer_norm_compute = AttnLayerNorm<T>(
        ctx.cuda_device_context(), epsilon, bsz_seq, dim_embed);

    bool compute_bias = true;
    if (qkv_bias == nullptr) {
      compute_bias = false;
    }
    // (transA, transB, compute_bias) = (false, true, true)
    auto qkv_compute = AttnMatMul<T>(ctx.cuda_device_context(),
                                     false,
                                     true,
                                     bsz_seq,
                                     output_size,
                                     input_size,
                                     compute_bias);

    AttnDropoutParam attn_dropout_param(is_test_1,
                                        dropout_implementation_1,
                                        attn_dropout_rate,
                                        is_upscale_in_train_1,
                                        is_fix_seed_1,
                                        seed_val_1,
                                        seed_1);
    auto fmha_ref_compute = FMHARef<T>(ctx.cuda_device_context(),
                                       batch_size,
                                       max_seq_len,
                                       num_head,
                                       dim_head,
                                       attn_dropout_param);

    output_size = hidden_size;
    // (transA, transB, compute_bias) = (false, false, false)
    // NOTE(Yuang Liu): For general input size == output size, change the
    // position won't have effects. For mp, the output size is mp_head * dkey
    // which is actually the input size. While the input size is hidden size,
    // which is actually the output size. So for out linear, switch the
    // input size and output size.
    auto out_linear_compute = AttnMatMul<T>(ctx.cuda_device_context(),
                                            false,
                                            false,
                                            bsz_seq,
                                            input_size,
                                            output_size,
                                            false);
    DropoutParam dropout_param2(ctx, 0);
    FusedDropoutLayerNormHelper<T, uint8_t> fused_dropout_layernorm_helper(
        ctx.cuda_device_context(),
        bsz_seq,
        dim_embed,
        dropout_param2,
        ln_epsilon);

    if (pre_layer_norm) {
      auto *ln_scale_data =
          (ln_scale == nullptr ? nullptr : ln_scale->data<U>());
      auto *ln_bias_data = (ln_bias == nullptr ? nullptr : ln_bias->data<U>());
      auto *ln_mean_data = ln_mean->mutable_data<U>(ctx.GetPlace());
      auto *ln_var_data = ln_var->mutable_data<U>(ctx.GetPlace());
      auto *ln_out_data = ln_out->mutable_data<T>(ctx.GetPlace());

      layer_norm_compute.ComputeForward(x_data,
                                        ln_scale_data,
                                        ln_bias_data,
                                        ln_out_data,
                                        ln_mean_data,
                                        ln_var_data);
      qkv_compute.ComputeForward(
          qkv_weight, ln_out, qkv_bias, qkv_out, qkv_bias_out);
    } else {
      qkv_compute.ComputeForward(
          qkv_weight, input_x, qkv_bias, qkv_out, qkv_bias_out);
    }
    if (qkv_bias == nullptr) {
      fmha_ref_compute.ComputeForward(*qkv_out,
                                      cache_kv,
                                      src_mask,
                                      transpose_out_2,
                                      cache_kv_out,
                                      qk_out,
                                      src_mask_out,
                                      softmax_out,
                                      attn_dropout_mask_out,
                                      attn_dropout_out,
                                      qktv_out,
                                      fmha_out);
    } else {
      fmha_ref_compute.ComputeForward(*qkv_bias_out,
                                      cache_kv,
                                      src_mask,
                                      transpose_out_2,
                                      cache_kv_out,
                                      qk_out,
                                      src_mask_out,
                                      softmax_out,
                                      attn_dropout_mask_out,
                                      attn_dropout_out,
                                      qktv_out,
                                      fmha_out);
    }

    // fmha_out: [batch_size, seq_len, num_head, head_dim]
    // weight:   [embed_dim, embed_dim]
    // out_linear_out: [batch_size, seq_len, embed_dim]
    out_linear_compute.ComputeForward(
        out_linear_weight, fmha_out, nullptr, out_linear_out, nullptr);
    // tensor model parallel
    AllReduce<T>(*out_linear_out, ring_id, ctx.cuda_device_context());

    bool add_residual = ctx.Attr<bool>("add_residual");
    const T *residual_ptr = add_residual ? x_data : nullptr;
    if (pre_layer_norm) {
      // output = (residual + dropout(input + bias))
      fused_dropout_layernorm_helper.ResidualDropoutBias(
          ctx.cuda_device_context(),
          out_linear_out_data,
          residual_ptr,
          out_linear_bias_data,
          final_out_data,
          dropout_mask_out_data);
    } else {
      // TODO(Xreki): support post layer_norm case when add_residual is false.
      PADDLE_ENFORCE_EQ(add_residual,
                        true,
                        platform::errors::InvalidArgument(
                            "Attribute add_residual is expected to be true "
                            "when pre_layer_norm is false."));

      const U *ln_scale_2_ptr = ln_scale_2 ? ln_scale_2->data<U>() : nullptr;
      const U *ln_bias_2_ptr = ln_bias_2 ? ln_bias_2->data<U>() : nullptr;
      T *bias_dropout_residual_out_ptr =
          bias_dropout_residual_out->mutable_data<T>(ctx.GetPlace());
      U *ln_mean_2_ptr = ln_mean_2->mutable_data<U>(ctx.GetPlace());
      U *ln_var_2_ptr = ln_var_2->mutable_data<U>(ctx.GetPlace());
      // output = layernorm(residual + dropout(input + bias))
      fused_dropout_layernorm_helper.LayernormResidualDropoutBias(
          ctx.cuda_device_context(),
          out_linear_out_data,
          residual_ptr,
          out_linear_bias_data,
          ln_scale_2_ptr,
          ln_bias_2_ptr,
          bias_dropout_residual_out_ptr,
          dropout_mask_out_data,
          final_out_data,
          ln_mean_2_ptr,
          ln_var_2_ptr);
    }
  }
};

template <typename T>
class FusedAttentionGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override {
    using U = LayerNormParamType<T>;
    const auto pre_layer_norm = ctx.Attr<bool>("pre_layer_norm");
    const float epsilon = ctx.Attr<float>("epsilon");
    const float ln2epsilon = ctx.Attr<float>("ln_epsilon");

    float attn_dropout_prob = ctx.Attr<float>("attn_dropout_rate");
    bool is_test_1 = ctx.Attr<bool>("is_test");
    auto &dropout_implementation_1 =
        ctx.Attr<std::string>("attn_dropout_implementation");
    bool is_upscale_in_train_1 =
        (dropout_implementation_1 == "upscale_in_train");
    auto *seed_1 = ctx.HasInput("Seed1") ? ctx.Input<Tensor>("Seed1") : nullptr;
    bool is_fix_seed_1 = ctx.Attr<bool>("attn_dropout_fix_seed");
    int seed_val_1 = ctx.Attr<int>("attn_dropout_seed");
    int ring_id = ctx.Attr<int>("ring_id");

    // get inputs.
    auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
    auto *d_y_data = d_y->data<T>();

    // fw input
    auto *input_x = ctx.Input<Tensor>("X");
    auto *ln_scale = ctx.Input<Tensor>("LnScale");
    auto *ln_2_scale = ctx.Input<Tensor>("Ln2Scale");
    auto *x_data = input_x->data<T>();
    auto *ln_scale_data = (ln_scale == nullptr ? nullptr : ln_scale->data<U>());
    auto *ln_2_scale_data =
        (ln_2_scale == nullptr ? nullptr : ln_2_scale->data<U>());
    // fw parameters.
    auto *src_mask = ctx.Input<Tensor>("SrcMask");
    auto *qkv_weight = ctx.Input<Tensor>("QKVW");
    auto *qkv_bias = ctx.Input<Tensor>("QKVBias");
    auto *out_linear_weight = ctx.Input<Tensor>("OutLinearW");
    auto *out_linear_bias = ctx.Input<Tensor>("OutLinearBias");
    auto *src_mask_data = (src_mask == nullptr ? nullptr : src_mask->data<T>());
    auto *qkv_weight_data = qkv_weight->data<T>();
    auto *qkv_bias_data = (qkv_bias == nullptr) ? nullptr : qkv_bias->data<T>();
    auto *out_linear_weight_data = out_linear_weight->data<T>();
    auto *out_linear_bias_data =
        (out_linear_bias == nullptr) ? nullptr : out_linear_bias->data<T>();

    // fw output
    auto *fmha_out = ctx.Input<Tensor>("FMHAOut");
    auto *transpose_out_2 = ctx.Input<Tensor>("TransposeOut2");
    auto *qk_out = ctx.Input<Tensor>("QKOut");
    auto *qktv_out = ctx.Input<Tensor>("QKTVOut");
    auto *softmax_out = ctx.Input<Tensor>("SoftmaxOut");
    auto *attn_dropout_mask_out = ctx.Input<Tensor>("AttnDropoutMaskOut");
    auto *attn_dropout_out = ctx.Input<Tensor>("AttnDropoutOut");
    auto *src_mask_out = ctx.Input<Tensor>("SrcMaskOut");
    auto *out_linear_out = ctx.Input<Tensor>("OutLinearOut");
    auto *ln_2_mean = ctx.Input<Tensor>("Ln2Mean");
    auto *ln_2_var = ctx.Input<Tensor>("Ln2Variance");
    auto *dropout_mask_out = ctx.Input<Tensor>("DropoutMaskOut");
    auto *bias_dropout_residual_out =
        ctx.Input<Tensor>("BiasDropoutResidualOut");
    auto *fmha_out_data = fmha_out->data<T>();
    auto *transpose_out_2_data = transpose_out_2->data<T>();
    auto *qk_out_data = qk_out->data<T>();
    auto *qktv_out_data = qktv_out->data<T>();
    auto *softmax_out_data = softmax_out->data<T>();
    auto *src_mask_out_data =
        (src_mask == nullptr) ? nullptr : src_mask_out->data<T>();
    auto *out_linear_out_data = out_linear_out->data<T>();
    auto *dropout_mask_out_data = dropout_mask_out->data<uint8_t>();

    // output's grad
    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto *d_qkv_out = ctx.Output<Tensor>(framework::GradVarName("QKVOut"));
    auto *d_qkv_bias_out =
        ctx.Output<Tensor>(framework::GradVarName("QKVBiasOut"));
    auto *d_qktv_out = ctx.Output<Tensor>(framework::GradVarName("QKTVOut"));
    auto *d_transpose_out_2 =
        ctx.Output<Tensor>(framework::GradVarName("TransposeOut2"));
    auto *d_qk_out = ctx.Output<Tensor>(framework::GradVarName("QKOut"));
    auto *d_softmax_out =
        ctx.Output<Tensor>(framework::GradVarName("SoftmaxOut"));
    auto *d_attn_dropout_out =
        ctx.Output<Tensor>(framework::GradVarName("AttnDropoutOut"));
    auto *d_src_mask_out =
        ctx.Output<Tensor>(framework::GradVarName("SrcMaskOut"));
    auto *d_fmha_out = ctx.Output<Tensor>(framework::GradVarName("FMHAOut"));
    auto *d_out_linear_out =
        ctx.Output<Tensor>(framework::GradVarName("OutLinearOut"));
    auto *d_bias_dropout_residual_out =
        ctx.Output<Tensor>(framework::GradVarName("BiasDropoutResidualOut"));
    auto *d_x_data = d_x->mutable_data<T>(ctx.GetPlace());
    // when qkv_bias is not nullptr, d_qkv_out is equals to d_qkv_bias_out, the
    // space can be reused.
    auto *d_qkv_out_data = (d_qkv_bias_out != nullptr)
                               ? nullptr
                               : d_qkv_out->mutable_data<T>(ctx.GetPlace());
    auto *d_qkv_bias_out_data =
        (d_qkv_bias_out == nullptr)
            ? nullptr
            : d_qkv_bias_out->mutable_data<T>(ctx.GetPlace());
    auto *d_qktv_out_data = d_qktv_out->mutable_data<T>(ctx.GetPlace());
    auto *d_transpose_out_2_data =
        d_transpose_out_2->mutable_data<T>(ctx.GetPlace());
    auto *d_qk_out_data = d_qk_out->mutable_data<T>(ctx.GetPlace());
    auto *d_softmax_out_data = d_softmax_out->mutable_data<T>(ctx.GetPlace());
    auto *d_attn_dropout_out_data =
        d_attn_dropout_out->mutable_data<T>(ctx.GetPlace());
    auto *d_src_mask_out_data =
        (src_mask == nullptr) ? nullptr
                              : d_src_mask_out->mutable_data<T>(ctx.GetPlace());
    auto *d_fmha_out_data = d_fmha_out->mutable_data<T>(ctx.GetPlace());
    auto *d_out_linear_out_data =
        d_out_linear_out->mutable_data<T>(ctx.GetPlace());

    // parameter grad
    auto *d_qkv_weight = ctx.Output<Tensor>(framework::GradVarName("QKVW"));
    auto *d_qkv_bias = ctx.Output<Tensor>(framework::GradVarName("QKVBias"));
    auto *d_out_linear_weight =
        ctx.Output<Tensor>(framework::GradVarName("OutLinearW"));
    auto *d_out_linear_bias =
        ctx.Output<Tensor>(framework::GradVarName("OutLinearBias"));
    auto *d_ln_2_scale = ctx.Output<Tensor>(framework::GradVarName("Ln2Scale"));
    auto *d_ln_2_bias = ctx.Output<Tensor>(framework::GradVarName("Ln2Bias"));

    auto *d_qkv_weight_data = d_qkv_weight->mutable_data<T>(ctx.GetPlace());
    auto *d_qkv_bias_data = (d_qkv_bias == nullptr)
                                ? nullptr
                                : d_qkv_bias->mutable_data<T>(ctx.GetPlace());
    auto *d_out_linear_weight_data =
        d_out_linear_weight->mutable_data<T>(ctx.GetPlace());
    auto *d_out_linear_bias_data =
        (d_out_linear_bias == nullptr)
            ? nullptr
            : d_out_linear_bias->mutable_data<T>(ctx.GetPlace());

    const auto input_x_dims = input_x->dims();
    const auto qkv_w_dims = qkv_weight->dims();

    int batch_size = input_x_dims[0];
    int max_seq_len = input_x_dims[1];
    int dim_embed = input_x_dims[2];
    int num_head = qkv_w_dims[1];
    int dim_head = qkv_w_dims[2];

    int bsz_seq = batch_size * max_seq_len;
    int hidden_size = num_head * dim_head;
    int output_size = 3 * hidden_size;
    int input_size = dim_embed;

    bool add_residual = ctx.Attr<bool>("add_residual");
    Tensor d_residual;
    T *d_residual_data = nullptr;
    if (add_residual) {
      d_residual.Resize(input_x_dims);
      d_residual_data = d_residual.mutable_data<T>(ctx.GetPlace());
    }

    bool transA = false;
    bool transB = true;
    bool compute_qkv_bias = qkv_bias ? true : false;
    auto layer_norm_compute = AttnLayerNorm<T>(
        ctx.cuda_device_context(), epsilon, bsz_seq, dim_embed);
    auto qkv_compute = AttnMatMul<T>(ctx.cuda_device_context(),
                                     transA,
                                     transB,
                                     bsz_seq,
                                     output_size,
                                     input_size,
                                     compute_qkv_bias);
    AttnDropoutParam attn_dropout_param(is_test_1,
                                        dropout_implementation_1,
                                        attn_dropout_prob,
                                        is_upscale_in_train_1,
                                        is_fix_seed_1,
                                        seed_val_1,
                                        seed_1);
    auto fmha_ref_compute = FMHARef<T>(ctx.cuda_device_context(),
                                       batch_size,
                                       max_seq_len,
                                       num_head,
                                       dim_head,
                                       attn_dropout_param);
    output_size = hidden_size;
    transA = false;
    transB = false;
    bool compute_bias = false;
    // (b*s, num_head * dim_head) * (num_head * dim_head, dim_embed)
    auto out_linear_compute = AttnMatMul<T>(ctx.cuda_device_context(),
                                            transA,
                                            transB,
                                            bsz_seq,
                                            input_size,
                                            output_size,
                                            compute_bias);
    DropoutParam dropout_param2(ctx, 0);
    FusedDropoutLayerNormHelper<T, uint8_t> fused_dropout_layernorm_helper(
        ctx.cuda_device_context(),
        bsz_seq,
        dim_embed,
        dropout_param2,
        ln2epsilon);

    if (pre_layer_norm) {
      fused_dropout_layernorm_helper.ResidualDropoutBiasGrad(
          ctx.cuda_device_context(),
          d_y_data,
          dropout_mask_out_data,
          d_out_linear_out_data,
          d_residual_data,
          d_out_linear_bias_data);
    } else {
      auto *ln_2_mean_data = ln_2_mean->data<U>();
      auto *ln_2_var_data = ln_2_var->data<U>();
      auto *bias_dropout_residual_out_data =
          bias_dropout_residual_out->data<T>();
      auto *d_ln_2_scale_data =
          (d_ln_2_scale == nullptr
               ? nullptr
               : d_ln_2_scale->mutable_data<U>(ctx.GetPlace()));
      auto *d_ln_2_bias_data =
          (d_ln_2_bias == nullptr
               ? nullptr
               : d_ln_2_bias->mutable_data<U>(ctx.GetPlace()));
      auto *d_bias_dropout_residual_out_data =
          d_bias_dropout_residual_out->mutable_data<T>(ctx.GetPlace());

      fused_dropout_layernorm_helper.LayernormResidualDropoutBiasGrad(
          ctx.cuda_device_context(),
          d_y_data,
          bias_dropout_residual_out_data,
          dropout_mask_out_data,
          ln_2_scale_data,
          ln_2_mean_data,
          ln_2_var_data,
          d_bias_dropout_residual_out_data,
          d_ln_2_scale_data,
          d_ln_2_bias_data,
          d_out_linear_out_data,
          d_out_linear_bias_data,
          d_residual_data);
    }

    out_linear_compute.ComputeBackward(fmha_out,
                                       out_linear_weight,
                                       d_out_linear_out,
                                       d_fmha_out,
                                       d_out_linear_weight,
                                       nullptr);

    if (qkv_bias != nullptr) {
      fmha_ref_compute.ComputeBackward(*transpose_out_2,
                                       src_mask,
                                       *softmax_out,
                                       *attn_dropout_mask_out,
                                       *attn_dropout_out,
                                       *qk_out,
                                       *src_mask_out,
                                       *d_fmha_out,
                                       d_qktv_out,
                                       d_attn_dropout_out,
                                       d_softmax_out,
                                       d_src_mask_out,
                                       d_qk_out,
                                       d_transpose_out_2,
                                       nullptr,
                                       d_qkv_bias_out);
    } else {
      fmha_ref_compute.ComputeBackward(*transpose_out_2,
                                       src_mask,
                                       *softmax_out,
                                       *attn_dropout_mask_out,
                                       *attn_dropout_out,
                                       *qk_out,
                                       *src_mask_out,
                                       *d_fmha_out,
                                       d_qktv_out,
                                       d_attn_dropout_out,
                                       d_softmax_out,
                                       d_src_mask_out,
                                       d_qk_out,
                                       d_transpose_out_2,
                                       nullptr,
                                       d_qkv_out);
    }

    if (pre_layer_norm) {
      auto *ln_mean = ctx.Input<Tensor>("LnMean");
      auto *ln_var = ctx.Input<Tensor>("LnVariance");
      auto *ln_out = ctx.Input<Tensor>("LnOut");
      auto *ln_mean_data = ln_mean->data<U>();
      auto *ln_var_data = ln_var->data<U>();
      auto *ln_out_data = ln_out->data<T>();

      auto *d_ln_out = ctx.Output<Tensor>(framework::GradVarName("LnOut"));
      auto *d_ln_scale = ctx.Output<Tensor>(framework::GradVarName("LnScale"));
      auto *d_ln_bias = ctx.Output<Tensor>(framework::GradVarName("LnBias"));
      auto *d_ln_out_data = d_ln_out->mutable_data<T>(ctx.GetPlace());
      auto *d_ln_scale_data =
          (d_ln_scale == nullptr ? nullptr
                                 : d_ln_scale->mutable_data<U>(ctx.GetPlace()));
      auto *d_ln_bias_data =
          (d_ln_bias == nullptr ? nullptr
                                : d_ln_bias->mutable_data<U>(ctx.GetPlace()));
      if (qkv_bias != nullptr) {
        qkv_compute.ComputeBackward(ln_out,
                                    qkv_weight,
                                    d_qkv_bias_out,
                                    d_ln_out,
                                    d_qkv_weight,
                                    d_qkv_bias);
      } else {
        qkv_compute.ComputeBackward(
            ln_out, qkv_weight, d_qkv_out, d_ln_out, d_qkv_weight, d_qkv_bias);
      }
      // tensor model parallel
      AllReduce<T>(*d_ln_out, ring_id, ctx.cuda_device_context());
      layer_norm_compute.ComputeBackward(x_data,
                                         d_ln_out_data,
                                         ln_scale_data,
                                         ln_mean_data,
                                         ln_var_data,
                                         d_x_data,
                                         d_ln_scale_data,
                                         d_ln_bias_data);
    } else {
      if (qkv_bias != nullptr) {
        qkv_compute.ComputeBackward(
            input_x, qkv_weight, d_qkv_bias_out, d_x, d_qkv_weight, d_qkv_bias);
      } else {
        qkv_compute.ComputeBackward(
            input_x, qkv_weight, d_qkv_out, d_x, d_qkv_weight, d_qkv_bias);
      }
      // tensor model parallel
      AllReduce<T>(*d_x, ring_id, ctx.cuda_device_context());
    }

    if (add_residual) {
      // gradient accumulation
      std::vector<const Tensor *> ins = {&d_residual, d_x};
      std::vector<Tensor *> outs = {d_x};
      phi::funcs::ElementwiseKernel<T>(
          ctx.cuda_device_context(), ins, &outs, phi::funcs::AddFunctor<T>());
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(fused_attention,
                        ops::FusedAttentionOpKernel<float>,
                        ops::FusedAttentionOpKernel<double>,
                        ops::FusedAttentionOpKernel<plat::float16>);
REGISTER_OP_CUDA_KERNEL(fused_attention_grad,
                        ops::FusedAttentionGradKernel<float>,
                        ops::FusedAttentionGradKernel<double>,
                        ops::FusedAttentionGradKernel<plat::float16>);