Add fused_dropout wrapper to ease use. (#36185) (#36640)

In fused_attention op and fused_ffn op, the fused bias_add+dropout+residual+layernorm kernel or bias_add+dropout+residual kernel is used. To ease the use of this kernel, we provide a wrapper in this PR.
1.To reuse the increment computing code, we exact the corresponding code to "GetSeedDataAndIncrement" routine in dropout_impl_util.h.
2.The fused_dropout_helper.h provides the fused dropout kernel wrapper.

Note: the test of this warper will be provided in the following fused_attention_op and fused_ffn PRs.

paddle/fluid/operators/dropout_impl.cu.h

@@ -30,6 +30,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/generator.h"
 #include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/dropout_impl_util.h"
 #include "paddle/fluid/operators/dropout_op.h"
 #include "paddle/fluid/platform/aligned_vector.h"
 #include "paddle/fluid/platform/gpu_launch_config.h"
@@ -196,28 +197,8 @@ void DropoutFwGPUKernelDriver(const platform::CUDADeviceContext& dev_ctx,
                                     config.thread_per_block.x * vec_size) +
                    1) *
                   vec_size;
-    int device_id =
-        BOOST_GET_CONST(platform::CUDAPlace, dev_ctx.GetPlace()).GetDeviceId();
-    auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);
-
-    if ((seed) && platform::is_gpu_place(seed->place())) {
-      framework::Tensor seed_cpu_tensor;
-      TensorCopySync(*seed, platform::CPUPlace(), &seed_cpu_tensor);
-      seed_data = static_cast<uint64_t>(seed_cpu_tensor.data<int>()[0]);
-      increment = offset;
-    } else if (gen_cuda->GetIsInitPy() && (!is_fix_seed)) {
-      auto seed_offset = gen_cuda->IncrementOffset(offset);
-      seed_data = seed_offset.first;
-      increment = seed_offset.second;
-    } else {
-      if (seed) {
-        seed_data = *(seed->data<int>());
-      } else {
-        std::random_device rnd;
-        seed_data = is_fix_seed ? seed_val : rnd();
-      }
-      increment = offset;
-    }
+    GetSeedDataAndIncrement(dev_ctx, seed, is_fix_seed, seed_val, offset,
+                            &seed_data, &increment);
 
 #ifdef __HIPCC__
     if (vec_size == 4 && size % 4 == 0) {

paddle/fluid/operators/dropout_impl_util.h

+/* 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. */
+
+#pragma once
+
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/framework/tensor_util.h"
+
+namespace paddle {
+namespace operators {
+
+inline void GetSeedDataAndIncrement(const platform::CUDADeviceContext& dev_ctx,
+                                    const framework::Tensor* seed,
+                                    const bool is_fix_seed, const int seed_val,
+                                    const int offset, uint64_t* seed_data,
+                                    uint64_t* increment) {
+  int device_id =
+      BOOST_GET_CONST(platform::CUDAPlace, dev_ctx.GetPlace()).GetDeviceId();
+  auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);
+
+  if ((seed) && platform::is_gpu_place(seed->place())) {
+    framework::Tensor seed_cpu_tensor;
+    TensorCopySync(*seed, platform::CPUPlace(), &seed_cpu_tensor);
+    *seed_data = static_cast<uint64_t>(seed_cpu_tensor.data<int>()[0]);
+    *increment = offset;
+  } else if (gen_cuda->GetIsInitPy() && (!is_fix_seed)) {
+    auto seed_offset = gen_cuda->IncrementOffset(offset);
+    *seed_data = seed_offset.first;
+    *increment = seed_offset.second;
+  } else {
+    if (seed) {
+      *seed_data = *(seed->data<int>());
+    } else {
+      std::random_device rnd;
+      *seed_data = is_fix_seed ? seed_val : rnd();
+    }
+    *increment = offset;
+  }
+}
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/fused/fused_dropout_helper.h

+/* 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. */
+
+#pragma once
+
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/operators/dropout_impl_util.h"
+#include "paddle/fluid/operators/fused/fused_dropout_act_bias.h"
+#include "paddle/fluid/operators/fused/fused_layernorm_residual_dropout_bias.h"
+#include "paddle/fluid/operators/fused/fused_residual_dropout_bias.h"
+#include "paddle/fluid/operators/math/functors.h"
+
+namespace paddle {
+namespace operators {
+
+/**
+ * Support two Dropouts in the use senarieo.
+ * This warpper can be used in FFN op.
+ * The DropoutParam will be used in the fused_dropout_act_bias,
+ * fused_residual_dropout_bias(pre_layer_norm=ture) or
+ * fused_layernorm_residual_dropout_bias(pre_layer_norm=false).
+*/
+struct DropoutParam {
+  uint64_t seed;
+  float dropout_prob;
+  bool is_upscale_in_train;
+  bool is_test;
+  bool fix_seed;
+  int increment;
+  const framework::Tensor* tensor_seed;
+  int seed_val;
+
+  DropoutParam() {
+    fix_seed = false;
+    seed = 0;
+    is_test = false;
+    is_upscale_in_train = false;
+    dropout_prob = 0.5;
+    tensor_seed = nullptr;
+    seed_val = 0;
+  }
+
+  /**
+   * dropout_index: can be 0, 1, 2. 0 means there is only one dropout,
+   * 1 and 2 represent two dropout, the parameter name of dropout
+   * will be "dropout" + dropout_index + param name, such as dropout1_seed,
+   * dropout1_is_test.
+   */
+  DropoutParam(const framework::ExecutionContext& context,
+               const int dropout_index) {
+    std::string pre_fix = "dropout";
+    std::string str_index = std::to_string(dropout_index);
+    if (dropout_index > 0) {
+      pre_fix = pre_fix + str_index + "_";
+    } else {
+      pre_fix = pre_fix + "_";
+    }
+    dropout_prob = context.Attr<float>(pre_fix + "prob");
+    auto& dropout_implementation =
+        context.Attr<std::string>(pre_fix + "implementation");
+    is_upscale_in_train = (dropout_implementation == "upscale_in_train");
+    is_test = context.Attr<bool>(pre_fix + "is_test");
+    fix_seed = context.Attr<bool>(pre_fix + "fix_seed");
+
+    std::string str_seed = "Dropout";
+    if (dropout_index > 0) {
+      str_seed = str_seed + str_index + "Seed";
+    } else {
+      str_seed = str_seed + "Seed";
+    }
+    tensor_seed =
+        context.HasInput(str_seed) ? context.Input<Tensor>(str_seed) : nullptr;
+    seed_val = context.Attr<int>(pre_fix + "seed");
+  }
+
+  int UpdateSeedAndIncrement(const platform::CUDADeviceContext& ctx,
+                             const int offset) {
+    uint64_t tmp_increment;
+    GetSeedDataAndIncrement(ctx, tensor_seed, fix_seed, seed_val, offset, &seed,
+                            &tmp_increment);
+    increment = static_cast<int>(tmp_increment);
+    return increment;
+  }
+};
+
+template <typename T, typename MaskType>
+class FusedDropoutHelper {
+ private:
+  int GetIncrement(const platform::CUDADeviceContext& ctx) {
+    const int VecSize = MAX_CACHE_BYTES / sizeof(T);
+    const int real_vec_size = cols_ % VecSize == 0 ? VecSize : 1;
+    auto config =
+        Get1DBlocksAnd2DGrids(ctx, static_cast<uint64_t>(rows_),
+                              static_cast<uint64_t>(cols_), real_vec_size);
+    int increment = ((cols_ - 1) / (config.thread_per_block.x *
+                                    config.block_per_grid.x * real_vec_size) +
+                     1) *
+                    real_vec_size;
+    increment = dropout_param_.UpdateSeedAndIncrement(ctx, increment);
+    return increment;
+  }
+
+ public:
+  FusedDropoutHelper() {}
+  FusedDropoutHelper(const platform::CUDADeviceContext& ctx, const int rows,
+                     const int cols, const DropoutParam& dropout_param) {
+    rows_ = rows;
+    cols_ = cols;
+    dropout_param_ = dropout_param;
+  }
+
+  // out = residual + dropout( src + bias )
+  void ResidualDropoutBias(const platform::CUDADeviceContext& ctx, const T* src,
+                           const T* residual, const T* bias, T* out,
+                           MaskType* mask) {
+    auto increment = GetIncrement(ctx);
+    LaunchResidualDropoutBias<T, MaskType>(
+        rows_, cols_, increment, dropout_param_.seed,
+        dropout_param_.dropout_prob, dropout_param_.is_test,
+        dropout_param_.is_upscale_in_train, src, residual, bias, mask, out,
+        ctx);
+  }
+
+  void ResidualDropoutBiasGrad(const platform::CUDADeviceContext& ctx,
+                               const T* d_out, const MaskType* mask, T* d_src,
+                               T* d_residual, T* d_bias) {
+    LaunchResidualDropoutBiasGrad<T, uint8_t>(
+        d_out, mask, dropout_param_.dropout_prob,
+        dropout_param_.is_upscale_in_train, rows_, cols_, d_src, d_bias, ctx);
+    auto cuda_place = BOOST_GET_CONST(platform::CUDAPlace, ctx.GetPlace());
+    memory::Copy(cuda_place, d_residual, cuda_place, d_out,
+                 rows_ * cols_ * sizeof(T), ctx.stream());
+  }
+
+  // out = dropout(activation(src + bias))
+  void DropoutActBias(const platform::CUDADeviceContext& ctx, const T* src,
+                      const T* bias, const std::string& act_method, T* out,
+                      MaskType* mask) {
+    auto increment = GetIncrement(ctx);
+    if (act_method == "gelu") {
+      GeluFunctor<T> gelu;
+      LaunchDropoutActBias<T, MaskType, GeluFunctor<T>>(
+          gelu, dropout_param_.seed, rows_, cols_, dropout_param_.increment,
+          dropout_param_.dropout_prob, dropout_param_.is_upscale_in_train,
+          dropout_param_.is_test, src, bias, out, mask, ctx);
+    } else if (act_method == "relu") {
+      math::ReluFunctor<T> relu;
+      LaunchDropoutActBias<T, MaskType, math::ReluFunctor<T>>(
+          relu, dropout_param_.seed, rows_, cols_, increment,
+          dropout_param_.dropout_prob, dropout_param_.is_upscale_in_train,
+          dropout_param_.is_test, src, bias, out, mask, ctx);
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Currently only supports gelu or relu activation functions!"));
+    }
+  }
+
+  void DropoutActBiasGrad(const platform::CUDADeviceContext& ctx, const T* dout,
+                          const T* src, const T* bias, const MaskType* mask,
+                          T* d_src, T* d_bias, const std::string& act_method) {
+    if (act_method == "gelu") {
+      GeluGradFunctor<T> gelu_grad;
+      LaunchDropoutActBiasGrad<T, MaskType, GeluGradFunctor<T>>(
+          gelu_grad, dout, mask, src, bias, dropout_param_.dropout_prob,
+          dropout_param_.is_upscale_in_train, rows_, cols_, d_src, d_bias, ctx);
+    } else if (act_method == "relu") {
+      math::ReluGradFunctor<T> relu_grad;
+      LaunchDropoutActBiasGrad<T, MaskType, math::ReluGradFunctor<T>>(
+          relu_grad, dout, mask, src, bias, dropout_param_.dropout_prob,
+          dropout_param_.is_upscale_in_train, rows_, cols_, d_src, d_bias, ctx);
+    } else {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Currently only supports gelu or relu activation functions!"));
+    }
+  }
+
+ protected:
+  int rows_;
+  int cols_;
+  DropoutParam dropout_param_;
+};
+
+template <typename T, typename MaskType>
+class FusedDropoutLayerNormHelper : public FusedDropoutHelper<T, MaskType> {
+ public:
+  FusedDropoutLayerNormHelper() {}
+  FusedDropoutLayerNormHelper(const int rows, const int cols,
+                              const float epsilon) {
+    using U = LayerNormParamType<T>;
+    this->rows_ = rows;
+    this->cols_ = cols;
+    epsilon_ = epsilon;
+  }
+
+  FusedDropoutLayerNormHelper(const platform::CUDADeviceContext& ctx,
+                              const int rows, const int cols,
+                              const DropoutParam& dropout_param,
+                              const float epsilon)
+      : FusedDropoutHelper<T, MaskType>(ctx, rows, cols, dropout_param) {
+    using U = LayerNormParamType<T>;
+    epsilon_ = epsilon;
+  }
+
+  // call layer_norm
+  void LayerNorm(const platform::CUDADeviceContext& ctx, const T* src,
+                 const LayerNormParamType<T>* gamma,
+                 const LayerNormParamType<T>* beta, T* out,
+                 LayerNormParamType<T>* mean, LayerNormParamType<T>* variance) {
+    using U = LayerNormParamType<T>;
+    switch (GetDesiredBlockDim(this->cols_)) {
+      FIXED_BLOCK_DIM_CASE(
+          LayerNormForward<
+              T, U, kBlockDim><<<this->rows_, kBlockDim, 0, ctx.stream()>>>(
+              src, gamma, beta, out, mean, variance, epsilon_, this->cols_));
+    }
+  }
+
+  void LayerNormGrad(const platform::CUDADeviceContext& ctx, const T* dout,
+                     const T* src, const LayerNormParamType<T>* gamma,
+                     const LayerNormParamType<T>* mean,
+                     const LayerNormParamType<T>* variance, T* d_src,
+                     LayerNormParamType<T>* d_scale,
+                     LayerNormParamType<T>* d_bias) {
+    using U = LayerNormParamType<T>;
+    LayerNormBackward<T, U>(src, dout, gamma, mean, variance, d_src, d_scale,
+                            d_bias, epsilon_, this->rows_, this->cols_, ctx);
+  }
+
+  // out = layernorm(residual + dropout(src + bias))
+  void LayernormResidualDropoutBias(
+      const platform::CUDADeviceContext& ctx, const T* src, const T* residual,
+      const T* bias, const LayerNormParamType<T>* gamma,
+      const LayerNormParamType<T>* beta, T* dropout_out, MaskType* mask, T* out,
+      LayerNormParamType<T>* mean, LayerNormParamType<T>* variance) {
+    using U = LayerNormParamType<T>;
+    int vec_size = MAX_CACHE_BYTES / sizeof(T);
+    if (this->cols_ % vec_size != 0) {
+      vec_size = 1;
+    }
+    int threads = GetDesiredBlockDim(this->cols_ / vec_size);
+    int increment = ((this->cols_ - 1) / (threads * vec_size) + 1) * vec_size;
+    increment = this->dropout_param_.UpdateSeedAndIncrement(ctx, increment);
+    LaunchLayernormResidualDropoutBias<T, MaskType>(
+        this->rows_, this->cols_, increment, this->dropout_param_.seed,
+        this->dropout_param_.dropout_prob, epsilon_,
+        this->dropout_param_.is_upscale_in_train, this->dropout_param_.is_test,
+        src, residual, bias, gamma, beta, mask, dropout_out, out, mean,
+        variance, ctx);
+  }
+
+  void LayernormResidualDropoutBiasGrad(
+      const platform::CUDADeviceContext& ctx, const T* d_out,
+      const T* layernorm_src, const MaskType* mask,
+      const LayerNormParamType<T>* gamma, const LayerNormParamType<T>* mean,
+      const LayerNormParamType<T>* variance, T* d_layernorm_src,
+      LayerNormParamType<T>* d_scale, LayerNormParamType<T>* d_layernorm_bias,
+      T* d_dropout_src, T* d_bias, T* d_residual) {
+    using U = LayerNormParamType<T>;
+    LayerNormBackward<T, U>(layernorm_src, d_out, gamma, mean, variance,
+                            d_layernorm_src, d_scale, d_layernorm_bias,
+                            epsilon_, this->rows_, this->cols_, ctx);
+    this->ResidualDropoutBiasGrad(ctx, d_layernorm_src, mask, d_dropout_src,
+                                  d_residual, d_bias);
+  }
+
+ protected:
+  float epsilon_;
+};
+
+}  // namespace operators
+}  // namespace paddle