【PaddlePaddle Hackathon 2】16 新增 API RRelu (#41823)

* rrelu逻辑部分

* unregistered op kernel (unresolved)

* commit before merge

* 丰富测试用例

* 修复rrelu-sig的bug

* 修复cpu环境测试

* 修改拼写错误

* 修改code format

* 尝试优化测试用例timeout的问题

* 优化测试用例

* 移除seed, 优化随机函数

* update en doc for rrelu

* fix rrelu en docs, test=document_fix

* add paper link for en docs, test=document_fix

* udpate en doc

* add r,test=document_fix

paddle/fluid/operators/rrelu_op.cc

@@ -0,0 +1,126 @@
+/* Copyright (c) 2022 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 <memory>
+#include <string>
+#include "paddle/fluid/framework/infershape_utils.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/infermeta/unary.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class RReluOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+  }
+};
+
+class RReluOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "The input of RReLU op.");
+    AddOutput("Out", "The output of RReLU op.");
+    AddOutput("Noise", "The random sampled RReLU noise.")
+        .AsIntermediate()
+        .AsExtra();
+    AddAttr<bool>("is_test",
+                  "(bool, default false) Set to true for inference only, false "
+                  "for training. Some layers may run faster when this is true.")
+        .SetDefault(false);
+    float default_lower = 1. / 8.;
+    AddAttr<float>("lower", "Lower bound of the uniform distribution.")
+        .SetDefault(default_lower)
+        .AddCustomChecker([](const float& lower) {
+          PADDLE_ENFORCE_EQ(lower >= 0.0f && lower < 1.0f, true,
+                            platform::errors::InvalidArgument(
+                                "'RRelu_lower' must be between 0.0 and 1.0."));
+        });
+    float defalut_upper = 1. / 3.;
+    AddAttr<float>("upper", "Upper bound of the uniform distribution.")
+        .SetDefault(defalut_upper)
+        .AddCustomChecker([](const float& upper) {
+          PADDLE_ENFORCE_EQ(upper > 0.0f && upper <= 1.0f, true,
+                            platform::errors::InvalidArgument(
+                                "'RRelu_upper' must be between 0.0 and 1.0."));
+        });
+    AddComment(R"DOC(
+RReLU Operator.
+
+Applies the randomized leaky rectified liner unit function, element-wise,
+as described in the paper:
+
+`Empirical Evaluation of Rectified Activations in Convolutional Network`_.
+
+The function is defined as:
+
+.. math::
+    \text{RReLU}(x) =
+    \begin{cases}
+        x & \text{if } x \geq 0 \\
+        ax & \text{ otherwise }
+    \end{cases}
+
+where :math:`a` is randomly sampled from uniform distribution
+:math:`\mathcal{U}(\text{lower}, \text{upper})`.
+
+ See: https://arxiv.org/pdf/1505.00853.pdf
+
+)DOC");
+  }
+};
+
+class RReluGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+};
+
+template <typename T>
+class RReluGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("rrelu_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("Noise", this->Output("Noise"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+DECLARE_INFER_SHAPE_FUNCTOR(rrelu, RReluInferShapeFunctor,
+                            PD_INFER_META(phi::RReluInferMeta));
+
+REGISTER_OPERATOR(rrelu, ops::RReluOp, ops::RReluOpMaker,
+                  ops::RReluGradOpMaker<paddle::framework::OpDesc>,
+                  ops::RReluGradOpMaker<paddle::imperative::OpBase>,
+                  RReluInferShapeFunctor);
+
+DECLARE_INFER_SHAPE_FUNCTOR(rrelu_grad, RReluGradInferShapeFunctor,
+                            PD_INFER_META(phi::RReluGradInferMeta));
+REGISTER_OPERATOR(rrelu_grad, ops::RReluGradOp, RReluGradInferShapeFunctor);

paddle/phi/infermeta/unary.cc

@@ -1977,6 +1977,55 @@ void RollInferMeta(const MetaTensor& x,
   out->set_dtype(x.dtype());
 }
 
+void RReluInferMeta(const MetaTensor& x,
+                    float lower,
+                    float upper,
+                    bool is_test,
+                    MetaTensor* out,
+                    MetaTensor* noise) {
+  auto x_dims = x.dims();
+  PADDLE_ENFORCE_GE(lower,
+                    0,
+                    phi::errors::InvalidArgument(
+                        "The lower value should be greater than or equal to 0. "
+                        "But received lower value = %f.",
+                        lower));
+  PADDLE_ENFORCE_LE(upper,
+                    1,
+                    phi::errors::InvalidArgument(
+                        "The upper value should be less than or equal to 1. "
+                        "But received upper value = %f.",
+                        upper));
+  PADDLE_ENFORCE_GE(
+      upper,
+      lower,
+      phi::errors::InvalidArgument(
+          "The upper value should be greater than or equal to lower value "
+          "But received upper value = %f, lower value = %f.",
+          upper,
+          lower));
+
+  out->set_dims(x_dims);
+  out->set_dtype(x.dtype());
+  out->set_layout(x.layout());
+  out->share_lod(x);
+
+  if (noise != nullptr) {
+    noise->set_dims(x_dims);
+    noise->set_dtype(x.dtype());
+    noise->set_layout(x.layout());
+  }
+}
+
+void RReluGradInferMeta(const MetaTensor& out_grad,
+                        const MetaTensor& noise,
+                        MetaTensor* x_grad) {
+  auto do_dims = out_grad.dims();
+  x_grad->set_dims(do_dims);
+  x_grad->set_dtype(out_grad.dtype());
+  x_grad->share_lod(out_grad);
+}
+
 void SetValueInferMeta(const MetaTensor& x, MetaTensor* out) {
   auto in_dims = x.dims();
   PADDLE_ENFORCE_LT(

paddle/phi/infermeta/unary.h

@@ -281,6 +281,17 @@ void RollInferMeta(const MetaTensor& x,
                    const std::vector<int64_t>& axis,
                    MetaTensor* out);
 
+void RReluInferMeta(const MetaTensor& x,
+                    float lower,
+                    float upper,
+                    bool is_test,
+                    MetaTensor* out,
+                    MetaTensor* noise);
+
+void RReluGradInferMeta(const MetaTensor& out_grad,
+                        const MetaTensor& noise,
+                        MetaTensor* x_grad);
+
 void SetValueInferMeta(const MetaTensor& x, MetaTensor* out);
 
 void ShapeInferMeta(const MetaTensor& input, MetaTensor* out);

paddle/phi/kernels/cpu/rrelu_grad_kernel.cc

@@ -0,0 +1,44 @@
+// Copyright (c) 2022 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 "paddle/phi/kernels/rrelu_grad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void RReluGradKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const DenseTensor& noise,
+                     const DenseTensor& out_grad,
+                     DenseTensor* x_grad) {
+  const T* n_ptr = noise.data<T>();
+  const T* x_ptr = x.data<T>();
+  const T* out_grad_ptr = out_grad.data<T>();
+  int numel = x.numel();
+  if (!x_grad) return;
+
+  int i = 0;
+  T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
+  for (i = 0; i < numel; i++) {
+    x_grad_ptr[i] = x_ptr[i] > 0 ? out_grad_ptr[i] : n_ptr[i] * out_grad_ptr[i];
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    rrelu_grad, CPU, ALL_LAYOUT, phi::RReluGradKernel, float, double) {}

paddle/phi/kernels/cpu/rrelu_kernel.cc

@@ -0,0 +1,77 @@
+// Copyright (c) 2022 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 "paddle/phi/kernels/rrelu_kernel.h"
+
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void RReluKernel(const Context& dev_ctx,
+                 const DenseTensor& x,
+                 const float lower,
+                 const float upper,
+                 bool is_test,
+                 DenseTensor* out,
+                 DenseTensor* noise) {
+  const T* x_ptr = x.data<T>();
+  T* o_ptr = dev_ctx.template Alloc<T>(out);
+  T* n_ptr = dev_ctx.template Alloc<T>(noise);
+  T zero = static_cast<T>(0);
+  int numel = x.numel();
+  int i = 0;
+
+  if (is_test) {
+    T mid_val = static_cast<T>((lower + upper) / 2.0);
+    for (i = 0; i < numel; i++) {
+      if (x_ptr[i] < zero) {
+        o_ptr[i] = mid_val * x_ptr[i];
+        n_ptr[i] = mid_val;
+      } else {
+        o_ptr[i] = x_ptr[i];
+        n_ptr[i] = 1.0;
+      }
+    }
+
+    return;
+  }
+
+  auto engine = paddle::framework::GetCPURandomEngine(0);
+
+  std::uniform_real_distribution<float> dist(lower, upper);
+
+  for (i = 0; i < numel; i++) {
+    if (x_ptr[i] < zero) {
+      T scale = static_cast<T>(dist(*engine));
+      o_ptr[i] = scale * x_ptr[i];
+      n_ptr[i] = scale;
+    } else {
+      o_ptr[i] = x_ptr[i];
+      n_ptr[i] = 1.0;
+    }
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(rrelu,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::RReluKernel,
+                   float,
+                   phi::dtype::float16,
+                   double) {}