Merge pull request #7538 from JiayiFeng/dev_elementwise_max_min

elementwise max min

paddle/gserver/tests/sequence_recurrent_group.py

 #  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 #
-#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
+# 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.
+# 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.
 from paddle.trainer_config_helpers import *
 
 ######################## data source ################################

paddle/operators/elementwise_add_op.h

@@ -28,39 +28,7 @@ template <typename DeviceContext, typename T>
 class ElementwiseAddKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    using Tensor = framework::Tensor;
-
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* z = ctx.Output<Tensor>("Out");
-    z->mutable_data<T>(ctx.GetPlace());
-    TransformFunctor<AddFunctor<T>, T, DeviceContext> functor(
-        x, y, z, ctx.template device_context<DeviceContext>(), AddFunctor<T>());
-
-    auto x_dims = x->dims();
-    auto y_dims = y->dims();
-    PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
-                      "Rank of first input must >= rank of second input.");
-
-    if (x_dims == y_dims) {
-      functor.Run();
-      return;
-    }
-
-    int axis = ctx.Attr<int>("axis");
-    axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
-    PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
-                   "Axis should be in range [0, x_dims)");
-
-    int pre, n, post;
-    get_mid_dims(x_dims, y_dims, axis, pre, n, post);
-    if (post == 1) {
-      functor.RunRowWise(n, pre);
-      return;
-    } else {
-      functor.RunMidWise(n, pre, post);
-      return;
-    }
+    ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx);
   }
 };
 

paddle/operators/elementwise_max_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/operators/elementwise_max_op.h"
+#include "paddle/operators/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseMaxOpMaker : public ElementwiseOpMaker {
+ public:
+  ElementwiseMaxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("Max", "Out = max(X, Y)");
+    AddComment(comment_);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_max, ops::ElementwiseOp, ops::ElementwiseMaxOpMaker,
+            elementwise_max_grad, ops::ElementwiseOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_max,
+    ops::ElementwiseMaxKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ElementwiseMaxKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::ElementwiseMaxKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseMaxKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_max_grad,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/operators/elementwise_max_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_max_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_max,
+    ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, int64_t>);
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_max_grad,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext,
+                                  int64_t>);

paddle/operators/elementwise_max_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/operators/elementwise_op_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct MaxFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a > b ? a : b; }
+};
+
+template <typename DeviceContext, typename T>
+class ElementwiseMaxKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseComputeEx<MaxFunctor<T>, DeviceContext, T>(ctx);
+  }
+};
+
+template <typename T>
+struct ElementwiseMaxGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = (x_e > y_e).template cast<T>() * dz_e;
+    }
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (x_e <= y_e).template cast<T>() * dz_e;
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseMaxBroadCastGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
+                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = (x_e > y_e_bcast).template cast<T>() * dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = ((x_e <= y_e_bcast).template cast<T>() * dz_e)
+                           .reshape(Eigen::DSizes<int, 2>(pre, n))
+                           .sum(Eigen::array<int, 1>{{0}});
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseMaxBroadCast2GradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N, typename Post>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
+                  Post post) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
+                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = (x_e > y_e_bcast).template cast<T>() * dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = ((x_e <= y_e_bcast).template cast<T>() * dz_e)
+                           .reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                           .sum(Eigen::array<int, 2>{{0, 2}});
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ElementwiseMaxGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseGradCompute<DeviceContext, T, ElementwiseMaxGradFunctor<T>,
+                           ElementwiseMaxBroadCastGradFunctor<T>,
+                           ElementwiseMaxBroadCast2GradFunctor<T>>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/elementwise_min_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/operators/elementwise_min_op.h"
+#include "paddle/operators/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwiseMinOpMaker : public ElementwiseOpMaker {
+ public:
+  ElementwiseMinOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("Max", "Out = min(X, Y)");
+    AddComment(comment_);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(elementwise_min, ops::ElementwiseOp, ops::ElementwiseMinOpMaker,
+            elementwise_min_grad, ops::ElementwiseOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_min,
+    ops::ElementwiseMinKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ElementwiseMinKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::ElementwiseMinKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseMinKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_min_grad,
+    ops::ElementwiseMinGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ElementwiseMinGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::ElementwiseMinGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseMinGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/operators/elementwise_min_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_min_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_min,
+    ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMinKernel<paddle::platform::CUDADeviceContext, int64_t>);
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_min_grad,
+    ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext,
+                                  int64_t>);

paddle/operators/elementwise_min_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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/operators/elementwise_op_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct MinFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a < b ? a : b; }
+};
+
+template <typename DeviceContext, typename T>
+class ElementwiseMinKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseComputeEx<MinFunctor<T>, DeviceContext, T>(ctx);
+  }
+};
+
+template <typename T>
+struct ElementwiseMinGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = (x_e < y_e).template cast<T>() * dz_e;
+    }
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = (x_e >= y_e).template cast<T>() * dz_e;
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseMinBroadCastGradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))
+                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = (x_e < y_e_bcast).template cast<T>() * dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = ((x_e >= y_e_bcast).template cast<T>() * dz_e)
+                           .reshape(Eigen::DSizes<int, 2>(pre, n))
+                           .sum(Eigen::array<int, 1>{{0}});
+    }
+  }
+};
+
+template <typename T>
+struct ElementwiseMinBroadCast2GradFunctor {
+  template <typename Device, typename X, typename Y, typename Z, typename dX,
+            typename dY, typename dZ, typename Pre, typename N, typename Post>
+  void operator()(Device d, X x, Y y, Z z, dX dx, dY dy, dZ dz, Pre pre, N n,
+                  Post post) {
+    auto x_e = framework::EigenVector<T>::Flatten(*x);
+    auto y_e = framework::EigenVector<T>::Flatten(*y);
+    auto dz_e = framework::EigenVector<T>::Flatten(*dz);
+
+    auto y_e_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))
+                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))
+                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));
+    if (dx) {
+      auto dx_e = framework::EigenVector<T>::Flatten(*dx);
+      dx_e.device(d) = (x_e < y_e_bcast).template cast<T>() * dz_e;
+    }
+
+    if (dy) {
+      auto dy_e = framework::EigenVector<T>::Flatten(*dy);
+      dy_e.device(d) = ((x_e >= y_e_bcast).template cast<T>() * dz_e)
+                           .reshape(Eigen::DSizes<int, 3>(pre, n, post))
+                           .sum(Eigen::array<int, 2>{{0, 2}});
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class ElementwiseMinGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseGradCompute<DeviceContext, T, ElementwiseMinGradFunctor<T>,
+                           ElementwiseMinBroadCastGradFunctor<T>,
+                           ElementwiseMinBroadCast2GradFunctor<T>>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/elementwise_op_function.h

@@ -356,5 +356,43 @@ void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
     return;
   }
 }
+
+template <typename Functor, typename DeviceContext, typename T>
+void ElementwiseComputeEx(const framework::ExecutionContext& ctx) {
+  using Tensor = framework::Tensor;
+
+  auto* x = ctx.Input<Tensor>("X");
+  auto* y = ctx.Input<Tensor>("Y");
+  auto* z = ctx.Output<Tensor>("Out");
+  z->mutable_data<T>(ctx.GetPlace());
+  TransformFunctor<Functor, T, DeviceContext> functor(
+      x, y, z, ctx.template device_context<DeviceContext>(), Functor());
+
+  auto x_dims = x->dims();
+  auto y_dims = y->dims();
+  PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
+                    "Rank of first input must >= rank of second input.");
+
+  if (x_dims == y_dims) {
+    functor.Run();
+    return;
+  }
+
+  int axis = ctx.Attr<int>("axis");
+  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
+  PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
+                 "Axis should be in range [0, x_dims)");
+
+  int pre, n, post;
+  get_mid_dims(x_dims, y_dims, axis, pre, n, post);
+  if (post == 1) {
+    functor.RunRowWise(n, pre);
+    return;
+  } else {
+    functor.RunMidWise(n, pre, post);
+    return;
+  }
+}
+
 }  // namespace operators
 }  // namespace paddle

python/paddle/v2/fluid/layers/ops.py

@@ -55,6 +55,8 @@ __all__ = [
     'elementwise_div',
     'elementwise_sub',
     'elementwise_mul',
+    'elementwise_max',
+    'elementwise_min',
     'clip',
     'sequence_softmax',
 ] + __activations__

python/paddle/v2/fluid/tests/test_edit_distance_op.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#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.
 import unittest
 import numpy as np
 from op_test import OpTest

python/paddle/v2/fluid/tests/test_elementwise_max_op.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#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.
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        # If x and y have the same value, the max() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        x = np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        sgn = np.random.choice([-1, 1], [13, 17]).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
+
+
+class TestElementwiseMaxOp_Vector(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.random((32, )).astype("float32")
+        sgn = np.random.choice([-1, 1], (32, )).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, (32, )).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (2, )).astype(np.float32)
+        y = x[:, 0, 0] + sgn * \
+            np.random.uniform(1, 2, (2, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, )).astype(np.float32)
+        y = x[0, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (4, )).astype(np.float32)
+        y = x[0, 0, :] + sgn * \
+            np.random.uniform(1, 2, (4, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_max"
+        x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, 4)).astype(np.float32)
+        y = x[0, :, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, 4)).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1))
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/fluid/tests/test_elementwise_min_op.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#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.
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwiseOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        # If x and y have the same value, the min() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        x = np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        sgn = np.random.choice([-1, 1], [13, 17]).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
+
+
+class TestElementwiseMaxOp_Vector(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.random((32, )).astype("float32")
+        sgn = np.random.choice([-1, 1], (32, )).astype("float32")
+        y = x + sgn * np.random.uniform(0.1, 1, (32, )).astype("float32")
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}
+
+
+class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (2, )).astype(np.float32)
+        y = x[:, 0, 0] + sgn * \
+            np.random.uniform(1, 2, (2, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 0}
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, )).astype(np.float32)
+        y = x[0, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (4, )).astype(np.float32)
+        y = x[0, 0, :] + sgn * \
+            np.random.uniform(1, 2, (4, )).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4))
+        }
+
+
+class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
+    def setUp(self):
+        self.op_type = "elementwise_min"
+        x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float32)
+        sgn = np.random.choice([-1, 1], (3, 4)).astype(np.float32)
+        y = x[0, :, :, 0] + sgn * \
+            np.random.uniform(1, 2, (3, 4)).astype(np.float32)
+        self.inputs = {'X': x, 'Y': y}
+
+        self.attrs = {'axis': 1}
+        self.outputs = {
+            'Out':
+            np.minimum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1))
+        }
+
+
+if __name__ == '__main__':
+    unittest.main()