Merge branch 'develop' of github.com:PaddlePaddle/Paddle into feature/recurrent_op_backward_fix

paddle/operators/clip_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/clip_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::LoDTensor;
+
+class ClipOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input(X) of ClipOp should not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) of ClipOp should not be null.");
+    auto x_dims = ctx.Input<LoDTensor>("X")->dims();
+    auto max = Attr<float>("max");
+    auto min = Attr<float>("min");
+    PADDLE_ENFORCE_LT(min, max, "max should be greater than min.");
+    ctx.Output<LoDTensor>("Out")->Resize(x_dims);
+  }
+};
+
+template <typename AttrType>
+class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ClipOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(Tensor)The input of clip op."
+             "The input should be a k-D tensor(k > 0 and k < 7)");
+    AddOutput("Out", "(Tensor)The output of clip op with shape as input(X)");
+    AddAttr<AttrType>(
+        "min", "(float)Minimum value, under which element is replaced by min.");
+    AddAttr<AttrType>(
+        "max", "(float)Maximum value, above which element is replaced by max");
+    AddComment(R"DOC(
+Clip operator limits the given input within an interval. The interval is
+specified with arguments 'min' and 'max'.
+)DOC");
+  }
+};
+
+class ClipOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<LoDTensor>("X")->dims();
+    auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    if (x_grad != nullptr) {
+      x_grad->Resize(x_dims);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(clip, ops::ClipOp, ops::ClipOpMaker<float>, clip_grad,
+            ops::ClipOpGrad);
+REGISTER_OP_CPU_KERNEL(clip,
+                       ops::ClipKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(clip_grad,
+                       ops::ClipGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/clip_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. */
+
+#include "paddle/operators/clip_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(clip,
+                       ops::ClipKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(clip_grad,
+                       ops::ClipGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/clip_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/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using platform::Transform;
+
+template <typename T>
+class ClipFunctor {
+ public:
+  explicit ClipFunctor(const T min, const T max) : min_(min), max_(max) {}
+  HOSTDEVICE T operator()(const T& x) const {
+    if (x < min_)
+      return min_;
+    else if (x > max_)
+      return max_;
+    else
+      return x;
+  }
+
+ private:
+  T min_;
+  T max_;
+};
+
+template <typename T>
+class ClipGradFunctor {
+ public:
+  explicit ClipGradFunctor(const T min, const T max) : min_(min), max_(max) {}
+  HOSTDEVICE T operator()(const T& x, const T& y) const {
+    return (y > min_ && y < max_) ? x : 0;
+  }
+
+ private:
+  T min_;
+  T max_;
+};
+
+template <typename Place, typename T>
+class ClipKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto max = context.Attr<T>("max");
+    auto min = context.Attr<T>("min");
+    auto* x = context.Input<Tensor>("X");
+    auto* out = context.Output<Tensor>("Out");
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+    const T* x_data = x->data<T>();
+    int64_t numel = x->numel();
+    Transform<Place> trans;
+    trans(context.device_context(), x_data, x_data + numel, out_data,
+          ClipFunctor<T>(min, max));
+  }
+};
+
+template <typename Place, typename T>
+class ClipGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto max = context.Attr<T>("max");
+    auto min = context.Attr<T>("min");
+    auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
+    if (d_x != nullptr) {
+      auto* x = context.Input<Tensor>("X");
+      int64_t numel = d_out->numel();
+      auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
+      const T* d_out_data = d_out->data<T>();
+      const T* x_data = x->data<T>();
+      Transform<Place> trans;
+      trans(context.device_context(), d_out_data, d_out_data + numel, x_data,
+            d_x_data, ClipGradFunctor<T>(min, max));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/operators/transpose_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/transpose_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class TransposeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) should not be null");
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
+    size_t x_rank = x_dims.size();
+    size_t axis_size = axis.size();
+
+    PADDLE_ENFORCE_EQ(x_rank, axis_size,
+                      "the input tensor's rank(%d) "
+                      "should be equal to the axis's size(%d)",
+                      x_rank, axis_size);
+
+    std::vector<int> count(axis_size, 0);
+    for (size_t i = 0; i < axis_size; i++) {
+      PADDLE_ENFORCE(
+          axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1,
+          "Each element of Attribute axis should be a unique value "
+          "range from 0 to (dims - 1), "
+          "where the dims is the axis's size");
+    }
+
+    framework::DDim out_dims(x_dims);
+    for (size_t i = 0; i < axis_size; i++) {
+      out_dims[i] = x_dims[axis[i]];
+    }
+    ctx.Output<framework::LoDTensor>("Out")->Resize(out_dims);
+  }
+};
+
+class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  TransposeOpMaker(framework::OpProto *proto,
+                   framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(
+        "X",
+        "(Tensor)The input tensor, tensors with rank at most 6 are supported");
+    AddOutput("Out", "(Tensor)The output tensor");
+    AddAttr<std::vector<int>>(
+        "axis",
+        "(vector<int>)a list of values, and the size of the list should be "
+        "the same with the input tensor rank, the tensor will "
+        "permute the axes according the the values given");
+    AddComment(R"DOC(
+The Tensor will be permuted according to the axis values given.
+The op is very much like the numpy.transpose function in python
+For example:
+ >> input = numpy.arange(6).reshape((2,3))
+ >> input
+ array([[0, 1, 2],
+        [3, 4, 5]])
+ >> axis = [1, 0]
+ >> output = input.transpose(axis)
+ >> output 
+ array([[0, 3],
+        [1, 4],
+		[2, 5]])
+So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
+the output tensor shape will be (N, H, W, C)
+)DOC");
+  }
+};
+
+class TransposeOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto *x_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+
+    if (x_grad) x_grad->Resize(x_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(transpose, ops::TransposeOp, ops::TransposeOpMaker, transpose_grad,
+            ops::TransposeOpGrad);
+REGISTER_OP_CPU_KERNEL(transpose,
+                       ops::TransposeKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    transpose_grad,
+    ops::TransposeGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/transpose_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. */
+
+#include "paddle/operators/transpose_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(transpose,
+                       ops::TransposeKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    transpose_grad,
+    ops::TransposeGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/transpose_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/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T, int Rank>
+void EigenTranspose(const framework::ExecutionContext& context,
+                    const framework::Tensor& in, framework::Tensor& out,
+                    std::vector<int> axis) {
+  Eigen::array<int, Rank> permute;
+  for (int i = 0; i < Rank; i++) {
+    permute[i] = axis[i];
+  }
+  auto in_dim = in.dims();
+  auto out_dim = out.dims();
+
+  auto eigen_in = framework::EigenTensor<T, Rank>::From(in);
+  auto eigen_out = framework::EigenTensor<T, Rank>::From(out);
+  auto& dev = context.GetEigenDevice<Place>();
+  eigen_out.device(dev) = eigen_in.shuffle(permute);
+}
+
+template <typename Place, typename T>
+class TransposeKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<framework::Tensor>("X");
+    auto* out = context.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(context.GetPlace());
+
+    std::vector<int> axis = context.Attr<std::vector<int>>("axis");
+    int ndims = axis.size();
+    switch (ndims) {
+      case 1:
+        EigenTranspose<Place, T, 1>(context, *x, *out, axis);
+        break;
+      case 2:
+        EigenTranspose<Place, T, 2>(context, *x, *out, axis);
+        break;
+      case 3:
+        EigenTranspose<Place, T, 3>(context, *x, *out, axis);
+        break;
+      case 4:
+        EigenTranspose<Place, T, 4>(context, *x, *out, axis);
+        break;
+      case 5:
+        EigenTranspose<Place, T, 5>(context, *x, *out, axis);
+        break;
+      case 6:
+        EigenTranspose<Place, T, 6>(context, *x, *out, axis);
+        break;
+      default:
+        PADDLE_THROW("Tensors with rank at most 6 are supported");
+    }
+  }
+};
+
+template <typename Place, typename T>
+class TransposeGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* out_grad =
+        context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* x_grad =
+        context.Output<framework::Tensor>(framework::GradVarName("X"));
+    if (x_grad) {
+      x_grad->mutable_data<T>(context.GetPlace());
+
+      std::vector<int> axis = context.Attr<std::vector<int>>("axis");
+      std::vector<int> reversed_axis(axis);
+
+      for (size_t i = 0; i < axis.size(); i++) {
+        reversed_axis[axis[i]] = i;
+      }
+
+      int ndims = axis.size();
+
+      switch (ndims) {
+        case 1:
+          EigenTranspose<Place, T, 1>(context, *out_grad, *x_grad,
+                                      reversed_axis);
+          break;
+        case 2:
+          EigenTranspose<Place, T, 2>(context, *out_grad, *x_grad,
+                                      reversed_axis);
+          break;
+        case 3:
+          EigenTranspose<Place, T, 3>(context, *out_grad, *x_grad,
+                                      reversed_axis);
+          break;
+        case 4:
+          EigenTranspose<Place, T, 4>(context, *out_grad, *x_grad,
+                                      reversed_axis);
+          break;
+        case 5:
+          EigenTranspose<Place, T, 5>(context, *out_grad, *x_grad,
+                                      reversed_axis);
+          break;
+        case 6:
+          EigenTranspose<Place, T, 6>(context, *out_grad, *x_grad,
+                                      reversed_axis);
+          break;
+        default:
+          PADDLE_THROW("Tensors with rank at most 6 are supported");
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/test_clip_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestClipOp(OpTest):
+    def setUp(self):
+        self.max_relative_error = 0.006
+        self.initTestCase()
+        input = np.random.random(self.shape).astype("float32")
+        input[np.abs(input - self.min) < self.max_relative_error] = 0.5
+        input[np.abs(input - self.max) < self.max_relative_error] = 0.5
+        self.op_type = "clip"
+        self.inputs = {'X': input, }
+        self.attrs = {}
+        self.attrs['min'] = self.min
+        self.attrs['max'] = self.max
+        self.outputs = {
+            'Out': np.clip(self.inputs['X'], self.attrs['min'],
+                           self.attrs['max'])
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=self.max_relative_error)
+
+    def initTestCase(self):
+        self.shape = (4, 4)
+        self.max = 0.7
+        self.min = 0.1
+
+
+class TestCase1(TestClipOp):
+    def initTestCase(self):
+        self.shape = (8, 16, 8)
+        self.max = 0.7
+        self.min = 0
+
+
+class TestCase2(TestClipOp):
+    def initTestCase(self):
+        self.shape = (8, 16)
+        self.max = 1
+        self.min = 0
+
+
+class TestCase3(TestClipOp):
+    def initTestCase(self):
+        self.shape = (4, 8, 16)
+        self.max = 0.7
+        self.min = 0.2
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_transpose_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestTransposeOp(OpTest):
+    def setUp(self):
+        self.initTestCase()
+        self.op_type = "transpose"
+        self.inputs = {'X': np.random.random(self.shape).astype("float32")}
+        self.attrs = {'axis': list(self.axis)}
+        self.outputs = {'Out': self.inputs['X'].transpose(self.axis)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+    def initTestCase(self):
+        self.shape = (3, 4)
+        self.axis = (1, 0)
+
+
+class TestCase0(TestTransposeOp):
+    def initTestCase(self):
+        self.shape = (3, )
+        self.axis = (0, )
+
+
+class TestCase1(TestTransposeOp):
+    def initTestCase(self):
+        self.shape = (3, 4, 5)
+        self.axis = (0, 2, 1)
+
+
+class TestCase2(TestTransposeOp):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5)
+        self.axis = (0, 2, 3, 1)
+
+
+class TestCase3(TestTransposeOp):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6)
+        self.axis = (4, 2, 3, 1, 0)
+
+
+class TestCase4(TestTransposeOp):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6, 1)
+        self.axis = (4, 2, 3, 1, 0, 5)
+
+
+if __name__ == '__main__':
+    unittest.main()