Merge pull request #3592 from reyoung/feature/identity_op

Identity operator and its gradient

paddle/framework/CMakeLists.txt

@@ -56,5 +56,6 @@ cc_library(paddle_pybind SHARED
     recurrent_op
     uniform_random_op
     gaussian_random_op
-    fill_zeros_like_op)
+    fill_zeros_like_op
+    scale_op)
 endif(WITH_PYTHON)

paddle/framework/pybind.cc

@@ -42,6 +42,8 @@ USE_OP(fill_zeros_like);
 USE_OP_ITSELF(recurrent_op);
 USE_OP(gaussian_random);
 USE_OP(uniform_random);
+USE_OP(scale);
+USE_OP_ITSELF(identity);
 USE_CPU_ONLY_OP(gather);
 
 namespace paddle {

paddle/framework/tensor.h

@@ -105,7 +105,10 @@ class Tensor {
   template <typename T>
   inline Tensor Slice(const int& begin_idx, const int& end_idx) const;
 
-  platform::Place place() const { return holder_->place(); }
+  platform::Place place() const {
+    PADDLE_ENFORCE_NOT_NULL(holder_, "Tensor get place() must contains holder");
+    return holder_->place();
+  }
 
  private:
   template <typename T>

paddle/operators/CMakeLists.txt

@@ -69,3 +69,4 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
     DEPS framework_proto tensor op_registry operator net_op)
 op_library(uniform_random_op
         SRCS uniform_random_op.cc uniform_random_op.cu)
+op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)

paddle/operators/net_op.cc

@@ -68,10 +68,15 @@ std::string NetOp::DebugString() const {
 bool NetOp::IsNetOp() const { return true; }
 
 std::vector<std::string> NetOp::OutputVars(bool has_intermediate) const {
+  std::vector<std::string> all;
+  for (auto& pair : this->outputs_) {
+    for (auto& var_name : pair.second) {
+      all.push_back(var_name);
+    }
+  }
   if (has_intermediate) {
-    return this->outputs_.at(kAll);
+    return all;
   }
-  auto& all = this->outputs_.at(kAll);
   std::vector<std::string> ret_val;
   for (auto& each : all) {
     if (!Contains(intermediate_outputs_, each)) {

paddle/operators/scale_op.cc

@@ -0,0 +1,105 @@
+/* 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/scale_op.h"
+#include "paddle/operators/net_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ScaleOp : public framework::OperatorWithKernel {
+ public:
+  ScaleOp(const std::string &type, const framework::VariableNameMap &inputs,
+          const framework::VariableNameMap &outputs,
+          const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    auto *in = ctx.Input<framework::Tensor>("X");
+    auto *out = ctx.Output<framework::Tensor>("Out");
+    out->Resize(in->dims());
+  }
+};
+
+template <typename AttrType>
+class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input tensor of scale operator.").NotInGradient();
+    AddOutput("Out", "The output tensor of scale operator.").NotInGradient();
+    AddComment(R"DOC(Scale operator
+
+The equation is: Out = scale*X
+)DOC");
+    AddAttr<AttrType>("scale", "scale of scale operator.").SetDefault(1.0);
+  }
+};
+
+// Identity Op's gradient is identity op, too.
+// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
+template <typename AttrType>
+class ScaleGradOp : public NetOp {
+ public:
+  ScaleGradOp(const std::string &type, const framework::VariableNameMap &inputs,
+              const framework::VariableNameMap &outputs,
+              const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    AppendOp(framework::OpRegistry::CreateOp(
+        "scale", {{"X", {Input(framework::GradVarName("Out"))}}},
+        {{"Out", {Output(framework::GradVarName("X"))}}},
+        {{"scale", GetAttr<AttrType>("scale")}}));
+    CompleteAddOp(false);
+  }
+};
+
+// identity is a alias of scale op. This is also a example for creating a alias
+// operator.
+template <typename AttrType>
+class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  IdentityOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "input tensor of identity op");
+    AddOutput("Out", "output tensor of identity op");
+    AddComment("identity operator. Just a alias of scale op which scale = 1.0");
+  }
+};
+
+template <typename AttrType>
+class IdentityOp : public NetOp {
+ public:
+  IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
+             const framework::VariableNameMap &outputs,
+             const framework::AttributeMap &attrs)
+      : NetOp(type, inputs, outputs, attrs) {
+    AppendOp(framework::OpRegistry::CreateOp(
+        "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
+        {{"scale", static_cast<AttrType>(1)}}));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
+            ops::ScaleGradOp<float>);
+REGISTER_OP_CPU_KERNEL(scale,
+                       ops::ScaleKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
+                             ops::IdentityOpMaker<float>);

paddle/operators/scale_op.cu

@@ -0,0 +1,18 @@
+/* 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/scale_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+    scale, paddle::operators::ScaleKernel<paddle::platform::GPUPlace, float>);

paddle/operators/scale_op.h

@@ -0,0 +1,40 @@
+/* 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, typename AttrType = T>
+class ScaleKernel : public framework::OpKernel {
+ public:
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* tensor = context.Output<framework::Tensor>("Out");
+    auto* in = context.Input<framework::Tensor>("X");
+    tensor->mutable_data<T>(in->place());
+
+    auto scale = static_cast<T>(context.op_.GetAttr<AttrType>("scale"));
+
+    auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
+    auto eigen_in = framework::EigenVector<T>::Flatten(*in);
+    auto& dev = context.GetEigenDevice<Place>();
+    eigen_out.device(dev) = scale * eigen_in;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -28,3 +28,4 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
 py_test(test_recurrent_op SRCS test_recurrent_op.py)
 py_test(test_sgd_op SRCS test_sgd_op.py)
 py_test(test_gradient_checker SRCS test_gradient_checker.py)
+py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)

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

@@ -160,8 +160,13 @@ class GradientChecker(unittest.TestCase):
             grad_tensor.set(data, place)
 
         # run backward op
-        for name in backward_op.outputs():
+        backward_outs = backward_op.outputs()
+        backward_names = [
+            item for key in backward_outs for item in backward_outs[key]
+        ]
+        for name in backward_names:
             scope.new_var(name)
+
         backward_op.infer_shape(scope)
         backward_op.run(scope, ctx)
 

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

@@ -0,0 +1,43 @@
+import unittest
+from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
+import numpy as np
+from paddle.v2.framework.op import Operator
+
+
+class IdentityTest(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "identity"
+        self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+        self.outputs = {'Out': self.inputs['X']}
+
+
+class IdentityGradOpTest(GradientChecker):
+    def test_normal(self):
+        op = create_op("identity")
+        inputs = {"X": np.random.random((10, 10)).astype("float32")}
+        self.check_grad(op, inputs, set("X"), "Out")
+
+
+class ScaleTest(unittest.TestCase):
+    __metaclass__ = OpTestMeta
+
+    def setUp(self):
+        self.type = "scale"
+        self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+        self.attrs = {'scale': -2.3}
+        self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']}
+
+
+class ScaleGradTest(GradientChecker):
+    def test_normal(self):
+        op = Operator("scale", X="X", Out="Out", scale=3.2)
+        self.check_grad(op,
+                        {"X": np.random.random((10, 10)).astype("float32")},
+                        set("X"), "Out")
+
+
+if __name__ == '__main__':
+    unittest.main()