update to latest

paddle/operators/fc_op.cc

@@ -100,7 +100,7 @@ class FCOp : public NetOp {
 
       add_out = Output("AddOut");
       AppendOp(framework::OpRegistry::CreateOp(
-          "rowwise_add", {{"X", {sum_out}}, {"b", {Input("B")}}},
+          "elementwise_add", {{"X", {sum_out}}, {"Y", {Input("B")}}},
           {{"Out", {add_out}}}, {}));
     } else {
       if (Output("AddOut") != framework::kEmptyVarName) {

paddle/operators/rowwise_add_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/rowwise_add_op.h"
-
-namespace paddle {
-namespace operators {
-
-using framework::Tensor;
-
-class RowwiseAddOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContextBase* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of RowwiseAddOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("b"),
-                   "Input(b) of RowwiseAddOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of RowwiseAddOp should not be null.");
-
-    auto x_dims = ctx->GetInputDim("X");
-    auto b_dims = ctx->GetInputDim("b");
-    PADDLE_ENFORCE_GT(
-        x_dims.size(), b_dims.size(),
-        "The rank of input `X` must be larger than the one of input `b`.");
-
-    int num_col_dims = x_dims.size() - b_dims.size();
-
-    PADDLE_ENFORCE_EQ(
-        framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
-        "The width of two operands must be same");
-    PADDLE_ENFORCE_EQ(ctx->Outputs("Out").size(), 1,
-                      "The output size must be 1");
-    ctx->SetOutputDim("Out", x_dims);
-    ctx->ShareLoD("X", /*->*/ "Out");
-  }
-};
-
-class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  RowwiseAddOpMaker(framework::OpProto* proto,
-                    framework::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The left input of row-wise add op, must be matrix");
-    AddInput("b", "The right input of row-wise add op, must be vector");
-    AddOutput("Out", "The output of row-wise add op");
-    AddComment(R"DOC(Row-wise Add operator
-
-for i in xrange(X.shape[0]):
-  Out = X[i] + b
-)DOC");
-  }
-};
-class RowwiseAddGradOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContextBase* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "X should not be null");
-    PADDLE_ENFORCE(ctx->HasInput("b"), "b should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should not be null");
-    auto x_dims = ctx->GetInputDim("X");
-    auto b_dims = ctx->GetInputDim("b");
-    PADDLE_ENFORCE_GT(
-        x_dims.size(), b_dims.size(),
-        "The rank of input `X` must be larger than the one of input `b`.");
-
-    int64_t num_col_dims = x_dims.size() - b_dims.size();
-    PADDLE_ENFORCE_EQ(
-        framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
-        "The width of two operands must be same");
-    auto x_grad_name = framework::GradVarName("X");
-    auto b_grad_name = framework::GradVarName("b");
-    if (ctx->HasOutput(x_grad_name)) {
-      ctx->SetOutputDim(x_grad_name, x_dims);
-    }
-    if (ctx->HasOutput(b_grad_name)) {
-      ctx->SetOutputDim(b_grad_name, b_dims);
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-namespace ops = paddle::operators;
-REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
-            rowwise_add_grad, ops::RowwiseAddGradOp);
-REGISTER_OP_CPU_KERNEL(
-    rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(
-    rowwise_add_grad,
-    ops::RowwiseAddGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rowwise_add_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/rowwise_add_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(
-    rowwise_add, ops::RowwiseAddKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(
-    rowwise_add_grad,
-    ops::RowwiseAddGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/rowwise_add_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 {
-
-using Tensor = framework::Tensor;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
-template <typename T, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
-
-template <typename Place, typename T>
-class RowwiseAddKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto out = context.Output<Tensor>("Out");
-    out->mutable_data<T>(context.GetPlace());
-    int num_col_dims = context.Input<Tensor>("X")->dims().size() -
-                       context.Input<Tensor>("b")->dims().size();
-    auto input =
-        EigenMatrix<T>::Reshape(*context.Input<Tensor>("X"), num_col_dims);
-    auto bias = EigenVector<T>::Flatten(*context.Input<Tensor>("b"));
-    auto output = EigenMatrix<T>::Reshape(*out, num_col_dims);
-
-    const int bias_size = bias.dimension(0);
-    const int rest_size = input.size() / bias_size;
-    Eigen::DSizes<int, 1> one_d(input.size());
-    Eigen::DSizes<int, 1> bcast(rest_size);
-    output.reshape(one_d).device(context.GetEigenDevice<Place>()) =
-        input.reshape(one_d) + bias.broadcast(bcast).reshape(one_d);
-  }
-};
-
-template <typename Place, typename T>
-class RowwiseAddGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
-    auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
-    auto* db = context.Output<Tensor>(framework::GradVarName("b"));
-    int num_col_dims = context.Input<Tensor>("X")->dims().size() -
-                       context.Input<Tensor>("b")->dims().size();
-
-    auto out_grad = EigenMatrix<T>::Reshape(*dout, num_col_dims);
-    auto place = context.GetEigenDevice<Place>();
-
-    if (dx) {
-      dx->mutable_data<T>(context.GetPlace());
-      EigenMatrix<T>::Reshape(*dx, num_col_dims).device(place) = out_grad;
-    }
-
-    if (db) {
-      db->mutable_data<T>(context.GetPlace());
-      // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
-      // colwise add
-      Eigen::array<int, 1> dims{{0}}; /* dimension to reduce */
-      EigenVector<T>::Flatten(*db).device(place) = out_grad.sum(dims);
-    }
-  }
-};
-}  // namespace operators
-}  // namespace paddle

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

 import unittest
 import numpy as np
+import random
 import itertools
 import paddle.v2.framework.core as core
 from paddle.v2.framework.op import Operator
@@ -192,6 +193,21 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
 
 
 class OpTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        '''Fix random seeds to remove randomness from tests'''
+        cls._np_rand_state = np.random.get_state()
+        cls._py_rand_state = random.getstate()
+
+        np.random.seed(123)
+        random.seed(124)
+
+    @classmethod
+    def tearDownClass(cls):
+        '''Restore random seeds'''
+        np.random.set_state(cls._np_rand_state)
+        random.setstate(cls._py_rand_state)
+
     def check_output_with_place(self, place, atol):
         self.scope = core.Scope()
         op_inputs = self.inputs if hasattr(self, "inputs") else dict()

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

-import unittest
-import numpy as np
-from op_test import OpTest
-
-
-class TestRowwiseAddOp(OpTest):
-    def setUp(self):
-        self.op_type = "rowwise_add"
-        self.inputs = {
-            'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
-            'b': np.random.uniform(0.1, 1, [10]).astype("float32")
-        }
-        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'b'], 'Out')
-
-    def test_check_grad_ingore_b(self):
-        self.check_grad(['X'], 'Out', no_grad_set=set('b'))
-
-    def test_check_grad_ingore_x(self):
-        self.check_grad(['b'], 'Out', no_grad_set=set('X'))
-
-
-class TestRowwiseAddOp2(OpTest):
-    def setUp(self):
-        self.op_type = "rowwise_add"
-        self.inputs = {
-            'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
-            'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32")
-        }
-        self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
-
-    def test_check_output(self):
-        self.check_output()
-
-    def test_check_grad_normal(self):
-        self.check_grad(['X', 'b'], 'Out')
-
-    def test_check_grad_ignore_b(self):
-        self.check_grad(['X'], 'Out', no_grad_set=set('b'))
-
-    def test_check_grad_ignore_x(self):
-        self.check_grad(['b'], 'Out', no_grad_set=set('X'))
-
-
-if __name__ == "__main__":
-    unittest.main()

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

@@ -43,7 +43,7 @@ class TestSoftmaxWithCrossEntropyOp2(OpTest):
     def setUp(self):
         self.op_type = "softmax_with_cross_entropy"
         batch_size = 2
-        class_num = 17
+        class_num = 37
 
         logits = np.random.uniform(0.1, 1.0,
                                    [batch_size, class_num]).astype("float32")