Implemented by boost preprocessor.

paddle/operators/expand_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/expand_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class ExpandOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
+    std::vector<int> expand_times = Attr<std::vector<int>>("expandTimes");
+    auto* x = ctx.Input<Tensor>("X");
+    auto x_dims = x->dims();
+
+    PADDLE_ENFORCE_EQ(static_cast<size_t>(framework::arity(x_dims)),
+                      expand_times.size(),
+                      "Number of attribute (expandTimes) value must be equal "
+                      "to rank of X.");
+    PADDLE_ENFORCE_LE(framework::arity(x_dims), 6,
+                      "Rank of X must not be greater than 6.");
+
+    std::vector<int64_t> out_shape(x_dims.size());
+    for (size_t i = 0; i < expand_times.size(); ++i) {
+      PADDLE_ENFORCE_GE(expand_times[i], 1,
+                        "Each value of expand times should not be "
+                        "less than 1.");
+      out_shape[i] = x_dims[i] * expand_times[i];
+    }
+    auto* out = ctx.Output<Tensor>("Out");
+    out->Resize(framework::make_ddim(out_shape));
+  }
+};
+
+class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ExpandOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input tensor.");
+    AddOutput("Out", "Expanded result by tiling input X.");
+    AddAttr<std::vector<int>>("expandTimes",
+                              "Expand times for each dimension.");
+    AddComment(R"DOC(
+Expand operator tiles the input by given times. You should set times for each
+dimension by providing attribute 'expandTimes'. Rank of input tensor should be
+in [1, 6]. Please draw an inttention that size of 'expandTimes' must be same
+with rank of input tensor.
+)DOC");
+  }
+};
+
+class ExpandGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null.");
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    std::vector<int> expand_times = Attr<std::vector<int>>("expandTimes");
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    auto* x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+
+    for (size_t i = 0; i < expand_times.size(); ++i) {
+      PADDLE_ENFORCE_EQ(x_dims[i] * expand_times[i], out_dims[i],
+                        "Size of each dimension of Input(Out@GRAD) should be "
+                        "equal to multiplication of crroresponding sizes of "
+                        "Input(X) and expandTimes.");
+    }
+
+    if (x_grad) x_grad->Resize(x_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(expand, ops::ExpandOp, ops::ExpandOpMaker, expand_grad,
+            ops::ExpandGradOp);
+REGISTER_OP_CPU_KERNEL(expand,
+                       ops::ExpandKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    expand_grad, ops::ExpandGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/expand_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/expand_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(expand,
+                       ops::ExpandKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    expand_grad, ops::ExpandGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/expand_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 <boost/preprocessor/arithmetic/div.hpp>
+#include <boost/preprocessor/arithmetic/mod.hpp>
+#include <boost/preprocessor/comparison/greater.hpp>
+#include <boost/preprocessor/comparison/greater_equal.hpp>
+#include <boost/preprocessor/control/if.hpp>
+#include <boost/preprocessor/repetition/repeat.hpp>
+#include <iostream>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/operator.h"
+
+#define EXPAND_TEMPLATE(z, n, data) \
+  case n + 1: {                     \
+    Expand<n + 1>(context);         \
+    break;                          \
+  }
+#define REP_EXPAND_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE, ~)
+
+#define COND(n) BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, 6), BOOST_PP_MOD(n, 6))
+#define EXPAND_GRAD_CASE(n)                                        \
+  case n: {                                                        \
+    ExpandBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
+    break;                                                         \
+  }
+#define EXPAND_TEMPLATE_GRAD(z, n, data) \
+  BOOST_PP_IF(COND(n), EXPAND_GRAD_CASE(n), )
+#define REP_EXPAND_GRAD_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE_GRAD, ~)
+
+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, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class ExpandKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto rank = framework::arity(context.Input<Tensor>("X")->dims());
+    switch (rank) {
+      REP_EXPAND_TEMPLATE(6)
+      default:
+        PADDLE_ENFORCE(false, "Only support tensor whose rank in [1, 6].");
+    };
+  }
+
+ protected:
+  template <int Rank>
+  void Expand(const framework::ExecutionContext& context) const {
+    auto* in0 = context.Input<Tensor>("X");
+    auto expand_times = context.Attr<std::vector<int>>("expandTimes");
+    auto* out0 = context.Output<Tensor>("Out");
+    Eigen::DSizes<int, Rank> bcast_dims;
+    auto x_dims = in0->dims();
+    for (size_t i = 0; i < expand_times.size(); ++i) {
+      bcast_dims[i] = expand_times[i];
+    }
+    auto x = EigenTensor<T, Rank>::From(*in0);
+    out0->mutable_data<T>(context.GetPlace());
+    auto y = EigenTensor<T, Rank>::From(*out0);
+    auto place = context.GetEigenDevice<Place>();
+    y.device(place) = x.broadcast(bcast_dims);
+  }
+};
+
+template <typename Place, typename T>
+class ExpandGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in0 = context.Input<Tensor>("X");
+    auto expand_times = context.Attr<std::vector<int>>("expandTimes");
+    auto x_dims = in0->dims();
+    std::vector<int> reshape_dims_vec;
+    std::vector<int> reduce_dims_vec;
+    for (size_t i = 0; i < expand_times.size(); ++i) {
+      if (expand_times[i] == 1) {
+        reshape_dims_vec.push_back(x_dims[i]);
+      } else {
+        if (x_dims[i] == 1) {
+          reduce_dims_vec.push_back(reshape_dims_vec.size());
+          reshape_dims_vec.push_back(expand_times[i]);
+        } else {
+          reduce_dims_vec.push_back(reshape_dims_vec.size());
+          reshape_dims_vec.push_back(expand_times[i]);
+          reshape_dims_vec.push_back(x_dims[i]);
+        }
+      }
+    }
+
+    int dims = reshape_dims_vec.size() * 6 + reduce_dims_vec.size() - 7;
+    switch (dims) {
+      REP_EXPAND_GRAD_TEMPLATE(72)
+      default:
+        PADDLE_ENFORCE(false, "Only support tensor whose rank in [1, 6].");
+    };
+  }
+
+ protected:
+  template <int Dims>
+  void ExpandBackward(const framework::ExecutionContext& context,
+                      const std::vector<int>& reshape_dims_vec,
+                      const std::vector<int>& reduce_dims_vec) const {
+    size_t reshape_size = Dims / 6 + 1;
+    size_t reduce_size = Dims % 6 + 1;
+    PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
+                      "Inconsistent size between Dims and "
+                      "reshape dimensions.");
+    PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
+                      "Inconsistent size between Dims and "
+                      "reduce dimensions.");
+    auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
+    auto x = EigenVector<T>::Flatten(*(context.Input<Tensor>("X")));
+    out0->mutable_data<T>(context.GetPlace());
+    auto x_grad = EigenVector<T>::Flatten(*out0);
+    Eigen::DSizes<int, Dims / 6 + 1> reshape_dims;
+    for (size_t i = 0; i < reshape_size; ++i) {
+      reshape_dims[i] = reshape_dims_vec[i];
+    }
+    Eigen::DSizes<int, Dims % 6 + 1> reduce_dims;
+    for (size_t i = 0; i < reduce_size; ++i) {
+      reduce_dims[i] = reduce_dims_vec[i];
+    }
+    auto out_grad = EigenVector<T>::Flatten(*in0);
+    x_grad.device(context.GetEigenDevice<Place>()) =
+        out_grad.reshape(reshape_dims).sum(reduce_dims).reshape(x.dimensions());
+  }
+};
+
+}  // operators
+}  // paddle

paddle/pybind/pybind.cc

@@ -54,6 +54,7 @@ USE_CPU_ONLY_OP(concat);
 USE_OP(top_k);
 USE_OP(squared_l2_distance);
 USE_OP(sum);
+USE_OP(expand);
 
 namespace paddle {
 namespace framework {

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

@@ -35,3 +35,4 @@ py_test(test_sum_op SRCS test_sum_op.py)
 py_test(mnist SRCS mnist.py)
 py_test(test_concat_op SRCS test_concat_op.py)
 py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
+py_test(test_expand_op SRCS test_expand_op.py)

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestExpandOpRank1(OpTest):
+    def setUp(self):
+        self.op_type = "expand"
+        self.inputs = {'X': np.random.random(12).astype("float32")}
+        self.attrs = {'expandTimes': [2]}
+        output = np.tile(self.inputs['X'], 2)
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestExpandOpRank2(OpTest):
+    def setUp(self):
+        self.op_type = "expand"
+        self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
+        self.attrs = {'expandTimes': [3, 4]}
+        output = np.tile(self.inputs['X'], (3, 4))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestExpandOpRank3(OpTest):
+    def setUp(self):
+        self.op_type = "expand"
+        self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
+        self.attrs = {'expandTimes': [3, 2, 1]}
+        output = np.tile(self.inputs['X'], (3, 2, 1))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestExpandOpRank4(OpTest):
+    def setUp(self):
+        self.op_type = "expand"
+        self.inputs = {'X': np.random.random((2, 4, 5, 7)).astype("float32")}
+        self.attrs = {'expandTimes': [3, 2, 1, 2]}
+        output = np.tile(self.inputs['X'], (3, 2, 1, 2))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+if __name__ == "__main__":
+    unittest.main()