[API 2.0] add paddle.tile op (#26245)

* add tile_op, test=develop

paddle/fluid/operators/tile_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/operators/tile_op.h"
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class TileOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Tile");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Tile");
+    auto x_dims = ctx->GetInputDim("X");
+    auto repeat_times = ctx->Attrs().Get<std::vector<int>>("repeat_times");
+    if (repeat_times.size() == 0) {
+      repeat_times = std::vector<int>(x_dims.size(), -1);
+    }
+
+    PADDLE_ENFORCE_LE(
+        x_dims.size(), MAX_RANK_SUPPORTED,
+        platform::errors::InvalidArgument(
+            "The rank of the input 'x' for tile op "
+            "must not be greater than %d, but the value received is %d.",
+            MAX_RANK_SUPPORTED, x_dims.size()));
+    PADDLE_ENFORCE_LE(
+        repeat_times.size(), MAX_RANK_SUPPORTED,
+        platform::errors::InvalidArgument(
+            "The size of the shape of input 'repeat_times' for tile op "
+            "must not be greater than %d, but the value received is %d.",
+            MAX_RANK_SUPPORTED, repeat_times.size()));
+    PADDLE_ENFORCE_GE(
+        repeat_times.size(), 1,
+        platform::errors::InvalidArgument(
+            "The size of the shape of input 'repeat_times' for tile op "
+            "must be positive integers, but the value received is %d.",
+            repeat_times.size()));
+
+    auto out_rank =
+        std::max(static_cast<size_t>(x_dims.size()), repeat_times.size());
+    std::vector<int64_t> out_shape(out_rank);
+    auto x_dim_vec = framework::vectorize<int>(x_dims);
+    if (x_dim_vec.size() > repeat_times.size()) {
+      auto diff = x_dim_vec.size() - repeat_times.size();
+      repeat_times.insert(repeat_times.begin(), diff, -1);
+    } else {
+      auto diff = repeat_times.size() - x_dim_vec.size();
+      x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
+    }
+    for (size_t i = 0; i < repeat_times.size(); ++i) {
+      if (x_dim_vec[i] == -1 || repeat_times[i] == -1) {
+        out_shape[i] = -1;
+      } else {
+        PADDLE_ENFORCE_GT(
+            repeat_times[i], 0,
+            platform::errors::InvalidArgument(
+                "Every element of the input 'repeat_times' for tile op must be "
+                "greater than 0, but the value given is %d.",
+                repeat_times[i]));
+        out_shape[i] = x_dim_vec[i] * repeat_times[i];
+      }
+    }
+
+    ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
+    if (out_shape[0] == x_dims[0]) {
+      ctx->ShareLoD("X", "Out");
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
+        ctx.device_context());
+  }
+
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string& var_name, const Tensor& tensor,
+      const framework::OpKernelType& expected_kernel_type) const override {
+    if (var_name == "repeat_times_tensor" || var_name == "RepeatTimes") {
+      return expected_kernel_type;
+    }
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   tensor.place(), tensor.layout());
+  }
+};
+
+class TileOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor, default Tensor<float>). X is the input to be titled.");
+    AddInput(
+        "RepeatTimes",
+        "(Tensor<int>, optional). If provided, it is the number of repeat times"
+        " along specific axis. It has a higher priority than "
+        "repeat_times_tensor and the repeat_times attribute.")
+        .AsDispensable();
+    AddInput("repeat_times_tensor",
+             "(Tensor Tensor<int>), repeat times for X."
+             "It has a higher priority than repeat_times, but a lower priority "
+             "than RepeatTimes")
+        .AsDuplicable()
+        .AsDispensable();
+    AddOutput("Out",
+              "(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
+              "After tiling, size of each dimension of Output(Out) is equal "
+              "to size of the corresponding dimension of Input(X) multiplying "
+              "the corresponding value given by Attr(repeat_times).");
+    AddAttr<std::vector<int>>("repeat_times",
+                              "The number of repeat times for each dimension.")
+        .SetDefault({});
+    AddComment(R"DOC(
+Tile operator repeats the input by given times number. You should set times
+number for each dimension by providing attribute 'repeat_times'. The rank of X
+should be in [1, 6]. Please note that size of 'repeat_times' must be the same
+with X's rank. Following is a using case:
+
+Input(X) is a 3-D tensor with shape [2, 3, 1]:
+
+        [
+           [[1], [2], [3]],
+           [[4], [5], [6]]
+        ]
+
+Attr(repeat_times):  [1, 2, 2]
+
+Output(Out) is a 3-D tensor with shape [2, 6, 2]:
+
+        [
+            [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
+            [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
+        ]
+
+)DOC");
+  }
+};
+
+class TileGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "TileGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "TileGrad");
+
+    auto x_dims = ctx->GetInputDim("X");
+    std::vector<int> repeat_times =
+        ctx->Attrs().Get<std::vector<int>>("repeat_times");
+    if (repeat_times.size() == 0) {
+      repeat_times = std::vector<int>(x_dims.size(), -1);
+    }
+
+    auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
+    auto x_dim_vec = framework::vectorize<int>(x_dims);
+    if (x_dim_vec.size() > repeat_times.size()) {
+      auto diff = x_dim_vec.size() - repeat_times.size();
+      repeat_times.insert(repeat_times.begin(), diff, -1);
+    } else {
+      auto diff = repeat_times.size() - x_dim_vec.size();
+      x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
+    }
+
+    for (size_t i = 0; i < repeat_times.size(); ++i) {
+      if (repeat_times[i] == -1 || x_dim_vec[i] == -1) {
+        continue;
+      } else {
+        if (ctx->IsRuntime()) {
+          PADDLE_ENFORCE_EQ(
+              x_dim_vec[i] * repeat_times[i], out_dims[i],
+              platform::errors::InvalidArgument(
+                  "The size (%d) of the dimension %d of Input(Out@GRAD) should "
+                  "be equal to the multiplication of the crroresponding "
+                  "dimension size of Input(X) (%d) and repeat_times (%d).",
+                  out_dims[i], i, x_dim_vec[i], repeat_times[i]));
+        }
+      }
+    }
+    auto x_grad_name = framework::GradVarName("X");
+
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, x_dims);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
+                                       ctx, framework::GradVarName("Out")),
+                                   ctx.device_context());
+  }
+
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string& var_name, const Tensor& tensor,
+      const framework::OpKernelType& expected_kernel_type) const override {
+    if (var_name == "repeat_times_tensor" || var_name == "RepeatTimes") {
+      return expected_kernel_type;
+    }
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   tensor.place(), tensor.layout());
+  }
+};
+
+template <typename T>
+class TileGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("tile_grad");
+    op->SetInput("X", this->Input("X"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetInput("repeat_times_tensor", this->Input("repeat_times_tensor"));
+    op->SetInput("RepeatTimes", this->Input("RepeatTimes"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(TileGradNoNeedBufVarsInferer, "X");
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(tile, ops::TileOp, ops::TileOpMaker,
+                  ops::TileGradOpMaker<paddle::framework::OpDesc>,
+                  ops::TileGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(tile_grad, ops::TileGradOp,
+                  ops::TileGradNoNeedBufVarsInferer);
+REGISTER_OP_CPU_KERNEL(
+    tile, ops::TileKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TileKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::TileKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::TileKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::TileKernel<paddle::platform::CPUDeviceContext, bool>);
+REGISTER_OP_CPU_KERNEL(
+    tile_grad, ops::TileGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TileGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::TileGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::TileGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/tile_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/operators/tile_op.h"
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_CUDA_KERNEL(
+    tile, ops::TileKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TileKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::TileKernel<paddle::platform::CUDADeviceContext, plat::float16>,
+    ops::TileKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::TileKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::TileKernel<paddle::platform::CUDADeviceContext, bool>);
+REGISTER_OP_CUDA_KERNEL(
+    tile_grad, ops::TileGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TileGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::TileGradKernel<paddle::platform::CUDADeviceContext, plat::float16>,
+    ops::TileGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::TileGradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/tile_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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 <algorithm>
+#include <vector>
+
+#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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+
+#define MAX_RANK_SUPPORTED 6
+
+#define TILE_TEMPLATE(z, n, data) \
+  case n + 1: {                   \
+    Tile<n + 1>(context);         \
+    break;                        \
+  }
+#define REP_TILE_TEMPLATE(n) BOOST_PP_REPEAT(n, TILE_TEMPLATE, ~)
+#define COND(n) BOOST_PP_GREATER_EQUAL(n, BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
+#define TILE_GRAD_CASE(n)                                        \
+  case n: {                                                      \
+    TileBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
+    break;                                                       \
+  }
+#define TILE_GRAD_TEMPLATE(z, n, data) BOOST_PP_IF(COND(n), TILE_GRAD_CASE(n), )
+#define REP_TILE_GRAD_TEMPLATE(n) BOOST_PP_REPEAT(n, TILE_GRAD_TEMPLATE, ~)
+
+namespace paddle {
+namespace operators {
+inline std::vector<int> get_repeat_times(
+    const framework::ExecutionContext& ctx) {
+  if (ctx.HasInput("RepeatTimes")) {
+    auto* repeat_tensor = ctx.Input<framework::LoDTensor>("RepeatTimes");
+    auto* repeat_data = repeat_tensor->data<int>();
+    framework::Tensor cpu_repeat_tensor;
+    if (platform::is_gpu_place(repeat_tensor->place())) {
+      TensorCopySync(*repeat_tensor, platform::CPUPlace(), &cpu_repeat_tensor);
+      repeat_data = cpu_repeat_tensor.data<int>();
+    }
+    auto vec_repeat_times =
+        std::vector<int>(repeat_data, repeat_data + repeat_tensor->numel());
+    return vec_repeat_times;
+  }
+
+  auto list_repeat_times_tensor =
+      ctx.MultiInput<framework::Tensor>("repeat_times_tensor");
+  if (list_repeat_times_tensor.size() > 0) {
+    // get tensor from
+    std::vector<int> vec_repeat_times;
+    for (size_t i = 0; i < list_repeat_times_tensor.size(); ++i) {
+      auto tensor = list_repeat_times_tensor[i];
+      if (platform::is_gpu_place(tensor->place())) {
+        framework::Tensor temp;
+        TensorCopySync(*tensor, platform::CPUPlace(), &temp);
+        vec_repeat_times.push_back(*temp.data<int32_t>());
+      } else {
+        vec_repeat_times.push_back(*tensor->data<int32_t>());
+      }
+    }
+    return vec_repeat_times;
+  } else {
+    return ctx.Attr<std::vector<int>>("repeat_times");
+  }
+}
+
+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>;
+using framework::To32BitIndex;
+
+template <typename DeviceContext, typename T>
+class TileKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto rank = context.Input<Tensor>("X")->dims().size();
+    PADDLE_ENFORCE_GE(
+        rank, 1, platform::errors::InvalidArgument(
+                     "The rank of the input 'x' for tile op must be a positive "
+                     "integer, but the value received is %d.",
+                     rank));
+    PADDLE_ENFORCE_LE(
+        rank, MAX_RANK_SUPPORTED,
+        platform::errors::InvalidArgument(
+            "The rank of the input 'x' for tile op "
+            "must be less than or equal to %d, but the value received is %d.",
+            MAX_RANK_SUPPORTED, rank));
+    auto repeat_times = get_repeat_times(context);
+    int repeat_times_size = repeat_times.size();
+    PADDLE_ENFORCE_GE(
+        repeat_times_size, 1,
+        platform::errors::InvalidArgument(
+            "The number of elements of the input 'repeat_times' for tile "
+            "op must be positive, but the value received is %d.",
+            repeat_times_size));
+    PADDLE_ENFORCE_LE(
+        repeat_times_size, MAX_RANK_SUPPORTED,
+        platform::errors::InvalidArgument(
+            "The number of elements of the input 'repeat_times' for tile op "
+            "must be less than or equal to %d, but the value received is %d.",
+            MAX_RANK_SUPPORTED, repeat_times_size));
+    rank = std::max(rank, repeat_times_size);
+    switch (rank) { REP_TILE_TEMPLATE(MAX_RANK_SUPPORTED) }
+  }
+
+ protected:
+  template <int Rank>
+  void Tile(const framework::ExecutionContext& context) const {
+    auto* in0 = context.Input<Tensor>("X");
+
+    auto in_dims = in0->dims();
+    auto repeat_times = get_repeat_times(context);
+    for (size_t i = 0; i < repeat_times.size(); ++i) {
+      PADDLE_ENFORCE_GT(
+          repeat_times[i], 0,
+          platform::errors::InvalidArgument(
+              "All elements of the input 'repeat_times' for tile op must "
+              "be positive integers, but the value received is %d.",
+              repeat_times[i]));
+    }
+    auto vec_in_dims = framework::vectorize<int>(in_dims);
+    if (repeat_times.size() < vec_in_dims.size()) {
+      int diff = vec_in_dims.size() - repeat_times.size();
+      repeat_times.insert(repeat_times.begin(), diff, 1);
+    } else {
+      int diff = repeat_times.size() - vec_in_dims.size();
+      vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
+    }
+    PADDLE_ENFORCE_EQ(
+        repeat_times.size(), vec_in_dims.size(),
+        platform::errors::InvalidArgument(
+            "The rank (%d) of the input 'x' and the rank (%d) of the input "
+            "'repeat_times' for tile op must match after promotion.",
+            vec_in_dims.size(), repeat_times.size()));
+    auto* out0 = context.Output<Tensor>("Out");
+    Eigen::DSizes<int, Rank> bcast_dims;
+    for (size_t i = 0; i < repeat_times.size(); ++i) {
+      bcast_dims[i] = repeat_times[i];
+    }
+
+    framework::DDim new_in_dims = framework::make_ddim(vec_in_dims);
+    framework::DDim out_dims(new_in_dims);
+    for (size_t i = 0; i < repeat_times.size(); ++i) {
+      out_dims[i] *= repeat_times[i];
+    }
+
+    out0->Resize(out_dims);
+    auto x = EigenTensor<T, Rank>::From(*in0, new_in_dims);
+    out0->mutable_data<T>(context.GetPlace());
+    auto y = EigenTensor<T, Rank>::From(*out0, out_dims);
+    auto& place =
+        *context.template device_context<DeviceContext>().eigen_device();
+    // use 32-bit index to speed up
+    bool use_32bit_index = y.size() < Eigen::NumTraits<int>::highest();
+    if (use_32bit_index) {
+      To32BitIndex(y).device(place) = To32BitIndex(x).broadcast(bcast_dims);
+    } else {
+      y.device(place) = x.broadcast(bcast_dims);
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+class TileGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in0 = context.Input<Tensor>("X");
+    auto repeat_times = get_repeat_times(context);
+    auto x_dims = in0->dims();
+    auto vec_in_dims = framework::vectorize<int>(x_dims);
+    if (repeat_times.size() < vec_in_dims.size()) {
+      int diff = vec_in_dims.size() - repeat_times.size();
+      repeat_times.insert(repeat_times.begin(), diff, 1);
+    } else {
+      int diff = repeat_times.size() - vec_in_dims.size();
+      vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
+    }
+    // 1. reshape_dims_vec is the broadcast parameter.
+    // 2. reduce_dims_vec is the dimension parameter to compute gradients. For
+    //    each dimension expanded, the gradients should be summed to original
+    //    size.
+    std::vector<int> reshape_dims_vec;
+    std::vector<int> reduce_dims_vec;
+    for (size_t i = 0; i < repeat_times.size(); ++i) {
+      reduce_dims_vec.push_back(reshape_dims_vec.size());
+      reshape_dims_vec.push_back(repeat_times[i]);
+      reshape_dims_vec.push_back(vec_in_dims[i]);
+    }
+
+    int dims = reduce_dims_vec.size();
+
+    bool just_copy = true;
+    for (size_t i = 0; i < repeat_times.size(); i++) {
+      if (repeat_times[i] != 1) {
+        just_copy = false;
+        break;
+      }
+    }
+    // no need reduce, just copy
+    if (just_copy) {
+      auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
+      auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
+      out0->mutable_data<T>(context.GetPlace());
+      framework::TensorCopy(*in0, context.GetPlace(), context.device_context(),
+                            out0);
+    } else {
+      PADDLE_ENFORCE_GE(dims, 1,
+                        platform::errors::InvalidArgument(
+                            "Th rank of the input 'Out@GRAD' for tile_grad op "
+                            " must be greater than or equal to 1, but "
+                            "the value received is %d.",
+                            dims));
+      PADDLE_ENFORCE_LE(dims, MAX_RANK_SUPPORTED,
+                        platform::errors::InvalidArgument(
+                            "The rank of the input 'Out@GRAD' for tile_grad op "
+                            "must be less than or equal "
+                            "to %d, but the value received is %d.",
+                            MAX_RANK_SUPPORTED, dims));
+      switch (dims) { REP_TILE_GRAD_TEMPLATE(MAX_RANK_SUPPORTED) }
+    }
+  }
+
+ protected:
+  template <int Dims>
+  void TileBackward(const framework::ExecutionContext& context,
+                    const std::vector<int>& reshape_dims_vec,
+                    const std::vector<int>& reduce_dims_vec) const {
+    size_t reshape_size = reshape_dims_vec.size();
+    size_t reduce_size = reduce_dims_vec.size();
+    auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
+    out0->mutable_data<T>(context.GetPlace());
+    auto x_grad = EigenVector<T>::Flatten(*out0);
+    Eigen::DSizes<int, Dims * 2> reshape_dims;
+    for (size_t i = 0; i < reshape_size; ++i) {
+      reshape_dims[i] = reshape_dims_vec[i];
+    }
+    Eigen::DSizes<int, Dims> 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.template device_context<DeviceContext>().eigen_device()) =
+        out_grad.reshape(reshape_dims)
+            .sum(reduce_dims)
+            .reshape(x_grad.dimensions());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/__init__.py

@@ -101,6 +101,7 @@ from .tensor.manipulation import cast  #DEFINE_ALIAS
 from .tensor.manipulation import concat  #DEFINE_ALIAS
 from .tensor.manipulation import expand  #DEFINE_ALIAS
 from .tensor.manipulation import expand_as  #DEFINE_ALIAS
+from .tensor.manipulation import tile  #DEFINE_ALIAS
 from .tensor.manipulation import flatten  #DEFINE_ALIAS
 from .tensor.manipulation import gather  #DEFINE_ALIAS
 from .tensor.manipulation import gather_nd  #DEFINE_ALIAS

python/paddle/fluid/tests/unittests/test_tile_op.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+
+
+# Situation 1: repeat_times is a list(without tensor)
+class TestTileOpRank1(OpTest):
+    def setUp(self):
+        self.op_type = "tile"
+        self.init_data()
+
+        self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
+        self.attrs = {'repeat_times': self.repeat_times}
+        output = np.tile(self.inputs['X'], self.repeat_times)
+        self.outputs = {'Out': output}
+
+    def init_data(self):
+        self.ori_shape = [100]
+        self.repeat_times = [2]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+# with dimension expanding
+class TestTileOpRank2Expanding(TestTileOpRank1):
+    def init_data(self):
+        self.ori_shape = [120]
+        self.repeat_times = [2, 2]
+
+
+class TestTileOpRank2(TestTileOpRank1):
+    def init_data(self):
+        self.ori_shape = [12, 14]
+        self.repeat_times = [2, 3]
+
+
+class TestTileOpRank3_Corner(TestTileOpRank1):
+    def init_data(self):
+        self.ori_shape = (2, 10, 5)
+        self.repeat_times = (1, 1, 1)
+
+
+class TestTileOpRank3_Corner2(TestTileOpRank1):
+    def init_data(self):
+        self.ori_shape = (2, 10, 5)
+        self.repeat_times = (2, 2)
+
+
+class TestTileOpRank3(TestTileOpRank1):
+    def init_data(self):
+        self.ori_shape = (2, 4, 15)
+        self.repeat_times = (2, 1, 4)
+
+
+class TestTileOpRank4(TestTileOpRank1):
+    def init_data(self):
+        self.ori_shape = (2, 4, 5, 7)
+        self.repeat_times = (3, 2, 1, 2)
+
+
+# Situation 2: repeat_times is a list(with tensor)
+class TestTileOpRank1_tensor_attr(OpTest):
+    def setUp(self):
+        self.op_type = "tile"
+        self.init_data()
+        repeat_times_tensor = []
+        for index, ele in enumerate(self.repeat_times):
+            repeat_times_tensor.append(("x" + str(index), np.ones(
+                (1)).astype('int32') * ele))
+
+        self.inputs = {
+            'X': np.random.random(self.ori_shape).astype("float64"),
+            'repeat_times_tensor': repeat_times_tensor,
+        }
+        self.attrs = {"repeat_times": self.infer_repeat_times}
+        output = np.tile(self.inputs['X'], self.repeat_times)
+        self.outputs = {'Out': output}
+
+    def init_data(self):
+        self.ori_shape = [100]
+        self.repeat_times = [2]
+        self.infer_repeat_times = [-1]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestTileOpRank2_Corner_tensor_attr(TestTileOpRank1_tensor_attr):
+    def init_data(self):
+        self.ori_shape = [12, 14]
+        self.repeat_times = [1, 1]
+        self.infer_repeat_times = [1, -1]
+
+
+class TestTileOpRank2_attr_tensor(TestTileOpRank1_tensor_attr):
+    def init_data(self):
+        self.ori_shape = [12, 14]
+        self.repeat_times = [2, 3]
+        self.infer_repeat_times = [-1, 3]
+
+
+# Situation 3: repeat_times is a tensor
+class TestTileOpRank1_tensor(OpTest):
+    def setUp(self):
+        self.op_type = "tile"
+        self.init_data()
+
+        self.inputs = {
+            'X': np.random.random(self.ori_shape).astype("float64"),
+            'RepeatTimes': np.array(self.repeat_times).astype("int32"),
+        }
+        self.attrs = {}
+        output = np.tile(self.inputs['X'], self.repeat_times)
+        self.outputs = {'Out': output}
+
+    def init_data(self):
+        self.ori_shape = [100]
+        self.repeat_times = [2]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestTileOpRank2_tensor(TestTileOpRank1_tensor):
+    def init_data(self):
+        self.ori_shape = [12, 14]
+        self.repeat_times = [2, 3]
+
+
+# Situation 4: input x is Integer
+class TestTileOpInteger(OpTest):
+    def setUp(self):
+        self.op_type = "tile"
+        self.inputs = {
+            'X': np.random.randint(
+                10, size=(2, 4, 5)).astype("int32")
+        }
+        self.attrs = {'repeat_times': [2, 1, 4]}
+        output = np.tile(self.inputs['X'], (2, 1, 4))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+# Situation 5: input x is Bool
+class TestTileOpBoolean(OpTest):
+    def setUp(self):
+        self.op_type = "tile"
+        self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
+        self.attrs = {'repeat_times': [2, 1, 4]}
+        output = np.tile(self.inputs['X'], (2, 1, 4))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+# Situation 56: input x is Integer
+class TestTileOpInt64_t(OpTest):
+    def setUp(self):
+        self.op_type = "tile"
+        self.inputs = {
+            'X': np.random.randint(
+                10, size=(2, 4, 5)).astype("int64")
+        }
+        self.attrs = {'repeat_times': [2, 1, 4]}
+        output = np.tile(self.inputs['X'], (2, 1, 4))
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestTileError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            repeat_times = [2, 2]
+            self.assertRaises(TypeError, paddle.tile, x1, repeat_times)
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
+            self.assertRaises(TypeError, paddle.tile, x2, repeat_times)
+            x3 = fluid.layers.data(name='x3', shape=[4], dtype="bool")
+            x3.stop_gradient = True
+            self.assertRaises(ValueError, paddle.tile, x3, repeat_times)
+
+
+# Test python API
+class TestTileAPI(unittest.TestCase):
+    def test_api(self):
+        input = np.random.random([12, 14]).astype("float32")
+        x = fluid.layers.data(
+            name='x', shape=[12, 14], append_batch_size=False, dtype="float32")
+
+        positive_2 = fluid.layers.fill_constant([1], "int32", 2)
+        repeat_times = fluid.layers.data(
+            name="repeat_times", shape=[2], append_batch_size=False)
+
+        out_1 = paddle.tile(x, repeat_times=[2, 3])
+        out_2 = paddle.tile(x, repeat_times=[positive_2, 3])
+        out_3 = paddle.tile(x, repeat_times=repeat_times)
+
+        g0 = fluid.backward.calc_gradient(out_2, x)
+
+        exe = fluid.Executor(place=fluid.CPUPlace())
+        res_1, res_2, res_3 = exe.run(fluid.default_main_program(),
+                                      feed={
+                                          "x": input,
+                                          "repeat_times":
+                                          np.array([1, 3]).astype("int32")
+                                      },
+                                      fetch_list=[out_1, out_2, out_3])
+        assert np.array_equal(res_1, np.tile(input, (2, 3)))
+        assert np.array_equal(res_2, np.tile(input, (2, 3)))
+        assert np.array_equal(res_3, np.tile(input, (1, 3)))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -77,6 +77,7 @@ from .manipulation import cast  #DEFINE_ALIAS
 from .manipulation import concat  #DEFINE_ALIAS
 from .manipulation import expand  #DEFINE_ALIAS
 from .manipulation import expand_as  #DEFINE_ALIAS
+from .manipulation import tile  #DEFINE_ALIAS
 from .manipulation import flatten  #DEFINE_ALIAS
 from .manipulation import gather  #DEFINE_ALIAS
 from .manipulation import gather_nd  #DEFINE_ALIAS

python/paddle/tensor/manipulation.py

@@ -68,6 +68,7 @@ __all__ = [
     'flip',
     'unbind',
     'roll',
+    'tile',
 ]
 
 
@@ -787,3 +788,122 @@ def unbind(input, axis=0):
         outputs={"Out": outs},
         attrs={"axis": axis})
     return outs
+
+
+def tile(x, repeat_times, name=None):
+    """
+    :alias_main: paddle.tile
+	:alias: paddle.tile,paddle.tensor.tile,paddle.tensor.manipulation.tile
+
+    Construct a new tensor by repeating ``x`` the number of times given by the parameter ``repeat_times``.
+    The rank of ``x`` should be less than or equal to 6, and the size of the shape of ``repeat_times`` should
+    be less than or equal to 6.
+    If the size of the parameter ``repeat_times`` is ``d``, and the rank for ``x`` is ``r``, then the number
+    of dimensions for the result is ``max(d, r)``.
+    If ``r < d``, ``x`` if first promoted to a d-dimensional tensor by inserting new axes at the beginning.
+    For example, a tensor ``x`` with the shape(3,) is promoted to a 2-D tensor with the shape (1, 3) if ``d`` is 2
+    and a 3-D tensor with the shape(1, 1, 3) if ``d`` is 3.
+    If ``r > d``, ``repeat_times`` is first promoted by inserting 1's at the beginning.
+    For example, if the tensor ``x`` is with a shape(4, 3, 2, 2) and ``repeat_times`` is a tuple (3, 2),
+    ``repeat_times`` is first promoted to a tuple (1, 1, 3, 2).
+    The following gives an using case:
+
+
+    .. code-block:: text
+
+        Input(x) is a 3-D tensor of shape (2, 3, 1):
+
+                [
+                   [[1], [2], [3]],
+                   [[4], [5], [6]]
+                ]
+
+        Attr(repeat_times):  [1, 2, 2]
+
+        Output(out) is a 3-D tensor of shape (2, 6, 2):
+
+                [
+                    [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
+                    [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
+                ]
+
+    Args:
+        x (Tensor): The input tensor, its data type should be bool, float32, float64, int32. The rank of ``x`` should be in [1, 6].
+        repeat_times (Tensor|tuple|list): The number of repeating times for each dimension of the input ``x``. If repeat_times is a list or tuple, the elements of
+                it should be integers or Tensors with shape [1]. If repeat_times is Tensor, it should be an 1-D Tensor. The size of its shape should be in [1, 6].
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name` .
+
+    Returns:
+        N-D Tensor. The data type is the same as ``x``. After tiling, each dimension of the output is equal to the corresponding dimension of ``x`` multiplying the corresponding value given by ``repeat_times`` .
+
+    Raises:
+        TypeError: The type of ``repeat_times`` must be list, tuple or Tensor.
+        ValueError: The elements of ``repeat_times`` cannot be negative.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            paddle.disable_static()
+
+            # example 1:
+            np_data_1 = np.array([1, 2, 3]).astype('int32')
+            data_1 = paddle..to_variable(np_data_1)
+            tiled_1 = paddle.tile(data_1, repeat_times=[2, 1])
+            # [[1, 2, 3], [1, 2, 3]]
+
+            # example 2:
+            np_repeat_times = np.array([2, 1]).astype("int32")
+            repeat_times = paddle.to_variable(np_repeat_times)
+            tiled_2 = paddle.tile(data_1, repeat_times=repeat_times)
+            # [[1, 2, 3], [1, 2, 3]]
+    """
+    if in_dygraph_mode():
+        if isinstance(repeat_times, (list, tuple)):
+            repeat_times = [
+                item.numpy()[0] if isinstance(item, Variable) else item
+                for item in repeat_times
+            ]
+
+            return core.ops.tile(x, 'repeat_times', repeat_times)
+
+    inputs = {"X": [x]}
+    attrs = {}
+    check_variable_and_dtype(
+        x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'tile')
+    check_type(repeat_times, 'repeat_times', (list, tuple, Variable), 'tile')
+    if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == True:
+        raise ValueError(
+            "When the date type is bool for the input 'x' of tile op, you "
+            "must set its stop_gradient to be False by "
+            "some_var.stop_gradient == True supporting some_var is the input.")
+
+    helper = LayerHelper('tile', input=x, **locals())
+
+    def get_attr_repeat_times(list_repeat_times):
+        attrs_repeat_times = []
+        for idx, times in enumerate(list_repeat_times):
+            if isinstance(times, Variable):
+                attrs_repeat_times.append(-1)
+            else:
+                attrs_repeat_times.append(times)
+                assert times > 0, (
+                    "Every element given in repeat_times must be positive.")
+        return attrs_repeat_times
+
+    if isinstance(repeat_times, Variable):
+        repeat_times.stop_gradient = True
+        inputs['RepeatTimes'] = repeat_times
+        attrs['repeat_times'] = [-1] * len(repeat_times.shape)
+    elif isinstance(repeat_times, (list, tuple)):
+        attrs['repeat_times'] = get_attr_repeat_times(repeat_times)
+        if utils._contain_var(repeat_times):
+            inputs['repeat_times_tensor'] = utils._convert_to_tensor_list(
+                repeat_times)
+
+    dtype = helper.input_dtype(input_param_name='x')
+    out = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type='tile', inputs=inputs, outputs={'Out': out}, attrs=attrs)
+    return out