slice_op.cc

/* 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. */

#include "paddle/fluid/operators/slice_op.h"
#include <algorithm>
#include <memory>
#include <string>
#include <vector>

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

class SliceOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {
    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
                      "Input (Input) of slice op should not be null.");

    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                      "Output (Out) of slice op should not be null.");

    auto in_dims = ctx->GetInputDim("Input");
    PADDLE_ENFORCE_LT(in_dims.size(), 7,
                      "The rank of input should be less than 7.");
    framework::DDim out_dims(in_dims);

    auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
    auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
    auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
    auto infer_flags = ctx->Attrs().Get<std::vector<int>>("infer_flags");
    auto decrease_axis = ctx->Attrs().Get<std::vector<int>>("decrease_axis");

    auto starts_size = starts.size();
    auto ends_size = ends.size();
    if (infer_flags.empty()) {
      // Initialize infer_flags with 1.
      // To be compatible with other op tests in which infer_flags is not set.
      infer_flags = std::vector<int>(axes.size(), 1);
    }

    if (ctx->HasInputs("StartsTensorList")) {
      auto StartsTensorList = ctx->Inputs("StartsTensorList");
      PADDLE_ENFORCE_GT(StartsTensorList.size(), 0,
                        "StartsTensorList size can't be zero");
      starts_size = StartsTensorList.size();
    }
    if (ctx->HasInputs("EndsTensorList")) {
      auto EndsTensorList = ctx->Inputs("EndsTensorList");
      PADDLE_ENFORCE_GT(EndsTensorList.size(), 0,
                        "EndsTensorList size can't be zero");
      ends_size = EndsTensorList.size();
    }

    if (ctx->HasInput("StartsTensor") == false) {
      PADDLE_ENFORCE_EQ(
          starts_size, axes.size(),
          "The size of starts must be equal to the size of axes.");
    }
    if (ctx->HasInput("EndsTensor") == false) {
      PADDLE_ENFORCE_EQ(ends_size, axes.size(),
                        "The size of ends must be equal to the size of axes.");
    }

    int dim_value, start, end;
    for (size_t i = 0; i < axes.size(); ++i) {
      PADDLE_ENFORCE_LT(static_cast<int>(axes[i]), in_dims.size(),
                        "The index of dimension in axes must be less "
                        "than the size of input shape.");
      if (infer_flags[i] == -1) {
        out_dims[axes[i]] = -1;
      } else {
        // infer out_dim shape
        dim_value = out_dims[axes[i]];
        if (dim_value > 0) {
          start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
          end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
          start = std::max(start, 0);
          end = std::max(end, 0);
          end = std::min(end, dim_value);
          PADDLE_ENFORCE_GT(end, start, "end should greater than start");
          out_dims[axes[i]] = end - start;
        }
      }
    }
    // generate new shape
    if (decrease_axis.size() > 0) {
      std::vector<int> new_out_shape;
      for (size_t i = 0; i < decrease_axis.size(); ++i) {
        if (ctx->IsRuntime() && infer_flags[i] != -1) {
          PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
                            "decrease dim should be 1");
        }
        out_dims[decrease_axis[i]] = 0;
      }

      for (int i = 0; i < out_dims.size(); ++i) {
        if (out_dims[i] != 0) {
          new_out_shape.push_back(out_dims[i]);
        }
      }
      if (new_out_shape.size() == 0) {
        new_out_shape.push_back(1);
      }

      out_dims = framework::make_ddim(new_out_shape);
    }
    ctx->SetOutputDim("Out", out_dims);
    if (axes[0] != 0) {
      ctx->ShareLoD("Input", /*->*/ "Out");
    }
  }

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    return framework::OpKernelType(
        OperatorWithKernel::IndicateVarDataType(ctx, "Input"),
        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 == "StartsTensor" || var_name == "EndsTensor") {
      return expected_kernel_type;
    }
    if (var_name == "StartsTensorList" || var_name == "EndsTensorList") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};

class SliceOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("Input", "(Tensor) Tensor of data to extract slices from.");
    AddInput("StartsTensor",
             "(Tensor<int32>, optional) If provided, slice will use this."
             "It has the highest priority of StartsTensor, StartsTensorList "
             "and attr(starts).")
        .AsDispensable();
    AddInput("EndsTensor",
             "(Tensor<int32>, optional) If provided, slice will use this."
             "It has the highest priority of EndsTensor, EndsTensorList and "
             "attr(ends).")
        .AsDispensable();
    AddInput(
        "StartsTensorList",
        "(vector<Tensor<int32>>, optional) If provided, slice will use this."
        "The shape of the tensor in vector MUST BE [1]."
        "It has higher priority compare with attr(starts).")
        .AsDuplicable()
        .AsDispensable();
    AddInput(
        "EndsTensorList",
        "(vector<Tensor<int32>>, optional) If provided, slice will use this."
        "The shape of the tensor in vector MUST BE [1]."
        "It has higher priority compare with attr(ends).")
        .AsDuplicable()
        .AsDispensable();
    AddOutput("Out", "Sliced data tensor.");
    AddAttr<std::vector<int>>(
        "axes",
        "(list<int>) Axes that `starts` and `ends` apply to. It's optional."
        "If not present, will be treated as [0, 1, ..., len(`starts`) - 1].");
    AddAttr<std::vector<int>>(
        "starts",
        "(list<int>) Starting indices of corresponding axis in `axes`")
        .SetDefault({});
    AddAttr<std::vector<int>>(
        "ends", "(list<int>) Ending indices of corresponding axis in `axes`.")
        .SetDefault({});
    AddAttr<std::vector<int>>(
        "infer_flags", "(list<int>) Flags of inferring dims in attributes.")
        .SetDefault({});
    AddAttr<std::vector<int>>("decrease_axis", "(list<int>) decrease_axis")
        .SetDefault({});
    AddComment(R"DOC(
Slice Operator.

Produces a slice of the input tensor along multiple axes. Similar to numpy:
https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Slice uses `axes`, `starts` and `ends` attributes to specify the start and
end dimension for each axis in the list of axes, it uses this information
to slice the input data tensor. If a negative value is passed for any of
the start or end indices, it represents number of elements before the end
of that dimension. If the value passed to start or end is larger than
the n (the number of elements in this dimension), it represents n.
For slicing to the end of a dimension with unknown size, it is recommended
to pass in INT_MAX. The size of axes must be equal to starts\' and ends\'.
Following examples will explain how slice works:

.. code-block:: text

    Case1:
        Given:
            data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
            axes = [0, 1]
            starts = [1, 0]
            ends = [2, 3]
        Then:
            result = [ [5, 6, 7], ]

    Case2:
        Given:
            data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
            starts = [0, 1]
            ends = [-1, 1000]
        Then:
            result = [ [2, 3, 4], ]
)DOC");
  }
};

class SliceOpGrad : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {
    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true, "Input should not be null");
    PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
                      "Input(Out@GRAD) should not be null");
    auto x_dims = ctx->GetInputDim("Input");
    auto x_grad_name = framework::GradVarName("Input");
    if (ctx->HasOutput(x_grad_name)) {
      ctx->SetOutputDim(x_grad_name, x_dims);
    }
  }
  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 == "StartsTensor" || var_name == "EndsTensor") {
      return expected_kernel_type;
    }
    if (var_name == "StartsTensorList" || var_name == "EndsTensorList") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};

template <typename T>
class SliceOpGradMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> bind) const override {
    bind->SetInput("Input", this->Input("Input"));
    if (this->HasInput("StartsTensor")) {
      bind->SetInput("StartsTensor", this->Input("StartsTensor"));
    }
    if (this->HasInput("EndsTensor")) {
      bind->SetInput("EndsTensor", this->Input("EndsTensor"));
    }
    if (this->HasInput("StartsTensorList")) {
      bind->SetInput("StartsTensorList", this->Input("StartsTensorList"));
    }
    if (this->HasInput("EndsTensorList")) {
      bind->SetInput("EndsTensorList", this->Input("EndsTensorList"));
    }
    bind->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    bind->SetOutput(framework::GradVarName("Input"), this->InputGrad("Input"));
    bind->SetAttrMap(this->Attrs());
    bind->SetType("slice_grad");
  }
};

template <typename T>
class SliceDoubleOpGradMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> bind) const override {
    if (this->HasInput("StartsTensor")) {
      bind->SetInput("StartsTensor", this->Input("StartsTensor"));
    }
    if (this->HasInput("EndsTensor")) {
      bind->SetInput("EndsTensor", this->Input("EndsTensor"));
    }
    if (this->HasInput("StartsTensorList")) {
      bind->SetInput("StartsTensorList", this->Input("StartsTensorList"));
    }
    if (this->HasInput("EndsTensorList")) {
      bind->SetInput("EndsTensorList", this->Input("EndsTensorList"));
    }
    bind->SetInput("Input", this->OutputGrad(framework::GradVarName("Input")));
    bind->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
    bind->SetAttrMap(this->Attrs());
    bind->SetType("slice");
  }
};

DECLARE_NO_NEED_BUFFER_VARS_INFERER(SliceOpGradNoNeedBufferVarsInference,
                                    "Input");

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(slice, ops::SliceOp, ops::SliceOpMaker,
                  ops::SliceOpGradMaker<paddle::framework::OpDesc>,
                  ops::SliceOpGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(slice_grad, ops::SliceOpGrad,
                  ops::SliceDoubleOpGradMaker<paddle::framework::OpDesc>,
                  ops::SliceDoubleOpGradMaker<paddle::imperative::OpBase>,
                  ops::SliceOpGradNoNeedBufferVarsInference);

REGISTER_OP_CPU_KERNEL(
    slice, ops::SliceKernel<paddle::platform::CPUDeviceContext, int>,
    ops::SliceKernel<paddle::platform::CPUDeviceContext, int64_t>,
    ops::SliceKernel<paddle::platform::CPUDeviceContext, float>,
    ops::SliceKernel<paddle::platform::CPUDeviceContext, double>);

REGISTER_OP_CPU_KERNEL(
    slice_grad, ops::SliceGradKernel<paddle::platform::CPUDeviceContext, int>,
    ops::SliceGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
    ops::SliceGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::SliceGradKernel<paddle::platform::CPUDeviceContext, double>);