slice_op_npu.cc

/* Copyright (c) 2021 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 Licnse. */

#include <memory>
#include <string>

#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/npu_op_runner.h"
#include "paddle/fluid/operators/slice_op.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

void UpdateAttr(const framework::DDim in_dims, const std::vector<int> axes,
                const std::vector<int> starts, const std::vector<int> ends,
                std::vector<int>* offsets, std::vector<int>* size) {
  int cnt = 0;
  for (int i = 0; i < in_dims.size(); ++i) {
    int start = 0;
    int end = in_dims[i];
    int axis = axes[cnt];

    if (axis == i) {
      start = starts[cnt];
      if (start < 0) {
        start = (start + in_dims[i]);
      }
      start = std::max(start, static_cast<int>(0));
      end = ends[cnt];
      if (end < 0) {
        end = (end + in_dims[i]);
      }
      end = std::min(end, static_cast<int>(in_dims[i]));
      cnt++;
    }

    (*offsets)[i] = start;
    (*size)[i] = end - start;
  }
}

template <typename DeviceContext, typename T>
class SliceNPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* input = ctx.Input<Tensor>("Input");
    auto* out = ctx.Output<Tensor>("Out");

    auto axes = ctx.Attr<std::vector<int>>("axes");
    auto starts = ctx.Attr<std::vector<int>>("starts");
    auto ends = ctx.Attr<std::vector<int>>("ends");

    out->mutable_data<T>(ctx.GetPlace());

    auto in_dims = input->dims();
    std::vector<int> offsets(in_dims.size());
    std::vector<int> size(in_dims.size());

    UpdateAttr(in_dims, axes, starts, ends, &offsets, &size);

    const auto& runner = NpuOpRunner("SliceD", {*input}, {*out},
                                     {{"offsets", offsets}, {"size", size}});

    auto stream =
        ctx.template device_context<paddle::platform::NPUDeviceContext>()
            .stream();
    runner.Run(stream);
  }
};

template <typename DeviceContext, typename T>
class SliceGradNPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* input = ctx.Input<Tensor>("Input");
    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto* dinput = ctx.Output<Tensor>(framework::GradVarName("Input"));

    auto axes = ctx.Attr<std::vector<int>>("axes");
    auto starts = ctx.Attr<std::vector<int>>("starts");
    auto ends = ctx.Attr<std::vector<int>>("ends");

    auto in_dims = input->dims();
    int rank = in_dims.size();

    std::vector<int> offsets(rank);
    std::vector<int> size(rank);
    UpdateAttr(in_dims, axes, starts, ends, &offsets, &size);

    std::vector<std::vector<int64_t>> paddings(rank, std::vector<int64_t>(2));
    for (int i = 0; i < rank; ++i) {
      paddings[i][0] = static_cast<int64_t>(offsets[i]);
      paddings[i][1] = static_cast<int64_t>(in_dims[i] - size[i] - offsets[i]);
    }

    dinput->mutable_data<T>(ctx.GetPlace());
    auto stream =
        ctx.template device_context<paddle::platform::NPUDeviceContext>()
            .stream();
    const auto& runner =
        NpuOpRunner("PadD", {*dout}, {*dinput}, {{"paddings", paddings}});
    runner.Run(stream);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_NPU_KERNEL(
    slice, ops::SliceNPUKernel<paddle::platform::NPUDeviceContext, float>,
    ops::SliceNPUKernel<paddle::platform::NPUDeviceContext, int>,
    ops::SliceNPUKernel<paddle::platform::NPUDeviceContext,
                        paddle::platform::float16>);

REGISTER_OP_NPU_KERNEL(
    slice_grad,
    ops::SliceGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
    ops::SliceGradNPUKernel<paddle::platform::NPUDeviceContext, int>,
    ops::SliceGradNPUKernel<paddle::platform::NPUDeviceContext,
                            paddle::platform::float16>);