set_value_op_mlu.cc

/* Copyright (c) 2022 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/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
#include "paddle/fluid/operators/set_value_op.h"

namespace paddle {
namespace operators {

using MLUDeviceContext = platform::MLUDeviceContext;

template <typename T>
class SetValueMLUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto* in = ctx.Input<Tensor>("Input");
    auto* value_tensor = ctx.Input<Tensor>("ValueTensor");
    auto* out = ctx.Output<Tensor>("Out");
    out->mutable_data<T>(ctx.GetPlace());

    auto starts_tensor_list = ctx.MultiInput<Tensor>("StartsTensorList");
    auto ends_tensor_list = ctx.MultiInput<Tensor>("EndsTensorList");
    auto steps_tensor_list = ctx.MultiInput<Tensor>("StepsTensorList");

    auto axes = ctx.Attr<std::vector<int64_t>>("axes");
    auto starts = ctx.Attr<std::vector<int64_t>>("starts");
    auto ends = ctx.Attr<std::vector<int64_t>>("ends");
    auto steps = ctx.Attr<std::vector<int64_t>>("steps");
    auto shape = ctx.Attr<std::vector<int64_t>>("shape");
    auto decrease_axes = ctx.Attr<std::vector<int64_t>>("decrease_axes");
    auto none_axes = ctx.Attr<std::vector<int64_t>>("none_axes");

    if (!starts_tensor_list.empty()) {
      starts = GetDataFromTensorList<int64_t>(starts_tensor_list);
    }
    if (!ends_tensor_list.empty()) {
      ends = GetDataFromTensorList<int64_t>(ends_tensor_list);
    }
    if (!steps_tensor_list.empty()) {
      steps = GetDataFromTensorList<int64_t>(steps_tensor_list);
    }

    auto in_dims = in->dims();
    phi::funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends, &steps);
    auto slice_dims =
        phi::funcs::GetSliceDims(in_dims, axes, starts, ends, &steps);
    auto decrease_slice_dims =
        phi::funcs::GetDecreasedDims(slice_dims, decrease_axes);

    auto slice_dims_for_assign = decrease_slice_dims;
    if (!none_axes.empty()) {
      std::vector<int64_t> slice_dims_with_none;

      size_t none_axes_cur = 0, decrease_axes_cur = 0;
      for (int i = 0; i < slice_dims.size(); ++i) {
        while (none_axes_cur < none_axes.size() &&
               none_axes[none_axes_cur] <= i) {
          slice_dims_with_none.push_back(1);
          none_axes_cur++;
        }
        if (decrease_axes_cur < decrease_axes.size() &&
            decrease_axes[decrease_axes_cur] == i) {
          decrease_axes_cur++;
        } else {
          slice_dims_with_none.push_back(slice_dims[i]);
        }
      }
      while (none_axes_cur < none_axes.size()) {
        slice_dims_with_none.push_back(1);
        none_axes_cur++;
      }

      slice_dims_for_assign = phi::make_ddim(slice_dims_with_none);
    }

    auto starts_indices = std::vector<int64_t>(in_dims.size(), 0);
    auto ends_indices = std::vector<int64_t>(in_dims.size(), 0);
    auto strides_indices = std::vector<int64_t>(in_dims.size(), 0);

    for (int i = 0; i < in_dims.size(); ++i) {
      starts_indices[i] = 0;
      ends_indices[i] = slice_dims[i];
      strides_indices[i] = 1;
    }
    for (size_t i = 0; i < axes.size(); i++) {
      int axis_index = axes[i];
      starts_indices[axis_index] = starts[i];
      ends_indices[axis_index] = ends[i];
      strides_indices[axis_index] = steps[i];
    }

    int64_t stride_step = phi::product(in_dims);
    std::vector<int64_t> index_indices(1, 0);
    for (size_t i = 0; i < strides_indices.size(); ++i) {
      auto index_size = index_indices.size();
      stride_step /= in_dims[i];
      for (size_t j = 0; j < index_size; ++j) {
        auto start_index = *index_indices.begin();
        if (strides_indices[i] > 0) {
          for (int64_t k = starts_indices[i]; k < ends_indices[i];
               k += strides_indices[i]) {
            index_indices.push_back(start_index + k * stride_step);
          }
        } else {
          for (int64_t k = starts_indices[i]; k > ends_indices[i];
               k += strides_indices[i]) {
            index_indices.push_back(start_index + k * stride_step);
          }
        }
        index_indices.erase(index_indices.begin());
      }
    }

    PADDLE_ENFORCE_EQ(
        static_cast<int64_t>(index_indices.size()),
        phi::product(slice_dims_for_assign),
        platform::errors::InvalidArgument(
            "OP(set_value) error index indices and value update not match "));

    Tensor value_t(in->type());
    if (value_tensor != nullptr) {
      value_t.ShareDataWith(*value_tensor);
    } else {
      auto value_dims = phi::make_ddim(shape);
      CheckIsDimsMatch(slice_dims_for_assign, value_dims);

      value_t.mutable_data<T>(value_dims, ctx.GetPlace());
      auto value_name =
          GetValueName(framework::TransToProtoVarType(in->dtype()));
      CopyVectorToTensor<T>(value_name.c_str(), &value_t, ctx);
      value_t.Resize(value_dims);
    }

    Tensor value_temp(in->type());
    if (slice_dims_for_assign == value_t.dims()) {
      value_temp.ShareDataWith(value_t);
    } else {
      value_temp.Resize(slice_dims_for_assign);
      value_temp.mutable_data<T>(ctx.GetPlace());
      MLUCnnlTensorDesc value_t_desc(value_t);
      MLUCnnlTensorDesc value_temp_desc(value_temp);
      MLUCnnl::BroadcastTo(ctx,
                           value_t_desc.get(),
                           GetBasePtr(&value_t),
                           value_temp_desc.get(),
                           GetBasePtr(&value_temp));
    }

    int64_t input_numel = phi::product(in_dims);
    int64_t value_numel = phi::product(value_temp.dims());
    Tensor in_temp, out_temp, val_temp;
    framework::Tensor index_temp;
    in_temp.ShareDataWith(*in);
    val_temp.ShareDataWith(value_temp);
    paddle::framework::TensorFromVector(
        index_indices, ctx.device_context(), &index_temp);
    auto new_in_dims = phi::make_ddim({input_numel});
    auto new_val_dims = phi::make_ddim({value_numel});
    in_temp.Resize(new_in_dims);
    val_temp.Resize(new_val_dims);
    cnnlScatterRefMode_t mode = CNNL_SCATTERREF_UPDATE;
    MLUCnnlTensorDesc x_desc(in_temp);
    MLUCnnlTensorDesc indices_desc(index_temp);
    MLUCnnlTensorDesc updates_desc(val_temp);
    MLUCnnlTensorDesc out_desc(*out);

    MLUCnnl::ScatterRefFunctor(ctx,
                               x_desc.get(),
                               GetBasePtr(&in_temp),
                               updates_desc.get(),
                               GetBasePtr(&val_temp),
                               indices_desc.get(),
                               GetBasePtr(&index_temp),
                               mode);
    in_temp.Resize(in_dims);
    paddle::framework::TensorCopy(in_temp, ctx.GetPlace(), out);
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_MLU_KERNEL(set_value,
                       ops::SetValueMLUKernel<int>,
                       ops::SetValueMLUKernel<float>);