elementwise_mul_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/operators/elementwise/elementwise_mul_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using MLUDeviceContext = platform::MLUDeviceContext;

static void GetReduceAxes(const int axis, const framework::DDim& src_ddims,
                          const framework::DDim& target_ddims,
                          std::vector<int>* axes) {
  int64_t src_dim_size = src_ddims.size();
  int64_t target_dim_size = target_ddims.size();
  for (int64_t i = 0; i < src_dim_size; ++i) {
    if (i < axis || i >= target_dim_size + axis) {
      axes->push_back(i);
      continue;
    }
    if (src_ddims[i] > target_ddims[i - axis]) {
      axes->push_back(i);
    }
  }
}

template <typename T>
class ElementwiseMulMLUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* x = ctx.Input<Tensor>("X");
    auto* y = ctx.Input<Tensor>("Y");
    auto* out = ctx.Output<Tensor>("Out");
    out->mutable_data<T>(ctx.GetPlace());

    int axis = ctx.Attr<int>("axis");
    const auto& x_dims = x->dims();
    const auto& y_dims = y->dims();
    axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
                     : axis);
    int max_dim = std::max(x_dims.size(), y_dims.size());
    std::vector<int> x_dims_array(max_dim);
    std::vector<int> y_dims_array(max_dim);
    std::vector<int> out_dims_array(max_dim);
    GetBroadcastDimsArrays(x_dims, y_dims, x_dims_array.data(),
                           y_dims_array.data(), out_dims_array.data(), max_dim,
                           axis);

    MLUCnnlTensorDesc x_desc(max_dim, x_dims_array.data(), ToCnnlDataType<T>());
    MLUCnnlTensorDesc y_desc(max_dim, y_dims_array.data(), ToCnnlDataType<T>());
    MLUCnnlTensorDesc out_desc(*out);
    MLUCnnlOpTensorDesc op_tensor_desc(CNNL_OP_TENSOR_MUL, ToCnnlDataType<T>(),
                                       CNNL_NOT_PROPAGATE_NAN);

    MLUCnnl::OpTensor(ctx, op_tensor_desc.get(), x_desc.get(), GetBasePtr(x),
                      y_desc.get(), GetBasePtr(y), out_desc.get(),
                      GetBasePtr(out), ToCnnlDataType<T>());
  }
};

template <typename T>
class ElementwiseMulGradMLUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto* x = ctx.Input<Tensor>("X");
    auto* y = ctx.Input<Tensor>("Y");
    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
    int axis = ctx.Attr<int>("axis");

    const auto& x_dims = x->dims();
    const auto& y_dims = y->dims();
    axis = (axis < 0 ? (std::abs(x_dims.size() - y_dims.size()) + axis + 1)
                     : axis);
    int max_dim = std::max(x_dims.size(), y_dims.size());
    std::vector<int> x_dims_array(max_dim);
    std::vector<int> y_dims_array(max_dim);
    std::vector<int> out_dims_array(max_dim);
    GetBroadcastDimsArrays(x_dims, y_dims, x_dims_array.data(),
                           y_dims_array.data(), out_dims_array.data(), max_dim,
                           axis);

    MLUCnnlTensorDesc x_desc(max_dim, x_dims_array.data(), ToCnnlDataType<T>());
    MLUCnnlTensorDesc y_desc(max_dim, y_dims_array.data(), ToCnnlDataType<T>());
    MLUCnnlTensorDesc dout_desc(*dout);
    MLUCnnlOpTensorDesc mul_op_desc(CNNL_OP_TENSOR_MUL, ToCnnlDataType<T>(),
                                    CNNL_NOT_PROPAGATE_NAN);
    if (dx) {
      dx->mutable_data<T>(ctx.GetPlace());
      if (dx->dims() == dout->dims()) {
        MLUCnnl::OpTensor(ctx, mul_op_desc.get(), dout_desc.get(),
                          GetBasePtr(dout), y_desc.get(), GetBasePtr(y),
                          x_desc.get(), GetBasePtr(dx), ToCnnlDataType<T>());
      } else {
        Tensor dx_temp(x->dtype());
        dx_temp.Resize(dout->dims());
        dx_temp.mutable_data<T>(ctx.GetPlace());
        MLUCnnl::OpTensor(ctx, mul_op_desc.get(), dout_desc.get(),
                          GetBasePtr(dout), y_desc.get(), GetBasePtr(y),
                          dout_desc.get(), GetBasePtr(&dx_temp),
                          ToCnnlDataType<T>());

        std::vector<int> reduce_axes;
        GetReduceAxes(axis, dx_temp.dims(), dx->dims(), &reduce_axes);
        MLUCnnlReduceDesc reduction_desc(
            reduce_axes, CNNL_REDUCE_ADD, ToCnnlDataType<T>(),
            CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
        MLUCnnlTensorDesc dx_desc(*dx);
        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
                        nullptr, dout_desc.get(), GetBasePtr(&dx_temp), 0,
                        nullptr, nullptr, dx_desc.get(), GetBasePtr(dx));
      }
    }
    if (dy) {
      dy->mutable_data<T>(ctx.GetPlace());
      if (dy->dims() == dout->dims()) {
        MLUCnnl::OpTensor(ctx, mul_op_desc.get(), dout_desc.get(),
                          GetBasePtr(dout), x_desc.get(), GetBasePtr(x),
                          y_desc.get(), GetBasePtr(dy), ToCnnlDataType<T>());
      } else {
        Tensor dy_temp(y->dtype());
        dy_temp.Resize(dout->dims());
        dy_temp.mutable_data<T>(ctx.GetPlace());
        MLUCnnl::OpTensor(ctx, mul_op_desc.get(), dout_desc.get(),
                          GetBasePtr(dout), x_desc.get(), GetBasePtr(x),
                          dout_desc.get(), GetBasePtr(&dy_temp),
                          ToCnnlDataType<T>());

        std::vector<int> reduce_axes;
        GetReduceAxes(axis, dy_temp.dims(), dy->dims(), &reduce_axes);
        MLUCnnlReduceDesc reduction_desc(
            reduce_axes, CNNL_REDUCE_ADD, ToCnnlDataType<T>(),
            CNNL_NOT_PROPAGATE_NAN, CNNL_REDUCE_NO_INDICES, CNNL_32BIT_INDICES);
        MLUCnnlTensorDesc dy_desc(*dy);
        MLUCnnl::Reduce(ctx, true /*need_workspace*/, reduction_desc.get(),
                        nullptr, dout_desc.get(), GetBasePtr(&dy_temp), 0,
                        nullptr, nullptr, dy_desc.get(), GetBasePtr(dy));
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_MLU_KERNEL(elementwise_mul, ops::ElementwiseMulMLUKernel<float>,
                       ops::ElementwiseMulMLUKernel<paddle::platform::float16>,
                       ops::ElementwiseMulMLUKernel<int>);

REGISTER_OP_MLU_KERNEL(
    elementwise_mul_grad, ops::ElementwiseMulGradMLUKernel<float>,
    ops::ElementwiseMulGradMLUKernel<paddle::platform::float16>,
    ops::ElementwiseMulGradMLUKernel<int>);