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106 lines
3.9 KiB
106 lines
3.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/operators/math/math_function.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class MulKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* x = context.Input<Tensor>("X");
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const Tensor* y = context.Input<Tensor>("Y");
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Tensor* z = context.Output<Tensor>("Out");
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const Tensor x_matrix =
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x->dims().size() > 2
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? framework::ReshapeToMatrix(
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*x, context.template Attr<int>("x_num_col_dims"))
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: *x;
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const Tensor y_matrix =
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y->dims().size() > 2
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? framework::ReshapeToMatrix(
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*y, context.template Attr<int>("y_num_col_dims"))
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: *y;
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z->mutable_data<T>(context.GetPlace());
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auto z_dim = z->dims();
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if (z_dim.size() != 2) {
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z->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
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}
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math::matmul<DeviceContext, T>(
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context.template device_context<DeviceContext>(), x_matrix, false,
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y_matrix, false, 1, z, 0);
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if (z_dim.size() != 2) {
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z->Resize(z_dim);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class MulGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims");
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int y_num_col_dims = ctx.template Attr<int>("y_num_col_dims");
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const Tensor* x = ctx.Input<Tensor>("X");
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const Tensor* y = ctx.Input<Tensor>("Y");
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const Tensor x_matrix = x->dims().size() > 2
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? framework::ReshapeToMatrix(*x, x_num_col_dims)
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: *x;
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const Tensor y_matrix = y->dims().size() > 2
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? framework::ReshapeToMatrix(*y, y_num_col_dims)
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: *y;
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const Tensor* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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Tensor dout_mat;
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dout_mat.ShareDataWith(*dout);
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dout_mat.Resize({framework::flatten_to_2d(x->dims(), x_num_col_dims)[0],
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framework::flatten_to_2d(y->dims(), y_num_col_dims)[1]});
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Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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Tensor* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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if (dx) {
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dx->mutable_data<T>(ctx.GetPlace());
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Tensor dx_matrix = dx->dims().size() > 2
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? framework::ReshapeToMatrix(*dx, x_num_col_dims)
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: *dx;
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// dx = dout * y'. dx: M x K, dout : M x N, y : K x N
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math::matmul<DeviceContext, T>(dev_ctx, dout_mat, false, y_matrix, true,
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1, &dx_matrix, 0);
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}
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if (dy) {
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dy->mutable_data<T>(ctx.GetPlace());
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Tensor dy_matrix = dy->dims().size() > 2
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? framework::ReshapeToMatrix(*dy, y_num_col_dims)
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: *dy;
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// dy = x' * dout. dy K x N, dout : M x N, x : M x K
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math::matmul<DeviceContext, T>(dev_ctx, x_matrix, true, dout_mat, false,
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1, &dy_matrix, 0);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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