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112 lines
3.9 KiB
112 lines
3.9 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/softmax_op.h"
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#include "paddle/fluid/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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using DDim = framework::DDim;
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template <typename DeviceContext, typename T>
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class SoftmaxXPUKernel : 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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auto* x = context.Input<Tensor>("X");
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auto* out = context.Output<Tensor>("Out");
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const int rank = x->dims().size();
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int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
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// allocate memory on device.
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out->mutable_data<T>(context.GetPlace());
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std::vector<int> x_dims;
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for (int i = 0; i < rank; i++) {
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x_dims.push_back(x->dims()[i]);
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}
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if (axis < 0) {
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axis += rank;
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}
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = XPU_SUCCESS;
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Tensor clip_x;
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int len = x->numel();
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T* clip_x_data =
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clip_x.mutable_data<T>(context.GetPlace(), len * sizeof(T));
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r = xpu::clip(dev_ctx.x_context(), x->data<float>(), clip_x_data, len,
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-1e30, 1e30);
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PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
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platform::errors::External("XPU API(clip) return wrong "
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"value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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r = xpu::softmax<T>(dev_ctx.x_context(), clip_x_data, out->data<float>(),
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x_dims, axis);
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PADDLE_ENFORCE_EQ(
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r, XPU_SUCCESS,
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platform::errors::External("XPU API(softmax2d_forward) return wrong "
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"value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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}
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};
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template <typename DeviceContext, typename T>
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class SoftmaxGradXPUKernel : 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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auto* out = context.Input<Tensor>("Out");
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auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
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auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
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const int rank = dx->dims().size();
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int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
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// allocate memory on device.
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dx->mutable_data<T>(context.GetPlace());
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std::vector<int> x_dims;
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for (int i = 0; i < rank; i++) {
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x_dims.push_back(dx->dims()[i]);
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}
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if (axis < 0) {
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axis += rank;
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}
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = xpu::softmax_grad<T>(dev_ctx.x_context(), out->data<float>(),
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dout->data<float>(), dx->data<float>(), x_dims,
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axis);
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PADDLE_ENFORCE_EQ(
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r, XPU_SUCCESS,
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platform::errors::External("XPU API(softmax2d_backward) return wrong "
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"value[%d %s]",
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r, XPUAPIErrorMsg[r]));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_XPU_KERNEL(
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softmax, ops::SoftmaxXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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softmax_grad,
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ops::SoftmaxGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif // PADDLE_WITH_XPU
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