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118 lines
4.0 KiB
118 lines
4.0 KiB
/* Copyright (c) 2021 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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#include "paddle/fluid/operators/mean_op.h"
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#include "paddle/fluid/platform/float16.h"
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#include "paddle/fluid/operators/npu_op_runner.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class MeanNPUKernel : 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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auto* x = ctx.Input<framework::LoDTensor>("X");
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auto* out = ctx.Output<framework::LoDTensor>("Out");
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std::vector<int> axes;
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framework::NPUAttributeMap attr_input = {
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{"keep_dims", false},
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{"axes", axes}};
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out->mutable_data<T>(ctx.GetPlace());
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auto runner = NpuOpRunner("ReduceMeanD",
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{*x},
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{*out},
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attr_input);
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auto stream =
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ctx.template device_context<
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paddle::platform::NPUDeviceContext>()
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.stream();
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runner.Run(stream);
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}
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};
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template <typename DeviceContext, typename T>
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class MeanGradNPUKernel : 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 stream =
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context.template device_context<
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paddle::platform::NPUDeviceContext>()
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.stream();
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auto grad = context.Input<Tensor>(framework::GradVarName("Out"));
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PADDLE_ENFORCE_EQ(grad->numel(), 1,
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platform::errors::InvalidArgument(
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"Mean Gradient Input Tensor len should be 1. But "
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"received Out@Grad's elements num is %d.",
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grad->numel()));
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auto IG = context.Output<Tensor>(framework::GradVarName("X"));
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IG->mutable_data<T>(context.GetPlace());
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// ones
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Tensor ones(grad->type());
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ones.mutable_data<T>(IG->dims(), context.GetPlace());
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auto runner_ones = NpuOpRunner("OnesLike", {*IG}, {ones}, {});
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runner_ones.Run(stream);
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// means
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Tensor mean_tensor(grad->type());
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mean_tensor.Resize({1});
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mean_tensor.mutable_data<T>(context.GetPlace());
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std::vector<float> mean_vec;
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mean_vec.push_back(1.0/static_cast<float>(IG->numel()));
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framework::TensorFromVector(mean_vec,
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context.device_context(),
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&mean_tensor);
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// means mul ones
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Tensor mean_ma(grad->type());
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mean_ma.Resize(IG->dims());
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mean_ma.mutable_data<T>(context.GetPlace());
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auto runner_mul_1 = NpuOpRunner("Mul", {mean_tensor, ones}, {mean_ma}, {});
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runner_mul_1.Run(stream);
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// and mul grad
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auto runner_mul_2 = NpuOpRunner("Mul", {mean_ma, *grad}, {*IG}, {});
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runner_mul_2.Run(stream);
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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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namespace plat = paddle::platform;
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REGISTER_OP_NPU_KERNEL(
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mean,
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ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, int>,
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ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, double>,
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ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, plat::float16>)
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REGISTER_OP_NPU_KERNEL(
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mean_grad,
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ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, int>,
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ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, double>,
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ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, plat::float16>)
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