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377 lines
12 KiB
377 lines
12 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 Licnse. */
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#include <memory>
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#include <string>
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#include "paddle/fluid/framework/ddim.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/operators/activation_op.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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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class PowNPUKernel : 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<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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auto factor = ctx.Attr<float>("factor");
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out->mutable_data<T>(ctx.GetPlace());
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auto runner = NpuOpRunner("Power", {*x}, {*out},
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{{"power", factor},
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{"scale", static_cast<float>(1.0)},
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{"shift", static_cast<float>(0.0)}});
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auto stream =
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ctx.template device_context<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 PowGradNPUKernel : 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<Tensor>("X");
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto factor = ctx.Attr<float>("factor");
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auto x_dims = x->dims();
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auto place = ctx.GetPlace();
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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// NOTE(liym27): dx = dout * factor * x.pow(factor-1)
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// Step1: Compute x_pow = x.pow(factor-1)
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Tensor x_pow(x->type());
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x_pow.mutable_data<T>(x->dims(), place);
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auto runner_pow = NpuOpRunner("Power", {*x}, {x_pow},
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{{"power", factor - static_cast<float>(1)}});
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runner_pow.Run(stream);
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// Step 2: Construct a broadcast factor, which has the same shape with x.
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// 2.1 Get a factor tensor with shape [1].
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Tensor factor_tensor(framework::proto::VarType::FP32);
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factor_tensor.mutable_data<float>({1}, place);
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TensorFromVector(std::vector<float>{factor}, ctx.device_context(),
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&factor_tensor);
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// 2.2 Get the factor which has the shape with x and the same value with
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// factor.
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Tensor factor_bc_tensor(framework::proto::VarType::FP32);
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factor_bc_tensor.mutable_data<float>(x_dims, place);
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auto runner_bc = NpuOpRunner("FillD", {factor_tensor}, {factor_bc_tensor},
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{{"dims", framework::vectorize(x_dims)}});
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runner_bc.Run(stream);
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// Step 3: Compute x_power_mul_factor = factor * x.pow(factor-1)
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Tensor x_power_mul_factor(x->type());
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x_power_mul_factor.mutable_data<T>(x->dims(), place);
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auto runner_mul_1 =
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NpuOpRunner("Mul", {factor_bc_tensor, *x}, {x_power_mul_factor}, {});
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runner_mul_1.Run(stream);
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// Step 4: Compute dx = dout * factor * x.pow(factor-1)
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dx->mutable_data<T>(place);
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auto runner_mul_2 =
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NpuOpRunner("Mul", {*dout, x_power_mul_factor}, {*dx}, {});
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runner_mul_2.Run(stream);
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}
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};
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template <typename DeviceContext, typename T>
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class ReluNPUKernel : 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<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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out->mutable_data<T>(ctx.GetPlace());
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auto runner = NpuOpRunner("Relu",
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{
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*x,
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},
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{*out}, {});
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auto stream =
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ctx.template device_context<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 ReluGradNPUKernel : 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* out = ctx.Input<Tensor>("Out");
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner = NpuOpRunner("ReluGrad", {*dout, *out}, {*dx}, {});
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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 SqrtNPUKernel : 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<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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auto place = ctx.GetPlace();
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out->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto runner = NpuOpRunner("Sqrt", {*x}, {*out}, {});
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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 SqrtGradNPUKernel : 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* out = ctx.Input<Tensor>("Out");
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auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto place = ctx.GetPlace();
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dx->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto dx_runner = NpuOpRunner("SqrtGrad", {*out, *dout}, {*dx}, {});
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dx_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 LogNPUKernel : 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<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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auto place = ctx.GetPlace();
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out->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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Tensor one(x->type());
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one.mutable_data<T>(x->dims(), place);
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auto one_runner = NpuOpRunner("OnesLike", {*x}, {one}, {});
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one_runner.Run(stream);
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Tensor sub(x->type());
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sub.mutable_data<T>(x->dims(), place);
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auto sub_runner = NpuOpRunner("Sub", {*x, one}, {sub}, {});
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sub_runner.Run(stream);
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auto out_runner = NpuOpRunner("Log1p", {sub}, {*out}, {});
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out_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 LogGradNPUKernel : 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* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* x = ctx.Input<Tensor>("X");
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto place = ctx.GetPlace();
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dx->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto runner = NpuOpRunner("DivNoNan", {*dout, *x}, {*dx}, {});
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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 TanhNPUKernel : 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<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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auto place = ctx.GetPlace();
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out->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto runner = NpuOpRunner("Tanh", {*x}, {*out}, {});
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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 TanhGradNPUKernel : 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* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto* out = ctx.Input<Tensor>("Out");
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto place = ctx.GetPlace();
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dx->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto dx_runner = NpuOpRunner("TanhGrad", {*out, *dout}, {*dx}, {});
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dx_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 SquareNPUKernel : 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<Tensor>("X");
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auto* out = ctx.Output<Tensor>("Out");
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auto place = ctx.GetPlace();
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out->mutable_data<T>(place);
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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auto runner = NpuOpRunner("Square", {*x}, {*out}, {});
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runner.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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REGISTER_OP_NPU_KERNEL(
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pow,
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ops::PowNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::PowNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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pow_grad,
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ops::PowGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::PowGradNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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relu,
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ops::ReluNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::ReluNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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relu_grad,
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ops::ReluGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::ReluGradNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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sqrt,
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ops::SqrtNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::SqrtNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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sqrt_grad,
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ops::SqrtGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::SqrtGradNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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log,
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ops::LogNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::LogNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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log_grad,
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ops::LogGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::LogGradNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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tanh,
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ops::TanhNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::TanhNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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tanh_grad,
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ops::TanhGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::TanhGradNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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square,
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ops::SquareNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::SquareNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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