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98 lines
3.4 KiB
98 lines
3.4 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/gather_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/operators/kron_op.h"
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#include "paddle/fluid/operators/npu_op_runner.h"
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#include "paddle/fluid/platform/npu_info.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 GatherOpNPUKernel : 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 *index = ctx.Input<Tensor>("Index");
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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("Gather", {*x, *index}, {*out},
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{{"validate_indices", true}});
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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 GatherGradOpNPUKernel : 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 *index = ctx.Input<Tensor>("Index");
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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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// step1: Unsqueeze index
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framework::Tensor tmp_tensor(index->type());
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const auto index_dims = index->dims();
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if (index_dims.size() == 1) {
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tmp_tensor.ShareDataWith(*index);
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std::vector<int64_t> new_dim = {index_dims[0], 1};
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tmp_tensor.Resize(framework::make_ddim(new_dim));
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index = &tmp_tensor;
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}
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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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// step2: ZerosLike x in device
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Tensor zeroslike_xout(x->type());
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zeroslike_xout.Resize(x->dims());
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auto p = zeroslike_xout.mutable_data<T>(ctx.GetPlace());
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platform::NPUMemsetAsync(static_cast<void *>(p), 0,
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zeroslike_xout.numel() * sizeof(T), stream);
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// step3: scatter(x_grad)
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner_scatter = NpuOpRunner(
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"TensorScatterUpdate", {zeroslike_xout, *index, *dout}, {*dx}, {});
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runner_scatter.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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gather, ops::GatherOpNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::GatherOpNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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gather_grad,
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ops::GatherGradOpNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::GatherGradOpNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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