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70 lines
2.6 KiB
70 lines
2.6 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 <vector>
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/strided_memcpy.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 ConcatKernel : 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 ins = ctx.MultiInput<framework::Tensor>("X");
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auto* out = ctx.Output<framework::Tensor>("Out");
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int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
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const size_t n = ins.size();
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size_t output_offset = 0;
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out->mutable_data<T>(ctx.GetPlace());
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auto out_stride = framework::stride(out->dims());
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for (size_t i = 0; i < n; i++) {
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auto& in = ins[i];
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auto axis_dim = in->dims()[axis];
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auto in_stride = framework::stride(in->dims());
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StridedMemcpy<T>(ctx.device_context(), in->data<T>(), in_stride,
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in->dims(), out_stride, out->data<T>() + output_offset);
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output_offset += axis_dim * in_stride[axis];
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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 ConcatGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* in = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
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int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
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const size_t n = outs.size();
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size_t input_offset = 0;
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auto in_stride = framework::stride(in->dims());
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for (size_t i = 0; i < n; i++) {
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auto& out = outs[i];
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out->mutable_data<T>(ctx.GetPlace());
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size_t axis_dim = out->dims()[axis];
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auto out_stride = framework::stride(out->dims());
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StridedMemcpy<T>(ctx.device_context(), in->data<T>() + input_offset,
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in_stride, out->dims(), out_stride, out->data<T>());
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input_offset += axis_dim * in_stride[axis];
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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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