wehub-resource-sync
149 lines
4.7 KiB
C++
149 lines
4.7 KiB
C++
/* Copyright (c) 2022 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/phi/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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namespace phi {
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namespace funcs {
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using XPUDeviceGuard = backends::xpu::XPUDeviceGuard;
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/*
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* All tensors' dimension should be the same and the values of
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* each dimension must be the same, except the axis dimension.
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*/
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template <typename T>
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class ConcatFunctor<XPUContext, T> {
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public:
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void operator()(const XPUContext& dev_ctx,
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const std::vector<DenseTensor>& input,
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int axis,
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DenseTensor* output) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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int dev_id = dev_ctx.GetPlace().GetDeviceId();
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XPUDeviceGuard guard(dev_id);
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int num = input.size();
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auto input_dims = input[0].dims();
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std::vector<std::vector<int64_t>> xdims_list(num);
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for (int i = 0; i < num; ++i) {
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std::vector<int64_t> tmp_dims(input_dims.size());
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for (int j = 0; j < input_dims.size(); ++j) {
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tmp_dims[j] = input[i].dims()[j];
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}
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xdims_list[i] = tmp_dims;
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}
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std::vector<const XPUType*> ptrs;
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for (int i = 0; i < num; ++i) {
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if (input[i].place() != dev_ctx.GetPlace()) {
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// data not on xpu, probably on cpu. move it now
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DenseTensor tmp_data = input[i];
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dev_ctx.template Alloc<T>(&tmp_data);
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ptrs.push_back(reinterpret_cast<const XPUType*>(tmp_data.data<T>()));
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} else {
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ptrs.push_back(reinterpret_cast<const XPUType*>(input[i].data<T>()));
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}
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}
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dev_ctx.template Alloc<T>(output);
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auto r = xpu::concat<XPUType>(dev_ctx.x_context(),
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ptrs,
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reinterpret_cast<XPUType*>(output->data<T>()),
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xdims_list,
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axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "concat");
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}
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};
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template <typename T>
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class SplitFunctor<XPUContext, T> {
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public:
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void operator()(const XPUContext& dev_ctx,
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const DenseTensor& input,
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const std::vector<const DenseTensor*>& ref_inputs,
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const int axis,
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std::vector<DenseTensor*>* outputs) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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int dev_id = dev_ctx.GetPlace().GetDeviceId();
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XPUDeviceGuard guard(dev_id);
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auto& ins = ref_inputs;
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int num = ins.size();
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auto input_dims = ins[0]->dims();
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// special for 0-dim shape
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if (input_dims.size() == 0) {
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input_dims = {1};
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}
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std::vector<int64_t> split_list(num);
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std::vector<int64_t> xdims_list(input_dims.size());
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int64_t total_length = 0;
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for (int i = 0; i < num; ++i) {
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auto ins_i_dims = ins[i]->dims();
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// special for 0-dim shape
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if (ins_i_dims.size() == 0) {
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ins_i_dims = {1};
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}
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split_list[i] = ins_i_dims[axis];
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total_length += ins_i_dims[axis];
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}
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for (int i = 0; i < input_dims.size(); ++i) {
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if (i == axis) continue;
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xdims_list[i] = input_dims[i];
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}
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xdims_list[axis] = total_length;
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std::vector<XPUType*> ptrs(num);
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for (int i = 0; i < num; ++i) {
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dev_ctx.template Alloc<T>(outputs->at(i));
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ptrs[i] = reinterpret_cast<XPUType*>(outputs->at(i)->data<T>());
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}
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DenseTensor tmp_data = input;
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if (input.place() != dev_ctx.GetPlace()) {
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// data not on xpu, probably on cpu. move it now
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dev_ctx.template Alloc<T>(&tmp_data);
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}
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// int split(Context* xpu_ctx, const T* x, const std::vector<T*>& y_list,
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// const std::vector<int64_t>& xshape, const std::vector<int64_t>&
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// split_list, int64_t axis);
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auto r = xpu::split<XPUType>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(tmp_data.data<T>()),
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ptrs,
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xdims_list,
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split_list,
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axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "split");
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}
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};
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#define DEFINE_XPU_FUNCTOR(type) \
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template class ConcatFunctor<XPUContext, type>; \
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template class SplitFunctor<XPUContext, type>;
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DEFINE_XPU_FUNCTOR(float)
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DEFINE_XPU_FUNCTOR(phi::float16)
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DEFINE_XPU_FUNCTOR(phi::bfloat16)
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DEFINE_XPU_FUNCTOR(int32_t)
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DEFINE_XPU_FUNCTOR(int64_t)
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DEFINE_XPU_FUNCTOR(uint8_t)
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} // namespace funcs
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} // namespace phi
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