wehub-resource-sync
134 lines
4.1 KiB
Plaintext
134 lines
4.1 KiB
Plaintext
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/concat_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/lod_utils.h"
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#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/phi/kernels/funcs/concat_funcs.h"
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#include "paddle/phi/kernels/funcs/strided_memcpy.h"
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namespace phi {
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template <typename T, typename Context>
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void ConcatKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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const Scalar& axis_scalar,
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DenseTensor* out) {
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int64_t axis = axis_scalar.to<int64_t>();
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axis = funcs::ComputeAxis(axis, x[0]->dims().size());
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std::vector<DDim> x_dims;
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for (size_t i = 0; i < x.size(); ++i) {
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x_dims.push_back(x[i]->dims());
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}
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DDim out_dims = funcs::ComputeAndCheckShape(true, x_dims, axis);
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out->Resize(out_dims);
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dev_ctx.template Alloc<T>(out);
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if (out->numel() == 0) {
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return;
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}
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// If axis is 0, the lod of the output is not the same as inputs.
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if (axis == 0 && x[0]->lod().size() > 0) {
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size_t lod_size_0 = x[0]->lod().size();
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size_t lod_size = lod_size_0;
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for (size_t i = 1; i < x.size(); ++i) {
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if (x[i]->lod().size() > 0) {
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PADDLE_ENFORCE_EQ(
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x[i]->lod().size(),
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lod_size_0,
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common::errors::Unimplemented(
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"The lod level of all input DenseTensors should be same. "
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"Maybe different lod level of input DenseTensors can concat,"
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"it is not supported currently. The lod level of %dth input "
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"is %d and first input is %d.",
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i,
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x[i]->lod().size(),
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lod_size_0));
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} else {
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lod_size = 0;
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break;
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}
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}
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if (lod_size) {
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auto* out_lod = out->mutable_lod();
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for (size_t i = 1; i < x.size(); ++i) {
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auto in_lod = ConvertToLengthBasedLegacyLoD(x[i]->lod());
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AppendLegacyLoD(out_lod, in_lod);
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}
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}
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}
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// Sometimes direct copies will be faster, this maybe need deeply analysis.
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if (axis == 0 && x.size() < 10) {
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size_t output_offset = 0;
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for (auto* in : x) {
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if (in->numel() == 0UL) {
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continue;
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}
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auto in_stride = common::stride_numel(in->dims());
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auto out_stride = common::stride_numel(out->dims());
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funcs::StridedNumelCopyWithAxis<T, Context>(
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dev_ctx,
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axis,
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out->data<T>() + output_offset,
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out_stride,
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in->data<T>(),
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in_stride,
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in_stride[axis]);
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output_offset += in_stride[axis];
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}
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} else {
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std::vector<DenseTensor> inputs;
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for (size_t j = 0; j < x.size(); ++j) {
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if (x[j]->numel() > 0) {
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inputs.push_back(*x[j]);
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} else {
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continue;
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}
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}
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funcs::ConcatFunctor<Context, T> functor;
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functor(dev_ctx, inputs, axis, out);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(concat,
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GPU,
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ALL_LAYOUT,
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phi::ConcatKernel,
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float,
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double,
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bool,
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int64_t,
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int,
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uint8_t,
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int8_t,
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int16_t,
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phi::float16,
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phi::bfloat16,
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phi::float8_e4m3fn,
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phi::float8_e5m2,
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phi::complex64,
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phi::complex128) {}
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