wehub-resource-sync
100 lines
3.4 KiB
C++
100 lines
3.4 KiB
C++
// Copyright (c) 2024 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 <string>
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#include <vector>
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/jit/kernels.h"
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namespace phi {
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template <typename T, typename Context>
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void FusionSeqPoolConcatKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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const std::string& pooltype,
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int axis,
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DenseTensor* out) {
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auto ins = x;
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auto x0_lod = ins[0]->lod();
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const auto& x0_dims = ins[0]->dims();
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const auto& y_dims = out->dims();
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size_t bs = x0_lod[0].size() - 1;
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out->Resize({static_cast<int64_t>(bs), y_dims[1]});
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LegacyLoD y_lod(1);
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y_lod[0].resize(bs + 1);
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for (size_t i = 0; i <= bs; ++i) {
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y_lod[0][i] = i;
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}
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out->set_lod(y_lod);
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T* y_data = dev_ctx.template Alloc<T>(out);
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int w = static_cast<int>(ins[0]->numel() / x0_dims[0]);
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PADDLE_ENFORCE_EQ(y_dims[1] % w,
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0,
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common::errors::InvalidArgument(
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"The output of dims[1] should be dividable of w, but "
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"dims[1] is %d, w is %d.",
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y_dims[1],
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w));
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jit::seq_pool_attr_t attr(w, jit::SeqPoolType::kSum);
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if (pooltype == "AVERAGE") {
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attr.type = jit::SeqPoolType::kAvg;
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} else if (pooltype == "SQRT") {
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attr.type = jit::SeqPoolType::kSqrt;
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}
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auto seqpool =
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jit::KernelFuncs<jit::SeqPoolTuple<T>, CPUPlace>::Cache().At(attr);
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size_t n = ins.size();
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size_t dst_step_size = n * w;
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for (size_t i = 0; i < n; ++i) {
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const auto& x_dims = ins[i]->dims();
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auto x_lod = ins[i]->lod()[0];
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const T* src = ins[i]->data<T>();
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T* dst = y_data + i * w;
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PADDLE_ENFORCE_EQ(
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static_cast<int>(ins[i]->numel() / x_dims[0]),
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w,
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common::errors::InvalidArgument(
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"Width of all inputs should be equal, but the width of the %d-th "
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"input %d is not equal to the previous %d",
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i,
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static_cast<int>(ins[i]->numel() / x_dims[0]),
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w));
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PADDLE_ENFORCE_EQ(
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x_lod.size(),
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bs + 1,
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common::errors::InvalidArgument(
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"Batchsize of all inputs should be equal, but the value of the "
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"%d-th %d is not equal to the previous %d.",
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i,
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x_lod.size(),
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bs + 1));
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for (size_t j = 0; j < bs; ++j) {
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attr.h = static_cast<int>(x_lod[j + 1] - x_lod[j]);
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seqpool(src, dst, &attr);
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dst += dst_step_size;
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src += attr.h * attr.w;
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(fusion_seqpool_concat,
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CPU,
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ALL_LAYOUT,
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phi::FusionSeqPoolConcatKernel,
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float,
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double) {}
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