wehub-resource-sync
84 lines
2.9 KiB
C++
84 lines
2.9 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 "paddle/phi/kernels/affine_channel_kernel.h"
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#include <string>
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#include <unordered_map>
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T>
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using EigenArrayMap =
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Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
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template <typename T>
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using ConstEigenArrayMap =
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Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
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template <typename T>
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using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
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template <typename T>
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using ConstEigenVectorArrayMap =
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Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
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template <typename T, typename Context>
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void AffineChannelKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& scale_in,
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const DenseTensor& bias_in,
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const std::string& data_layout,
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DenseTensor* out) {
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auto* x = &x_in;
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auto* scale = &scale_in;
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auto* bias = &bias_in;
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auto* y = out;
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dev_ctx.template Alloc<T>(y);
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const DataLayout layout = StringToDataLayout(data_layout);
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auto dims = x->dims();
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int N = static_cast<int>(dims[0]);
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int C = static_cast<int>(layout == DataLayout::NCHW ? dims[1]
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: dims[dims.size() - 1]);
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int HxW = static_cast<int>(x->numel() / N / C);
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auto* scale_d = scale->data<T>();
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auto* bias_d = bias->data<T>();
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ConstEigenVectorArrayMap<T> a_e(scale_d, C);
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ConstEigenVectorArrayMap<T> b_e(bias_d, C);
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auto* x_d = x->data<T>();
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auto* y_d = y->data<T>();
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if (layout == DataLayout::NCHW) {
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int64_t stride = static_cast<int64_t>(C) * HxW;
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for (int i = 0; i < N; i++) {
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ConstEigenArrayMap<T> x_e(x_d, HxW, C);
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EigenArrayMap<T> y_e(y_d, HxW, C);
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y_e = (x_e.rowwise() * a_e.transpose()).rowwise() + b_e.transpose();
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x_d += stride;
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y_d += stride;
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}
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} else {
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int64_t num = static_cast<int64_t>(N) * HxW;
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ConstEigenArrayMap<T> x_e(x_d, C, num);
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EigenArrayMap<T> y_e(y_d, C, num);
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y_e = (x_e.colwise() * a_e).colwise() + b_e;
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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affine_channel, CPU, ALL_LAYOUT, phi::AffineChannelKernel, float, double) {}
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