wehub-resource-sync
82 lines
2.6 KiB
C++
82 lines
2.6 KiB
C++
// 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/norm_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/common_shape.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void NormKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int axis,
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float epsilon,
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bool is_test,
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DenseTensor* out,
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DenseTensor* norm) {
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auto xdim = x.dims();
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T eps = epsilon;
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if (axis < 0) axis = xdim.size() + axis;
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int64_t pre = 0, n = 0, post = 0;
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funcs::GetPrePostNumel(xdim, axis, &pre, &n, &post);
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DenseTensor* out_norm = nullptr;
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DenseTensor out_norm_tmp;
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if (is_test) {
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auto out_dim = x.dims();
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out_dim[axis] = 1;
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out_norm = &out_norm_tmp;
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out_norm->Resize(out_dim);
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} else {
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out_norm = norm;
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}
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dev_ctx.template Alloc<T>(out);
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dev_ctx.template Alloc<T>(out_norm);
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auto* place = dev_ctx.eigen_device();
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Eigen::DSizes<int64_t, 3> shape(pre, n, post);
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Eigen::DSizes<int64_t, 2> norm_shape(pre, post);
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auto x_e = EigenVector<T>::Flatten(x);
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auto y_e = EigenVector<T>::Flatten(*out);
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auto norm_e = EigenVector<T>::Flatten(*out_norm);
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auto x_r = x_e.reshape(shape);
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auto y = y_e.reshape(shape);
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auto norm_reshape = norm_e.reshape(norm_shape);
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Eigen::DSizes<int, 1> rdim(1);
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// y = x / sqrt((sum(x * x) + epsilon))
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// norm = sqrt(sum(x * x) + epsilon)
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auto x2 = x_r * x_r;
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auto sum = x2.sum(rdim) + eps;
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norm_reshape.device(*place) = sum.sqrt();
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// y = x / norm
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Eigen::DSizes<int64_t, 3> rshape(pre, static_cast<int64_t>(1), post);
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Eigen::DSizes<int64_t, 3> bcast(
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static_cast<int64_t>(1), n, static_cast<int64_t>(1));
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y.device(*place) = x_r / norm_reshape.reshape(rshape).broadcast(bcast);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(norm, CPU, ALL_LAYOUT, phi::NormKernel, float, double) {}
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