wehub-resource-sync
127 lines
4.2 KiB
C++
127 lines
4.2 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/amp_kernel.h"
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#include <cmath>
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/impl/amp_kernel_impl.h"
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#include "paddle/phi/kernels/isfinite_kernel.h"
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#include "paddle/phi/kernels/reduce_all_kernel.h"
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namespace phi {
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// Utils
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template <typename T, bool IsFoundInfOnCPU>
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class UpdateLossScalingFunctor<CPUContext, T, IsFoundInfOnCPU> {
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public:
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void operator()(const CPUContext& dev_ctx UNUSED,
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const bool* found_inf_data,
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const T* pre_loss_scaling_data,
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const int* good_in_data,
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const int* bad_in_data,
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const int incr_every_n_steps,
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const int decr_every_n_nan_or_inf,
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const float incr_ratio,
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const float decr_ratio,
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T* updated_loss_scaling_data,
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int* good_out_data,
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int* bad_out_data) const {
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PADDLE_ENFORCE_EQ(
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IsFoundInfOnCPU,
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true,
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common::errors::InvalidArgument(
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"The Input(FoundInfinite) should be on the CPUPlace."));
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Update<T>(found_inf_data,
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pre_loss_scaling_data,
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good_in_data,
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bad_in_data,
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incr_every_n_steps,
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decr_every_n_nan_or_inf,
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incr_ratio,
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decr_ratio,
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updated_loss_scaling_data,
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good_out_data,
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bad_out_data);
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}
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};
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// Kernels
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template <typename T, typename Context>
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void CheckFiniteAndUnscaleKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& xs,
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const DenseTensor& scale,
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std::vector<DenseTensor*> outs,
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DenseTensor* found_infinite) {
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const T* scale_data = scale.data<T>();
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bool* found_inf_data = dev_ctx.template Alloc<bool>(found_infinite);
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*found_inf_data = false;
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DenseTensor is_finite = Empty<bool>(dev_ctx, {1});
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bool* is_finite_data = is_finite.template data<bool>();
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auto& dev = *dev_ctx.eigen_device();
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T inverse_scale = 1.0 / *scale_data;
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for (size_t i = 0; i < xs.size(); ++i) {
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const auto* x = xs[i];
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auto* out = outs[i];
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dev_ctx.template Alloc<T>(out);
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if (!(*found_inf_data)) {
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DenseTensor tmp;
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tmp.Resize(x->dims());
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IsfiniteKernel<T, Context>(dev_ctx, *x, &tmp);
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std::vector<int64_t> dims(x->dims().size());
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std::iota(dims.begin(), dims.end(), 0);
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AllKernel<bool, Context>(dev_ctx, tmp, dims, false, &is_finite);
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*found_inf_data = !(*is_finite_data);
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}
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auto eigen_out = EigenVector<T>::Flatten(*out);
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auto eigen_in = EigenVector<T>::Flatten(*x);
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if (!(*found_inf_data)) {
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eigen_out.device(dev) = eigen_in * inverse_scale;
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} else {
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eigen_out.device(dev) = eigen_in * static_cast<T>(0);
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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(check_finite_and_unscale,
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CPU,
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ALL_LAYOUT,
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phi::CheckFiniteAndUnscaleKernel,
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float,
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double) {
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kernel->OutputAt(1).SetDataType(phi::DataType::BOOL);
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}
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PD_REGISTER_KERNEL(update_loss_scaling,
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CPU,
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ALL_LAYOUT,
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phi::UpdateLossScalingKernel,
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float,
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double) {
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kernel->OutputAt(2).SetDataType(phi::DataType::INT32);
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kernel->OutputAt(3).SetDataType(phi::DataType::INT32);
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}
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