wehub-resource-sync
77 lines
2.7 KiB
C++
77 lines
2.7 KiB
C++
// Copyright (c) 2026 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/reduce_nansum_grad_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/reduce_functor.h"
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#include "paddle/phi/kernels/impl/reduce_grad.h"
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namespace phi {
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template <typename T, typename Context>
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void NansumGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const IntArray& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* x_grad) {
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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// Step 1: broadcast out_grad to x_grad shape (same as sum_grad)
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ReduceGradKernel<Context, T, funcs::SumGradFunctor, true>(dev_ctx,
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x,
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paddle::none,
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out_grad,
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dims.GetData(),
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keep_dim,
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reduce_all,
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x_grad);
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// Step 2: zero out gradient where x is NaN
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const T* x_data = x.data<T>();
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T* x_grad_data = x_grad->data<T>();
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int64_t numel = x.numel();
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for (int64_t i = 0; i < numel; ++i) {
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if (x_data[i] != x_data[i]) {
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x_grad_data[i] = 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(nansum_grad,
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CPU,
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ALL_LAYOUT,
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phi::NansumGradKernel,
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bool,
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float,
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double,
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phi::float16,
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phi::bfloat16,
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int16_t,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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