// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/phi/kernels/reduce_nansum_kernel.h" #include "paddle/phi/backends/cpu/cpu_context.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/full_kernel.h" #include "paddle/phi/kernels/reduce_sum_kernel.h" namespace phi { template void NansumKernel(const Context& dev_ctx, const DenseTensor& x, const IntArray& dims, DataType out_dtype, bool keep_dim, DenseTensor* out) { if (out_dtype == DataType::UNDEFINED && out->dtype() != x.dtype()) { out_dtype = out->dtype(); } if (x.numel() == 0) { dev_ctx.template Alloc(out); if (out_dtype == DataType::INT64) { Full(dev_ctx, out->dims(), 0, out); } else { Full(dev_ctx, out->dims(), 0, out); } return; } // Replace NaN with 0 DenseTensor cleaned_x; cleaned_x.Resize(x.dims()); dev_ctx.template Alloc(&cleaned_x); const T* x_data = x.data(); T* clean_data = cleaned_x.data(); int64_t numel = x.numel(); for (int64_t i = 0; i < numel; ++i) { clean_data[i] = (x_data[i] != x_data[i]) ? static_cast(0) : x_data[i]; } // Delegate to SumRawKernel bool reduce_all = recompute_reduce_all(x, dims); SumRawKernel( dev_ctx, cleaned_x, dims, keep_dim, reduce_all, out_dtype, out); } } // namespace phi PD_REGISTER_KERNEL(nansum, CPU, ALL_LAYOUT, phi::NansumKernel, bool, float, double, phi::float16, phi::bfloat16, int16_t, int8_t, uint8_t, int, int64_t, phi::complex64, phi::complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED); }