// Copyright (c) 2023 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/where_kernel.h" #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void WhereKernel(const Context& dev_ctx, const DenseTensor& condition, const DenseTensor& x, const DenseTensor& y, DenseTensor* out) { using XPUType = typename XPUTypeTrait::Type; const bool* cond_data = condition.data(); const XPUType* x_data = reinterpret_cast(x.data()); const XPUType* y_data = reinterpret_cast(y.data()); XPUType* out_data = reinterpret_cast(dev_ctx.template Alloc(out)); if (out && out->numel() == 0) { return; } auto cond_dims = vectorize(condition.dims()); auto x_dims = vectorize(x.dims()); // use [1] to replace [], because xpu not support [] if (cond_dims.size() == 0) { cond_dims = std::vector({1}); } if (x_dims.size() == 0) { x_dims = std::vector({1}); } int ret = xpu::where(dev_ctx.x_context(), cond_data, x_data, y_data, out_data, cond_dims, x_dims); PADDLE_ENFORCE_XDNN_SUCCESS(ret, "where"); } } // namespace phi PD_REGISTER_KERNEL(where, XPU, ALL_LAYOUT, phi::WhereKernel, float, double, int, int64_t, phi::float16, phi::bfloat16) {}