// Copyright (c) 2022 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/stack_kernel.h" #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void StackKernel(const Context& dev_ctx, const std::vector& x, int axis, DenseTensor* out) { using XPUType = typename XPUTypeTrait::Type; if (axis < 0) { axis += x[0]->dims().size() + 1; } // zero sized tensor case if (x[0]->numel() == 0) { dev_ctx.template Alloc(out); auto out_dims = out->dims(); out->Resize(out_dims); return; } dev_ctx.template Alloc(out); auto& dim = x[0]->dims(); std::vector xdims; for (auto i = 0; i < dim.size(); ++i) { xdims.push_back(dim[i]); } xdims.push_back(1); std::vector> xdims_list; int64_t n = static_cast(x.size()); for (int64_t i = 0; i < n; i++) { xdims_list.push_back(xdims); } std::vector x_list; for (int64_t i = 0; i < n; i++) { x_list.push_back(reinterpret_cast(x[i]->data())); } int r = xpu::concat(dev_ctx.x_context(), x_list, reinterpret_cast(out->data()), xdims_list, axis); PADDLE_ENFORCE_XDNN_SUCCESS(r, "concat in stack op"); } } // namespace phi PD_REGISTER_KERNEL(stack, XPU, ALL_LAYOUT, phi::StackKernel, double, float, phi::float16, phi::bfloat16, int64_t, int, int16_t, int8_t, uint8_t, bool) {}