/* Copyright (c) 2021 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/pten/api/include/creation.h" #include #include "glog/logging.h" #include "paddle/pten/api/include/registry.h" #include "paddle/pten/api/lib/kernel_dispatch.h" #include "paddle/pten/api/lib/utils/allocator.h" #include "paddle/pten/core/kernel_registry.h" #include "paddle/pten/include/core.h" #include "paddle/pten/include/infershape.h" PT_DECLARE_MODULE(CreationCPU); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) PT_DECLARE_MODULE(CreationCUDA); #endif namespace paddle { namespace experimental { PD_DLL_DECL Tensor full(const std::vector& shape, const Scalar& value, DataType dtype, Backend backend, DataLayout layout) { // 1. Get kernel signature and kernel pten::KernelKey kernel_key{backend, layout, dtype}; auto kernel = pten::KernelFactory::Instance().SelectKernelOrThrowError( "fill_constant.scalar", kernel_key); // 2. Get Device Context auto* dev_ctx = GetDeviceContextByBackend(kernel_key.backend()); auto kernel_context = pten::KernelContext(dev_ctx); // 3. Auto data transform kernel_context.EmplaceBackAttr(value); // 4. InferShape auto out_meta = pten::FullInferShape(shape, dtype, layout); // 5. Prepare outputs const auto allocator = std::make_shared( pten::TransToFluidPlace(kernel_key.backend())); auto dense_out = std::make_shared(allocator, out_meta); kernel_context.EmplaceBackOutput(dense_out); Tensor out; out.set_impl(dense_out); // 6. Call kernel kernel(&kernel_context); return out; } PD_DLL_DECL Tensor full_like(const Tensor& x, const Scalar& value, DataType dtype, Backend backend, DataLayout layout) { // 1. Get kernel signature and kernel auto kernel_key_set = ParseKernelKeyByInputArgs(x); auto kernel_key = kernel_key_set.GetHigestPriorityKernelKey(); DataType kernel_data_type = dtype == DataType::UNDEFINED ? kernel_key.dtype() : dtype; Backend kernel_backend = backend == Backend::UNDEFINED ? kernel_key.backend() : backend; DataLayout kernel_layout = layout == DataLayout::UNDEFINED ? kernel_key.layout() : layout; auto kernel = pten::KernelFactory::Instance().SelectKernelOrThrowError( "fill_any_like", {kernel_backend, kernel_layout, kernel_data_type}); // 2. Get Device Context auto* dev_ctx = GetDeviceContextByBackend(kernel_key.backend()); auto kernel_context = pten::KernelContext(dev_ctx); // 3. Auto data transform auto dense_x = std::dynamic_pointer_cast(x.impl()); kernel_context.EmplaceBackAttr(value); // 4. InferShape auto out_meta = FullLikeInferShape(dense_x->meta(), dtype, layout); // 5. Prepare outputs Tensor out; const auto allocator = std::make_shared( pten::TransToFluidPlace(kernel_backend)); auto dense_out = std::make_shared(allocator, out_meta); kernel_context.EmplaceBackOutput(dense_out); out.set_impl(dense_out); // 6. Call kernel kernel(&kernel_context); return out; } PD_DLL_DECL Tensor ones_like(const Tensor& x, DataType dtype, Backend backend, DataLayout layout) { return full_like(x, 1, dtype, backend, layout); } PD_DLL_DECL Tensor zeros_like(const Tensor& x, DataType dtype, Backend backend, DataLayout layout) { return full_like(x, 0, dtype, backend, layout); } } // namespace experimental } // namespace paddle PT_REGISTER_API(Creation);