/* 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/pten/api/include/sparse_api.h" #include #include "glog/logging.h" #include "paddle/pten/api/lib/api_registry.h" #include "paddle/pten/api/lib/kernel_dispatch.h" #include "paddle/pten/api/lib/utils/storage.h" #include "paddle/pten/core/kernel_registry.h" #include "paddle/pten/infermeta/unary.h" PT_DECLARE_KERNEL(dense_to_sparse_coo, CPU, ALL_LAYOUT); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) PT_DECLARE_KERNEL(dense_to_sparse_coo, GPU, ALL_LAYOUT); #endif namespace paddle { namespace experimental { namespace sparse { PADDLE_API Tensor to_sparse_coo(const Tensor& x, Backend backend, const int64_t sparse_dim) { if (x.layout() == pten::DataLayout::SPARSE_COO) { return x; } // 1. Get kernel signature and kernel auto kernel_key_set = ParseKernelKeyByInputArgs(x); kernel_key_set.backend_set = kernel_key_set.backend_set | BackendSet(backend); auto kernel_key = kernel_key_set.GetHigestPriorityKernelKey(); std::string kernel_name = "dense_to_sparse_coo"; if (x.layout() == pten::DataLayout::SPARSE_CSR) { kernel_name = "sparse_csr_to_coo"; } auto kernel = pten::KernelFactory::Instance().SelectKernelOrThrowError( kernel_name, kernel_key); VLOG(6) << "to API kernel key: " << kernel_key; VLOG(6) << "to API kernel: " << kernel; // 2. Get Device Context auto* dev_ctx = GetDeviceContextByBackend(kernel_key.backend()); auto kernel_context = pten::KernelContext(dev_ctx); // 3. Auto data transform if (x.layout() == pten::DataLayout::SPARSE_CSR) { auto input = std::dynamic_pointer_cast(x.impl()); kernel_context.EmplaceBackInput(input.get()); } else { auto input = std::dynamic_pointer_cast(x.impl()); kernel_context.EmplaceBackInput(input.get()); kernel_context.EmplaceBackAttr(sparse_dim); } // 4. InferMeta auto indices_meta = pten::DenseTensorMeta( pten::DataType::INT64, {-1}, pten::DataLayout::NCHW); auto elements_meta = pten::DenseTensorMeta(x.dtype(), {-1}, x.layout()); // 5. Prepare outputs // create empty SparseCooTensor pten::DenseTensor non_zero_indices( pten::make_intrusive( pten::TransToFluidPlace(backend)), std::move(indices_meta)); pten::DenseTensor non_zero_elements( pten::make_intrusive( pten::TransToFluidPlace(backend)), std::move(elements_meta)); auto coo = std::make_shared( non_zero_indices, non_zero_elements, x.dims()); kernel_context.EmplaceBackOutput(coo.get()); Tensor out; out.set_impl(coo); // 6. Call kernel kernel(&kernel_context); return out; } } // namespace sparse } // namespace experimental } // namespace paddle PT_REGISTER_API(SparseApi);