/* 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/fluid/framework/phi_utils.h" #include #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/op_info.h" #include "paddle/fluid/framework/selected_rows_utils.h" #include "paddle/fluid/framework/variable.h" #include "paddle/fluid/string/string_helper.h" #include "paddle/phi/core/compat/convert_utils.h" #include "paddle/phi/core/compat/op_utils.h" #include "paddle/phi/core/kernel_factory.h" #include "paddle/phi/core/type_defs.h" namespace paddle { namespace framework { class KernelArgsNameMakerByOpProto : public KernelArgsNameMaker { public: explicit KernelArgsNameMakerByOpProto( const framework::proto::OpProto* op_proto) : op_proto_(op_proto) { PADDLE_ENFORCE_NOT_NULL(op_proto_, platform::errors::InvalidArgument( "Op proto cannot be nullptr.")); } ~KernelArgsNameMakerByOpProto() {} const paddle::small_vector& GetInputArgsNames() override; const paddle::small_vector& GetOutputArgsNames() override; const paddle::small_vector& GetAttrsArgsNames() override; phi::KernelSignature GetKernelSignature(); private: DISABLE_COPY_AND_ASSIGN(KernelArgsNameMakerByOpProto); private: const framework::proto::OpProto* op_proto_; paddle::small_vector input_names_; paddle::small_vector output_names_; paddle::small_vector attr_names_; }; OpKernelType TransPhiKernelKeyToOpKernelType(const phi::KernelKey& kernel_key) { proto::VarType::Type data_type = paddle::framework::TransToProtoVarType(kernel_key.dtype()); // no need to set current device id here platform::Place place = phi::TransToPhiPlace(kernel_key.backend(), false); DataLayout data_layout = kernel_key.layout(); LibraryType library_type = LibraryType::kPlain; if (kernel_key.backend() == phi::Backend::MKLDNN) { library_type = LibraryType::kMKLDNN; } else if (kernel_key.backend() == phi::Backend::GPUDNN) { library_type = LibraryType::kCUDNN; } else if (kernel_key.backend() == phi::Backend::KPS) { library_type = LibraryType::kKP; } else { // do nothing } // TODO(chenweihang): the customized_type_value is lost return OpKernelType(data_type, place, data_layout, library_type); } phi::KernelKey TransOpKernelTypeToPhiKernelKey( const OpKernelType& kernel_type) { phi::Backend backend = phi::TransToPhiBackend(kernel_type.place_); switch (kernel_type.library_type_) { case LibraryType::kCUDNN: backend = phi::Backend::GPUDNN; break; case LibraryType::kMKLDNN: backend = phi::Backend::MKLDNN; break; case LibraryType::kKP: backend = phi::Backend::KPS; break; default: break; } return phi::KernelKey(backend, kernel_type.data_layout_, framework::TransToPhiDataType(kernel_type.data_type_)); } phi::KernelKey FallBackToCpu(const OpKernelType& expected_kernel_key, const phi::KernelKey& kernel_key, const framework::OperatorBase& op) { #ifdef PADDLE_WITH_XPU if (platform::is_xpu_place(expected_kernel_key.place_) || paddle::platform::is_in_xpu_black_list(op.Type())) { VLOG(3) << "phi missing XPU kernel: " << op.Type() << ", expected_kernel_key:" << expected_kernel_key << ", fallbacking to CPU one!"; return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(), kernel_key.dtype()); } #endif #ifdef PADDLE_WITH_ASCEND_CL if (platform::is_npu_place(expected_kernel_key.place_)) { VLOG(3) << "phi missing NPU kernel: " << op.Type() << ", expected_kernel_key:" << expected_kernel_key << ", fallbacking to CPU one!"; return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(), kernel_key.dtype()); } #endif #ifdef PADDLE_WITH_MLU if (platform::is_mlu_place(expected_kernel_key.place_)) { VLOG(3) << "phi missing MLU kernel: " << op.Type() << ", expected_kernel_key:" << expected_kernel_key << ", fallbacking to CPU one!"; return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(), kernel_key.dtype()); } #endif #ifdef PADDLE_WITH_IPU if (platform::is_ipu_place(expected_kernel_key.place_)) { VLOG(3) << "phi missing IPU kernel: " << op.Type() << ", expected_kernel_key:" << expected_kernel_key << ", fallbacking to CPU one!"; return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(), kernel_key.dtype()); } #endif #ifdef PADDLE_WITH_CUSTOM_DEVICE if (platform::is_custom_place(expected_kernel_key.place_)) { VLOG(3) << "phi missing " << expected_kernel_key.place_.GetDeviceType() << " kernel: " << op.Type() << ", expected_kernel_key:" << expected_kernel_key << ", fallbacking to CPU one!"; return phi::KernelKey(phi::Backend::CPU, kernel_key.layout(), kernel_key.dtype()); } #endif return phi::KernelKey(); } const paddle::small_vector& KernelArgsNameMakerByOpProto::GetInputArgsNames() { for (int i = 0; i < op_proto_->inputs_size(); ++i) { auto& in = op_proto_->inputs()[i]; auto& in_name = in.name(); if ((in.has_extra() && in.extra()) || (in.has_quant() && in.quant())) { continue; } // If contains dispensable input, we should override the // OpArgumentMapping method self in phi/ops/compat dir if (in.has_dispensable() && in.dispensable()) { continue; } input_names_.emplace_back(in_name.c_str()); } if (VLOG_IS_ON(10)) { std::ostringstream sout; sout << "PhiKernel inputs: "; std::copy(input_names_.begin(), input_names_.end(), std::ostream_iterator(sout, ", ")); VLOG(10) << sout.str(); } return input_names_; } const paddle::small_vector& KernelArgsNameMakerByOpProto::GetOutputArgsNames() { for (int i = 0; i < op_proto_->outputs_size(); ++i) { auto& out = op_proto_->outputs()[i]; auto& out_name = out.name(); if ((out.has_extra() && out.extra()) || (out.has_quant() && out.quant())) { continue; } output_names_.emplace_back(out_name.c_str()); } if (VLOG_IS_ON(10)) { std::ostringstream sout; sout << "PhiKernel outputs: "; std::copy(output_names_.begin(), output_names_.end(), std::ostream_iterator(sout, ", ")); VLOG(10) << sout.str(); } return output_names_; } const paddle::small_vector& KernelArgsNameMakerByOpProto::GetAttrsArgsNames() { for (int i = 0; i < op_proto_->attrs_size(); ++i) { auto& attr = op_proto_->attrs()[i]; auto& attr_name = attr.name(); if (attr_name == "use_mkldnn" || attr_name == "use_cudnn" || attr_name == "op_role" || attr_name == "op_role_var" || attr_name == "op_namescope" || attr_name == "op_callstack" || attr_name == "op_device") { continue; } if ((attr.has_extra() && attr.extra()) || (attr.has_quant() && attr.quant())) { continue; } attr_names_.emplace_back(attr_name.c_str()); } if (VLOG_IS_ON(10)) { std::ostringstream sout; sout << "PhiKernel attributes: "; std::copy(attr_names_.begin(), attr_names_.end(), std::ostream_iterator(sout, ", ")); VLOG(10) << sout.str(); } return attr_names_; } phi::KernelSignature KernelArgsNameMakerByOpProto::GetKernelSignature() { return phi::KernelSignature( phi::TransToPhiKernelName(op_proto_->type()).c_str(), GetInputArgsNames(), GetAttrsArgsNames(), GetOutputArgsNames()); } std::once_flag kernel_sig_map_init_flag; void InitDefaultKernelSignatureMap() { std::call_once(kernel_sig_map_init_flag, [] { for (const auto& pair : paddle::framework::OpInfoMap::Instance().map()) { const auto& op_type = pair.first; const auto* op_proto = pair.second.proto_; if (phi::KernelFactory::Instance().HasCompatiblePhiKernel(op_type) && op_proto) { paddle::framework::KernelArgsNameMakerByOpProto maker(op_proto); VLOG(10) << "Register `" << op_type << "` kernel signature:"; phi::DefaultKernelSignatureMap::Instance().Insert( op_type, std::move(maker.GetKernelSignature())); } } }); } static void SetAllocationForUninitializedDenseTensor( phi::DenseTensor* dense_tensor, const platform::Place& place) { int dtype_size = dense_tensor->dtype() == DataType::UNDEFINED ? 0 : experimental::SizeOf(dense_tensor->dtype()); int64_t numels = product(dense_tensor->dims()); numels = numels < 0 ? 0 : numels; auto tmp_allocation_ptr = memory::Alloc(place, numels * dtype_size); auto& deleter = tmp_allocation_ptr.get_deleter(); auto* allocation_ptr = tmp_allocation_ptr.release(); auto shared_allocation = std::shared_ptr(allocation_ptr, deleter); dense_tensor->ResetHolder(shared_allocation); } } // namespace framework } // namespace paddle