// Copyright (c) 2019 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 "lite/kernels/npu/bridges/engine.h" #include #include #include #include "lite/kernels/npu/bridges/registry.h" namespace paddle { namespace lite { namespace subgraph { int Engine::BuildDeviceProgram() { return FAILED; } int Engine::LaunchDeviceProgram() { return 0; } int Engine::BuildOriginProgram() { // TODO(hong19860320) The block_desc need to be divided into subgraphs during // the exection time. But only see them as a subgraph now. origin_program_.clear(); for (size_t op_idx = 0; op_idx < block_desc_->OpsSize(); op_idx++) { auto op_desc = block_desc_->GetOp(op_idx); CHECK(op_desc); std::string op_type = op_desc->Type(); auto op = LiteOpRegistry::Global().Create(op_desc->Type()); op->Attach(*op_desc, scope_); std::unique_ptr picked_kernel; if (op_desc->HasAttr(kKernelTypeAttr)) { // Create op and pick up kernel according to the kKernelTypeAttr attribute auto kernel_type = op_desc->GetAttr(kKernelTypeAttr); std::string alias; Place place; KernelBase::ParseKernelType(kernel_type, &op_type, &alias, &place); VLOG(3) << "Found the attr '" << kKernelTypeAttr << "': " << kernel_type << " for " << op_type; auto kernels = op->CreateKernels({place}); CHECK_GT(kernels.size(), 0u) << "No kernels found for " << op_type; auto it = std::find_if( kernels.begin(), kernels.end(), [&](std::unique_ptr& it) { return it->alias() == alias; }); CHECK(it != kernels.end()); picked_kernel = std::move(*it); } else { VLOG(3) << "The attr '" << kKernelTypeAttr << "' not found, pick the first kernel for " << op_type; std::vector> kernels; #if defined(LITE_WITH_ARM) kernels = op->CreateKernels({Place{TARGET(kARM)}, Place{TARGET(kHost)}}); #elif defined(LITE_WITH_X86) kernels = op->CreateKernels({Place{TARGET(kX86)}, Place{TARGET(kHost)}}); #endif if (kernels.size() > 0) { picked_kernel = std::move(kernels.front()); } else { LOG(WARNING) << "No kernels found for " << op_type; } } if (picked_kernel != nullptr) { picked_kernel->SetContext( ContextScheduler::Global().NewContext(picked_kernel->target())); } origin_program_.emplace_back(std::move(op), std::move(picked_kernel)); } return 0; } int Engine::LaunchOriginProgram() { for (auto& inst : origin_program_) { auto op_type = inst.op()->op_info()->Type(); if (op_type == "feed" || op_type == "fetch") continue; inst.Run(); } return 0; } int Engine::Build() { // In order to attach all of the ops of the block desc, we need to build the // original program firstly. BuildOriginProgram(); // Run InferShape() of all of ops, and convert Paddle ops to NPU/XPU IR graph build_device_program_status_ = BuildDeviceProgram(); return build_device_program_status_; } bool Engine::InputShapeChanged() { for (size_t i = 0; i < origin_itensors_.size(); i++) { if (origin_itensors_[i]->dims() != origin_idims_[i]) { return true; } } return false; } int Engine::Launch() { // Rebuild device program when the shapes of input tensors have been changed. if (CHECK_SUCCESS(build_device_program_status_) && CHECK_REBUILD_WHEN_SHAPE_CHANGED(build_device_program_status_) && InputShapeChanged()) { Build(); } if (CHECK_FAILED(build_device_program_status_)) { LaunchOriginProgram(); } else { LaunchDeviceProgram(); } return 0; } } // namespace subgraph } // namespace lite } // namespace paddle