trevor-m commented on a change in pull request #6395: URL: https://github.com/apache/incubator-tvm/pull/6395#discussion_r497068599
########## File path: src/runtime/contrib/tensorrt/tensorrt_runtime.cc ########## @@ -0,0 +1,311 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * \file src/runtime/contrib/tensorrt/tensorrt_runtime.cc + * \brief JSON runtime implementation for TensorRT. + */ + +#include <dmlc/parameter.h> +#include <tvm/runtime/ndarray.h> +#include <tvm/runtime/registry.h> + +#include <fstream> + +#include "../../file_util.h" +#include "../json/json_node.h" +#include "../json/json_runtime.h" + +#ifdef TVM_GRAPH_RUNTIME_TENSORRT +#include "NvInfer.h" +#include "tensorrt_builder.h" +#endif + +namespace tvm { +namespace runtime { +namespace contrib { + +using namespace tvm::runtime::json; + +class TensorRTRuntime : public JSONRuntimeBase { + public: + /*! + * \brief The TensorRT runtime module. Deserialize the provided functions + * on creation and store in the layer cache. + * + * \param symbol_name The name of the function. + * \param graph_json serialized JSON representation of a sub-graph. + * \param const_names The names of each constant in the sub-graph. + */ + explicit TensorRTRuntime(const std::string& symbol_name, const std::string& graph_json, + const Array<String>& const_names) + : JSONRuntimeBase(symbol_name, graph_json, const_names), use_implicit_batch_(true), + max_workspace_size_(size_t(1) << 30) {} + + /*! + * \brief The type key of the module. + * + * \return module type key. + */ + const char* type_key() const override { return "tensorrt"; } + + /*! + * \brief Initialize runtime. Create TensorRT layer from JSON + * representation. + * + * \param consts The constant params from compiled model. + */ + void Init(const Array<NDArray>& consts) override { + CHECK_EQ(consts.size(), const_idx_.size()) + << "The number of input constants must match the number of required."; + LoadGlobalAttributes(); + if (GetCachedEnginesFromDisk()) return; + SetupConstants(consts); + BuildEngine(); + CacheEngineToDisk(); + } + + void LoadGlobalAttributes() { + // These settings are global to the entire subgraph. Codegen will add them as attributes to all + // op nodes. Read from first one. + for (size_t i = 0; i < nodes_.size(); ++i) { + if (nodes_[i].HasAttr("use_implicit_batch") && nodes_[i].HasAttr("max_workspace_size")) { + use_implicit_batch_ = + std::stoi(nodes_[i].GetAttr<std::vector<std::string>>("use_implicit_batch")[0]); + // Allow max_workspace_size to be overridden at runtime. + size_t runtime_max_workspace_size = + dmlc::GetEnv("TVM_TENSORRT_MAX_WORKSPACE_SIZE", size_t(0)); + if (runtime_max_workspace_size != 0) { + max_workspace_size_ = runtime_max_workspace_size; + } else { + max_workspace_size_ = + std::stoul(nodes_[i].GetAttr<std::vector<std::string>>("max_workspace_size")[0]); + } + return; + } + } + } + +#ifdef TVM_GRAPH_RUNTIME_TENSORRT + /*! \brief Run inference using built engine. */ + void Run() override { + auto& engine_and_context = trt_engine_cache_.at(symbol_name_); + auto engine = engine_and_context.engine; + auto context = engine_and_context.context; + std::vector<void*> bindings(engine->getNbBindings(), nullptr); + + for (size_t i = 0; i < input_nodes_.size(); ++i) { + auto nid = input_nodes_[i]; + if (nodes_[nid].GetOpType() == "input") { + for (size_t j = 0; j < nodes_[nid].GetOpShape().size(); ++j) { + uint32_t eid = EntryID(nid, j); + const std::string name = nodes_[nid].GetOpName() + "_" + std::to_string(j); + int binding_index = engine->getBindingIndex(name.c_str()); + CHECK_NE(binding_index, -1); + bindings[binding_index] = data_entry_[eid]->data; + } + } + } + + for (size_t i = 0; i < outputs_.size(); ++i) { + uint32_t eid = EntryID(outputs_[i]); + const std::string& name = engine_and_context.outputs[i]; + int binding_index = engine->getBindingIndex(name.c_str()); + CHECK_NE(binding_index, -1); + bindings[binding_index] = data_entry_[eid]->data; + } + +#if TRT_VERSION_GE(6, 0, 1) + if (use_implicit_batch_) { + CHECK(context->execute(batch_size_, bindings.data())) << "Running TensorRT failed."; + } else { + CHECK(context->executeV2(bindings.data())) << "Running TensorRT failed."; + } +#else + CHECK(context->execute(batch_size_, bindings.data())) << "Running TensorRT failed."; +#endif + } + + private: + /*! + * \brief Build TensorRT engine from JSON representation. + */ + void BuildEngine() { + LOG(INFO) << "Building new TensorRT engine for subgraph " << symbol_name_; + const bool use_fp16 = dmlc::GetEnv("TVM_TENSORRT_USE_FP16", false); + batch_size_ = GetBatchSize(); + TensorRTBuilder builder(&logger_, max_workspace_size_, use_implicit_batch_, use_fp16, + batch_size_); + + // Add inputs and constants. + for (size_t i = 0; i < input_nodes_.size(); ++i) { + auto nid = input_nodes_[i]; + const auto& node = nodes_[nid]; + std::string name = node.GetOpName(); + if (node.GetOpType() == "input") { + builder.AddInput(nid, node); + } else { + CHECK_EQ(node.GetOpType(), "const"); + uint32_t eid = EntryID(nid, 0); + builder.AddConstant(nid, data_entry_[eid]); + } + } + + // Add layers. + for (size_t nid = 0; nid < nodes_.size(); ++nid) { + const auto& node = nodes_[nid]; + if (node.GetOpType() != "kernel") continue; + builder.AddLayer(nid, node); + } + + // Add outputs. + for (size_t i = 0; i < outputs_.size(); ++i) { + builder.AddOutput(outputs_[i]); + } + + // Build engine. + trt_engine_cache_[symbol_name_] = builder.BuildEngine(); + LOG(INFO) << "Finished building TensorRT engine for subgraph " << symbol_name_; + } + + /*! \brief If TVM_TENSORRT_CACHE_DIR is set, will check that directory for + * already built TRT engines and load into trt_engine_cache_ so they don't + * have to be built at first inference. + */ + bool GetCachedEnginesFromDisk() { Review comment: Thanks @comaniac that is correct. The engine is platform-dependent so it is not possible to create it during the compilation, it must be done at runtime. It think it is an interesting idea to update the `.so` with the built engine. I think the TVM runtime doesn't contain the necessary components to be able to serialize to .so. It could also introduce some weird behavior (you run a model on one NVIDIA device, it stores the built engine in the .so, then you take the model and try to run it on a different NVIDIA device and it wouldn't work). This extra serialization is not required to use TRT which is why it is only exposed via an optional environment variable. It is useful for edge devices however where building the TRT engine can take up to an hour. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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