juda commented on code in PR #11911: URL: https://github.com/apache/tvm/pull/11911#discussion_r918483831
########## src/contrib/torch/pt_call_tvm/RuntimeModuleWrapper.cc: ########## @@ -0,0 +1,262 @@ +/* + * 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. + */ +#include <ATen/DLConvertor.h> +#include <dlpack/dlpack.h> +#include <dmlc/memory_io.h> +#include <torch/custom_class.h> +#include <torch/script.h> +#include <tvm/runtime/module.h> +#include <tvm/runtime/registry.h> +#include <tvm/target/codegen.h> +#include <tvm/target/target.h> + +#include <cstdio> +#include <map> +#include <string> +#include <vector> + +#include "../../../runtime/graph_executor/graph_executor_factory.h" +#include "../base64.h" + +namespace tvm { +namespace contrib { + +/** + * We pass the TVM module by TVM's FFI because Torch's FFI cannot recognize such TVM objects + */ +struct ThreadLocalStore { + tvm::runtime::Module mod; + static ThreadLocalStore* ThreadLocal() { + thread_local ThreadLocalStore tls; + return &tls; + } +}; + +using SerializationType = std::string; // base64 stream + +SerializationType serialize(tvm::runtime::Module module) { + static const runtime::PackedFunc* f_to_str = + runtime::Registry::Get("script_torch.save_to_base64"); + ICHECK(f_to_str) << "IndexError: Cannot find the packed function " + "`script_torch.save_to_tar` in the global registry"; + return (*f_to_str)(module); +} + +struct Deleter { // deleter + explicit Deleter(std::string file_name) { this->file_name = file_name; } + void operator()(FILE* p) const { + fclose(p); + ICHECK(remove(file_name.c_str()) == 0) + << "remove temporary file (" << file_name << ") unsuccessfully"; + } + std::string file_name; +}; + +tvm::runtime::Module deserialize(SerializationType state) { + auto length = tvm::support::b64strlen(state); + + std::vector<u_char> bytes(length); + tvm::support::b64decode(state, bytes.data()); + + const std::string name = tmpnam(NULL); + auto file_name = name + ".so"; + std::unique_ptr<FILE, Deleter> pFile(fopen(file_name.c_str(), "wb"), Deleter(file_name)); + fwrite(bytes.data(), sizeof(u_char), length, pFile.get()); + fflush(pFile.get()); + + std::string load_f_name = "runtime.module.loadfile_so"; + const PackedFunc* f = runtime::Registry::Get(load_f_name); + ICHECK(f != nullptr) << "Loader for `.so` files is not registered," + << " resolved to (" << load_f_name << ") in the global registry." + << "Ensure that you have loaded the correct runtime code, and" + << "that you are on the correct hardware architecture."; + + tvm::runtime::Module ret = (*f)(file_name, ""); + + return ret; +} + +/** + * @brief A Torch's module which wraps TVM's OperatorModule Class. + * The basic forward function calling TVM's runtime is provided. + * The TVM module can be serialized/deserialized as a Torch module. + */ +class OperatorModuleWrapper : public torch::jit::CustomClassHolder { + public: + OperatorModuleWrapper() { runtime_module = ThreadLocalStore::ThreadLocal()->mod; } + + void forward(const c10::List<at::Tensor>& inputs) { + int input_length = inputs.size(); + + std::vector<DLManagedTensor*> tensors; + + for (int i = 0; i < input_length; ++i) tensors.push_back(toDLPack(inputs[i])); + + tvm::runtime::PackedFunc run = runtime_module.GetFunction("__tvm_main__"); + + std::vector<TVMValue> tvm_values(input_length); + std::vector<int> tvm_type_codes(input_length); + tvm::runtime::TVMArgsSetter setter(tvm_values.data(), tvm_type_codes.data()); + for (int k = 0; k < input_length; ++k) { + setter(k, &tensors[k]->dl_tensor); + } + + run.CallPacked(tvm::runtime::TVMArgs(tvm_values.data(), tvm_type_codes.data(), input_length), + nullptr); + + for (int k = 0; k < input_length; ++k) { + tensors[k]->deleter(tensors[k]); + } + } + + SerializationType Serialize() { return serialize(runtime_module); } + + explicit OperatorModuleWrapper(SerializationType state) { runtime_module = deserialize(state); } + + private: + tvm::runtime::Module runtime_module; +}; + +tvm::Device getDevice(const at::Tensor& tensor) { + tvm::Device dev; + dev.device_id = tensor.get_device(); + switch (tensor.device().type()) { + case at::DeviceType::CPU: + dev.device_type = DLDeviceType::kDLCPU; + if (dev.device_id == -1) { + /* + * In PyTorch the device ID for cpu is -1, sometimes causing error during tuning + * Thus we manually set the device ID as 0 for avoiding potentially error of index out of + * bounds + */ + dev.device_id = 0; + } + break; + case at::DeviceType::CUDA: + dev.device_type = DLDeviceType::kDLCUDA; + break; + default: + TORCH_CHECK(false, "PyTorch TVM integration doesn't support device " + tensor.device().str()); + } + return dev; +} + +/** + * @brief A Torch's module which wraps TVM's GraphExecutorFactory Class. + * The basic forward function calling TVM's runtime is provided. + * The TVM module can be serialized/deserialized as a Torch module. + */ +class GraphExecutorFactoryWrapper : public torch::jit::CustomClassHolder { Review Comment: one reason is that we don't want to use temp files to transmit data, as bytedance's approach, but use TVM's FFI. @yelite -- This is an automated message from the Apache Git Service. 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