juda commented on code in PR #12232: URL: https://github.com/apache/tvm/pull/12232#discussion_r934092594
########## src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc: ########## @@ -0,0 +1,179 @@ +/* + * 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 <dlpack/dlpack.h> +#include <dmlc/memory_io.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" +#include "runtime_bridge.h" + +struct ThreadLocalStore { + tvm::runtime::Module mod; + static ThreadLocalStore* ThreadLocal() { + thread_local ThreadLocalStore tls; + return &tls; + } +}; + +namespace tvm { +namespace contrib { + +std::string 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_base64` 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(std::string 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; +} + +tvm::Device getDeviceInfo(DLManagedTensor* input_device) { + return {.device_type = input_device->dl_tensor.device.device_type, + .device_id = input_device->dl_tensor.device.device_id}; +} + +TVM_REGISTER_GLOBAL("tvmtorch.save_runtime_mod").set_body_typed([](tvm::runtime::Module mod) { + ThreadLocalStore::ThreadLocal()->mod = mod; +}); + +} // namespace contrib +} // namespace tvm + +extern "C" { + +struct TVMContribTorchRuntimeModule { + tvm::runtime::Module mod; + + explicit TVMContribTorchRuntimeModule(tvm::runtime::Module mod) : mod(mod) {} +}; + +TVMContribTorchRuntimeModule* tvm_contrib_torch_get_last_saved_runtime_module() { + return new TVMContribTorchRuntimeModule(ThreadLocalStore::ThreadLocal()->mod); +} + +void tvm_contrib_torch_operator_module_forward(TVMContribTorchRuntimeModule* runtime_module, + DLPackTensorExt* inputs, size_t input_size) { + tvm::runtime::PackedFunc run = runtime_module->mod.GetFunction("__tvm_main__"); + + std::vector<TVMValue> tvm_values(input_size); + std::vector<int> tvm_type_codes(input_size); + tvm::runtime::TVMArgsSetter setter(tvm_values.data(), tvm_type_codes.data()); + for (int k = 0; k < input_size; ++k) { + setter(k, &inputs[k].dl_managed_tensor->dl_tensor); + } + run.CallPacked(tvm::runtime::TVMArgs(tvm_values.data(), tvm_type_codes.data(), input_size), + nullptr); +} + +int64_t tvm_contrib_torch_graph_executor_module_forward(TVMContribTorchRuntimeModule* graph_module, + DLPackTensorExt* inputs, size_t input_size, + DLPackTensorExt** outputs) { + tvm::runtime::PackedFunc built_module = graph_module->mod.GetFunction("default"); + auto device_info = tvm::contrib::getDeviceInfo(inputs[0].dl_managed_tensor); + tvm::runtime::Module runtime_module = built_module(device_info); + tvm::runtime::PackedFunc run = runtime_module.GetFunction("run"); + tvm::runtime::PackedFunc set_input = runtime_module.GetFunction("set_input"); + tvm::runtime::PackedFunc get_output = runtime_module.GetFunction("get_output"); + tvm::runtime::PackedFunc get_num_outputs = runtime_module.GetFunction("get_num_outputs"); + + for (int k = 0; k < input_size; ++k) { + set_input(k, &inputs[k].dl_managed_tensor->dl_tensor); + } + + run(); + + int64_t output_length = get_num_outputs(); + + auto outputs_ptr = new DLPackTensorExt[output_length]; + *outputs = outputs_ptr; + + for (int k = 0; k < output_length; ++k) { + tvm::runtime::NDArray results = get_output(k); + auto is_bool = results.DataType().is_bool(); + DLManagedTensor* tensor; + if (is_bool) { + auto tmp = + tvm::runtime::NDArray::Empty(results.Shape(), DLDataType{kDLInt, 8, 1}, device_info); + results.CopyTo(tmp); + tensor = tmp.ToDLPack(); + } else { + tensor = results.ToDLPack(); + } + outputs_ptr[k] = {.dl_managed_tensor = tensor, .is_bool = is_bool}; + } + + return output_length; +} + +char* tvm_contrib_torch_encode(TVMContribTorchRuntimeModule* runtime_module) { + auto std = tvm::contrib::serialize(runtime_module->mod); + auto* ret = new char[std.length() + 1]; 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