[GitHub] [tvm] yelite commented on a diff in pull request #12232: [TVM PyTorch Integration] libstdc++ CXX11 ABI Compatibility & boolean tensor support

[GitBox <gi...@apache.org>]

yelite commented on code in PR #12232:
URL: https://github.com/apache/tvm/pull/12232#discussion_r944074708


##########
src/contrib/torch/tvm_module_wrapper/runtime_bridge.h:
##########
@@ -0,0 +1,114 @@
+/*
+ * 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 runtime_bridge.h
+ * \brief Util functions for pytorch tvm interaction.
+ */
+#ifndef TVM_CONTRIB_TORCH_TVM_MODULE_WRAPPER_RUNTIME_BRIDGE_H_
+#define TVM_CONTRIB_TORCH_TVM_MODULE_WRAPPER_RUNTIME_BRIDGE_H_
+
+extern "C" {
+
+/*
+ * DLPack data structure extend with `is_bool` flag.
+ * DLPack haven't support boolean tensor,

Review Comment:
   Can you add a link to the comment, pointing to the source code of boolean tensor handling? https://github.com/pytorch/pytorch/blob/4618371da56c887195e2e1d16dad2b9686302800/aten/src/ATen/DLConvertor.cpp#L42 and https://github.com/apache/tvm/blob/de124862714e747764aa8b7f41a90bcb25f3c6a8/python/tvm/_ffi/runtime_ctypes.py#L91



##########
src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc:
##########
@@ -0,0 +1,281 @@
+/*
+ * 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"
+
+namespace tvm {
+namespace contrib {
+
+struct ThreadLocalStore {
+  tvm::runtime::Module mod;
+  static ThreadLocalStore* ThreadLocal() {
+    thread_local ThreadLocalStore tls;
+    return &tls;
+  }
+};
+
+/*
+ * Encode TVM runtime module to base64 stream
+ */
+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;
+};
+
+/*
+ * Decode TVM runtime module from base64 stream
+ */
+tvm::runtime::Module deserialize(std::string state) {
+  auto length = tvm::support::b64strlen(state);
+
+  std::vector<u_char> bytes(length);  // bytes stream
+  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) {
+  tvm::Device ret{input_device->dl_tensor.device.device_type,
+                  input_device->dl_tensor.device.device_id};
+  return ret;
+}
+
+TVM_REGISTER_GLOBAL("tvmtorch.save_runtime_mod").set_body_typed([](tvm::runtime::Module mod) {
+  ThreadLocalStore::ThreadLocal()->mod = mod;
+});
+
+/*
+ * Convert NDArray to DLPack extend tensor. It should be zero-cost.
+ * @param src Pointer to NDArray
+ * @return DLPack extended tensor
+ */
+DLPackTensorExt create_dlpack_tensor_ext(tvm::runtime::NDArray* src, bool is_bool) {
+  DLManagedTensor* tensor;
+  if (is_bool) {
+    // If we change DLDataType{kDLInt, 8, 1} to DataType::Bool()
+    // we will get `RuntimeError: Unsupported kUInt bits 1`
+    auto tmp = src->CreateView(src->Shape(), DLDataType{kDLInt, 8, 1});
+    tensor = tmp.ToDLPack();
+  } else {
+    tensor = src->ToDLPack();
+  }
+  DLPackTensorExt ret{tensor, is_bool};
+  return ret;
+}
+
+/*
+ * Create an empty NDArray with boolean type.
+ * @param src DLpack extended tensor
+ * @return an empty NDArray
+ */
+tvm::runtime::NDArray create_empty_bool_ndarray(DLPackTensorExt* src) {
+  auto& tensor = src->dl_managed_tensor->dl_tensor;
+  std::vector<int64_t> shape;
+  for (int64_t i = 0; i < tensor.ndim; i++) {
+    shape.push_back(tensor.shape[i]);
+  }
+  auto ret = tvm::runtime::NDArray::Empty(shape, DataType::Bool(), tensor.device);
+  return ret;
+}
+
+/*
+ * Create an NDArray with boolean type. (One memory copy)
+ * @param src DLpack extended tensor
+ * @return a new NDArray
+ */
+tvm::runtime::NDArray create_bool_ndarray(DLPackTensorExt* src) {
+  auto&& ret = create_empty_bool_ndarray(src);
+  ret.CopyFrom(&src->dl_managed_tensor->dl_tensor);
+  return std::move(ret);
+}
+
+bool is_zero_copy(DLPackTensorExt* src) {
+  auto& dl_tensor = src->dl_managed_tensor->dl_tensor;
+  bool is_zero_copy =
+      tvm::runtime::NDArray::AbilityOfZeroCopyForDLTensor(&dl_tensor, dl_tensor.device);
+  return is_zero_copy;
+}
+
+/*
+ * Create an NDArray from DLpack extended tensor.
+ * @param src DLpack extended tensor
+ * @return a new NDArray
+ */
+tvm::runtime::NDArray ndarray_from_dlpack(DLPackTensorExt* src) {
+  using tvm::runtime::NDArray;
+
+  NDArray array;
+  auto& dl_tensor = src->dl_managed_tensor->dl_tensor;
+  if (src->is_bool) {
+    // one memory copy
+    // the code is similar to NewFromDLTensor except for the type
+    array = create_bool_ndarray(src);
+  } else if (is_zero_copy(src)) {
+    array = NDArray::FromExternalDLTensor(src->dl_managed_tensor->dl_tensor);
+  } else {
+    // one memory copy
+    array = NDArray::NewFromDLTensor(&dl_tensor, dl_tensor.device);
+  }
+  return array;
+}
+
+}  // namespace contrib
+}  // namespace tvm
+
+extern "C" {
+
+struct TVMContribTorchRuntimeModule {
+  tvm::runtime::Module mod;
+
+  explicit TVMContribTorchRuntimeModule(tvm::runtime::Module& mod) : mod(mod) {}
+};
+
+bool tvm_contrib_torch_is_be_copied(DLPackTensorExt* src) {
+  return (src->is_bool) || (!tvm::contrib::is_zero_copy(src));
+}
+
+TVMContribTorchRuntimeModule* tvm_contrib_torch_get_last_saved_runtime_module() {
+  return new TVMContribTorchRuntimeModule(tvm::contrib::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());
+
+  std::vector<tvm::runtime::NDArray> input_cache(input_size);
+
+  for (int k = 0; k < input_size; ++k) {
+    auto datum = tvm::contrib::ndarray_from_dlpack(&inputs[k]);  // could have one memory copy
+    input_cache[k] = datum;  // we keep the datum in a vector for future use, otherwise the datum
+                             // will be freed after the loop
+    setter(k, datum);
+  }
+
+  run.CallPacked(tvm::runtime::TVMArgs(tvm_values.data(), tvm_type_codes.data(), input_size),
+                 nullptr);
+
+  for (int k = 0; k < input_size; ++k) {
+    if (tvm_contrib_torch_is_be_copied(&inputs[k]))
+      input_cache[k].CopyTo(&inputs[k].dl_managed_tensor->dl_tensor);
+  }
+}
+
+TVMContribTorchRuntimeModule* tvm_contrib_torch_create_graph_runtime_module(
+    TVMContribTorchRuntimeModule* graph_module, DLManagedTensor* input_example) {
+  tvm::runtime::PackedFunc built_module = graph_module->mod.GetFunction("default");
+  tvm::Device device_info = tvm::contrib::getDeviceInfo(input_example);
+  tvm::runtime::Module runtime_module = built_module(device_info);
+  return new TVMContribTorchRuntimeModule(runtime_module);
+}
+
+size_t tvm_contrib_torch_graph_executor_module_forward(TVMContribTorchRuntimeModule* runtime_module,
+                                                       DLPackTensorExt* inputs, size_t input_size,
+                                                       DLPackTensorExt** outputs) {
+  tvm::runtime::PackedFunc run = runtime_module->mod.GetFunction("run");
+  tvm::runtime::PackedFunc set_input = runtime_module->mod.GetFunction("set_input");
+  tvm::runtime::PackedFunc get_output = runtime_module->mod.GetFunction("get_output");
+  tvm::runtime::PackedFunc get_num_outputs = runtime_module->mod.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();
+
+  DLPackTensorExt* outputs_ptr = new DLPackTensorExt[output_length];
+  *outputs = outputs_ptr;
+
+  for (int k = 0; k < output_length; ++k) {
+    tvm::runtime::NDArray results = get_output(k);
+    bool is_bool = results.DataType().is_bool();
+    outputs_ptr[k] = tvm::contrib::create_dlpack_tensor_ext(&results, is_bool);
+  }
+
+  return output_length;
+}
+
+char* tvm_contrib_torch_encode(TVMContribTorchRuntimeModule* runtime_module) {
+  std::string std = tvm::contrib::serialize(runtime_module->mod);
+  char* ret = new char[std.length() + 1];
+  snprintf(ret, std.length() + 1, "%s", std.c_str());
+  return ret;
+}
+
+TVMContribTorchRuntimeModule* tvm_contrib_torch_decode(const char* state) {
+  tvm::runtime::Module ret = tvm::contrib::deserialize(state);
+  return new TVMContribTorchRuntimeModule(ret);
+}
+
+void tvm_contrib_torch_free_runtime_module(TVMContribTorchRuntimeModule* module_ptr) {
+  delete module_ptr;
+}
+
+void tvm_contrib_torch_free_dlpack_tensor_ext_array(DLPackTensorExt* dlpack_ptr) {
+  delete dlpack_ptr;

Review Comment:
   I think this needs `delete[]` instead of `delete`. 
   
   https://en.cppreference.com/w/cpp/language/delete
   > For the first (non-array) form, ... If expression is anything else, including if it is a pointer obtained by the array form of [new-expression](https://en.cppreference.com/w/cpp/language/new), the behavior is [undefined](https://en.cppreference.com/w/cpp/language/ub).



##########
src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc:
##########
@@ -0,0 +1,281 @@
+/*
+ * 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"
+
+namespace tvm {
+namespace contrib {
+
+struct ThreadLocalStore {
+  tvm::runtime::Module mod;
+  static ThreadLocalStore* ThreadLocal() {
+    thread_local ThreadLocalStore tls;
+    return &tls;
+  }
+};
+
+/*
+ * Encode TVM runtime module to base64 stream
+ */
+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;
+};
+
+/*
+ * Decode TVM runtime module from base64 stream
+ */
+tvm::runtime::Module deserialize(std::string state) {
+  auto length = tvm::support::b64strlen(state);
+
+  std::vector<u_char> bytes(length);  // bytes stream
+  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) {
+  tvm::Device ret{input_device->dl_tensor.device.device_type,
+                  input_device->dl_tensor.device.device_id};
+  return ret;
+}
+
+TVM_REGISTER_GLOBAL("tvmtorch.save_runtime_mod").set_body_typed([](tvm::runtime::Module mod) {
+  ThreadLocalStore::ThreadLocal()->mod = mod;
+});
+
+/*
+ * Convert NDArray to DLPack extend tensor. It should be zero-cost.
+ * @param src Pointer to NDArray
+ * @return DLPack extended tensor
+ */
+DLPackTensorExt create_dlpack_tensor_ext(tvm::runtime::NDArray* src, bool is_bool) {
+  DLManagedTensor* tensor;
+  if (is_bool) {
+    // If we change DLDataType{kDLInt, 8, 1} to DataType::Bool()
+    // we will get `RuntimeError: Unsupported kUInt bits 1`
+    auto tmp = src->CreateView(src->Shape(), DLDataType{kDLInt, 8, 1});
+    tensor = tmp.ToDLPack();
+  } else {
+    tensor = src->ToDLPack();
+  }
+  DLPackTensorExt ret{tensor, is_bool};
+  return ret;
+}
+
+/*
+ * Create an empty NDArray with boolean type.
+ * @param src DLpack extended tensor
+ * @return an empty NDArray
+ */
+tvm::runtime::NDArray create_empty_bool_ndarray(DLPackTensorExt* src) {

Review Comment:
   Maybe consider merging this function into the one below if it's only used once.



##########
src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc:
##########
@@ -0,0 +1,281 @@
+/*
+ * 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"
+
+namespace tvm {
+namespace contrib {
+
+struct ThreadLocalStore {
+  tvm::runtime::Module mod;
+  static ThreadLocalStore* ThreadLocal() {
+    thread_local ThreadLocalStore tls;
+    return &tls;
+  }
+};
+
+/*
+ * Encode TVM runtime module to base64 stream
+ */
+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;
+};
+
+/*
+ * Decode TVM runtime module from base64 stream
+ */
+tvm::runtime::Module deserialize(std::string state) {
+  auto length = tvm::support::b64strlen(state);
+
+  std::vector<u_char> bytes(length);  // bytes stream
+  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) {
+  tvm::Device ret{input_device->dl_tensor.device.device_type,
+                  input_device->dl_tensor.device.device_id};
+  return ret;
+}
+
+TVM_REGISTER_GLOBAL("tvmtorch.save_runtime_mod").set_body_typed([](tvm::runtime::Module mod) {
+  ThreadLocalStore::ThreadLocal()->mod = mod;
+});
+
+/*
+ * Convert NDArray to DLPack extend tensor. It should be zero-cost.
+ * @param src Pointer to NDArray
+ * @return DLPack extended tensor
+ */
+DLPackTensorExt create_dlpack_tensor_ext(tvm::runtime::NDArray* src, bool is_bool) {

Review Comment:
   Can you make sure all C++ functions follow the camel case naming?



##########
src/contrib/torch/tvm_module_wrapper/runtime_bridge.h:
##########
@@ -0,0 +1,114 @@
+/*
+ * 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 runtime_bridge.h
+ * \brief Util functions for pytorch tvm interaction.
+ */
+#ifndef TVM_CONTRIB_TORCH_TVM_MODULE_WRAPPER_RUNTIME_BRIDGE_H_
+#define TVM_CONTRIB_TORCH_TVM_MODULE_WRAPPER_RUNTIME_BRIDGE_H_
+
+extern "C" {
+
+/*
+ * DLPack data structure extend with `is_bool` flag.
+ * DLPack haven't support boolean tensor,
+ * thus a boolean tensor will be regarded as a UInt8 tensor.
+ */
+struct DLPackTensorExt {
+  DLManagedTensor* dl_managed_tensor;
+  bool is_bool;
+};
+
+/*
+ * A wrapper pointing to TVM runtime module.
+ */
+struct TVMContribTorchRuntimeModule;
+
+/*
+ * Obtain a saved runtime module passed by TVM FFI.
+ * @return A TVM runtime module wrapper.
+ */
+TVMContribTorchRuntimeModule* tvm_contrib_torch_get_last_saved_runtime_module();
+
+/*
+ * Delete TVMContribTorchRuntimeModule pointer.
+ */
+void tvm_contrib_torch_free_runtime_module(TVMContribTorchRuntimeModule* module_ptr);
+
+/*
+ * Obtain ExecutorFactory runtime module from ExecutorFactory class.
+ * @param graph_module ExecutorFactory class
+ * @param input_example For obtaining device information
+ * @return ExecutorFactory TVM runtime module wrapper
+ */
+TVMContribTorchRuntimeModule* tvm_contrib_torch_create_graph_runtime_module(
+    TVMContribTorchRuntimeModule* graph_module, DLManagedTensor* input_example);

Review Comment:
   ```suggestion
       TVMContribTorchRuntimeModule* graph_executor_factory, DLManagedTensor* input_example);
   ```



##########
src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc:
##########
@@ -0,0 +1,281 @@
+/*
+ * 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"
+
+namespace tvm {
+namespace contrib {
+
+struct ThreadLocalStore {
+  tvm::runtime::Module mod;
+  static ThreadLocalStore* ThreadLocal() {
+    thread_local ThreadLocalStore tls;
+    return &tls;
+  }
+};
+
+/*
+ * Encode TVM runtime module to base64 stream
+ */
+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;
+};
+
+/*
+ * Decode TVM runtime module from base64 stream
+ */
+tvm::runtime::Module deserialize(std::string state) {
+  auto length = tvm::support::b64strlen(state);
+
+  std::vector<u_char> bytes(length);  // bytes stream
+  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) {

Review Comment:
   tvm::Device is an alias to DLDevice. Why do we want to do this instead of using `input_device->dl_tensor.device`? Also just noticed the variable name doesn't look correct...



##########
src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc:
##########
@@ -0,0 +1,281 @@
+/*
+ * 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"
+
+namespace tvm {
+namespace contrib {
+
+struct ThreadLocalStore {
+  tvm::runtime::Module mod;
+  static ThreadLocalStore* ThreadLocal() {
+    thread_local ThreadLocalStore tls;
+    return &tls;
+  }
+};
+
+/*
+ * Encode TVM runtime module to base64 stream
+ */
+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;
+};
+
+/*
+ * Decode TVM runtime module from base64 stream
+ */
+tvm::runtime::Module deserialize(std::string state) {
+  auto length = tvm::support::b64strlen(state);
+
+  std::vector<u_char> bytes(length);  // bytes stream
+  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) {
+  tvm::Device ret{input_device->dl_tensor.device.device_type,
+                  input_device->dl_tensor.device.device_id};
+  return ret;
+}
+
+TVM_REGISTER_GLOBAL("tvmtorch.save_runtime_mod").set_body_typed([](tvm::runtime::Module mod) {
+  ThreadLocalStore::ThreadLocal()->mod = mod;
+});
+
+/*
+ * Convert NDArray to DLPack extend tensor. It should be zero-cost.
+ * @param src Pointer to NDArray
+ * @return DLPack extended tensor
+ */
+DLPackTensorExt create_dlpack_tensor_ext(tvm::runtime::NDArray* src, bool is_bool) {

Review Comment:
   Can it get the `is_bool` inside this function by looking at `src`? If yes we can remove this parameter



##########
src/contrib/torch/tvm_module_wrapper/RuntimeModuleWrapperTVM.cc:
##########
@@ -0,0 +1,281 @@
+/*
+ * 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"
+
+namespace tvm {
+namespace contrib {
+
+struct ThreadLocalStore {
+  tvm::runtime::Module mod;
+  static ThreadLocalStore* ThreadLocal() {
+    thread_local ThreadLocalStore tls;
+    return &tls;
+  }
+};
+
+/*
+ * Encode TVM runtime module to base64 stream
+ */
+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;
+};
+
+/*
+ * Decode TVM runtime module from base64 stream
+ */
+tvm::runtime::Module deserialize(std::string state) {
+  auto length = tvm::support::b64strlen(state);
+
+  std::vector<u_char> bytes(length);  // bytes stream
+  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) {
+  tvm::Device ret{input_device->dl_tensor.device.device_type,
+                  input_device->dl_tensor.device.device_id};
+  return ret;
+}
+
+TVM_REGISTER_GLOBAL("tvmtorch.save_runtime_mod").set_body_typed([](tvm::runtime::Module mod) {
+  ThreadLocalStore::ThreadLocal()->mod = mod;
+});
+
+/*
+ * Convert NDArray to DLPack extend tensor. It should be zero-cost.
+ * @param src Pointer to NDArray
+ * @return DLPack extended tensor
+ */
+DLPackTensorExt create_dlpack_tensor_ext(tvm::runtime::NDArray* src, bool is_bool) {
+  DLManagedTensor* tensor;
+  if (is_bool) {
+    // If we change DLDataType{kDLInt, 8, 1} to DataType::Bool()
+    // we will get `RuntimeError: Unsupported kUInt bits 1`
+    auto tmp = src->CreateView(src->Shape(), DLDataType{kDLInt, 8, 1});
+    tensor = tmp.ToDLPack();
+  } else {
+    tensor = src->ToDLPack();
+  }
+  DLPackTensorExt ret{tensor, is_bool};
+  return ret;
+}
+
+/*
+ * Create an empty NDArray with boolean type.
+ * @param src DLpack extended tensor
+ * @return an empty NDArray
+ */
+tvm::runtime::NDArray create_empty_bool_ndarray(DLPackTensorExt* src) {
+  auto& tensor = src->dl_managed_tensor->dl_tensor;
+  std::vector<int64_t> shape;
+  for (int64_t i = 0; i < tensor.ndim; i++) {
+    shape.push_back(tensor.shape[i]);
+  }
+  auto ret = tvm::runtime::NDArray::Empty(shape, DataType::Bool(), tensor.device);
+  return ret;
+}
+
+/*
+ * Create an NDArray with boolean type. (One memory copy)
+ * @param src DLpack extended tensor
+ * @return a new NDArray
+ */
+tvm::runtime::NDArray create_bool_ndarray(DLPackTensorExt* src) {
+  auto&& ret = create_empty_bool_ndarray(src);
+  ret.CopyFrom(&src->dl_managed_tensor->dl_tensor);
+  return std::move(ret);
+}
+
+bool is_zero_copy(DLPackTensorExt* src) {
+  auto& dl_tensor = src->dl_managed_tensor->dl_tensor;
+  bool is_zero_copy =
+      tvm::runtime::NDArray::AbilityOfZeroCopyForDLTensor(&dl_tensor, dl_tensor.device);
+  return is_zero_copy;
+}
+
+/*
+ * Create an NDArray from DLpack extended tensor.
+ * @param src DLpack extended tensor
+ * @return a new NDArray
+ */
+tvm::runtime::NDArray ndarray_from_dlpack(DLPackTensorExt* src) {
+  using tvm::runtime::NDArray;
+
+  NDArray array;
+  auto& dl_tensor = src->dl_managed_tensor->dl_tensor;
+  if (src->is_bool) {
+    // one memory copy
+    // the code is similar to NewFromDLTensor except for the type
+    array = create_bool_ndarray(src);
+  } else if (is_zero_copy(src)) {
+    array = NDArray::FromExternalDLTensor(src->dl_managed_tensor->dl_tensor);
+  } else {
+    // one memory copy
+    array = NDArray::NewFromDLTensor(&dl_tensor, dl_tensor.device);
+  }
+  return array;
+}
+
+}  // namespace contrib
+}  // namespace tvm
+
+extern "C" {
+
+struct TVMContribTorchRuntimeModule {
+  tvm::runtime::Module mod;
+
+  explicit TVMContribTorchRuntimeModule(tvm::runtime::Module& mod) : mod(mod) {}
+};
+
+bool tvm_contrib_torch_is_be_copied(DLPackTensorExt* src) {
+  return (src->is_bool) || (!tvm::contrib::is_zero_copy(src));
+}
+
+TVMContribTorchRuntimeModule* tvm_contrib_torch_get_last_saved_runtime_module() {
+  return new TVMContribTorchRuntimeModule(tvm::contrib::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());
+
+  std::vector<tvm::runtime::NDArray> input_cache(input_size);
+
+  for (int k = 0; k < input_size; ++k) {
+    auto datum = tvm::contrib::ndarray_from_dlpack(&inputs[k]);  // could have one memory copy
+    input_cache[k] = datum;  // we keep the datum in a vector for future use, otherwise the datum
+                             // will be freed after the loop
+    setter(k, datum);
+  }
+
+  run.CallPacked(tvm::runtime::TVMArgs(tvm_values.data(), tvm_type_codes.data(), input_size),
+                 nullptr);
+
+  for (int k = 0; k < input_size; ++k) {
+    if (tvm_contrib_torch_is_be_copied(&inputs[k]))
+      input_cache[k].CopyTo(&inputs[k].dl_managed_tensor->dl_tensor);
+  }
+}
+
+TVMContribTorchRuntimeModule* tvm_contrib_torch_create_graph_runtime_module(
+    TVMContribTorchRuntimeModule* graph_module, DLManagedTensor* input_example) {
+  tvm::runtime::PackedFunc built_module = graph_module->mod.GetFunction("default");
+  tvm::Device device_info = tvm::contrib::getDeviceInfo(input_example);
+  tvm::runtime::Module runtime_module = built_module(device_info);
+  return new TVMContribTorchRuntimeModule(runtime_module);
+}
+
+size_t tvm_contrib_torch_graph_executor_module_forward(TVMContribTorchRuntimeModule* runtime_module,
+                                                       DLPackTensorExt* inputs, size_t input_size,
+                                                       DLPackTensorExt** outputs) {
+  tvm::runtime::PackedFunc run = runtime_module->mod.GetFunction("run");
+  tvm::runtime::PackedFunc set_input = runtime_module->mod.GetFunction("set_input");
+  tvm::runtime::PackedFunc get_output = runtime_module->mod.GetFunction("get_output");
+  tvm::runtime::PackedFunc get_num_outputs = runtime_module->mod.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();
+
+  DLPackTensorExt* outputs_ptr = new DLPackTensorExt[output_length];
+  *outputs = outputs_ptr;
+
+  for (int k = 0; k < output_length; ++k) {
+    tvm::runtime::NDArray results = get_output(k);
+    bool is_bool = results.DataType().is_bool();
+    outputs_ptr[k] = tvm::contrib::create_dlpack_tensor_ext(&results, is_bool);
+  }
+
+  return output_length;
+}
+
+char* tvm_contrib_torch_encode(TVMContribTorchRuntimeModule* runtime_module) {
+  std::string std = tvm::contrib::serialize(runtime_module->mod);
+  char* ret = new char[std.length() + 1];
+  snprintf(ret, std.length() + 1, "%s", std.c_str());
+  return ret;
+}
+
+TVMContribTorchRuntimeModule* tvm_contrib_torch_decode(const char* state) {
+  tvm::runtime::Module ret = tvm::contrib::deserialize(state);
+  return new TVMContribTorchRuntimeModule(ret);
+}
+
+void tvm_contrib_torch_free_runtime_module(TVMContribTorchRuntimeModule* module_ptr) {
+  delete module_ptr;
+}
+
+void tvm_contrib_torch_free_dlpack_tensor_ext_array(DLPackTensorExt* dlpack_ptr) {
+  delete dlpack_ptr;
+}
+
+void tvm_contrib_torch_free_encoding(char* encoding) { delete encoding; }

Review Comment:
   Same problem on `delete[]` vs `delete`



##########
src/contrib/torch/tvm_module_wrapper/runtime_bridge.h:
##########
@@ -0,0 +1,114 @@
+/*
+ * 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 runtime_bridge.h
+ * \brief Util functions for pytorch tvm interaction.
+ */
+#ifndef TVM_CONTRIB_TORCH_TVM_MODULE_WRAPPER_RUNTIME_BRIDGE_H_
+#define TVM_CONTRIB_TORCH_TVM_MODULE_WRAPPER_RUNTIME_BRIDGE_H_
+
+extern "C" {
+
+/*
+ * DLPack data structure extend with `is_bool` flag.
+ * DLPack haven't support boolean tensor,
+ * thus a boolean tensor will be regarded as a UInt8 tensor.
+ */
+struct DLPackTensorExt {
+  DLManagedTensor* dl_managed_tensor;
+  bool is_bool;
+};
+
+/*
+ * A wrapper pointing to TVM runtime module.
+ */
+struct TVMContribTorchRuntimeModule;
+
+/*
+ * Obtain a saved runtime module passed by TVM FFI.
+ * @return A TVM runtime module wrapper.
+ */
+TVMContribTorchRuntimeModule* tvm_contrib_torch_get_last_saved_runtime_module();
+
+/*
+ * Delete TVMContribTorchRuntimeModule pointer.
+ */
+void tvm_contrib_torch_free_runtime_module(TVMContribTorchRuntimeModule* module_ptr);
+
+/*
+ * Obtain ExecutorFactory runtime module from ExecutorFactory class.
+ * @param graph_module ExecutorFactory class
+ * @param input_example For obtaining device information
+ * @return ExecutorFactory TVM runtime module wrapper
+ */
+TVMContribTorchRuntimeModule* tvm_contrib_torch_create_graph_runtime_module(
+    TVMContribTorchRuntimeModule* graph_module, DLManagedTensor* input_example);
+
+/*
+ * Forward method for OperatorModuleWrapper.
+ * @param runtime_module TVM runtime module wrapper
+ * @param inputs Array pointer of the input tensors
+ * @param input_size The number of input tensors
+ */
+void tvm_contrib_torch_operator_module_forward(TVMContribTorchRuntimeModule* runtime_module,
+                                               DLPackTensorExt* inputs, size_t input_size);
+
+/*
+ * Forward method for GraphExecutorFactoryWrapper.
+ * @param graph_module TVM runtime module wrapper
+ * @param inputs Array pointer of the input tensors
+ * @param input_size The number of input tensors
+ * @param outputs The resulting output tensors pointer
+ * @return The number of output tensors
+ */
+size_t tvm_contrib_torch_graph_executor_module_forward(TVMContribTorchRuntimeModule* graph_module,
+                                                       DLPackTensorExt* inputs, size_t input_size,
+                                                       DLPackTensorExt** outputs);
+
+/*
+ * Encode TVM runtime module.
+ * @param runtime_module TVM runtime module wrapper
+ * @return The encoding stream (char array)
+ */
+char* tvm_contrib_torch_encode(TVMContribTorchRuntimeModule* runtime_module);
+
+/*
+ * Decode TVM runtime module.
+ * @param state The encoding stream (char array) of TVM runtime module
+ * @return TVM runtime module wrapper
+ */
+TVMContribTorchRuntimeModule* tvm_contrib_torch_decode(const char* state);
+
+/*
+ * Delete DLPackTensorExt pointer.
+ */
+void tvm_contrib_torch_free_dlpack_tensor_ext_array(DLPackTensorExt*);
+
+/*
+ * Delete char array pointer.
+ */
+void tvm_contrib_torch_free_encoding(char* encoding);
+
+/*
+ * Checking if a DLPackTensorExt is boolean or cannot be copied in zero cost.
+ */
+bool tvm_contrib_torch_is_be_copied(DLPackTensorExt*);

Review Comment:
   ```suggestion
   bool tvm_contrib_torch_tensor_ability_of_zero_copy(DLPackTensorExt*);
   ```



-- 
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.

To unsubscribe, e-mail: commits-unsubscribe@tvm.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org