juda commented on code in PR #11911: URL: https://github.com/apache/tvm/pull/11911#discussion_r919959997
########## python/tvm/contrib/torch/as_torch.py: ########## @@ -0,0 +1,88 @@ +# pylint: disable=inconsistent-return-statements +#!/usr/bin/env python + +# 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. +# pylint: disable=missing-module-docstring +# pylint: disable=missing-class-docstring +# pylint: disable=missing-function-docstring +""" +as_torch: a decorator, which is used to wrap the TVMscript code to `torch.nn.module`. +""" +from typing import Callable, List, Union + +import torch +import torch.utils.dlpack + +import tvm + + +# python wrapper for OperatorModule +class OperatorModuleWrapper(torch.nn.Module): + def __init__( + self, + module: Union[ + tvm.ir.module.IRModule, + tvm.tir.function.PrimFunc, + ], + ): + super().__init__() + self.rt_module = None # runtime module + self.ir_module = module # IR modules + + def build(self, target=None): + runtime_module = tvm.build(self.ir_module, target=target) + func = tvm.get_global_func("tvmtorch.save_runtime_mod") + func(runtime_module) + + self.rt_module = torch.classes.tvm_torch.OperatorModuleWrapper() + + def forward(self, *torch_inputs: List[torch.Tensor]) -> List[torch.Tensor]: + if self.rt_module is None: + if torch_inputs[0].is_cuda: + self.build(target="cuda") + elif torch_inputs[0].device.type == "cpu": + self.build() + else: + raise Exception(f"the target {torch_inputs[0].device.type} is not supported yet") + + return self.rt_module.forward(torch_inputs) + + +def as_torch(func: Union[tvm.ir.module.IRModule, tvm.tir.function.PrimFunc, Callable]): Review Comment: My understanding is that `as_torch` is just used to convert TVMscript to Torch. Need to confirm with @yelite to see if we need to do more. -- 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-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
