[MetaSchedule] Improve the script for TorchBench model tuning & benchmarking

[yelite]

This PR adds features to the python/tvm/meta_schedule/testing/torchbench/run.py.

• Integrate with the TVM PyTorch integration to handle boolean tensor and unaligned memory.
• Deduplicate collected tuning tasks to prevent thousands of tasks created by hundreds of subgraphs with similar structure.
• Add option to cast model to float32, which are more stable numerically than float16 and prevents inaccurate result from many models.
• Add option to choose search strategy in MetaSchedule.
• Inspect output error if the actual output doesn't match the expectation. Also save the actual output and expected output for further analysis if needed.
• Save subgraphs and their example input for debug purpose.
• Print MetaSchedule profiling information at the end of execution.
• Detach PyTorch tensor before exporting to dlpack.
• Fix the sys path to avoid conflict with the benchmarks package installed by TorchBench dependency.
• Trim all command line args passed in, in order to prevent breaking some TorchBench model that depends on args.
• Empty cuda cache before starting the actual benchmark.

cc: @junrushao @zxybazh

[tvm-bot]

Thanks for contributing to TVM! Please refer to the contributing guidelines https://tvm.apache.org/docs/contribute/ for useful information and tips. Please request code reviews from Reviewers by @-ing them in a comment.

• cc @Hzfengsy, @elvin-n, @junrushao _{See #10317 for details}
• Built docs for commit 1e54702 can be found here.

_{Generated by tvm-bot}

[junrushao]

LGTM!