mirror of https://github.com/llvm/torch-mlir
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
#!/usr/bin/env python3
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"""
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Utility for handling common tasks for exported `.pt` model files.
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Usage:
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# Dump PyTorch data structures for .pt file.
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# This does not involve any MLIR code.
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$ pt_util.py --dump model.pt
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# Import the .pt file into MLIR.
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$ pt_util.py --import model.pt
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"""
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import torch
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import torch_mlir
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import argparse
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def main():
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parser = argparse.ArgumentParser(
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description="Utility for .pt files")
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parser.add_argument("pt_file", metavar="PT_FILE", type=str,
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help="the .pt file to import")
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parser.add_argument("--dump", action="store_true",
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help="dump the pytorch module")
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parser.add_argument("--import", action="store_true",
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help="import the pytorch module")
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parser.add_argument("--exported-name", action="append",
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help="""
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Name to export, such as `my.submodule.forward`(default = export all).
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Can pass repeatedly.
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""")
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args = parser.parse_args()
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# TODO: Investigate why "cpu" is needed.
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module = torch.jit.load(args.pt_file, map_location="cpu")
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if args.dump:
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module._c.dump(code=True, attrs=False, params=False)
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# `import` is a Python keyword, so getattr is needed.
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if getattr(args, "import", False):
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class_annotator = torch_mlir.ClassAnnotator()
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if args.exported_name is not None:
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class_annotator.exportNone(module._c._type())
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for name in args.exported_name:
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class_annotator.exportPath(module._c._type(), name.split("."))
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mb = torch_mlir.ModuleBuilder()
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mb.import_module(module._c, class_annotator)
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mb.module.operation.print(large_elements_limit=16)
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if __name__ == "__main__":
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main()
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