torch-mlir/frontends/pytorch/utils/pt_util.py

55 lines
1.7 KiB
Python

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