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)
return
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.s = Submodule()
def forward(self, tensor, value_tensor):
return self.s.forward()
test_module = TestModule()
recursivescriptmodule = torch.jit.script(test_module)
annotator = torch_mlir.ClassAnnotator()
class_type = recursivescriptmodule._c._type()
annotator.exportNone(class_type)
annotator.exportPath(class_type, ['s', 'exported'])
annotator.exportPath(class_type, ['s', 'forward'])
annotator.annotateArgs(class_type, ['forward'], [
None,
((1024, 2), torch.float32, False),
((42, -1, 7), torch.int8, True),
])
# "Change detector" test + "documentation" for the repr of `ClassAnnotator`.
# This is semi-load-bearing because users interact with this class and repr
# will show up in error messages, so should be pretty readable.
#
def compile(self, program: torch.nn.Module) -> Any:
mb = torch_mlir.ModuleBuilder()
scripted = torch.jit.script(program)
class_annotator = torch_mlir.ClassAnnotator()
extract_annotations(program, scripted, class_annotator)
# TODO: Find a way to make each of these calls own its own
# "debuggable error report" situation.
try:
sys.stderr = StringIO()
# Import the TorchScript module to MLIR
mb.import_module(scripted._c, class_annotator)
except Exception as e:
raise Exception(f"""
PyTorch TorchScript module -> NPCOMP Object Graph IR import failed with:
Exception:
{e}
Diagnostics:
{sys.stderr.getvalue()}
""") from None
finally:
sys.stderr = sys.__stderr__
try:
sys.stderr = StringIO()
asm_for_error_report = mb.module.operation.get_asm(
large_elements_limit=10, enable_debug_info=True)
pipeline_str = "torchscript-to-npcomp-backend-pipeline"
# Lower module in place to make it ready for compiler backends.
with mb.module.context:
pm = PassManager.parse(pipeline_str)
pm.run(mb.module)
except Exception as e:
# TODO: More robust.
# - don't arbitrarily clutter up /tmp. When a test suite has many
# tests, this can be a big disk cost (also, /tmp/ is frequently a
# RAM fs, which increases worries about capacity).
# - don't have colliding filenames (hard to do without cluttering
# up /tmp)
# - if we do have have colliding filenames, writes should at least
# avoid being racy.
filename = os.path.join(tempfile.gettempdir(),
scripted.original_name + '.mlir')
with open(filename, 'w') as f:
f.write(asm_for_error_report)
raise Exception(f"""
NPCOMP TorchScript Object Graph IR -> NPCOMP Backend IR lowering failed with the following diagnostics:
{sys.stderr.getvalue()}
Error can be reproduced with:
$ npcomp-opt -{pipeline_str} {filename}
""") from None
finally:
sys.stderr = sys.__stderr__
try:
sys.stderr = StringIO()
asm_for_error_report = mb.module.operation.get_asm(
large_elements_limit=10, enable_debug_info=True)
return self.backend.compile(mb.module)
except Exception as e:
filename = os.path.join(tempfile.gettempdir(),
scripted.original_name + '.mlir')
with open(filename, 'w') as f:
f.write(asm_for_error_report)
raise Exception(f"""
NPCOMP Backend lowering for {self.backend.__class__.__name__} failed with the following diagnostics:
## Exception:
{e}
## Stderr:
{sys.stderr.getvalue()}
## Input IR has been saved in {filename}
""") from None
finally:
sys.stderr = sys.__stderr__
