mirror of https://github.com/llvm/torch-mlir
57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
# See https://llvm.org/LICENSE.txt for license information.
|
|
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
# Also available under a BSD-style license. See LICENSE.
|
|
|
|
from io import StringIO
|
|
import os
|
|
import sys
|
|
import tempfile
|
|
from torch_mlir.passmanager import PassManager
|
|
from torch_mlir.ir import StringAttr
|
|
|
|
def get_module_name_for_debug_dump(module):
|
|
"""Gets a name suitable for a debug dump.
|
|
|
|
The name is not guaranteed to be unique.
|
|
"""
|
|
if not "torch.debug_module_name" in module.operation.attributes:
|
|
return "UnnammedModule"
|
|
return StringAttr(module.operation.attributes["torch.debug_module_name"]).value
|
|
|
|
def run_pipeline_with_repro_report(module,
|
|
pipeline: str,
|
|
description: str):
|
|
"""Runs `pipeline` on `module`, with a nice repro report if it fails."""
|
|
module_name = get_module_name_for_debug_dump(module)
|
|
try:
|
|
original_stderr = sys.stderr
|
|
sys.stderr = StringIO()
|
|
asm_for_error_report = module.operation.get_asm(
|
|
large_elements_limit=10, enable_debug_info=True)
|
|
# Lower module in place to make it ready for compiler backends.
|
|
with module.context:
|
|
pm = PassManager.parse(pipeline)
|
|
pm.run(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(), module_name + ".mlir")
|
|
with open(filename, 'w') as f:
|
|
f.write(asm_for_error_report)
|
|
raise Exception(f"""
|
|
{description} failed with the following diagnostics:
|
|
{sys.stderr.getvalue()}
|
|
|
|
Error can be reproduced with:
|
|
$ torch-mlir-opt -pass-pipeline='{pipeline}' {filename}
|
|
""") from None
|
|
finally:
|
|
sys.stderr = original_stderr
|