mirror of https://github.com/llvm/torch-mlir
72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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# Also available under a BSD-style license. See LICENSE.
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from io import StringIO
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import os
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import sys
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import tempfile
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from torch_mlir.passmanager import PassManager
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from torch_mlir.ir import StringAttr
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def get_module_name_for_debug_dump(module):
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"""Gets a name suitable for a debug dump.
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The name is not guaranteed to be unique.
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"""
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if not "torch.debug_module_name" in module.operation.attributes:
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return "UnnammedModule"
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return StringAttr(module.operation.attributes["torch.debug_module_name"]).value
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class TorchMlirCompilerError(Exception):
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pass
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def run_pipeline_with_repro_report(module,
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pipeline: str,
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description: str):
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"""Runs `pipeline` on `module`, with a nice repro report if it fails."""
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module_name = get_module_name_for_debug_dump(module)
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try:
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original_stderr = sys.stderr
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sys.stderr = StringIO()
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asm_for_error_report = module.operation.get_asm(
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large_elements_limit=10, enable_debug_info=True)
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# Lower module in place to make it ready for compiler backends.
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with module.context:
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pm = PassManager.parse(pipeline)
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pm.run(module.operation)
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except Exception as e:
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# TODO: More robust.
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# - don't arbitrarily clutter up /tmp. When a test suite has many
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# tests, this can be a big disk cost (also, /tmp/ is frequently a
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# RAM fs, which increases worries about capacity).
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# - don't have colliding filenames (hard to do without cluttering
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# up /tmp)
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# - if we do have have colliding filenames, writes should at least
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# avoid being racy.
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filename = os.path.join(tempfile.gettempdir(), module_name + ".mlir")
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with open(filename, 'w') as f:
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f.write(asm_for_error_report)
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debug_options="-mlir-print-ir-after-all -mlir-disable-threading"
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# Put something descriptive here even if description is empty.
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description = description or f"{module_name} compile"
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message = f"""\
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{description} failed with the following diagnostics:
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{sys.stderr.getvalue()}
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python exception: {e}
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For Torch-MLIR developers, the error can be reproduced with:
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$ torch-mlir-opt -pass-pipeline='{pipeline}' {filename}
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Add '{debug_options}' to get the IR dump for debugging purpose.
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"""
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trimmed_message = '\n'.join([m.lstrip() for m in message.split('\n')])
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raise TorchMlirCompilerError(trimmed_message) from None
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finally:
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sys.stderr = original_stderr
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