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[error] Save mlir stderr and add to run_pipeline_with_repro_report error message #4444

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[error] Save mlir stderr and add to run_pipeline_with_repro_report error message #4444

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14 changes: 10 additions & 4 deletions python/torch_mlir/compiler_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
from .passmanager import PassManager
from .ir import StringAttr

ERROR_MSG_MAX_LENGTH = 10000

class TensorPlaceholder:
"""A class that represents a formal parameter of a given shape and dtype.
Expand Down Expand Up @@ -90,9 +91,10 @@ def run_pipeline_with_repro_report(
):
"""Runs `pipeline` on `module`, with a nice repro report if it fails."""
module_name = get_module_name_for_debug_dump(module)
original_stderr = sys.stderr
saved_stderr_fd = os.dup(2)
read_fd, write_fd = os.pipe()
os.dup2(write_fd, 2)
try:
sys.stderr = StringIO()
asm_for_error_report = module.operation.get_asm(
large_elements_limit=10, enable_debug_info=True
)
Expand Down Expand Up @@ -122,9 +124,10 @@ def run_pipeline_with_repro_report(
# Put something descriptive here even if description is empty.
description = description or f"{module_name} compile"

stderr_out = os.read(read_fd, ERROR_MSG_MAX_LENGTH).decode()
message = f"""\
{description} failed with the following diagnostics:
{sys.stderr.getvalue()}
{stderr_out}

python exception: {e}

Expand All @@ -135,7 +138,10 @@ def run_pipeline_with_repro_report(
trimmed_message = "\n".join([m.lstrip() for m in message.split("\n")])
raise TorchMlirCompilerError(trimmed_message) from None
finally:
sys.stderr = original_stderr
os.dup2(saved_stderr_fd, 2)
os.close(saved_stderr_fd)
os.close(write_fd)
os.close(read_fd)


class OutputType(Enum):
Expand Down