pytorch/test/onnx/error_reproduction.py

"""Error reproduction utilities for op consistency tests."""

from __future__ import annotations

import difflib
import platform
import sys
import time
import traceback
from pathlib import Path

import numpy as np

import onnx
import onnxruntime as ort
import onnxscript

import torch


_MISMATCH_MARKDOWN_TEMPLATE = """\
### Summary

The output of ONNX Runtime does not match that of PyTorch when executing test
`{test_name}`, `sample {sample_num}` in ONNX Script `TorchLib`.

To recreate this report, use

```bash
CREATE_REPRODUCTION_REPORT=1 python -m pytest onnxscript/tests/function_libs/torch_lib/ops_test.py -k {short_test_name}
```

### ONNX Model

```
{onnx_model_text}
```

### Inputs

Shapes: `{input_shapes}`

<details><summary>Details</summary>
<p>

```python
kwargs = {kwargs}
inputs = {inputs}
```

</p>
</details>

### Expected output

Shape: `{expected_shape}`

<details><summary>Details</summary>
<p>

```python
expected = {expected}
```

</p>
</details>

### Actual output

Shape: `{actual_shape}`

<details><summary>Details</summary>
<p>

```python
actual = {actual}
```

</p>
</details>

### Difference

<details><summary>Details</summary>
<p>

```diff
{diff}
```

</p>
</details>

### Full error stack

```
{error_stack}
```

### Environment

```
{sys_info}
```

"""


def create_mismatch_report(
    test_name: str,
    sample_num: int,
    onnx_model: onnx.ModelProto,
    inputs,
    kwargs,
    actual,
    expected,
    error: Exception,
) -> None:
    torch.set_printoptions(threshold=sys.maxsize)

    error_text = str(error)
    error_stack = error_text + "\n" + "".join(traceback.format_tb(error.__traceback__))
    short_test_name = test_name.split(".")[-1]
    diff = difflib.unified_diff(
        str(actual).splitlines(),
        str(expected).splitlines(),
        fromfile="actual",
        tofile="expected",
        lineterm="",
    )
    onnx_model_text = onnx.printer.to_text(onnx_model)
    input_shapes = repr(
        [
            f"Tensor<{inp.shape}, dtype={inp.dtype}>"
            if isinstance(inp, torch.Tensor)
            else inp
            for inp in inputs
        ]
    )
    sys_info = f"""\
OS: {platform.platform()}
Python version: {sys.version}
onnx=={onnx.__version__}
onnxruntime=={ort.__version__}
onnxscript=={onnxscript.__version__}
numpy=={np.__version__}
torch=={torch.__version__}"""

    markdown = _MISMATCH_MARKDOWN_TEMPLATE.format(
        test_name=test_name,
        short_test_name=short_test_name,
        sample_num=sample_num,
        input_shapes=input_shapes,
        inputs=inputs,
        kwargs=kwargs,
        expected=expected,
        expected_shape=expected.shape if isinstance(expected, torch.Tensor) else None,
        actual=actual,
        actual_shape=actual.shape if isinstance(actual, torch.Tensor) else None,
        diff="\n".join(diff),
        error_stack=error_stack,
        sys_info=sys_info,
        onnx_model_text=onnx_model_text,
    )

    markdown_file_name = f'mismatch-{short_test_name.replace("/", "-").replace(":", "-")}-{str(time.time()).replace(".", "_")}.md'
    markdown_file_path = save_error_report(markdown_file_name, markdown)
    print(f"Created reproduction report at {markdown_file_path}")


def save_error_report(file_name: str, text: str):
    reports_dir = Path("error_reports")
    reports_dir.mkdir(parents=True, exist_ok=True)
    file_path = reports_dir / file_name
    with open(file_path, "w", encoding="utf-8") as f:
        f.write(text)

    return file_path