import torch import numpy as np from transformers import Mask2FormerForUniversalSegmentation, DeformableDetrForObjectDetection, AutoImageProcessor from PIL import Image import httpx from io import BytesIO

url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png" response = httpx.get(url, follow_redirects=True) image = Image.open(BytesIO(response.content))

mask2former

try: model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic").eval() image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-ade-semantic") inputs = image_processor(image, return_tensors="pt") exported = torch.export.export(model, args=(inputs["pixel_values"], inputs["pixel_mask"]), strict=True) print(f"{len(exported.graph.nodes)} nodes") outputs = exported.module()(inputs["pixel_values"], inputs["pixel_mask"]) print(f"class_queries_logits: {outputs.class_queries_logits.shape}") print(f"masks_queries_logits: {outputs.masks_queries_logits.shape}") except Exception as e: print(f"Mask2Former FAILED: {chr(10).join(str(e).split(chr(10))[:2])}")

deformabledetr

try: model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr").eval() image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") inputs = image_processor(image, return_tensors="pt") exported = torch.export.export(model, args=(inputs["pixel_values"], inputs["pixel_mask"]), strict=True) print(f"{len(exported.graph.nodes)} nodes") outputs = exported.module()(inputs["pixel_values"], inputs["pixel_mask"]) print(f"logits: {outputs.logits.shape}") print(f"pred_boxes: {outputs.pred_boxes.shape}") except Exception as e: print(f"DeformableDetr FAILED: {chr(10).join(str(e).split(chr(10))[:2])}")

Problem Summary

Fixing torch_compilable_check issue with torch.export.export(strict=True)

Root Cause Analysis

The issue is caused by torch_compilable_check calling torch._check_with with ValueError as an argument, but during strict tracing, Dynamo can't convert exception types to proxy objects.

Fix Plan

Step 1: Update torch version

Update torch to the latest version (2.12.0 or later) which includes a fix for this issue.

pip install torch==2.12.0

Step 2: Disable strict tracing

Disable strict tracing by setting strict=False in torch.export.export.

exported = torch.export.export(model, args=(inputs["pixel_values"], inputs["pixel_mask"]), strict=False)

Step 3: Use torch._check_with workaround

Alternatively, you can use a workaround by importing torch._check_with and redefining it to not raise an exception.

import torch._check_with as _check_with
def _check_with_workaround(error_type, cond, msg_callable):
    if not cond:
        print(f"Warning: {msg_callable()}")
    else:
        _check_with(error_type, cond, msg_callable)
torch._check_with = _check_with_workaround

Verification

Verify that the fix works by running the code with the updated torch version or with the workaround.

Extra Tips

• Make sure to update torch to the latest version to avoid any other potential issues.
• If you're using a custom torch version, consider updating to the latest version to avoid compatibility issues.
• The workaround is a temporary solution and should be removed once the issue is fixed in torch.