change

Author: xadupre

_unittests/ut_tasks/test_tasks.py

@@ -45,6 +45,8 @@ def test_image_classification(self):
         model, inputs, ds = data["model"], data["inputs"], data["dynamic_shapes"]
         model(**inputs)
         model(**data["inputs2"])
+        if not has_transformers("4.51"):
+            raise unittest.SkipTest("_patch_make_causal_mask patch fails when an issue arises")
         with bypass_export_some_errors(patch_transformers=True, verbose=10):
             torch.export.export(
                 model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False
@@ -131,7 +133,7 @@ def test_image_text_to_text(self):
         model, inputs, ds = data["model"], data["inputs"], data["dynamic_shapes"]
         model(**inputs)
         model(**data["inputs2"])
-        if not has_torch("2.8"):
+        if not has_torch("2.7.99"):
             raise unittest.SkipTest("sym_max does not work with dynamic dimension")
         with bypass_export_some_errors(patch_transformers=True, verbose=10):
             torch.export.export(
@@ -219,6 +221,8 @@ def test_zero_shot_image_classification(self):
         model, inputs, ds = data["model"], data["inputs"], data["dynamic_shapes"]
         model(**inputs)
         model(**data["inputs2"])
+        if not has_transformers("4.51"):
+            raise unittest.SkipTest("_patch_make_causal_mask patch fails when an issue arises")
         with bypass_export_some_errors(patch_transformers=True, verbose=10):
             torch.export.export(
                 model, (), kwargs=inputs, dynamic_shapes=use_dyn_not_str(ds), strict=False

onnx_diagnostic/torch_export_patches/patches/patch_torch.py

@@ -44,7 +44,7 @@ def _catch_produce_guards_and_solve_constraints(
             raise
         if verbose:
             print(
-                f"[_catch_produce_guards_and_solve_constraints] ERROR"
+                f"[_catch_produce_guards_and_solve_constraints] ERROR: "
                 f"produce_guards_and_solve_constraints failed, "
                 f"use SKIP_SOLVE_CONSTRAINTS=0 to avoid skipping\n"
                 f"fake_mode={fake_mode}\n"
@@ -54,6 +54,7 @@ def _catch_produce_guards_and_solve_constraints(
                 f"_is_torch_jit_trace={_is_torch_jit_trace}\n"
                 f"exc={e}\ngm={gm}"
             )
+        torch._dynamo.reset()
 
 
 def patch__check_input_constraints_for_graph(
@@ -70,13 +71,14 @@ def patch__check_input_constraints_for_graph(
             raise
         if verbose:
             print(
-                f"[_check_input_constraints_for_graph] ERROR"
+                f"[_check_input_constraints_for_graph] ERROR: "
                 f"_check_input_constraints_for_graph failed, "
                 f"use SKIP_SOLVE_CONSTRAINTS=0 to avoid skipping\n"
                 f"input_placeholders={input_placeholders}\n"
                 f"range_constraints={range_constraints}\n"
                 f"exc={e}"
             )
+        torch._dynamo.reset()
 
 
 def patched_infer_size(a, b):

onnx_diagnostic/torch_export_patches/patches/patch_transformers.py

@@ -5,6 +5,7 @@
 import transformers
 from transformers.modeling_attn_mask_utils import AttentionMaskConverter
 from transformers.cache_utils import StaticCache, Cache, DynamicCache
+from ...ext_test_case import has_transformers
 from ...helpers.torch_test_helper import is_torchdynamo_exporting
 
 
@@ -50,7 +51,8 @@ class patched_AttentionMaskConverter:
     ``transformers.modeling_attn_mask_utils.AttentionMaskConverter._make_causal_mask``.
     """
 
-    _PATCHES_ = ["_make_causal_mask"]
+    # This method was fixed in 4.51 at least.
+    _PATCHES_ = ["_make_causal_mask"] if not has_transformers("4.50.9999") else []
     _PATCHED_CLASS_ = AttentionMaskConverter
 
     @staticmethod