Repository: huggingface/transformers
Pull request #36734 (2025-03-14 22:25:08)

Fix `TrainingArguments.torch_empty_cache_steps` post_init check
# What does this PR do?

PR #31546 introduced the `torch_empty_cache_steps` attribute to `TrainingArguments`, requiring it to be either a positive integer or `None`. This constraint is enforced via an explicit check in `TrainingArguments.__post_init__()`.

However, the current condition incorrectly uses:

`not ([is int] OR [> 0])`

instead of:

`not ([is int] AND [> 0])`

as intended to trigger a `ValueError`. As a result, the following invalid values are erroneously accepted:

```python
# Accepted because 1.0 > 0, but should raise an error
TrainingArguments(torch_empty_cache_steps=1.0)

# Accepted because 0 is an int, but should raise an error
TrainingArguments(torch_empty_cache_steps=0)
```

This PR fixes the condition to correctly enforce the “positive int or None” requirement and adds a corresponding test to verify the fix.

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Patch:
diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py
index d2ec76091f..a1f172801a 100644
--- a/src/transformers/training_args.py
+++ b/src/transformers/training_args.py
@@ -1641,7 +1641,7 @@ class TrainingArguments:
             self.do_eval = True
 
         if self.torch_empty_cache_steps is not None:
-            if not (isinstance(self.torch_empty_cache_steps, int) or self.torch_empty_cache_steps > 0):
+            if not (isinstance(self.torch_empty_cache_steps, int) and self.torch_empty_cache_steps > 0):
                 raise ValueError(
                     f"`torch_empty_cache_steps` must be an integer bigger than 0, got {self.torch_empty_cache_steps}."
                 )
diff --git a/src/transformers/utils/quantization_config.py b/src/transformers/utils/quantization_config.py
index 0988d8ac14..851249b270 100755
--- a/src/transformers/utils/quantization_config.py
+++ b/src/transformers/utils/quantization_config.py
@@ -902,6 +902,9 @@ class AwqConfig(QuantizationConfigMixin):
             )
 
         if self.backend == AwqBackendPackingMethod.LLMAWQ:
+            # Only cuda device can run this function
+            if not torch.cuda.is_available():
+                raise ValueError("LLM-AWQ backend is only supported on CUDA")
             compute_capability = torch.cuda.get_device_capability()
             major, minor = compute_capability
             if major < 8:


Test patch:
diff --git a/tests/models/llava_next_video/test_modeling_llava_next_video.py b/tests/models/llava_next_video/test_modeling_llava_next_video.py
index 44c7deeffc..ba7323a075 100644
--- a/tests/models/llava_next_video/test_modeling_llava_next_video.py
+++ b/tests/models/llava_next_video/test_modeling_llava_next_video.py
@@ -430,13 +430,13 @@ class LlavaNextVideoForConditionalGenerationIntegrationTest(unittest.TestCase):
 
         # verify generation
         output = model.generate(**inputs, do_sample=False, max_new_tokens=40)
-        EXPECTED_DECODED_TEXT = 'USER: \nWhy is this video funny? ASSISTANT: The humor in this video comes from the unexpected and somewhat comical situation of a young child reading a book while another child is attempting to read the same book. The child who is reading the book seems'  # fmt: skip
-
-        self.assertEqual(
-            self.processor.decode(output[0], skip_special_tokens=True),
-            EXPECTED_DECODED_TEXT,
+        EXPECTED_DECODED_TEXT = (
+            "USER: \nWhy is this video funny? ASSISTANT: The humor in this video comes from the unexpected and somewhat comical situation of a young child reading a book while another child is attempting to read the same book. The child who is reading the book seems",  # cuda output
+            "USER: \nWhy is this video funny? ASSISTANT: The humor in this video comes from the unexpected and somewhat comical situation of a young child reading a book while wearing a pair of glasses that are too large for them. The glasses are",  # xpu output
         )
 
+        self.assertTrue(self.processor.decode(output[0], skip_special_tokens=True) in EXPECTED_DECODED_TEXT)
+
     @slow
     @require_bitsandbytes
     def test_small_model_integration_test_batch(self):
diff --git a/tests/quantization/autoawq/test_awq.py b/tests/quantization/autoawq/test_awq.py
index 5238c29a9c..d597f8de71 100644
--- a/tests/quantization/autoawq/test_awq.py
+++ b/tests/quantization/autoawq/test_awq.py
@@ -59,16 +59,21 @@ class AwqConfigTest(unittest.TestCase):
         with self.assertRaises(ValueError):
             AwqConfig(bits=4, backend="unexisting-backend")
 
-        compute_capability = torch.cuda.get_device_capability()
-        major, minor = compute_capability
-
-        if major < 8:
+        # Only cuda device can run this function
+        support_llm_awq = False
+        if torch.cuda.is_available():
+            compute_capability = torch.cuda.get_device_capability()
+            major, minor = compute_capability
+            if major >= 8:
+                support_llm_awq = True
+
+        if support_llm_awq:
+            # LLMAWQ should work on an A100
+            AwqConfig(bits=4, backend="llm-awq")
+        else:
             # LLMAWQ does not work on a T4
             with self.assertRaises(ValueError):
                 AwqConfig(bits=4, backend="llm-awq")
-        else:
-            # LLMAWQ should work on an A100
-            AwqConfig(bits=4, backend="llm-awq")
 
     def test_to_dict(self):
         """
diff --git a/tests/test_training_args.py b/tests/test_training_args.py
index a4da834582..c207196abc 100644
--- a/tests/test_training_args.py
+++ b/tests/test_training_args.py
@@ -40,3 +40,28 @@ class TestTrainingArguments(unittest.TestCase):
             self.assertFalse(os.path.exists(output_dir))  # Still shouldn't exist
 
             # Directory should be created when actually needed (e.g. in Trainer)
+
+    def test_torch_empty_cache_steps_requirements(self):
+        """Test that torch_empty_cache_steps is a positive integer or None."""
+
+        # None is acceptable (feature is disabled):
+        args = TrainingArguments(torch_empty_cache_steps=None)
+        self.assertIsNone(args.torch_empty_cache_steps)
+
+        # non-int is unacceptable:
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps=1.0)
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps="none")
+
+        # negative int is unacceptable:
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps=-1)
+
+        # zero is unacceptable:
+        with self.assertRaises(ValueError):
+            TrainingArguments(torch_empty_cache_steps=0)
+
+        # positive int is acceptable:
+        args = TrainingArguments(torch_empty_cache_steps=1)
+        self.assertEqual(args.torch_empty_cache_steps, 1)
diff --git a/tests/utils/test_import_utils.py b/tests/utils/test_import_utils.py
index 801d1c5484..fe616e9cfb 100644
--- a/tests/utils/test_import_utils.py
+++ b/tests/utils/test_import_utils.py
@@ -1,23 +1,26 @@
 import sys
 
+from transformers.testing_utils import run_test_using_subprocess
 from transformers.utils.import_utils import clear_import_cache
 
 
+@run_test_using_subprocess
 def test_clear_import_cache():
-    # Import some transformers modules
+    """Test the clear_import_cache function."""
 
-    # Get initial module count
+    # Save initial state
     initial_modules = {name: mod for name, mod in sys.modules.items() if name.startswith("transformers.")}
+    assert len(initial_modules) > 0, "No transformers modules loaded before test"
 
-    # Verify we have some modules loaded
-    assert len(initial_modules) > 0
-
-    # Clear cache
+    # Execute clear_import_cache() function
     clear_import_cache()
 
-    # Check modules were removed
+    # Verify modules were removed
     remaining_modules = {name: mod for name, mod in sys.modules.items() if name.startswith("transformers.")}
-    assert len(remaining_modules) < len(initial_modules)
+    assert len(remaining_modules) < len(initial_modules), "No modules were removed"
+
+    # Import and verify module exists
+    from transformers.models.auto import modeling_auto
 
-    # Verify we can reimport
-    assert "transformers" in sys.modules
+    assert "transformers.models.auto.modeling_auto" in sys.modules
+    assert modeling_auto.__name__ == "transformers.models.auto.modeling_auto"