fix: resolving errors in the hf decorator function

nemo_automodel/components/models/biencoder/llama_bidirectional_model.py

@@ -44,14 +44,16 @@
 )
 from transformers.processing_utils import Unpack
 from transformers.utils import TransformersKwargs, auto_docstring, logging
-from transformers.utils.generic import check_model_inputs
 
 try:
     from nemo_automodel.components.models.biencoder.state_dict_adapter import BiencoderStateDictAdapter
 except ImportError:
     BiencoderStateDictAdapter = object
 
+from nemo_automodel.shared.import_utils import get_check_model_inputs_decorator
+
 logger = logging.get_logger(__name__)
+check_model_inputs = get_check_model_inputs_decorator()
 
 
 def contrastive_scores_and_labels(
@@ -177,7 +179,7 @@ def _update_causal_mask(
             return attention_mask
         return None
 
-    @check_model_inputs()
+    @check_model_inputs
     @auto_docstring
     def forward(
         self,

nemo_automodel/components/models/llama/model.py

@@ -50,18 +50,20 @@
 )
 from transformers.processing_utils import Unpack
 from transformers.utils import TransformersKwargs, can_return_tuple
-from transformers.utils.generic import check_model_inputs
 
 from nemo_automodel.components.models.common.combined_projection import (
     CombinedGateUpMLP,
     CombinedQKVAttentionMixin,
 )
 from nemo_automodel.components.models.llama.state_dict_adapter import LlamaStateDictAdapter
 from nemo_automodel.components.moe.utils import BackendConfig
+from nemo_automodel.shared.import_utils import get_check_model_inputs_decorator
 from nemo_automodel.shared.utils import dtype_from_str
 
 __all__ = ["build_llama_model", "LlamaForCausalLM"]
 
+check_model_inputs = get_check_model_inputs_decorator()
+
 
 class LlamaAttention(CombinedQKVAttentionMixin, nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper with combined QKV projection."""
@@ -279,7 +281,7 @@ def __init__(
         # Initialize weights and apply final processing
         self.post_init()
 
-    @check_model_inputs()
+    @check_model_inputs
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,

nemo_automodel/components/models/qwen2/model.py

@@ -50,18 +50,20 @@
 )
 from transformers.processing_utils import Unpack
 from transformers.utils import TransformersKwargs, can_return_tuple
-from transformers.utils.generic import check_model_inputs
 
 from nemo_automodel.components.models.common.combined_projection import (
     CombinedGateUpMLP,
     CombinedQKVAttentionMixin,
 )
 from nemo_automodel.components.models.qwen2.state_dict_adapter import Qwen2StateDictAdapter
 from nemo_automodel.components.moe.utils import BackendConfig
+from nemo_automodel.shared.import_utils import get_check_model_inputs_decorator
 from nemo_automodel.shared.utils import dtype_from_str
 
 __all__ = ["build_qwen2_model", "Qwen2ForCausalLM"]
 
+check_model_inputs = get_check_model_inputs_decorator()
+
 
 class Qwen2Attention(CombinedQKVAttentionMixin, nn.Module):
     """Multi-headed attention with combined QKV projection.
@@ -252,7 +254,7 @@ def __init__(
         # Initialize weights and apply final processing
         self.post_init()
 
-    @check_model_inputs()
+    @check_model_inputs
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,

nemo_automodel/shared/import_utils.py

@@ -419,3 +419,50 @@ def is_te_min_version(version, check_equality=True):
     if check_equality:
         return get_te_version() >= PkgVersion(version)
     return get_te_version() > PkgVersion(version)
+
+
+def get_transformers_version():
+    """Get transformers version from __version__."""
+    try:
+        import transformers
+
+        if hasattr(transformers, "__version__"):
+            _version = str(transformers.__version__)
+        else:
+            from importlib.metadata import version
+
+            _version = version("transformers")
+    except ImportError:
+        _version = "0.0.0"
+    return PkgVersion(_version)
+
+
+def is_transformers_min_version(version, check_equality=True):
+    """Check if minimum version of `transformers` is installed."""
+    if check_equality:
+        return get_transformers_version() >= PkgVersion(version)
+    return get_transformers_version() > PkgVersion(version)
+
+
+def get_check_model_inputs_decorator():
+    """
+    Get the appropriate check_model_inputs decorator based on transformers version.
+
+    In transformers >= 4.57.3, check_model_inputs became a function that returns a decorator.
+    In older versions, it was directly a decorator.
+
+    Returns:
+        Decorator function to validate model inputs.
+    """
+    try:
+        from transformers.utils.generic import check_model_inputs
+
+        if is_transformers_min_version("4.57.3"):
+            # New API: check_model_inputs() returns a decorator
+            return check_model_inputs()
+        else:
+            # Old API: check_model_inputs is directly a decorator
+            return check_model_inputs
+    except ImportError:
+        # If transformers is not available, return a no-op decorator
+        return null_decorator

tests/unit_tests/shared/test_import_utils.py

@@ -174,3 +174,29 @@ def test_is_te_min_version():
     """
     assert si.is_te_min_version("0.0.0") is True
     assert si.is_te_min_version("9999.0.0", check_equality=False) is False
+
+
+def test_get_transformers_version_type():
+    """
+    ``get_transformers_version`` should *never* raise – even when transformers is unavailable
+    while building docs – and must always return a ``packaging.version.Version``.
+    """
+    ver = si.get_transformers_version()
+    assert isinstance(ver, PkgVersion)
+
+
+def test_is_transformers_min_version():
+    """
+    * A ridiculously low requirement must be satisfied.
+    * A far-future version must *not* be satisfied.
+    """
+    assert si.is_transformers_min_version("0.0.0") is True
+    assert si.is_transformers_min_version("9999.0.0", check_equality=False) is False
+
+
+def test_get_check_model_inputs_decorator():
+    """
+    ``get_check_model_inputs_decorator`` should always return a callable decorator.
+    """
+    decorator = si.get_check_model_inputs_decorator()
+    assert callable(decorator)