Display warning for unknown quants config instead of an error (#35963)

* add supports_quant_method check

* fix

* add test and fix suggestions

* change logic slightly

---------

Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>

src/transformers/modeling_utils.py

@@ -3634,7 +3634,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
 
             model_kwargs = kwargs
 
-        pre_quantized = getattr(config, "quantization_config", None) is not None
+        pre_quantized = hasattr(config, "quantization_config")
+        if pre_quantized and not AutoHfQuantizer.supports_quant_method(config.quantization_config):
+            pre_quantized = False
+
         if pre_quantized or quantization_config is not None:
             if pre_quantized:
                 config.quantization_config = AutoHfQuantizer.merge_quantization_configs(
@@ -3647,7 +3650,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 config.quantization_config,
                 pre_quantized=pre_quantized,
             )
-
         else:
             hf_quantizer = None
 

src/transformers/quantizers/auto.py

@@ -15,6 +15,7 @@ import warnings
 from typing import Dict, Optional, Union
 
 from ..models.auto.configuration_auto import AutoConfig
+from ..utils import logging
 from ..utils.quantization_config import (
     AqlmConfig,
     AwqConfig,
@@ -82,6 +83,8 @@ AUTO_QUANTIZATION_CONFIG_MAPPING = {
     "vptq": VptqConfig,
 }
 
+logger = logging.get_logger(__name__)
+
 
 class AutoQuantizationConfig:
     """
@@ -195,3 +198,23 @@ class AutoHfQuantizer:
             warnings.warn(warning_msg)
 
         return quantization_config
+
+    @staticmethod
+    def supports_quant_method(quantization_config_dict):
+        quant_method = quantization_config_dict.get("quant_method", None)
+        if quantization_config_dict.get("load_in_8bit", False) or quantization_config_dict.get("load_in_4bit", False):
+            suffix = "_4bit" if quantization_config_dict.get("load_in_4bit", False) else "_8bit"
+            quant_method = QuantizationMethod.BITS_AND_BYTES + suffix
+        elif quant_method is None:
+            raise ValueError(
+                "The model's quantization config from the arguments has no `quant_method` attribute. Make sure that the model has been correctly quantized"
+            )
+
+        if quant_method not in AUTO_QUANTIZATION_CONFIG_MAPPING.keys():
+            logger.warning(
+                f"Unknown quantization type, got {quant_method} - supported types are:"
+                f" {list(AUTO_QUANTIZER_MAPPING.keys())}. Hence, we will skip the quantization. "
+                "To remove the warning, you can delete the quantization_config attribute in config.json"
+            )
+            return False
+        return True

tests/utils/test_modeling_utils.py

@@ -1819,6 +1819,19 @@ class ModelUtilsTest(TestCasePlus):
         self.assertIsNone(model_outputs.past_key_values)
         self.assertTrue(model.training)
 
+    def test_unknown_quantization_config(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            config = BertConfig(
+                vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
+            )
+            model = BertModel(config)
+            config.quantization_config = {"quant_method": "unknown"}
+            model.save_pretrained(tmpdir)
+            with self.assertLogs("transformers", level="WARNING") as cm:
+                BertModel.from_pretrained(tmpdir)
+            self.assertEqual(len(cm.records), 1)
+            self.assertTrue(cm.records[0].message.startswith("Unknown quantization type, got"))
+
 
 @slow
 @require_torch