[Regression] Fix Quark quantized model loading after refactorization (#37407)

src/transformers/modeling_utils.py

@@ -649,7 +649,10 @@ def _infer_parameter_dtype(
     try:
         old_param = model.get_parameter_or_buffer(param_name)
     except Exception as e:
-        if hf_quantizer is not None and hf_quantizer.quantization_config.quant_method == QuantizationMethod.HQQ:
+        if hf_quantizer is not None and hf_quantizer.quantization_config.quant_method in {
+            QuantizationMethod.HQQ,
+            QuantizationMethod.QUARK,
+        }:
             return True, None
         else:
             raise e
@@ -708,11 +711,12 @@ def _load_state_dict_into_meta_model(
         device_map_regex = "|".join([re.escape(k) for k in sorted(device_map.keys(), reverse=True)])
 
     is_quantized = hf_quantizer is not None
-    is_hqq_or_bnb = is_quantized and hf_quantizer.quantization_config.quant_method in [
+    is_hqq_or_bnb_or_quark = is_quantized and hf_quantizer.quantization_config.quant_method in {
         QuantizationMethod.HQQ,
         QuantizationMethod.BITS_AND_BYTES,
-    ]
+        QuantizationMethod.QUARK,
-    is_meta_state_dict = shard_file.endswith(".safetensors") and not is_hqq_or_bnb
+    }
+    is_meta_state_dict = shard_file.endswith(".safetensors") and not is_hqq_or_bnb_or_quark
     file_pointer = None
     if is_meta_state_dict:
         file_pointer = safe_open(shard_file, framework="pt", device=tensor_device)
@@ -4632,11 +4636,15 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
     ):
         # Useful flags
         is_quantized = hf_quantizer is not None
-        is_hqq = is_quantized and hf_quantizer.quantization_config.quant_method == QuantizationMethod.HQQ
+        is_hqq_or_quark = is_quantized and hf_quantizer.quantization_config.quant_method in {
-        is_hqq_or_bnb = is_quantized and hf_quantizer.quantization_config.quant_method in [
+            QuantizationMethod.HQQ,
+            QuantizationMethod.QUARK,
+        }
+        is_hqq_or_bnb_or_quark = is_quantized and hf_quantizer.quantization_config.quant_method in {
             QuantizationMethod.HQQ,
             QuantizationMethod.BITS_AND_BYTES,
-        ]
+            QuantizationMethod.QUARK,
+        }
 
         # Get all the keys of the state dicts that we have to initialize the model
         if sharded_metadata is not None:
@@ -4798,7 +4806,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
             expected_keys = hf_quantizer.update_expected_keys(model_to_load, expected_keys, checkpoint_keys)
 
         # Warmup cuda to load the weights much faster on devices
-        if device_map is not None and not is_hqq:
+        if device_map is not None and not is_hqq_or_quark:
             expanded_device_map = expand_device_map(device_map, expected_keys)
             caching_allocator_warmup(model_to_load, expanded_device_map, factor=2 if hf_quantizer is None else 4)
 
@@ -4812,7 +4820,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
             map_location = "cpu"
             if (
                 shard_file.endswith(".safetensors")
-                and not is_hqq_or_bnb
+                and not is_hqq_or_bnb_or_quark
                 and not (is_deepspeed_zero3_enabled() and not is_quantized)
             ):
                 map_location = "meta"

tests/quantization/quark_integration/test_quark.py

@@ -53,6 +53,7 @@ class QuarkTest(unittest.TestCase):
     EXPECTED_OUTPUTS.add("Today I am in Paris and I am not in Paris, France\nToday I am in Paris, Illinois")
     EXPECTED_OUTPUTS.add("Today I am in Paris and I am enjoying the city of light. I am not just any ordinary Paris")
     EXPECTED_OUTPUTS.add("Today I am in Paris and I am enjoying my day off! The sun is shining, the birds are")
+    EXPECTED_OUTPUTS.add("Today I am in Paris and I'm here to tell you about it. It's a beautiful day,")
 
     EXPECTED_RELATIVE_DIFFERENCE = 1.66
     device_map = None