improve type hinting and fix use_cache (#680)

Author: casper-hansen

awq/models/auto.py

@@ -74,18 +74,15 @@ def from_pretrained(
             model_path, trust_remote_code, **model_init_kwargs
         )
 
-        if model_init_kwargs.get("low_cpu_mem_usage") is None:
-            model_init_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
-        if model_init_kwargs.get("use_cache") is None:
-            model_init_kwargs["use_cache"] = use_cache
-
         return AWQ_CAUSAL_LM_MODEL_MAP[model_type].from_pretrained(
             model_path,
             model_type,
             trust_remote_code=trust_remote_code,
             safetensors=safetensors,
             device_map=device_map,
             download_kwargs=download_kwargs,
+            low_cpu_mem_usage=low_cpu_mem_usage,
+            use_cache=use_cache,
             **model_init_kwargs,
         )
 

awq/models/base.py

@@ -36,6 +36,7 @@
     PretrainedConfig,
     AutoProcessor,
     BaseImageProcessor,
+    ProcessorMixin,
     PreTrainedTokenizer,
 )
 from accelerate.big_modeling import (
@@ -112,7 +113,7 @@ def __init__(
         self.search_result = None
         self.config: PretrainedConfig = config
         self.quant_config: AwqConfig = quant_config
-        self.processor: BaseImageProcessor = processor
+        self.processor: ProcessorMixin = processor
 
     def to(self, device: Annotated[str, Doc("The device to move your model to.")]):
         """A utility function for moving the model to a device."""
@@ -342,6 +343,14 @@ def from_pretrained(
             Dict,
             Doc("Used for configure download model"),
         ] = None,
+        low_cpu_mem_usage: Annotated[
+            bool,
+            Doc("Use low_cpu_mem_usage when loading from transformers.")
+        ] = True,
+        use_cache: Annotated[
+            bool,
+            Doc("Use use_cache argument in transformers")
+        ] = False,
         **model_init_kwargs: Annotated[
             Dict,
             Doc(
@@ -367,6 +376,11 @@ def from_pretrained(
         if target_cls_name == "AutoModelForVision2Seq":
             processor = AutoProcessor.from_pretrained(model_weights_path)
 
+        if model_init_kwargs.get("low_cpu_mem_usage") is None:
+            model_init_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
+        if model_init_kwargs.get("use_cache") is None and target_cls_name != "AutoModelForVision2Seq":
+            model_init_kwargs["use_cache"] = use_cache
+
         # If not quantized, must load with AutoModelForCausalLM
         model = target_cls.from_pretrained(
             model_weights_path,

awq/models/llava.py

@@ -35,7 +35,8 @@ def move_embed(model: OldLlavaForConditionalGeneration, device: str):
         model.language_model.model.embed_tokens = model.get_input_embeddings().to(
             device
         )
-        model.language_model.model.rotary_emb = model.language_model.model.rotary_emb.to(device)
+        if hasattr(model.language_model.model, "rotary_emb"):
+            model.language_model.model.rotary_emb = model.language_model.model.rotary_emb.to(device)
 
     @staticmethod
     def get_layers_for_scaling(module: OldLlamaDecoderLayer, input_feat, module_kwargs):