fix exllama_set_max_input_length

optimum_benchmark/backends/pytorch/backend.py

@@ -108,9 +108,9 @@ def load_model_from_pretrained(self) -> None:
             self.quantization_config = GPTQConfig(**self.config.quantization_config)
         elif self.config.quantization_scheme == "awq" and self.config.quantization_config:
             LOGGER.info("\t+ Processing AWQ config")
-            from transformers import AWQConfig
+            from transformers import AwqConfig
 
-            self.quantization_config = AWQConfig(**self.config.quantization_config)
+            self.quantization_config = AwqConfig(**self.config.quantization_config)
         elif self.config.quantization_scheme == "bnb":
             LOGGER.info("\t+ Processing BitsAndBytesConfig")
             from transformers import BitsAndBytesConfig
@@ -238,10 +238,10 @@ def load_model_from_config(self) -> None:
 
     def prepare_for_inference(self, input_shapes: Dict[str, int], **kwargs) -> None:
         super().prepare_for_inference(input_shapes=input_shapes, **kwargs)
-        if (self.config.quantization_scheme == "gptq" and self.config.quantization_config["desc_act"]) or (
+        if (self.config.quantization_scheme == "gptq" and self.config.quantization_config.get("desc_act", None)) or (
             hasattr(self.pretrained_config, "quantization_config")
-            and self.pretrained_config.quantization_config["desc_act"]
-            and self.pretrained_config.quantization_config["quant_method"] == "gptq"
+            and self.pretrained_config.quantization_config.get("quant_method", None) == "gptq"
+            and self.pretrained_config.quantization_config.get("desc_act", None)
         ):
             LOGGER.info("\t+ Setting GPTQ's max_input_length")
             from auto_gptq import exllama_set_max_input_length