remove gptq exllama's max_input_length setting

optimum_benchmark/backends/pytorch/backend.py

@@ -184,7 +184,7 @@ def automodel_kwargs(self) -> Dict[str, Any]:
         kwargs = {}
 
         if hasattr(self.pretrained_config, "quantization_config") or self.quantization_config is not None:
-            kwargs["low_cpu_memory_usage"] = True
+            kwargs["low_cpu_mem_usage"] = True
 
         if self.quantization_config is not None:
             kwargs["quantization_config"] = self.quantization_config
@@ -256,21 +256,6 @@ def load_model_from_config(self) -> None:
         LOGGER.info("\t+ Tying weights")
         self.pretrained_model.tie_weights()
 
-    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.get("desc_act", None)) or (
-            hasattr(self.pretrained_config, "quantization_config")
-            and self.pretrained_config.quantization_config.quant_method == "gptq"
-            and hasattr(self.pretrained_config.quantization_config, "desc_act")
-            and self.pretrained_config.quantization_config.desc_act
-        ):
-            LOGGER.info("\t+ Setting GPTQ's max_input_length")
-            from auto_gptq import exllama_set_max_input_length  # type: ignore
-
-            max_input_length = to_pow2(input_shapes["batch_size"] * input_shapes["sequence_length"])
-            self.pretrained_model = exllama_set_max_input_length(self.pretrained_model, max_input_length)
-
     def forward(self, input: Dict[str, Any], kwargs: Dict[str, Any]) -> "ModelOutput":
         if self.is_diffusion_pipeline():
             return super().forward(input, kwargs)