fixed segfault when loading no weights exllama v2

optimum_benchmark/backends/pytorch/backend.py

@@ -6,7 +6,7 @@
 
 import torch
 from datasets import Dataset
-from safetensors.torch import save_model
+from safetensors.torch import save_file
 from transformers import TrainerCallback, TrainerState
 from transformers.utils import ModelOutput
 from transformers.utils.logging import set_verbosity_error
@@ -78,6 +78,8 @@ def configure(self, config: PyTorchConfig) -> None:
             self.quantization_config = None
 
         # Load model
+        if self.config.no_weights and self.is_diffusion_pipeline():
+            raise ValueError("Diffusion Pipelines are not supported with no_weights=True")
         if self.config.no_weights:
             LOGGER.info("\t+ Loading model with no weights")
             self.load_model_with_no_weights()
@@ -161,7 +163,7 @@ def load_model_from_pretrained(self) -> None:
                 **self.hub_kwargs,
             )
         elif self.is_gptq_quantized() or self.is_awq_quantized():
-            LOGGER.info("\t+ Loading GPTQ quantized model")
+            LOGGER.info("\t+ Loading quantized model")
             self.pretrained_model = self.automodel_class.from_pretrained(
                 pretrained_model_name_or_path=self.model,
                 # for gptq, we need to specify the device_map to either auto
@@ -172,6 +174,7 @@ def load_model_from_pretrained(self) -> None:
                 **self.automodel_kwargs,
                 **self.hub_kwargs,
             )
+            print(torch.cuda.max_memory_allocated())
         elif self.config.device_map is not None:
             LOGGER.info(f"\t+ Loading model with device map: {self.config.device_map}")
             self.pretrained_model = self.automodel_class.from_pretrained(
@@ -193,31 +196,40 @@ def load_model_from_pretrained(self) -> None:
     def load_model_with_no_weights(self) -> None:
         self.tmp_dir = TemporaryDirectory()
 
-        if self.is_diffusion_pipeline():
-            raise ValueError("Diffusion pipelines are not supported with no_weights=True")
-
         original_model = self.model
         no_weights_model = os.path.join(self.tmp_dir.name, "no_weights")
 
         LOGGER.info("\t+ Creating no weights model directory")
-        os.makedirs(no_weights_model, exist_ok=True)
+        if not os.path.exists(no_weights_model):
+            os.makedirs(no_weights_model)
 
         if self.is_quantized():
-            # so that from_pretrained acts as if the model is quantized
+            # tricking from_pretrained to load the model as if it was quantized
             self.pretrained_config.quantization_config = self.quantization_config.to_dict()
 
         LOGGER.info(f"\t+ Saving pretrained config to {no_weights_model}")
         self.pretrained_config.save_pretrained(save_directory=no_weights_model)
 
-        if self.pretrained_processor is not None:
-            LOGGER.info(f"\t+ Saving pretrained processor to {no_weights_model}")
-            self.pretrained_processor.save_pretrained(save_directory=no_weights_model)
+        LOGGER.info(f"\t+ Creating no weights model to {no_weights_model}")
+        state_dict = torch.nn.Linear(1, 1).state_dict()
+
+        if self.is_exllamav2():
+            # for exllamav2 we need to add g_idx to the state_dict
+            LOGGER.info("\t+ Loading meta model")
+            with torch.device("meta"):
+                meta_model = self.automodel_class.from_config(self.pretrained_config)
+
+            LOGGER.info("\t+ Setting g_idx for ExllamaV2")
+            for name, module in meta_model.named_modules():
+                # loading to exllama v2's QuantLinear creates g_idx with bad values
+                if hasattr(module, "in_features"):
+                    state_dict[name + ".g_idx"] = torch.ones((module.in_features,), dtype=torch.int32)
 
         LOGGER.info(f"\t+ Saving no weights model to {no_weights_model}")
-        save_model(
+        save_file(
             filename=os.path.join(no_weights_model, "model.safetensors"),
-            model=torch.nn.Linear(1, 1),
             metadata={"format": "pt"},
+            tensors=state_dict,
         )
 
         LOGGER.info("\t+ Loading no weights model")
@@ -282,6 +294,14 @@ def is_awq_quantized(self) -> bool:
             and self.pretrained_config.quantization_config.get("quant_method", None) == "awq"
         )
 
+    def is_exllamav2(self) -> bool:
+        return (
+            self.is_quantized()
+            and self.is_gptq_quantized()
+            and "exllama_config" in self.config.quantization_config
+            and self.config.quantization_config["exllama_config"]["version"] == 2
+        )
+
     @property
     def automodel_kwargs(self) -> Dict[str, Any]:
         kwargs = {}