Fix code for llmc llama4 quantization

auto_round/compressors/base.py

@@ -3003,10 +3003,16 @@ def _quantize_block(
         if not self.not_use_best_mse:
             last_loss = best_loss
             best_iter = last_best_iter
-        dump_info = (
-            f"quantized {len(quantized_layer_names)}/{(len(quantized_layer_names) + len(unquantized_layer_names))} "
-            f"layers in the block, loss iter 0: {init_loss:.6f} -> iter {best_iter}: {last_loss:.6f}"
-        )
+        if self.iters > 0:
+            dump_info = (
+                f"quantized {len(quantized_layer_names)}/{(len(quantized_layer_names) + len(unquantized_layer_names))} "
+                f"layers in the block, loss iter 0: {init_loss:.6f} -> iter {best_iter}: {last_loss:.6f}"
+            )
+        else:
+            dump_info = (
+                f"quantized {len(quantized_layer_names)}/{(len(quantized_layer_names) + len(unquantized_layer_names))} "
+                "layers in the block"
+            )
 
         if self.low_gpu_mem_usage:
             clear_memory(device_list=self.device_list)  # clear cached memory during training

auto_round/compressors/mllm/compressor.py

@@ -24,7 +24,7 @@
 
 from auto_round.compressors.base import BaseCompressor
 from auto_round.compressors.mllm.dataset import get_mllm_dataloader
-from auto_round.compressors.mllm.template import Template, get_template
+from auto_round.compressors.mllm.template import TEMPLATES, Template, get_template
 from auto_round.logger import logger
 from auto_round.schemes import QuantizationScheme
 from auto_round.special_model_handler import (
@@ -200,23 +200,27 @@ def __init__(
         self.image_processor = image_processor
         from transformers import PreTrainedModel
 
-        if model.config.model_type == "llava" and isinstance(model, PreTrainedModel):
+        # if model is not the object of transformers PreTrainedModel, there maybe no config attribute
+        if isinstance(model, PreTrainedModel) and model.config.model_type == "llava":
             template = "default"
         if hasattr(model, "name_or_path") and any([name in model.name_or_path for name in MISTRAL_3_2_MODELS]):
             template = "mistral3_2"
         if iters > 0:
-            self.template = template if template is not None else model.config.model_type
-            if not isinstance(dataset, torch.utils.data.DataLoader):
-                self.template = get_template(
-                    self.template,
-                    model=model,
-                    tokenizer=tokenizer,
-                    processor=processor,
-                    image_processor=image_processor,
-                    use_rtn=iters == 0,
-                    quiet=not self.quant_nontext_module,
-                )
-                dataset = self.template.default_dataset if dataset is None else dataset
+            if template is None and model.config.model_type not in TEMPLATES:
+                self.template = None
+            else:
+                self.template = template if template is not None else model.config.model_type
+                if not isinstance(dataset, torch.utils.data.DataLoader):
+                    self.template = get_template(
+                        self.template,
+                        model=model,
+                        tokenizer=tokenizer,
+                        processor=processor,
+                        image_processor=image_processor,
+                        use_rtn=iters == 0,
+                        quiet=not self.quant_nontext_module,
+                    )
+                    dataset = self.template.default_dataset if dataset is None else dataset
         else:
             self.template = None
 
@@ -233,7 +237,9 @@ def __init__(
                     " switching to liuhaotian/llava_conv_58k"
                 )
                 dataset = "liuhaotian/llava_conv_58k"
-            elif not _only_text_test(model, tokenizer, self.device, self.template.model_type):
+            elif self.template is not None and not _only_text_test(
+                model, tokenizer, self.device, self.template.model_type
+            ):
                 logger.warning(
                     f"{model.config.model_type} does not support for {dataset},"
                     " will use liuhaotian/llava_conv_58k with default config as an alternative."

auto_round/special_model_handler.py

@@ -60,7 +60,7 @@ def _get_moe_converter(config):
 
 
 def _handle_special_model(model):
-    if model.config.model_type == "deepseek_vl_v2":
+    if hasattr(model, "config") and model.config.model_type == "deepseek_vl_v2":
         from functools import partial
 
         model.forward = partial(_deepseek_vl2_forward, model)