fix gguf fp8 input model and vram issue

auto_round/compressors/base.py

@@ -1195,9 +1195,6 @@ def _quant_rtn_with_imatrix(self, all_to_quantized_module_names: list[str]) -> N
         # Load dataset
         from auto_round.calib_dataset import get_dataloader
 
-        if _is_fp8_model(self.model):
-            convert_fp8_model_to_16b_model(self.model, self.amp_dtype)
-
         if isinstance(self.dataset, str):
             if self.tokenizer is None:
                 raise ValueError("A tokenizer must be set for the model when using a dataset string.")
@@ -1244,6 +1241,8 @@ def get_imatrix_hook(module, input, output):
                 dispatch_model(self.model, self.model.hf_device_map)
             else:
                 model = model.to(self.device)
+            if _is_fp8_model(self.model):
+                convert_fp8_model_to_16b_model(self.model, self.amp_dtype)
             cnt = 0
 
             # Run forward pass to accumulate imatrix
@@ -1422,7 +1421,6 @@ def _quantize_layer_via_rtn(self, name: str) -> None:
         """
         m = get_module(self.model, name)
 
-        # if m.__class__.__name__ == "FP8Linear":
         if _is_fp8_linear(m):
             m = convert_fp8_layer_to_linear(m, self.amp_dtype)
             set_module(self.model, name, m)

auto_round/export/export_to_gguf/convert.py

@@ -394,7 +394,10 @@ def prepare_tensors(cls):
         clean_weight_list = []
 
         modify_name = _special_name_handle(cls, name)
+        orig_device = data_torch.device
+        data_torch = data_torch.to("cpu")
         for new_name, data_torch in cls.modify_tensors(data_torch, modify_name, bid):
+            data_torch.to(orig_device)
             skip = False
             for tensor_info in cls.gguf_writer.tensors:
                 if new_name in tensor_info:
@@ -545,7 +548,7 @@ def prepare_tensors(cls):
                                 attr_tensor = getattr(module, attr)
                             else:
                                 attr_tensor = getattr(module, "w_" + attr)
-                            if attr_tensor is None:
+                            if attr_tensor is None or not isinstance(attr_tensor, torch.Tensor):
                                 continue
                             kv_b = attr_tensor.view(n_head_kv, v_head_dim + qk_nope_head_dim, -1)
                             k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)

auto_round/export/export_to_gguf/convert_hf_to_gguf.py

@@ -41,9 +41,11 @@
 
 if "NO_LOCAL_GGUF" not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / "gguf-py"))
+
 from auto_round.utils import LazyImport
 
 gguf = LazyImport("gguf")
+
 MistralTokenizerType = LazyImport("gguf.vocab.MistralTokenizerType")
 MistralVocab = LazyImport("gguf.vocab.MistralVocab")
 DATASET_MEAN = LazyImport("mistral_common.tokens.tokenizers.multimodal.DATASET_MEAN")

auto_round/utils.py

@@ -79,8 +79,7 @@ def __getitem__(self, key):
 
 # Changed to str as it relies on triton or others lib to load this
 INNER_SUPPORTED_LAYER_TYPES = ("FP8Linear",)
-# INNER_SUPPORTED_LAYER_TYPES = (transformers.integrations.finegrained_fp8.FP8Linear,)
-
+# transformers.integrations.finegrained_fp8.FP8Linear
 if deepspeed_exists:
     from deepspeed.module_inject import LinearAllreduce, LinearLayer
 
@@ -2298,10 +2297,14 @@ def convert_fp8_model_to_16b_model(model, dtype=torch.bfloat16):
     Convert a model with FP8 quantized layers to a model with 16-bit linear layers.
     This is useful for compatibility with other frameworks or for further processing.
     """
+    cnt = 0
     for n, m in model.named_modules():
         if m.__class__.__name__ == "FP8Linear":
             new_module = convert_fp8_layer_to_linear(m, dtype=dtype)
             set_module(model, n, new_module)
+            cnt += 1
+            if cnt % 10 == 0:  # Tricky setting
+                clear_memory()
     return model
 
 
@@ -2344,7 +2347,6 @@ def download_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):
     """Download hugging face model from hf hub."""
     from huggingface_hub.constants import DEFAULT_REVISION, HUGGINGFACE_HUB_CACHE
     from huggingface_hub.file_download import REGEX_COMMIT_HASH, repo_folder_name
-    from huggingface_hub.utils import EntryNotFoundError
 
     if cache_dir is None:
         cache_dir = HUGGINGFACE_HUB_CACHE