Keep model in CPU during ONNX export

src/sparseml/transformers/export.py

@@ -75,10 +75,12 @@
 import math
 import os
 import shutil
+from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Union
 
 from torch.nn import Module
 from transformers import AutoConfig, AutoTokenizer
+from transformers import TrainingArguments as HFTrainingArgs
 from transformers.tokenization_utils_base import PaddingStrategy
 
 from sparseml.optim import parse_recipe_variables
@@ -107,6 +109,14 @@
 _LOGGER = logging.getLogger(__name__)
 
 
+@dataclass
+class DeviceCPUTrainingArgs(HFTrainingArgs):
+    @property
+    def place_model_on_device(self):
+        # Ensure model remains in CPU during ONNX export
+        return False
+
+
 def load_task_model(task: str, model_path: str, config: Any) -> Module:
     if task == "masked-language-modeling" or task == "mlm":
         return SparseAutoModel.masked_language_modeling_from_pretrained(
@@ -294,15 +304,18 @@ def export_transformer_to_onnx(
         _LOGGER.info(f"loaded validation dataset for args {data_args}")
 
     model = model.train()
+
+    args = DeviceCPUTrainingArgs(output_dir="tmp_trainer")
     trainer = Trainer(
         model=model,
+        args=args,
         model_state_path=model_path,
         eval_dataset=eval_dataset,
         recipe=None,
         recipe_args=None,
         teacher=None,
     )
-    model = model.cpu()
+
     applied = trainer.apply_manager(epoch=math.inf, checkpoint=None)
 
     if not applied:

src/sparseml/transformers/sparsification/trainer.py

@@ -125,7 +125,7 @@ def __init__(
                 training_args_dict=training_args.to_dict(),
                 data_args_dict=asdict(data_args) if data_args else {},
             )
-            if training_args
+            if training_args and metadata_args
             else None
         )
 
@@ -762,7 +762,6 @@ def _get_fake_dataloader(
         num_samples: int,
         tokenizer: "PreTrainedTokenizerBase",  # noqa: F821
     ):
-
         # Rearrange inputs' keys to match those defined by model foward func, which
         # seem to define how the order of inputs is determined in the exported model
         forward_args_spec = inspect.getfullargspec(self.model.__class__.forward)
@@ -820,7 +819,6 @@ def __init__(
         teacher: Optional[Union[Module, str]] = None,
         **kwargs,
     ):
-
         super().__init__(
             model=model,
             model_state_path=model_state_path,