Update checkpoint handling in QuantizationRecipe to use checkpointer.…

…save_checkpoint

recipes/quantize.py

@@ -3,10 +3,8 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
-import os
 import sys
 import time
-from pathlib import Path
 from typing import Any, Dict
 
 import torch
@@ -53,6 +51,11 @@ def __init__(self, cfg: DictConfig) -> None:
         training.set_seed(seed=cfg.seed)
 
     def load_checkpoint(self, checkpointer_cfg: DictConfig) -> Dict[str, Any]:
+        logger.info(
+            "Setting safe_serialization to False. TorchAO quantization is compatible "
+            "only with HuggingFace's non-safetensor serialization and deserialization."
+        )
+        checkpointer_cfg.safe_serialization = False
         self._checkpointer = config.instantiate(checkpointer_cfg)
         checkpoint_dict = self._checkpointer.load_checkpoint()
         return checkpoint_dict
@@ -95,21 +98,8 @@ def quantize(self, cfg: DictConfig):
         logger.info(f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB")
 
     def save_checkpoint(self, cfg: DictConfig):
-        ckpt_dict = self._model.state_dict()
-        file_name = cfg.checkpointer.checkpoint_files[0].split(".")[0]
-
-        output_dir = Path(cfg.checkpointer.output_dir)
-        output_dir.mkdir(exist_ok=True)
-        checkpoint_file = Path.joinpath(
-            output_dir, f"{file_name}-{self._quantization_mode}".rstrip("-qat")
-        ).with_suffix(".pt")
-
-        torch.save(ckpt_dict, checkpoint_file)
-        logger.info(
-            "Model checkpoint of size "
-            f"{os.path.getsize(checkpoint_file) / 1024**3:.2f} GiB "
-            f"saved to {checkpoint_file}"
-        )
+        ckpt_dict = {training.MODEL_KEY: self._model.state_dict()}
+        self._checkpointer.save_checkpoint(ckpt_dict, epoch=0)
 
 
 @config.parse