[0.5/3] Diffusion ckpt export for NVFP4 & FP8 (#783)

See #781

This is the MR that only includes the refactoring of the llm export,
please ignore the change on quantize.py from the diffusion example.

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## Summary by CodeRabbit

## Release Notes

* **New Features**
* Added `--hf-ckpt-dir` CLI option to save checkpoints in HuggingFace
format
  * Enabled support for exporting Diffusers-based pipelines
* Unified export system now handles both transformer and diffusion model
architectures

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

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---------

Signed-off-by: Jingyu Xin <jingyux@nvidia.com>

Author: jingyu-ml

examples/diffusers/quantization/quantize.py

@@ -66,6 +66,7 @@
 
 import modelopt.torch.opt as mto
 import modelopt.torch.quantization as mtq
+from modelopt.torch.export import export_hf_checkpoint
 
 
 class ModelType(str, Enum):
@@ -348,6 +349,7 @@ class ExportConfig:
 
     quantized_torch_ckpt_path: Path | None = None
     onnx_dir: Path | None = None
+    hf_ckpt_dir: Path | None = None
     restore_from: Path | None = None
 
     def validate(self) -> None:
@@ -363,6 +365,9 @@ def validate(self) -> None:
         if self.onnx_dir and not self.onnx_dir.exists():
             self.onnx_dir.mkdir(parents=True, exist_ok=True)
 
+        if self.hf_ckpt_dir and not self.hf_ckpt_dir.exists():
+            self.hf_ckpt_dir.mkdir(parents=True, exist_ok=True)
+
 
 def setup_logging(verbose: bool = False) -> logging.Logger:
     """
@@ -862,6 +867,20 @@ def restore_checkpoint(self, backbone: nn.Module) -> None:
         mto.restore(backbone, str(self.config.restore_from))
         self.logger.info("Model restored successfully")
 
+    def export_hf_ckpt(self, pipe: DiffusionPipeline) -> None:
+        """
+        Export quantized model to HuggingFace checkpoint format.
+
+        Args:
+            pipe: Diffusion pipeline containing the quantized model
+        """
+        if not self.config.hf_ckpt_dir:
+            return
+
+        self.logger.info(f"Exporting HuggingFace checkpoint to {self.config.hf_ckpt_dir}")
+        export_hf_checkpoint(pipe, export_dir=self.config.hf_ckpt_dir)
+        self.logger.info("HuggingFace checkpoint export completed successfully")
+
 
 def create_argument_parser() -> argparse.ArgumentParser:
     """
@@ -994,6 +1013,11 @@ def create_argument_parser() -> argparse.ArgumentParser:
         help="Path to save quantized PyTorch checkpoint",
     )
     export_group.add_argument("--onnx-dir", type=str, help="Directory for ONNX export")
+    export_group.add_argument(
+        "--hf-ckpt-dir",
+        type=str,
+        help="Directory for HuggingFace checkpoint export",
+    )
     export_group.add_argument(
         "--restore-from", type=str, help="Path to restore from previous checkpoint"
     )
@@ -1070,6 +1094,7 @@ def main() -> None:
             if args.quantized_torch_ckpt_save_path
             else None,
             onnx_dir=Path(args.onnx_dir) if args.onnx_dir else None,
+            hf_ckpt_dir=Path(args.hf_ckpt_dir) if args.hf_ckpt_dir else None,
             restore_from=Path(args.restore_from) if args.restore_from else None,
         )
 
@@ -1125,6 +1150,9 @@ def forward_loop(mod):
             model_config.model_type,
             quant_config.format,
         )
+
+        export_manager.export_hf_ckpt(pipe)
+
         logger.info(
             f"Quantization process completed successfully! Time taken = {time.time() - s} seconds"
         )

examples/llm_ptq/multinode_ptq.py

@@ -36,7 +36,7 @@
 import modelopt.torch.quantization as mtq
 from modelopt.torch.export import get_model_type
 from modelopt.torch.export.convert_hf_config import convert_hf_quant_config_format
-from modelopt.torch.export.unified_export_hf import _export_hf_checkpoint
+from modelopt.torch.export.unified_export_hf import _export_transformers_checkpoint
 from modelopt.torch.quantization.config import need_calibration
 from modelopt.torch.quantization.utils import patch_fsdp_mp_dtypes
 from modelopt.torch.utils.dataset_utils import get_dataset_dataloader, get_supported_datasets
@@ -243,7 +243,7 @@ def export_model(
     export_dir = Path(export_path)
     export_dir.mkdir(parents=True, exist_ok=True)
 
-    post_state_dict, hf_quant_config = _export_hf_checkpoint(
+    post_state_dict, hf_quant_config = _export_transformers_checkpoint(
         model, torch.bfloat16, accelerator=accelerator
     )
 

examples/llm_qat/export.py

@@ -23,7 +23,7 @@
 
 import modelopt.torch.opt as mto
 from modelopt.torch.export.convert_hf_config import convert_hf_quant_config_format
-from modelopt.torch.export.unified_export_hf import _export_hf_checkpoint
+from modelopt.torch.export.unified_export_hf import _export_transformers_checkpoint
 from modelopt.torch.opt.conversion import restore_from_modelopt_state
 from modelopt.torch.quantization.utils import set_quantizer_state_dict
 from modelopt.torch.utils import print_rank_0
@@ -81,7 +81,9 @@ def main(args):
         base_model_dir = export_dir
 
     try:
-        post_state_dict, hf_quant_config = _export_hf_checkpoint(model, is_modelopt_qlora=is_qlora)
+        post_state_dict, hf_quant_config = _export_transformers_checkpoint(
+            model, is_modelopt_qlora=is_qlora
+        )
 
         with open(f"{base_model_dir}/hf_quant_config.json", "w") as file:
             json.dump(hf_quant_config, file, indent=4)