[v1] support training with fsdp2 (#9773)

Co-authored-by: frozenleaves <frozen@Mac.local>
Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>

scripts/hf2dcp.py

@@ -0,0 +1,55 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert a HuggingFace model to DCP checkpoint format.
+
+Usage:
+  python scripts/hf2dcp.py convert --hf_path=/path/to/hf --dcp_path=/path/to/dcp
+
+Arguments:
+  hf_path: Path to the HuggingFace model directory.
+  dcp_path: Output path (directory) for DCP checkpoint.
+"""
+
+import fire
+import torch
+import torch.distributed.checkpoint as dcp
+from transformers import AutoModelForCausalLM
+
+
+def convert(hf_path: str, dcp_path: str) -> None:
+    """Convert HF model weights to DCP.
+
+    Args:
+        hf_path: HuggingFace model directory.
+        dcp_path: Output path (directory) for DCP checkpoint.
+    """
+    if not hf_path or not dcp_path:
+        raise ValueError("Both 'hf_path' and 'dcp_path' are required.")
+
+    print(f"Loading HF model from {hf_path}...")
+    model = AutoModelForCausalLM.from_pretrained(hf_path, device_map="cpu", torch_dtype=torch.bfloat16)
+
+    print(f"Saving to DCP format at {dcp_path}...")
+    dcp.save(model.state_dict(), checkpoint_id=dcp_path)
+    print("Done!")
+
+
+def help() -> None:
+    """Show help message."""
+    print(__doc__)
+
+
+if __name__ == "__main__":
+    fire.Fire({"convert": convert, "help": help, "--convert": convert})