[v1] support resume training from checkpoint (#10280)

Co-authored-by: frozenleaves <frozen@Mac.local>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

scripts/dcp2hf.py

@@ -0,0 +1,76 @@
+# 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 DCP checkpoint to HuggingFace model format.
+
+Usage:
+  python scripts/dcp2hf.py convert --dcp_path=/path/to/dcp --hf_path=/path/to/hf --config_path=/path/to/config
+
+Arguments:
+  dcp_path: Path to the DCP checkpoint directory.
+  hf_path: Output path (directory) for HuggingFace model.
+  config_path: Path to the HuggingFace model directory containing config.json.
+"""
+
+import fire
+import torch
+import torch.distributed.checkpoint as dcp
+import transformers
+from transformers import AutoConfig
+
+
+def convert(dcp_path: str, hf_path: str, config_path: str) -> None:
+    """Convert DCP model weights to HF.
+
+    Note: this script is used to convert a DCP checkpoint to HuggingFace model format,
+    it will just convert the DCP checkpoint to a HuggingFace model format, for the tokenizer,
+    you may need to copy from the original model.
+
+    Args:
+        dcp_path: DCP checkpoint directory.
+        hf_path: Output path (directory) for HuggingFace model.
+        config_path: Path to the HuggingFace model directory containing config.json.
+    """
+    if not dcp_path or not hf_path or not config_path:
+        raise ValueError("All 'dcp_path', 'hf_path', and 'config_path' are required.")
+
+    print(f"Loading config from {config_path}...")
+    config = AutoConfig.from_pretrained(config_path)
+    architectures = getattr(config, "architectures", [])
+    if architectures:
+        model_cls = getattr(transformers, architectures[0], transformers.AutoModelForCausalLM)
+    else:
+        model_cls = transformers.AutoModelForCausalLM
+
+    print("Initializing model on CPU...")
+    model = model_cls(config).to(torch.bfloat16)
+
+    print(f"Loading DCP from {dcp_path}...")
+    state_dict = model.state_dict()
+    dcp.load(state_dict, checkpoint_id=dcp_path)
+    model.load_state_dict(state_dict)
+
+    print(f"Saving to HF format at {hf_path}...")
+    model.save_pretrained(hf_path)
+    config.save_pretrained(hf_path)
+    print("Done!")
+
+
+def help() -> None:
+    """Show help message."""
+    print(__doc__)
+
+
+if __name__ == "__main__":
+    fire.Fire({"convert": convert, "help": help, "--convert": convert})