[v1] support training with fsdp2 (#9773)

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

examples/v1/train_full/train_full_fsdp2.yaml

@@ -0,0 +1,34 @@
+model: Qwen/Qwen3-0.6B
+trust_remote_code: true
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+  name: auto
+  include_kernels: auto # choice: null/true/false/auto/kernel_id1,kernel_id2,kernel_id3, default is null
+
+quant_config: null
+
+dist_config:
+  name: fsdp2
+  dcp_path: null # /mnt/f/pretrain_models/Qwen3-0.6B-dcp
+
+init_config:
+  name: init_on_meta
+
+### data
+train_dataset: data/v1_sft_demo.yaml 
+
+### training
+output_dir: outputs/test_fsdp2
+micro_batch_size: 1
+global_batch_size: 1
+cutoff_len: 2048
+learning_rate: 1.0e-4
+bf16: false
+max_steps: 10
+
+### sample
+sample_backend: hf
+max_new_tokens: 128

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})

src/llamafactory/v1/accelerator/helper.py

@@ -180,6 +180,16 @@ def operate_tensorlike(fn: Callable[[...], Tensor], data: TensorLike, **kwargs)
         return result.tolist()
 
 
+def get_process_group_backend() -> str:
+    """Get backend for init process group."""
+    if get_current_accelerator().type == DeviceType.NPU:
+        return "hccl"
+    elif get_current_accelerator().type == DeviceType.CUDA:
+        return "nccl"
+    else:
+        return "gloo"
+
+
 def all_gather(tensor: Tensor, group: Optional[ProcessGroup] = None) -> Tensor:
     """Gathers the tensor from all ranks and stacks them at the first dim."""
     world_size = get_world_size()

src/llamafactory/v1/accelerator/interface.py

@@ -145,7 +145,7 @@ class DistributedInterface:
             timeout = config.get("timeout", 18000)
 
         if self._is_distributed:
-            init_process_group(timeout=timedelta(seconds=timeout))
+            init_process_group(timeout=timedelta(seconds=timeout), backend=helper.get_process_group_backend())
             self.model_device_mesh = init_device_mesh(
                 device_type=self.current_device.type,
                 mesh_shape=self.strategy.model_mesh_shape,

src/llamafactory/v1/config/arg_parser.py

@@ -20,7 +20,7 @@ from typing import Any
 from omegaconf import OmegaConf
 from transformers import HfArgumentParser
 
-from ...extras.misc import is_env_enabled
+from ..utils.env import is_env_enabled
 from .data_args import DataArguments
 from .model_args import ModelArguments
 from .sample_args import SampleArguments

src/llamafactory/v1/config/training_args.py

@@ -45,6 +45,10 @@ class TrainingArguments:
         default=3,
         metadata={"help": "Number of training epochs."},
     )
+    max_steps: int | None = field(
+        default=None,
+        metadata={"help": "Maximum number of training steps. If set, overrides num_train_epochs."},
+    )
     max_grad_norm: float = field(
         default=1.0,
         metadata={"help": "Maximum gradient norm for training."},

src/llamafactory/v1/core/base_trainer.py

@@ -67,6 +67,10 @@ class BaseTrainer:
         self.model_input_names = self.renderer.processor.model_input_names
 
         self._create_batch_generator()
+        # Calculate num_training_steps: max_steps takes priority if set
+        if self.args.max_steps is not None and self.args.max_steps > 0:
+            self.num_training_steps = self.args.max_steps
+        else:
             self.num_training_steps = self.args.num_train_epochs * len(self.train_batch_generator)
 
         if self.args.enable_activation_checkpointing:
@@ -98,7 +102,22 @@ class BaseTrainer:
         )
 
     def _shard_model(self) -> None:
-        pass
+        if self.args.dist_config is None:
+            if DistributedInterface().get_world_size(Dim.DP) > 1:
+                from torch.nn.parallel import DistributedDataParallel as DDP
+
+                logger.warning_rank0(
+                    "dist_config is None but distributed training is enabled; falling back to DistributedDataParallel."
+                )
+                device_ids = None if self.device.type == "cpu" else [self.device.index]
+                self.model = DDP(self.model, device_ids=device_ids)
+        else:
+            from ..plugins.trainer_plugins.distributed.hub import DistributedPlugin
+
+            self.model = DistributedPlugin(self.args.dist_config.name)(
+                self.model,
+                self.args.dist_config,
+            )
 
     def _init_optimizer(self) -> None:
         """Init optimizer."""
@@ -162,7 +181,9 @@ class BaseTrainer:
                     step_loss += loss.item()
 
                 grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm).item()
-                if not torch.isfinite(grad_norm):
+
+                # isfinite(): argument 'input' (position 1) must be Tensor, not float
+                if not torch.isfinite(torch.tensor(grad_norm)):  # type: ignore # pyright: ignore [reportUnknownReturnType]
                     logger.warning_rank0(f"Gradient norm is not finite: {grad_norm}")
                 else:
                     self.optimizer.step()
@@ -172,10 +193,17 @@ class BaseTrainer:
 
                 step_loss, grad_norm = DistributedInterface().all_reduce([step_loss, grad_norm])
                 DistributedInterface().sync()
+                if DistributedInterface().get_rank() == 0:
                     print(f"Epoch {epoch}, Step {self.global_step}, Loss: {step_loss:.4f}, Grad Norm: {grad_norm:.4f}")
 
+                # Check if max_steps is reached
+                if self.global_step >= self.num_training_steps:
+                    logger.info_rank0(f"Reached max_steps ({self.num_training_steps}), stopping training.")
+                    return
+
     def save_model(self) -> None:
         """Save the model."""
-        self.model.save_pretrained(self.args.output_dir)
+        model_to_save = self.model.module if hasattr(self.model, "module") else self.model
+        model_to_save.save_pretrained(self.args.output_dir)
         self.renderer.processor.save_pretrained(self.args.output_dir)
         logger.info_rank0(f"Model saved to {self.args.output_dir}")

src/llamafactory/v1/core/utils/batching.py

@@ -30,7 +30,7 @@ from torch.utils.data import default_collate
 from torchdata.stateful_dataloader import StatefulDataLoader
 from torchdata.stateful_dataloader.sampler import StatefulDistributedSampler
 
-from ...accelerator.interface import DistributedInterface
+from ...accelerator.interface import Dim, DistributedInterface
 from ...config import BatchingStrategy
 from ...utils import logging
 from ...utils.helper import pad_and_truncate
@@ -83,8 +83,7 @@ class BatchGenerator(Iterator):
         self.pin_memory = pin_memory
         self.drop_last = drop_last
         # TODO: support length and infinity
-
-        dp_size = DistributedInterface().get_world_size("dp")
+        dp_size = DistributedInterface().get_world_size(Dim.DP)
 
         if self.global_batch_size is None:
             self.global_batch_size = dp_size * micro_batch_size
@@ -126,8 +125,8 @@ class BatchGenerator(Iterator):
         if len(self.dataset) != -1:
             sampler = StatefulDistributedSampler(
                 self.dataset,
-                num_replicas=DistributedInterface().get_world_size("dp"),
-                rank=DistributedInterface().get_rank("dp"),
+                num_replicas=DistributedInterface().get_world_size(Dim.DP),
+                rank=DistributedInterface().get_rank(Dim.DP),
                 shuffle=True,
                 seed=0,
                 drop_last=self.drop_last,
@@ -142,6 +141,7 @@ class BatchGenerator(Iterator):
             num_workers=self.batching_workers,
             collate_fn=self.renderer.process_samples,
             pin_memory=self.pin_memory,
+            pin_memory_device=DistributedInterface().current_device.type,
             drop_last=self.drop_last,
         )
         if self.batching_strategy == BatchingStrategy.NORMAL:

src/llamafactory/v1/launcher.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
+import subprocess
 import sys
-
-from ..extras.env import VERSION, print_env
+from copy import deepcopy
 
 
 USAGE = (
@@ -27,27 +28,97 @@ USAGE = (
     + "-" * 70
 )
 
-
-WELCOME = (
-    "-" * 58
-    + "\n"
-    + f"| Welcome to LLaMA Factory, version {VERSION}"
-    + " " * (21 - len(VERSION))
-    + "|\n|"
-    + " " * 56
-    + "|\n"
-    + "| Project page: https://github.com/hiyouga/LLaMA-Factory |\n"
-    + "-" * 58
-)
+_DIST_TRAIN_COMMANDS = ("train", "sft", "dpo", "rm")
 
 
 def launch():
+    from .accelerator.helper import get_device_count
+    from .utils.env import find_available_port, is_env_enabled, use_kt, use_ray
+    from .utils.logging import get_logger
+
+    logger = get_logger(__name__)
+
+    # NOTE:
+    # `llamafactory-cli <command> ...` enters here first.
+    # We may re-launch via `torchrun` for distributed training. In that case we must
+    # forward `<command>` as argv[1] to the re-executed script, otherwise the script
+    # will misinterpret the first user argument (e.g. yaml config) as the command.
     command = sys.argv.pop(1) if len(sys.argv) > 1 else "help"
 
-    if command == "sft":  # train command will fallback to sft command
-        from .trainers.sft_trainer import run_sft
+    if command in _DIST_TRAIN_COMMANDS and (
+        is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray() and not use_kt())
+    ):
+        nnodes = os.getenv("NNODES", "1")
+        node_rank = os.getenv("NODE_RANK", "0")
+        nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
+        master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
+        master_port = os.getenv("MASTER_PORT", str(find_available_port()))
+        logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
+        if int(nnodes) > 1:
+            logger.info_rank0(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
 
-        run_sft()
+        # elastic launch support
+        max_restarts = os.getenv("MAX_RESTARTS", "0")
+        rdzv_id = os.getenv("RDZV_ID")
+        min_nnodes = os.getenv("MIN_NNODES")
+        max_nnodes = os.getenv("MAX_NNODES")
+
+        env = deepcopy(os.environ)
+        if is_env_enabled("OPTIM_TORCH", "1"):
+            # optimize DDP, see https://zhuanlan.zhihu.com/p/671834539
+            env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+            env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+        torchrun_args = [
+            "torchrun",
+            "--nproc-per-node",
+            nproc_per_node,
+        ]
+        if rdzv_id is not None:
+            # launch elastic job with fault tolerant support when possible
+            # see also https://docs.pytorch.org/docs/stable/elastic/train_script.html
+            rdzv_nnodes = nnodes
+            # elastic number of nodes if MIN_NNODES and MAX_NNODES are set
+            if min_nnodes is not None and max_nnodes is not None:
+                rdzv_nnodes = f"{min_nnodes}:{max_nnodes}"
+
+            torchrun_args.extend(
+                [
+                    "--nnodes",
+                    rdzv_nnodes,
+                    "--rdzv-id",
+                    rdzv_id,
+                    "--rdzv-backend",
+                    "c10d",
+                    "--rdzv-endpoint",
+                    f"{master_addr}:{master_port}",
+                    "--max-restarts",
+                    max_restarts,
+                ]
+            )
+        else:
+            # NOTE: DO NOT USE shell=True to avoid security risk
+            torchrun_args.extend(
+                [
+                    "--nnodes",
+                    nnodes,
+                    "--node_rank",
+                    node_rank,
+                    "--master_addr",
+                    master_addr,
+                    "--master_port",
+                    master_port,
+                ]
+            )
+
+        script_args = [__file__, command] + sys.argv[1:]
+        process = subprocess.run(
+            torchrun_args + script_args,
+            env=env,
+            check=True,
+        )
+
+        sys.exit(process.returncode)
 
     elif command == "chat":
         from .samplers.cli_sampler import run_chat
@@ -55,17 +126,54 @@ def launch():
         run_chat()
 
     elif command == "env":
-        print_env()
+        raise NotImplementedError("Environment information is not implemented yet.")
 
     elif command == "version":
-        print(WELCOME)
+        raise NotImplementedError("Version information is not implemented yet.")
 
     elif command == "help":
         print(USAGE)
 
+    elif command in _DIST_TRAIN_COMMANDS:
+        # Single GPU training without torchrun
+        if command in ("train", "sft"):
+            from llamafactory.v1.trainers.sft_trainer import run_sft
+
+            run_sft()
+        elif command == "dpo":
+            raise NotImplementedError("DPO trainer is not implemented yet.")
+        elif command == "rm":
+            raise NotImplementedError("RM trainer is not implemented yet.")
+
     else:
         print(f"Unknown command: {command}.\n{USAGE}")
 
 
+def main():
+    # sys.argv[1] contains the command (sft/dpo/rm/train), sys.argv[2:] contains the rest args
+    command = sys.argv[1] if len(sys.argv) > 1 else "sft"
+
+    # Routing needs the sub-command, but downstream trainers usually expect argv without it.
+    if command in _DIST_TRAIN_COMMANDS:
+        sys.argv.pop(1)
+    else:
+        # Backward-compat: if someone runs `torchrun launcher.py config.yaml`,
+        # treat it as sft by default.
+        if len(sys.argv) > 1 and sys.argv[1].endswith((".yaml", ".yml")):
+            command = "sft"
+    if command in ("train", "sft"):
+        from llamafactory.v1.trainers.sft_trainer import run_sft
+
+        run_sft()
+    elif command == "dpo":
+        # from llamafactory.v1.trainers.dpo_trainer import run_dpo
+        # run_dpo()
+        raise NotImplementedError("DPO trainer is not implemented yet.")
+    elif command == "rm":
+        # from llamafactory.v1.trainers.rm_trainer import run_rm
+        # run_rm()
+        raise NotImplementedError("RM trainer is not implemented yet.")
+
+
 if __name__ == "__main__":
-    pass
+    main()

src/llamafactory/v1/plugins/trainer_plugins/distributed/fsdp2.py

@@ -0,0 +1,399 @@
+# 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.
+
+import gc
+import os
+
+import torch
+import torch.nn as nn
+from torch.distributed.checkpoint.state_dict import StateDictOptions, get_model_state_dict, set_model_state_dict
+from torch.distributed.fsdp import (
+    CPUOffloadPolicy,
+    MixedPrecisionPolicy,
+    fully_shard,
+)
+from transformers import PreTrainedModel
+
+from ....accelerator.helper import get_current_accelerator
+from ....accelerator.interface import DistributedInterface
+from ....utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+
+def get_transformer_layer_cls(model: PreTrainedModel) -> type[nn.Module] | None:
+    no_split_modules = getattr(model, "_no_split_modules", None)
+    if no_split_modules:
+        if isinstance(no_split_modules, (list, tuple)):
+            for name, module in model.named_modules():
+                for cls_name in no_split_modules:
+                    if module.__class__.__name__ == cls_name:
+                        return module.__class__
+    if hasattr(model, "model") and hasattr(model.model, "layers"):
+        return type(model.model.layers[0])
+    if hasattr(model, "layers"):
+        return type(model.layers[0])
+
+    return None
+
+
+class FSDP2Engine:
+    def __init__(self, dist_config: dict):
+        self.dist_interface = DistributedInterface()
+        self.rank = self.dist_interface.get_rank()
+        self.local_rank = self.dist_interface.get_local_rank()
+        self.world_size = self.dist_interface.get_world_size()
+        self.mixed_precision = dist_config.get("mixed_precision", "bf16")
+        self.reshard_after_forward = dist_config.get("reshard_after_forward", True)
+        self.offload_params = dist_config.get("offload_params", False)
+        self.pin_memory = dist_config.get("pin_memory", True)
+        self.dcp_path = dist_config.get("dcp_path", None)
+        self.device_mesh = self.dist_interface.data_device_mesh
+
+        if self.device_mesh is None:
+            logger.warning(
+                "Device Mesh not found in DistributedInterface. FSDP2 might fail if not running in distributed mode."
+            )
+
+        if self.device_mesh is not None:
+            try:
+                self.fsdp_mesh = self.device_mesh["dp"]
+            except Exception:
+                self.fsdp_mesh = self.device_mesh
+
+            logger.info(f"Using Device Mesh: {self.fsdp_mesh}")
+        else:
+            self.fsdp_mesh = None
+
+    def get_mp_policy(self) -> MixedPrecisionPolicy:
+        if self.mixed_precision == "bf16":
+            param_dtype = torch.bfloat16
+            reduce_dtype = torch.float32
+        elif self.mixed_precision == "fp16":
+            param_dtype = torch.float16
+            reduce_dtype = torch.float32
+        else:
+            param_dtype = torch.float32
+            reduce_dtype = torch.float32
+
+        return MixedPrecisionPolicy(
+            param_dtype=param_dtype,
+            reduce_dtype=reduce_dtype,
+            cast_forward_inputs=True,
+        )
+
+    def prepare_model(self, model: PreTrainedModel) -> PreTrainedModel:
+        if self.fsdp_mesh is None:
+            logger.warning("No FSDP Mesh available, skipping FSDP wrapping.")
+            return model
+
+        mp_policy = self.get_mp_policy()
+        layer_cls = get_transformer_layer_cls(model)
+
+        if layer_cls is None:
+            logger.warning(
+                "Could not identify Transformer Layer class, applying FSDP to the whole model structure only."
+            )
+            transformer_layer_cls_to_wrap = set()
+        else:
+            logger.info(f"Applying per-layer FSDP to {layer_cls.__name__}")
+            transformer_layer_cls_to_wrap = {layer_cls}
+
+        for name, module in model.named_modules():
+            should_wrap = False
+
+            if type(module) in transformer_layer_cls_to_wrap:
+                should_wrap = True
+            elif isinstance(module, nn.Embedding):
+                if not getattr(model.config, "tie_word_embeddings", True):
+                    should_wrap = True
+
+            if should_wrap:
+                fully_shard(
+                    module,
+                    mesh=self.fsdp_mesh,
+                    reshard_after_forward=self.reshard_after_forward,
+                    mp_policy=mp_policy,
+                    offload_policy=CPUOffloadPolicy(pin_memory=self.pin_memory) if self.offload_params else None,
+                )
+
+        use_gradient_checkpointing = True  # Could be configurable
+        if use_gradient_checkpointing:
+            if self.rank == 0:
+                logger.info("Enabling gradient checkpointing (transformers native)...")
+
+            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
+
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        fully_shard(
+            model,
+            mesh=self.fsdp_mesh,
+            reshard_after_forward=self.reshard_after_forward,
+            mp_policy=mp_policy,
+            offload_policy=CPUOffloadPolicy(pin_memory=self.pin_memory) if self.offload_params else None,
+        )
+
+        return model
+
+    @torch.no_grad()
+    def materialize_and_load(self, model: PreTrainedModel, hf_model_path: str, dcp_path: str = None):
+        if self.rank == 0:
+            logger.info("Materializing sharded model params...")
+
+        device = get_current_accelerator()
+        model.to_empty(device=device)
+
+        if dcp_path and os.path.exists(dcp_path):
+            if self.rank == 0:
+                logger.info(f"DCP path found at {dcp_path}. Using efficient Sharded Loading (DCP Load).")
+            self._load_from_dcp(model, dcp_path)
+        else:
+            if self.rank == 0:
+                if dcp_path:
+                    logger.warning(f"DCP path {dcp_path} not found.")
+                logger.info("Using HF Meta Loading (Chunk Load).")
+            self._load_weights_from_hf_checkpoint(model, hf_model_path)
+
+        return model
+
+    def shard_model(self, model: PreTrainedModel) -> PreTrainedModel:
+        if model.device.type == "meta":
+            model = self.prepare_model(model)
+            model = self.materialize_and_load(model, hf_model_path=model.config.name_or_path, dcp_path=self.dcp_path)
+        else:
+            model = self.prepare_model(model)
+        return model
+
+    def _load_from_dcp(self, model: PreTrainedModel, dcp_path: str):
+        import torch.distributed.checkpoint as dcp
+
+        try:
+            if self.rank == 0:
+                logger.info(f"Loading distributed checkpoint from {dcp_path} ...")
+
+            options = StateDictOptions(full_state_dict=False, cpu_offload=True)
+            local_state_dict = get_model_state_dict(model, options=options)
+            dcp.load(state_dict=local_state_dict, checkpoint_id=dcp_path)
+            set_model_state_dict(model, local_state_dict, options=options)
+
+            if self.rank == 0:
+                logger.info("DCP weights loaded successfully.")
+
+        except Exception as e:
+            logger.error(f"Failed to load from DCP: {e}")
+            raise e
+
+    def _load_weights_from_hf_checkpoint(self, model, hf_model_path):
+        import glob
+        import json
+
+        hf_model_path = self._resolve_hf_checkpoint_dir(hf_model_path)
+
+        if self.rank == 0:
+            logger.info(f"Loading weights from {hf_model_path} ...")
+
+        index_file = os.path.join(hf_model_path, "model.safetensors.index.json")
+        is_safetensors = True
+        checkpoint_files = []
+
+        if os.path.exists(index_file):
+            with open(index_file) as f:
+                index = json.load(f)
+            checkpoint_files = sorted(set(index["weight_map"].values()))
+            checkpoint_files = [os.path.join(hf_model_path, f) for f in checkpoint_files]
+        elif os.path.exists(os.path.join(hf_model_path, "model.safetensors")):
+            checkpoint_files = [os.path.join(hf_model_path, "model.safetensors")]
+        else:
+            is_safetensors = False
+            index_file = os.path.join(hf_model_path, "pytorch_model.bin.index.json")
+            if os.path.exists(index_file):
+                with open(index_file) as f:
+                    index = json.load(f)
+                checkpoint_files = sorted(set(index["weight_map"].values()))
+                checkpoint_files = [os.path.join(hf_model_path, f) for f in checkpoint_files]
+            elif os.path.exists(os.path.join(hf_model_path, "pytorch_model.bin")):
+                checkpoint_files = [os.path.join(hf_model_path, "pytorch_model.bin")]
+            else:
+                checkpoint_files = sorted(glob.glob(os.path.join(hf_model_path, "*.safetensors")))
+                if checkpoint_files:
+                    is_safetensors = True
+                else:
+                    checkpoint_files = sorted(glob.glob(os.path.join(hf_model_path, "*.bin")))
+
+        if not checkpoint_files:
+            raise ValueError(f"No checkpoint files found in {hf_model_path}")
+
+        param_map = dict(model.named_parameters())
+        total_files = len(checkpoint_files)
+
+        for i, ckpt_file in enumerate(checkpoint_files):
+            if self.rank == 0:
+                logger.info(f"[{i + 1}/{total_files}] Loading {os.path.basename(ckpt_file)} ...")
+
+            if is_safetensors:
+                from safetensors import safe_open
+
+                with safe_open(ckpt_file, framework="pt", device="cpu") as f:
+                    for key in f.keys():
+                        if key in param_map:
+                            tensor = f.get_tensor(key)
+                            self._copy_weights(param_map[key], tensor)
+            else:
+                state_dict = torch.load(ckpt_file, map_location="cpu")
+                for key, tensor in state_dict.items():
+                    if key in param_map:
+                        self._copy_weights(param_map[key], tensor)
+                del state_dict
+                gc.collect()
+
+    def _resolve_hf_checkpoint_dir(self, hf_model_path: str) -> str:
+        """Resolve a HF model identifier or local path to a local directory containing checkpoint files.
+
+        - If `hf_model_path` is an existing directory, return it.
+        - If it's a file path, return its parent directory.
+        - Otherwise treat it as a Hugging Face Hub repo id and download/resolve to the local cache dir.
+        """
+        if not hf_model_path:
+            return hf_model_path
+
+        # Local directory or file path.
+        if os.path.isdir(hf_model_path):
+            return hf_model_path
+        if os.path.isfile(hf_model_path):
+            return os.path.dirname(hf_model_path)
+
+        # HuggingFace Hub repo id: snapshot to local cache so we can glob/index files.
+        try:
+            from huggingface_hub import snapshot_download
+        except ImportError as e:
+            raise ValueError(
+                f"hf_model_path='{hf_model_path}' does not exist locally and huggingface_hub is not available "
+                f"to download it. Please provide a local model directory or install huggingface_hub. Error: {e}"
+            ) from e
+
+        revision = os.getenv("HF_REVISION")
+        offline = os.getenv("HF_HUB_OFFLINE") == "1" or os.getenv("TRANSFORMERS_OFFLINE") == "1"
+
+        # In distributed runs, let rank0 download first to avoid N-way concurrent downloads.
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            if self.rank == 0:
+                local_dir = snapshot_download(
+                    repo_id=hf_model_path,
+                    revision=revision,
+                    local_files_only=offline,
+                    allow_patterns=[
+                        "*.safetensors",
+                        "*.bin",
+                        "*.index.json",
+                        "model.safetensors",
+                        "model.safetensors.index.json",
+                        "pytorch_model.bin",
+                        "pytorch_model.bin.index.json",
+                        "config.json",
+                    ],
+                )
+                logger.info(f"Resolved HF repo id '{hf_model_path}' to local dir: {local_dir}")
+            torch.distributed.barrier()
+            if self.rank != 0:
+                local_dir = snapshot_download(
+                    repo_id=hf_model_path,
+                    revision=revision,
+                    local_files_only=True,
+                    allow_patterns=[
+                        "*.safetensors",
+                        "*.bin",
+                        "*.index.json",
+                        "model.safetensors",
+                        "model.safetensors.index.json",
+                        "pytorch_model.bin",
+                        "pytorch_model.bin.index.json",
+                        "config.json",
+                    ],
+                )
+            return local_dir
+
+        local_dir = snapshot_download(
+            repo_id=hf_model_path,
+            revision=revision,
+            local_files_only=offline,
+            allow_patterns=[
+                "*.safetensors",
+                "*.bin",
+                "*.index.json",
+                "model.safetensors",
+                "model.safetensors.index.json",
+                "pytorch_model.bin",
+                "pytorch_model.bin.index.json",
+                "config.json",
+            ],
+        )
+        if self.rank == 0:
+            logger.info(f"Resolved HF repo id '{hf_model_path}' to local dir: {local_dir}")
+        return local_dir
+
+    def _copy_weights(self, param, loaded_tensor):
+        from torch.distributed._tensor import DTensor, Shard
+
+        if loaded_tensor.dtype != param.dtype:
+            loaded_tensor = loaded_tensor.to(param.dtype)
+
+        if isinstance(param, DTensor):
+            shard_placement = None
+            mesh_dim = -1
+
+            for i, placement in enumerate(param.placements):
+                if isinstance(placement, Shard):
+                    shard_placement = placement
+                    mesh_dim = i
+                    break
+
+            local_tensor = param.to_local()
+
+            if shard_placement is None:
+                local_tensor.copy_(loaded_tensor)
+            else:
+                dim = shard_placement.dim
+                mesh = param.device_mesh
+                my_coordinate = mesh.get_coordinate()
+                if my_coordinate is None:
+                    return
+
+                rank_in_dim = my_coordinate[mesh_dim]
+                world_size_in_dim = mesh.size(mesh_dim)
+
+                full_size = param.shape[dim]
+                chunk_size = (full_size + world_size_in_dim - 1) // world_size_in_dim
+
+                start = rank_in_dim * chunk_size
+                end = min(start + chunk_size, full_size)
+
+                if start >= full_size:
+                    return
+
+                sliced_tensor = loaded_tensor.narrow(dim, start, end - start)
+
+                slices = [slice(None)] * local_tensor.ndim
+                slices[dim] = slice(0, sliced_tensor.shape[dim])
+                local_tensor[tuple(slices)].copy_(sliced_tensor)
+        else:
+            param.data.copy_(loaded_tensor)

src/llamafactory/v1/plugins/trainer_plugins/distributed/hub.py

@@ -0,0 +1,34 @@
+# 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.
+
+from ....config.arg_utils import PluginConfig
+from ....utils.plugin import BasePlugin
+from ....utils.types import HFModel
+
+
+class DistributedPlugin(BasePlugin):
+    def __call__(self, model: HFModel, dist_config: PluginConfig, **kwargs) -> HFModel:
+        return super().__call__(model, dist_config, **kwargs)
+
+
+@DistributedPlugin("fsdp2").register()
+def shard_model_fsdp2(model: HFModel, dist_config: PluginConfig) -> HFModel:
+    from .fsdp2 import FSDP2Engine
+
+    return FSDP2Engine(dist_config).shard_model(model)
+
+
+@DistributedPlugin("deepspeed").register()
+def shard_model_deepspeed(model: HFModel, dist_config: PluginConfig) -> HFModel:
+    return model

src/llamafactory/v1/utils/env.py

@@ -28,3 +28,11 @@ def find_available_port() -> int:
 def is_env_enabled(env_var: str, default: str = "0") -> bool:
     """Check if the environment variable is enabled."""
     return os.getenv(env_var, default).lower() in ["true", "yes", "on", "t", "y", "1"]
+
+
+def use_ray() -> bool:
+    return False
+
+
+def use_kt() -> bool:
+    return False

tests_v1/plugins/model_plugins/test_kernel_plugin.py

@@ -15,7 +15,6 @@
 import sys
 from unittest.mock import MagicMock, patch
 
-import pytest
 import torch.multiprocessing as mp
 from transformers import AutoModelForCausalLM
 

tests_v1/trainers/test_fsdp2_sft_trainer.py

@@ -0,0 +1,89 @@
+# 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.
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+
+@pytest.mark.xfail(reason="CI machines may OOM when heavily loaded.")
+@pytest.mark.runs_on(["cuda", "npu"])
+def test_fsdp2_sft_trainer(tmp_path: Path):
+    """Test FSDP2 SFT trainer by simulating `llamafactory-cli sft config.yaml` behavior."""
+    config_yaml = """\
+model: Qwen/Qwen3-0.6B
+trust_remote_code: true
+model_class: llm
+
+template: qwen3_nothink
+
+kernel_config:
+    name: auto
+    include_kernels: auto
+
+quant_config: null
+
+dist_config:
+    name: fsdp2
+    dcp_path: null
+
+init_config:
+    name: init_on_meta
+
+### data
+train_dataset: data/v1_sft_demo.yaml
+
+### training
+output_dir: {output_dir}
+micro_batch_size: 1
+global_batch_size: 1
+cutoff_len: 2048
+learning_rate: 1.0e-4
+bf16: false
+max_steps: 1
+
+### sample
+sample_backend: hf
+max_new_tokens: 128
+"""
+    # Create output directory
+    output_dir = tmp_path / "outputs"
+    output_dir.mkdir(parents=True, exist_ok=True)
+    config_file = tmp_path / "config.yaml"
+    config_file.write_text(config_yaml.format(output_dir=str(output_dir)))
+
+    # Set up environment variables
+    env = os.environ.copy()
+    env["USE_V1"] = "1"  # Use v1 launcher
+    env["FORCE_TORCHRUN"] = "1"  # Force distributed training via torchrun
+
+    # Run the CLI command via subprocess
+    # This simulates: llamafactory-cli sft config.yaml
+    result = subprocess.run(
+        [sys.executable, "-m", "llamafactory.cli", "sft", str(config_file)],
+        env=env,
+        capture_output=True,
+        cwd=str(Path(__file__).parent.parent.parent),  # LLaMA-Factory root
+    )
+
+    # Decode output with error handling (progress bars may contain non-UTF-8 bytes)
+    stderr = result.stderr.decode("utf-8", errors="replace")
+
+    # Check the result
+    assert result.returncode == 0, f"Training failed with return code {result.returncode}\nSTDERR: {stderr}"
+
+    # Verify output files exist (optional - adjust based on what run_sft produces)
+    # assert (output_dir / "some_expected_file").exists()