[feat] support LlamaFactory SFT training by HyperParallel FSDP2 backend (#10289)

src/llamafactory/extras/packages.py

@@ -70,6 +70,10 @@ def is_matplotlib_available():
     return _is_package_available("matplotlib")
 
 
+def is_hyper_parallel_available():
+    return _is_package_available("hyper_parallel")
+
+
 def is_mcore_adapter_available():
     return _is_package_available("mcore_adapter")
 

src/llamafactory/hparams/finetuning_args.py

@@ -482,6 +482,24 @@ class FinetuningArguments(
             )
         },
     )
+    use_hyper_parallel: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to use HyperParallel distributed training backend (FSDP/TP). "
+                "Only supported for the 'sft' stage with full fine-tuning."
+            )
+        },
+    )
+    hyper_parallel_args: str | None = field(
+        default=None,
+        metadata={
+            "help": (
+                "Path to a JSON file containing HyperParallel strategy arguments "
+                "(e.g., tp_size, param_dtype). Used when use_hyper_parallel=True."
+            )
+        },
+    )
     use_muon: bool = field(
         default=False,
         metadata={"help": "Whether or not to use the Muon optimizer."},

src/llamafactory/train/hyper_parallel/__init__.py

@@ -0,0 +1,18 @@
+# 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 .workflow import run_sft
+
+
+__all__ = ["run_sft"]

src/llamafactory/train/hyper_parallel/workflow.py

@@ -0,0 +1,179 @@
+# 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 typing import TYPE_CHECKING, Optional
+
+from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
+from ...extras.constants import IGNORE_INDEX
+from ...extras.logging import get_logger
+from ...extras.misc import calculate_tps
+from ...extras.packages import is_hyper_parallel_available, is_transformers_version_greater_than
+from ...extras.ploting import plot_loss
+from ...model import load_model, load_tokenizer
+from ..callbacks import SaveProcessorCallback
+from ..sft.metric import ComputeAccuracy, ComputeSimilarity, eval_logit_processor
+from ..trainer_utils import asft_loss_func, create_modelcard_and_push, create_ref_model, dft_loss_func, eaft_loss_func
+
+
+if TYPE_CHECKING:
+    from transformers import Seq2SeqTrainingArguments, TrainerCallback
+
+    from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def run_sft(
+    model_args: "ModelArguments",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+    generating_args: "GeneratingArguments",
+    callbacks: Optional[list["TrainerCallback"]] = None,
+):
+    if not is_hyper_parallel_available():
+        raise ImportError(
+            "hyper_parallel is not installed. Please install it with `pip install hyper_parallel`."
+        )
+
+    from hyper_parallel.integration.llamafactory import HyperParallelArguments, HyperParallelTrainer  # pylint: disable=C0415
+
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
+
+    ref_model = None
+    if finetuning_args.use_asft_loss:
+        ref_model = create_ref_model(model_args, finetuning_args)
+
+    data_collator = SFTDataCollatorWith4DAttentionMask(
+        template=template,
+        model=model if not training_args.predict_with_generate else None,
+        pad_to_multiple_of=8 if training_args.do_train else None,
+        label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
+        block_diag_attn=model_args.block_diag_attn,
+        attn_implementation=getattr(model.config, "_attn_implementation", None),
+        compute_dtype=model_args.compute_dtype,
+        **tokenizer_module,
+    )
+
+    # Metric utils
+    metric_module = {}
+    if training_args.predict_with_generate:
+        metric_module["compute_metrics"] = ComputeSimilarity(tokenizer=tokenizer)
+    elif finetuning_args.compute_accuracy:
+        metric_module["compute_metrics"] = ComputeAccuracy()
+        metric_module["preprocess_logits_for_metrics"] = eval_logit_processor
+
+    # Keyword arguments for `model.generate`
+    gen_kwargs = generating_args.to_dict(obey_generation_config=True)
+    if is_transformers_version_greater_than("4.58.0"):
+        extra_ids = getattr(tokenizer, "additional_special_tokens_ids", None)
+        if not isinstance(extra_ids, list):
+            extra_special_tokens = getattr(tokenizer, "_extra_special_tokens", [])
+            string_tokens = [str(t) for t in extra_special_tokens]
+            extra_ids = tokenizer.convert_tokens_to_ids(string_tokens)
+        all_eos_ids = [tokenizer.eos_token_id] + [i for i in extra_ids if i != -1]
+        gen_kwargs["eos_token_id"] = list(dict.fromkeys(all_eos_ids))
+    else:
+        gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
+    gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
+
+    hp_args = HyperParallelArguments.from_finetuning_args(finetuning_args)
+
+    callbacks = list(callbacks or [])
+    processor = tokenizer_module.get("processor")
+    if processor is not None:
+        callbacks.append(SaveProcessorCallback(processor))
+
+    compute_loss_func = None
+    if finetuning_args.use_dft_loss:
+        compute_loss_func = dft_loss_func
+    elif finetuning_args.use_eaft_loss:
+        compute_loss_func = lambda outputs, labels, num_items_in_batch=None: eaft_loss_func(  # noqa: E731
+            outputs, labels, num_items_in_batch, finetuning_args.eaft_alpha
+        )
+    elif finetuning_args.use_asft_loss:
+        from functools import partial
+
+        compute_loss_func = partial(asft_loss_func, asft_alpha=finetuning_args.asft_alpha)
+
+    trainer = HyperParallelTrainer(
+        hp_args=hp_args,
+        model=model,
+        args=training_args,
+        finetuning_args=finetuning_args,
+        data_collator=data_collator,
+        callbacks=callbacks,
+        gen_kwargs=gen_kwargs,
+        ref_model=ref_model,
+        compute_loss_func=compute_loss_func,
+        **dataset_module,
+        **tokenizer_module,
+        **metric_module,
+    )
+
+    if finetuning_args.use_badam:
+        from badam import BAdamCallback, clip_grad_norm_old_version  # type: ignore[import]
+        from types import MethodType
+
+        trainer.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, trainer.accelerator)
+        trainer.add_callback(BAdamCallback)
+
+    # Training
+    if training_args.do_train:
+        train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
+        trainer.save_model()
+        if finetuning_args.include_effective_tokens_per_second:
+            train_result.metrics["effective_tokens_per_sec"] = calculate_tps(
+                dataset_module["train_dataset"], train_result.metrics, stage="sft"
+            )
+
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
+            keys = ["loss"]
+            if isinstance(dataset_module.get("eval_dataset"), dict):
+                keys += sum(
+                    [[f"eval_{key}_loss", f"eval_{key}_accuracy"] for key in dataset_module["eval_dataset"].keys()],
+                    [],
+                )
+            else:
+                keys += ["eval_loss", "eval_accuracy"]
+
+            plot_loss(training_args.output_dir, keys=keys)
+
+    if training_args.predict_with_generate:
+        tokenizer.padding_side = "left"
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Predict
+    if training_args.do_predict:
+        logger.warning_rank0_once("Batch generation can be very slow. Consider using `scripts/vllm_infer.py` instead.")
+        predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict", **gen_kwargs)
+        trainer.log_metrics("predict", predict_results.metrics)
+        trainer.save_metrics("predict", predict_results.metrics)
+        trainer.save_predictions(dataset_module["eval_dataset"], predict_results, generating_args.skip_special_tokens)
+
+    # Create model card
+    create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args)

src/llamafactory/train/tuner.py

@@ -24,7 +24,12 @@ from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.misc import find_available_port, get_device_name, get_torch_device, infer_optim_dtype
-from ..extras.packages import is_mcore_adapter_available, is_ray_available, is_transformers_version_greater_than
+from ..extras.packages import (
+    is_hyper_parallel_available,
+    is_mcore_adapter_available,
+    is_ray_available,
+    is_transformers_version_greater_than,
+)
 from ..hparams import RayArguments, get_infer_args, get_ray_args, get_train_args, read_args
 from ..model import load_model, load_tokenizer
 from .callbacks import LogCallback, PissaConvertCallback, ReporterCallback
@@ -71,7 +76,16 @@ def _training_function(config: dict[str, Any]) -> None:
 
     callbacks.append(ReporterCallback(model_args, data_args, finetuning_args, generating_args))  # add to last
 
-    if finetuning_args.stage in ["pt", "sft", "dpo"] and finetuning_args.use_mca:
+    if finetuning_args.stage == "sft" and finetuning_args.use_hyper_parallel:
+        if not is_hyper_parallel_available():
+            raise ImportError(
+                "hyper_parallel is not installed. Please install it with `pip install hyper_parallel`."
+            )
+        from .hyper_parallel import run_sft as run_sft_hp
+
+        run_sft_hp(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
+
+    elif finetuning_args.stage in ["pt", "sft", "dpo"] and finetuning_args.use_mca:
         if not is_mcore_adapter_available():
             raise ImportError("mcore_adapter is not installed. Please install it with `pip install mcore-adapter`.")
         if finetuning_args.stage == "pt":