mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-01 11:12:50 +08:00
parent
0d8aa6e6ef
commit
825ea1c72d
@ -10,6 +10,7 @@ DISABLE_VERSION_CHECK=
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FORCE_CHECK_IMPORTS=
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LLAMAFACTORY_VERBOSITY=
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USE_MODELSCOPE_HUB=
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USE_OPENMIND_HUB=
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RECORD_VRAM=
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# torchrun
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FORCE_TORCHRUN=
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1
.gitignore
vendored
1
.gitignore
vendored
@ -162,6 +162,7 @@ cython_debug/
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# custom .gitignore
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ms_cache/
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hf_cache/
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om_cache/
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cache/
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config/
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saves/
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2
Makefile
2
Makefile
@ -18,4 +18,4 @@ style:
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ruff format $(check_dirs)
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test:
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CUDA_VISIBLE_DEVICES= pytest tests/
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CUDA_VISIBLE_DEVICES= WANDB_DISABLED=true pytest tests/
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@ -79,6 +79,11 @@ def is_transformers_version_greater_than_4_43():
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return _get_package_version("transformers") >= version.parse("4.43.0")
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@lru_cache
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def is_transformers_version_equal_to_4_46():
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return _get_package_version("transformers") == version.parse("4.46.0")
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def is_uvicorn_available():
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return _is_package_available("uvicorn")
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@ -29,6 +29,7 @@ from trl.trainer import disable_dropout_in_model
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from typing_extensions import override
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from ...extras.constants import IGNORE_INDEX
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from ...extras.packages import is_transformers_version_equal_to_4_46
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from ..callbacks import PissaConvertCallback, SaveProcessorCallback
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from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
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@ -118,6 +119,13 @@ class CustomDPOTrainer(DPOTrainer):
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create_custom_scheduler(self.args, num_training_steps, optimizer)
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return super().create_scheduler(num_training_steps, optimizer)
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@override
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def get_batch_samples(self, epoch_iterator, num_batches):
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r"""
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Replaces the method of KTO Trainer with the one of the standard Trainer.
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"""
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return Trainer.get_batch_samples(self, epoch_iterator, num_batches)
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def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
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r"""
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Computes ORPO's odds ratio (OR) loss for batched log probabilities of the policy model.
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@ -258,3 +266,15 @@ class CustomDPOTrainer(DPOTrainer):
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metrics[f"{prefix}odds_ratio_loss"] = ((losses - sft_loss) / self.beta).detach().mean().cpu()
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return losses.mean(), metrics
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@override
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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r"""
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Fixes the loss value for transformers 4.46.0.
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https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
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"""
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loss = super().compute_loss(model, inputs, return_outputs)
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if kwargs.pop("num_items_in_batch", False) and is_transformers_version_equal_to_4_46():
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loss /= self.args.gradient_accumulation_steps
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return loss
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@ -28,6 +28,7 @@ from trl.trainer import disable_dropout_in_model
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from typing_extensions import override
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from ...extras.constants import IGNORE_INDEX
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from ...extras.packages import is_transformers_version_equal_to_4_46
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from ..callbacks import SaveProcessorCallback
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from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
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@ -120,6 +121,13 @@ class CustomKTOTrainer(KTOTrainer):
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"""
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return Trainer._get_train_sampler(self)
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@override
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def get_batch_samples(self, epoch_iterator, num_batches):
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r"""
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Replaces the method of KTO Trainer with the one of the standard Trainer.
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"""
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return Trainer.get_batch_samples(self, epoch_iterator, num_batches)
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@override
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def forward(
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self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
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@ -231,3 +239,15 @@ class CustomKTOTrainer(KTOTrainer):
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metrics["kl"] = kl.item()
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return losses, metrics
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@override
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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r"""
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Fixes the loss value for transformers 4.46.0.
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https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
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"""
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loss = super().compute_loss(model, inputs, return_outputs)
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if kwargs.pop("num_items_in_batch", False) and is_transformers_version_equal_to_4_46():
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loss /= self.args.gradient_accumulation_steps
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return loss
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@ -25,6 +25,7 @@ from transformers import Trainer
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from typing_extensions import override
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from ...extras.logging import get_logger
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from ...extras.packages import is_transformers_version_equal_to_4_46
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from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
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from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
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@ -79,7 +80,7 @@ class PairwiseTrainer(Trainer):
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@override
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def compute_loss(
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self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False
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self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
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) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
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r"""
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Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
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@ -98,6 +99,10 @@ class PairwiseTrainer(Trainer):
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chosen_scores, rejected_scores = chosen_scores.squeeze(), rejected_scores.squeeze()
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loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
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if kwargs.pop("num_items_in_batch", False) and is_transformers_version_equal_to_4_46():
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loss /= self.args.gradient_accumulation_steps # fixes the loss value for transformers 4.46.0
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if return_outputs:
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return loss, (loss, chosen_scores, rejected_scores)
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else:
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@ -44,7 +44,6 @@ INFER_ARGS = {
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"finetuning_type": "lora",
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"template": "llama3",
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"infer_dtype": "float16",
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"export_dir": "llama3_export",
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}
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OS_NAME = os.environ.get("OS_NAME", "")
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@ -61,11 +60,12 @@ OS_NAME = os.environ.get("OS_NAME", "")
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],
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)
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def test_run_exp(stage: str, dataset: str):
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output_dir = f"train_{stage}"
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output_dir = os.path.join("output", f"train_{stage}")
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run_exp({"stage": stage, "dataset": dataset, "output_dir": output_dir, **TRAIN_ARGS})
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assert os.path.exists(output_dir)
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def test_export():
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export_model(INFER_ARGS)
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assert os.path.exists("llama3_export")
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export_dir = os.path.join("output", "llama3_export")
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export_model({"export_dir": export_dir, **INFER_ARGS})
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assert os.path.exists(export_dir)
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