diff --git a/README.md b/README.md index a0df0e2c..1de08dc2 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846 ## Changelog +[23/10/21] We supported [NEFTune](https://arxiv.org/abs/2310.05914) optimization . Try `--neftune_noise_alpha` argument to activate NEFTune, e.g., `--neftune_noise_alpha 5`. + [23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention. [23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. diff --git a/README_zh.md b/README_zh.md index 7caf80c6..4511c418 100644 --- a/README_zh.md +++ b/README_zh.md @@ -22,6 +22,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846 ## 更新日志 +[23/10/21] 我们支持了 [NEFTune](https://arxiv.org/abs/2310.05914) 优化。试试`--neftune_noise_alpha` 参数来激活 NEFTune，例如，`--neftune_noise_alpha 5`。 + [23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。 [23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。 diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py index b0e99193..a09a0c9f 100644 --- a/src/llmtuner/hparams/finetuning_args.py +++ b/src/llmtuner/hparams/finetuning_args.py @@ -75,6 +75,10 @@ class FinetuningArguments: default=0.1, metadata={"help": "The beta parameter for the DPO loss."} ) + neftune_noise_alpha: Optional[float] = field( + default=None, + metadata={"help": "The alpha parameter for the NEFTune noise. By setting this the NEFTune optimization will be activated."} + ) def __post_init__(self): if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA diff --git a/src/llmtuner/tuner/sft/trainer.py b/src/llmtuner/tuner/sft/trainer.py index 4fafc76b..397286bd 100644 --- a/src/llmtuner/tuner/sft/trainer.py +++ b/src/llmtuner/tuner/sft/trainer.py @@ -3,8 +3,10 @@ import json import torch import numpy as np import torch.nn as nn +from functools import wraps from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union -from transformers import Seq2SeqTrainer +from transformers import Seq2SeqTrainer, PreTrainedModel, Trainer +from peft import PeftModel from llmtuner.extras.constants import IGNORE_INDEX from llmtuner.extras.logging import get_logger @@ -21,6 +23,14 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer): Inherits PeftTrainer to compute generative metrics such as BLEU and ROUGE. """ + def __init__(self, model: Union["PreTrainedModel", nn.Module] = None, neftune_noise_alpha: Optional[float] = 0, **kwargs): + super().__init__(model, **kwargs) + self.neftune_noise_alpha = neftune_noise_alpha + self._neftune_activated = False + + if self.neftune_noise_alpha: + self._activate_neftune(model) + def prediction_step( self, model: nn.Module, @@ -99,3 +109,71 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer): for pred, label in zip(decoded_preds, decoded_labels): res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False)) writer.write("\n".join(res)) + + + @wraps(Trainer.train) + def train(self, *args, **kwargs): + output = super().train(*args, **kwargs) + + # After training we make sure to retrieve back the original forward pass method + # for the embedding layer. + if self.neftune_noise_alpha is not None: + self._deactivate_neftune(self.model) + + return output + + def _toggle_neftune(self, model, activate=True): + """Toggle NEFTune optimization for a model (i.e. activate or deactivate). + This optimization based on this paper: https://arxiv.org/abs/2310.05914 + + Parameters: + model : PreTrainedModel or PeftModel + The model to toggle the noise for. + activate : bool, optional (default=True) + Whether to activate the noise or not. + """ + if activate == self._neftune_activated: + return + + self._neftune_activated = activate + + embeddings = (model.get_input_embeddings() if isinstance(model, PreTrainedModel) + else model.base_model.get_input_embeddings() if isinstance(model, PeftModel) + else None) + + if embeddings: + if activate: + embeddings.neftune_noise_alpha = self.neftune_noise_alpha + embeddings._trl_old_forward = embeddings.forward + neftune_method = _neftune_forward_function.__get__(embeddings, embeddings.__class__) + setattr(embeddings, "forward", neftune_method) + logger.info("NEFTune activated with alpha: ", self.neftune_noise_alpha) + elif hasattr(embeddings, "_trl_old_forward"): + embeddings.forward = embeddings._trl_old_forward + del embeddings._trl_old_forward + del embeddings.neftune_noise_alpha + logger.info("NEFTune deactivated") + + _activate_neftune = lambda self, model: self._toggle_neftune(model, activate=True) + _deactivate_neftune = lambda self, model: self._toggle_neftune(model, activate=False) + + +def _neftune_forward_function(self, input: torch.Tensor) -> torch.Tensor: + """ + This code is adapted from the original source code that can be found here: https://github.com/neelsjain/NEFTune + """ + embeddings = torch.nn.functional.embedding( + input, + self.weight, + self.padding_idx, + self.max_norm, + self.norm_type, + self.scale_grad_by_freq, + self.sparse) + + if self.training: + dims = torch.tensor(embeddings.size(1) * embeddings.size(2)) + mag_norm = self.neftune_noise_alpha / torch.sqrt(dims) + embeddings += torch.zeros_like(embeddings).uniform_(-mag_norm, mag_norm) + + return embeddings diff --git a/src/llmtuner/tuner/sft/workflow.py b/src/llmtuner/tuner/sft/workflow.py index 63070965..52af9b76 100644 --- a/src/llmtuner/tuner/sft/workflow.py +++ b/src/llmtuner/tuner/sft/workflow.py @@ -53,6 +53,7 @@ def run_sft( data_collator=data_collator, callbacks=callbacks, compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None, + neftune_noise_alpha=finetuning_args.neftune_noise_alpha, **split_dataset(dataset, data_args, training_args) )