diff --git a/README.md b/README.md index be43a481..a93a82f0 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,8 @@ ## Changelog +[23/08/18] Now we support **resuming training**, upgrade `transformers` to `4.31.0` to enjoy this feature. + [23/08/12] Now we support **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings. [23/08/11] Now we support **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models (experimental feature). @@ -158,6 +160,8 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl CUDA_VISIBLE_DEVICES=0 python src/train_web.py ``` +We strongly recommend using the all-in-one Web UI for newcomers since it can also generate training scripts **automatically**. + Currently the web UI only supports training on **a single GPU**. ### Pre-Training diff --git a/README_zh.md b/README_zh.md index 2a84e697..fe96c55a 100644 --- a/README_zh.md +++ b/README_zh.md @@ -12,6 +12,8 @@ ## 更新日志 +[23/08/18] 现在我们支持了**训练状态恢复**，请将 `transformers` 升级至 `4.31.0` 以启用此功能。 + [23/08/12] 现在我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请尝试使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。 [23/08/11] 现在我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。详情请参阅[此示例](#dpo-训练)（实验性功能）。 @@ -24,7 +26,7 @@ [23/07/19] 现在我们支持了 **LLaMA-2** 模型的训练。请尝试使用 `--model_name_or_path meta-llama/Llama-2-7b-hf` 参数。使用 LLaMA-2-chat 模型时请添加 `--template llama2` 参数。 -[23/07/18] 我们开发了支持训练和测试的**一体化浏览器界面**。请尝试使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。 +[23/07/18] 我们开发了支持训练和测试的**浏览器一体化界面**。请尝试使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。 [23/07/11] 现在我们支持了 **Baichuan-13B** 模型的训练。请尝试使用 `--model_name_or_path baichuan-inc/Baichuan-13B-Base` 和 `--lora_target W_pack` 参数。使用 Baichuan-13B-Chat 模型时请添加 `--template baichuan` 参数。 @@ -152,12 +154,14 @@ pip install -r requirements.txt pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl ``` -### 浏览器一键微调/测试 +### 浏览器一体化界面 ```bash CUDA_VISIBLE_DEVICES=0 python src/train_web.py ``` +我们极力推荐新手使用浏览器一体化界面，因为它还可以**自动**生成运行所需的命令行脚本。 + 目前网页 UI 仅支持**单卡训练**。 ### 预训练 @@ -451,6 +455,8 @@ python src/export_model.py \ - [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) - [InternLM](https://github.com/InternLM/InternLM#open-source-license) - [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE) +- [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) +- [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B/blob/main/MODEL_LICENSE) ## 引用 diff --git a/src/llmtuner/tuner/core/loader.py b/src/llmtuner/tuner/core/loader.py index 4bf767a6..1f6d39b1 100644 --- a/src/llmtuner/tuner/core/loader.py +++ b/src/llmtuner/tuner/core/loader.py @@ -121,6 +121,9 @@ def load_model_and_tokenizer( # Quantization configurations (using bitsandbytes library). is_mergeable = True if model_args.quantization_bit is not None: + if is_deepspeed_zero3_enabled(): + raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.") + if model_args.quantization_bit == 8: require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0") config_kwargs["load_in_8bit"] = True @@ -144,7 +147,7 @@ def load_model_and_tokenizer( model = AutoModelForCausalLM.from_pretrained( model_to_load, config=config, - torch_dtype=torch.bfloat16 if model_args.compute_dtype == torch.bfloat16 else torch.float16, + torch_dtype=model_args.compute_dtype, low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()), **config_kwargs ) diff --git a/src/llmtuner/tuner/core/parser.py b/src/llmtuner/tuner/core/parser.py index e039513d..e80deebc 100644 --- a/src/llmtuner/tuner/core/parser.py +++ b/src/llmtuner/tuner/core/parser.py @@ -5,6 +5,7 @@ import datasets import transformers from typing import Any, Dict, Optional, Tuple from transformers import HfArgumentParser, Seq2SeqTrainingArguments +from transformers.trainer_utils import get_last_checkpoint from llmtuner.extras.logging import get_logger from llmtuner.hparams import ( @@ -97,30 +98,33 @@ def get_train_args( if general_args.stage != "sft" and training_args.predict_with_generate: raise ValueError("`predict_with_generate` cannot be set as True except SFT.") - if training_args.do_train and training_args.predict_with_generate: - raise ValueError("`predict_with_generate` cannot be set as True while training.") - if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate: raise ValueError("Please enable `predict_with_generate` to save model predictions.") if general_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora": - raise ValueError("RM and PPO training can only be performed with the LoRA method.") + raise ValueError("RM and PPO stages can only be performed with the LoRA method.") + + if general_args.stage in ["rm", "ppo"] and training_args.resume_from_checkpoint is not None: + raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.") if general_args.stage in ["ppo", "dpo"] and not training_args.do_train: - raise ValueError("PPO and DPO stage can only be performed at training.") + raise ValueError("PPO and DPO stages can only be performed at training.") if general_args.stage == "ppo" and model_args.reward_model is None: raise ValueError("Reward model is necessary for PPO training.") - if training_args.max_steps == -1 and data_args.streaming: - raise ValueError("Please specify `max_steps` in streaming mode.") - if general_args.stage == "ppo" and data_args.streaming: raise ValueError("Streaming mode does not suppport PPO training currently.") + if training_args.max_steps == -1 and data_args.streaming: + raise ValueError("Please specify `max_steps` in streaming mode.") + if data_args.val_size > 1e-6 and data_args.val_size < 1 and data_args.streaming: raise ValueError("Streaming mode should have an integer val size.") + if training_args.do_train and training_args.predict_with_generate: + raise ValueError("`predict_with_generate` cannot be set as True while training.") + if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora": raise ValueError("Quantization is only compatible with the LoRA method.") @@ -134,9 +138,15 @@ def get_train_args( if model_args.quantization_bit is not None and (not training_args.do_train): logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.") - if training_args.do_train and (not training_args.fp16): - logger.warning("We recommend enable fp16 mixed precision training.") + if training_args.do_train and (not training_args.fp16) and (not training_args.bf16): + logger.warning("We recommend enable mixed precision training.") + # postprocess data_args + if data_args.max_samples is not None and data_args.streaming: + logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.") + data_args.max_samples = None + + # postprocess training_args if ( training_args.local_rank != -1 and training_args.ddp_find_unused_parameters is None @@ -145,12 +155,26 @@ def get_train_args( logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.") training_args.ddp_find_unused_parameters = False - if data_args.max_samples is not None and data_args.streaming: - logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.") - data_args.max_samples = None + if training_args.optim == "adamw_hf": + training_args.optim = "adamw_torch" # suppress warning - training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning + if ( + training_args.resume_from_checkpoint is None + and training_args.do_train + and os.path.isdir(training_args.output_dir) + and not training_args.overwrite_output_dir + ): + last_checkpoint = get_last_checkpoint(training_args.output_dir) + if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: + raise ValueError("Output directory already exists and is not empty. Use `overwrite_output_dir`.") + if last_checkpoint is not None: + training_args.resume_from_checkpoint = last_checkpoint + logger.info( + "Resuming from checkpoint. Change `output_dir` or use `overwrite_output_dir` to avoid." + ) + + # postprocess model_args if training_args.bf16: if not torch.cuda.is_bf16_supported(): raise ValueError("Current device does not support bf16 training.") diff --git a/src/llmtuner/tuner/dpo/workflow.py b/src/llmtuner/tuner/dpo/workflow.py index 350d64a7..af184ce5 100644 --- a/src/llmtuner/tuner/dpo/workflow.py +++ b/src/llmtuner/tuner/dpo/workflow.py @@ -50,7 +50,7 @@ def run_dpo( # Training if training_args.do_train: - train_result = trainer.train() + train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() diff --git a/src/llmtuner/tuner/pt/workflow.py b/src/llmtuner/tuner/pt/workflow.py index 865ec218..9908dece 100644 --- a/src/llmtuner/tuner/pt/workflow.py +++ b/src/llmtuner/tuner/pt/workflow.py @@ -39,7 +39,7 @@ def run_pt( # Training if training_args.do_train: - train_result = trainer.train() + train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() diff --git a/src/llmtuner/tuner/sft/workflow.py b/src/llmtuner/tuner/sft/workflow.py index ebb16edd..aeda0369 100644 --- a/src/llmtuner/tuner/sft/workflow.py +++ b/src/llmtuner/tuner/sft/workflow.py @@ -1,5 +1,4 @@ # Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py -import os from typing import TYPE_CHECKING, Optional, List from transformers import DataCollatorForSeq2Seq @@ -11,14 +10,11 @@ from llmtuner.extras.ploting import plot_loss from llmtuner.tuner.core import load_model_and_tokenizer from llmtuner.tuner.sft.metric import ComputeMetrics from llmtuner.tuner.sft.trainer import Seq2SeqPeftTrainer -from transformers.trainer_utils import get_last_checkpoint -from llmtuner.extras.logging import reset_logging, get_logger if TYPE_CHECKING: from transformers import Seq2SeqTrainingArguments, TrainerCallback from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments -logger = get_logger(__name__) def run_sft( model_args: "ModelArguments", @@ -62,12 +58,7 @@ def run_sft( # Training if training_args.do_train: - checkpoint = None - if training_args.resume_from_checkpoint is not None: - checkpoint = training_args.resume_from_checkpoint - elif last_checkpoint is not None: - checkpoint = last_checkpoint - train_result = trainer.train(resume_from_checkpoint=checkpoint) + train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state()