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update examples

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hiyouga 2024-04-15 22:14:34 +08:00
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47
examples/README.md
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@ -3,41 +3,42 @@ We provide diverse examples about fine-tuning LLMs.
```
examples/
├── lora_single_gpu/
│ ├── pretrain.sh: Do pre-training
│ ├── sft.sh: Do supervised fine-tuning
│ ├── reward.sh: Do reward modeling
│ ├── ppo.sh: Do PPO training
│ ├── dpo.sh: Do DPO training
│ ├── orpo.sh: Do ORPO training
│ ├── pretrain.sh: Do pre-training using LoRA
│ ├── sft.sh: Do supervised fine-tuning using LoRA
│ ├── reward.sh: Do reward modeling using LoRA
│ ├── ppo.sh: Do PPO training using LoRA
│ ├── dpo.sh: Do DPO training using LoRA
│ ├── orpo.sh: Do ORPO training using LoRA
│ ├── prepare.sh: Save tokenized dataset
│ └── predict.sh: Do batch predict
│ └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after LoRA tuning
├── qlora_single_gpu/
│ ├── bitsandbytes.sh: Fine-tune 4/8-bit BNB models
│ ├── gptq.sh: Fine-tune 4/8-bit GPTQ models
│ ├── awq.sh: Fine-tune 4-bit AWQ models
│ └── aqlm.sh: Fine-tune 2-bit AQLM models
│ ├── bitsandbytes.sh: Fine-tune 4/8-bit BNB models using QLoRA
│ ├── gptq.sh: Fine-tune 4/8-bit GPTQ models using QLoRA
│ ├── awq.sh: Fine-tune 4-bit AWQ models using QLoRA
│ └── aqlm.sh: Fine-tune 2-bit AQLM models using QLoRA
├── lora_multi_gpu/
│ ├── single_node.sh: Fine-tune model with Accelerate on single node
│ └── multi_node.sh: Fine-tune model with Accelerate on multiple nodes
│ ├── single_node.sh: Fine-tune model with Accelerate on single node using LoRA
│ └── multi_node.sh: Fine-tune model with Accelerate on multiple nodes using LoRA
├── full_multi_gpu/
│ ├── single_node.sh: Fine-tune model with DeepSpeed on single node
│ └── multi_node.sh: Fine-tune model with DeepSpeed on multiple nodes
│ ├── single_node.sh: Full fine-tune model with DeepSpeed on single node
│ ├── multi_node.sh: Full fine-tune model with DeepSpeed on multiple nodes
│ └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after full tuning
├── merge_lora/
│ ├── merge.sh: Merge LoRA weights into the pre-trained models
│ └── quantize.sh: Quantize fine-tuned model with AutoGPTQ
│ └── quantize.sh: Quantize the fine-tuned model with AutoGPTQ
├── inference/
│ ├── cli_demo.sh: Launch a command line interface
│ ├── api_demo.sh: Launch an OpenAI-style API
│ ├── web_demo.sh: Launch a web interface
│ └── evaluate.sh: Evaluate model on the MMLU benchmark
│ ├── cli_demo.sh: Launch a command line interface with LoRA adapters
│ ├── api_demo.sh: Launch an OpenAI-style API with LoRA adapters
│ ├── web_demo.sh: Launch a web interface with LoRA adapters
│ └── evaluate.sh: Evaluate model on the MMLU/CMMLU/C-Eval benchmarks with LoRA adapters
└── extras/
├── galore/
│ └── sft.sh: Fine-tune model with GaLore
├── loraplus/
│ └── sft.sh: Fine-tune model with LoRA+
│ └── sft.sh: Fine-tune model using LoRA+
├── llama_pro/
│ ├── expand.sh: Expand layers in the model
│ └── sft.sh: Fine-tune expanded model
│ └── sft.sh: Fine-tune the expanded model
└── fsdp_qlora/
└── sft.sh: Fine-tune quantized model with FSDP
└── sft.sh: Fine-tune quantized model with FSDP+QLoRA
```

46
examples/README_zh.md
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@ -1,36 +1,36 @@
我们提供了多样化的示例脚本。
我们提供了多样化的大模型微调示例脚本。
```
examples/
├── lora_single_gpu/
│ ├── pretrain.sh: 进行预训练
│ ├── sft.sh: 进行指令监督微调
│ ├── reward.sh: 进行奖励模型训练
│ ├── ppo.sh: 进行 PPO 训练
│ ├── dpo.sh: 进行 DPO 训练
│ ├── orpo.sh: 进行 ORPO 训练
│ ├── pretrain.sh: 基于 LoRA 进行预训练
│ ├── sft.sh: 基于 LoRA 进行指令监督微调
│ ├── reward.sh: 基于 LoRA 进行奖励模型训练
│ ├── ppo.sh: 基于 LoRA 进行 PPO 训练
│ ├── dpo.sh: 基于 LoRA 进行 DPO 训练
│ ├── orpo.sh: 基于 LoRA 进行 ORPO 训练
│ ├── prepare.sh: 保存预处理后的数据集
│ └── predict.sh: 进行批量预测
│ └── predict.sh: 基于 LoRA 进行批量预测并计算 BLEU 和 ROUGE 分数
├── qlora_single_gpu/
│ ├── bitsandbytes.sh: 微调 4/8 比特 BNB 模型
│ ├── gptq.sh: 微调 4/8 比特 GPTQ 模型
│ ├── awq.sh: 微调 4 比特 AWQ 模型
│ └── aqlm.sh: 微调 2 比特 AQLM 模型
│ ├── bitsandbytes.sh: 基于 QLoRA 微调 4/8 比特 BNB 模型
│ ├── gptq.sh: 基于 QLoRA 微调 4/8 比特 GPTQ 模型
│ ├── awq.sh: 基于 QLoRA 微调 4 比特 AWQ 模型
│ └── aqlm.sh: 基于 QLoRA 微调 2 比特 AQLM 模型
├── lora_multi_gpu/
│ ├── single_node.sh: 使用 Accelerate 进行单节点训练
│ └── multi_node.sh: 使用 Accelerate 进行多节点训练
│ ├── single_node.sh: 使用 Accelerate 进行单节点 LoRA 训练
│ └── multi_node.sh: 使用 Accelerate 进行多节点 LoRA 训练
├── full_multi_gpu/
│ ├── single_node.sh: 使用 DeepSpeed 进行单节点训练
│ └── multi_node.sh: 使用 DeepSpeed 进行多节点训练
| └── predict.sh: 使用单卡做全参批量预测
│ ├── single_node.sh: 使用 DeepSpeed 进行单节点全量训练
│ ├── multi_node.sh: 使用 DeepSpeed 进行多节点全量训练
│ └── predict.sh: 基于全量训练进行批量预测并计算 BLEU 和 ROUGE 分数
├── merge_lora/
│ ├── merge.sh: 将 LoRA 权重合并到预训练模型中
│ └── quantize.sh: 使用 AutoGPTQ 量化模型
│ └── quantize.sh: 使用 AutoGPTQ 量化微调后的模型
├── inference/
│ ├── cli_demo.sh: 启动命令行推理接口
│ ├── api_demo.sh: 启动 OpenAI 风格 API
│ ├── web_demo.sh: 启动浏览器推理接口
│ └── evaluate.sh: 在 MMLU 数据集上评测模型
│ ├── cli_demo.sh: 启动 LoRA 模型的命令行推理接口
│ ├── api_demo.sh: 启动 LoRA 模型的 OpenAI 风格 API
│ ├── web_demo.sh: 启动 LoRA 模型的浏览器推理接口
│ └── evaluate.sh: 在 MMLU/CMMLU/C-Eval 数据集上评测 LoRA 模型
└── extras/
├── galore/
│ └── sft.sh: 使用 GaLore 训练模型
@ -40,5 +40,5 @@ examples/
│ ├── expand.sh: 扩展模型中的层
│ └── sft.sh: 训练扩展后的模型
└── fsdp_qlora/
└── sft.sh: 使用 FSDP 微调量化模型
└── sft.sh: 使用 FSDP+QLoRA 微调量化模型
```

4
examples/extras/loraplus/sft.sh
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@ -9,6 +9,7 @@ CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--loraplus_lr_ratio 16.0 \
--output_dir ../../saves/LLaMA2-7B/loraplus/sft \
--overwrite_cache \
--overwrite_output_dir \
@ -29,5 +30,4 @@ CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16 \
--loraplus_lr_ratio 16.0
--fp16

2
examples/full_multi_gpu/predict.sh
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@ -3,7 +3,7 @@
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_predict \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--model_name_or_path ../../saves/LLaMA2-7B/full/sft \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \

2
examples/merge_lora/merge.sh
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@ -1,7 +1,7 @@
#!/bin/bash
# DO NOT use quantized model or quantization_bit when merging lora weights
CUDA_VISIBLE_DEVICES= python ../../src/export_model.py \
CUDA_VISIBLE_DEVICES=0 python ../../src/export_model.py \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--template default \

3
src/llmtuner/chat/vllm_engine.py
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@ -1,8 +1,6 @@
import uuid
from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
from transformers.utils.versions import require_version
from ..data import get_template_and_fix_tokenizer
from ..extras.misc import get_device_count
from ..extras.packages import is_vllm_available
@ -25,7 +23,6 @@ class VllmEngine(BaseEngine):
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
) -> None:
require_version("vllm>=0.3.3", "To fix: pip install vllm>=0.3.3")
self.can_generate = finetuning_args.stage == "sft"
engine_args = AsyncEngineArgs(
model=model_args.model_name_or_path,

4
src/llmtuner/extras/packages.py
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@ -49,10 +49,6 @@ def is_starlette_available():
return _is_package_available("sse_starlette")
def is_unsloth_available():
return _is_package_available("unsloth")
def is_uvicorn_available():
return _is_package_available("uvicorn")

28
src/llmtuner/hparams/parser.py
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@ -8,10 +8,10 @@ import transformers
from transformers import HfArgumentParser, Seq2SeqTrainingArguments
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import is_torch_bf16_gpu_available
from transformers.utils.versions import require_version
from ..extras.logging import get_logger
from ..extras.misc import check_dependencies, get_current_device
from ..extras.packages import is_unsloth_available
from .data_args import DataArguments
from .evaluation_args import EvaluationArguments
from .finetuning_args import FinetuningArguments
@ -74,6 +74,26 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
def _check_extra_dependencies(
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
training_args: Optional["Seq2SeqTrainingArguments"] = None,
) -> None:
if model_args.use_unsloth:
require_version("unsloth", "Please install unsloth: https://github.com/unslothai/unsloth")
if model_args.infer_backend == "vllm":
require_version("vllm>=0.3.3", "To fix: pip install vllm>=0.3.3")
if finetuning_args.use_galore:
require_version("galore_torch", "To fix: pip install galore_torch")
if training_args is not None and training_args.predict_with_generate:
require_version("jieba", "To fix: pip install jieba")
require_version("nltk", "To fix: pip install nltk")
require_version("rouge_chinese", "To fix: pip install rouge-chinese")
def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
parser = HfArgumentParser(_TRAIN_ARGS)
return _parse_args(parser, args)
@ -131,9 +151,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if training_args.do_train and training_args.predict_with_generate:
raise ValueError("`predict_with_generate` cannot be set as True while training.")
if training_args.do_train and model_args.use_unsloth and not is_unsloth_available():
raise ValueError("Unsloth was not installed: https://github.com/unslothai/unsloth")
if finetuning_args.use_dora and model_args.use_unsloth:
raise ValueError("Unsloth does not support DoRA.")
@ -158,6 +175,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("vLLM backend is only available for API, CLI and Web.")
_verify_model_args(model_args, finetuning_args)
_check_extra_dependencies(model_args, finetuning_args, training_args)
if (
training_args.do_train
@ -277,6 +295,7 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
raise ValueError("vLLM engine does not support RoPE scaling.")
_verify_model_args(model_args, finetuning_args)
_check_extra_dependencies(model_args, finetuning_args)
if model_args.export_dir is not None:
model_args.device_map = {"": torch.device(model_args.export_device)}
@ -298,6 +317,7 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
raise ValueError("vLLM backend is only available for API, CLI and Web.")
_verify_model_args(model_args, finetuning_args)
_check_extra_dependencies(model_args, finetuning_args)
model_args.device_map = "auto"

4
src/llmtuner/model/loader.py
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@ -85,7 +85,9 @@ def load_model(
logger.warning("Unsloth does not support loading adapters.")
if model is None:
model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, config=config, **init_kwargs)
init_kwargs["config"] = config
init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path
model: "PreTrainedModel" = AutoModelForCausalLM.from_pretrained(**init_kwargs)
patch_model(model, tokenizer, model_args, is_trainable)
register_autoclass(config, model, tokenizer)

5
src/llmtuner/train/sft/metric.py
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@ -2,7 +2,6 @@ from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
import numpy as np
from transformers.utils.versions import require_version
from ...extras.constants import IGNORE_INDEX
from ...extras.packages import is_jieba_available, is_nltk_available, is_rouge_available
@ -33,10 +32,6 @@ class ComputeMetrics:
r"""
Uses the model predictions to compute metrics.
"""
require_version("jieba", "To fix: pip install jieba")
require_version("nltk", "To fix: pip install nltk")
require_version("rouge_chinese", "To fix: pip install rouge-chinese")
preds, labels = eval_preds
score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}

3
src/llmtuner/train/utils.py
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@ -5,7 +5,6 @@ from transformers import Trainer
from transformers.optimization import get_scheduler
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.trainer_pt_utils import get_parameter_names
from transformers.utils.versions import require_version
from ..extras.logging import get_logger
from ..extras.packages import is_galore_available
@ -168,8 +167,6 @@ def _create_galore_optimizer(
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
) -> "torch.optim.Optimizer":
require_version("galore_torch", "To fix: pip install galore_torch")
if len(finetuning_args.galore_target) == 1 and finetuning_args.galore_target[0] == "all":
galore_targets = find_all_linear_modules(model)
else: