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release v0.5.3

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hiyouga 2024-02-29 00:34:19 +08:00
parent 804c1e7083
commit fa5ab21ebc
10 changed files with 116 additions and 67 deletions
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6
README.md
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@ -42,9 +42,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
- **Scalable resources**: 32-bit full-tuning, 16-bit freeze tuning, 16-bit LoRA tuning, 2/4/8-bit QLoRA with AQLM/AWQ/GPTQ/LLM.int8.
- **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA, 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
- **Advanced algorithms**: DoRA, LongLoRA, LLaMA Pro, LoftQ, agent tuning.
- **Intriguing tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune, rsLoRA.
- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune, rsLoRA.
- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
## Benchmark
@ -140,7 +140,7 @@ Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list
## Supported Training Approaches
| Approach | Full-parameter | Partial-parameter | LoRA | QLoRA |
| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA |
| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |

8
README_zh.md
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@ -41,10 +41,10 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
## 项目特色
- **多种模型**：LLaMA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练、DPO 训练。
- **多种精度**：32 比特全参数训练、16 比特部分参数训练、16比特 LoRA 训练、基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 LoRA 训练。
- **先进算法**: DoRA、LongLoRA、LLaMA Pro、LoftQ、agent tuning。
- **新鲜技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune、rsLoRA。
- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练和 DPO 训练。
- **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
- **先进算法**：DoRA、LongLoRA、LLaMA Pro、LoftQ 和 Agent 微调。
- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
## 性能指标

2
src/llmtuner/__init__.py
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@ -7,5 +7,5 @@ from .train import export_model, run_exp
from .webui import create_ui, create_web_demo
__version__ = "0.5.2"
__version__ = "0.5.3"
__all__ = ["create_app", "ChatModel", "Evaluator", "export_model", "run_exp", "create_ui", "create_web_demo"]

3
src/llmtuner/hparams/parser.py
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@ -3,7 +3,6 @@ import os
import sys
from typing import Any, Dict, Optional, Tuple
import datasets
import torch
import transformers
from transformers import HfArgumentParser, Seq2SeqTrainingArguments
@ -62,7 +61,6 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
def _set_transformers_logging(log_level: Optional[int] = logging.INFO) -> None:
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
@ -243,7 +241,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
str(model_args.compute_dtype),
)
)
logger.info(f"Training/evaluation parameters {training_args}")
transformers.set_seed(training_args.seed)

2
src/llmtuner/model/loader.py
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@ -51,7 +51,7 @@ def load_model_and_tokenizer(
patch_tokenizer(tokenizer)
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
patch_config(config, tokenizer, model_args, finetuning_args, config_kwargs, is_trainable)
patch_config(config, tokenizer, model_args, config_kwargs, is_trainable)
model = None
if is_trainable and model_args.use_unsloth:

18
src/llmtuner/model/patcher.py
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@ -24,7 +24,7 @@ if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedTokenizer
from trl import AutoModelForCausalLMWithValueHead
from ..hparams import FinetuningArguments, ModelArguments
from ..hparams import ModelArguments
logger = get_logger(__name__)
@ -157,7 +157,7 @@ def _configure_quantization(
config_kwargs: Dict[str, Any],
) -> None:
r"""
Priority: GPTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
"""
if getattr(config, "quantization_config", None): # gptq
if is_deepspeed_zero3_enabled():
@ -167,7 +167,15 @@ def _configure_quantization(
quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
if quantization_config.get("quant_method", None) == "gptq" and quantization_config.get("bits", -1) == 4:
quantization_config["use_exllama"] = False # disable exllama
logger.info("Loading {}-bit GPTQ-quantized model.".format(quantization_config.get("bits", -1)))
if quantization_config.get("quant_method", None) == "aqlm":
quantization_config["bits"] = 2
logger.info(
"Loading {}-bit {}-quantized model.".format(
quantization_config.get("bits", "?"), quantization_config.get("quant_method", None)
)
)
elif model_args.export_quantization_bit is not None: # auto-gptq
require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
@ -253,7 +261,6 @@ def patch_config(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
config_kwargs: Dict[str, Any],
is_trainable: bool,
) -> None:
@ -274,9 +281,6 @@ def patch_config(
_configure_quantization(config, tokenizer, model_args, config_kwargs)
if finetuning_args.use_dora:
config_kwargs["device_map"] = {"": get_current_device()}
def patch_model(
model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments", is_trainable: bool

30
src/llmtuner/webui/components/train.py
View File

@ -34,7 +34,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
with gr.Row():
cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
cutoff_len = gr.Slider(value=1024, minimum=4, maximum=16384, step=1)
learning_rate = gr.Textbox(value="5e-5")
num_train_epochs = gr.Textbox(value="3.0")
max_samples = gr.Textbox(value="100000")
@ -52,8 +52,8 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
)
with gr.Row():
batch_size = gr.Slider(value=4, minimum=1, maximum=1024, step=1)
gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=1024, step=1)
batch_size = gr.Slider(value=2, minimum=1, maximum=1024, step=1)
gradient_accumulation_steps = gr.Slider(value=8, minimum=1, maximum=1024, step=1)
lr_scheduler_type = gr.Dropdown(choices=[scheduler.value for scheduler in SchedulerType], value="cosine")
max_grad_norm = gr.Textbox(value="1.0")
val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
@ -122,25 +122,31 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
with gr.Accordion(label="LoRA config", open=False) as lora_tab:
with gr.Row():
lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1)
lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01)
lora_target = gr.Textbox()
additional_target = gr.Textbox()
lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1, scale=1)
lora_alpha = gr.Slider(value=16, minimum=1, maximum=2048, step=0.1, scale=1)
lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
lora_target = gr.Textbox(scale=2)
with gr.Column():
use_rslora = gr.Checkbox()
create_new_adapter = gr.Checkbox()
with gr.Row():
use_rslora = gr.Checkbox(scale=1)
use_dora = gr.Checkbox(scale=1)
create_new_adapter = gr.Checkbox(scale=1)
additional_target = gr.Textbox(scale=2)
input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, use_rslora, create_new_adapter})
input_elems.update(
{lora_rank, lora_alpha, lora_dropout, lora_target, use_rslora, use_dora, create_new_adapter, additional_target}
)
elem_dict.update(
dict(
lora_tab=lora_tab,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
lora_target=lora_target,
additional_target=additional_target,
use_rslora=use_rslora,
use_dora=use_dora,
create_new_adapter=create_new_adapter,
additional_target=additional_target,
)
)

89
src/llmtuner/webui/locales.py
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@ -572,6 +572,20 @@ LOCALES = {
"info": "LoRA 矩阵的秩。",
},
},
"lora_alpha": {
"en": {
"label": "LoRA Alpha",
"info": "Lora scaling coefficient.",
},
"ru": {
"label": "LoRA Alpha",
"info": "Коэффициент масштабирования LoRA.",
},
"zh": {
"label": "LoRA 缩放系数",
"info": "LoRA 缩放系数大小。",
},
},
"lora_dropout": {
"en": {
"label": "LoRA Dropout",
@ -600,6 +614,48 @@ LOCALES = {
"info": "应用 LoRA 的目标模块名称。使用英文逗号分隔多个名称。",
},
},
"use_rslora": {
"en": {
"label": "Use rslora",
"info": "Use the rank stabilization scaling factor for LoRA layer.",
},
"ru": {
"label": "Использовать rslora",
"info": "Использовать коэффициент масштабирования стабилизации ранга для слоя LoRA.",
},
"zh": {
"label": "使用 rslora",
"info": "对 LoRA 层使用秩稳定缩放方法。",
},
},
"use_dora": {
"en": {
"label": "Use DoRA",
"info": "Use weight-decomposed LoRA.",
},
"ru": {
"label": "Используйте DoRA",
"info": "Используйте LoRA с декомпозицией весов.",
},
"zh": {
"label": "使用 DoRA",
"info": "使用权重分解的 LoRA。",
},
},
"create_new_adapter": {
"en": {
"label": "Create new adapter",
"info": "Create a new adapter with randomly initialized weight upon the existing one.",
},
"ru": {
"label": "Создать новый адаптер",
"info": "Создать новый адаптер с случайной инициализацией веса на основе существующего.",
},
"zh": {
"label": "新建适配器",
"info": "在现有的适配器上创建一个随机初始化后的新适配器。",
},
},
"additional_target": {
"en": {
"label": "Additional modules (optional)",
@ -617,34 +673,6 @@ LOCALES = {
"info": "除 LoRA 层以外的可训练模块名称。使用英文逗号分隔多个名称。",
},
},
"use_rslora": {
"en": {
"label": "Use rslora",
"info": "Use the rank stabilization scaling factor for LoRA layer.",
},
"ru": {
"label": "Использовать rslora",
"info": "Использовать коэффициент масштабирования стабилизации ранга для слоя LoRA.",
},
"zh": {
"label": "使用 rslora",
"info": "对 LoRA 层使用秩稳定缩放方法。",
},
},
"create_new_adapter": {
"en": {
"label": "Create new adapter",
"info": "Create a new adapter with randomly initialized weight upon the existing one.",
},
"ru": {
"label": "Создать новый адаптер",
"info": "Создать новый адаптер с случайной инициализацией веса на основе существующего.",
},
"zh": {
"label": "新建适配器",
"info": "在现有的适配器上创建一个随机初始化后的新适配器。",
},
},
"rlhf_tab": {
"en": {
"label": "RLHF configurations",
@ -1055,6 +1083,11 @@ ALERTS = {
"ru": "Неверная схема JSON.",
"zh": "Json 格式错误。",
},
"warn_no_cuda": {
"en": "CUDA environment was not detected.",
"ru": "Среда CUDA не обнаружена.",
"zh": "未检测到 CUDA 环境。",
},
"info_aborting": {
"en": "Aborted, wait for terminating...",
"ru": "Прервано, ожидание завершения...",

14
src/llmtuner/webui/runner.py
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@ -8,6 +8,7 @@ import gradio as gr
import transformers
from gradio.components import Component # cannot use TYPE_CHECKING here
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.utils import is_torch_cuda_available
from ..extras.callbacks import LogCallback
from ..extras.constants import TRAINING_STAGES
@ -64,12 +65,15 @@ class Runner:
if len(dataset) == 0:
return ALERTS["err_no_dataset"][lang]
if self.demo_mode and (not from_preview):
if not from_preview and self.demo_mode:
return ALERTS["err_demo"][lang]
if not from_preview and get_device_count() > 1:
return ALERTS["err_device_count"][lang]
if not from_preview and not is_torch_cuda_available():
gr.Warning(ALERTS["warn_no_cuda"][lang])
self.aborted = False
self.logger_handler.reset()
self.trainer_callback = LogCallback(self)
@ -139,11 +143,13 @@ class Runner:
args["num_layer_trainable"] = int(get("train.num_layer_trainable"))
args["name_module_trainable"] = get("train.name_module_trainable")
elif args["finetuning_type"] == "lora":
args["lora_rank"] = get("train.lora_rank")
args["lora_dropout"] = get("train.lora_dropout")
args["lora_rank"] = int(get("train.lora_rank"))
args["lora_alpha"] = float(get("train.lora_alpha"))
args["lora_dropout"] = float(get("train.lora_dropout"))
args["lora_target"] = get("train.lora_target") or get_module(get("top.model_name"))
args["additional_target"] = get("train.additional_target") or None
args["use_rslora"] = get("train.use_rslora")
args["use_dora"] = get("train.use_dora")
args["additional_target"] = get("train.additional_target") or None
if args["stage"] in ["rm", "ppo", "dpo"]:
args["create_new_adapter"] = args["quantization_bit"] is None
else:

9
src/llmtuner/webui/utils.py
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@ -44,11 +44,14 @@ def can_quantize(finetuning_type: str) -> Dict[str, Any]:
def check_json_schema(text: str, lang: str) -> None:
try:
tools = json.loads(text)
if tools:
assert isinstance(tools, list)
for tool in tools:
assert "name" in tool
except AssertionError:
if "name" not in tool:
raise ValueError("Name not found.")
except ValueError:
gr.Warning(ALERTS["err_tool_name"][lang])
except json.JSONDecodeError:
except Exception:
gr.Warning(ALERTS["err_json_schema"][lang])