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update webui

This commit is contained in:
hiyouga 2023-08-14 22:45:26 +08:00
parent adb0f186e9
commit 9d0f6214b6
8 changed files with 47 additions and 78 deletions
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36
src/llmtuner/extras/constants.py
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@ -10,7 +10,13 @@ LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp"]
METHODS = ["full", "freeze", "lora"]
STAGES = ["Supervised Finetuning", "Reward Modeling", "PPO", "DPO", "Pretraining"]
STAGES = [
"SFT",
"Reward Modeling",
"PPO",
"DPO",
"Pre-Training"
]
SUPPORTED_MODELS = {
"LLaMA-7B": "huggyllama/llama-7b",
@ -23,6 +29,10 @@ SUPPORTED_MODELS = {
"LLaMA2-7B-Chat": "meta-llama/Llama-2-7b-chat-hf",
"LLaMA2-13B-Chat": "meta-llama/Llama-2-13b-chat-hf",
"LLaMA2-70B-Chat": "meta-llama/Llama-2-70b-chat-hf",
"ChineseLLaMA2-7B": "ziqingyang/chinese-llama-2-7b",
"ChineseLLaMA2-13B": "ziqingyang/chinese-llama-2-13b",
"ChineseLLaMA2-7B-Chat": "ziqingyang/chinese-alpaca-2-7b",
"ChineseLLaMA2-13B-Chat": "ziqingyang/chinese-alpaca-2-13b",
"BLOOM-560M": "bigscience/bloom-560m",
"BLOOM-3B": "bigscience/bloom-3b",
"BLOOM-7B1": "bigscience/bloom-7b1",
@ -41,12 +51,13 @@ SUPPORTED_MODELS = {
"Qwen-7B": "Qwen/Qwen-7B",
"Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
"XVERSE-13B": "xverse/XVERSE-13B",
"ChatGLM2-6B": "THUDM/chatglm2-6b"
"ChatGLM2-6B-Chat": "THUDM/chatglm2-6b"
}
DEFAULT_MODULE = {
"LLaMA": "q_proj,v_proj",
"LLaMA2": "q_proj,v_proj",
"ChineseLLaMA2": "q_proj,v_proj",
"BLOOM": "query_key_value",
"BLOOMZ": "query_key_value",
"Falcon": "query_key_value",
@ -59,28 +70,9 @@ DEFAULT_MODULE = {
DEFAULT_TEMPLATE = {
"LLaMA2": "llama2",
"ChineseLLaMA2": "llama2_zh",
"Baichuan": "baichuan",
"InternLM": "intern",
"Qwen": "chatml",
"ChatGLM2": "chatglm2"
}
# huggingface model name prefix 2 template
DEFAULT_TEMPLATE_WITH_CUSTOM_MODEL = {
"Llama-2": "llama2",
"chinese-alpaca-2": "llama2_zh",
"alpaca-7b-wdiff": "alpaca",
"vicuna": "vicuna",
"BELLE": "belle",
"Chinese-LLaMA-2": "linly",
"BiLLa": "billa",
"Ziya": "ziya",
"aquilachat": "aquila",
"internlm": "intern",
"aquilachat": "aquila",
"internlm": "intern",
"Baichuan":"baichuan",
"starchat":"starchat",
"Qwen":"chatml",
"chatglm2":"chatglm2"
}

4
src/llmtuner/extras/misc.py
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@ -95,7 +95,6 @@ def prepare_model_for_training(
use_gradient_checkpointing: Optional[bool] = True,
layer_norm_names: Optional[List[str]] = LAYERNORM_NAMES
) -> "PreTrainedModel":
for name, param in model.named_parameters():
if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
param.data = param.data.to(torch.float32)
@ -112,9 +111,6 @@ def prepare_model_for_training(
model.config.use_cache = False # turn off when gradient checkpointing is enabled
if finetuning_type != "full" and hasattr(model, output_layer_name):
if hasattr(model, "config") and hasattr(model.config, "pretraining_tp"):
model.config.pretraining_tp = 1 # disable TP for LoRA (https://github.com/huggingface/peft/pull/728)
output_layer: torch.nn.Linear = getattr(model, output_layer_name)
input_dtype = output_layer.weight.dtype

4
src/llmtuner/extras/template.py
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@ -273,8 +273,8 @@ register_template(
r"""
Supports: https://huggingface.co/ziqingyang/chinese-alpaca-2-7b
https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
Supports: https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
https://huggingface.co/ziqingyang/chinese-alpaca-2-7b
"""
register_template(
name="llama2_zh",

20
src/llmtuner/webui/common.py
View File

@ -6,7 +6,7 @@ import gradio as gr
from peft.utils import WEIGHTS_NAME as PEFT_WEIGHTS_NAME
from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
from llmtuner.extras.constants import SUPPORTED_MODELS, DEFAULT_TEMPLATE_WITH_CUSTOM_MODEL, DEFAULT_TEMPLATE
from llmtuner.extras.constants import DEFAULT_TEMPLATE, SUPPORTED_MODELS
DEFAULT_CACHE_DIR = "cache"
@ -48,20 +48,10 @@ def get_model_path(model_name: str) -> str:
return user_config["path_dict"].get(model_name, SUPPORTED_MODELS.get(model_name, ""))
def get_template(
model_name: str,
) -> str:
if model_name == "Custom":
model_name_or_path = get_model_path(model_name)
# get last dir
basename = os.path.basename(model_name_or_path)
# prefix match
for k, v in DEFAULT_TEMPLATE_WITH_CUSTOM_MODEL.items():
if basename.startswith(k):
return v
return "default"
return DEFAULT_TEMPLATE.get(model_name.split("-")[0], "default")
def get_template(model_name: str) -> str:
if model_name.endswith("Chat") and model_name.split("-")[0] in DEFAULT_TEMPLATE:
return DEFAULT_TEMPLATE[model_name.split("-")[0]]
return "default"
def list_checkpoint(model_name: str, finetuning_type: str) -> Dict[str, Any]:

5
src/llmtuner/webui/components/top.py
View File

@ -4,7 +4,7 @@ import gradio as gr
from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
from llmtuner.extras.template import templates
from llmtuner.webui.common import list_checkpoint, get_model_path, save_config, get_template
from llmtuner.webui.common import list_checkpoint, get_model_path, get_template, save_config
from llmtuner.webui.utils import can_quantize
if TYPE_CHECKING:
@ -36,10 +36,11 @@ def create_top() -> Dict[str, "Component"]:
list_checkpoint, [model_name, finetuning_type], [checkpoints]
).then(
get_model_path, [model_name], [model_path]
).then(
get_template, [model_name], [template]
) # do not save config since the below line will save
model_path.change(save_config, [lang, model_name, model_path])
model_path.change(get_template, [model_name], [template])
finetuning_type.change(
list_checkpoint, [model_name, finetuning_type], [checkpoints]

10
src/llmtuner/webui/components/train.py
View File

@ -3,10 +3,10 @@ from transformers.trainer_utils import SchedulerType
import gradio as gr
from llmtuner.extras.constants import STAGES
from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR
from llmtuner.webui.components.data import create_preview_box
from llmtuner.webui.utils import can_preview, get_preview, gen_plot
from llmtuner.extras.constants import STAGES
if TYPE_CHECKING:
from gradio.components import Component
@ -15,9 +15,7 @@ if TYPE_CHECKING:
def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
with gr.Row():
stage = gr.Dropdown(choices=STAGES,
value="Supervised Finetuning", scale=2)
training_stage = gr.Dropdown(choices=STAGES, value=STAGES[0], scale=2)
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
dataset = gr.Dropdown(multiselect=True, scale=4)
data_preview_btn = gr.Button(interactive=False, scale=1)
@ -104,7 +102,7 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
top_elems["quantization_bit"],
top_elems["template"],
top_elems["source_prefix"],
stage,
training_stage,
dataset_dir,
dataset,
max_source_length,
@ -145,7 +143,7 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
)
return dict(
stage=stage,
training_stage=training_stage,
dataset_dir=dataset_dir,
dataset=dataset,
data_preview_btn=data_preview_btn,

30
src/llmtuner/webui/locales.py
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@ -87,6 +87,16 @@ LOCALES = {
"info": "默认使用的系统提示词"
}
},
"training_stage": {
"en": {
"label": "Stage",
"info": "The stage to perform in training."
},
"zh": {
"label": "训练阶段",
"info": "目前采用的训练方式。"
}
},
"dataset_dir": {
"en": {
"label": "Data dir",
@ -343,16 +353,6 @@ LOCALES = {
"label": "RLHF 参数设置"
}
},
"rlhf_method": {
"en": {
"label": "RLHF method",
"info": "The RLHF algorithm to adopt."
},
"zh": {
"label": "RLHF 方法",
"info": "RLHF 阶段使用的算法。"
}
},
"dpo_beta": {
"en": {
"label": "DPO beta",
@ -546,15 +546,7 @@ LOCALES = {
"zh": {
"value": "开始导出"
}
},
"stage": {
"en": {
"label": "train stage"
},
"zh": {
"label": "训练阶段"
}
},
}
}

16
src/llmtuner/webui/runner.py
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@ -8,11 +8,11 @@ from transformers.trainer import TRAINING_ARGS_NAME
from typing import Any, Dict, Generator, List, Tuple
from llmtuner.extras.callbacks import LogCallback
from llmtuner.extras.constants import DEFAULT_MODULE, DEFAULT_TEMPLATE, DEFAULT_TEMPLATE_WITH_CUSTOM_MODEL
from llmtuner.extras.constants import DEFAULT_MODULE
from llmtuner.extras.logging import LoggerHandler
from llmtuner.extras.misc import torch_gc
from llmtuner.tuner import run_exp
from llmtuner.webui.common import get_model_path, get_save_dir, get_template
from llmtuner.webui.common import get_model_path, get_save_dir
from llmtuner.webui.locales import ALERTS
from llmtuner.webui.utils import gen_cmd, get_eval_results, update_process_bar
@ -70,7 +70,7 @@ class Runner:
quantization_bit: str,
template: str,
source_prefix: str,
stage: str,
training_stage: str,
dataset_dir: str,
dataset: List[str],
max_source_length: int,
@ -138,21 +138,21 @@ class Runner:
)
args[compute_type] = True
if stage == "Pretraining":
args["stage"] = "pt"
if stage == "Reward Modeling":
if training_stage == "Reward Modeling":
args["stage"] = "rm"
args["resume_lora_training"] = False
elif stage == "PPO":
elif training_stage == "PPO":
args["stage"] = "ppo"
args["resume_lora_training"] = False
args["reward_model"] = reward_model
args["padding_side"] = "left"
val_size = 0
elif stage == "DPO":
elif training_stage == "DPO":
args["stage"] = "dpo"
args["resume_lora_training"] = False
args["dpo_beta"] = dpo_beta
elif training_stage == "Pre-Training":
args["stage"] = "pt"
if val_size > 1e-6:
args["val_size"] = val_size