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allow non-packing pretraining

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Former-commit-id: 3fee5cc5a3db9ce874ad90f2500ec092d904bd4e
This commit is contained in:
hiyouga
2024-03-09 22:21:46 +08:00
parent c631799f5d
commit 4881f4e631
22 changed files with 64 additions and 67 deletions
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4
src/llmtuner/webui/chatter.py
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@@ -18,9 +18,7 @@ if TYPE_CHECKING:
class WebChatModel(ChatModel):
def __init__(
self, manager: "Manager", demo_mode: Optional[bool] = False, lazy_init: Optional[bool] = True
) -> None:
def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
self.manager = manager
self.demo_mode = demo_mode
self.engine: Optional["BaseEngine"] = None

8
src/llmtuner/webui/common.py
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@@ -104,10 +104,12 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
return {}
def list_dataset(
dataset_dir: Optional[str] = None, training_stage: Optional[str] = list(TRAINING_STAGES.keys())[0]
) -> Dict[str, Any]:
def list_dataset(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Dict[str, Any]:
dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
ranking = TRAINING_STAGES[training_stage] in ["rm", "dpo"]
datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
return gr.update(value=[], choices=datasets)
def autoset_packing(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Dict[str, Any]:
return gr.update(value=(TRAINING_STAGES[training_stage] == "pt"))

4
src/llmtuner/webui/components/chatbot.py
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@@ -1,4 +1,4 @@
from typing import TYPE_CHECKING, Dict, Optional, Tuple
from typing import TYPE_CHECKING, Dict, Tuple
import gradio as gr
@@ -14,7 +14,7 @@ if TYPE_CHECKING:
def create_chat_box(
engine: "Engine", visible: Optional[bool] = False
engine: "Engine", visible: bool = False
) -> Tuple["Block", "Component", "Component", Dict[str, "Component"]]:
with gr.Box(visible=visible) as chat_box:
chatbot = gr.Chatbot()

6
src/llmtuner/webui/components/top.py
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@@ -1,4 +1,4 @@
from typing import TYPE_CHECKING, Dict
from typing import TYPE_CHECKING, Dict, Tuple
import gradio as gr
@@ -12,7 +12,7 @@ if TYPE_CHECKING:
from gradio.components import Component
def create_top() -> Dict[str, "Component"]:
def create_top() -> Tuple["gr.Dropdown", Dict[str, "Component"]]:
available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
with gr.Row():
@@ -44,7 +44,7 @@ def create_top() -> Dict[str, "Component"]:
refresh_btn.click(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False)
return dict(
return lang, dict(
lang=lang,
model_name=model_name,
model_path=model_path,

10
src/llmtuner/webui/components/train.py
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@@ -4,7 +4,7 @@ import gradio as gr
from transformers.trainer_utils import SchedulerType
from ...extras.constants import TRAINING_STAGES
from ..common import DEFAULT_DATA_DIR, list_adapters, list_dataset
from ..common import DEFAULT_DATA_DIR, autoset_packing, list_adapters, list_dataset
from ..components.data import create_preview_box
from ..utils import gen_plot
@@ -78,7 +78,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
with gr.Row():
resize_vocab = gr.Checkbox()
sft_packing = gr.Checkbox()
packing = gr.Checkbox()
upcast_layernorm = gr.Checkbox()
use_llama_pro = gr.Checkbox()
shift_attn = gr.Checkbox()
@@ -91,7 +91,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
neftune_alpha,
optim,
resize_vocab,
sft_packing,
packing,
upcast_layernorm,
use_llama_pro,
shift_attn,
@@ -106,7 +106,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
neftune_alpha=neftune_alpha,
optim=optim,
resize_vocab=resize_vocab,
sft_packing=sft_packing,
packing=packing,
upcast_layernorm=upcast_layernorm,
use_llama_pro=use_llama_pro,
shift_attn=shift_attn,
@@ -166,7 +166,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
[engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
[reward_model],
queue=False,
)
).then(autoset_packing, [training_stage], [packing], queue=False)
input_elems.update({dpo_beta, dpo_ftx, reward_model})
elem_dict.update(dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, dpo_ftx=dpo_ftx, reward_model=reward_model))

4
src/llmtuner/webui/engine.py
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@@ -1,4 +1,4 @@
from typing import Any, Dict, Generator, Optional
from typing import Any, Dict, Generator
import gradio as gr
from gradio.components import Component # cannot use TYPE_CHECKING here
@@ -12,7 +12,7 @@ from .utils import get_time
class Engine:
def __init__(self, demo_mode: Optional[bool] = False, pure_chat: Optional[bool] = False) -> None:
def __init__(self, demo_mode: bool = False, pure_chat: bool = False) -> None:
self.demo_mode = demo_mode
self.pure_chat = pure_chat
self.manager = Manager()

7
src/llmtuner/webui/interface.py
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@@ -1,5 +1,3 @@
from typing import Optional
import gradio as gr
from transformers.utils.versions import require_version
@@ -19,7 +17,7 @@ from .engine import Engine
require_version("gradio>=3.38.0,<4.0.0", 'To fix: pip install "gradio>=3.38.0,<4.0.0"')
def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
def create_ui(demo_mode: bool = False) -> gr.Blocks:
engine = Engine(demo_mode=demo_mode, pure_chat=False)
with gr.Blocks(title="LLaMA Board", css=CSS) as demo:
@@ -31,8 +29,7 @@ def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
)
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
engine.manager.all_elems["top"] = create_top()
lang: "gr.Dropdown" = engine.manager.get_elem_by_name("top.lang")
lang, engine.manager.all_elems["top"] = create_top()
with gr.Tab("Train"):
engine.manager.all_elems["train"] = create_train_tab(engine)

8
src/llmtuner/webui/locales.py
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@@ -480,18 +480,18 @@ LOCALES = {
"info": "更改分词器词表和嵌入层的大小。",
},
},
"sft_packing": {
"packing": {
"en": {
"label": "Pack sequences",
"info": "Pack sequences into samples of fixed length in supervised fine-tuning.",
"info": "Pack sequences into samples of fixed length.",
},
"ru": {
"label": "Упаковка последовательностей",
"info": "Упаковка последовательностей в образцы фиксированной длины при контролируемой тонкой настройке.",
"info": "Упаковка последовательностей в образцы фиксированной длины.",
},
"zh": {
"label": "序列打包",
"info": "在指令监督微调时将序列打包为等长样本。",
"info": "将序列打包为等长样本。",
},
},
"upcast_layernorm": {

6
src/llmtuner/webui/runner.py
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@@ -2,7 +2,7 @@ import logging
import os
import time
from threading import Thread
from typing import TYPE_CHECKING, Any, Dict, Generator, Optional, Tuple
from typing import TYPE_CHECKING, Any, Dict, Generator, Tuple
import gradio as gr
import transformers
@@ -25,7 +25,7 @@ if TYPE_CHECKING:
class Runner:
def __init__(self, manager: "Manager", demo_mode: Optional[bool] = False) -> None:
def __init__(self, manager: "Manager", demo_mode: bool = False) -> None:
self.manager = manager
self.demo_mode = demo_mode
""" Resume """
@@ -136,7 +136,7 @@ class Runner:
neftune_noise_alpha=get("train.neftune_alpha") or None,
optim=get("train.optim"),
resize_vocab=get("train.resize_vocab"),
sft_packing=get("train.sft_packing"),
packing=get("train.packing"),
upcast_layernorm=get("train.upcast_layernorm"),
use_llama_pro=get("train.use_llama_pro"),
shift_attn=get("train.shift_attn"),