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refactor adapter hparam

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Former-commit-id: f82aece9ebd6df83a7a005cc7cbbcec07fa6e14d
This commit is contained in:
hiyouga
2023-12-15 20:53:11 +08:00
parent 27ef5b1aa7
commit f902b0d420
21 changed files with 302 additions and 311 deletions
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14
src/llmtuner/webui/components/train.py
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@@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Dict
from transformers.trainer_utils import SchedulerType
from llmtuner.extras.constants import TRAINING_STAGES
from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR
from llmtuner.webui.common import list_adapters, list_dataset, DEFAULT_DATA_DIR
from llmtuner.webui.components.data import create_preview_box
from llmtuner.webui.utils import gen_plot
@@ -60,7 +60,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
lr_scheduler_type=lr_scheduler_type, max_grad_norm=max_grad_norm, val_size=val_size
))
with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
with gr.Accordion(label="Extra config", open=False) as extra_tab:
with gr.Row():
logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
@@ -73,7 +73,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, train_on_prompt, upcast_layernorm})
elem_dict.update(dict(
advanced_tab=advanced_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
extra_tab=extra_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
neftune_alpha=neftune_alpha, train_on_prompt=train_on_prompt, upcast_layernorm=upcast_layernorm
))
@@ -83,12 +83,12 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
lora_target = gr.Textbox(scale=1)
additional_target = gr.Textbox(scale=1)
resume_lora_training = gr.Checkbox(value=True, scale=1)
create_new_adapter = gr.Checkbox(scale=1)
input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, resume_lora_training})
input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, create_new_adapter})
elem_dict.update(dict(
lora_tab=lora_tab, lora_rank=lora_rank, lora_dropout=lora_dropout, lora_target=lora_target,
additional_target=additional_target, resume_lora_training=resume_lora_training,
additional_target=additional_target, create_new_adapter=create_new_adapter
))
with gr.Accordion(label="RLHF config", open=False) as rlhf_tab:
@@ -98,7 +98,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
refresh_btn = gr.Button(scale=1)
refresh_btn.click(
list_checkpoint,
list_adapters,
[engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
[reward_model],
queue=False