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update webui

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Former-commit-id: 9e0f7c362d40b78d57e77d52eaa96e678cebadcd
This commit is contained in:
hiyouga
2024-02-19 16:49:58 +08:00
parent 7bd1991513
commit 5ccf8fcd6b
6 changed files with 93 additions and 18 deletions
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5
src/llmtuner/webui/components/export.py
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@@ -26,6 +26,7 @@ def save_model(
max_shard_size: int,
export_quantization_bit: int,
export_quantization_dataset: str,
export_legacy_format: bool,
export_dir: str,
) -> Generator[str, None, None]:
error = ""
@@ -61,6 +62,7 @@ def save_model(
export_size=max_shard_size,
export_quantization_bit=int(export_quantization_bit) if export_quantization_bit in GPTQ_BITS else None,
export_quantization_dataset=export_quantization_dataset,
export_legacy_format=export_legacy_format,
)
yield ALERTS["info_exporting"][lang]
@@ -73,6 +75,7 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
max_shard_size = gr.Slider(value=1, minimum=1, maximum=100)
export_quantization_bit = gr.Dropdown(choices=["none", "8", "4", "3", "2"], value="none")
export_quantization_dataset = gr.Textbox(value="data/c4_demo.json")
export_legacy_format = gr.Checkbox()
export_dir = gr.Textbox()
export_btn = gr.Button()
@@ -90,6 +93,7 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
max_shard_size,
export_quantization_bit,
export_quantization_dataset,
export_legacy_format,
export_dir,
],
[info_box],
@@ -99,6 +103,7 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
max_shard_size=max_shard_size,
export_quantization_bit=export_quantization_bit,
export_quantization_dataset=export_quantization_dataset,
export_legacy_format=export_legacy_format,
export_dir=export_dir,
export_btn=export_btn,
info_box=info_box,

2
src/llmtuner/webui/components/top.py
View File

@@ -30,7 +30,7 @@ def create_top() -> Dict[str, "Component"]:
quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none")
template = gr.Dropdown(choices=list(templates.keys()), value="default")
rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
booster = gr.Radio(choices=["none", "flash_attn", "unsloth"], value="none")
booster = gr.Radio(choices=["none", "flashattn", "unsloth"], value="none")
model_name.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
get_model_path, [model_name], [model_path], queue=False

34
src/llmtuner/webui/components/train.py
View File

@@ -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=512, step=1)
gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=512, step=1)
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)
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)
@@ -76,11 +76,13 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
neftune_alpha = gr.Slider(value=0, minimum=0, maximum=10, step=0.1)
with gr.Column():
sft_packing = gr.Checkbox(value=False)
upcast_layernorm = gr.Checkbox(value=False)
with gr.Row():
resize_vocab = gr.Checkbox()
sft_packing = gr.Checkbox()
upcast_layernorm = gr.Checkbox()
use_llama_pro = gr.Checkbox()
input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, sft_packing, upcast_layernorm})
input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, resize_vocab, sft_packing, upcast_layernorm, use_llama_pro})
elem_dict.update(
dict(
extra_tab=extra_tab,
@@ -88,20 +90,25 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
save_steps=save_steps,
warmup_steps=warmup_steps,
neftune_alpha=neftune_alpha,
resize_vocab=resize_vocab,
sft_packing=sft_packing,
upcast_layernorm=upcast_layernorm,
use_llama_pro=use_llama_pro,
)
)
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, scale=1)
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)
create_new_adapter = gr.Checkbox(scale=1)
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()
input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, create_new_adapter})
with gr.Column():
use_rslora = gr.Checkbox()
create_new_adapter = gr.Checkbox()
input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, use_rslora, create_new_adapter})
elem_dict.update(
dict(
lora_tab=lora_tab,
@@ -109,6 +116,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
lora_dropout=lora_dropout,
lora_target=lora_target,
additional_target=additional_target,
use_rslora=use_rslora,
create_new_adapter=create_new_adapter,
)
)
@@ -143,7 +151,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
output_dir = gr.Textbox()
with gr.Row():
resume_btn = gr.Checkbox(visible=False, interactive=False, value=False)
resume_btn = gr.Checkbox(visible=False, interactive=False)
process_bar = gr.Slider(visible=False, interactive=False)
with gr.Box():