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better llamaboard

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* easily resume from checkpoint
* support full and freeze checkpoints
* faster ui


Former-commit-id: 84cfb2452cc86b037ccddee6e833f8eb7c129fa4
This commit is contained in:
hiyouga
2024-05-29 23:55:38 +08:00
parent f90c4ca672
commit 87aa332583
14 changed files with 303 additions and 193 deletions
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29
src/llamafactory/webui/components/export.py
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@@ -1,5 +1,6 @@
from typing import TYPE_CHECKING, Dict, Generator, List
from typing import TYPE_CHECKING, Dict, Generator, List, Union
from ...extras.constants import PEFT_METHODS
from ...extras.misc import torch_gc
from ...extras.packages import is_gradio_available
from ...train.tuner import export_model
@@ -24,8 +25,8 @@ def save_model(
lang: str,
model_name: str,
model_path: str,
adapter_path: List[str],
finetuning_type: str,
checkpoint_path: Union[str, List[str]],
template: str,
visual_inputs: bool,
export_size: int,
@@ -45,9 +46,9 @@ def save_model(
error = ALERTS["err_no_export_dir"][lang]
elif export_quantization_bit in GPTQ_BITS and not export_quantization_dataset:
error = ALERTS["err_no_dataset"][lang]
elif export_quantization_bit not in GPTQ_BITS and not adapter_path:
elif export_quantization_bit not in GPTQ_BITS and not checkpoint_path:
error = ALERTS["err_no_adapter"][lang]
elif export_quantization_bit in GPTQ_BITS and adapter_path:
elif export_quantization_bit in GPTQ_BITS and isinstance(checkpoint_path, list):
error = ALERTS["err_gptq_lora"][lang]
if error:
@@ -55,16 +56,8 @@ def save_model(
yield error
return
if adapter_path:
adapter_name_or_path = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in adapter_path]
)
else:
adapter_name_or_path = None
args = dict(
model_name_or_path=model_path,
adapter_name_or_path=adapter_name_or_path,
finetuning_type=finetuning_type,
template=template,
visual_inputs=visual_inputs,
@@ -77,6 +70,14 @@ def save_model(
export_legacy_format=export_legacy_format,
)
if checkpoint_path:
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in checkpoint_path]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, checkpoint_path)
yield ALERTS["info_exporting"][lang]
export_model(args)
torch_gc()
@@ -86,7 +87,7 @@ def save_model(
def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
with gr.Row():
export_size = gr.Slider(minimum=1, maximum=100, value=1, step=1)
export_quantization_bit = gr.Dropdown(choices=["none", "8", "4", "3", "2"], value="none")
export_quantization_bit = gr.Dropdown(choices=["none"] + GPTQ_BITS, value="none")
export_quantization_dataset = gr.Textbox(value="data/c4_demo.json")
export_device = gr.Radio(choices=["cpu", "cuda"], value="cpu")
export_legacy_format = gr.Checkbox()
@@ -104,8 +105,8 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
engine.manager.get_elem_by_id("top.lang"),
engine.manager.get_elem_by_id("top.model_name"),
engine.manager.get_elem_by_id("top.model_path"),
engine.manager.get_elem_by_id("top.adapter_path"),
engine.manager.get_elem_by_id("top.finetuning_type"),
engine.manager.get_elem_by_id("top.checkpoint_path"),
engine.manager.get_elem_by_id("top.template"),
engine.manager.get_elem_by_id("top.visual_inputs"),
export_size,