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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
from ..common import get_save_dir
from ..locales import ALERTS
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
GPTQ_BITS = ["8", "4", "3", "2"]
def can_quantize(checkpoint_path: Union[str, List[str]]) -> "gr.Dropdown":
if isinstance(checkpoint_path, list) and len(checkpoint_path) != 0:
return gr.Dropdown(value="none", interactive=False)
else:
return gr.Dropdown(interactive=True)
def save_model(
lang: str,
model_name: str,
model_path: str,
finetuning_type: str,
checkpoint_path: Union[str, List[str]],
template: str,
visual_inputs: bool,
export_size: int,
export_quantization_bit: int,
export_quantization_dataset: str,
export_device: str,
export_legacy_format: bool,
export_dir: str,
export_hub_model_id: str,
) -> Generator[str, None, None]:
error = ""
if not model_name:
error = ALERTS["err_no_model"][lang]
elif not model_path:
error = ALERTS["err_no_path"][lang]
elif not export_dir:
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 checkpoint_path:
error = ALERTS["err_no_adapter"][lang]
elif export_quantization_bit in GPTQ_BITS and isinstance(checkpoint_path, list):
error = ALERTS["err_gptq_lora"][lang]
if error:
gr.Warning(error)
yield error
return
args = dict(
model_name_or_path=model_path,
finetuning_type=finetuning_type,
template=template,
visual_inputs=visual_inputs,
export_dir=export_dir,
export_hub_model_id=export_hub_model_id or None,
export_size=export_size,
export_quantization_bit=int(export_quantization_bit) if export_quantization_bit in GPTQ_BITS else None,
export_quantization_dataset=export_quantization_dataset,
export_device=export_device,
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()
yield ALERTS["info_exported"][lang]
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"] + GPTQ_BITS, value="none")
export_quantization_dataset = gr.Textbox(value="data/c4_demo.json")
export_device = gr.Radio(choices=["cpu", "auto"], value="cpu")
export_legacy_format = gr.Checkbox()
with gr.Row():
export_dir = gr.Textbox()
export_hub_model_id = gr.Textbox()
checkpoint_path: gr.Dropdown = engine.manager.get_elem_by_id("top.checkpoint_path")
checkpoint_path.change(can_quantize, [checkpoint_path], [export_quantization_bit], queue=False)
export_btn = gr.Button()
info_box = gr.Textbox(show_label=False, interactive=False)
export_btn.click(
save_model,
[
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.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,
export_quantization_bit,
export_quantization_dataset,
export_device,
export_legacy_format,
export_dir,
export_hub_model_id,
],
[info_box],
)
return dict(
export_size=export_size,
export_quantization_bit=export_quantization_bit,
export_quantization_dataset=export_quantization_dataset,
export_device=export_device,
export_legacy_format=export_legacy_format,
export_dir=export_dir,
export_hub_model_id=export_hub_model_id,
export_btn=export_btn,
info_box=info_box,
)
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