from typing import TYPE_CHECKING, Dict, Generator, List from ...extras.misc import torch_gc from ...extras.packages import is_gradio_available from ...train 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 save_model( lang: str, model_name: str, model_path: str, adapter_path: List[str], finetuning_type: 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 adapter_path: error = ALERTS["err_no_adapter"][lang] elif export_quantization_bit in GPTQ_BITS and adapter_path: error = ALERTS["err_gptq_lora"][lang] if error: gr.Warning(error) 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, 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, ) 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(value=1, minimum=1, maximum=100, step=1) export_quantization_bit = gr.Dropdown(choices=["none", "8", "4", "3", "2"], 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() with gr.Row(): export_dir = gr.Textbox() export_hub_model_id = gr.Textbox() 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.adapter_path"), engine.manager.get_elem_by_id("top.finetuning_type"), 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, )