mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 11:42:49 +08:00
133 lines
4.1 KiB
Python
133 lines
4.1 KiB
Python
from typing import TYPE_CHECKING, Dict, Generator, List
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from ...extras.misc import torch_gc
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from ...extras.packages import is_gradio_available
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from ...train import export_model
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from ..common import get_save_dir
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from ..locales import ALERTS
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if is_gradio_available():
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import gradio as gr
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if TYPE_CHECKING:
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from gradio.components import Component
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from ..engine import Engine
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GPTQ_BITS = ["8", "4", "3", "2"]
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def save_model(
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lang: str,
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model_name: str,
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model_path: str,
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adapter_path: List[str],
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finetuning_type: str,
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template: str,
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visual_inputs: bool,
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export_size: int,
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export_quantization_bit: int,
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export_quantization_dataset: str,
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export_device: str,
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export_legacy_format: bool,
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export_dir: str,
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export_hub_model_id: str,
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) -> Generator[str, None, None]:
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error = ""
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if not model_name:
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error = ALERTS["err_no_model"][lang]
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elif not model_path:
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error = ALERTS["err_no_path"][lang]
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elif not export_dir:
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error = ALERTS["err_no_export_dir"][lang]
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elif export_quantization_bit in GPTQ_BITS and not export_quantization_dataset:
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error = ALERTS["err_no_dataset"][lang]
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elif export_quantization_bit not in GPTQ_BITS and not adapter_path:
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error = ALERTS["err_no_adapter"][lang]
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elif export_quantization_bit in GPTQ_BITS and adapter_path:
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error = ALERTS["err_gptq_lora"][lang]
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if error:
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gr.Warning(error)
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yield error
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return
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if adapter_path:
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adapter_name_or_path = ",".join(
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[get_save_dir(model_name, finetuning_type, adapter) for adapter in adapter_path]
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)
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else:
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adapter_name_or_path = None
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args = dict(
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model_name_or_path=model_path,
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adapter_name_or_path=adapter_name_or_path,
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finetuning_type=finetuning_type,
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template=template,
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visual_inputs=visual_inputs,
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export_dir=export_dir,
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export_hub_model_id=export_hub_model_id or None,
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export_size=export_size,
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export_quantization_bit=int(export_quantization_bit) if export_quantization_bit in GPTQ_BITS else None,
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export_quantization_dataset=export_quantization_dataset,
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export_device=export_device,
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export_legacy_format=export_legacy_format,
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)
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yield ALERTS["info_exporting"][lang]
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export_model(args)
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torch_gc()
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yield ALERTS["info_exported"][lang]
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def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
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with gr.Row():
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export_size = gr.Slider(value=1, minimum=1, maximum=100, step=1)
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export_quantization_bit = gr.Dropdown(choices=["none", "8", "4", "3", "2"], value="none")
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export_quantization_dataset = gr.Textbox(value="data/c4_demo.json")
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export_device = gr.Radio(choices=["cpu", "cuda"], value="cpu")
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export_legacy_format = gr.Checkbox()
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with gr.Row():
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export_dir = gr.Textbox()
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export_hub_model_id = gr.Textbox()
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export_btn = gr.Button()
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info_box = gr.Textbox(show_label=False, interactive=False)
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export_btn.click(
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save_model,
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[
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engine.manager.get_elem_by_id("top.lang"),
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engine.manager.get_elem_by_id("top.model_name"),
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engine.manager.get_elem_by_id("top.model_path"),
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engine.manager.get_elem_by_id("top.adapter_path"),
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engine.manager.get_elem_by_id("top.finetuning_type"),
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engine.manager.get_elem_by_id("top.template"),
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engine.manager.get_elem_by_id("top.visual_inputs"),
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export_size,
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export_quantization_bit,
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export_quantization_dataset,
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export_device,
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export_legacy_format,
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export_dir,
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export_hub_model_id,
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],
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[info_box],
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)
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return dict(
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export_size=export_size,
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export_quantization_bit=export_quantization_bit,
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export_quantization_dataset=export_quantization_dataset,
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export_device=export_device,
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export_legacy_format=export_legacy_format,
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export_dir=export_dir,
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export_hub_model_id=export_hub_model_id,
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export_btn=export_btn,
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info_box=info_box,
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)
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