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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 | |
| from ...extras.packages import is_gradio_available | |
| from ..common import DEFAULT_DATA_DIR, list_datasets | |
| from .data import create_preview_box | |
| if is_gradio_available(): | |
| import gradio as gr | |
| if TYPE_CHECKING: | |
| from gradio.components import Component | |
| from ..engine import Engine | |
| def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]: | |
| input_elems = engine.manager.get_base_elems() | |
| elem_dict = dict() | |
| with gr.Row(): | |
| dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2) | |
| dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4) | |
| preview_elems = create_preview_box(dataset_dir, dataset) | |
| input_elems.update({dataset_dir, dataset}) | |
| elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems)) | |
| with gr.Row(): | |
| cutoff_len = gr.Slider(minimum=4, maximum=65536, value=1024, step=1) | |
| max_samples = gr.Textbox(value="100000") | |
| batch_size = gr.Slider(minimum=1, maximum=1024, value=2, step=1) | |
| predict = gr.Checkbox(value=True) | |
| input_elems.update({cutoff_len, max_samples, batch_size, predict}) | |
| elem_dict.update(dict(cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict)) | |
| with gr.Row(): | |
| max_new_tokens = gr.Slider(minimum=8, maximum=4096, value=512, step=1) | |
| top_p = gr.Slider(minimum=0.01, maximum=1, value=0.7, step=0.01) | |
| temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01) | |
| output_dir = gr.Textbox() | |
| input_elems.update({max_new_tokens, top_p, temperature, output_dir}) | |
| elem_dict.update(dict(max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir)) | |
| with gr.Row(): | |
| cmd_preview_btn = gr.Button() | |
| start_btn = gr.Button(variant="primary") | |
| stop_btn = gr.Button(variant="stop") | |
| with gr.Row(): | |
| resume_btn = gr.Checkbox(visible=False, interactive=False) | |
| progress_bar = gr.Slider(visible=False, interactive=False) | |
| with gr.Row(): | |
| output_box = gr.Markdown() | |
| elem_dict.update( | |
| dict( | |
| cmd_preview_btn=cmd_preview_btn, | |
| start_btn=start_btn, | |
| stop_btn=stop_btn, | |
| resume_btn=resume_btn, | |
| progress_bar=progress_bar, | |
| output_box=output_box, | |
| ) | |
| ) | |
| output_elems = [output_box, progress_bar] | |
| cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None) | |
| start_btn.click(engine.runner.run_eval, input_elems, output_elems) | |
| stop_btn.click(engine.runner.set_abort) | |
| resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None) | |
| dataset.focus(list_datasets, [dataset_dir], [dataset], queue=False) | |
| return elem_dict | |