# Copyright (c) ModelScope Contributors. All rights reserved. import gradio as gr import json import os import re import sys import time from datetime import datetime from functools import partial from json import JSONDecodeError from transformers.utils import is_torch_cuda_available, is_torch_npu_available from typing import Type from swift.arguments import SamplingArguments from swift.dataset import get_dataset_list from swift.utils import get_device_count, get_logger from ..base import BaseUI from ..llm_train import run_command_in_background_with_popen from .model import Model from .runtime import SampleRuntime from .sample import Sample logger = get_logger() class LLMSample(BaseUI): group = 'llm_sample' is_multimodal = True sub_ui = [Model, Sample, SampleRuntime] locale_dict = { 'llm_sample': { 'label': { 'zh': 'LLM采样', 'en': 'LLM Sampling', } }, 'sample': { 'value': { 'zh': '开始采样', 'en': 'Start sampling', } }, 'load_alert': { 'value': { 'zh': '采样中，请点击"展示采样状态"查看', 'en': 'Start to sample, ' 'please Click "Show running ' 'status" to view details', } }, 'gpu_id': { 'label': { 'zh': '选择可用GPU', 'en': 'Choose GPU' }, 'info': { 'zh': '选择采样使用的GPU号，如CUDA不可用只能选择CPU', 'en': 'Select GPU to sample' } }, 'dataset': { 'label': { 'zh': '数据集名称', 'en': 'Dataset id/path' }, 'info': { 'zh': '选择采样的数据集，支持复选/本地路径', 'en': 'The dataset(s) to train the models, support multi select and local folder/files' } }, 'num_sampling_batch_size': { 'label': { 'zh': '每次采样的批次大小', 'en': 'The batch size of sampling' } }, 'num_sampling_batches': { 'label': { 'zh': '采样批次数量', 'en': 'Num of Sampling batches' } }, 'output_dir': { 'label': { 'zh': '存储目录', 'en': 'The output dir', }, 'info': { 'zh': '设置采样结果存储在哪个文件夹下', 'en': 'Set the output folder', } }, 'envs': { 'label': { 'zh': '环境变量', 'en': 'Extra env vars' }, }, 'more_params': { 'label': { 'zh': '更多参数', 'en': 'More params' }, 'info': { 'zh': '以json格式或--xxx xxx命令行格式填入', 'en': 'Fill in with json format or --xxx xxx cmd format' } }, } choice_dict = BaseUI.get_choices_from_dataclass(SamplingArguments) default_dict = BaseUI.get_default_value_from_dataclass(SamplingArguments) arguments = BaseUI.get_argument_names(SamplingArguments) @classmethod def do_build_ui(cls, base_tab: Type['BaseUI']): with gr.TabItem(elem_id='llm_sample', label=''): default_device = 'cpu' device_count = get_device_count() if device_count > 0: default_device = '0' with gr.Blocks(): Model.build_ui(base_tab) Sample.build_ui(base_tab) with gr.Row(): gr.Dropdown( elem_id='dataset', multiselect=True, choices=get_dataset_list(), scale=20, allow_custom_value=True) gr.Slider(elem_id='num_sampling_batch_size', minimum=1, maximum=128, step=1, value=1, scale=10) gr.Slider(elem_id='num_sampling_batches', minimum=1, maximum=128, step=1, value=1, scale=10) SampleRuntime.build_ui(base_tab) with gr.Row(equal_height=True): gr.Dropdown( elem_id='gpu_id', multiselect=True, choices=[str(i) for i in range(device_count)] + ['cpu'], value=default_device, scale=20) gr.Textbox(elem_id='output_dir', value='sample_output', scale=20) gr.Textbox(elem_id='envs', scale=20) gr.Button(elem_id='sample', scale=2, variant='primary') with gr.Row(): gr.Textbox(elem_id='more_params', lines=4) cls.element('sample').click( cls.sample_model, list(base_tab.valid_elements().values()), [cls.element('runtime_tab'), cls.element('running_tasks')]) base_tab.element('running_tasks').change( partial(SampleRuntime.task_changed, base_tab=base_tab), [base_tab.element('running_tasks')], list(cls.valid_elements().values()) + [cls.element('log')]) SampleRuntime.element('kill_task').click( SampleRuntime.kill_task, [SampleRuntime.element('running_tasks')], [SampleRuntime.element('running_tasks')] + [SampleRuntime.element('log')], ) @classmethod def sample(cls, *args): sample_args = cls.get_default_value_from_dataclass(SamplingArguments) kwargs = {} kwargs_is_list = {} other_kwargs = {} more_params = {} more_params_cmd = '' keys = cls.valid_element_keys() for key, value in zip(keys, args): compare_value = sample_args.get(key) compare_value_arg = str(compare_value) if not isinstance(compare_value, (list, dict)) else compare_value compare_value_ui = str(value) if not isinstance(value, (list, dict)) else value if key in sample_args and compare_value_ui != compare_value_arg and value: if isinstance(value, str) and re.fullmatch(cls.int_regex, value): value = int(value) elif isinstance(value, str) and re.fullmatch(cls.float_regex, value): value = float(value) elif isinstance(value, str) and re.fullmatch(cls.bool_regex, value): value = True if value.lower() == 'true' else False kwargs[key] = value if not isinstance(value, list) else ' '.join(value) kwargs_is_list[key] = isinstance(value, list) or getattr(cls.element(key), 'is_list', False) else: other_kwargs[key] = value if key == 'more_params' and value: try: more_params = json.loads(value) except (JSONDecodeError or TypeError): more_params_cmd = value kwargs.update(more_params) model = kwargs.get('model') if os.path.exists(model) and os.path.exists(os.path.join(model, 'args.json')): args_path = os.path.join(model, 'args.json') if os.path.exists(os.path.join(model, 'adapter_config.json')): kwargs['adapters'] = kwargs.pop('model') with open(args_path, 'r', encoding='utf-8') as f: _json = json.load(f) kwargs['model_type'] = _json['model_type'] kwargs['tuner_type'] = _json['tuner_type'] sample_args = SamplingArguments( **{ key: value.split(' ') if key in kwargs_is_list and kwargs_is_list[key] else value for key, value in kwargs.items() }) params = '' command = ['swift', 'sample'] sep = f'{cls.quote} {cls.quote}' for e in kwargs: if isinstance(kwargs[e], list): params += f'--{e} {cls.quote}{sep.join(kwargs[e])}{cls.quote} ' command.extend([f'--{e}'] + kwargs[e]) elif e in kwargs_is_list and kwargs_is_list[e]: all_args = [arg for arg in kwargs[e].split(' ') if arg.strip()] params += f'--{e} {cls.quote}{sep.join(all_args)}{cls.quote} ' command.extend([f'--{e}'] + all_args) else: params += f'--{e} {cls.quote}{kwargs[e]}{cls.quote} ' command.extend([f'--{e}', f'{kwargs[e]}']) if more_params_cmd != '': params += more_params_cmd + ' ' more_params_cmd = [param.strip() for param in more_params_cmd.split('--')] more_params_cmd = [param.split(' ') for param in more_params_cmd if param] for param in more_params_cmd: command.extend([f'--{param[0]}'] + param[1:]) all_envs = {} devices = other_kwargs['gpu_id'] devices = [d for d in devices if d] assert (len(devices) == 1 or 'cpu' not in devices) gpus = ','.join(devices) cuda_param = '' if gpus != 'cpu': if is_torch_npu_available(): cuda_param = f'ASCEND_RT_VISIBLE_DEVICES={gpus}' all_envs['ASCEND_RT_VISIBLE_DEVICES'] = gpus elif is_torch_cuda_available(): cuda_param = f'CUDA_VISIBLE_DEVICES={gpus}' all_envs['CUDA_VISIBLE_DEVICES'] = gpus else: cuda_param = '' now = datetime.now() time_str = f'{now.year}{now.month}{now.day}{now.hour}{now.minute}{now.second}' file_path = f'output/{sample_args.model_type}-{time_str}' if not os.path.exists(file_path): os.makedirs(file_path, exist_ok=True) log_file = os.path.join(os.getcwd(), f'{file_path}/run_sample.log') sample_args.log_file = log_file params += f'--log_file "{log_file}" ' command.extend(['--log_file', f'{log_file}']) params += '--ignore_args_error true ' command.extend(['--ignore_args_error', 'true']) if sys.platform == 'win32': if cuda_param: cuda_param = f'set {cuda_param} && ' run_command = f'{cuda_param}start /b swift sample {params} > {log_file} 2>&1' else: run_command = f'{cuda_param} nohup swift sample {params} > {log_file} 2>&1 &' return command, all_envs, run_command, sample_args, log_file @classmethod def sample_model(cls, *args): command, all_envs, run_command, sample_args, log_file = cls.sample(*args) logger.info(f'Running sample command: {run_command}') run_command_in_background_with_popen(command, all_envs, log_file) gr.Info(cls.locale('load_alert', cls.lang)['value']) time.sleep(2) running_task = SampleRuntime.refresh_tasks(log_file) return gr.update(open=True), running_task