| |
| import collections |
| import gradio as gr |
| import json |
| import matplotlib.pyplot as plt |
| import os |
| import psutil |
| import re |
| import subprocess |
| import sys |
| import time |
| import webbrowser |
| from datetime import datetime |
| from functools import partial |
| from packaging import version |
| from transformers import is_tensorboard_available |
| from typing import Dict, List, Tuple, Type |
|
|
| from swift.utils import TB_COLOR, TB_COLOR_SMOOTH, format_time, get_logger, read_tensorboard_file, tensorboard_smoothing |
| from ..base import BaseUI |
| from .utils import close_loop, run_command_in_subprocess |
|
|
| logger = get_logger() |
|
|
|
|
| class Runtime(BaseUI): |
|
|
| handlers: Dict[str, Tuple[List, Tuple]] = {} |
|
|
| group = 'llm_train' |
|
|
| all_plots = None |
|
|
| log_event = {} |
|
|
| sft_plot = [ |
| { |
| 'name': 'train/loss', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/acc', |
| 'smooth': None, |
| }, |
| { |
| 'name': 'train/learning_rate', |
| 'smooth': None, |
| }, |
| { |
| 'name': 'eval/loss', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'eval/acc', |
| 'smooth': None, |
| }, |
| ] |
|
|
| dpo_plot = [ |
| { |
| 'name': 'train/loss', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/rewards/accuracies', |
| 'smooth': None, |
| }, |
| { |
| 'name': 'train/rewards/margins', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/logps/chosen', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/logps/rejected', |
| 'smooth': 0.9, |
| }, |
| ] |
|
|
| kto_plot = [ |
| { |
| 'name': 'kl', |
| 'smooth': None, |
| }, |
| { |
| 'name': 'rewards/chosen_sum', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'logps/chosen_sum', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'rewards/rejected_sum', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'logps/rejected_sum', |
| 'smooth': 0.9, |
| }, |
| ] |
|
|
| orpo_plot = [ |
| { |
| 'name': 'train/loss', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/rewards/accuracies', |
| 'smooth': None, |
| }, |
| { |
| 'name': 'train/rewards/margins', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/rewards/chosen', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/log_odds_ratio', |
| 'smooth': 0.9, |
| }, |
| ] |
|
|
| grpo_plot = [ |
| { |
| 'name': 'train/loss', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/reward', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/learning_rate', |
| 'smooth': None, |
| }, |
| { |
| 'name': 'train/completions/mean_length', |
| 'smooth': 0.9, |
| }, |
| { |
| 'name': 'train/kl', |
| 'smooth': 0.9, |
| }, |
| ] |
|
|
| locale_dict = { |
| 'runtime_tab': { |
| 'label': { |
| 'zh': '运行时', |
| 'en': 'Runtime' |
| }, |
| }, |
| 'tb_not_found': { |
| 'value': { |
| 'zh': 'TensorBoard未安装,使用`pip install tensorboard`进行安装', |
| 'en': 'TensorBoard not found, install it by `pip install tensorboard`', |
| } |
| }, |
| 'running_cmd': { |
| 'label': { |
| 'zh': '运行命令', |
| 'en': 'Command line' |
| }, |
| 'info': { |
| 'zh': '执行的实际命令', |
| 'en': 'The actual command' |
| } |
| }, |
| 'show_running_cmd': { |
| 'value': { |
| 'zh': '展示运行命令', |
| 'en': 'Show running command line' |
| }, |
| }, |
| 'show_sh': { |
| 'label': { |
| 'zh': '展示sh命令行', |
| 'en': 'Show sh command line' |
| }, |
| }, |
| 'cmd_sh': { |
| 'label': { |
| 'zh': '训练命令行', |
| 'en': 'Training command line' |
| }, |
| 'info': { |
| 'zh': |
| '如果训练命令行没有展示请再次点击"展示运行命令",点击下方的"保存训练命令"可以保存sh脚本', |
| 'en': ('Please press "Show running command line" if the content is none, ' |
| 'click the "Save training command" below to save the sh script') |
| } |
| }, |
| 'save_cmd_as_sh': { |
| 'value': { |
| 'zh': '保存训练命令', |
| 'en': 'Save training command' |
| } |
| }, |
| 'save_cmd_alert': { |
| 'value': { |
| 'zh': '训练命令行将被保存在:{}', |
| 'en': 'The training command line will be saved in: {}' |
| } |
| }, |
| 'close_cmd_show': { |
| 'value': { |
| 'zh': '关闭训练命令展示', |
| 'en': 'Close training command show' |
| } |
| }, |
| 'show_log': { |
| 'value': { |
| 'zh': '展示运行状态', |
| 'en': 'Show running status' |
| }, |
| }, |
| 'stop_show_log': { |
| 'value': { |
| 'zh': '停止展示运行状态', |
| 'en': 'Stop showing running status' |
| }, |
| }, |
| 'logging_dir': { |
| 'label': { |
| 'zh': '日志路径', |
| 'en': 'Logging dir' |
| }, |
| 'info': { |
| 'zh': '支持手动传入文件路径', |
| 'en': 'Support fill custom path in' |
| } |
| }, |
| 'log': { |
| 'label': { |
| 'zh': '日志输出', |
| 'en': 'Logging content' |
| }, |
| 'info': { |
| 'zh': '如果日志无更新请再次点击"展示运行状态"', |
| 'en': 'Please press "Show running status" if the log content is not updating' |
| } |
| }, |
| 'running_tasks': { |
| 'label': { |
| 'zh': '运行中任务', |
| 'en': 'Running Tasks' |
| }, |
| 'info': { |
| 'zh': '运行中的任务(所有的swift sft/pt命令)', |
| 'en': 'All running tasks(started by swift sft/pt)' |
| } |
| }, |
| 'refresh_tasks': { |
| 'value': { |
| 'zh': '找回运行时任务', |
| 'en': 'Find running tasks' |
| }, |
| }, |
| 'kill_task': { |
| 'value': { |
| 'zh': '杀死任务', |
| 'en': 'Kill running task' |
| }, |
| }, |
| 'tb_url': { |
| 'label': { |
| 'zh': 'Tensorboard链接', |
| 'en': 'Tensorboard URL' |
| }, |
| 'info': { |
| 'zh': '仅展示,不可编辑', |
| 'en': 'Not editable' |
| } |
| }, |
| 'start_tb': { |
| 'value': { |
| 'zh': '打开TensorBoard', |
| 'en': 'Start TensorBoard' |
| }, |
| }, |
| 'close_tb': { |
| 'value': { |
| 'zh': '关闭TensorBoard', |
| 'en': 'Close TensorBoard' |
| }, |
| }, |
| } |
|
|
| @classmethod |
| def do_build_ui(cls, base_tab: Type['BaseUI']): |
| with gr.Accordion(elem_id='runtime_tab', open=False): |
| with gr.Blocks(): |
| with gr.Row(): |
| with gr.Column(scale=3): |
| with gr.Row(): |
| gr.Textbox(elem_id='running_cmd', lines=1, scale=3, interactive=False, max_lines=1) |
| gr.Textbox(elem_id='logging_dir', lines=1, scale=3, max_lines=1) |
| with gr.Row(): |
| gr.Button(elem_id='show_running_cmd', scale=2, variant='primary') |
| gr.Button(elem_id='show_log', scale=2, variant='primary') |
| gr.Button(elem_id='stop_show_log', scale=2) |
| with gr.Column(scale=2): |
| with gr.Row(): |
| gr.Textbox(elem_id='tb_url', lines=1, scale=4, interactive=False, max_lines=1) |
| with gr.Row(): |
| gr.Button(elem_id='start_tb', scale=2, variant='primary') |
| gr.Button(elem_id='close_tb', scale=2) |
| with gr.Accordion(elem_id='show_sh', open=False, visible=False): |
| with gr.Blocks(): |
| gr.Textbox(elem_id='cmd_sh', lines=8) |
| with gr.Row(equal_height=True): |
| gr.Button(elem_id='save_cmd_as_sh', variant='primary', scale=2) |
| gr.Button(elem_id='close_cmd_show', scale=2) |
| with gr.Row(): |
| gr.Textbox(elem_id='log', lines=6, visible=False) |
| with gr.Row(equal_height=True): |
| gr.Dropdown(elem_id='running_tasks', scale=10) |
| gr.Button(elem_id='refresh_tasks', scale=1) |
| gr.Button(elem_id='kill_task', scale=1) |
|
|
| with gr.Row(): |
| cls.all_plots = [] |
| plot = Runtime.sft_plot |
| if base_tab.group == 'llm_rlhf': |
| plot = Runtime.dpo_plot |
| elif base_tab.group == 'llm_grpo': |
| plot = Runtime.grpo_plot |
| for idx, k in enumerate(plot): |
| name = k['name'] |
| cls.all_plots.append(gr.Plot(elem_id=str(idx), label=name)) |
|
|
| concurrency_limit = {} |
| if version.parse(gr.__version__) >= version.parse('4.0.0'): |
| concurrency_limit = {'concurrency_limit': 5} |
| base_tab.element('show_log').click( |
| Runtime.update_log, [base_tab.element('running_tasks')], [cls.element('log')] + cls.all_plots).then( |
| Runtime.wait, [base_tab.element('logging_dir'), |
| base_tab.element('running_tasks')], [cls.element('log')] + cls.all_plots, |
| **concurrency_limit) |
|
|
| base_tab.element('stop_show_log').click(cls.break_log_event, [cls.element('running_tasks')], []) |
|
|
| base_tab.element('start_tb').click( |
| Runtime.start_tb, |
| [base_tab.element('logging_dir')], |
| [base_tab.element('tb_url')], |
| ) |
|
|
| base_tab.element('close_tb').click( |
| Runtime.close_tb, |
| [base_tab.element('logging_dir')], |
| [], |
| ) |
|
|
| base_tab.element('refresh_tasks').click( |
| partial(Runtime.refresh_tasks, group=cls.group), |
| [base_tab.element('running_tasks')], |
| [base_tab.element('running_tasks')], |
| ) |
|
|
| @classmethod |
| def after_build_ui(cls, base_tab: Type['BaseUI']): |
| cls.element('show_running_cmd').click(Runtime.show_train_sh, cls.element('running_cmd'), |
| [cls.element('show_sh')] + [cls.element('cmd_sh')]) |
| cls.element('save_cmd_as_sh').click(cls.save_cmd, cls.element('running_cmd'), []) |
| cls.element('close_cmd_show').click(Runtime.close_cmd_show, [], [cls.element('show_sh')]) |
|
|
| @classmethod |
| def get_plot(cls, task): |
| if not task or 'swift sft' in task or 'swift pt' in task: |
| return cls.sft_plot |
|
|
| args: dict = cls.parse_info_from_cmdline(task)[1] |
| rlhf_type = args.get('rlhf_type', 'dpo') |
| if rlhf_type in ('dpo', 'cpo', 'simpo'): |
| return cls.dpo_plot |
| elif rlhf_type == 'kto': |
| return cls.kto_plot |
| elif rlhf_type == 'orpo': |
| return cls.orpo_plot |
| elif rlhf_type == 'grpo': |
| return cls.grpo_plot |
|
|
| @classmethod |
| def update_log(cls, task): |
| ret = [gr.update(visible=True)] |
| plot = Runtime.get_plot(task) |
| for i in range(len(plot)): |
| p = plot[i] |
| ret.append(gr.update(visible=True, label=p['name'])) |
| return ret |
|
|
| @classmethod |
| def get_initial(cls, line): |
| tqdm_starts = ['Train:', 'Map:', 'Val:', 'Filter:'] |
| for start in tqdm_starts: |
| if line.startswith(start): |
| return start |
| return None |
|
|
| @classmethod |
| def wait(cls, logging_dir, task): |
| if not logging_dir: |
| return [None] + Runtime.plot(task) |
| log_file = os.path.join(logging_dir, 'run.log') |
| cls.log_event[logging_dir] = False |
| offset = 0 |
| latest_data = '' |
| lines = collections.deque(maxlen=int(os.environ.get('MAX_LOG_LINES', 100))) |
| try: |
| with open(log_file, 'r', encoding='utf-8') as input: |
| input.seek(offset) |
| fail_cnt = 0 |
| while True: |
| try: |
| latest_data += input.read() |
| except UnicodeDecodeError: |
| continue |
| if not latest_data: |
| time.sleep(0.5) |
| fail_cnt += 1 |
| if fail_cnt > 50: |
| break |
|
|
| if cls.log_event.get(logging_dir, False): |
| cls.log_event[logging_dir] = False |
| break |
|
|
| if '\n' not in latest_data: |
| continue |
| latest_lines = latest_data.split('\n') |
| if latest_data[-1] != '\n': |
| latest_data = latest_lines[-1] |
| latest_lines = latest_lines[:-1] |
| else: |
| latest_data = '' |
| lines.extend(latest_lines) |
| start = cls.get_initial(lines[-1]) |
| if start: |
| i = len(lines) - 2 |
| while i >= 0: |
| if lines[i].startswith(start): |
| del lines[i] |
| i -= 1 |
| else: |
| break |
| yield [gr.update(value='\n'.join(lines))] + Runtime.plot(task) |
| time.sleep(0.5) |
| except IOError: |
| pass |
|
|
| @classmethod |
| def break_log_event(cls, task): |
| if not task: |
| return |
| pid, all_args = Runtime.parse_info_from_cmdline(task) |
| cls.log_event[all_args['logging_dir']] = True |
|
|
| @classmethod |
| def show_log(cls, logging_dir): |
| webbrowser.open('file://' + os.path.join(logging_dir, 'run.log'), new=2) |
|
|
| @classmethod |
| def start_tb(cls, logging_dir): |
| if not is_tensorboard_available(): |
| gr.Error(cls.locale('tb_not_found', cls.lang)['value']) |
| return '' |
|
|
| logging_dir = logging_dir.strip() |
| logging_dir = logging_dir if not logging_dir.endswith(os.sep) else logging_dir[:-1] |
| if logging_dir in cls.handlers: |
| return cls.handlers[logging_dir][1] |
|
|
| handler, lines = run_command_in_subprocess('tensorboard', '--logdir', logging_dir, timeout=2) |
| localhost_addr = '' |
| for line in lines: |
| if 'http://localhost:' in line: |
| line = line[line.index('http://localhost:'):] |
| localhost_addr = line[:line.index(' ')] |
| cls.handlers[logging_dir] = (handler, localhost_addr) |
| logger.info('===========Tensorboard Log============') |
| logger.info('\n'.join(lines)) |
| webbrowser.open(localhost_addr, new=2) |
| return localhost_addr |
|
|
| @staticmethod |
| def close_tb(logging_dir): |
| if logging_dir in Runtime.handlers: |
| close_loop(Runtime.handlers[logging_dir][0]) |
| Runtime.handlers.pop(logging_dir) |
|
|
| @staticmethod |
| def refresh_tasks(running_task=None, group=None): |
| output_dir = running_task if not running_task or 'pid:' not in running_task else None |
| process_name = 'swift' |
| negative_names = ['swift.exe', 'swift-script.py'] |
| cmd_name = ['pt', 'sft'] if group == 'llm_train' else ['rlhf'] |
| process = [] |
| selected = None |
| for proc in psutil.process_iter(): |
| try: |
| cmdlines = proc.cmdline() |
| except (psutil.ZombieProcess, psutil.AccessDenied, psutil.NoSuchProcess): |
| cmdlines = [] |
| if any([ |
| process_name in cmdline for cmdline in cmdlines |
| ]) and not any([ |
| negative_name in cmdline for negative_name in negative_names |
| for cmdline in cmdlines |
| ]) and any([cmdline in cmd_name for cmdline in cmdlines]): |
| if any([group == 'llm_rlhf' and 'grpo' in cmdline for cmdline in cmdlines]): |
| continue |
| if group == 'llm_grpo' and all(['grpo' not in cmdline for cmdline in cmdlines]): |
| continue |
| process.append(Runtime.construct_running_task(proc)) |
| if output_dir is not None and any( |
| [output_dir == cmdline for cmdline in cmdlines]): |
| selected = Runtime.construct_running_task(proc) |
| if not selected: |
| if running_task and running_task in process: |
| selected = running_task |
| if not selected and process: |
| selected = process[0] |
| return gr.update(choices=process, value=selected) |
|
|
| @staticmethod |
| def construct_running_task(proc): |
| pid = proc.pid |
| ts = time.time() |
| create_time = proc.create_time() |
| create_time_formatted = datetime.fromtimestamp(create_time).strftime('%Y-%m-%d, %H:%M') |
|
|
| return f'pid:{pid}/create:{create_time_formatted}' \ |
| f'/running:{format_time(ts - create_time)}/cmd:{" ".join(proc.cmdline())}' |
|
|
| @staticmethod |
| def parse_info_from_cmdline(task): |
| pid = None |
| if '/cmd:' in task: |
| for i in range(3): |
| slash = task.find('/') |
| if i == 0: |
| pid = task[:slash].split(':')[1] |
| task = task[slash + 1:] |
| if 'swift sft' in task: |
| args = task.split('swift sft')[1] |
| elif 'swift pt' in task: |
| args = task.split('swift pt')[1] |
| elif 'swift rlhf' in task: |
| args = task.split('swift rlhf')[1] |
| else: |
| raise ValueError(f'Cannot parse cmd line: {task}') |
| args = [arg.strip() for arg in args.split('--') if arg.strip()] |
| all_args = {} |
| for i in range(len(args)): |
| space = args[i].find(' ') |
| splits = args[i][:space], args[i][space + 1:] |
| all_args[splits[0]] = str(splits[1]) if isinstance(splits[1], int) else splits[1] |
|
|
| output_dir = all_args['output_dir'] |
| if os.path.exists(os.path.join(output_dir, 'args.json')): |
| with open(os.path.join(output_dir, 'args.json'), 'r', encoding='utf-8') as f: |
| _json = json.load(f) |
| for key in all_args.keys(): |
| all_args[key] = str(_json.get(key)) if isinstance(_json.get(key), int) else _json.get(key) |
| if isinstance(all_args[key], list): |
| if any([' ' in value for value in all_args[key] if isinstance(value, str)]): |
| all_args[key] = [f'"{value}"' for value in all_args[key]] |
| if len(all_args[key]) > 0 and isinstance(all_args[key][0], str): |
| all_args[key] = ' '.join(all_args[key]) |
| return pid, all_args |
|
|
| @staticmethod |
| def kill_task(task): |
| if task: |
| pid, all_args = Runtime.parse_info_from_cmdline(task) |
| output_dir = all_args['output_dir'] |
| if sys.platform == 'win32': |
| command = ['taskkill', '/f', '/t', '/pid', pid] |
| else: |
| command = ['pkill', '-9', '-f', output_dir] |
| try: |
| result = subprocess.run(command, capture_output=True, text=True) |
| assert result.returncode == 0 |
| except Exception as e: |
| raise e |
| Runtime.break_log_event(task) |
| return [Runtime.refresh_tasks()] + [gr.update(value=None)] * (len(Runtime.get_plot(task)) + 1) |
|
|
| @staticmethod |
| def reset(): |
| return None, 'output' |
|
|
| @staticmethod |
| def task_changed(task, base_tab): |
| if task: |
| _, all_args = Runtime.parse_info_from_cmdline(task) |
| else: |
| all_args = {} |
| elements = list(base_tab.valid_elements().values()) |
| ret = [] |
| for e in elements: |
| if e.elem_id in all_args: |
| if isinstance(e, gr.Dropdown) and e.multiselect: |
| arg = all_args[e.elem_id].split(' ') |
| else: |
| arg = all_args[e.elem_id] |
| if isinstance(e, gr.Slider) and isinstance(arg, str) and re.fullmatch(base_tab.int_regex, arg): |
| arg = int(arg) |
| elif isinstance(e, gr.Slider) and isinstance(arg, str) and re.fullmatch(base_tab.float_regex, arg): |
| arg = float(arg) |
| elif isinstance(e, gr.Checkbox) and isinstance(arg, str) and re.fullmatch(base_tab.bool_regex, arg): |
| arg = True if arg.lower() == 'true' else False |
| ret.append(gr.update(value=arg)) |
| else: |
| ret.append(gr.update()) |
| Runtime.break_log_event(task) |
| return ret + [gr.update(value=None)] * (len(Runtime.get_plot(task)) + 1) |
|
|
| @staticmethod |
| def plot(task): |
| plot = Runtime.get_plot(task) |
| if not task: |
| return [None] * len(plot) |
| _, all_args = Runtime.parse_info_from_cmdline(task) |
| tb_dir = all_args['logging_dir'] |
| if not os.path.exists(tb_dir): |
| return [None] * len(plot) |
| fname = [ |
| fname for fname in os.listdir(tb_dir) |
| if os.path.isfile(os.path.join(tb_dir, fname)) and fname.startswith('events.out') |
| ] |
| if fname: |
| fname = fname[0] |
| else: |
| return [None] * len(plot) |
| tb_path = os.path.join(tb_dir, fname) |
| data = read_tensorboard_file(tb_path) |
|
|
| plots = [] |
| for k in plot: |
| name = k['name'] |
| smooth = k['smooth'] |
| if name == 'train/acc': |
| if 'train/token_acc' in data: |
| name = 'train/token_acc' |
| if 'train/seq_acc' in data: |
| name = 'train/seq_acc' |
| if name == 'eval/acc': |
| if 'eval/token_acc' in data: |
| name = 'eval/token_acc' |
| if 'eval/seq_acc' in data: |
| name = 'eval/seq_acc' |
| if name not in data: |
| plots.append(None) |
| continue |
| _data = data[name] |
| steps = [d['step'] for d in _data] |
| values = [d['value'] for d in _data] |
| if len(values) == 0: |
| continue |
|
|
| plt.close('all') |
| fig = plt.figure() |
| ax = fig.add_subplot() |
| |
| ax.set_title(name) |
| if len(values) == 1: |
| ax.scatter(steps, values, color=TB_COLOR_SMOOTH) |
| elif smooth is not None: |
| ax.plot(steps, values, color=TB_COLOR) |
| values_s = tensorboard_smoothing(values, smooth) |
| ax.plot(steps, values_s, color=TB_COLOR_SMOOTH) |
| else: |
| ax.plot(steps, values, color=TB_COLOR_SMOOTH) |
| plots.append(fig) |
| return plots |
|
|
| @classmethod |
| def save_cmd(cls, cmd): |
| if len(cmd) > 0: |
| cmd_sh, output_dir = Runtime.cmd_to_sh_format(cmd) |
| os.makedirs(output_dir, exist_ok=True) |
| sh_file_path = os.path.join(output_dir, 'train.sh') |
| gr.Info(cls.locale('save_cmd_alert', cls.lang)['value'].format(sh_file_path)) |
| with open(sh_file_path, 'w', encoding='utf-8') as f: |
| f.write(cmd_sh) |
|
|
| @staticmethod |
| def show_train_sh(cmd): |
| if len(cmd) == 0: |
| return gr.update(visible=False, open=False), None |
| cmd_sh, _ = Runtime.cmd_to_sh_format(cmd) |
| return gr.update(visible=True, open=True), cmd_sh |
|
|
| @staticmethod |
| def cmd_to_sh_format(cmd): |
| cmd_sh = '' |
| params = cmd.split('--') |
| env_params = params[0].split('nohup')[0].strip() |
| cmd_sh += (env_params + ' \\\n') |
| swift_cmd = params[0].split('nohup')[1].strip() |
| cmd_sh += ('nohup ' + swift_cmd + ' \\\n') |
| for param in params[1:]: |
| if param.startswith('output_dir'): |
| output_dir = param.split(' ')[1].strip() |
| cmd_sh += ('--' + param.strip() + ' \\\n') |
| return cmd_sh, output_dir |
|
|
| @staticmethod |
| def close_cmd_show(): |
| return gr.update(visible=False) |
|
|