494 lines
17 KiB
Python
494 lines
17 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Gradio UI + EvalScope 调度脚本（量产版）
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关键改动
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1. 独立 Stop Evaluation 按钮，queue=False，立即触发 `stop_eval()`
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2. `stop_eval()` 使用 psutil 递归杀进程树并 wait()，杜绝僵尸
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3. 所有生成器统一返回 4 个输出：output_text ‖ is_running ‖ run_button 更新 ‖ stop_button 更新
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"""
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import time
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import os
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import glob
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import threading
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import subprocess
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import gradio as gr
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import psutil
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import signal
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import shlex
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import pathlib
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# ---------------- 全局进程句柄 ----------------
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current_process = None
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should_stop = False
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# ---------------- 可选数据集 ----------------
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EVAL_DATASETS = [
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"arc", "bbh", "ceval", "cmmlu", "competition_math", "gsm8k",
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"hellaswag", "humaneval", "mmlu", "mmlu_pro", "race",
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"trivia_qa", "truthful_qa"
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]
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PERF_DATASETS = [
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"openqa", "flickr8k", "longalpaca",
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"line_by_line", "speed_benchmark"
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]
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def toggle_dataset_file_visibility(ds):
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return gr.update(visible=(ds == "line_by_line"))
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# ---------------- perf 模式运行 ----------------
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def run_perf(
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inputs, native, other, output_choices,
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api_url, api_token,
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api_provider, dataset,
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dataset_path,
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max_tokens, min_tokens, parallel_reqs,
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max_prompt_len, num_requests,
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model_override,
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extra_args
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):
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global current_process
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if dataset == "line_by_line" and dataset_path is None:
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msg = "[❌] 请选择 line_by_line 数据集文件 (.txt)"
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yield msg, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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return
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timestamp = time.strftime("%Y%m%d-%H%M%S")
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model_name = model_override.strip() or timestamp
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command = [
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"evalscope", "perf",
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"--url", api_url.strip(),
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"--api", api_provider,
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"--model", model_name,
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"--dataset", dataset,
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"--max-tokens", str(int(max_tokens)),
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"--min-tokens", str(int(min_tokens)),
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"--parallel", str(int(parallel_reqs)),
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"--max-prompt-length", str(int(max_prompt_len)),
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"--number", str(int(num_requests)),
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"--api-key", api_token.strip(),
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]
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if dataset == "line_by_line" and dataset_path:
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command += ["--dataset-path", dataset_path]
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if extra_args.strip():
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command += shlex.split(extra_args.strip())
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full_output = f"[Eval Started @ {timestamp}]\nCmd: {' '.join(command)}\n"
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yield full_output, True, gr.update(interactive=False), gr.update(visible=True)
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try:
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current_process = subprocess.Popen(
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command,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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bufsize=1,
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start_new_session=True, # 独立进程组，便于后续 killpg / psutil
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)
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for line in current_process.stdout:
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if should_stop:
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break
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full_output += line
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yield full_output, True, gr.update(interactive=False), gr.update(visible=True)
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current_process.stdout.close()
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current_process.wait()
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except Exception as e:
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full_output += f"[Error] {e}\n"
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yield full_output, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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finally:
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current_process = None
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if dataset_path:
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pathlib.Path(dataset_path).unlink(missing_ok=True)
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full_output += "[Eval Finished]\n"
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# 自动启动可视化
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if "Evaluation Report" in output_choices:
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vis_port = 7901
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outputs_root = "./outputs"
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try:
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latest_output = max(
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glob.glob(os.path.join(outputs_root, "*")),
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key=os.path.getmtime
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)
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except ValueError:
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latest_output = outputs_root
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vis_cmd = [
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"evalscope", "app",
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"--outputs", outputs_root,
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"--server-name", "0.0.0.0",
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"--server-port", str(vis_port),
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]
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threading.Thread(
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target=subprocess.Popen,
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args=(vis_cmd,),
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kwargs={"stdout": subprocess.DEVNULL, "stderr": subprocess.STDOUT},
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daemon=True
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).start()
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full_output += f"[Visualization 👉] http://localhost:{vis_port}\n"
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yield full_output, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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# ---------------- eval 模式运行 ----------------
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def run_eval_tool(
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inputs, native, other, output_choices,
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api_url, api_token,
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api_provider, dataset,
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dataset_path,
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max_tokens, min_tokens, parallel_reqs,
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max_prompt_len, num_requests,
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model_override, extra_args
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):
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global current_process
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if dataset == "line_by_line" and dataset_path is None:
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msg = "[❌] 请选择 line_by_line 数据集文件 (.txt)"
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yield msg, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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return
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timestamp = time.strftime("%Y%m%d-%H%M%S")
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model_name = model_override.strip() or timestamp
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command = [
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"evalscope", "eval",
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"--model", model_name,
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"--datasets", dataset
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]
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if api_url.strip():
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command += [
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"--eval-type", "service",
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"--api-url", api_url.strip(),
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"--api-key", api_token.strip()
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]
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if num_requests:
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command += ["--limit", str(int(num_requests))]
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if extra_args.strip():
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command += shlex.split(extra_args.strip())
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if dataset == "line_by_line" and dataset_path:
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command += ["--dataset-path", dataset_path]
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full_output = f"[Eval Started @ {timestamp}]\nCmd: {' '.join(command)}\n"
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yield full_output, True, gr.update(interactive=False), gr.update(visible=True)
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try:
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current_process = subprocess.Popen(
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command,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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bufsize=1,
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start_new_session=True
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)
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for line in current_process.stdout:
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if should_stop:
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break
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full_output += line
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yield full_output, True, gr.update(interactive=False), gr.update(visible=True)
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current_process.stdout.close()
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current_process.wait()
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except Exception as e:
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full_output += f"[Error] {e}\n"
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yield full_output, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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finally:
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current_process = None
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if dataset_path:
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pathlib.Path(dataset_path).unlink(missing_ok=True)
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full_output += "[Eval Finished]\n"
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if "Evaluation Report" in output_choices:
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vis_port = 7901
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outputs_root = "./outputs"
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try:
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latest_output = max(
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glob.glob(os.path.join(outputs_root, "*")),
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key=os.path.getmtime
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)
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except ValueError:
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latest_output = outputs_root
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vis_cmd = [
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"evalscope", "app",
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"--outputs", outputs_root,
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"--server-name", "0.0.0.0",
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"--server-port", str(vis_port),
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]
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threading.Thread(
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target=subprocess.Popen,
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args=(vis_cmd,),
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kwargs={"stdout": subprocess.DEVNULL, "stderr": subprocess.STDOUT},
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daemon=True
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).start()
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full_output += f"[Visualization 👉] http://localhost:{vis_port}\n"
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yield full_output, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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# ---------------- 停止函数 ----------------
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def stop_eval() -> str:
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"""
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彻底终止 current_process 及其全部子孙进程：
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1. SIGINT（优雅退出，3 秒宽限）
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2. 仍存活则 SIGKILL
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3. wait() 主进程，防止僵尸
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"""
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global current_process, should_stop
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should_stop = True
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if not (current_process and current_process.poll() is None):
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return "[⚠️ 无活动 evalscope 进程]\n"
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try:
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parent = psutil.Process(current_process.pid)
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family = parent.children(recursive=True) + [parent]
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# 1) SIGINT
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for p in family:
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p.send_signal(signal.SIGINT)
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_, alive = psutil.wait_procs(family, timeout=3)
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# 2) SIGKILL
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for p in alive:
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p.kill()
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psutil.wait_procs(alive, timeout=3)
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# 3) reap
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current_process.wait(timeout=3)
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return "[✅ 已终止进程树 (SIGINT ➜ SIGKILL fallback)]\n"
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except Exception as exc:
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return f"[❌ 终止失败: {exc}]\n"
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finally:
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current_process = None
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# ---------------- 控制器（仅负责启动） ----------------
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def toggle_run(
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inputs, native, other, output_choices,
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api_url, api_token,
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api_provider, dataset, dataset_file,
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max_tokens, min_tokens, parallel_reqs,
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max_prompt_len, num_requests,
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model_override,
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extra_args,
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is_running,
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run_mode
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):
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global should_stop
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dataset_path = dataset_file.name if dataset_file else None
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if not inputs:
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msg = "[❌ 错误] 必须至少选择一个输入源（API、本地、基准或自定义）才能开始运行。\n"
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yield msg, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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return
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should_stop = False
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if run_mode == "perf":
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yield from run_perf(
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inputs, native, other, output_choices,
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api_url, api_token,
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api_provider, dataset,
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dataset_path,
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max_tokens, min_tokens, parallel_reqs,
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max_prompt_len, num_requests,
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model_override,
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extra_args
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)
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elif run_mode == "eval":
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yield from run_eval_tool(
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inputs, native, other, output_choices,
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api_url, api_token,
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api_provider, dataset,
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dataset_path,
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max_tokens, min_tokens, parallel_reqs,
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max_prompt_len, num_requests,
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model_override,
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extra_args
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)
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elif run_mode == "app":
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info = "[⚠️ 当前为 app 模式，请手动打开 http://localhost:7901 查看报告]\n"
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yield info, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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# ---------------- 输入源互斥逻辑 ----------------
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def enforce_input_exclusive_and_toggle_fields(selected):
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order = ["API Models", "Local Models", "Benchmarks", "Custom Datasets"]
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group1 = {"API Models", "Local Models"}
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group2 = {"Benchmarks", "Custom Datasets"}
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def keep_only_one(group):
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filtered = [item for item in selected if item in group]
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return filtered[-1:]
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final_sel = set(selected)
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final_sel -= group1
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final_sel |= set(keep_only_one(group1))
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final_sel -= group2
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final_sel |= set(keep_only_one(group2))
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final_list = [itm for itm in order if itm in final_sel]
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input_update = gr.update() if list(selected) == final_list else gr.update(value=final_list)
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api_field_update = gr.update(visible="API Models" in final_sel)
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return input_update, api_field_update
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# ---------------- UI 构建 ----------------
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with gr.Blocks(title="EvalScope 全功能界面") as demo:
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is_running = gr.State(value=False)
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# ── 顶栏：模式选择 ─────────────────────────────
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with gr.Group():
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with gr.Row():
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mode_dropdown = gr.Dropdown(
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label="评测类型",
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choices=["eval", "perf", "app"],
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value="perf",
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info="eval: 智力评测；perf: 性能评测；app: 可视化"
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)
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# ── 输入源选择 ────────────────────────────────
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with gr.Group():
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with gr.Row():
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input_choices = gr.CheckboxGroup(
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label="选择输入源",
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choices=["API Models", "Local Models", "Benchmarks", "Custom Datasets"],
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interactive=True
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)
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# ── API 参数 ─────────────────────────────────
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with gr.Column(visible=False) as api_fields:
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api_url_input = gr.Textbox(label="API 地址", placeholder="https://.../v1/chat/completions")
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api_token_input = gr.Textbox(label="Token 密钥", type="password", placeholder="sk-xxx")
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with gr.Accordion("运行参数（可选修改）", open=False):
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with gr.Row():
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api_provider_dropdown = gr.Dropdown(
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label="API Provider",
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choices=["openai", "azure", "ollama", "gemini"],
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value="openai"
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)
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dataset_dropdown = gr.Dropdown(
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label="评测数据集 (--dataset)",
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choices=PERF_DATASETS,
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value=PERF_DATASETS[0]
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)
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dataset_file_input = gr.File(
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label="Line‑by‑line 数据集文件（txt）",
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file_types=[".txt"], # 可改为 ["text/plain"]
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visible=False # 默认隐藏，选了 line_by_line 时再显示
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)
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model_override_input = gr.Textbox(label="自定义模型名 (--model)", placeholder="llm-name")
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extra_args_input = gr.Textbox(label="额外 EvalScope 参数", placeholder="例如: --disable-cache --temperature 0.7")
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with gr.Row():
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max_tokens_slider = gr.Slider(label="Max Tokens", minimum=256, maximum=8192, step=256, value=1024)
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min_tokens_slider = gr.Slider(label="Min Tokens", minimum=0, maximum=4096, step=64, value=1024)
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with gr.Row():
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parallel_slider = gr.Slider(label="并发请求数", minimum=1, maximum=100, step=1, value=1)
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num_req_slider = gr.Slider(label="请求条数", minimum=1, maximum=1000, step=1, value=100)
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max_prompt_len_slider = gr.Slider(
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label="最大 Prompt 长度", minimum=2048, maximum=262144, step=512, value=15360
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)
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# ── 本地/外部模块勾选 ──────────────────────────
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with gr.Row():
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with gr.Column():
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native_choices = gr.CheckboxGroup(
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label="启用本地模块",
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choices=["Model Adapter", "Data Adapter", "Evaluator", "Perf Monitor"]
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)
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with gr.Column():
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other_choices = gr.CheckboxGroup(
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label="启用外部后端",
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choices=["OpenCompass", "VLMEvalKit", "RAGAS", "MTEB/CMTEB"]
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)
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# ── 输出开关 ─────────────────────────────────
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output_choices = gr.CheckboxGroup(
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label="输出形式",
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choices=["Evaluation Report", "Gradio", "WandB", "Swanlab"]
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)
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# ── Run & Stop 按钮 ─────────────────────────
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run_button = gr.Button("Run Evaluation", variant="primary")
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stop_button = gr.Button("Stop Evaluation", variant="stop", visible=False)
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# ── 输出区域 ─────────────────────────────────
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output_text = gr.TextArea(
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label="执行结果", lines=20, interactive=False, show_copy_button=True
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)
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# ── 逻辑绑定 ─────────────────────────────────
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input_choices.change(
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fn=enforce_input_exclusive_and_toggle_fields,
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inputs=input_choices,
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outputs=[input_choices, api_fields]
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)
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mode_dropdown.change(
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lambda mode: gr.update(
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choices=EVAL_DATASETS if mode == "eval" else PERF_DATASETS,
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value=EVAL_DATASETS[0] if mode == "eval" else PERF_DATASETS[0]
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),
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inputs=mode_dropdown,
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outputs=dataset_dropdown
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)
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dataset_dropdown.change(
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toggle_dataset_file_visibility,
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inputs=dataset_dropdown,
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outputs=dataset_file_input
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)
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# ---- Run 按钮（queue=True）----
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run_button.click(
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fn=toggle_run,
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inputs=[
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input_choices, native_choices, other_choices,
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output_choices,
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api_url_input, api_token_input,
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api_provider_dropdown, dataset_dropdown, dataset_file_input,
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max_tokens_slider, min_tokens_slider, parallel_slider,
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max_prompt_len_slider, num_req_slider,
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model_override_input,
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extra_args_input,
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is_running,
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mode_dropdown
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],
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outputs=[output_text, is_running, run_button, stop_button],
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show_progress=True,
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queue=True
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)
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# ---- Stop 按钮（queue=False）----
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def stop_action():
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msg = stop_eval()
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return msg, False, gr.update(value="Run Evaluation", interactive=True), gr.update(visible=False)
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stop_button.click(
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fn=stop_action,
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inputs=None,
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outputs=[output_text, is_running, run_button, stop_button],
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queue=False
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)
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# ---------------- 入口 ----------------
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=7900)
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