.

gradio_ui.py

@@ -188,24 +188,47 @@ def run_eval_tool(
 
 # ---------------- 停止函数 ----------------
 def stop_eval():
+    """
+    彻底终止 current_process 及其全部子孙进程：
+    1. 先发 SIGINT（Ctrl‑C）尝试优雅退出
+    2. 3 秒内仍存活的进程升级为 SIGKILL
+    3. 最后 wait() 主进程，防止僵尸
+    """
     global current_process, should_stop
     should_stop = True
 
-    if current_process and current_process.poll() is None:
-        try:
-            pgid = os.getpgid(current_process.pid)
-            os.killpg(pgid, signal.SIGINT)
-            time.sleep(2)
-            if current_process.poll() is None:
-                os.killpg(pgid, signal.SIGKILL)
-            return "[✅ 已发送终止信号 (SIGINT → SIGKILL fallback)]\n"
-        except Exception as e:
-            return f"[❌ 终止失败: {e}]\n"
-        finally:
-            current_process = None
-    else:
+    if not (current_process and current_process.poll() is None):
         return "[⚠️ 无活动 evalscope 进程]\n"
 
+    try:
+        parent = psutil.Process(current_process.pid)
+        family = parent.children(recursive=True) + [parent]  # 整棵进程树
+
+        # ── 1) 尝试优雅终止 ──────────────────────
+        for p in family:
+            p.send_signal(signal.SIGINT)
+
+        # 给 10 秒宽限期
+        _, alive = psutil.wait_procs(family, timeout=10)
+
+        # ── 2) 强制 kill 仍存活的 ────────────────
+        for p in alive:
+            p.kill()
+        psutil.wait_procs(alive, timeout=10)
+
+        # ── 3) 回收僵尸，确保句柄关闭 ────────────
+        current_process.wait(timeout=10)
+
+        return "[✅ 已终止进程树 (SIGINT ➜ SIGKILL fallback)]\n"
+
+    except Exception as e:
+        return f"[❌ 终止失败: {e}]\n"
+
+    finally:
+        current_process = None
+
+
+
 # ---------------- 控制器 ----------------
 def toggle_run(
     inputs, native, other, output_choices,
@@ -300,14 +323,14 @@ with gr.Blocks(title="EvalScope 全功能界面") as demo:
             with gr.Row():
                 api_provider_dropdown = gr.Dropdown(label="API Provider", choices=["openai", "azure", "ollama", "gemini"], value="openai")
                 dataset_dropdown = gr.Dropdown(label="评测数据集 (--dataset)", choices=PERF_DATASETS, value=PERF_DATASETS[0])
-            model_override_input = gr.Textbox(label="自定义模型名 (--model)", placeholder="my-llm")
+            model_override_input = gr.Textbox(label="自定义模型名 (--model)", placeholder="llm-name")
             with gr.Row():
                 max_tokens_slider = gr.Slider(label="Max Tokens", minimum=256, maximum=8192, step=256, value=1024)
                 min_tokens_slider = gr.Slider(label="Min Tokens", minimum=0, maximum=4096, step=64, value=1024)
             with gr.Row():
-                parallel_slider = gr.Slider(label="并发请求数", minimum=1, maximum=16, step=1, value=1)
+                parallel_slider = gr.Slider(label="并发请求数", minimum=1, maximum=100, step=1, value=1)
                 num_req_slider = gr.Slider(label="请求条数", minimum=1, maximum=1000, step=1, value=100)
-            max_prompt_len_slider = gr.Slider(label="最大 Prompt 长度", minimum=2048, maximum=32768, step=512, value=15360)
+            max_prompt_len_slider = gr.Slider(label="最大 Prompt 长度", minimum=2048, maximum=262144, step=512, value=15360)
 
     with gr.Row():
         with gr.Column():