feat: 模型推理端到端闭环 — 真实流式推理 + 释放/删除 + GPU 状态同步

后端 (platform.py + platform_store.py):
- 新增 _build_messages_payload() 转换前端格式为 OpenAI messages
- 新增 _stream_chat_proxy() SSE 流式代理到算力节点
- 新增 _unload_from_compute_node() 真正释放算力节点 GPU 显存
- 重写 model_compare_load: 从假 PID/端口改为真正调用算力节点加载模型
- 修复 model_compare_unload: 调用 _unload_from_compute_node 释放 GPU
- 修复 model_compare_delete: 先释放 GPU 再删除记录
- 修复 model_compare_stream_chat: 从 mock 改为 StreamingResponse 代理
- 修复 model_chat_local/stream: 消息格式转换 + 路径修正
- PlatformStore 新增 _inference_nodes 追踪，gpus() 同步推理占用状态
- preload/unload 端点标记/清除推理节点占用

算力节点 (compute):
- inference.py: 适配新版 LLaMA-Factory API (get_infer_args 4 返回值、ChatModel args dict、stream_chat 新签名)
- inference.py: unload() 增加 gc.collect + torch.cuda.empty_cache + synchronize 彻底释放显存
- main.py: inference/load 移除 HTTPException(500)，错误以 200 正常返回

前端:
- InferenceChatView: 真实模式下走 SSE 流式推理，mock 模式保留兼容
- InferenceCreateView: 调用 preloadLocalModel + createCompare 真实创建推理任务，失败回退 mock
- InferenceListView: 「停止」改为「释放」，删除前先释放算力节点，改进错误提示
- compare.ts: 新增 streamChatReal() fetch SSE，preload 超时提升至 5 分钟
- useStreamChat.ts: send() 支持 useMock 参数，真实模式调用 streamChatReal
- GPU 选择过滤: 仅显示在线算力节点上的空闲 GPU

Co-Authored-By: Claude <noreply@anthropic.com>

compute/engines/llama_factory/inference.py

@@ -59,10 +59,18 @@ class InferenceSession:
                 if adapter_name_or_path:
                     args["adapter_name_or_path"] = adapter_name_or_path
                 args.update(kwargs)
-                model_args, generating_args = get_infer_args(args)
-                self._model = ChatModel(model_args)
-                self._tokenizer = self._model.tokenizer
-                self._generating_args = generating_args
+                infer_result = get_infer_args(args)
+                # ChatModel internally re-parses the args dict via get_infer_args,
+                # so pass the original args (not the parsed dataclass objects).
+                self._model = ChatModel(args)
+                self._tokenizer = getattr(self._model, 'tokenizer', None) or self._model.engine.tokenizer
+                # Extract generating_args (last element) for later use in chat()
+                generating_args = infer_result[-1]
+                if hasattr(generating_args, '__dataclass_fields__'):
+                    self._generating_args = {k: v for k, v in vars(generating_args).items()
+                                              if not k.startswith('_')}
+                else:
+                    self._generating_args = dict(generating_args)
                 self._loaded_at = time.time()
                 self._status = "ready"
                 return {"loaded": True, "status": "ready"}
@@ -80,6 +88,16 @@ class InferenceSession:
                     pass
                 self._model = None
                 self._tokenizer = None
+                # 强制释放 PyTorch CUDA 缓存，真正归还 GPU 显存
+                try:
+                    import gc
+                    gc.collect()
+                    import torch
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+                        torch.cuda.synchronize()
+                except Exception:
+                    pass
             self._status = "idle"
             self._model_name = ""
             self._adapter_path = ""
@@ -91,11 +109,12 @@ class InferenceSession:
             if self._status != "ready" or self._model is None:
                 return {"error": "model not loaded", "response": ""}
             try:
-                generate_kwargs = {**self._generating_args, "temperature": temperature, "top_p": top_p, "max_new_tokens": max_new_tokens, "do_sample": do_sample}
+                generate_kwargs = {"temperature": temperature, "top_p": top_p, "max_new_tokens": max_new_tokens, "do_sample": do_sample}
                 generate_kwargs.update(kwargs)
-                formatted = self._model.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+                system = next((m["content"] for m in messages if m["role"] == "system"), None)
+                user_messages = [m for m in messages if m["role"] != "system"]
                 responses = []
-                for response in self._model.stream_chat(formatted, generate_kwargs):
+                for response in self._model.stream_chat(user_messages, system=system, **generate_kwargs):
                     responses.append(response)
                 full_response = "".join(str(r) for r in responses)
                 return {"response": full_response}
@@ -108,9 +127,10 @@ class InferenceSession:
                 yield 'data: {"error": "model not loaded"}\n\n'
                 return
             try:
-                generate_kwargs = {**self._generating_args, **kwargs}
-                formatted = self._model.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-                for new_text in self._model.stream_chat(formatted, generate_kwargs):
+                generate_kwargs = {**kwargs}
+                system = next((m["content"] for m in messages if m["role"] == "system"), None)
+                user_messages = [m for m in messages if m["role"] != "system"]
+                for new_text in self._model.stream_chat(user_messages, system=system, **generate_kwargs):
                     yield new_text
             except Exception as exc:
                 yield 'data: {"error": "' + str(exc) + '"}\n\n'