chore: import upstream snapshot with attribution

python/sglang/srt/entrypoints/http_server_engine.py

@@ -0,0 +1,146 @@
+import multiprocessing
+import time
+from typing import List, Optional, Tuple
+
+import requests
+import torch
+
+from sglang.srt.entrypoints.EngineBase import EngineBase
+from sglang.srt.entrypoints.http_server import launch_server
+from sglang.srt.server_args import ServerArgs
+from sglang.srt.utils import MultiprocessingSerializer, kill_process_tree
+
+
+def launch_server_process(server_args: ServerArgs) -> multiprocessing.Process:
+
+    p = multiprocessing.Process(target=launch_server, args=(server_args,))
+    p.start()
+
+    base_url = server_args.url()
+    timeout = 300.0  # Increased timeout to 5 minutes for downloading large models
+    start_time = time.perf_counter()
+
+    ssl_verify = server_args.ssl_verify()
+
+    with requests.Session() as session:
+        while time.perf_counter() - start_time < timeout:
+            try:
+                headers = {
+                    "Content-Type": "application/json; charset=utf-8",
+                    "Authorization": f"Bearer {server_args.api_key}",
+                }
+                response = session.get(
+                    f"{base_url}/health_generate", headers=headers, verify=ssl_verify
+                )
+                if response.status_code == 200:
+                    return p
+            except requests.RequestException:
+                pass
+
+            if not p.is_alive():
+                raise Exception("Server process terminated unexpectedly.")
+
+            time.sleep(2)
+
+    p.terminate()
+    raise TimeoutError("Server failed to start within the timeout period.")
+
+
+class HttpServerEngineAdapter(EngineBase):
+    """
+    You can use this class to launch a server from a VerlEngine instance.
+    We recommend using this class only you need to use http server.
+    Otherwise, you can use Engine directly.
+    """
+
+    def __init__(self, **kwargs):
+        self.server_args = ServerArgs(**kwargs)
+        print(
+            f"Launch HttpServerEngineAdapter at: {self.server_args.host}:{self.server_args.port}"
+        )
+        self.process = launch_server_process(self.server_args)
+
+    def _make_request(self, endpoint: str, payload: Optional[dict] = None):
+        """Make a POST request to the specified endpoint with the given payload.
+        Args:
+            endpoint: The API endpoint to call
+            payload: The JSON payload to send (default: empty dict)
+        Returns:
+            The JSON response from the server
+        """
+        url = f"{self.server_args.url()}/{endpoint}"
+        response = requests.post(
+            url, json=payload or {}, verify=self.server_args.ssl_verify()
+        )
+        response.raise_for_status()
+        return response.json()
+
+    def update_weights_from_tensor(
+        self,
+        named_tensors: List[Tuple[str, torch.Tensor]],
+        load_format: Optional[str] = None,
+        flush_cache: bool = False,
+    ):
+        """
+        Update model weights from tensor data. The HTTP server will only post meta data, and the real weights will be copied directly from GPUs.
+        Note: The model should be on GPUs rather than CPU for this functionality to work properly.
+        If you encounter issues, ensure your model is loaded on GPU devices rather than CPU.
+        """
+
+        return self._make_request(
+            "update_weights_from_tensor",
+            {
+                "serialized_named_tensors": [
+                    MultiprocessingSerializer.serialize(named_tensors, output_str=True)
+                    for _ in range(self.server_args.tp_size)
+                ],
+                "load_format": load_format,
+                "flush_cache": flush_cache,
+            },
+        )
+
+    def shutdown(self):
+        kill_process_tree(self.process.pid, wait_timeout=60)
+
+    def generate(
+        self,
+        prompt=None,
+        sampling_params=None,
+        input_ids=None,
+        image_data=None,
+        return_logprob=False,
+        logprob_start_len=None,
+        top_logprobs_num=None,
+        token_ids_logprob=None,
+        lora_path=None,
+        custom_logit_processor=None,
+        priority=None,
+        session_id=None,
+    ):
+        payload = {
+            "text": prompt,
+            "sampling_params": sampling_params,
+            "input_ids": input_ids,
+            "image_data": image_data,
+            "return_logprob": return_logprob,
+            "logprob_start_len": logprob_start_len,
+            "top_logprobs_num": top_logprobs_num,
+            "token_ids_logprob": token_ids_logprob,
+            "lora_path": lora_path,
+            "custom_logit_processor": custom_logit_processor,
+            "priority": priority,
+            "session_id": session_id,
+        }
+        # Filter out None values
+        payload = {k: v for k, v in payload.items() if v is not None}
+
+        return self._make_request("generate", payload)
+
+    def release_memory_occupation(self):
+        return self._make_request("release_memory_occupation")
+
+    def resume_memory_occupation(self):
+        return self._make_request("resume_memory_occupation")
+
+    def flush_cache(self):
+        return self._make_request("flush_cache")