adapt to sglang v0.5.2rc1 on dcu

examples/runtime/vertex_predict.py

@@ -0,0 +1,66 @@
+"""
+Usage:
+python -m sglang.launch_server --model meta-llama/Llama-2-7b-hf --port 30000
+python vertex_predict.py
+
+This example shows the request and response formats of the prediction route for
+Google Cloud Vertex AI Online Predictions.
+
+Vertex AI SDK for Python is recommended for deploying models to Vertex AI
+instead of a local server. After deploying the model to a Vertex AI Online
+Prediction Endpoint, send requests via the Python SDK:
+
+response = endpoint.predict(
+    instances=[
+        {"text": "The capital of France is"},
+        {"text": "What is a car?"},
+    ],
+    parameters={"sampling_params": {"max_new_tokens": 16}},
+)
+print(response.predictions)
+
+More details about get online predictions from Vertex AI can be found at
+https://cloud.google.com/vertex-ai/docs/predictions/get-online-predictions.
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+
+import requests
+
+
+@dataclass
+class VertexPrediction:
+    predictions: List
+
+
+class LocalVertexEndpoint:
+    def __init__(self) -> None:
+        self.base_url = "http://127.0.0.1:30000"
+
+    def predict(self, instances: List[dict], parameters: Optional[dict] = None):
+        response = requests.post(
+            self.base_url + "/vertex_generate",
+            json={
+                "instances": instances,
+                "parameters": parameters,
+            },
+        )
+        return VertexPrediction(predictions=response.json()["predictions"])
+
+
+endpoint = LocalVertexEndpoint()
+
+# Predict with a single prompt.
+response = endpoint.predict(instances=[{"text": "The capital of France is"}])
+print(response.predictions)
+
+# Predict with multiple prompts and parameters.
+response = endpoint.predict(
+    instances=[
+        {"text": "The capital of France is"},
+        {"text": "What is a car?"},
+    ],
+    parameters={"sampling_params": {"max_new_tokens": 16}},
+)
+print(response.predictions)