chore: import upstream snapshot with attribution

python/ray/llm/examples/sglang/batch_sglang_example.py

@@ -0,0 +1,49 @@
+"""Batch inference with SGLang using Ray Data.
+
+Usage:
+    RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=0 python batch_sglang_example.py
+"""
+
+import ray
+from ray.data.llm import SGLangEngineProcessorConfig, build_processor
+
+config = SGLangEngineProcessorConfig(
+    model_source="unsloth/Llama-3.1-8B-Instruct",
+    engine_kwargs=dict(
+        dtype="half",
+        mem_fraction_static=0.8,
+    ),
+    batch_size=32,
+    concurrency=1,
+)
+
+processor = build_processor(
+    config,
+    preprocess=lambda row: dict(
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": row["prompt"]},
+        ],
+        sampling_params=dict(
+            temperature=0.7,
+            max_new_tokens=256,
+        ),
+    ),
+    postprocess=lambda row: dict(
+        prompt=row["prompt"],
+        response=row["generated_text"],
+    ),
+)
+
+ds = ray.data.from_items(
+    [
+        {"prompt": "What is the capital of France?"},
+        {"prompt": "Explain photosynthesis in one sentence."},
+        {"prompt": "Write a haiku about programming."},
+    ]
+)
+ds = processor(ds)
+
+for row in ds.take_all():
+    print(f"Prompt: {row['prompt']}")
+    print(f"Response: {row['response']}\n")