Litellm Backend (#502)

python/pyproject.toml

@@ -23,7 +23,8 @@ srt = ["aiohttp", "fastapi", "psutil", "rpyc", "torch", "uvloop", "uvicorn",
        "zmq", "vllm==0.4.3", "interegular", "pydantic", "pillow", "packaging", "huggingface_hub", "hf_transfer", "outlines>=0.0.34"]
 openai = ["openai>=1.0", "numpy", "tiktoken"]
 anthropic = ["anthropic>=0.20.0", "numpy"]
-all = ["sglang[srt]", "sglang[openai]", "sglang[anthropic]"]
+litellm = ["litellm>=1.0.0"]
+all = ["sglang[srt]", "sglang[openai]", "sglang[anthropic]", "sglang[litellm]"]
 
 [project.urls]
 "Homepage" = "https://github.com/sgl-project/sglang"

python/sglang/__init__.py

@@ -27,6 +27,7 @@ from sglang.backend.anthropic import Anthropic
 from sglang.backend.openai import OpenAI
 from sglang.backend.runtime_endpoint import RuntimeEndpoint
 from sglang.backend.vertexai import VertexAI
+from sglang.backend.litellm import LiteLLM
 
 # Global Configurations
 from sglang.global_config import global_config
@@ -35,6 +36,7 @@ from sglang.global_config import global_config
 __all__ = [
     "global_config",
     "Anthropic",
+    "LiteLLM",
     "OpenAI",
     "RuntimeEndpoint",
     "VertexAI",

python/sglang/backend/litellm.py

+from typing import Mapping, Optional
+
+from sglang.backend.base_backend import BaseBackend
+from sglang.lang.chat_template import get_chat_template_by_model_path
+from sglang.lang.interpreter import StreamExecutor
+from sglang.lang.ir import SglSamplingParams
+
+try:
+    import litellm
+except ImportError as e:
+    litellm = e
+
+
+class LiteLLM(BaseBackend):
+
+    def __init__(
+        self,
+        model_name,
+        chat_template=None,
+        api_key=None,
+        organization: Optional[str] = None,
+        base_url: Optional[str] = None,
+        timeout: Optional[float] = 600,
+        max_retries: Optional[int] = litellm.num_retries,
+        default_headers: Optional[Mapping[str, str]] = None,
+    ):
+        super().__init__()
+
+        if isinstance(litellm, Exception):
+            raise litellm
+
+        self.model_name = model_name
+
+        self.chat_template = chat_template or get_chat_template_by_model_path(
+            model_name)
+
+        self.client_params = {
+            "api_key": api_key,
+            "organization": organization,
+            "base_url": base_url,
+            "timeout": timeout,
+            "max_retries": max_retries,
+            "default_headers": default_headers,
+        }
+
+    def get_chat_template(self):
+        return self.chat_template
+
+    def generate(
+        self,
+        s: StreamExecutor,
+        sampling_params: SglSamplingParams,
+    ):
+        if s.messages_:
+            messages = s.messages_
+        else:
+            messages = [{"role": "user", "content": s.text_}]
+
+        ret = litellm.completion(
+            model=self.model_name,
+            messages=messages,
+            **self.client_params,
+            **sampling_params.to_anthropic_kwargs(),
+        )
+        comp = ret.choices[0].message.content
+
+        return comp, {}
+
+    def generate_stream(
+        self,
+        s: StreamExecutor,
+        sampling_params: SglSamplingParams,
+    ):
+        if s.messages_:
+            messages = s.messages_
+        else:
+            messages = [{"role": "user", "content": s.text_}]
+
+        ret = litellm.completion(
+            model=self.model_name,
+            messages=messages,
+            stream=True,
+            **self.client_params,
+            **sampling_params.to_litellm_kwargs(),
+        )
+        for chunk in ret:
+            text = chunk.choices[0].delta.content
+            if text is not None:
+                yield text, {}

python/sglang/lang/ir.py

@@ -82,6 +82,21 @@ class SglSamplingParams:
             "top_k": self.top_k,
         }
         
+    def to_litellm_kwargs(self):
+        if self.regex is not None:
+            warnings.warn(
+                "Regular expression is not supported in the LiteLLM backend."
+            )
+        return {
+            "max_tokens": self.max_new_tokens,
+            "stop": self.stop or None,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "top_k": self.top_k,
+            "frequency_penalty": self.frequency_penalty,
+            "presence_penalty": self.presence_penalty,
+        }
+
     def to_srt_kwargs(self):
         return {
             "max_new_tokens": self.max_new_tokens,

test/lang/test_litellm_backend.py

+import json
+import unittest
+
+from sglang import LiteLLM, set_default_backend
+from sglang.test.test_programs import test_mt_bench, test_stream
+
+
+class TestAnthropicBackend(unittest.TestCase):
+    backend = None
+    chat_backend = None
+
+    def setUp(self):
+        cls = type(self)
+
+        if cls.backend is None:
+            cls.backend = LiteLLM("gpt-3.5-turbo")
+            set_default_backend(cls.backend)
+
+    def test_mt_bench(self):
+        test_mt_bench()
+
+    def test_stream(self):
+        test_stream()
+
+
+if __name__ == "__main__":
+    unittest.main(warnings="ignore")