add managed vllm server

atroposlib/envs/server_handling/vllm_server.py

@@ -0,0 +1,344 @@
+# This requires a customized vLLM api server
+# see example_trainer/vllm_api_server.py for an example
+
+import asyncio
+import warnings
+
+import aiohttp
+import openai
+from openai.types.chat.chat_completion import ChatCompletion
+from openai.types.completion import Completion
+from pydantic_cli import FailedExecutionException
+from transformers import AutoTokenizer
+
+from atroposlib.envs.constants import NAMESPACE_SEP, OPENAI_NAMESPACE
+from atroposlib.envs.server_handling.server_baseline import APIServer, APIServerConfig
+
+
+class VLLMServer(APIServer):
+    """
+    VLLM server handling.
+    """
+
+    def __init__(self, config: APIServerConfig):
+        self.openai = openai.AsyncClient(
+            api_key=config.api_key,
+            base_url=config.base_url,
+            timeout=config.timeout,
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(config.model_name)
+        super().__init__(config)
+
+    async def check_server_status_task(self, chat_completion: bool = True):
+        while True:
+            try:
+                async with aiohttp.ClientSession() as session:
+                    async with session.get(
+                        f"{self.config.base_url.replace('/v1', '')}/health",
+                        headers=(
+                            {"Authorization": f"Bearer {self.config.api_key}"}
+                            if self.config.api_key
+                            else {}
+                        ),
+                        timeout=aiohttp.ClientTimeout(total=self.config.timeout),
+                    ) as response:
+                        response.raise_for_status()
+                self.server_healthy = True
+            except (
+                aiohttp.ClientError,
+                openai.OpenAIError,
+                openai.APITimeoutError,
+                Exception,
+            ):
+                self.server_healthy = False
+            await asyncio.sleep(1)
+
+    async def _chat_completion_wrapper(self, **kwargs) -> ChatCompletion:
+        """
+        Wrapper for the chat completion using the openai client.
+        """
+        assert (
+            kwargs.get("model", None) is not None
+        ), "Model is required for chat completion!"
+        assert (
+            kwargs.get("messages", None) is not None
+        ), "Messages are required for chat completion!"
+        if self.config.n_kwarg_is_ignored:
+            n = kwargs.pop("n", 1)
+            completion_list = await asyncio.gather(
+                *[self.openai.chat.completions.create(**kwargs) for _ in range(n)]
+            )
+            completions = completion_list[0]
+            if n > 1:
+                for c in completion_list[1:]:
+                    completions.choices.extend(c.choices)
+            else:
+                completions = await self.openai.chat.completions.create(**kwargs)
+        else:
+            if "n" in kwargs:
+                n = kwargs["n"]
+            else:
+                n = 1
+            completions = await self.openai.chat.completions.create(**kwargs)
+            if len(completions.choices) != n:
+                if len(completions.choices) != 1:
+                    raise ValueError(
+                        f"Expected 1 or {n} completions, got {len(completions.choices)}!"
+                    )
+                else:
+                    warnings.warn("n kwarg is ignored by the API, setting to True")
+                    self.config.n_kwarg_is_ignored = True
+                    completion_list = await asyncio.gather(
+                        *[
+                            self.openai.chat.completions.create(**kwargs)
+                            for _ in range(1, n)
+                        ]
+                    )
+                    for c in completion_list:
+                        completions.choices.extend(c.choices)
+        return completions
+
+    async def _completion_wrapper(self, **kwargs) -> Completion:
+        """
+        Wrapper for the completion using the openai client.
+        """
+        assert (
+            kwargs.get("model", None) is not None
+        ), "Model is required for completion!"
+        assert (
+            kwargs.get("prompt", None) is not None
+        ), "Prompt is required for completion!"
+        if self.config.n_kwarg_is_ignored:
+            n = kwargs.pop("n", 1)
+            completion_list = await asyncio.gather(
+                *[self.openai.completions.create(**kwargs) for _ in range(n)]
+            )
+            completions = completion_list[0]
+            if n > 1:
+                for c in completion_list[1:]:
+                    completions.choices.extend(c.choices)
+        else:
+            if "n" in kwargs:
+                n = kwargs["n"]
+            else:
+                n = 1
+            completions = await self.openai.completions.create(**kwargs)
+            if len(completions.choices) != n:
+                if len(completions.choices) != 1:
+                    raise ValueError(
+                        f"Expected 1 or {n} completions, got {len(completions.choices)}!"
+                    )
+                else:
+                    warnings.warn("n kwarg is ignored by the API, setting to True")
+                    self.config.n_kwarg_is_ignored = True
+                    completion_list = await asyncio.gather(
+                        *[self.openai.completions.create(**kwargs) for _ in range(1, n)]
+                    )
+                    for c in completion_list:
+                        completions.choices.extend(c.choices)
+        return completions
+
+    async def _tokens_and_logprobs_completion_wrapper(
+        self, **kwargs
+    ) -> tuple[list, list, list, list]:
+        """
+        Wrapper for tokens and logprobs completion using VLLM's native API.
+        Returns a tuple of (prompt_tokens, output_tokens, output_logprobs, finish_reasons).
+        Each element is a list of lists (one per completion in the batch).
+        """
+        assert (
+            kwargs.get("model", None) is not None
+        ), "Model is required for completion!"
+        assert (
+            kwargs.get("prompt", None) is not None
+            or kwargs.get("input_ids", None) is not None
+        ), "Prompt or input_ids is required for completion!"
+
+        # Use input_ids if provided (from ManagedServer), otherwise tokenize prompt
+        if "input_ids" in kwargs:
+            prompt_tokens = kwargs.pop("input_ids")
+            kwargs.pop("prompt", None)  # Remove prompt if it exists
+        else:
+            prompt_tokens = self.tokenizer.encode(kwargs.pop("prompt"))
+
+        # Check for double BOS token, can happen if you use chat templates and forget that they insert a BOS token
+        if (
+            len(prompt_tokens) >= 2
+            and prompt_tokens[0] == self.tokenizer.bos_token_id == prompt_tokens[1]
+        ):
+            prompt_tokens = prompt_tokens[1:]
+        if "max_new_tokens" in kwargs:
+            kwargs["max_tokens"] = kwargs.pop("max_new_tokens")
+        if "model" in kwargs:
+            kwargs.pop("model")
+        # Prepare request for VLLM native API
+        request_data = {"prompt": {"prompt_token_ids": prompt_tokens}, "logprobs": 0}
+        request_data.update(kwargs)
+
+        # Make async request to VLLM /generate endpoint
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                f"{self.config.base_url.replace('/v1', '')}/generate",
+                json=request_data,
+                headers=(
+                    {"Authorization": f"Bearer {self.config.api_key}"}
+                    if self.config.api_key
+                    else {}
+                ),
+                timeout=aiohttp.ClientTimeout(total=self.config.timeout),
+            ) as response:
+                response.raise_for_status()
+                results = await response.json()
+        output_tokens_list = []
+        output_logprobs_list = []
+        finish_reasons_list = []
+        for output_token_logprobs, finish_reason in zip(
+            results["logprobs"], results["finish_reasons"]
+        ):
+            logprobs = [
+                list(item[0].values())[0] for item in output_token_logprobs
+            ]  # Extract logprob from [{id: logprob}]
+            output_ids = [
+                int(list(item[0].keys())[0]) for item in output_token_logprobs
+            ]  # Extract token ID from [{id: logprob}]
+
+            # Get finish reason
+            finish_reason = finish_reason
+
+            output_tokens_list.append(output_ids)
+            output_logprobs_list.append(logprobs)
+            finish_reasons_list.append(finish_reason)
+
+        return (
+            prompt_tokens,
+            output_tokens_list,
+            output_logprobs_list,
+            finish_reasons_list,
+        )
+
+
+def resolve_openai_configs(
+    default_server_configs,
+    openai_config_dict,
+    yaml_config,
+    cli_passed_flags,
+    logger,
+):
+    """
+    Helper to resolve the final server_configs, handling single, multiple servers, and overrides.
+    """
+    from atroposlib.envs.server_handling.server_manager import ServerBaseline
+
+    openai_full_prefix = f"{OPENAI_NAMESPACE}{NAMESPACE_SEP}"
+    openai_yaml_config = yaml_config.get(OPENAI_NAMESPACE, None)
+    openai_cli_config = {
+        k: v for k, v in cli_passed_flags.items() if k.startswith(openai_full_prefix)
+    }
+
+    is_multi_server_yaml = (
+        isinstance(openai_yaml_config, list) and len(openai_yaml_config) >= 2
+    )
+    is_multi_server_default = (
+        (not is_multi_server_yaml)
+        and isinstance(default_server_configs, list)
+        and len(default_server_configs) >= 2
+    )
+
+    if (is_multi_server_yaml or is_multi_server_default) and openai_cli_config:
+        raise FailedExecutionException(
+            message=f"CLI overrides for OpenAI settings (--{openai_full_prefix}*) are not supported "
+            f"when multiple servers are defined (either via YAML list under '{OPENAI_NAMESPACE}' "
+            "or a default list with length >= 2).",
+            exit_code=2,
+        )
+
+    if is_multi_server_yaml:
+        logger.info(
+            f"Using multi-server configuration defined in YAML under '{OPENAI_NAMESPACE}'."
+        )
+        try:
+            server_configs = [APIServerConfig(**cfg) for cfg in openai_yaml_config]
+        except Exception as e:
+            raise FailedExecutionException(
+                f"Error parsing multi-server OpenAI configuration from YAML under '{OPENAI_NAMESPACE}': {e}"
+            ) from e
+    elif isinstance(default_server_configs, ServerBaseline):
+        logger.info("Using ServerBaseline configuration.")
+        server_configs = default_server_configs
+    elif is_multi_server_default:
+        logger.info("Using default multi-server configuration (length >= 2).")
+        server_configs = default_server_configs
+    else:
+        logger.info(
+            "Using single OpenAI server configuration based on merged settings (default/YAML/CLI)."
+        )
+        try:
+            final_openai_config = APIServerConfig(**openai_config_dict)
+        except Exception as e:
+            raise FailedExecutionException(
+                f"Error creating final OpenAI configuration from merged settings: {e}\n"
+                f"Merged Dict: {openai_config_dict}"
+            ) from e
+
+        if isinstance(default_server_configs, APIServerConfig):
+            server_configs = final_openai_config
+        elif isinstance(default_server_configs, list):
+            server_configs = [final_openai_config]
+        else:
+            logger.warning(
+                f"Unexpected type for default_server_configs: {type(default_server_configs)}. "
+                f"Proceeding with single OpenAI server configuration based on merged settings."
+            )
+            server_configs = [final_openai_config]
+
+    return server_configs
+
+
+if __name__ == "__main__":
+
+    async def test_tokens_and_logprobs():
+        # Configure the server - update these values for your setup
+        config = APIServerConfig(
+            api_key="",  # Add your API key if needed
+            base_url="http://localhost:8000",  # Update to your VLLM server URL
+            model_name="Qwen/Qwen2.5-7B",  # Update to your model name
+            timeout=120,
+        )
+
+        server = VLLMServer(config)
+
+        # Test the tokens_and_logprobs_completion method
+        print("Testing tokens_and_logprobs_completion...")
+        try:
+            prompt_tokens, output_tokens, output_logprobs, finish_reasons = (
+                await server.tokens_and_logprobs_completion(
+                    prompt="The capital of France is",
+                    n=4,
+                    max_tokens=32,
+                    temperature=1.0,
+                    top_p=1.0,
+                    stop=["User:", "Human:", "Assistant:", "</answer>"],
+                )
+            )
+
+            print("\nResults:")
+            print(f"Prompt tokens: {prompt_tokens}")
+            print(f"Output tokens: {output_tokens}")
+            print(f"Output logprobs (first 5): {[lp[:5] for lp in output_logprobs]}")
+            print(f"Finish reasons: {finish_reasons}")
+            print(f"\nNumber of completions: {len(output_tokens)}")
+            print(f"Output length: {[len(tokens) for tokens in output_tokens]}")
+            responses = "\n\n".join(
+                [server.tokenizer.decode(tokens) for tokens in output_tokens]
+            )
+            print(f"Responses:\n-{responses}")
+            print(f"Health: {server.server_healthy}")
+
+        except Exception as e:
+            print(f"Error: {e}")
+            import traceback
+
+            traceback.print_exc()
+
+    # Run the test
+    asyncio.run(test_tokens_and_logprobs())