diff --git a/Containerfile.vllm b/Containerfile.vllm new file mode 100644 index 000000000..f86def8ca --- /dev/null +++ b/Containerfile.vllm @@ -0,0 +1,53 @@ +# Base image: vllm/vllm-openai (has vLLM pre-installed). Override with build arg. +ARG BASE_IMAGE=vllm/vllm-openai + +FROM $BASE_IMAGE + +# release: take the last version and add a post if build iteration +# candidate: increment to next minor, add 'rc' with build iteration +# nightly: increment to next minor, add 'a' with build iteration +# alpha: increment to next minor, add 'a' with build iteration +# dev: increment to next minor, add 'dev' with build iteration +ARG GUIDELLM_BUILD_TYPE=dev + +# Extra dependencies to install (e.g. recommended, all) +ARG GUIDELLM_BUILD_EXTRAS=recommended,audio + +# Switch to root for installing system deps and pip install +USER root + +# Install git for setuptools-git-versioning (version discovery) +RUN apt-get update && apt-get install -y --no-install-recommends git \ + && rm -rf /var/lib/apt/lists/* + +ENV GUIDELLM_BUILD_TYPE=$GUIDELLM_BUILD_TYPE + +# Copy repository and install GuideLLM from source with pip (no uv, to avoid +# conflicting with the pre-installed vLLM in the base image) +COPY / /src +WORKDIR /src +RUN pip install --no-cache-dir ".[${GUIDELLM_BUILD_EXTRAS}]" + +# Metadata +LABEL io.k8s.display-name="GuideLLM" \ + org.opencontainers.image.description="GuideLLM Performance Benchmarking Container" \ + org.opencontainers.image.source="https://github.com/vllm-project/guidellm" \ + org.opencontainers.image.documentation="https://blog.vllm.ai/guidellm/stable" \ + org.opencontainers.image.license="Apache-2.0" + +ENV HOME="/home/guidellm" \ + GUIDELLM_OUTPUT_DIR="/results" + +WORKDIR $HOME + +# Ensure that the user home dir can be used by any user +# (OpenShift Pods can't use the cache otherwise) +RUN chgrp -R 0 "$HOME" && chmod -R g=u "$HOME" + +VOLUME /results + +# Root group for k8s +USER 1001:0 + +ENTRYPOINT [ "guidellm" ] +CMD [ "benchmark", "run" ] diff --git a/docs/getting-started/benchmark.md b/docs/getting-started/benchmark.md index 11a04d19a..2d46df3c6 100644 --- a/docs/getting-started/benchmark.md +++ b/docs/getting-started/benchmark.md @@ -4,7 +4,7 @@ weight: -6 # Run a Benchmark -After [installing GuideLLM](install.md) and [starting a server](server.md), you're ready to run benchmarks to evaluate your LLM deployment's performance. +After [installing GuideLLM](install.md) and [starting a server](server.md), you're ready to run benchmarks to evaluate your LLM deployment's performance. Alternatively, you can run benchmarks with the vLLM Python backend (`--backend vllm_python`) without a separate server; see [vLLM Python backend](../guides/vllm-python-backend.md). Running a GuideLLM benchmark is straightforward. The basic command structure is: diff --git a/docs/getting-started/install.md b/docs/getting-started/install.md index 73421ecc7..4e47e2f64 100644 --- a/docs/getting-started/install.md +++ b/docs/getting-started/install.md @@ -86,6 +86,8 @@ guidellm --help This should display the installed version of GuideLLM. +To use the vLLM Python backend (in-process inference), see [vLLM Python backend](../guides/vllm-python-backend.md) for recommended installation (container or existing vLLM environment) and pip installation notes. + ## Troubleshooting If you encounter any issues during installation, ensure that your Python and pip versions meet the prerequisites. For further assistance, please refer to the [GitHub Issues](https://github.com/vllm-project/guidellm/issues) page or consult the [Documentation](https://github.com/vllm-project/guidellm/tree/main/docs). diff --git a/docs/guides/backends.md b/docs/guides/backends.md index 8696932bd..e4da624ba 100644 --- a/docs/guides/backends.md +++ b/docs/guides/backends.md @@ -8,6 +8,10 @@ GuideLLM is designed to work with OpenAI-compatible HTTP servers, enabling seaml GuideLLM supports OpenAI-compatible HTTP servers, which provide a standardized API for interacting with LLMs. This includes popular implementations such as [vLLM](https://github.com/vllm-project/vllm) and [Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference). These servers allow GuideLLM to perform evaluations, benchmarks, and optimizations with minimal setup. +### vLLM Python backend + +GuideLLM supports running inference in the same process using the **vLLM Python backend** (`vllm_python`). This backend runs inference in the same process as GuideLLM's using vLLM's python API (AsyncLLMEngine), without an HTTP server. For setup, installation options (container, existing vLLM, pip), and examples, see [vLLM Python backend](vllm-python-backend.md). + ## Examples for Spinning Up Compatible Servers ### 1. vLLM diff --git a/docs/guides/multimodal/audio.md b/docs/guides/multimodal/audio.md index 51c417de8..bbea507f9 100644 --- a/docs/guides/multimodal/audio.md +++ b/docs/guides/multimodal/audio.md @@ -106,10 +106,10 @@ For example, to specify French as the target language for an audio translation r #### "stream" -Turn streaming responses on or off (if supported by the server) using a boolean value. By default, streaming is enabled. +Turn streaming responses on or off (if supported by the backend) using a boolean value. By default, streaming is enabled. Use `--backend-kwargs`: ```bash ---request-formatter-kwargs '{"stream": false}' +--backend-kwargs '{"stream": false}' ``` ## Expected Results diff --git a/docs/guides/multimodal/image.md b/docs/guides/multimodal/image.md index b891eb1c2..912957ad8 100644 --- a/docs/guides/multimodal/image.md +++ b/docs/guides/multimodal/image.md @@ -97,10 +97,10 @@ For example, to specify a specific system prompt or other body parameter: #### "stream" -Turn streaming responses on or off (if supported by the server) using a boolean value. By default, streaming is enabled. +Turn streaming responses on or off (if supported by the backend) using a boolean value. By default, streaming is enabled. Use `--backend-kwargs`: ```bash ---request-formatter-kwargs '{"stream": false}' +--backend-kwargs '{"stream": false}' ``` ## Expected Results diff --git a/docs/guides/multimodal/video.md b/docs/guides/multimodal/video.md index 87e2a3cab..6f4085aa7 100644 --- a/docs/guides/multimodal/video.md +++ b/docs/guides/multimodal/video.md @@ -92,10 +92,10 @@ For example, to specify a specific system prompt or other body parameter: #### "stream" -Turn streaming responses on or off (if supported by the server) using a boolean value. By default, streaming is enabled. +Turn streaming responses on or off (if supported by the backend) using a boolean value. By default, streaming is enabled. Use `--backend-kwargs`: ```bash ---request-formatter-kwargs '{"stream": false}' +--backend-kwargs '{"stream": false}' ``` ## Expected Results diff --git a/docs/guides/vllm-python-backend.md b/docs/guides/vllm-python-backend.md new file mode 100644 index 000000000..f2b95114c --- /dev/null +++ b/docs/guides/vllm-python-backend.md @@ -0,0 +1,66 @@ +# vLLM Python Backend + +The **vLLM Python backend** (`vllm_python`) runs inference in the **same process** as GuideLLM using vLLM's [AsyncLLMEngine](https://docs.vllm.ai/). No HTTP server is involved, reducing overheat and variables. This is useful for isolating performance bottlenecks or simplifying your benchmark setup. You do **not** pass `--target`; you **must** pass `--model`. + +For all engine options and supported models, see vLLM's [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) and the [vLLM documentation](https://docs.vllm.ai/). + +## Installation + +### Recommended methods + +- **Official GuideLLM + vLLM image**\ + Build and run the image that uses the vLLM base image (e.g. [Containerfile.vllm](https://github.com/vllm-project/guidellm/blob/main/Containerfile.vllm)). It is based on `vllm/vllm-openai` and installs GuideLLM on top, giving a known-good vLLM + GuideLLM stack with hardware support as provided by the base image. + + **Note:** This method will result in the preference for vllm's requirements as opposed to GuideLLM's requirements. Since vLLM is the more complex project, this is the recommended configuration, but this may result in an older Python or dependency version, resulting in sub-optimal GuideLLM performance and behavior in some scenarios. + +- **Existing vLLM installation**\ + Install vLLM first for your environment (GPU/CPU, CUDA, etc.), then install GuideLLM in the same environment (e.g. `pip install guidellm` or with extras). You avoid a duplicate vLLM install and reuse your existing acceleration setup. + + **Note:** Using [uv](https://github.com/astral-sh/uv) is not recommended for the vLLM Python backend because of potentially incompatible requirements between the two projects. Prefer pip or the container / existing vLLM environment. + +It is also possible to install GuideLLM and vLLM via pip using `pip install guidellm[vllm]`. This method may make **hardware acceleration** (e.g. CUDA) harder to get working. See [vLLM installation](https://docs.vllm.ai/en/latest/getting_started/installation) and GPU/hardware-specific docs there. For production or GPU use, the container or existing-install path is recommended. + +## Basic example + +Run a benchmark with the vLLM Python backend (no `--target`): + +```bash +guidellm benchmark run \ + --backend vllm_python \ + --model "Qwen/Qwen3-0.6B" \ + --data "prompt_tokens=256,output_tokens=128" \ + --max-seconds 20 \ + --rate 3 +``` + +Engine behavior (device, memory, etc.) follows vLLM defaults unless you override it via `--backend-kwargs` (e.g. `vllm_config`). When running without a GPU (e.g. the GuideLLM + vLLM container without GPU access), the backend automatically uses the CPU device unless you set `device` in `vllm_config`. For engine configuration options, see vLLM's [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/). + +## Request format and backend options + +- **`--request-format`**\ + Controls how chat prompts are built. Options: `plain` (no chat template; message content is concatenated as plain text), `default-template` (use the tokenizer’s default chat template), or a file path / single-line template string per vLLM’s supported options. The value is passed through to vLLM's chat template handling. For details, see vLLM's [Chat templates](https://docs.vllm.ai/en/latest/api/vllm/transformers_utils/chat_templates/) documentation. + +- **`--backend-kwargs`**\ + Backend-specific options are passed here as a JSON object: pass a `vllm_config` key whose value is a dict of engine option names and values. You can also pass `request_format` here as an alternative to `--request-format`. + + **Using Engine Arguments in `vllm_config`:** The [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) documentation describes options in **CLI form** (e.g. `--gpu-memory-utilization`, `--max-model-len`). For `vllm_config` you must use the **Python parameter names** instead: strip the leading `--` and replace dashes with underscores (e.g. `gpu_memory_utilization`, `max_model_len`). The keys are the same as the field names on vLLM's `EngineArgs` and `AsyncEngineArgs` dataclasses; for the exact list of allowed keys and types, see the [vLLM source: `vllm/engine/arg_utils.py`](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) (search for `class EngineArgs`). + + Example — limit GPU memory use and context length: + + ```bash + --backend-kwargs '{"vllm_config": {"gpu_memory_utilization": 0.8, "max_model_len": 4096}}' + ``` + + For the full list of options and their types, see vLLM's [Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) (CLI form) and the [EngineArgs source](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) (Python field names for `vllm_config`). + +## See also + +- [Backends](backends.md) — Overview of supported backends. +- [Run a benchmark](../getting-started/benchmark.md) — General benchmark options. +- [vLLM Engine Arguments](https://docs.vllm.ai/en/stable/configuration/engine_args/) — CLI-oriented docs; use Python names (e.g. `gpu_memory_utilization`) in `vllm_config`. +- [vLLM source: `vllm/engine/arg_utils.py`](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) — `EngineArgs` / `AsyncEngineArgs` field names and types for `vllm_config` keys. +- [vLLM AsyncEngineArgs API](https://docs.vllm.ai/en/stable/api/vllm/engine/arg_utils/#vllm.engine.arg_utils.AsyncEngineArgs) — API reference for the class that receives these options. +- [vLLM Chat templates](https://docs.vllm.ai/en/latest/api/vllm/transformers_utils/chat_templates/) — For `--request-format` behavior. +- [vLLM documentation](https://docs.vllm.ai/) +- [vLLM installation](https://docs.vllm.ai/en/latest/getting_started/installation) +- [vLLM OpenAI-compatible server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) — When using the HTTP server instead of the Python backend. diff --git a/pyproject.toml b/pyproject.toml index 20daf9d09..8266b2f83 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -91,6 +91,9 @@ vision = [ "datasets[vision]", "pillow", ] +vllm = [ + "vllm" +] # Dev Tooling dev = [ # Install all optional dependencies diff --git a/src/guidellm/__main__.py b/src/guidellm/__main__.py index 7e9dab87f..6e562450f 100644 --- a/src/guidellm/__main__.py +++ b/src/guidellm/__main__.py @@ -46,6 +46,10 @@ get_builtin_scenarios, reimport_benchmarks_report, ) +from guidellm.benchmark.schemas.generative.entrypoints import ( + backend_requires_model, + backend_requires_target, +) from guidellm.mock_server import MockServer, MockServerConfig from guidellm.scheduler import StrategyType from guidellm.settings import print_config @@ -186,7 +190,12 @@ def benchmark(): default=BenchmarkGenerativeTextArgs.get_default("request_format"), help=( "Format to use for requests. Options depend on backend. " - "If not provided, uses backend default." + "For vLLM backend: plain (no chat template, text appending only), " + "default-template (use tokenizer default), or a file path / single-line " + "template per vLLM docs. Default: default-template" + "For openai backend: http endpoint path (/v1/chat/completions, " + "/v1/completions, /v1/audio/transcriptions, /v1/audio/translations) or " + "alias (e.g. chat_completions); default /v1/chat/completions." ), ) @click.option( @@ -460,6 +469,40 @@ def run(**kwargs): # noqa: C901 status="warning", ) + # Early validation: check target and model parameters based on backend requirements + backend = kwargs.get("backend", BenchmarkGenerativeTextArgs.get_default("backend")) + target = kwargs.get("target", None) + model = kwargs.get("model", None) + requires_target = backend_requires_target(backend) + requires_model = backend_requires_model(backend) + backend_type = backend.type_ if hasattr(backend, "type_") else backend + + # Validate target parameter + if requires_target and target is None: + raise click.BadParameter( + f"Backend '{backend_type}' requires a target parameter. " + "Please provide --target with a valid endpoint URL.", + ctx=click.get_current_context(), + param_hint="--target", + ) + + if not requires_target and target is not None: + raise click.BadParameter( + f"Backend '{backend_type}' does not support a target parameter. " + "Please remove --target as this backend runs locally.", + ctx=click.get_current_context(), + param_hint="--target", + ) + + # Validate model parameter + if requires_model and model is None: + raise click.BadParameter( + f"Backend '{backend_type}' requires a model parameter. " + "Please provide --model with a valid model identifier.", + ctx=click.get_current_context(), + param_hint="--model", + ) + try: args = BenchmarkGenerativeTextArgs.create( scenario=kwargs.pop("scenario", None), **kwargs diff --git a/src/guidellm/backends/__init__.py b/src/guidellm/backends/__init__.py index 1a902f97c..13a5f580f 100644 --- a/src/guidellm/backends/__init__.py +++ b/src/guidellm/backends/__init__.py @@ -19,6 +19,17 @@ TextCompletionsRequestHandler, ) +# Conditionally import VLLM backend if available +try: + from .vllm_python.vllm import VLLMPythonBackend + from .vllm_python.vllm_response import VLLMResponseHandler + + HAS_VLLM_BACKEND = True +except ImportError: + VLLMPythonBackend = None # type: ignore[assignment, misc] + VLLMResponseHandler = None # type: ignore[assignment, misc] + HAS_VLLM_BACKEND = False + __all__ = [ "AudioRequestHandler", "Backend", @@ -29,3 +40,7 @@ "OpenAIRequestHandlerFactory", "TextCompletionsRequestHandler", ] + +# Conditionally add VLLM backend and handler to exports +if HAS_VLLM_BACKEND: + __all__.extend(["VLLMPythonBackend", "VLLMResponseHandler"]) diff --git a/src/guidellm/backends/backend.py b/src/guidellm/backends/backend.py index bc3fe37a5..998f3bf78 100644 --- a/src/guidellm/backends/backend.py +++ b/src/guidellm/backends/backend.py @@ -21,7 +21,7 @@ ] -BackendType = Literal["openai_http"] +BackendType = Literal["openai_http", "vllm_python"] class Backend( @@ -101,6 +101,24 @@ def requests_limit(self) -> int | None: """ return None + @classmethod + def requires_target(cls) -> bool: + """ + Indicate whether this backend requires a target parameter. + + :return: True if the backend requires a target parameter, False otherwise + """ + return True # Default to True for safety (most backends need a target) + + @classmethod + def requires_model(cls) -> bool: + """ + Indicate whether this backend requires a model parameter. + + :return: True if the backend requires a model parameter, False otherwise + """ + return False # Default to False (model is optional by default) + @abstractmethod async def default_model(self) -> str: """ diff --git a/src/guidellm/backends/openai/http.py b/src/guidellm/backends/openai/http.py index 7abb0432e..87d7b97a7 100644 --- a/src/guidellm/backends/openai/http.py +++ b/src/guidellm/backends/openai/http.py @@ -78,6 +78,25 @@ class OpenAIHTTPBackend(Backend): await backend.process_shutdown() """ + @classmethod + def requires_target(cls) -> bool: + """ + OpenAI HTTP backend requires a target URL. + + :return: True, as this backend requires a target endpoint URL + """ + return True + + @classmethod + def requires_model(cls) -> bool: + """ + OpenAI HTTP backend does not require a model parameter. + The model can be optional as we can use the server's default model. + + :return: False, as this backend does not require a model parameter + """ + return False + def __init__( self, target: str, @@ -274,7 +293,7 @@ async def default_model(self) -> str: self.model = models[0] if models else "" return self.model - async def resolve( # type: ignore[override] + async def resolve( # type: ignore[override, misc] self, request: GenerationRequest, request_info: RequestInfo, diff --git a/src/guidellm/backends/vllm_python/__init__.py b/src/guidellm/backends/vllm_python/__init__.py new file mode 100644 index 000000000..b69bb9d9f --- /dev/null +++ b/src/guidellm/backends/vllm_python/__init__.py @@ -0,0 +1,10 @@ +""" +VLLM Python API backend package. + +Provides the VLLM Python backend and response handler for compiling +OpenAI-style response dicts into GenerationResponse. +""" + +from .vllm_response import VLLMResponseHandler + +__all__ = ["VLLMResponseHandler"] diff --git a/src/guidellm/backends/vllm_python/vllm.py b/src/guidellm/backends/vllm_python/vllm.py new file mode 100644 index 000000000..9f4a37dce --- /dev/null +++ b/src/guidellm/backends/vllm_python/vllm.py @@ -0,0 +1,993 @@ +""" +VLLM Python API backend implementation for GuideLLM. + +Provides direct Python API integration with VLLM's AsyncLLMEngine, enabling local +inference without HTTP overhead. Supports both GPU-accelerated and CPU-only +inference, true async streaming with token-by-token generation, and flexible +configuration through dict-style parameters. +""" + +from __future__ import annotations + +import asyncio +import time +import uuid +from collections.abc import AsyncIterator +from pathlib import Path +from typing import Any, Literal, cast + +import numpy as np + +from guidellm.backends.backend import Backend +from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler +from guidellm.extras.vllm import ( + HAS_VLLM, + AsyncEngineArgs, + AsyncLLMEngine, + RequestOutput, + SamplingParams, +) +from guidellm.logger import logger +from guidellm.schemas import ( + GenerationRequest, + GenerationResponse, + RequestInfo, +) + +try: + from guidellm.extras.audio import _decode_audio + HAS_AUDIO = True +except ImportError: + _decode_audio = None # type: ignore[assignment] + HAS_AUDIO = False + +_Mode = Literal["audio", "chat", "text"] + +# Audio file tuple: (path_or_name, data_bytes) or (path_or_name, data_bytes, mimetype) +_AUDIO_FILE_MIN_LEN = 2 +_AUDIO_FILE_DATA_INDEX = 1 +_AUDIO_FILE_MIMETYPE_INDEX = 2 + +__all__ = ["VLLMPythonBackend"] + + +class _RequestContext: + """Resolved mode, body, stream, and files for a generation request.""" + + __slots__ = ("mode", "body", "stream", "files") + + def __init__( + self, + mode: _Mode, + body: dict[str, Any], + stream: bool, + files: dict[str, Any], + ) -> None: + self.mode = mode + self.body = body + self.stream = stream + self.files = files + + +def _check_vllm_available() -> None: + """Check if vllm is available and raise helpful error if not.""" + if not HAS_VLLM: + raise ImportError( + "vllm is not installed. Please install it using " + "'pip install guidellm[vllm]' or 'pip install vllm>=0.6.0'" + ) + + +@Backend.register("vllm_python") +class VLLMPythonBackend(Backend): + """ + Python API backend for VLLM inference engine. + + Directly uses VLLM's AsyncLLMEngine for local async inference. When CUDA is not + available and ``device`` is not set in vllm_config, the backend sets + ``device="cpu"`` so the engine runs on CPU; otherwise vLLM uses CUDA if + available. You can pass ``device`` in vllm_config (e.g. ``"cpu"``, ``"cuda"``) + and it is passed through to AsyncEngineArgs. Handles request/response conversion + between GuideLLM schemas and VLLM's native API, with async support for finer + token-by-token processing and timings. + + Engine parameters not set in vllm_config use vLLM's AsyncEngineArgs defaults. + Example (optional overrides): + :: + backend = VLLMPythonBackend(model="meta-llama/Llama-2-7b-chat-hf") + # Or: vllm_config={"tensor_parallel_size": 1, "gpu_memory_utilization": 0.9} + + await backend.process_startup() + async for response, request_info in backend.resolve(request, info): + process_response(response) + await backend.process_shutdown() + """ + + @classmethod + def requires_target(cls) -> bool: + """ + VLLM Python backend does not require a target URL. + + This backend runs locally and does not connect to a remote server. + + :return: False, as this backend does not require a target + """ + return False + + @classmethod + def requires_model(cls) -> bool: + """ + VLLM Python backend requires a model parameter. + + The model must be specified to know which model to load for inference. + + :return: True, as this backend requires a model parameter + """ + return True + + def __init__( + self, + model: str, + vllm_config: dict[str, Any] | None = None, + request_format: str | None = None, + target: str | None = None, # Backend.create API; unused (local) + stream: bool = True, + ): + """ + Initialize VLLM Python backend with model and configuration. + + :param model: Model identifier or path for VLLM to load + :param vllm_config: Optional dict of VLLM AsyncEngineArgs parameters. + Passed through with no GuideLLM defaults; only model (and optionally + chat_template) are set by the backend. When CUDA is not available and + ``device`` is not set here, the backend sets ``device="cpu"``. You can + pass ``device`` (e.g. ``"cpu"``, ``"cuda"``) and it is passed through. + Unset parameters use vLLM's defaults. Common options include + tensor_parallel_size, gpu_memory_utilization, max_model_len, and any + other parameter accepted by vllm.AsyncEngineArgs. + :param request_format: "plain" (no chat template), "default-template" + (use tokenizer default), or a chat template path / single-line string. + :param target: Target URL (ignored for VLLM Python backend, runs locally) + :param stream: Whether to stream responses (default True). Can be overridden per + request via request.arguments.stream. + """ + _check_vllm_available() + super().__init__(type_="vllm_python") + _ = target # Required by Backend.create(); unused for local backend + + self.model = model + self.request_format = request_format + self.stream = stream + self.vllm_config = self._merge_config(vllm_config or {}) + + # Runtime state + self._in_process = False + self._engine: AsyncLLMEngine | None = None + + @property + def processes_limit(self) -> int | None: + """ + Limits VLLM Python to a single process, since it starts up an entire + VLLM engine. + """ + return 1 + + def _merge_config(self, user_config: dict[str, Any]) -> dict[str, Any]: + """ + Build engine config from user config plus required model and optional + chat_template. + + No GuideLLM defaults are applied; any parameter not set here or in + user_config is left to vLLM's AsyncEngineArgs defaults. When + request_format is a custom template (not plain or default-template), + adds chat_template to config. + + :param user_config: User-provided configuration dictionary + :return: Config dict for AsyncEngineArgs (model set; chat_template if set) + """ + config = dict(user_config) + + # Ensure model is set in config (required; overrides user if they passed it) + config["model"] = self.model + + # Pass custom chat template to vLLM engine when applicable + if self.request_format is not None and self.request_format not in ( + "plain", + "default-template", + ): + config["chat_template"] = self.request_format + + return config + + @property + def info(self) -> dict[str, Any]: + """ + Get backend configuration details. + + :return: Dictionary containing backend configuration details + """ + return { + "model": self.model, + "vllm_config": self.vllm_config, + "stream": self.stream, + "in_process": self._in_process, + "engine_initialized": self._engine is not None, + } + + async def process_startup(self): + """ + Initialize VLLM AsyncLLMEngine instance with configured parameters. + + :raises RuntimeError: If backend is already initialized + :raises ImportError: If vllm is not available + """ + _check_vllm_available() + + if self._in_process: + raise RuntimeError("Backend already started up for process.") + + engine_args = AsyncEngineArgs(**self.vllm_config) # type: ignore[misc] + self._engine = AsyncLLMEngine.from_engine_args(engine_args) # type: ignore[misc] + self._in_process = True + + async def process_shutdown(self): + """ + Clean up VLLM AsyncLLMEngine instance and resources. + + :raises RuntimeError: If backend was not properly initialized + """ + if not self._in_process: + raise RuntimeError("Backend not started up for process.") + + # Shutdown the async engine if it has a shutdown method + if self._engine is not None: + self._engine.shutdown() + self._engine = None + self._in_process = False + + async def validate(self): + """ + Validate backend readiness by attempting a test generation. + + :raises RuntimeError: If backend cannot generate or is not initialized + """ + if self._engine is None: + raise RuntimeError("Backend not started up for process.") + + # Perform a minimal test generation to verify the model is working + try: + test_params = SamplingParams(temperature=0.0, max_tokens=1) # type: ignore[misc] + request_id = str(uuid.uuid4()) + # Use async generation for validation + async for _ in self._engine.generate("test", test_params, request_id): # type: ignore[misc] + # Just consume the first output to verify it works + break + except (RuntimeError, ValueError, TypeError, OSError) as exc: + raise RuntimeError( + "Backend validation failed. VLLM model could not generate text." + ) from exc + + async def available_models(self) -> list[str]: + """ + Get available models from this backend. + + :return: List containing the configured model identifier + """ + # VLLM only supports one model per VLLM instance. + return [self.model] + + async def default_model(self) -> str: + """ + Get the default model for this backend. + + :return: Model name or identifier + """ + return self.model + + def _validate_backend_initialized(self) -> None: + """ + Validate that the backend is initialized and ready. + + :raises RuntimeError: If backend is not initialized + """ + if self._engine is None: + raise RuntimeError("Backend not started up for process.") + + def _validate_history( + self, history: list[tuple[GenerationRequest, GenerationResponse]] | None + ) -> None: + """ + Validate that history is not provided (not yet supported). + + :param history: Conversation history + :raises NotImplementedError: If history is provided + """ + if history is not None: + raise NotImplementedError("Multi-turn requests not yet supported") + + def _get_request_context(self, request: GenerationRequest) -> _RequestContext: + """ + Resolve body, stream, and files from request; infer mode from that data. + + When the request has arguments, use body/stream/files from them. Otherwise + build body from request.columns (always messages) so the standard benchmark + pipeline works. Mode is inferred from body and files (audio/chat/text). + """ + arguments = getattr(request, "arguments", None) + + if arguments is not None: + body = getattr(arguments, "body", None) or {} + stream_override = getattr(arguments, "stream", None) + stream = self.stream if stream_override is None else bool(stream_override) + files = getattr(arguments, "files", None) or {} + else: + columns = getattr(request, "columns", {}) or {} + prefix_parts = list(columns.get("prefix_column", []) or []) + text_parts = list(columns.get("text_column", []) or []) + messages: list[dict[str, Any]] = [] + for p in prefix_parts: + if p: + messages.append({"role": "system", "content": str(p)}) + for t in text_parts: + if t: + messages.append( + { + "role": "user", + "content": [{"type": "text", "text": str(t)}], + } + ) + body = {"messages": messages} + stream = self.stream + files = {} + + if files: + mode: _Mode = "audio" + elif body.get("messages"): + mode = "chat" + elif ( + body.get("prompt") is not None + and body.get("prompt") != "" + or "prompt" in body + ): + mode = "text" + else: + raise ValueError("Request must include prompt, messages, or audio files.") + + return _RequestContext(mode=mode, body=body, stream=stream, files=files) + + def _update_request_timing( + self, request_info: RequestInfo, iter_time: float + ) -> None: + """ + Update request iteration timing information. + + :param request_info: Request tracking info to update + :param iter_time: Current iteration time + """ + if request_info.timings.first_request_iteration is None: + request_info.timings.first_request_iteration = iter_time + request_info.timings.last_request_iteration = iter_time + request_info.timings.request_iterations += 1 + + def _update_token_timing( + self, request_info: RequestInfo, iter_time: float, iterations: int = 1 + ) -> None: + """ + Update token iteration timing information. + + :param request_info: Request tracking info to update + :param iter_time: Current iteration time + :param iterations: Number of token iterations (default: 1) + """ + if request_info.timings.first_token_iteration is None: + request_info.timings.first_token_iteration = iter_time + request_info.timings.token_iterations = 0 + + request_info.timings.last_token_iteration = iter_time + request_info.timings.token_iterations += iterations + + def _text_from_output(self, output: RequestOutput | None) -> str: + """ + Extract generated text from VLLM RequestOutput. + + :param output: VLLM request output (may be None) + :return: output.outputs[0].text if present, else "" + """ + if output is None or not output.outputs or len(output.outputs) == 0: + return "" + return output.outputs[0].text or "" + + def _openai_payload_for_mode(self, mode: _Mode, text: str) -> dict[str, Any]: + """ + Build OpenAI-style payload (content/choices only, no usage or id) for a mode. + + :param mode: Inferred mode (audio, chat, text) + :param text: Content text + :return: Dict with "text" (audio) or "choices" (text/chat) + """ + if mode == "audio": + return {"text": text} + if mode == "text": + return {"choices": [{"text": text}]} + return {"choices": [{"message": {"content": text, "role": "assistant"}}]} + + def _stream_usage_tokens( + self, + output: RequestOutput, + accumulated_output_tokens: int | None, + accumulated_text: str, + request_info: RequestInfo, + ) -> tuple[int, int]: + """ + Stream path: compute output_tokens; return input and output token counts + in the form of (input_tokens, output_tokens). + """ + input_tokens = len(output.prompt_token_ids or []) + output_tokens = 0 + source = "unknown" + generated_text = accumulated_text + + if accumulated_output_tokens is not None and accumulated_output_tokens > 0: + output_tokens = accumulated_output_tokens + source = "token_ids" + elif output.outputs and len(output.outputs) > 0: + out = output.outputs[0] + if not generated_text and out.text: + generated_text = out.text + if out.token_ids is not None: + output_tokens = len(out.token_ids) + source = "token_ids" + else: + logger.debug( + "[vllm_python streaming] final_output.outputs[0].token_ids " + "is None, len(generated_text)={}", + len(generated_text or ""), + ) + if output_tokens == 0 and generated_text and self._engine is not None: + try: + tokenizer = getattr(self._engine, "tokenizer", None) + if tokenizer is not None: + ids = tokenizer.encode( # type: ignore[union-attr] + generated_text, add_special_tokens=False + ) + output_tokens = len(ids) + source = "tokenizer" + else: + logger.debug( + "[vllm_python streaming] engine has no tokenizer attribute" + ) + except (TypeError, ValueError, RuntimeError) as exc: + logger.debug( + "[vllm_python streaming] tokenizer.encode failed: {}", + exc, + ) + if output_tokens == 0 and request_info.timings.token_iterations: + output_tokens = request_info.timings.token_iterations + source = "token_iterations" + logger.debug( + "[vllm_python streaming usage] source={} completion_tokens={} " + "prompt_tokens={} token_iterations={}", + source, + output_tokens, + input_tokens, + request_info.timings.token_iterations, + ) + return input_tokens, output_tokens + + def _usage_from_output( + self, + output: RequestOutput | None, + *, + accumulated_output_tokens: int | None = None, + accumulated_text: str = "", + request_info: RequestInfo | None = None, + ) -> dict[str, int] | None: + """ + Build usage dict (prompt_tokens, completion_tokens, total_tokens). + + When request_info is None (non-stream), uses only output token counts. + When request_info is set (stream), uses fallback chain via + _stream_usage_tokens. + """ + if output is None: + return None + input_tokens = len(output.prompt_token_ids or []) + output_tokens = 0 + + if request_info is None: + if output.outputs and len(output.outputs) > 0: + out = output.outputs[0] + if out.token_ids is not None: + output_tokens = len(out.token_ids) + return { + "prompt_tokens": input_tokens, + "completion_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + } + + input_tokens, output_tokens = self._stream_usage_tokens( + output, accumulated_output_tokens, accumulated_text, request_info + ) + return { + "prompt_tokens": input_tokens, + "completion_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + } + + def _build_final_response( + self, + request: GenerationRequest, + request_info: RequestInfo, + final_output: RequestOutput | None, + ctx: _RequestContext, + response_handler: VLLMResponseHandler, + stream: bool, + accumulated_text: str = "", + total_output_tokens: int = 0, + ) -> tuple[GenerationResponse, RequestInfo] | None: + """Build and return the final (response, request_info) to yield, or None.""" + if final_output is None: + return None + if stream: + # Use provided text (e.g. from cancel) or final vLLM output. + final_text = accumulated_text or self._text_from_output(final_output) + usage = self._usage_from_output( + final_output, + accumulated_output_tokens=total_output_tokens, + accumulated_text=final_text, + request_info=request_info, + ) + if usage is not None: + response_handler.streaming_usage = usage + # We do not send streaming lines during the stream loop, so the handler + # never receives an id from the stream. Set it from the final vLLM output + # so the response has a usable id. + if ( + response_handler.streaming_response_id is None + and hasattr(final_output, "request_id") + and final_output.request_id + ): + response_handler.streaming_response_id = final_output.request_id + # Build response with final text only (no streamed chunks). + return response_handler.compile_streaming( + request, text_override=final_text + ), request_info + openai_format = self._convert_vllm_output_to_openai_format( + final_output, ctx.mode + ) + return response_handler.compile_non_streaming( + request, openai_format + ), request_info + + def _extract_text_from_content( + self, content: str | list[dict[str, Any]] | Any + ) -> str: + """ + Extract text content from message content field. + + Handles both string content and list-based multimodal content blocks. + For list-based content, extracts text from blocks with type "text" and + concatenates them together. + + :param content: Content field which can be a string or list of content blocks + :return: Extracted text string + """ + if isinstance(content, str): + return content + if isinstance(content, list): + # Extract text from content blocks with type "text" + text_parts = [] + for block in content: + if isinstance(block, dict): + block_type = block.get("type", "") + if block_type == "text": + text = block.get("text", "") + if text: + text_parts.append(text) + return "".join(text_parts) + # Fallback: convert to string + return str(content) if content is not None else "" + + def _extract_audio_from_request(self, files: dict[str, Any]) -> tuple[bytes, str]: + """ + Extract audio file from request files dict. + + Caller must only call when mode is audio. Audio files are expected in + the format (filename, bytes, mimetype) or as raw bytes. + + :param files: Files dict from request context + :return: Tuple of (audio_bytes, mimetype) + :raises ValueError: If files is empty or no valid audio file found + """ + if not files: + raise ValueError("Audio request must include audio file in 'files'.") + + audio_file = None + for value in files.values(): + if isinstance(value, list | tuple) and len(value) >= _AUDIO_FILE_MIN_LEN: + audio_file = value + break + if isinstance(value, bytes): + audio_file = ("audio.wav", value, "audio/wav") + break + + if audio_file is None: + raise ValueError("Audio request must include audio file in 'files'.") + + is_audio_tuple = ( + isinstance(audio_file, list | tuple) + and len(audio_file) >= _AUDIO_FILE_MIN_LEN + ) + if is_audio_tuple: + audio_data = audio_file[_AUDIO_FILE_DATA_INDEX] + if not isinstance(audio_data, bytes): + raise ValueError( + f"Expected bytes for audio data, got {type(audio_data)}: " + f"{audio_data}" + ) + mimetype = ( + audio_file[_AUDIO_FILE_MIMETYPE_INDEX] + if len(audio_file) > _AUDIO_FILE_MIMETYPE_INDEX + else "audio/wav" + ) + return (audio_data, mimetype) + + raise ValueError("Invalid audio file format in 'files'.") + + def _extract_prompt_text(self, body: dict[str, Any]) -> str: + """Extract prompt for text mode.""" + if ( + self._engine is not None + and getattr(self._engine, "tokenizer", None) is not None + ): + logger.warning( + "Tokenizer is set (from model) but not used: request has 'prompt' " + "so mode was inferred as text; prompt is passed through without " + "tokenizer formatting." + ) + prompt = body.get("prompt", "") + if not prompt: + raise ValueError("Text request must include 'prompt' in body.") + return prompt if isinstance(prompt, str) else str(prompt) + + def _extract_prompt_chat_plain( + self, formatted_messages: list[dict[str, Any]] + ) -> str: + """Format messages as plain 'Role: content' lines.""" + parts = [] + for msg in formatted_messages: + role = msg.get("role", "user") + content = msg.get("content", "") + if role and content: + parts.append(f"{role.capitalize()}: {content}") + parts.append("Assistant: ") + return "\n".join(parts) + + def _extract_prompt_chat_tokenizer( + self, formatted_messages: list[dict[str, Any]] + ) -> str: + """Apply tokenizer chat template to formatted messages.""" + if self._engine is None: + raise RuntimeError("Backend not started up while extracting prompt.") + tokenizer = self._engine.tokenizer + if tokenizer is None: + raise RuntimeError("Backend engine has no tokenizer.") + if self.request_format is not None and self.request_format not in ( + "plain", + "default-template", + ): + template_value = self.request_format + path = Path(template_value) + if path.exists() and path.is_file(): + tokenizer.chat_template = path.read_text() # type: ignore[attr-defined] + else: + tokenizer.chat_template = template_value # type: ignore[assignment, attr-defined] + prompt = tokenizer.apply_chat_template( + formatted_messages, # type: ignore[arg-type] + tokenize=False, + add_generation_prompt=True, + ) + if isinstance(prompt, str): + return prompt + raise RuntimeError("Backend received unexpected type from tokenizer.") + + def _extract_prompt_chat(self, body: dict[str, Any]) -> str: + """Extract prompt for chat mode (plain or tokenizer).""" + messages = body.get("messages", []) + if not messages: + raise ValueError("Chat request must include 'messages' in body.") + formatted_messages = [] + for msg in messages: + if isinstance(msg, dict): + raw_content = msg.get("content", "") + text_content = self._extract_text_from_content(raw_content) + formatted_messages.append( + {"role": msg.get("role", ""), "content": text_content} + ) + else: + formatted_messages.append(msg) + if self.request_format == "plain": + return self._extract_prompt_chat_plain(formatted_messages) + return self._extract_prompt_chat_tokenizer(formatted_messages) + + def _extract_prompt( + self, + body: dict[str, Any], + mode: _Mode, + ) -> str: + """ + Extract prompt text from request body based on mode. + + For chat mode, uses tokenizer chat template or plain concat per + request_format. For text mode uses body prompt. For audio, prompt + is optional. + + :param body: Request body dict (prompt or messages) + :param mode: Inferred mode (audio, chat, text) + :return: Extracted prompt string + :raises ValueError: If prompt cannot be extracted + """ + if mode == "text": + return self._extract_prompt_text(body) + if mode == "chat": + return self._extract_prompt_chat(body) + if mode == "audio": + prompt = body.get("prompt", "") + return ( + prompt if isinstance(prompt, str) else str(prompt) if prompt else "" + ) + raise ValueError(f"Unsupported mode: {mode!r}.") + + def _create_sampling_params( + self, + body: dict[str, Any], + max_tokens_override: int | None = None, + ) -> SamplingParams: + """ + Create VLLM SamplingParams from request body. + + :param body: Request body dict with sampling parameters + :param max_tokens_override: Optional max_tokens from request (e.g. benchmark) + :return: Configured SamplingParams instance + """ + max_tokens = ( + body.get("max_tokens") + if body.get("max_tokens") is not None + else (max_tokens_override if max_tokens_override is not None else 16) + ) + if max_tokens == 0: + max_tokens = 16 + + using_benchmark_target = ( + body.get("max_tokens") is None and max_tokens_override is not None + ) + if using_benchmark_target: + ignore_eos = True + stop: list[str] | None = [] + else: + ignore_eos = body.get("ignore_eos", False) + stop = body.get("stop") + + params = { + "temperature": body.get("temperature", 1.0), + "top_p": body.get("top_p", 1.0), + "max_tokens": max_tokens, + "min_tokens": body.get("min_tokens", 0), + "stop": stop, + "ignore_eos": ignore_eos, + "frequency_penalty": body.get("frequency_penalty", 0.0), + "presence_penalty": body.get("presence_penalty", 0.0), + } + + params = {k: v for k, v in params.items() if v is not None} + + return SamplingParams(**params) # type: ignore[misc] + + def _convert_vllm_output_to_openai_format( + self, + output: RequestOutput, + mode: _Mode, + ) -> dict[str, Any]: + """ + Convert VLLM RequestOutput to OpenAI-style dict format. + + Converts VLLM's native output format to the format expected by + VLLMResponseHandler, matching OpenAI API response structure. + + :param output: VLLM request output + :param mode: Inferred mode (audio, chat, text) for response shape + :return: OpenAI-style response dictionary + """ + generated_text = self._text_from_output(output) + usage = self._usage_from_output(output) or {} + + response_dict: dict[str, Any] = { + **self._openai_payload_for_mode(mode, generated_text), + "usage": usage, + } + if hasattr(output, "request_id") and output.request_id: + response_dict["id"] = output.request_id + return response_dict + + def _build_audio_generate_input( + self, prompt: str, ctx: _RequestContext + ) -> dict[str, Any]: + """Decode audio from request and build multimodal generate_input dict.""" + audio_bytes, _mimetype = self._extract_audio_from_request(ctx.files) + if not isinstance(audio_bytes, bytes): + raise TypeError( + f"Expected bytes for audio data, got {type(audio_bytes)}: " + f"{audio_bytes}" + ) + if len(audio_bytes) == 0: + raise ValueError("Audio data cannot be empty") + if not HAS_AUDIO or _decode_audio is None: + raise ImportError( + "Audio support requires guidellm[audio] (torchcodec). " + "Install with: pip install 'guidellm[audio]'" + ) + try: + audio_samples = _decode_audio(audio_bytes) + if hasattr(audio_samples.data, "numpy"): + audio_array = audio_samples.data.numpy() + elif hasattr(audio_samples.data, "cpu"): + audio_array = audio_samples.data.cpu().numpy() + else: + audio_array = np.asarray(audio_samples.data) + return { + "prompt": prompt, + "multi_modal_data": {"audio": audio_array}, + } + except (ValueError, TypeError, OSError, RuntimeError) as exc: + raise ValueError( + f"Failed to decode audio data for vLLM: {exc}" + ) from exc + + def _raise_generation_error(self, exc: BaseException) -> None: + """Re-raise generation failure with context; special case for audio.""" + error_msg = str(exc) + if ( + "At most 0 audio" in error_msg + or "audio(s) may be provided" in error_msg + ): + raise RuntimeError( + f"Generation failed: The model '{self.model}' does not " + f"support audio inputs. Use an audio-capable model " + f"(e.g. Whisper-based). Original error: {exc}" + ) from exc + raise RuntimeError(f"Generation failed: {exc}") from exc + + async def _run_generation( + self, + request: GenerationRequest, + request_info: RequestInfo, + ctx: _RequestContext, + response_handler: VLLMResponseHandler, + generate_input: str | dict[str, Any], + sampling_params: SamplingParams, + request_id: str, + state: dict[str, Any], + ) -> AsyncIterator[tuple[GenerationResponse, RequestInfo]]: + """Run engine.generate loop and yield final (response, request_info).""" + state["final_output"] = None # Only state we keep; text/usage from final output + stream = ctx.stream + engine = self._engine + if engine is None: + raise RuntimeError("Backend not started up.") + + prompt_arg = cast("Any", generate_input) + async for request_output in engine.generate( # type: ignore[misc] + prompt_arg, sampling_params, request_id + ): + iter_time = time.time() + self._update_request_timing(request_info, iter_time) + state["final_output"] = request_output + + if ( + request_output.outputs + and len(request_output.outputs) > 0 + and request_output.outputs[0].finish_reason is not None + ): + break + + if stream: + # The main advantage of streaming is more granular timing information + # can be saved here. + self._update_token_timing(request_info, iter_time, 1) + + request_info.timings.request_end = time.time() + final_output = state["final_output"] + # Text and usage from final output only; pass 0 for accumulated tokens. + result = self._build_final_response( + request, + request_info, + final_output, + ctx, + response_handler, + stream, + self._text_from_output(final_output) if final_output else "", + 0, + ) + if result is not None: + yield result + + async def resolve( # type: ignore[override, misc] + self, + request: GenerationRequest, + request_info: RequestInfo, + history: list[tuple[GenerationRequest, GenerationResponse]] | None = None, + ) -> AsyncIterator[tuple[GenerationResponse, RequestInfo]]: + """ + Process generation request and yield a single response. + + Handles both streaming and non-streaming requests using AsyncLLMEngine's + native async generation. For streaming, records per-token timings and + yields once with a response compiled from the stream. For non-streaming, + yields once with the complete response. In both cases the caller receives + exactly one (response, request_info) pair. + + :param request: Generation request with content and parameters + :param request_info: Request tracking info updated with timing metadata + :param history: Conversation history (currently not supported) + :raises NotImplementedError: If history is provided + :raises RuntimeError: If backend is not initialized + :raises ValueError: If request body/files do not imply a valid mode + :yields: Single tuple of (response, updated_request_info) + """ + ctx = self._get_request_context(request) + self._validate_backend_initialized() + self._validate_history(history) + response_handler = VLLMResponseHandler() + prompt = self._extract_prompt(ctx.body, ctx.mode) + sampling_params = self._create_sampling_params( + ctx.body, + max_tokens_override=( + request.output_metrics.text_tokens + if request.output_metrics.text_tokens + else None + ), + ) + generate_input: str | dict[str, Any] + if ctx.mode == "audio": + prompt_str = prompt if isinstance(prompt, str) else "" + generate_input = self._build_audio_generate_input(prompt_str, ctx) + else: + generate_input = prompt + + request_id = str(uuid.uuid4()) + request_info.timings.request_start = time.time() + engine = self._engine + if engine is None: + raise RuntimeError("Backend not started up.") + + gen_state: dict[str, Any] = {} + try: + async for result in self._run_generation( + request, + request_info, + ctx, + response_handler, + generate_input, + sampling_params, + request_id, + gen_state, + ): + yield result + except asyncio.CancelledError as err: + # Yield whatever we have so far (same pattern: text from output, 0 tokens). + final_output = gen_state.get("final_output") + cancel_result = self._build_final_response( + request, + request_info, + final_output, + ctx, + response_handler, + ctx.stream, + self._text_from_output(final_output) if final_output else "", + 0, + ) + if cancel_result is not None: + yield cancel_result + raise err + except (RuntimeError, ValueError, TypeError, OSError, KeyError) as exc: + self._raise_generation_error(exc) diff --git a/src/guidellm/backends/vllm_python/vllm_response.py b/src/guidellm/backends/vllm_python/vllm_response.py new file mode 100644 index 000000000..792f22cd3 --- /dev/null +++ b/src/guidellm/backends/vllm_python/vllm_response.py @@ -0,0 +1,224 @@ +""" +VLLM-specific response handler for compiling OpenAI-style response dicts. + +Compiles response dictionaries (choices+usage or text+usage) and streaming +SSE lines into GenerationResponse. Infers format from response shape; no +request type parameter required. +""" + +from __future__ import annotations + +from typing import Any, cast + +from guidellm.schemas import GenerationRequest, GenerationResponse, UsageMetrics +from guidellm.utils import json + +__all__ = ["VLLMResponseHandler"] + + +class VLLMResponseHandler: + """ + Response handler for the vLLM Python backend. + + Compiles OpenAI-style response dicts into GenerationResponse by inferring + shape: choices+usage (text/chat) or text+usage (audio). Parses streaming + deltas (choices[].text, choices[].delta.content, or text) without + requiring a request type. + """ + + def __init__(self) -> None: + """Initialize streaming accumulation state.""" + self.streaming_texts: list[str] = [] + self.streaming_usage: dict[str, Any] | None = None + self.streaming_response_id: str | None = None + + def compile_non_streaming( + self, + request: GenerationRequest, + response_dict: dict[str, Any], + ) -> GenerationResponse: + """ + Compile a non-streaming response dict into GenerationResponse. + + Infers format from keys: "text" (audio-style) vs "choices" (text/chat). + + :param request: Original generation request + :param response_dict: OpenAI-style response (choices+usage or text+usage) + :return: GenerationResponse with text and metrics + """ + text, usage = self._extract_text_and_usage(response_dict) + input_metrics, output_metrics = self._extract_metrics(usage, text) + request_args = self._get_request_args(request) + + return GenerationResponse( + request_id=request.request_id, + response_id=response_dict.get("id"), + request_args=request_args, + text=text or None, + input_metrics=input_metrics, + output_metrics=output_metrics, + ) + + def add_streaming_line(self, line: str) -> int | None: + """ + Process a single SSE line from a streaming response. + + Infers delta shape: "text", choices[].text, or choices[].delta.content. + Returns None if line indicates completion (e.g. data: [DONE]). + + :param line: Raw SSE line (e.g. "data: {...}") + :return: 1 if content was extracted, 0 if ignored, None if stream done + """ + data = self._parse_line(line) + if data is None: + return None + if not data: + return 0 + + if "id" in data and self.streaming_response_id is None: + self.streaming_response_id = data.get("id") + + if "usage" in data: + self.streaming_usage = data.get("usage") or self.streaming_usage + + result = 0 + if "text" in data and data["text"]: + self.streaming_texts.append(data["text"]) + result = 1 + else: + choices = data.get("choices", []) + if choices: + choice = choices[0] + if "text" in choice and choice["text"]: + self.streaming_texts.append(choice["text"]) + result = 1 + else: + delta = choice.get("delta", {}) + if isinstance(delta, dict) and delta.get("content"): + self.streaming_texts.append(delta["content"]) + result = 1 + return result + + def compile_streaming( + self, + request: GenerationRequest, + *, + text_override: str | None = None, + ) -> GenerationResponse: + """ + Compile accumulated streaming chunks into a final GenerationResponse. + + :param request: Original generation request + :param text_override: If provided, use as response text instead of + joining streaming_texts (e.g. when backend supplies final text only). + :return: GenerationResponse with text and metrics + """ + text = ( + text_override + if text_override is not None + else "".join(self.streaming_texts) + ) + input_metrics, output_metrics = self._extract_metrics( + self.streaming_usage, text + ) + request_args = self._get_request_args(request) + + return GenerationResponse( + request_id=request.request_id, + response_id=self.streaming_response_id, + request_args=request_args, + text=text or None, + input_metrics=input_metrics, + output_metrics=output_metrics, + ) + + def _extract_text_and_usage( + self, response_dict: dict[str, Any] + ) -> tuple[str, dict[str, Any]]: + """Extract text and usage from response dict; infer shape from keys.""" + usage = response_dict.get("usage", {}) or {} + + if "text" in response_dict: + text = response_dict["text"] + return text if isinstance(text, str) else str(text), usage + + choices = response_dict.get("choices", []) + if not choices: + return "", usage + + choice = choices[0] + if "text" in choice: + text = choice["text"] + elif "message" in choice: + msg = choice["message"] + text = msg.get("content", "") if isinstance(msg, dict) else "" + else: + text = "" + + return text if isinstance(text, str) else str(text), usage + + def _extract_metrics( + self, usage: dict[str, Any] | None, text: str + ) -> tuple[UsageMetrics, UsageMetrics]: + """Build UsageMetrics from usage dict and text (word/char counts).""" + if not usage: + return UsageMetrics(), UsageMetrics( + text_words=len(text.split()) if text else 0, + text_characters=len(text) if text else 0, + ) + + usage_metrics = cast("dict[str, int]", usage) + input_details = cast( + "dict[str, int]", usage.get("prompt_tokens_details", {}) or {} + ) + output_details = cast( + "dict[str, int]", usage.get("completion_tokens_details", {}) or {} + ) + + input_metrics = UsageMetrics( + text_tokens=( + input_details.get("prompt_tokens") + or usage_metrics.get("prompt_tokens") + or 0 + ), + image_tokens=input_details.get("image_tokens"), + video_tokens=input_details.get("video_tokens"), + audio_tokens=input_details.get("audio_tokens"), + audio_seconds=input_details.get("seconds"), + ) + output_metrics = UsageMetrics( + text_tokens=( + output_details.get("completion_tokens") + or usage_metrics.get("completion_tokens") + or 0 + ), + text_words=len(text.split()) if text else 0, + text_characters=len(text) if text else 0, + image_tokens=output_details.get("image_tokens"), + video_tokens=output_details.get("video_tokens"), + audio_tokens=output_details.get("audio_tokens"), + audio_seconds=output_details.get("seconds"), + ) + return input_metrics, output_metrics + + def _parse_line(self, line: str) -> dict[str, Any] | None: + """Parse SSE line to JSON dict; return None if done or invalid.""" + if line == "data: [DONE]": + return None + line = (line or "").strip() + if not line or not line.startswith("data:"): + return {} if line else None + line = line[len("data:") :].strip() + try: + return json.loads(line) + except (ValueError, TypeError): + return {} + + def _get_request_args(self, request: GenerationRequest) -> str | None: + """Get request_args string from request.arguments if present.""" + arguments = getattr(request, "arguments", None) + if arguments is None: + return None + if hasattr(arguments, "model_dump_json"): + return arguments.model_dump_json() + return None diff --git a/src/guidellm/benchmark/entrypoints.py b/src/guidellm/benchmark/entrypoints.py index dd634d9a5..ed89e5c93 100644 --- a/src/guidellm/benchmark/entrypoints.py +++ b/src/guidellm/benchmark/entrypoints.py @@ -78,7 +78,7 @@ async def resolve_backend( backend: BackendType | Backend, - target: str, + target: str | None, model: str | None, console: Console | None = None, **backend_kwargs: Any, @@ -92,7 +92,7 @@ async def resolve_backend( benchmark execution. :param backend: Backend type identifier or pre-configured Backend instance - :param target: Target endpoint URL or connection string for the backend + :param target: Target endpoint URL or connection string for the backend, or None :param model: Model identifier to use with the backend, or None to use default :param console: Console instance for progress reporting, or None :param backend_kwargs: Additional keyword arguments passed to backend initialization @@ -103,8 +103,16 @@ async def resolve_backend( if console else None ) + + # Only pass target if it's provided (some backends don't need it) + create_kwargs = {**(backend_kwargs or {})} + if target is not None: + create_kwargs["target"] = target + if model is not None: + create_kwargs["model"] = model + backend_instance = ( - Backend.create(backend, target=target, model=model, **(backend_kwargs or {})) + Backend.create(backend, **create_kwargs) if not isinstance(backend, Backend) else backend ) diff --git a/src/guidellm/benchmark/schemas/generative/entrypoints.py b/src/guidellm/benchmark/schemas/generative/entrypoints.py index e85a5ba58..522d8497f 100644 --- a/src/guidellm/benchmark/schemas/generative/entrypoints.py +++ b/src/guidellm/benchmark/schemas/generative/entrypoints.py @@ -27,6 +27,7 @@ ValidatorFunctionWrapHandler, field_serializer, field_validator, + model_validator, ) from torch.utils.data import Sampler from transformers import PreTrainedTokenizerBase @@ -39,7 +40,57 @@ from guidellm.scheduler import StrategyType from guidellm.schemas import StandardBaseModel -__all__ = ["BenchmarkGenerativeTextArgs"] +__all__ = [ + "BenchmarkGenerativeTextArgs", + "backend_requires_model", + "backend_requires_target", +] + + +def backend_requires_target(backend: BackendType | Backend) -> bool: + """ + Determine if a backend requires a target parameter. + + Uses the backend's class method to determine if it requires target, + making it extensible for new backend implementations. + + :param backend: Backend type identifier or Backend instance + :return: True if the backend requires target, False otherwise + """ + if isinstance(backend, Backend): + # If it's already a Backend instance, use its class method + return backend.__class__.requires_target() + + # If it's a BackendType string, get the registered backend class + backend_class = Backend.get_registered_object(backend) + if backend_class is None: + # Unknown backend type, default to True for safety + return True + + return backend_class.requires_target() + + +def backend_requires_model(backend: BackendType | Backend) -> bool: + """ + Determine if a backend requires a model parameter. + + Uses the backend's class method to determine if it requires model, + making it extensible for new backend implementations. + + :param backend: Backend type identifier or Backend instance + :return: True if the backend requires model, False otherwise + """ + if isinstance(backend, Backend): + # If it's already a Backend instance, use its class method + return backend.__class__.requires_model() + + # If it's a BackendType string, get the registered backend class + backend_class = Backend.get_registered_object(backend) + if backend_class is None: + # Unknown backend type, default to False for safety + return False + + return backend_class.requires_model() class BenchmarkGenerativeTextArgs(StandardBaseModel): @@ -159,7 +210,9 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any: ) # Required - target: str = Field(description="Target endpoint URL for benchmark execution") + target: str | None = Field( + default=None, description="Target endpoint URL for benchmark execution" + ) data: list[Any] = Field( description="List of dataset sources or data files", default_factory=list, @@ -182,8 +235,9 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any: request_format: str | None = Field( default=None, description=( - "Query format for backend operations;" - " shorthand for backend_kwargs['request_format']" + "Query format for backend operations; shorthand for " + "backend_kwargs['request_format']. For vLLM backend: plain, " + "default-template, or chat template path/single-line string." ), ) model: str | None = Field(default=None, description="Model identifier for backend") @@ -325,6 +379,52 @@ def single_to_list( else: raise + @model_validator(mode="after") + def validate_target_required(self) -> BenchmarkGenerativeTextArgs: + """ + Validate target and model parameters based on backend requirements. + + Target validation: + - If backend requires target: target must be provided (not None) + - If backend does not require target: target must be None (not provided) + + Model validation: + - If backend requires model: model must be provided (not None) + - If backend does not require model: model can be None or provided + + :return: Self if validation passes + :raises ValueError: If target is provided when backend doesn't support it, + if target is missing when backend requires it, + or if model is missing when backend requires it + """ + backend_type = ( + self.backend.type_ if isinstance(self.backend, Backend) else self.backend + ) + requires_target = backend_requires_target(self.backend) + requires_model = backend_requires_model(self.backend) + + # Validate target parameter + if requires_target and self.target is None: + raise ValueError( + f"Backend '{backend_type}' requires a target parameter. " + "Please provide --target with a valid endpoint URL." + ) + + if not requires_target and self.target is not None: + raise ValueError( + f"Backend '{backend_type}' does not support a target parameter. " + "Please remove --target as this backend runs locally." + ) + + # Validate model parameter + if requires_model and self.model is None: + raise ValueError( + f"Backend '{backend_type}' requires a model parameter. " + "Please provide --model with a valid model identifier." + ) + + return self + @field_serializer("backend") def serialize_backend(self, backend: BackendType | Backend) -> str: """Serialize backend to type string.""" diff --git a/src/guidellm/extras/vllm.py b/src/guidellm/extras/vllm.py new file mode 100644 index 000000000..a415e966f --- /dev/null +++ b/src/guidellm/extras/vllm.py @@ -0,0 +1,13 @@ +try: + from vllm import SamplingParams + from vllm.engine.arg_utils import AsyncEngineArgs + from vllm.engine.async_llm_engine import AsyncLLMEngine + from vllm.outputs import RequestOutput + + HAS_VLLM = True +except ImportError: + AsyncLLMEngine = None # type: ignore[assignment, misc] + AsyncEngineArgs = None # type: ignore[assignment, misc] + SamplingParams = None # type: ignore[assignment, misc] + RequestOutput = None # type: ignore[assignment, misc] + HAS_VLLM = False diff --git a/tests/unit/backends/test_backend.py b/tests/unit/backends/test_backend.py index b7b5ed0bf..8157bac27 100644 --- a/tests/unit/backends/test_backend.py +++ b/tests/unit/backends/test_backend.py @@ -303,6 +303,23 @@ def test_openai_backend_registered(self): assert isinstance(backend, OpenAIHTTPBackend) assert backend.type_ == "openai_http" + @pytest.mark.smoke + def test_vllm_python_backend_registered(self): + """ + Test that vllm_python backend is registered and createable. + ## WRITTEN BY AI ## + """ + from unittest.mock import patch + + from guidellm.backends.vllm_python.vllm import VLLMPythonBackend + + assert Backend.is_registered("vllm_python") + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend = Backend.create("vllm_python", model="test-model") + assert isinstance(backend, VLLMPythonBackend) + assert backend.model == "test-model" + assert backend.type_ == "vllm_python" + @pytest.mark.smoke def test_backend_registry_functionality(self): """Test that backend registry functions work.""" diff --git a/tests/unit/backends/vllm_python/__init__.py b/tests/unit/backends/vllm_python/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit/backends/vllm_python/test_vllm.py b/tests/unit/backends/vllm_python/test_vllm.py new file mode 100644 index 000000000..1d8db29fb --- /dev/null +++ b/tests/unit/backends/vllm_python/test_vllm.py @@ -0,0 +1,1471 @@ +""" +Unit tests for VLLM Python backend. + +Tests _get_request_context (mode from body/files; body from columns or arguments) +and request_format (plain, default-template, custom template) for chat prompts. +""" + +from __future__ import annotations + +import asyncio +from unittest.mock import Mock, patch + +import numpy as np +import pytest + +from guidellm.backends.vllm_python.vllm import VLLMPythonBackend, _RequestContext +from guidellm.schemas import ( + GenerationRequest, + GenerationResponse, + RequestInfo, + UsageMetrics, +) + + +@pytest.fixture +def backend(): + """VLLMPythonBackend instance without requiring vllm to be installed.""" + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + return VLLMPythonBackend(model="test-model") + + +class TestVLLMGetRequestContext: + """ + Test _get_request_context: mode inferred from body and files. + ## WRITTEN BY AI ## + """ + + @pytest.mark.smoke + def test_columns_only_infers_chat(self, backend): + """ + Request with only columns (no arguments) builds messages and infers mode chat. + ## WRITTEN BY AI ## + """ + request = GenerationRequest(columns={"text_column": ["hello"]}) + ctx = backend._get_request_context(request) + assert ctx.mode == "chat" + assert "messages" in ctx.body + assert len(ctx.body["messages"]) == 1 + assert ctx.body["messages"][0]["role"] == "user" + assert ctx.stream is True # backend default stream=True (match HTTP behavior) + assert ctx.files == {} + + @pytest.mark.smoke + def test_columns_only_uses_backend_stream_false(self): + """ + When backend.stream=False and no arguments, ctx.stream is False. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend = VLLMPythonBackend(model="test-model", stream=False) + request = GenerationRequest(columns={"text_column": ["hello"]}) + ctx = backend._get_request_context(request) + assert ctx.stream is False + + @pytest.mark.smoke + def test_arguments_stream_overrides_backend_stream(self): + """ + When request.arguments.stream is set, it overrides backend.stream. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend = VLLMPythonBackend(model="test-model", stream=False) + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = {"messages": [{"role": "user", "content": "Hi"}]} + request.arguments.stream = True # override backend's stream=False + request.arguments.files = {} + ctx = backend._get_request_context(request) + assert ctx.stream is True + + @pytest.mark.smoke + def test_arguments_body_prompt_infers_text(self, backend): + """ + Request with arguments.body containing only prompt infers mode text. + ## WRITTEN BY AI ## + """ + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = {"prompt": "Complete this"} + request.arguments.stream = False + request.arguments.files = {} + ctx = backend._get_request_context(request) + assert ctx.mode == "text" + assert ctx.body == {"prompt": "Complete this"} + assert ctx.files == {} + + @pytest.mark.smoke + def test_arguments_body_messages_infers_chat(self, backend): + """ + Request with arguments.body containing messages infers mode chat. + ## WRITTEN BY AI ## + """ + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = { + "messages": [{"role": "user", "content": "Hi"}], + } + request.arguments.stream = True + request.arguments.files = {} + ctx = backend._get_request_context(request) + assert ctx.mode == "chat" + assert ctx.body["messages"] == [{"role": "user", "content": "Hi"}] + assert ctx.stream is True + assert ctx.files == {} + + @pytest.mark.smoke + def test_arguments_non_empty_files_infers_audio(self, backend): + """ + Request with non-empty files infers mode audio. + ## WRITTEN BY AI ## + """ + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = {} + request.arguments.stream = False + request.arguments.files = {"file": (b"audio-data", "audio/wav")} + ctx = backend._get_request_context(request) + assert ctx.mode == "audio" + assert ctx.files == {"file": (b"audio-data", "audio/wav")} + + @pytest.mark.smoke + def test_arguments_files_take_precedence_over_body(self, backend): + """ + When files is non-empty, mode is audio even if body has messages. + ## WRITTEN BY AI ## + """ + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = {"messages": [{"role": "user", "content": "Hi"}]} + request.arguments.stream = False + request.arguments.files = {"file": (b"x", "audio/wav")} + ctx = backend._get_request_context(request) + assert ctx.mode == "audio" + + @pytest.mark.smoke + def test_arguments_empty_body_no_files_raises(self, backend): + """ + Request with no prompt, no messages, and no files raises ValueError. + ## WRITTEN BY AI ## + """ + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = {} + request.arguments.stream = False + request.arguments.files = {} + with pytest.raises( + ValueError, match="Request must include prompt, messages, or audio files" + ): + backend._get_request_context(request) + + @pytest.mark.smoke + def test_arguments_body_prompt_key_empty_string_infers_text(self, backend): + """ + Body with key 'prompt' present (even empty string) infers mode text. + ## WRITTEN BY AI ## + """ + request = Mock(spec=GenerationRequest) + request.arguments = Mock() + request.arguments.body = {"prompt": ""} + request.arguments.stream = False + request.arguments.files = {} + ctx = backend._get_request_context(request) + assert ctx.mode == "text" + assert ctx.body["prompt"] == "" + + @pytest.mark.smoke + def test_columns_prefix_and_text_build_messages(self, backend): + """ + Columns with prefix_column and text_column build multiple messages, mode chat. + ## WRITTEN BY AI ## + """ + request = GenerationRequest( + columns={ + "prefix_column": ["System prompt"], + "text_column": ["User question"], + } + ) + ctx = backend._get_request_context(request) + assert ctx.mode == "chat" + assert len(ctx.body["messages"]) == 2 + assert ctx.body["messages"][0]["role"] == "system" + assert ctx.body["messages"][0]["content"] == "System prompt" + assert ctx.body["messages"][1]["role"] == "user" + assert ctx.body["messages"][1]["content"][0]["text"] == "User question" + + +class TestVLLMRequestFormat: + """ + Test request_format: plain, default-template, and custom template. + """ + + @pytest.mark.smoke + def test_request_format_plain_produces_concatenated_prompt(self, backend): + """ + With request_format=plain, chat prompt is plain concatenation (no template). + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_plain = VLLMPythonBackend( + model="test-model", request_format="plain" + ) + body = { + "messages": [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi"}, + {"role": "user", "content": "Bye"}, + ] + } + # _extract_prompt for chat uses self._engine.tokenizer only when not plain + prompt = backend_plain._extract_prompt(body, "chat") + assert "User: Hello" in prompt + assert "Assistant: Hi" in prompt + assert "User: Bye" in prompt + assert "Assistant: " in prompt + # No template markers like [INST] or <|im_start|> + assert "[INST]" not in prompt + assert "<|im_start|>" not in prompt + + @pytest.mark.smoke + def test_request_format_default_template_uses_apply_chat_template(self, backend): + """ + With request_format=default-template or None, apply_chat_template is used. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + mock_tokenizer = Mock() + mock_tokenizer.apply_chat_template.return_value = "formatted_prompt" + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_default = VLLMPythonBackend( + model="test-model", request_format="default-template" + ) + backend_default._engine = Mock() + backend_default._engine.tokenizer = mock_tokenizer + prompt = backend_default._extract_prompt(body, "chat") + assert prompt == "formatted_prompt" + mock_tokenizer.apply_chat_template.assert_called_once() + call_kw = mock_tokenizer.apply_chat_template.call_args[1] + assert call_kw.get("tokenize") is False + assert call_kw.get("add_generation_prompt") is True + + @pytest.mark.smoke + def test_request_format_none_uses_apply_chat_template(self, backend): + """ + With request_format=None, tokenizer.apply_chat_template is used. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + mock_tokenizer = Mock() + mock_tokenizer.apply_chat_template.return_value = "default_prompt" + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_none = VLLMPythonBackend(model="test-model") + backend_none._engine = Mock() + backend_none._engine.tokenizer = mock_tokenizer + prompt = backend_none._extract_prompt(body, "chat") + assert prompt == "default_prompt" + mock_tokenizer.apply_chat_template.assert_called_once() + + @pytest.mark.smoke + def test_request_format_custom_template_string_sets_tokenizer_and_applies( + self, backend + ): + """ + With request_format=custom, chat_template is set then apply_chat_template. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + mock_tokenizer = Mock() + mock_tokenizer.apply_chat_template.return_value = "custom_prompt" + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_custom = VLLMPythonBackend( + model="test-model", + request_format="{{ messages[0]['content'] }}", + ) + backend_custom._engine = Mock() + backend_custom._engine.tokenizer = mock_tokenizer + prompt = backend_custom._extract_prompt(body, "chat") + assert prompt == "custom_prompt" + assert mock_tokenizer.chat_template == "{{ messages[0]['content'] }}" + mock_tokenizer.apply_chat_template.assert_called_once() + + @pytest.mark.smoke + def test_request_format_custom_template_from_file_sets_tokenizer_and_applies( + self, backend, tmp_path + ): + """ + With request_format=file path, chat_template is set from file then applied. + ## WRITTEN BY AI ## + """ + template_file = tmp_path / "template.jinja" + template_file.write_text("Custom: {{ messages[0]['content'] }}") + body = {"messages": [{"role": "user", "content": "Hi"}]} + mock_tokenizer = Mock() + mock_tokenizer.apply_chat_template.return_value = "Custom: Hi" + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_file = VLLMPythonBackend( + model="test-model", request_format=str(template_file) + ) + backend_file._engine = Mock() + backend_file._engine.tokenizer = mock_tokenizer + prompt = backend_file._extract_prompt(body, "chat") + assert prompt == "Custom: Hi" + assert mock_tokenizer.chat_template == "Custom: {{ messages[0]['content'] }}" + mock_tokenizer.apply_chat_template.assert_called_once() + + @pytest.mark.smoke + def test_request_format_stored_and_passed_to_vllm_config(self, backend): + """ + Custom request_format is added to vllm_config as chat_template. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_custom = VLLMPythonBackend( + model="test-model", + request_format="/path/to/template.jinja", + ) + assert backend_custom.request_format == "/path/to/template.jinja" + assert ( + backend_custom.vllm_config.get("chat_template") == "/path/to/template.jinja" + ) + + @pytest.mark.smoke + def test_request_format_plain_not_in_vllm_config(self, backend): + """ + request_format=plain does not add chat_template to vllm_config. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_plain = VLLMPythonBackend( + model="test-model", request_format="plain" + ) + assert backend_plain.request_format == "plain" + assert "chat_template" not in backend_plain.vllm_config + + @pytest.mark.smoke + def test_request_format_default_template_not_in_vllm_config(self, backend): + """ + request_format=default-template does not add chat_template to vllm_config. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_def = VLLMPythonBackend( + model="test-model", request_format="default-template" + ) + assert backend_def.request_format == "default-template" + assert "chat_template" not in backend_def.vllm_config + + @pytest.mark.smoke + def test_vllm_config_empty_uses_vllm_defaults(self): + """ + With vllm_config empty or None, backend only sets model; no extra keys. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_empty = VLLMPythonBackend(model="test-model", vllm_config={}) + backend_none = VLLMPythonBackend(model="test-model", vllm_config=None) + for b in (backend_empty, backend_none): + assert b.vllm_config.get("model") == "test-model" + assert "tensor_parallel_size" not in b.vllm_config + assert "gpu_memory_utilization" not in b.vllm_config + + +class TestVLLMExtractPromptTokenizerWarnings: + """ + Test warnings when tokenizer is set but not used for prompt extraction. + """ + + @pytest.mark.smoke + def test_text_mode_warns_when_engine_has_tokenizer(self, backend): + """ + With mode=text and engine.tokenizer set, a warning is logged. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend._engine = Mock() + backend._engine.tokenizer = Mock() + with patch("guidellm.backends.vllm_python.vllm.logger") as mock_logger: + result = backend._extract_prompt({"prompt": "Complete this"}, "text") + assert result == "Complete this" + mock_logger.warning.assert_called_once() + call_msg = mock_logger.warning.call_args[0][0] + assert "Tokenizer is set" in call_msg + assert "mode was inferred as text" in call_msg + + @pytest.mark.smoke + def test_text_mode_no_warning_when_engine_has_no_tokenizer(self, backend): + """ + With mode=text and no engine tokenizer, no warning is logged. + ## WRITTEN BY AI ## + """ + backend._engine = None + with patch("guidellm.backends.vllm_python.vllm.logger") as mock_logger: + result = backend._extract_prompt({"prompt": "Complete this"}, "text") + assert result == "Complete this" + mock_logger.warning.assert_not_called() + + @pytest.mark.smoke + def test_chat_plain_no_warning_when_engine_has_no_tokenizer(self, backend): + """ + With request_format=plain and no engine tokenizer, no warning is logged. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_plain = VLLMPythonBackend( + model="test-model", request_format="plain" + ) + backend_plain._engine = None + body = {"messages": [{"role": "user", "content": "Hi"}]} + with patch("guidellm.backends.vllm_python.vllm.logger") as mock_logger: + backend_plain._extract_prompt(body, "chat") + mock_logger.warning.assert_not_called() + + +class TestVLLMStreamingUsageFromOutput: + """ + Test _usage_from_output (stream path): token_ids vs tokenizer fallback. + """ + + @pytest.mark.smoke + def test_streaming_usage_from_output_token_ids_none_uses_tokenizer(self, backend): + """ + When token_ids is None, output_tokens are derived from tokenizer.encode. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.token_ids = None + mock_out.text = "Hello world" + mock_final = Mock() + mock_final.prompt_token_ids = [1, 2, 3] + mock_final.outputs = [mock_out] + mock_info = Mock() + mock_info.timings.token_iterations = 10 + mock_tokenizer = Mock() + mock_tokenizer.encode.return_value = [10, 20, 30, 40, 50] # 5 tokens + backend._engine = Mock() + backend._engine.tokenizer = mock_tokenizer + usage = backend._usage_from_output( + mock_final, + accumulated_text="Hello world", + request_info=mock_info, + ) + assert usage is not None + assert usage["prompt_tokens"] == 3 + assert usage["completion_tokens"] == 5 + assert usage["total_tokens"] == 8 + mock_tokenizer.encode.assert_called_once_with( + "Hello world", add_special_tokens=False + ) + + @pytest.mark.smoke + def test_streaming_usage_from_output_uses_token_ids_when_available(self, backend): + """ + When token_ids is set, it is used and tokenizer is not called. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.token_ids = [100, 101, 102, 103] + mock_out.text = "Hi" + mock_final = Mock() + mock_final.prompt_token_ids = [1, 2] + mock_final.outputs = [mock_out] + mock_info = Mock() + mock_info.timings.token_iterations = 1 + backend._engine = Mock() + backend._engine.tokenizer = Mock() + usage = backend._usage_from_output( + mock_final, + accumulated_text="Hi", + request_info=mock_info, + ) + assert usage is not None + assert usage["prompt_tokens"] == 2 + assert usage["completion_tokens"] == 4 + assert usage["total_tokens"] == 6 + backend._engine.tokenizer.encode.assert_not_called() + + @pytest.mark.smoke + def test_streaming_usage_from_output_fallback_to_token_iterations(self, backend): + """ + When token_ids is None and tokenizer fails, use token_iterations. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.token_ids = None + mock_out.text = "Hi" + mock_final = Mock() + mock_final.prompt_token_ids = [1] + mock_final.outputs = [mock_out] + mock_info = Mock() + mock_info.timings.token_iterations = 7 + backend._engine = Mock() + backend._engine.tokenizer = Mock(side_effect=RuntimeError("no tokenizer")) + usage = backend._usage_from_output( + mock_final, + accumulated_text="Hi", + request_info=mock_info, + ) + assert usage is not None + assert usage["completion_tokens"] == 7 + assert usage["total_tokens"] == 8 + + +class TestVLLMStreamingCumulativeTokenCount: + """ + Test that streaming correctly counts tokens when vLLM yields CUMULATIVE token_ids. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_cumulative_token_ids_counted_as_delta(self, backend): + """ + vLLM yields cumulative token_ids; we add delta only. + ## WRITTEN BY AI ## + """ + # Simulate 3 chunks, cumulative token_ids [1],[1,2],[1,2,3] -> 3 tokens + out1 = Mock() + out1.text = "A" + out1.token_ids = [100] + out1.finish_reason = None + out2 = Mock() + out2.text = "AB" + out2.token_ids = [100, 101] + out2.finish_reason = None + out3 = Mock() + out3.text = "ABC" + out3.token_ids = [100, 101, 102] + out3.finish_reason = None + req1, req2, req3 = Mock(), Mock(), Mock() + for req in (req1, req2, req3): + req.request_id = None + req.prompt_token_ids = [1, 2] # for _usage_from_output + req1.outputs, req2.outputs, req3.outputs = [out1], [out2], [out3] + + async def mock_generate(_prompt, _params, _req_id): + for r in (req1, req2, req3): + yield r + + backend._engine = Mock() + backend._engine.generate = mock_generate + request = GenerationRequest( + columns={"text_column": ["Hi"]}, + output_metrics=UsageMetrics(text_tokens=10), + ) + request_info = RequestInfo() + + with patch.object(backend, "_extract_prompt", return_value="Hi"): + final_response = None + async for response, _ in backend.resolve(request, request_info): + final_response = response + + assert final_response is not None + assert final_response.output_metrics.text_tokens == 3 + + +class TestVLLMCreateSamplingParams: + """ + Test _create_sampling_params: max_tokens from body vs request override. + """ + + @pytest.mark.smoke + def test_max_tokens_override_used_when_body_has_no_max_tokens(self, backend): + """ + When body has no max_tokens, max_tokens_override is used. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + params = backend._create_sampling_params(body, max_tokens_override=2000) + assert params.max_tokens == 2000 + + @pytest.mark.smoke + def test_body_max_tokens_takes_precedence_over_override(self, backend): + """ + When body has max_tokens, override is ignored. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}], "max_tokens": 64} + params = backend._create_sampling_params(body, max_tokens_override=2000) + assert params.max_tokens == 64 + + @pytest.mark.smoke + def test_default_max_tokens_when_no_body_no_override(self, backend): + """ + When body has no max_tokens and no override, default is 16. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + params = backend._create_sampling_params(body) + assert params.max_tokens == 16 + + @pytest.mark.smoke + def test_override_used_sets_ignore_eos_and_stop_like_http(self, backend): + """ + When max_tokens_override is used, ignore_eos=True and stop=[] to match HTTP. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + params = backend._create_sampling_params(body, max_tokens_override=2000) + assert params.max_tokens == 2000 + assert params.ignore_eos is True + assert params.stop == [] + + @pytest.mark.smoke + def test_override_used_with_600_also_sets_ignore_eos_and_stop(self, backend): + """ + When max_tokens_override is used (e.g. 600), same: ignore_eos=True, stop=[]. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}]} + params = backend._create_sampling_params(body, max_tokens_override=600) + assert params.max_tokens == 600 + assert params.ignore_eos is True + assert params.stop == [] + + @pytest.mark.smoke + def test_body_max_tokens_uses_body_ignore_eos_and_stop(self, backend): + """ + When body has max_tokens, ignore_eos and stop come from body. + ## WRITTEN BY AI ## + """ + body = { + "messages": [{"role": "user", "content": "Hi"}], + "max_tokens": 64, + "ignore_eos": True, + "stop": [""], + } + params = backend._create_sampling_params(body, max_tokens_override=2000) + assert params.max_tokens == 64 + assert params.ignore_eos is True + assert params.stop == [""] + + @pytest.mark.smoke + def test_max_tokens_zero_safeguard(self, backend): + """ + When body has max_tokens=0, we use 16 so vLLM never receives 0. + ## WRITTEN BY AI ## + """ + body = {"messages": [{"role": "user", "content": "Hi"}], "max_tokens": 0} + params = backend._create_sampling_params(body) + assert params.max_tokens == 16 + + +class TestVLLMLifecycle: + """ + process_startup/shutdown/validate with mocked AsyncEngineArgs and AsyncLLMEngine. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_process_startup_success(self): + """ + Success: mock AsyncEngineArgs and from_engine_args; set _engine, _in_process. + ## WRITTEN BY AI ## + """ + mock_engine = Mock() + with ( + patch("guidellm.backends.vllm_python.vllm._check_vllm_available"), + patch( + "guidellm.backends.vllm_python.vllm.AsyncEngineArgs", + return_value=Mock(), + ), + patch( + "guidellm.backends.vllm_python.vllm.AsyncLLMEngine" + ) as mock_engine_cls, + ): + mock_engine_cls.from_engine_args = Mock(return_value=mock_engine) + backend = VLLMPythonBackend(model="test-model") + await backend.process_startup() + assert backend._engine is mock_engine + assert backend._in_process is True + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_process_startup_idempotency_raises(self): + """ + Idempotency: raise RuntimeError when _in_process already True. + ## WRITTEN BY AI ## + """ + mock_engine = Mock() + with ( + patch("guidellm.backends.vllm_python.vllm._check_vllm_available"), + patch( + "guidellm.backends.vllm_python.vllm.AsyncEngineArgs", + return_value=Mock(), + ), + patch( + "guidellm.backends.vllm_python.vllm.AsyncLLMEngine" + ) as mock_engine_cls, + ): + mock_engine_cls.from_engine_args = Mock(return_value=mock_engine) + backend = VLLMPythonBackend(model="test-model") + await backend.process_startup() + with pytest.raises(RuntimeError, match="Backend already started up"): + await backend.process_startup() + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_process_shutdown_success(self): + """ + Success: _engine.shutdown() called; _engine None, _in_process False. + ## WRITTEN BY AI ## + """ + mock_engine = Mock() + with ( + patch("guidellm.backends.vllm_python.vllm._check_vllm_available"), + patch( + "guidellm.backends.vllm_python.vllm.AsyncEngineArgs", + return_value=Mock(), + ), + patch( + "guidellm.backends.vllm_python.vllm.AsyncLLMEngine" + ) as mock_engine_cls, + ): + mock_engine_cls.from_engine_args = Mock(return_value=mock_engine) + backend = VLLMPythonBackend(model="test-model") + await backend.process_startup() + await backend.process_shutdown() + mock_engine.shutdown.assert_called_once() + assert backend._engine is None + assert backend._in_process is False + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_process_shutdown_not_started_raises(self): + """ + Raise RuntimeError when not started. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend = VLLMPythonBackend(model="test-model") + backend._in_process = False + backend._engine = None + with pytest.raises(RuntimeError, match="Backend not started up"): + await backend.process_shutdown() + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_validate_engine_none_raises(self, backend): + """ + Raise RuntimeError when _engine is None. + ## WRITTEN BY AI ## + """ + backend._engine = None + with pytest.raises(RuntimeError, match="Backend not started up"): + await backend.validate() + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_validate_success(self, backend): + """ + Success: mock _engine.generate async iterator yielding one item; no raise. + ## WRITTEN BY AI ## + """ + + async def one_yield(*args, **kwargs): + yield Mock(outputs=[Mock()], prompt_token_ids=[]) + + backend._engine = Mock() + backend._engine.generate = one_yield + with patch( + "guidellm.backends.vllm_python.vllm.SamplingParams", return_value=Mock() + ): + await backend.validate() + + +class TestVLLMModels: + """ + available_models and default_model. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_available_models_returns_model_list(self, backend): + """ + Returns [self.model]. + ## WRITTEN BY AI ## + """ + models = await backend.available_models() + assert models == ["test-model"] + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_default_model_returns_model(self, backend): + """ + Returns self.model. + ## WRITTEN BY AI ## + """ + model = await backend.default_model() + assert model == "test-model" + + +class TestVLLMValidateHelpers: + """ + _validate_backend_initialized and _validate_history. + """ + + @pytest.mark.smoke + def test_validate_backend_initialized_raises_when_engine_none(self, backend): + """ + Raises RuntimeError when _engine is None. + ## WRITTEN BY AI ## + """ + backend._engine = None + with pytest.raises(RuntimeError, match="Backend not started up"): + backend._validate_backend_initialized() + + @pytest.mark.smoke + def test_validate_backend_initialized_passes_when_engine_set(self, backend): + """ + Does not raise when _engine is set. + ## WRITTEN BY AI ## + """ + backend._engine = Mock() + backend._validate_backend_initialized() + + @pytest.mark.smoke + def test_validate_history_raises_when_history_not_none(self, backend): + """ + Raises NotImplementedError when history is not None. + ## WRITTEN BY AI ## + """ + with pytest.raises( + NotImplementedError, match="Multi-turn requests not yet supported" + ): + backend._validate_history([(Mock(), Mock())]) + + @pytest.mark.smoke + def test_validate_history_passes_when_history_none(self, backend): + """ + Does not raise when history is None. + ## WRITTEN BY AI ## + """ + backend._validate_history(None) + + +class TestVLLMTextFromOutput: + """ + _text_from_output: None, empty outputs, normal. + """ + + @pytest.mark.smoke + def test_text_from_output_none_returns_empty(self, backend): + """ + output is None -> "". + ## WRITTEN BY AI ## + """ + assert backend._text_from_output(None) == "" + + @pytest.mark.smoke + def test_text_from_output_empty_outputs_returns_empty(self, backend): + """ + output.outputs empty -> "". + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.outputs = [] + assert backend._text_from_output(mock_out) == "" + + @pytest.mark.smoke + def test_text_from_output_normal_returns_first_text(self, backend): + """ + Normal -> output.outputs[0].text. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.outputs = [Mock(text="Hello world")] + assert backend._text_from_output(mock_out) == "Hello world" + + @pytest.mark.smoke + def test_text_from_output_none_text_returns_empty(self, backend): + """ + output.outputs[0].text is None -> "". + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.outputs = [Mock(text=None)] + assert backend._text_from_output(mock_out) == "" + + +class TestVLLMOpenAIPayloadForMode: + """ + _openai_payload_for_mode: audio, text, chat delta/message. + """ + + @pytest.mark.smoke + def test_openai_payload_audio_returns_text_key(self, backend): + """ + mode audio -> {"text": ...}. + ## WRITTEN BY AI ## + """ + out = backend._openai_payload_for_mode("audio", "transcribed") + assert out == {"text": "transcribed"} + + @pytest.mark.smoke + def test_openai_payload_text_returns_choices_text(self, backend): + """ + mode text -> choices[].text. + ## WRITTEN BY AI ## + """ + out = backend._openai_payload_for_mode("text", "completion") + assert out == {"choices": [{"text": "completion"}]} + + @pytest.mark.smoke + def test_openai_payload_chat_returns_choices_message(self, backend): + """ + mode chat -> choices[].message (no delta shape; message only). + ## WRITTEN BY AI ## + """ + out = backend._openai_payload_for_mode("chat", "hi") + assert out == { + "choices": [{"message": {"content": "hi", "role": "assistant"}}] + } + + @pytest.mark.smoke + def test_openai_payload_chat_message_returns_choices_message(self, backend): + """ + mode chat -> choices[].message. + ## WRITTEN BY AI ## + """ + out = backend._openai_payload_for_mode("chat", "hello") + assert out == { + "choices": [{"message": {"content": "hello", "role": "assistant"}}] + } + + +class TestVLLMUsageFromOutputNonStream: + """ + _usage_from_output with request_info=None (non-stream path). + """ + + @pytest.mark.smoke + def test_usage_from_output_non_stream_uses_output_token_counts(self, backend): + """ + Non-stream: use only output token counts (no tokenizer/request_info). + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.prompt_token_ids = [1, 2, 3] + mock_out.outputs = [Mock(token_ids=[10, 20])] + usage = backend._usage_from_output(mock_out, request_info=None) + assert usage == { + "prompt_tokens": 3, + "completion_tokens": 2, + "total_tokens": 5, + } + + @pytest.mark.smoke + def test_usage_from_output_non_stream_outputs_empty(self, backend): + """ + Non-stream with no outputs -> completion_tokens 0. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.prompt_token_ids = [1] + mock_out.outputs = [] + usage = backend._usage_from_output(mock_out, request_info=None) + assert usage["completion_tokens"] == 0 + assert usage["prompt_tokens"] == 1 + + +class TestVLLMBuildFinalResponse: + """ + _build_final_response: final_output None; stream vs non-stream. + """ + + @pytest.mark.smoke + def test_build_final_response_none_returns_none(self, backend): + """ + final_output is None -> return None. + ## WRITTEN BY AI ## + """ + from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler + + request = GenerationRequest(columns={"text_column": ["x"]}) + request_info = RequestInfo() + ctx = _RequestContext(mode="chat", body={}, stream=True, files={}) + handler = VLLMResponseHandler() + result = backend._build_final_response( + request, request_info, None, ctx, handler, stream=True + ) + assert result is None + + @pytest.mark.smoke + def test_build_final_response_stream_returns_compile_streaming(self, backend): + """ + Stream path uses compile_streaming. + ## WRITTEN BY AI ## + """ + from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler + + mock_final = Mock() + mock_final.prompt_token_ids = [1, 2] + mock_final.outputs = [Mock(token_ids=[3, 4], text="ab")] + mock_final.request_id = "req-1" + request = GenerationRequest(columns={"text_column": ["x"]}) + request_info = RequestInfo() + ctx = _RequestContext(mode="chat", body={}, stream=True, files={}) + handler = VLLMResponseHandler() + handler.add_streaming_line('data: {"choices": [{"delta": {"content": "ab"}}]}') + result = backend._build_final_response( + request, + request_info, + mock_final, + ctx, + handler, + stream=True, + accumulated_text="ab", + total_output_tokens=2, + ) + assert result is not None + resp, info = result + assert isinstance(resp, GenerationResponse) + assert resp.text == "ab" + assert info is request_info + + @pytest.mark.smoke + def test_build_final_response_non_stream_returns_compile_non_streaming( + self, backend + ): + """ + Non-stream: convert to OpenAI format and compile_non_streaming. + ## WRITTEN BY AI ## + """ + from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler + + mock_final = Mock() + mock_final.prompt_token_ids = [1, 2] + mock_final.outputs = [Mock(token_ids=[3, 4], text="hello")] + mock_final.request_id = "req-1" + request = GenerationRequest(columns={"text_column": ["x"]}) + request_info = RequestInfo() + ctx = _RequestContext(mode="text", body={}, stream=False, files={}) + handler = VLLMResponseHandler() + result = backend._build_final_response( + request, request_info, mock_final, ctx, handler, stream=False + ) + assert result is not None + resp, info = result + assert isinstance(resp, GenerationResponse) + assert resp.text == "hello" + assert info is request_info + + +class TestVLLMExtractTextFromContent: + """ + _extract_text_from_content: string, list of text blocks, fallback. + """ + + @pytest.mark.smoke + def test_extract_text_from_content_string_returns_same(self, backend): + """ + String -> same string. + ## WRITTEN BY AI ## + """ + assert backend._extract_text_from_content("hello") == "hello" + + @pytest.mark.smoke + def test_extract_text_from_content_list_text_blocks_concatenated(self, backend): + """ + List of {type:text, text:x} -> concatenated. + ## WRITTEN BY AI ## + """ + content = [ + {"type": "text", "text": "a"}, + {"type": "image_url", "url": "x"}, + {"type": "text", "text": "b"}, + ] + assert backend._extract_text_from_content(content) == "ab" + + @pytest.mark.smoke + def test_extract_text_from_content_list_no_text_blocks_returns_empty(self, backend): + """ + List without text blocks -> "". + ## WRITTEN BY AI ## + """ + content = [{"type": "image_url", "url": "x"}] + assert backend._extract_text_from_content(content) == "" + + @pytest.mark.smoke + def test_extract_text_from_content_fallback_str(self, backend): + """ + Non-str non-list -> str(content). + ## WRITTEN BY AI ## + """ + assert backend._extract_text_from_content(123) == "123" + + @pytest.mark.smoke + def test_extract_text_from_content_none_returns_empty(self, backend): + """ + content None -> "". + ## WRITTEN BY AI ## + """ + assert backend._extract_text_from_content(None) == "" + + +class TestVLLMExtractAudioFromRequest: + """ + _extract_audio_from_request: empty, no file, tuple, raw bytes, non-bytes, invalid. + """ + + @pytest.mark.smoke + def test_extract_audio_empty_files_raises(self, backend): + """ + Empty files -> ValueError. + ## WRITTEN BY AI ## + """ + with pytest.raises(ValueError, match="Audio request must include audio file"): + backend._extract_audio_from_request({}) + + @pytest.mark.smoke + def test_extract_audio_no_valid_file_raises(self, backend): + """ + No tuple/bytes in values -> ValueError. + ## WRITTEN BY AI ## + """ + with pytest.raises(ValueError, match="Audio request must include audio file"): + backend._extract_audio_from_request({"k": "not-bytes"}) + + @pytest.mark.smoke + def test_extract_audio_tuple_returns_bytes_and_mimetype(self, backend): + """ + Tuple (name, bytes, mimetype) -> return bytes and mimetype. + ## WRITTEN BY AI ## + """ + data = b"audio-bytes" + out = backend._extract_audio_from_request({"f": ("x.wav", data, "audio/wav")}) + assert out == (data, "audio/wav") + + @pytest.mark.smoke + def test_extract_audio_tuple_two_elements_default_mimetype(self, backend): + """ + Tuple (name, bytes) -> mimetype default audio/wav. + ## WRITTEN BY AI ## + """ + data = b"audio" + out = backend._extract_audio_from_request({"f": ("x.wav", data)}) + assert out == (data, "audio/wav") + + @pytest.mark.smoke + def test_extract_audio_raw_bytes_returns_wav_tuple(self, backend): + """ + Raw bytes value -> ("audio.wav", bytes, "audio/wav"). + ## WRITTEN BY AI ## + """ + data = b"raw" + out = backend._extract_audio_from_request({"f": data}) + assert out == (data, "audio/wav") + + @pytest.mark.smoke + def test_extract_audio_tuple_non_bytes_at_index_1_raises(self, backend): + """ + Tuple with non-bytes at index 1 -> ValueError. + ## WRITTEN BY AI ## + """ + with pytest.raises(ValueError, match="Expected bytes for audio data"): + backend._extract_audio_from_request( + {"f": ("x.wav", "not-bytes", "audio/wav")} + ) + + +class TestVLLMExtractPromptEdgeCases: + """ + _extract_prompt: text/chat empty -> ValueError; audio optional prompt. + """ + + @pytest.mark.smoke + def test_extract_prompt_text_empty_prompt_raises(self, backend): + """ + Text mode: body prompt missing or empty -> ValueError. + ## WRITTEN BY AI ## + """ + with pytest.raises(ValueError, match="Text request must include 'prompt'"): + backend._extract_prompt({"prompt": ""}, "text") + with pytest.raises(ValueError, match="Text request must include 'prompt'"): + backend._extract_prompt({}, "text") + + @pytest.mark.smoke + def test_extract_prompt_chat_empty_messages_raises(self, backend): + """ + Chat mode: messages empty -> ValueError. + ## WRITTEN BY AI ## + """ + with patch("guidellm.backends.vllm_python.vllm._check_vllm_available"): + backend_plain = VLLMPythonBackend( + model="test-model", request_format="plain" + ) + backend_plain._engine = None + with pytest.raises(ValueError, match="Chat request must include 'messages'"): + backend_plain._extract_prompt({"messages": []}, "chat") + + @pytest.mark.smoke + def test_extract_prompt_audio_returns_prompt_or_empty(self, backend): + """ + Audio mode: return body.get("prompt", "") (optional prompt). + ## WRITTEN BY AI ## + """ + assert backend._extract_prompt({"prompt": "caption"}, "audio") == "caption" + assert backend._extract_prompt({}, "audio") == "" + assert backend._extract_prompt({"prompt": ""}, "audio") == "" + + +class TestVLLMConvertVllmOutputToOpenAIFormat: + """ + _convert_vllm_output_to_openai_format: OpenAI-shaped dict with usage and id. + """ + + @pytest.mark.smoke + def test_convert_vllm_output_to_openai_format_includes_usage_and_id(self, backend): + """ + Builds dict with mode-specific payload, usage, and id from output.request_id. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.prompt_token_ids = [1, 2] + mock_out.outputs = [Mock(token_ids=[3], text="hi")] + mock_out.request_id = "vllm-req-123" + result = backend._convert_vllm_output_to_openai_format(mock_out, "chat") + assert result["choices"] == [ + {"message": {"content": "hi", "role": "assistant"}} + ] + assert result["usage"] == { + "prompt_tokens": 2, + "completion_tokens": 1, + "total_tokens": 3, + } + assert result["id"] == "vllm-req-123" + + @pytest.mark.smoke + def test_convert_vllm_output_to_openai_format_no_request_id_omits_id(self, backend): + """ + When output has no request_id or falsy, id is omitted. + ## WRITTEN BY AI ## + """ + mock_out = Mock() + mock_out.prompt_token_ids = [] + mock_out.outputs = [Mock(token_ids=[], text="")] + mock_out.request_id = None + result = backend._convert_vllm_output_to_openai_format(mock_out, "text") + assert "choices" in result + assert "usage" in result + assert "id" not in result + + +class TestVLLMResolveNonStream: + """ + resolve() with stream=False: one generate yield, compile_non_streaming. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_resolve_non_stream_yields_one_response(self, backend): + """ + stream=False; mock generate yields once; one response via compile_non_streaming. + ## WRITTEN BY AI ## + """ + out = Mock() + out.text = "done" + out.token_ids = [1, 2, 3] + out.finish_reason = "stop" + req_out = Mock() + req_out.prompt_token_ids = [1, 2] + req_out.outputs = [out] + req_out.request_id = "r1" + + async def one_yield(prompt, sampling_params, request_id): + yield req_out + + backend._engine = Mock() + backend._engine.generate = one_yield + request = Mock(spec=GenerationRequest) + request.columns = None + request.arguments = Mock() + request.arguments.body = {"messages": [{"role": "user", "content": "Hi"}]} + request.arguments.stream = False + request.arguments.files = {} + request.output_metrics = UsageMetrics() + request.request_id = "req-1" + request.arguments.model_dump_json = Mock(return_value="{}") + request_info = RequestInfo() + + results = [] + with patch.object( + backend, "_extract_prompt", return_value="User: Hi\nAssistant: " + ): + async for response, info in backend.resolve(request, request_info): + results.append((response, info)) + + assert len(results) == 1 + resp, info = results[0] + assert isinstance(resp, GenerationResponse) + assert resp.text == "done" + assert info is request_info + + +class TestVLLMResolveValidation: + """ + resolve(): history -> NotImplementedError; _engine None -> RuntimeError. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_resolve_with_history_raises_not_implemented(self, backend): + """ + history=[(...)] -> NotImplementedError. + ## WRITTEN BY AI ## + """ + backend._engine = Mock() + request = GenerationRequest(columns={"text_column": ["Hi"]}) + request_info = RequestInfo() + history = [(Mock(), Mock())] + with pytest.raises( + NotImplementedError, match="Multi-turn requests not yet supported" + ): + async for _ in backend.resolve(request, request_info, history=history): + pass + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_resolve_engine_none_raises_runtime_error(self, backend): + """ + _engine is None -> RuntimeError. + ## WRITTEN BY AI ## + """ + backend._engine = None + request = GenerationRequest(columns={"text_column": ["Hi"]}) + request_info = RequestInfo() + with pytest.raises(RuntimeError, match="Backend not started up"): + async for _ in backend.resolve(request, request_info): + pass + + +class TestVLLMResolveCancelledError: + """ + resolve(): CancelledError after yield; response still yielded, then re-raise. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_resolve_cancelled_error_yields_then_reraises(self, backend): + """ + During generate raise CancelledError; assert response yielded then re-raise. + ## WRITTEN BY AI ## + """ + out = Mock() + out.text = "partial" + out.token_ids = [1] + out.finish_reason = None + req_out = Mock() + req_out.prompt_token_ids = [1] + req_out.outputs = [out] + req_out.request_id = "r1" + + async def yield_then_cancel(prompt, sampling_params, request_id): + yield req_out + raise asyncio.CancelledError + + backend._engine = Mock() + backend._engine.generate = yield_then_cancel + request = GenerationRequest(columns={"text_column": ["Hi"]}) + request_info = RequestInfo() + results = [] + async def collect(): + async for response, info in backend.resolve(request, request_info): + results.append((response, info)) + + with patch.object( + backend, "_extract_prompt", return_value="User: Hi\nAssistant: " + ), pytest.raises(asyncio.CancelledError): + await collect() + assert len(results) == 1 + assert results[0][0].text == "partial" + + +class TestVLLMResolveAudioError: + """ + resolve(): At most 0 audio / audio(s) may be provided -> RuntimeError with guidance. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_resolve_audio_error_wraps_with_guidance(self, backend): + """ + 'At most 0 audio' -> RuntimeError with audio-capable model guidance. + ## WRITTEN BY AI ## + """ + + async def mock_generate_raise(*args, **kwargs): + raise ValueError("At most 0 audio(s) may be provided.") + yield # makes this an async generator so async for raises + + with patch("guidellm.backends.vllm_python.vllm._decode_audio") as mock_decode: + mock_decode.return_value = Mock(data=np.array([0.0])) + backend._engine = Mock() + backend._engine.generate = mock_generate_raise + request = Mock(spec=GenerationRequest) + request.columns = None + request.arguments = Mock() + request.arguments.body = {} + request.arguments.stream = False + request.arguments.files = {"f": ("audio.wav", b"audio", "audio/wav")} + request.output_metrics = UsageMetrics() + request.request_id = "r1" + request_info = RequestInfo() + with pytest.raises(RuntimeError) as exc_info: + async for _ in backend.resolve(request, request_info): + pass + assert "does not support audio" in str(exc_info.value) + assert "audio-capable model" in str(exc_info.value) + + +class TestVLLMResolveAudioMode: + """ + resolve() audio: mock _decode_audio and generate; one response. + """ + + @pytest.mark.asyncio + @pytest.mark.smoke + async def test_resolve_audio_mode_yields_one_response(self, backend): + """ + Mode audio; mock _decode_audio and engine.generate; assert one response. + ## WRITTEN BY AI ## + """ + mock_audio_array = np.array([0.0, 0.1]) + mock_decode_result = Mock() + mock_decode_result.data = mock_audio_array + + out = Mock() + out.text = "transcribed" + out.token_ids = [1, 2] + out.finish_reason = "stop" + req_out = Mock() + req_out.prompt_token_ids = [1] + req_out.outputs = [out] + req_out.request_id = "r1" + + async def mock_generate(prompt, sampling_params, request_id): + assert isinstance(prompt, dict) + assert "prompt" in prompt + assert "multi_modal_data" in prompt + assert "audio" in prompt["multi_modal_data"] + yield req_out + + with patch( + "guidellm.backends.vllm_python.vllm._decode_audio", + return_value=mock_decode_result, + ): + backend._engine = Mock() + backend._engine.generate = mock_generate + request = Mock(spec=GenerationRequest) + request.columns = None + request.arguments = Mock() + request.arguments.body = {"prompt": ""} + request.arguments.stream = False + request.arguments.files = {"f": ("audio.wav", b"audio-bytes", "audio/wav")} + request.output_metrics = UsageMetrics() + request.request_id = "r1" + request.arguments.model_dump_json = Mock(return_value="{}") + request_info = RequestInfo() + results = [] + async for response, info in backend.resolve(request, request_info): + results.append((response, info)) + assert len(results) == 1 + assert results[0][0].text == "transcribed" diff --git a/tests/unit/backends/vllm_python/test_vllm_response.py b/tests/unit/backends/vllm_python/test_vllm_response.py new file mode 100644 index 000000000..79af25d75 --- /dev/null +++ b/tests/unit/backends/vllm_python/test_vllm_response.py @@ -0,0 +1,240 @@ +""" +Unit tests for VLLMResponseHandler. + +Tests compilation of OpenAI-style response dicts (choices+usage and text+usage) +and streaming SSE line parsing without request type. +""" + +from __future__ import annotations + +import pytest + +from guidellm.backends.vllm_python.vllm_response import VLLMResponseHandler +from guidellm.schemas import GenerationRequest, GenerationResponse + + +@pytest.fixture +def request_without_arguments(): + """GenerationRequest with no arguments (columns only).""" + return GenerationRequest(columns={"text_column": ["test"]}) + + +@pytest.fixture +def handler(): + """Fresh VLLMResponseHandler instance.""" + return VLLMResponseHandler() + + +class TestVLLMResponseHandler: + """ + Test cases for VLLMResponseHandler. + ## WRITTEN BY AI ## + """ + + @pytest.mark.smoke + def test_has_required_methods(self): + """ + Handler exposes compile_non_streaming, add_streaming_line, compile_streaming. + ## WRITTEN BY AI ## + """ + assert hasattr(VLLMResponseHandler, "compile_non_streaming") + assert hasattr(VLLMResponseHandler, "add_streaming_line") + assert hasattr(VLLMResponseHandler, "compile_streaming") + + @pytest.mark.smoke + def test_compile_non_streaming_choices_usage( + self, handler, request_without_arguments + ): + """ + compile_non_streaming with choices+usage (text/chat) shape. + ## WRITTEN BY AI ## + """ + response_dict = { + "id": "gen-123", + "choices": [{"text": "Hello world"}], + "usage": { + "prompt_tokens": 5, + "completion_tokens": 2, + "total_tokens": 7, + }, + } + out = handler.compile_non_streaming(request_without_arguments, response_dict) + assert isinstance(out, GenerationResponse) + assert out.request_id == request_without_arguments.request_id + assert out.response_id == "gen-123" + assert out.text == "Hello world" + assert out.input_metrics.text_tokens == 5 + assert out.output_metrics.text_tokens == 2 + assert out.output_metrics.text_words == 2 + assert out.output_metrics.text_characters == 11 + + @pytest.mark.smoke + def test_compile_non_streaming_choices_message( + self, handler, request_without_arguments + ): + """ + compile_non_streaming with choices[].message (chat) shape. + ## WRITTEN BY AI ## + """ + response_dict = { + "choices": [{"message": {"content": "Hi there", "role": "assistant"}}], + "usage": {"prompt_tokens": 1, "completion_tokens": 2}, + } + out = handler.compile_non_streaming(request_without_arguments, response_dict) + assert out.text == "Hi there" + assert out.input_metrics.text_tokens == 1 + assert out.output_metrics.text_tokens == 2 + + @pytest.mark.smoke + def test_compile_non_streaming_text_usage(self, handler, request_without_arguments): + """ + compile_non_streaming with text+usage (audio) shape. + ## WRITTEN BY AI ## + """ + response_dict = { + "text": "Transcribed text", + "usage": {"prompt_tokens": 0, "completion_tokens": 3}, + } + out = handler.compile_non_streaming(request_without_arguments, response_dict) + assert out.text == "Transcribed text" + assert out.output_metrics.text_tokens == 3 + + @pytest.mark.smoke + def test_compile_non_streaming_empty_usage( + self, handler, request_without_arguments + ): + """ + compile_non_streaming with no usage still fills text and word/char counts. + ## WRITTEN BY AI ## + """ + response_dict = {"choices": [{"text": "Hi"}]} + out = handler.compile_non_streaming(request_without_arguments, response_dict) + assert out.text == "Hi" + assert out.output_metrics.text_words == 1 + assert out.output_metrics.text_characters == 2 + + @pytest.mark.smoke + def test_add_streaming_line_choices_text(self, handler): + """ + add_streaming_line parses choices[].text delta. + ## WRITTEN BY AI ## + """ + line = 'data: {"choices": [{"text": "ab"}]}' + assert handler.add_streaming_line(line) == 1 + assert handler.add_streaming_line('data: {"choices": [{"text": "c"}]}') == 1 + assert handler.streaming_texts == ["ab", "c"] + + @pytest.mark.smoke + def test_add_streaming_line_choices_delta(self, handler): + """ + add_streaming_line parses choices[].delta.content (chat) delta. + ## WRITTEN BY AI ## + """ + handler.add_streaming_line('data: {"choices": [{"delta": {"content": "x"}}]}') + handler.add_streaming_line('data: {"choices": [{"delta": {"content": "y"}}]}') + assert handler.streaming_texts == ["x", "y"] + + @pytest.mark.smoke + def test_add_streaming_line_text(self, handler): + """ + add_streaming_line parses audio-style text delta. + ## WRITTEN BY AI ## + """ + handler.add_streaming_line('data: {"text": "hello"}') + assert handler.add_streaming_line('data: {"text": " world"}') == 1 + assert handler.streaming_texts == ["hello", " world"] + + @pytest.mark.smoke + def test_add_streaming_line_done_returns_none(self, handler): + """ + add_streaming_line returns None for data: [DONE]. + ## WRITTEN BY AI ## + """ + assert handler.add_streaming_line("data: [DONE]") is None + + @pytest.mark.smoke + def test_add_streaming_line_empty_returns_zero(self, handler): + """ + add_streaming_line returns 0 for line with no content. + ## WRITTEN BY AI ## + """ + assert handler.add_streaming_line("data: {}") == 0 + + @pytest.mark.smoke + def test_add_streaming_line_invalid_json_returns_zero(self, handler): + """ + add_streaming_line with invalid JSON in data returns 0; no crash. + ## WRITTEN BY AI ## + """ + result = handler.add_streaming_line("data: { invalid }") + assert result == 0 + assert handler.streaming_texts == [] + + @pytest.mark.smoke + def test_compile_streaming_uses_accumulated_text( + self, handler, request_without_arguments + ): + """ + compile_streaming builds response from accumulated chunks. + ## WRITTEN BY AI ## + """ + handler.add_streaming_line('data: {"choices": [{"text": "Hello"}]}') + handler.add_streaming_line('data: {"choices": [{"text": " "}]}') + handler.add_streaming_line('data: {"choices": [{"text": "world"}]}') + out = handler.compile_streaming(request_without_arguments) + assert out.text == "Hello world" + assert out.request_id == request_without_arguments.request_id + + @pytest.mark.smoke + def test_compile_streaming_text_override_uses_given_text( + self, handler, request_without_arguments + ): + """ + compile_streaming with text_override uses that text instead of streaming_texts. + ## WRITTEN BY AI ## + """ + handler.add_streaming_line('data: {"choices": [{"text": "ignored"}]}') + out = handler.compile_streaming( + request_without_arguments, text_override="final text" + ) + assert out.text == "final text" + assert out.request_id == request_without_arguments.request_id + + @pytest.mark.smoke + def test_compile_streaming_stores_response_id( + self, handler, request_without_arguments + ): + """ + compile_streaming uses response_id from streaming chunks. + ## WRITTEN BY AI ## + """ + handler.add_streaming_line( + 'data: {"id": "stream-1", "choices": [{"text": "x"}]}' + ) + out = handler.compile_streaming(request_without_arguments) + assert out.response_id == "stream-1" + assert out.text == "x" + + @pytest.mark.smoke + def test_request_args_from_arguments_if_present(self, request_without_arguments): + """ + When request has arguments, request_args is serialized in response. + ## WRITTEN BY AI ## + """ + from unittest.mock import Mock + + # Request has no 'arguments'; handler uses getattr(request, "arguments", None). + # Mock request with arguments.model_dump_json (e.g. from alternate pipelines). + request_with_arguments = Mock(spec=GenerationRequest) + request_with_arguments.request_id = request_without_arguments.request_id + request_with_arguments.arguments = Mock() + request_with_arguments.arguments.model_dump_json.return_value = ( + '{"body":{"prompt":"hi","max_tokens":10}}' + ) + + response_dict = {"choices": [{"text": "hey"}], "usage": {}} + handler = VLLMResponseHandler() + out = handler.compile_non_streaming(request_with_arguments, response_dict) + assert out.request_args is not None + assert "prompt" in out.request_args + assert "max_tokens" in out.request_args diff --git a/tox.ini b/tox.ini index b6ae685e6..8f0a94555 100644 --- a/tox.ini +++ b/tox.ini @@ -6,6 +6,7 @@ env_list = py3{10,11,12,13} [testenv:tests] description = Run all tests dependency_groups = dev +deps = vllm commands = python -m pytest {posargs:tests/} diff --git a/uv.lock b/uv.lock index 4fd7b1bb2..de2f48723 100644 --- a/uv.lock +++ b/uv.lock @@ -2,9 +2,13 @@ version = 1 revision = 3 requires-python = ">=3.10.0, <4.0" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform != 'darwin'", + "python_full_version >= '3.14' and sys_platform != 'darwin'", + "python_full_version == '3.13.*' and sys_platform != 'darwin'", + 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