From 7f40ec3476038f9bbdd4f7333492a4c7818c0418 Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Thu, 2 Apr 2026 14:16:26 -0700 Subject: [PATCH 1/8] first pass at removing vllm --- mellea/backends/vllm.py | 643 --------- mellea/telemetry/backend_instrumentation.py | 2 - pyproject.toml | 17 +- test/backends/test_vllm.py | 227 --- test/backends/test_vllm_tools.py | 103 -- test/conftest.py | 104 +- test/package/test_dependency_isolation.py | 24 +- uv.lock | 1397 +------------------ 8 files changed, 9 insertions(+), 2508 deletions(-) delete mode 100644 mellea/backends/vllm.py delete mode 100644 test/backends/test_vllm.py delete mode 100644 test/backends/test_vllm_tools.py diff --git a/mellea/backends/vllm.py b/mellea/backends/vllm.py deleted file mode 100644 index 5a5b1aa03..000000000 --- a/mellea/backends/vllm.py +++ /dev/null @@ -1,643 +0,0 @@ -"""A backend that uses a VLLM in the current process. - -The purpose of the VLLM backend is to provide a locally running fast inference engine. -""" - -from __future__ import annotations - -import asyncio -import dataclasses -import datetime -import functools -import importlib -import json -import os -import shutil -from collections.abc import Callable, Sequence -from typing import Any, overload - -try: - import msgspec - import torch - import vllm - from transformers import AutoTokenizer - from transformers.tokenization_utils_base import PreTrainedTokenizerBase -except ImportError as e: - raise ImportError( - "vLLM backend dependencies are not installed. " - "Please install with: pip install mellea[vllm]\n" - "Note: vLLM is not supported on macOS. Use other backends like Ollama or HuggingFace instead." - ) from e - -from ..backends import ModelIdentifier -from ..core import ( - BaseModelSubclass, - C, - CBlock, - Component, - Context, - FancyLogger, - GenerateLog, - GenerateType, - ModelOutputThunk, -) -from ..core.base import AbstractMelleaTool -from ..formatters import ChatFormatter, TemplateFormatter -from ..helpers import get_current_event_loop, send_to_queue -from .backend import FormatterBackend -from .model_options import ModelOption -from .tools import ( - add_tools_from_context_actions, - add_tools_from_model_options, - convert_tools_to_json, -) -from .utils import to_chat, to_tool_calls - -format: None = None # typing this variable in order to shadow the global format function and ensure mypy checks for errors - - -class LocalVLLMBackend(FormatterBackend): - """The LocalVLLMBackend uses vLLM's python interface for inference, and uses a Formatter to convert `Component`s into prompts. - - The support for Activated LoRAs (ALoras)](https://arxiv.org/pdf/2504.12397) is planned. - - This backend is designed for running an HF model for small-scale inference locally on your machine. - - Its throughput is generally higher than that of LocalHFBackend. - However, it takes longer to load the weights during the instantiation. - Also, if you submit a request one by one, it can be slower. - - Note: vLLM defaults to ~16 tokens. Always set ModelOption.MAX_NEW_TOKENS explicitly (100-1000+). - Structured output needs 200-500+ tokens. - - Args: - model_id (str | ModelIdentifier): HuggingFace model ID used to load model weights via vLLM. - formatter (ChatFormatter | None): Formatter for rendering components into prompts. - Defaults to a ``TemplateFormatter`` for the given ``model_id``. - model_options (dict | None): Default model options for generation requests. - - Attributes: - to_mellea_model_opts_map (dict): Mapping from backend-specific option names to - Mellea ``ModelOption`` sentinel keys. - from_mellea_model_opts_map (dict): Mapping from Mellea ``ModelOption`` sentinel - keys to backend-specific option names. - engine_args (dict): vLLM engine arguments used at instantiation; retained so the - engine can be restarted when the event loop changes. - """ - - def __init__( - self, - model_id: str | ModelIdentifier, - formatter: ChatFormatter | None = None, - *, - model_options: dict | None = None, - ): - """Load model weights via vLLM and initialize the async inference engine.""" - formatter = ( - formatter if formatter is not None else TemplateFormatter(model_id=model_id) - ) - - super().__init__(model_id, formatter, model_options=model_options) - - # A mapping of common options for this backend mapped to their Mellea ModelOptions equivalent. - # These are usually values that must be extracted before hand or that are common among backend providers - self.to_mellea_model_opts_map = { - # "system": ModelOption.SYSTEM_PROMPT, - "max_tokens": ModelOption.MAX_NEW_TOKENS, - "seed": ModelOption.SEED, - "temperature": ModelOption.TEMPERATURE, - } - - # A mapping of Mellea specific ModelOptions to the specific names for this backend. - # These options should almost always be a subset of those specified in the `to_mellea_model_opts_map`. - # Usually, values that are intentionally extracted while prepping for the backend generate call - # will be omitted here so that they will be removed when model_options are processed - # for the call to the model. - self.from_mellea_model_opts_map = { - ModelOption.MAX_NEW_TOKENS: "max_tokens", - ModelOption.SEED: "seed", - ModelOption.TEMPERATURE: "temperature", - } - - # Either use the custom config or load the model from its model_id - match model_id: - case str(): - self._hf_model_id = model_id - case ModelIdentifier(): - assert model_id.hf_model_name is not None, ( - "model_id is None. This can also happen if the ModelIdentifier has no hf_model_id name set." - ) - self._hf_model_id = model_id.hf_model_name - - # vllm requires some model options during instantiation. - engine_args = self._simplify_and_merge(model_options) - engine_args = self._make_backend_specific_and_remove( - engine_args, vllm.AsyncEngineArgs - ) - - logger = FancyLogger.get_logger() - # Get the model and tokenizer. - # Getting vllm instantiated is tricky as it does not automatically detect some of these parameters. - engine_args["gpu_memory_utilization"] = engine_args.get( - "gpu_memory_utilization", 0.9 - ) - engine_args["max_num_seqs"] = engine_args.get("max_num_seqs", 16) - engine_args["max_model_len"] = engine_args.get("max_model_len", 16384) - logger.info( - f"Instantiating vllm with the following model parameters:\n" - f"gpu_memory_utilization: {engine_args['gpu_memory_utilization']}\n" - f"max_model_len: {engine_args['max_model_len']}\n" - f"max_num_seqs: {engine_args['max_num_seqs']}\n" - ) - retry = 0 - while True: - retry += 1 - try: - self._underlying_model = vllm.AsyncLLMEngine.from_engine_args( - vllm.AsyncEngineArgs(model=self._hf_model_id, **engine_args) - ) - self._tokenizer = self._underlying_model.get_tokenizer() - break - except torch._dynamo.exc.BackendCompilerFailed as e: # type: ignore - # example: - # torch._dynamo.exc.BackendCompilerFailed: backend='' raised: - # RuntimeError: vLLM failed to compile the model. The most likely reason for this is that a previous compilation failed, leading to a corrupted compilation artifact. We recommend trying to remove ~/.cache/vllm/torch_compile_cache and try again to see the real issue. - - if "~/.cache/vllm/torch_compile_cache" in str(e.inner_exception): - logger.warning( - "removing ~/.cache/vllm/torch_compile_cache and retry" - ) - shutil.rmtree("~/.cache/vllm/torch_compile_cache") - # then retry - - except Exception as e: - logger.info(e) - if retry % 3 == 0: - engine_args["max_model_len"] //= 2 - elif retry % 3 == 1: - engine_args["max_num_seqs"] //= 2 - elif retry % 3 == 2: - engine_args["gpu_memory_utilization"] *= 0.9 - if ( - engine_args["max_model_len"] == 0 - or engine_args["max_num_seqs"] == 0 - or engine_args["gpu_memory_utilization"] < 0.1 - ): - raise RuntimeError( - f"Failed to initialize vLLM engine (last error: {e}).\n" - "\n" - "Exhausted all retry attempts:\n" - f" • gpu_memory_utilization: {engine_args['gpu_memory_utilization']:.2f}\n" - f" • max_model_len: {engine_args['max_model_len']}\n" - f" • max_num_seqs: {engine_args['max_num_seqs']}\n" - "\n" - "Please check the full stack trace above for details.\n" - "Common issues: OOM, CUDA fork errors, GPU exclusive_process mode.\n" - ) - logger.info( - f"Reducing vllm model parameters to make it fit in the GPU memory.\n" - "current values:\n" - f"gpu_memory_utilization: {engine_args['gpu_memory_utilization']}\n" - f"max_model_len: {engine_args['max_model_len']}\n" - f"max_num_seqs: {engine_args['max_num_seqs']}\n" - ) - - logger.info( - f"vllm instantiated.\n" - "final model parameters:\n" - f"gpu_memory_utilization: {engine_args['gpu_memory_utilization']}\n" - f"max_model_len: {engine_args['max_model_len']}\n" - f"max_num_seqs: {engine_args['max_num_seqs']}\n" - ) - - # Keep track of the event loop the engine was instantiated in. - self._event_loop = get_current_event_loop() - # we store the engine args because we have to reset the engine with a different event loop. See _model . - self.engine_args = engine_args - - @property - def _model(self) -> vllm.AsyncLLMEngine: - """Use model when making generation requests.""" - # 2026/01/06 Masa: Temporarily canceling the mechanism below. - # After vllm 0.11.0, start/shutdown_background_loop is gone. - # 2026/01/07 Masa: Rewrote it to reinstantiate the engine. - - el = get_current_event_loop() - - # vLLM attaches itself to the event loop that is running when instantiated / - # the first generate request is made. Thankfully, they provide helpers to - # reset that. We do that here if the event loop changes. - - # Most of the time, this should be a no-op. The event loop will only change - # if switching between async and sync calls. - if el != self._event_loop: - FancyLogger.get_logger().warning("restarting the vllm event loop") - # self._underlying_model.shutdown_background_loop() - # self._underlying_model.start_background_loop() - self._underlying_model.shutdown() - self._underlying_model = vllm.AsyncLLMEngine.from_engine_args( - vllm.AsyncEngineArgs(model=self._hf_model_id, **self.engine_args) - ) - self._tokenizer = self._underlying_model.get_tokenizer() - self._event_loop = el - - return self._underlying_model - - async def _generate_from_context( - self, - action: Component[C] | CBlock, - ctx: Context, - *, - format: type[BaseModelSubclass] | None = None, - model_options: dict | None = None, - generate_logs: list[GenerateLog] | None = None, - tool_calls: bool = False, - ) -> tuple[ModelOutputThunk[C], Context]: - """Generate a completion for ``action`` given the current ``ctx`` using the vLLM engine. - - Args: - action (Component[C] | CBlock): The component or content block to generate a - completion for. - ctx (Context): The current generation context (must be a chat context). - format (type[BaseModelSubclass] | None): Optional Pydantic model class for - structured/constrained output decoding. - model_options (dict | None): Per-call model options that override the - backend's defaults. - generate_logs (list[GenerateLog] | None): Optional list to which a - ``GenerateLog`` entry will be appended. - tool_calls (bool): If ``True``, expose available tools to the model and - parse tool-call responses. - - Returns: - tuple[ModelOutputThunk[C], Context]: A thunk holding the (lazy) model output - and an updated context that includes ``action`` and the new output. - """ - await self.do_generate_walk(action) - - # Upsert model options. - model_options = self._simplify_and_merge(model_options) - - # TODO: insert the alora code here. - - mot = await self._generate_from_context_standard( - action, - ctx, - _format=format, - model_options=model_options, - generate_logs=generate_logs, - tool_calls=tool_calls, - ) - return mot, ctx.add(action).add(mot) - - async def _generate_from_context_standard( - self, - action: Component[C] | CBlock, - ctx: Context, - *, - _format: type[BaseModelSubclass] | None = None, - model_options: dict[str, Any], - generate_logs: list[GenerateLog] | None = None, - tool_calls: bool = False, - ) -> ModelOutputThunk[C]: - # Construct input. - # If the Context is a ChatHistory then we will pretty-print each content as a message and then use apply_chat_template. - # Otherwise, we will linearize the context and treat it as a raw input. - if ctx.is_chat_context: - system_prompt = model_options.get(ModelOption.SYSTEM_PROMPT, None) - ctx_as_chat = to_chat(action, ctx, self.formatter, system_prompt) - - # Append tool call information if applicable. - tools: dict[str, AbstractMelleaTool] = dict() - if tool_calls: - if _format: - FancyLogger.get_logger().warning( - f"Tool calling typically uses constrained generation, but you have specified a `format` in your generate call. NB: tool calling is superseded by format; we will NOT call tools for your request: {action}" - ) - else: - add_tools_from_model_options(tools, model_options) - add_tools_from_context_actions( - tools, ctx.actions_for_available_tools() - ) - - # Add the tools from the action for this generation last so that - # they overwrite conflicting names. - add_tools_from_context_actions(tools, [action]) - FancyLogger.get_logger().info(f"Tools for call: {tools.keys()}") - - input_str: str = self._tokenizer.apply_chat_template( # type: ignore - ctx_as_chat, # type: ignore - tokenize=False, - tools=convert_tools_to_json(tools), # type: ignore - ) - - sampling_params = vllm.SamplingParams( - **self._make_backend_specific_and_remove( - model_options, vllm.SamplingParams - ), - output_kind=( - # returns results incrementally - vllm.sampling_params.RequestOutputKind.DELTA # type: ignore - if model_options.get(ModelOption.STREAM, False) - # returns only the final result - else vllm.sampling_params.RequestOutputKind.FINAL_ONLY # type: ignore - ), - ) - - if _format is not None: - sampling_params.structured_outputs = ( - vllm.sampling_params.StructuredOutputsParams( - json=_format.model_json_schema() - ) - ) - - # stream = model_options.get(ModelOption.STREAM, False) - # if stream: - - output = ModelOutputThunk(None) - output._start = datetime.datetime.now() - - generator = self._model.generate( # type: ignore - request_id=str(id(output)), - prompt=input_str, - sampling_params=sampling_params, - ) # type: ignore - - output._context = ctx.view_for_generation() - output._action = action - output._model_options = model_options - - output._process = self.processing - output._post_process = functools.partial( - self.post_processing, - conversation=ctx_as_chat, - _format=_format, - tool_calls=tool_calls, - tools=tools, - seed=model_options.get(ModelOption.SEED, None), - ) - - try: - # This function should always be called from a running event loop so we don't have to worry about - # scheduling the task to a specific event loop here. - output._generate = asyncio.create_task( - send_to_queue(generator, output._async_queue) # type: ignore - ) - output._generate_type = GenerateType.ASYNC - except RuntimeError as e: - # Most likely cause is running this function without an event loop present. - raise e - - return output - - else: - raise Exception("Does not yet support non-chat contexts.") - - async def processing(self, mot: ModelOutputThunk, chunk: vllm.RequestOutput): - """Accumulate text from a single vLLM output chunk into the model output thunk. - - Called during streaming or final generation to add each incremental result to - ``mot._underlying_value``. - - Args: - mot (ModelOutputThunk): The output thunk being populated. - chunk (vllm.RequestOutput): A single output from the vLLM generate stream. - """ - if mot._underlying_value is None: - mot._underlying_value = "" - mot._underlying_value += chunk.outputs[0].text - - async def post_processing( - self, - mot: ModelOutputThunk, - conversation: list[dict], - _format: type[BaseModelSubclass] | None, - tool_calls: bool, - tools: dict[str, AbstractMelleaTool], - seed, - ): - """Finalize the model output thunk after generation completes. - - Parses any tool calls from the raw output, attaches the generate log, and - records metadata needed for telemetry. - - Args: - mot (ModelOutputThunk): The output thunk to finalize. - conversation (list[dict]): The chat conversation sent to the model, - used for logging. - _format (type[BaseModelSubclass] | None): The structured output format - class used during generation, if any. - tool_calls (bool): Whether tool calling was enabled for this request. - tools (dict[str, AbstractMelleaTool]): Available tools, keyed by name. - seed: The random seed used during generation, or ``None``. - """ - # The ModelOutputThunk must be computed by this point. - assert mot.value is not None - - # Only scan for tools if we are not doing structured output and tool calls were provided to the model. - if _format is None and tool_calls: - mot.tool_calls = to_tool_calls(tools, mot.value) - - assert mot._action is not None, ( - "ModelOutputThunks should have their action assigned during generation" - ) - assert mot._model_options is not None, ( - "ModelOutputThunks should have their model_opts assigned during generation" - ) - - # Generate the log for this ModelOutputThunk. - generate_log = GenerateLog() - generate_log.prompt = conversation - generate_log.backend = f"vllm::{self.model_id!s}" - generate_log.model_options = mot._model_options - generate_log.date = datetime.datetime.now() - generate_log.model_output = mot.value - generate_log.extra = { - "format": _format, - "tools_available": tools, - "tools_called": mot.tool_calls, - "seed": seed, - } - generate_log.action = mot._action - generate_log.result = mot - - mot._generate_log = generate_log - - @overload - async def generate_from_raw( - self, - actions: list[Component[C]], - ctx: Context, - *, - format: type[BaseModelSubclass] | None = None, - model_options: dict | None = None, - tool_calls: bool = False, - ) -> list[ModelOutputThunk[C]]: ... - - @overload - async def generate_from_raw( - self, - actions: list[Component[C] | CBlock], - ctx: Context, - *, - format: type[BaseModelSubclass] | None = None, - model_options: dict | None = None, - tool_calls: bool = False, - ) -> list[ModelOutputThunk[C | str]]: ... - - async def generate_from_raw( - self, - actions: Sequence[Component[C] | CBlock], - ctx: Context, - *, - format: type[BaseModelSubclass] | None = None, - model_options: dict | None = None, - tool_calls: bool = False, - ) -> list[ModelOutputThunk]: - """Generate completions for multiple actions without chat templating. - - Passes the formatted prompt strings directly to vLLM's completion endpoint. - Tool calling is not supported by this method. - - Args: - actions (Sequence[Component[C] | CBlock]): Actions to generate completions for. - ctx (Context): The current generation context. - format (type[BaseModelSubclass] | None): Optional Pydantic model for - structured output decoding. - model_options (dict | None): Per-call model options. - tool_calls (bool): Ignored; tool calling is not supported on this endpoint. - - Returns: - list[ModelOutputThunk]: A list of model output thunks, one per action. - """ - from ..telemetry.backend_instrumentation import instrument_generate_from_raw - - with instrument_generate_from_raw( - backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls - ): - await self.do_generate_walks(list(actions)) - - if tool_calls: - FancyLogger.get_logger().warning( - "The completion endpoint does not support tool calling at the moment." - ) - - model_options = self._simplify_and_merge(model_options) - - prompts = [self.formatter.print(action) for action in actions] - - sampling_params = vllm.SamplingParams( - **self._make_backend_specific_and_remove( - model_options, vllm.SamplingParams - ), - output_kind=vllm.sampling_params.RequestOutputKind.FINAL_ONLY, # returns only the final results # type: ignore - ) - - if format is not None: - sampling_params.structured_outputs = ( - vllm.sampling_params.StructuredOutputsParams( - json=format.model_json_schema() - ) - ) - - async def generate(prompt, request_id): - async for result_output in self._model.generate( - request_id=request_id, - prompt=prompt, - sampling_params=sampling_params, - ): - assert result_output.finished - return result_output.outputs[0].text - - tasks = [generate(p, f"{id(prompts)}-{i}") for i, p in enumerate(prompts)] - decoded_results = await asyncio.gather(*tasks) - - results = [ModelOutputThunk(value=text) for text in decoded_results] - - for i, result in enumerate(results): - date = datetime.datetime.now() - - action = actions[i] - result.parsed_repr = ( - action.parse(result) if isinstance(action, Component) else result.value - ) - - generate_log = GenerateLog() - generate_log.prompt = prompts[i] - generate_log.backend = f"vllm::{self.model_id!s}" - generate_log.model_options = model_options - generate_log.date = date - generate_log.model_output = decoded_results - generate_log.extra = { - "format": format, - "seed": model_options.get(ModelOption.SEED, None), - } - generate_log.action = action - generate_log.result = results[i] - result._generate_log = generate_log - - return results - - def _simplify_and_merge( - self, model_options: dict[str, Any] | None - ) -> dict[str, Any]: - """Simplifies model_options to use the Mellea specific ModelOption.Option and merges the backend's model_options with those passed into this call. - - Rules: - - Within a model_options dict, existing keys take precedence. This means remapping to mellea specific keys will maintain the value of the mellea specific key if one already exists. - - When merging, the keys/values from the dictionary passed into this function take precedence. - - Because this function simplifies and then merges, non-Mellea keys from the passed in model_options will replace - Mellea specific keys from the backend's model_options. - - Common model options: https://huggingface.co/docs/transformers/en/llm_tutorial#common-options - - Args: - model_options: the model_options for this call - - Returns: - a new dict - """ - backend_model_opts = ModelOption.replace_keys( - self.model_options, self.to_mellea_model_opts_map - ) - - if model_options is None: - return backend_model_opts - - generate_call_model_opts = ModelOption.replace_keys( - model_options, self.to_mellea_model_opts_map - ) - return ModelOption.merge_model_options( - backend_model_opts, generate_call_model_opts - ) - - def _make_backend_specific_and_remove( - self, model_options: dict[str, Any], cls: type[Any] - ) -> dict[str, Any]: - """Maps specified Mellea specific keys to their backend specific version and removes any remaining Mellea keys. - - Args: - model_options: the model_options for this call - cls: the target class. the returned dict contains the keys of this class - - Returns: - a new dict - """ - backend_specific = ModelOption.replace_keys( - model_options, self.from_mellea_model_opts_map - ) - backend_specific = ModelOption.remove_special_keys(backend_specific) - try: - # note: dataclasses.Field objects - return { - field.name: backend_specific[field.name] - for field in dataclasses.fields(cls) - if field.name in backend_specific - } - except TypeError: - # note: msgspec.structs.FieldInfo objects - return { - field.name: backend_specific[field.name] - for field in msgspec.structs.fields(cls) - if field.name in backend_specific - } diff --git a/mellea/telemetry/backend_instrumentation.py b/mellea/telemetry/backend_instrumentation.py index 73bfd82f8..684d723aa 100644 --- a/mellea/telemetry/backend_instrumentation.py +++ b/mellea/telemetry/backend_instrumentation.py @@ -47,8 +47,6 @@ def get_system_name(backend: Any) -> str: return "watsonx" elif "litellm" in backend_class: return "litellm" - elif "vllm" in backend_class: - return "vllm" else: return backend.__class__.__name__ diff --git a/pyproject.toml b/pyproject.toml index e16e3f571..ec5f58e24 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,10 +51,6 @@ hf = [ "huggingface-hub>=0.33.4", ] -vllm = [ - "vllm>=0.13.0; sys_platform != 'darwin'", - "numpy<=2.2", # ImportError: Numba needs NumPy 2.2 or less. Got NumPy 2.4. -] litellm = [ "litellm>=1.76", @@ -101,7 +97,7 @@ sandbox = [ "llm-sandbox[docker]>=0.3.23", ] -backends = ["mellea[watsonx,hf,vllm,litellm]"] +backends = ["mellea[watsonx,hf,litellm]"] hooks = [ "cpex>=0.1.0.dev10; python_version >= '3.11'", @@ -302,12 +298,6 @@ disable_error_code = [ "import-not-found", ] -[[tool.mypy.overrides]] -# vLLM is an optional backend that doesn't work on macOS -module = "mellea.backends.vllm" -disable_error_code = [ - "import-not-found", -] # ----------------------------- # Codespell - Spell Checking @@ -337,7 +327,6 @@ markers = [ "openai: Tests requiring OpenAI API (requires API key)", "watsonx: Tests requiring Watsonx API (requires API key)", "huggingface: Tests requiring HuggingFace backend (local, heavy)", - "vllm: Tests requiring vLLM backend (local, GPU required)", "litellm: Tests requiring LiteLLM backend", "bedrock: Tests requiring AWS Bedrock backend (requires credentials)", @@ -368,10 +357,6 @@ filterwarnings = [ # Deduplicate noisy Pydantic serialization warnings "once:.*PydanticSerializationUnexpectedValue.*:UserWarning", - # Ignore SWIG C/C++ binding warnings from vLLM (Python 3.12 issue) - "ignore:builtin type SwigPyPacked has no __module__ attribute:DeprecationWarning", - "ignore:builtin type SwigPyObject has no __module__ attribute:DeprecationWarning", - "ignore:builtin type swigvarlink has no __module__ attribute:DeprecationWarning", # Keep Watsonx deprecation visible (important for migration) "default:.*Watsonx Backend is deprecated.*:DeprecationWarning", diff --git a/test/backends/test_vllm.py b/test/backends/test_vllm.py deleted file mode 100644 index d9bc8a092..000000000 --- a/test/backends/test_vllm.py +++ /dev/null @@ -1,227 +0,0 @@ -import asyncio -import os -from typing import Annotated - -import pydantic -import pytest - -from test.predicates import require_gpu - -# Mark all tests in this module with backend and resource requirements -pytestmark = [ - pytest.mark.vllm, - pytest.mark.e2e, - require_gpu(min_vram_gb=4), - # Skip entire module in CI since all 8 tests are qualitative - pytest.mark.skipif( - int(os.environ.get("CICD", 0)) == 1, - reason="Skipping vLLM tests in CI - all qualitative tests", - ), -] - -# Try to import vLLM backend - skip all tests if not available -try: - import mellea.backends.model_ids as model_ids - from mellea import MelleaSession - from mellea.backends import ModelOption - from mellea.backends.vllm import LocalVLLMBackend - from mellea.core import CBlock - from mellea.stdlib.context import ChatContext, SimpleContext -except ImportError as e: - pytest.skip( - f"vLLM backend not available: {e}. Install with: pip install mellea[vllm]", - allow_module_level=True, - ) - - -# vLLM tests use a shared session-scoped backend (see conftest.py shared_vllm_backend). -# Falls back to a module-scoped backend when --group-by-backend delays shared creation. -@pytest.fixture(scope="module") -def backend(shared_vllm_backend): - """Use shared session-scoped backend, or create module-scoped as fallback.""" - if shared_vllm_backend is not None: - yield shared_vllm_backend - return # skip cleanup — shared backend cleaned up by conftest - - # Fallback: shared backend not available, create module-scoped backend - backend = LocalVLLMBackend( - model_id=model_ids.IBM_GRANITE_4_MICRO_3B, - model_options={ - "gpu_memory_utilization": 0.6, - "max_model_len": 4096, - "max_num_seqs": 4, - }, - ) - yield backend - - from test.conftest import cleanup_gpu_backend - - cleanup_gpu_backend(backend, "vllm") - - -@pytest.fixture(scope="function") -def session(backend): - """Fresh HuggingFace session for each test.""" - session = MelleaSession(backend, ctx=ChatContext()) - yield session - session.reset() - - -@pytest.mark.qualitative -def test_system_prompt(session) -> None: - result = session.chat( - "Where are we going?", - model_options={ModelOption.SYSTEM_PROMPT: "Talk like a pirate."}, - ) - print(result) - - -@pytest.mark.qualitative -def test_instruct(session) -> None: - result = session.instruct("Compute 1+1.") - print(result) - - -@pytest.mark.qualitative -def test_multiturn(session) -> None: - session.instruct("Compute 1+1") - session.instruct( - "Take the result of the previous sum and find the corresponding letter in the greek alphabet." - ) - words = session.instruct("Now list five English words that start with that letter.") - print(words) - - -@pytest.mark.qualitative -def test_format(session) -> None: - class Person(pydantic.BaseModel): - name: str - email_address: Annotated[ - str, pydantic.StringConstraints(pattern=r"[a-zA-Z]{5,10}@example\.com") - ] - - class Email(pydantic.BaseModel): - to: Person - subject: str - body: str - - output = session.instruct( - "Write a short email to Olivia, thanking her for organizing a sailing activity. Her email server is example.com. No more than two sentences. ", - format=Email, - model_options={ModelOption.MAX_NEW_TOKENS: 2**8}, - ) - print("Formatted output:") - email = Email.model_validate_json( - output.value - ) # this should succeed because the output should be JSON because we passed in a format= argument... - print(email) - - print("address:", email.to.email_address) - assert "@" in email.to.email_address, "The @ sign should be in the meail address." - assert email.to.email_address.endswith("example.com"), ( - "The email address should be at example.com" - ) - - -@pytest.mark.qualitative -async def test_generate_from_raw(session) -> None: - prompts = ["what is 1+1?", "what is 2+2?", "what is 3+3?", "what is 4+4?"] - - results = await session.backend.generate_from_raw( - actions=[CBlock(value=prompt) for prompt in prompts], ctx=session.ctx - ) - - assert len(results) == len(prompts) - assert results[0].value is not None - - -@pytest.mark.qualitative -async def test_generate_from_raw_with_format(session) -> None: - prompts = ["what is 1+1?", "what is 2+2?", "what is 3+3?", "what is 4+4?"] - - # The original prompts in this test case told the model to return a number, after - # which the constrained decoding below would prevent the model from producing - # a number. High-quality models will correctly output garbage in this case, and - # this garbage can include unbounded amounts of whitespace, causing this test case - # to fail randomly in the face of correct model operation. - # Tweak the prompts a bit to make the test pass. - prompts = [p + " Answer with a JSON record please." for p in prompts] - - class Answer(pydantic.BaseModel): - name: str - value: int - - results = await session.backend.generate_from_raw( - actions=[CBlock(value=prompt) for prompt in prompts], - ctx=session.ctx, - format=Answer, - model_options={ModelOption.MAX_NEW_TOKENS: 100}, - ) - - assert len(results) == len(prompts) - - random_result = results[0] - try: - Answer.model_validate_json(random_result.value) - except pydantic.ValidationError as e: - assert False, ( - f"formatting directive failed for {random_result.value}: {e.json()}" - ) - - -@pytest.mark.qualitative -def test_async_parallel_requests(session) -> None: - async def parallel_requests(): - model_opts = {ModelOption.STREAM: True} - mot1, _ = await session.backend.generate_from_context( - CBlock("Say Hello."), SimpleContext(), model_options=model_opts - ) - mot2, _ = await session.backend.generate_from_context( - CBlock("Say Goodbye!"), SimpleContext(), model_options=model_opts - ) - - m1_val = None - m2_val = None - if not mot1.is_computed(): - m1_val = await mot1.astream() - if not mot2.is_computed(): - m2_val = await mot2.astream() - - assert m1_val is not None, "should be a string val after generation" - assert m2_val is not None, "should be a string val after generation" - - m1_final_val = await mot1.avalue() - m2_final_val = await mot2.avalue() - - # Ideally, we would be able to assert that m1_final_val != m1_val, but sometimes the first streaming response - # contains the full response. - assert m1_final_val.startswith(m1_val), ( - "final val should contain the first streamed chunk" - ) - assert m2_final_val.startswith(m2_val), ( - "final val should contain the first streamed chunk" - ) - - assert m1_final_val == mot1.value - assert m2_final_val == mot2.value - - asyncio.run(parallel_requests()) - - -@pytest.mark.qualitative -def test_async_avalue(session) -> None: - async def avalue(): - mot1, _ = await session.backend.generate_from_context( - CBlock("Say Hello."), SimpleContext() - ) - m1_final_val = await mot1.avalue() - assert m1_final_val is not None - assert m1_final_val == mot1.value - - asyncio.run(avalue()) - - -if __name__ == "__main__": - import pytest - - pytest.main([__file__]) diff --git a/test/backends/test_vllm_tools.py b/test/backends/test_vllm_tools.py deleted file mode 100644 index 861f2a51e..000000000 --- a/test/backends/test_vllm_tools.py +++ /dev/null @@ -1,103 +0,0 @@ -import os - -import pytest - -from mellea.backends.tools import MelleaTool -from test.predicates import require_gpu - -# Skip entire module in CI since the single test is qualitative -pytestmark = [ - pytest.mark.vllm, - pytest.mark.e2e, - require_gpu(min_vram_gb=18), - pytest.mark.skipif( - int(os.environ.get("CICD", 0)) == 1, - reason="Skipping vLLM tools tests in CI - qualitative test", - ), -] - -# Try to import vLLM backend - skip all tests if not available -try: - import mellea.backends.model_ids as model_ids - from mellea import MelleaSession - from mellea.backends import ModelOption - from mellea.backends.vllm import LocalVLLMBackend - from mellea.stdlib.context import ChatContext -except ImportError as e: - pytest.skip( - f"vLLM backend not available: {e}. Install with: pip install mellea[vllm]", - allow_module_level=True, - ) - - -# vLLM tests use a shared session-scoped backend (see conftest.py shared_vllm_backend). -# Falls back to a module-scoped backend when --group-by-backend delays shared creation. -# Note: Originally used Mistral-7B, now uses Granite 4 Micro for consistency. -# Granite 4 Micro supports tool calling and is sufficient for testing. -@pytest.fixture(scope="module") -def backend(shared_vllm_backend): - """Use shared session-scoped backend, or create module-scoped as fallback.""" - if shared_vllm_backend is not None: - yield shared_vllm_backend - return - - # Fallback: shared backend not available, create module-scoped backend - backend = LocalVLLMBackend( - model_id=model_ids.IBM_GRANITE_4_MICRO_3B, - model_options={ - "gpu_memory_utilization": 0.6, - "max_model_len": 4096, - "max_num_seqs": 4, - }, - ) - yield backend - - from test.conftest import cleanup_gpu_backend - - cleanup_gpu_backend(backend, "vllm-tools") - - -@pytest.fixture(scope="function") -def session(backend): - """Fresh HuggingFace session for each test.""" - session = MelleaSession(backend, ctx=ChatContext()) - yield session - session.reset() - - -@pytest.mark.qualitative -def test_tool(session): - tool_call_history = [] - - def get_temperature(location: str) -> int: - """Returns today's temperature of the given city in Celsius. - - Args: - location: a city name. - """ - tool_call_history.append(location) - return 21 - - output = session.instruct( - "What is today's temperature in Boston? Answer in Celsius. Reply the number only.", - model_options={ - ModelOption.TOOLS: [MelleaTool.from_callable(get_temperature)], - ModelOption.MAX_NEW_TOKENS: 1000, - }, - tool_calls=True, - ) - - assert output.tool_calls is not None - - result = output.tool_calls["get_temperature"].call_func() - print(result) - - assert len(tool_call_history) > 0 - assert tool_call_history[0].lower() == "boston" - assert 21 == result - - -if __name__ == "__main__": - import pytest - - pytest.main([__file__]) diff --git a/test/conftest.py b/test/conftest.py index ae3baf0eb..6a0330087 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -147,65 +147,6 @@ def gh_run() -> int: return int(os.environ.get("CICD", 0)) # type: ignore -@pytest.fixture(scope="session") -def shared_vllm_backend(request): - """Shared vLLM backend for ALL vLLM tests across all modules. - - When --group-by-backend is used, delays creation until after openai_vllm group. - Uses IBM Granite 4 Micro as a small, fast model suitable for all vLLM tests. - """ - # When using --group-by-backend, delay backend creation until after openai_vllm group - if request.config.getoption("--group-by-backend", default=False): - # Check if we're currently in the openai_vllm group - if hasattr(pytest_runtest_setup, "_last_backend_group"): - current_group = pytest_runtest_setup._last_backend_group - if current_group == "openai_vllm": - logger = FancyLogger.get_logger() - logger.info( - "Backend grouping enabled: Delaying vLLM backend creation until after openai_vllm group" - ) - yield None - return - - try: - import mellea.backends.model_ids as model_ids - from mellea.backends.vllm import LocalVLLMBackend - except ImportError: - pytest.skip("vLLM backend not available") - return - - try: - import torch - - if not torch.cuda.is_available(): - pytest.skip("CUDA not available for vLLM tests") - return - except ImportError: - pytest.skip("PyTorch not available") - return - - logger = FancyLogger.get_logger() - logger.info( - "Creating shared vLLM backend (session-scoped) for all vLLM tests. " - "This backend will be reused to avoid GPU memory fragmentation." - ) - - backend = LocalVLLMBackend( - model_id=model_ids.IBM_GRANITE_4_MICRO_3B, - model_options={ - "gpu_memory_utilization": 0.6, - "max_model_len": 4096, - "max_num_seqs": 4, - }, - ) - - logger.info("Shared vLLM backend created successfully.") - yield backend - - logger.info("Cleaning up shared vLLM backend (end of test session)") - cleanup_gpu_backend(backend, "shared-vllm") - - # ============================================================================ # Backend Test Grouping Configuration # ============================================================================ @@ -220,11 +161,7 @@ def shared_vllm_backend(request): }, "openai_vllm": { "marker": "openai", - "description": "OpenAI backend tests with vLLM server (subprocess)", - }, - "vllm": { - "marker": "vllm", - "description": "vLLM backend tests (GPU, shared in-process backend)", + "description": "OpenAI backend tests (including tests with vLLM server subprocess)", }, "ollama": { "marker": "ollama", @@ -237,7 +174,7 @@ def shared_vllm_backend(request): } # Execution order when --group-by-backend is used -BACKEND_GROUP_ORDER = ["huggingface", "openai_vllm", "vllm", "ollama", "api"] +BACKEND_GROUP_ORDER = ["huggingface", "openai_vllm", "ollama", "api"] # ============================================================================ @@ -279,7 +216,6 @@ def add_option_safe(option_name, **kwargs): "openai": "Tests requiring OpenAI API (requires API key)", "watsonx": "Tests requiring Watsonx API (requires API key)", "huggingface": "Tests requiring HuggingFace backend (local, heavy)", - "vllm": "Tests requiring vLLM backend (local, GPU required)", "litellm": "Tests requiring LiteLLM backend", "bedrock": "Tests requiring AWS Bedrock backend (requires credentials)", } @@ -318,9 +254,6 @@ def pytest_configure(config): "markers", "llm: Tests that make LLM calls (deprecated — use e2e instead)" ) - # Store vLLM isolation flag in config - config._vllm_process_isolation = False - # ============================================================================ # Heavy GPU Test Process Isolation @@ -417,21 +350,13 @@ def cleanup_gpu_backend(backend, backend_name="unknown"): try: del backend._model except AttributeError: - pass # _model is a @property on vLLM backends (no deleter) + pass # 6. Delete tokenizer if hasattr(backend, "_tokenizer"): del backend._tokenizer - # 7. vLLM backends - if hasattr(backend, "_underlying_model"): - try: - backend._underlying_model.shutdown() - except Exception: - pass - del backend._underlying_model - - # 8. Force garbage collection and flush device caches + # 7. Force garbage collection and flush device caches gc.collect() gc.collect() @@ -553,23 +478,6 @@ def pytest_runtest_setup(item): "Running GPU cleanup." ) - # Clean up shared vLLM backend if leaving vLLM group - if prev_group in ("vllm", "openai_vllm"): - try: - shared_backend_defs = ( - item.session._fixturemanager._arg2fixturedefs.get( - "shared_vllm_backend" - ) - ) - if shared_backend_defs: - backend_instance = shared_backend_defs[-1].cached_result[0] - if backend_instance is not None: - cleanup_gpu_backend( - backend_instance, "shared-vllm-transition" - ) - except Exception as e: - logger.warning(f"Failed to cleanup vLLM backend on transition: {e}") - # General GPU flush for any transition try: import torch @@ -637,10 +545,6 @@ def pytest_runtest_setup(item): "Skipping test: Watsonx API credentials not found in environment" ) - if item.get_closest_marker("vllm"): - if not capabilities["has_gpu"]: - pytest.skip("Skipping test: vLLM requires GPU") - # Note: Ollama tests are now skipped at collection time in pytest_collection_modifyitems # to prevent fixture setup errors diff --git a/test/package/test_dependency_isolation.py b/test/package/test_dependency_isolation.py index 9a6f60cd6..becee24fc 100644 --- a/test/package/test_dependency_isolation.py +++ b/test/package/test_dependency_isolation.py @@ -29,18 +29,12 @@ PROJECT_ROOT = Path(__file__).resolve().parents[2] -IS_MACOS = sys.platform == "darwin" - # --------------------------------------------------------------------------- # Import statements keyed by extra name ("core" for base dependencies) # --------------------------------------------------------------------------- # NOTE: There's some risk that we hit overlap situations (ie if a group imports another group). # In those cases, special handling is needed (see telemetry). -# There's also some risk that a group could unintentionally fully import the dependencies of -# another group (without explicitly listing it). That will lead to false positives here on -# the should_fail side. This happens with vllm (which imports all the parts of hf) so we just -# exclude the hf import statements from that test. IMPORTS: dict[str, list[str]] = { "core": [ "import mellea", @@ -50,7 +44,6 @@ "from mellea.core import Backend", ], "hf": ["from mellea.backends.huggingface import LocalHFBackend"], - "vllm": ["from mellea.backends.vllm import LocalVLLMBackend"], "litellm": ["from mellea.backends.litellm import LiteLLMBackend"], "watsonx": ["from mellea.backends.watsonx import WatsonxAIBackend"], "tools": ["import langchain_core", "import smolagents"], @@ -83,7 +76,7 @@ } # There are a few special tests for backends. -BACKEND_EXTRAS = {"hf", "vllm", "litellm", "watsonx"} +BACKEND_EXTRAS = {"hf", "litellm", "watsonx"} # Meta-groups that compose other extras (no dedicated isolation test needed). META_GROUPS = {"backends", "all"} @@ -92,7 +85,6 @@ # Used to determine if we are missing tests for a given optional-dependency group. TESTED_EXTRAS = { "hf", - "vllm", "litellm", "watsonx", "tools", @@ -141,8 +133,6 @@ def _backend_fail_imports(exclude: set[str] | str = "") -> list[str]: if isinstance(exclude, str): exclude = {exclude} if exclude else set() extras = BACKEND_EXTRAS - exclude - if IS_MACOS: - extras -= {"vllm"} return [stmt for name in sorted(extras) for stmt in IMPORTS[name]] @@ -304,16 +294,6 @@ def test_hf() -> None: ) -@pytest.mark.skipif(IS_MACOS, reason="vllm is not supported on macOS") -def test_vllm() -> None: - """mellea[vllm]: vLLM backend imports succeed, others fail.""" - _run_check( - extra="vllm", - should_succeed=[*IMPORTS["core"], *IMPORTS["vllm"]], - should_fail=_backend_fail_imports(exclude={"vllm", "hf"}), - ) - - def test_litellm() -> None: """mellea[litellm]: LiteLLM backend imports succeed, others fail.""" _run_check( @@ -380,7 +360,7 @@ def test_hooks() -> None: def test_backends_meta() -> None: """mellea[backends]: all backend imports work.""" - extras = BACKEND_EXTRAS - ({"vllm"} if IS_MACOS else set()) + extras = BACKEND_EXTRAS should_succeed = [*IMPORTS["core"]] for name in sorted(extras): should_succeed.extend(IMPORTS[name]) diff --git a/uv.lock b/uv.lock index 8c39705b8..7c0e28c76 100644 --- a/uv.lock +++ b/uv.lock @@ -197,25 +197,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, ] -[[package]] -name = "anthropic" -version = "0.79.0" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - 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{ url = "https://files.pythonhosted.org/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390", size = 4083, upload-time = "2024-12-07T15:28:26.465Z" }, -] - [[package]] name = "wrapt" version = "1.17.3" From dbeb5d8de90e29401b444a57aa56c0b58abacbc5 Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Fri, 3 Apr 2026 14:12:40 -0700 Subject: [PATCH 2/8] adding an externally managed vllm for tests --- ...a.sh => run_tests_with_ollama_and_vllm.sh} | 135 ++++++++++++++++-- 1 file changed, 120 insertions(+), 15 deletions(-) rename test/scripts/{run_tests_with_ollama.sh => run_tests_with_ollama_and_vllm.sh} (51%) diff --git a/test/scripts/run_tests_with_ollama.sh b/test/scripts/run_tests_with_ollama_and_vllm.sh similarity index 51% rename from test/scripts/run_tests_with_ollama.sh rename to test/scripts/run_tests_with_ollama_and_vllm.sh index 91cd65e26..3f70ca32f 100755 --- a/test/scripts/run_tests_with_ollama.sh +++ b/test/scripts/run_tests_with_ollama_and_vllm.sh @@ -1,17 +1,24 @@ #!/bin/bash -# run_tests_with_ollama.sh -# Starts a local ollama server (no sudo), pulls required models, runs tests, -# and shuts everything down cleanly. +# run_tests_with_ollama_and_vllm.sh +# Starts a local ollama server (no sudo), optionally starts a local vLLM server, +# pulls/installs required models, runs tests, and shuts everything down cleanly. +# +# vLLM is enabled automatically when a CUDA GPU is detected, or explicitly with +# WITH_VLLM=1. Override with WITH_VLLM=0 to force-disable even on GPU hosts. # # Usage: -# ./run_tests_with_ollama.sh # run all tests -# ./run_tests_with_ollama.sh -m ollama # only ollama tests -# ./run_tests_with_ollama.sh --group-by-backend -v -s # custom pytest args +# ./run_tests_with_ollama_and_vllm.sh # auto (vLLM on GPU hosts) +# ./run_tests_with_ollama_and_vllm.sh -m ollama # only ollama tests +# ./run_tests_with_ollama_and_vllm.sh --group-by-backend -v -s # custom pytest args +# WITH_VLLM=1 ./run_tests_with_ollama_and_vllm.sh # force-enable vLLM +# WITH_VLLM=0 ./run_tests_with_ollama_and_vllm.sh # force-disable vLLM +# WITH_VLLM=1 VLLM_MODEL=ibm-granite/granite-3.3-8b-instruct \ +# ./run_tests_with_ollama_and_vllm.sh --group-by-backend -v -s # # LSF example: # bsub -n 1 -G grp_preemptable -q preemptable \ # -gpu "num=1/task:mode=shared:j_exclusive=yes" \ -# "./run_tests_with_ollama.sh --group-by-backend -v -s" +# "./run_tests_with_ollama_and_vllm.sh --group-by-backend -v -s" set -euo pipefail @@ -19,7 +26,7 @@ set -euo pipefail log() { echo "[$(date +%H:%M:%S)] $*"; } die() { log "ERROR: $*" >&2; exit 1; } -# --- Configuration --- +# --- Ollama configuration --- OLLAMA_HOST="${OLLAMA_HOST:-127.0.0.1}" OLLAMA_PORT="${OLLAMA_PORT:-11434}" if [[ -n "${CACHE_DIR:-}" ]]; then @@ -35,6 +42,25 @@ OLLAMA_MODELS=( "granite3.2-vision" ) +# --- vLLM configuration --- +# Auto-enable vLLM when a CUDA GPU is available (nvidia-smi honours CUDA_VISIBLE_DEVICES +# so this is safe on multi-tenant LSF hosts). Override with WITH_VLLM=0 or WITH_VLLM=1. +if [[ -z "${WITH_VLLM:-}" ]]; then + if command -v nvidia-smi >/dev/null 2>&1 && nvidia-smi -L >/dev/null 2>&1; then + WITH_VLLM=1 + log "CUDA GPU detected — enabling vLLM (set WITH_VLLM=0 to disable)" + else + WITH_VLLM=0 + fi +fi +VLLM_PORT="${VLLM_PORT:-8100}" +VLLM_MODEL="${VLLM_MODEL:-ibm-granite/granite-3.3-8b-instruct}" +VLLM_GPU_MEM="${VLLM_GPU_MEM:-0.85}" +VLLM_MAX_MODEL_LEN="${VLLM_MAX_MODEL_LEN:-4096}" +VLLM_VENV="${CACHE_DIR:+${CACHE_DIR}/.vllm-venv}" +VLLM_VENV="${VLLM_VENV:-.vllm-venv}" +VLLM_PID="" + # Log directory - use MELLEA_LOGDIR if set (from nightly.py), otherwise create standalone if [[ -n "${MELLEA_LOGDIR:-}" ]]; then LOGDIR="$MELLEA_LOGDIR" @@ -48,14 +74,28 @@ mkdir -p "$LOGDIR" cleanup() { if [[ "${OLLAMA_EXTERNAL:-0}" == "1" ]]; then log "Ollama managed externally (OLLAMA_EXTERNAL=1) — skipping shutdown" - return + else + log "Shutting down ollama server..." + if [[ -n "${OLLAMA_PID:-}" ]] && kill -0 "$OLLAMA_PID" 2>/dev/null; then + kill "$OLLAMA_PID" 2>/dev/null + wait "$OLLAMA_PID" 2>/dev/null || true + fi + log "Ollama stopped." fi - log "Shutting down ollama server..." - if [[ -n "${OLLAMA_PID:-}" ]] && kill -0 "$OLLAMA_PID" 2>/dev/null; then - kill "$OLLAMA_PID" 2>/dev/null - wait "$OLLAMA_PID" 2>/dev/null || true + + if [[ "$WITH_VLLM" == "1" ]]; then + if [[ "${VLLM_EXTERNAL:-0}" == "1" ]]; then + log "vLLM managed externally (VLLM_EXTERNAL=1) — skipping shutdown" + elif [[ -n "$VLLM_PID" ]] && kill -0 "$VLLM_PID" 2>/dev/null; then + log "Shutting down vLLM server..." + kill "$VLLM_PID" 2>/dev/null + wait "$VLLM_PID" 2>/dev/null || true + log "vLLM stopped." + fi + if [[ "${KEEP_VLLM_VENV:-0}" != "1" ]]; then + rm -rf "$VLLM_VENV" + fi fi - log "Ollama stopped." } trap cleanup EXIT @@ -139,7 +179,7 @@ for model in "${OLLAMA_MODELS[@]}"; do fi done -log "All models ready." +log "All ollama models ready." # --- Warm up models (first load into memory is slow) --- if [[ "${OLLAMA_SKIP_WARMUP:-0}" == "1" ]]; then @@ -155,6 +195,71 @@ else log "Warmup complete." fi +# --- vLLM server (optional, auto-enabled on CUDA hosts) --- +if [[ "$WITH_VLLM" == "1" ]]; then + if [[ "${VLLM_EXTERNAL:-0}" == "1" ]]; then + log "vLLM managed externally (VLLM_EXTERNAL=1) — skipping startup" + if ! curl -sf "http://127.0.0.1:${VLLM_PORT}/v1/models" >/dev/null 2>&1; then + die "VLLM_EXTERNAL=1 but no vLLM server found on port ${VLLM_PORT}" + fi + else + # Find a free port starting from VLLM_PORT + while ss -tln 2>/dev/null | grep -q ":${VLLM_PORT} " || \ + netstat -tln 2>/dev/null | grep -q ":${VLLM_PORT} "; do + log "Port $VLLM_PORT in use, trying $((VLLM_PORT + 1))..." + VLLM_PORT=$((VLLM_PORT + 1)) + done + + # Install vLLM into an isolated venv so it never enters mellea's own deps + if [[ -d "$VLLM_VENV" ]] && [[ "${KEEP_VLLM_VENV:-0}" == "1" ]]; then + log "Reusing existing vLLM venv at $VLLM_VENV (KEEP_VLLM_VENV=1)" + else + log "Creating isolated vLLM venv at $VLLM_VENV ..." + uv venv "$VLLM_VENV" --python 3.11 + log "Installing vllm into $VLLM_VENV ..." + uv pip install --python "$VLLM_VENV/bin/python" vllm \ + > "$LOGDIR/vllm_install.log" 2>&1 \ + || die "vllm install failed. Check $LOGDIR/vllm_install.log" + log "vllm installed." + fi + + # Start vllm serve in the background + log "Starting vLLM server — model: $VLLM_MODEL, port: $VLLM_PORT ..." + "$VLLM_VENV/bin/python" -m vllm.entrypoints.openai.api_server \ + --model "$VLLM_MODEL" \ + --port "$VLLM_PORT" \ + --max-model-len "$VLLM_MAX_MODEL_LEN" \ + --gpu-memory-utilization "$VLLM_GPU_MEM" \ + > "$LOGDIR/vllm.log" 2>&1 & + VLLM_PID=$! + log "vLLM server PID: $VLLM_PID" + + # Wait for vLLM to be ready (model load can take 60-90s) + log "Waiting for vLLM to be ready..." + for i in $(seq 1 120); do + if curl -sf "http://127.0.0.1:${VLLM_PORT}/v1/models" >/dev/null 2>&1; then + log "vLLM ready after ${i}s" + break + fi + if ! kill -0 "$VLLM_PID" 2>/dev/null; then + die "vLLM process died during startup. Check $LOGDIR/vllm.log" + fi + sleep 1 + done + + if ! curl -sf "http://127.0.0.1:${VLLM_PORT}/v1/models" >/dev/null 2>&1; then + die "vLLM failed to start within 120s. Check $LOGDIR/vllm.log" + fi + fi + + # Export for pytest fixtures + export VLLM_TEST_BASE_URL="http://127.0.0.1:${VLLM_PORT}" + export VLLM_TEST_MODEL="$VLLM_MODEL" + log "vLLM ready. VLLM_TEST_BASE_URL=$VLLM_TEST_BASE_URL" +else + log "vLLM disabled (WITH_VLLM=0). Pass WITH_VLLM=1 to enable, or run on a CUDA host for auto-detection." +fi + # --- Run tests --- log "Starting pytest..." log "Log directory: $LOGDIR" From d9d22b31f273e9f88fab2aeae9df2b9c762fba41 Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Fri, 3 Apr 2026 14:20:41 -0700 Subject: [PATCH 3/8] removing vllm backend references from docs --- README.md | 2 +- docs/docs/community/contributing-guide.md | 1 - docs/docs/docs.json | 1 - docs/docs/guide/glossary.md | 3 +- docs/docs/how-to/use-images-and-vision.md | 1 - docs/docs/index.mdx | 3 - docs/docs/integrations/vllm.md | 89 ----------------------- 7 files changed, 2 insertions(+), 98 deletions(-) delete mode 100644 docs/docs/integrations/vllm.md diff --git a/README.md b/README.md index aee08d70a..64304ed43 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ print(user.age) # 31 — always an int, guaranteed by the schema - **Structured output** — `@generative` turns typed functions into LLM calls; Pydantic schemas are enforced at generation time - **Requirements & repair** — attach natural-language requirements to any call; Mellea validates and retries automatically - **Sampling strategies** — run a generation multiple times and pick the best result; swap between rejection sampling, majority voting, and more with one parameter change -- **Multiple backends** — Ollama, OpenAI, vLLM, HuggingFace, WatsonX, LiteLLM, Bedrock +- **Multiple backends** — Ollama, OpenAI, HuggingFace, WatsonX, LiteLLM, Bedrock - **Legacy integration** — easily drop Mellea into existing codebases with `mify` - **MCP compatible** — expose any generative program as an MCP tool diff --git a/docs/docs/community/contributing-guide.md b/docs/docs/community/contributing-guide.md index be8766748..bb583292c 100644 --- a/docs/docs/community/contributing-guide.md +++ b/docs/docs/community/contributing-guide.md @@ -215,7 +215,6 @@ Tests use a four-tier granularity system. Every test belongs to exactly one tier | `openai` | OpenAI API or compatible | API calls (may use Ollama `/v1`) | | `watsonx` | Watsonx API | API calls, requires credentials | | `huggingface` | HuggingFace transformers | Local, GPU required | -| `vllm` | vLLM | Local, GPU required | | `litellm` | LiteLLM (wraps other backends) | Depends on underlying backend | | `bedrock` | AWS Bedrock | API calls, requires credentials | diff --git a/docs/docs/docs.json b/docs/docs/docs.json index c9516bc1b..59d4835e4 100644 --- a/docs/docs/docs.json +++ b/docs/docs/docs.json @@ -87,7 +87,6 @@ "pages": [ "integrations/ollama", "integrations/huggingface", - "integrations/vllm", "integrations/openai", "integrations/vertex-ai", "integrations/bedrock", diff --git a/docs/docs/guide/glossary.md b/docs/docs/guide/glossary.md index d5af369ea..933d8d54f 100644 --- a/docs/docs/guide/glossary.md +++ b/docs/docs/guide/glossary.md @@ -60,8 +60,7 @@ See: [Generative Functions](./generative-functions) ## Backend A backend is an inference engine that Mellea uses to run LLM calls. Examples: -`OllamaModelBackend`, `OpenAIBackend`, `LocalHFBackend`, `LocalVLLMBackend`, -`WatsonxAIBackend`. Backends are configured via `MelleaSession` or +`OllamaModelBackend`, `OpenAIBackend`, `LocalHFBackend`, `WatsonxAIBackend`. Backends are configured via `MelleaSession` or `start_session()`. See: [Backends and Configuration](./backends-and-configuration) diff --git a/docs/docs/how-to/use-images-and-vision.md b/docs/docs/how-to/use-images-and-vision.md index 25e03e2f8..0c1630313 100644 --- a/docs/docs/how-to/use-images-and-vision.md +++ b/docs/docs/how-to/use-images-and-vision.md @@ -114,7 +114,6 @@ To remove images from context on the next turn, pass `images=[]` explicitly. | `OpenAIBackend` | ✓ | Use with `gpt-4o`, or a local vision model via OpenAI-compatible endpoint | | `LiteLLMBackend` | ✓ | Depends on the underlying provider | | `LocalHFBackend` | Partial | Model-dependent; experimental | -| `LocalVLLMBackend` | Partial | Model-dependent | | `WatsonxAIBackend` | ✗ | Not currently supported | > **Full example (Ollama):** [`docs/examples/image_text_models/vision_ollama_chat.py`](https://github.com/generative-computing/mellea/blob/main/docs/examples/image_text_models/vision_ollama_chat.py) diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index dff38e8fa..6ddccaf2e 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -97,9 +97,6 @@ Mellea is backend-agnostic. The same program runs on any inference engine. Local inference with Transformers — aLoRA and constrained decoding. - - High-throughput batched local inference on Linux + CUDA. - Google Vertex AI, Anthropic, and 100+ providers via LiteLLM. diff --git a/docs/docs/integrations/vllm.md b/docs/docs/integrations/vllm.md deleted file mode 100644 index b55fd1fd7..000000000 --- a/docs/docs/integrations/vllm.md +++ /dev/null @@ -1,89 +0,0 @@ ---- -title: "vLLM" -description: "Run Mellea with high-throughput local inference using LocalVLLMBackend and vLLM." -# diataxis: how-to ---- - -`LocalVLLMBackend` uses [vLLM](https://vllm.ai/) for higher-throughput local inference. -It is a good choice when you are running many requests in parallel — for example, batch -evaluation or load testing. vLLM takes longer to initialise than `LocalHFBackend` but -sustains higher throughput once warm. - -**Prerequisites:** `pip install 'mellea[vllm]'`, Linux, CUDA GPU. - -> **Platform note:** vLLM is not supported on macOS. Use -> [`LocalHFBackend`](./huggingface) or [Ollama](./ollama) on Apple Silicon. - -## Install - -```bash -pip install 'mellea[vllm]' -``` - -## Basic usage - -```python -from mellea import MelleaSession -from mellea.backends import ModelOption, model_ids -from mellea.backends.vllm import LocalVLLMBackend - -m = MelleaSession( - LocalVLLMBackend( - model_ids.IBM_GRANITE_4_HYBRID_MICRO, - model_options={ModelOption.MAX_NEW_TOKENS: 256}, - ) -) - -result = m.instruct("Explain the difference between precision and recall.") -print(str(result)) -# Output will vary — LLM responses depend on model and temperature. -``` - -> **Always set `MAX_NEW_TOKENS` explicitly.** vLLM defaults to approximately 16 tokens. -> For structured output or longer responses, set `ModelOption.MAX_NEW_TOKENS` to -> 200–1000+ tokens. - -## High-throughput batched inference - -vLLM processes requests in continuous batches. For batch evaluation, send requests -concurrently rather than sequentially to take advantage of the batching: - -```python -import asyncio -from mellea import MelleaSession -from mellea.backends import ModelOption, model_ids -from mellea.backends.vllm import LocalVLLMBackend - -backend = LocalVLLMBackend( - model_ids.IBM_GRANITE_4_HYBRID_MICRO, - model_options={ModelOption.MAX_NEW_TOKENS: 512}, -) - -async def run_batch(prompts: list[str]) -> list[str]: - m = MelleaSession(backend) - tasks = [m.ainstruct(p) for p in prompts] - results = await asyncio.gather(*tasks) - return [str(r) for r in results] -``` - -## Vision support - -Vision support for `LocalVLLMBackend` is model-dependent. Pass a PIL image or an -[`ImageBlock`](../guide/glossary#imageblock) via `images=[...]` when using a -vision-capable model. See [Use Images and Vision Models](../how-to/use-images-and-vision). - -## Troubleshooting - -### Output truncated at ~16 tokens - -vLLM defaults to approximately 16 tokens. Set [`ModelOption`](../guide/glossary#modeloption) -`MAX_NEW_TOKENS` explicitly: - -```python -model_options={ModelOption.MAX_NEW_TOKENS: 512} -``` - ---- - -**See also:** [Backends and Configuration](../guide/backends-and-configuration) | -[LoRA and aLoRA Adapters](../advanced/lora-and-alora-adapters) From 94ef5b3d3edded242f17d7fcf5f30b39a9dbda56 Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Mon, 6 Apr 2026 09:20:55 -0700 Subject: [PATCH 4/8] adding back some comments --- test/package/test_dependency_isolation.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/test/package/test_dependency_isolation.py b/test/package/test_dependency_isolation.py index becee24fc..402078adc 100644 --- a/test/package/test_dependency_isolation.py +++ b/test/package/test_dependency_isolation.py @@ -35,6 +35,11 @@ # NOTE: There's some risk that we hit overlap situations (ie if a group imports another group). # In those cases, special handling is needed (see telemetry). +# There's also some risk that a group could unintentionally fully import the dependencies of +# another group (without explicitly listing it). That will lead to false positives here on +# the should_fail side. This happens with vllm (which imports all the parts of hf) so we just +# exclude the hf import statements from that test. + IMPORTS: dict[str, list[str]] = { "core": [ "import mellea", From b07800df8ac659328a96e5e19723bffac40984eb Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Mon, 6 Apr 2026 10:07:33 -0700 Subject: [PATCH 5/8] adding some logic to use a custom venv for vllm tests --- test/backends/test_openai_vllm.py | 39 ++++++++++++++++--- .../scripts/run_tests_with_ollama_and_vllm.sh | 1 + 2 files changed, 35 insertions(+), 5 deletions(-) diff --git a/test/backends/test_openai_vllm.py b/test/backends/test_openai_vllm.py index 1ad21d585..6498f701f 100644 --- a/test/backends/test_openai_vllm.py +++ b/test/backends/test_openai_vllm.py @@ -38,13 +38,41 @@ @pytest.fixture(scope="module") def vllm_process(): - """Shared vllm process for all tests in this module.""" + """Shared vllm process for all tests in this module. + + If VLLM_TEST_BASE_URL is set (e.g. by run_tests_with_ollama_and_vllm.sh), + the server is already running externally — skip subprocess entirely. + """ + if os.environ.get("VLLM_TEST_BASE_URL"): + yield None + return + + # Skip if no CUDA GPU is available (mirrors auto-detection in the script) + try: + subprocess.run(["nvidia-smi", "-L"], check=True, capture_output=True) + except (subprocess.CalledProcessError, FileNotFoundError): + pytest.skip( + "No CUDA GPU detected — skipping vLLM openai tests", allow_module_level=True + ) + + # Resolve the vLLM venv — created by run_tests_with_ollama_and_vllm.sh or + # bootstrapped here for direct pytest invocations. + vllm_venv = os.environ.get("VLLM_VENV_PATH", ".vllm-venv") + vllm_python = os.path.join(vllm_venv, "bin", "python") + if not os.path.isfile(vllm_python): + subprocess.run(["uv", "venv", vllm_venv, "--python", "3.11"], check=True) + subprocess.run( + ["uv", "pip", "install", "--python", vllm_python, "vllm"], check=True + ) + process = None try: process = subprocess.Popen( [ - "vllm", - "serve", + vllm_python, + "-m", + "vllm.entrypoints.openai.api_server", + "--model", IBM_GRANITE_4_MICRO_3B.hf_model_name, "--served-model-name", IBM_GRANITE_4_MICRO_3B.hf_model_name, @@ -130,11 +158,12 @@ def vllm_process(): @pytest.fixture(scope="module") def backend(gh_run: int, vllm_process: subprocess.Popen): - """Shared OpenAI backend configured for Ollama.""" + """Shared OpenAI backend configured for vLLM.""" + base_url = os.environ.get("VLLM_TEST_BASE_URL", "http://127.0.0.1:8000") + "/v1" return OpenAIBackend( model_id=IBM_GRANITE_4_MICRO_3B.hf_model_name, # type: ignore formatter=TemplateFormatter(model_id=IBM_GRANITE_4_MICRO_3B.hf_model_name), # type: ignore - base_url="http://0.0.0.0:8000/v1", + base_url=base_url, api_key="EMPTY", ) diff --git a/test/scripts/run_tests_with_ollama_and_vllm.sh b/test/scripts/run_tests_with_ollama_and_vllm.sh index 3f70ca32f..cb748905b 100755 --- a/test/scripts/run_tests_with_ollama_and_vllm.sh +++ b/test/scripts/run_tests_with_ollama_and_vllm.sh @@ -255,6 +255,7 @@ if [[ "$WITH_VLLM" == "1" ]]; then # Export for pytest fixtures export VLLM_TEST_BASE_URL="http://127.0.0.1:${VLLM_PORT}" export VLLM_TEST_MODEL="$VLLM_MODEL" + export VLLM_VENV_PATH="$VLLM_VENV" log "vLLM ready. VLLM_TEST_BASE_URL=$VLLM_TEST_BASE_URL" else log "vLLM disabled (WITH_VLLM=0). Pass WITH_VLLM=1 to enable, or run on a CUDA host for auto-detection." From e92235a633eaba5ae2260097a429f88e6a92bb81 Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Mon, 6 Apr 2026 11:15:35 -0700 Subject: [PATCH 6/8] adding some vllm params to reduc gpu footprint --- test/backends/test_openai_vllm.py | 6 ++++++ test/scripts/run_tests_with_ollama_and_vllm.sh | 6 ++++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/test/backends/test_openai_vllm.py b/test/backends/test_openai_vllm.py index 6498f701f..ac134e34a 100644 --- a/test/backends/test_openai_vllm.py +++ b/test/backends/test_openai_vllm.py @@ -82,6 +82,12 @@ def vllm_process(): "--max-lora-rank", "64", "--enable-prefix-caching", + "--gpu-memory-utilization", + "0.65", + "--max-num-seqs", + "256", + "--max-model-len", + "4096", ], # the process will have a new session id, so # entire process tree is killable at once diff --git a/test/scripts/run_tests_with_ollama_and_vllm.sh b/test/scripts/run_tests_with_ollama_and_vllm.sh index cb748905b..8f36b3844 100755 --- a/test/scripts/run_tests_with_ollama_and_vllm.sh +++ b/test/scripts/run_tests_with_ollama_and_vllm.sh @@ -55,8 +55,9 @@ if [[ -z "${WITH_VLLM:-}" ]]; then fi VLLM_PORT="${VLLM_PORT:-8100}" VLLM_MODEL="${VLLM_MODEL:-ibm-granite/granite-3.3-8b-instruct}" -VLLM_GPU_MEM="${VLLM_GPU_MEM:-0.85}" +VLLM_GPU_MEM="${VLLM_GPU_MEM:-0.65}" VLLM_MAX_MODEL_LEN="${VLLM_MAX_MODEL_LEN:-4096}" +VLLM_MAX_NUM_SEQS="${VLLM_MAX_NUM_SEQS:-256}" VLLM_VENV="${CACHE_DIR:+${CACHE_DIR}/.vllm-venv}" VLLM_VENV="${VLLM_VENV:-.vllm-venv}" VLLM_PID="" @@ -228,8 +229,9 @@ if [[ "$WITH_VLLM" == "1" ]]; then "$VLLM_VENV/bin/python" -m vllm.entrypoints.openai.api_server \ --model "$VLLM_MODEL" \ --port "$VLLM_PORT" \ - --max-model-len "$VLLM_MAX_MODEL_LEN" \ --gpu-memory-utilization "$VLLM_GPU_MEM" \ + --max-num-seqs "$VLLM_MAX_NUM_SEQS" \ + --max-model-len "$VLLM_MAX_MODEL_LEN" \ > "$LOGDIR/vllm.log" 2>&1 & VLLM_PID=$! log "vLLM server PID: $VLLM_PID" From 7eb430256483a56239d75db96860fc30e4c17682 Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Mon, 6 Apr 2026 12:26:29 -0700 Subject: [PATCH 7/8] defaulting to granite 4 micro --- test/scripts/run_tests_with_ollama_and_vllm.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/scripts/run_tests_with_ollama_and_vllm.sh b/test/scripts/run_tests_with_ollama_and_vllm.sh index 8f36b3844..28898a3ee 100755 --- a/test/scripts/run_tests_with_ollama_and_vllm.sh +++ b/test/scripts/run_tests_with_ollama_and_vllm.sh @@ -54,7 +54,7 @@ if [[ -z "${WITH_VLLM:-}" ]]; then fi fi VLLM_PORT="${VLLM_PORT:-8100}" -VLLM_MODEL="${VLLM_MODEL:-ibm-granite/granite-3.3-8b-instruct}" +VLLM_MODEL="${VLLM_MODEL:-ibm-granite/granite-4.0-micro}" VLLM_GPU_MEM="${VLLM_GPU_MEM:-0.65}" VLLM_MAX_MODEL_LEN="${VLLM_MAX_MODEL_LEN:-4096}" VLLM_MAX_NUM_SEQS="${VLLM_MAX_NUM_SEQS:-256}" From bc85f3bec71e1fab9a6e61c8517d3b1c22a4bd7a Mon Sep 17 00:00:00 2001 From: Avinash Balakrishnan Date: Mon, 6 Apr 2026 14:30:34 -0700 Subject: [PATCH 8/8] adding a skip warmup flag (making it general --- test/backends/test_openai_vllm.py | 2 +- test/scripts/run_tests_with_ollama_and_vllm.sh | 9 ++++++--- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/test/backends/test_openai_vllm.py b/test/backends/test_openai_vllm.py index ac134e34a..f788fcde6 100644 --- a/test/backends/test_openai_vllm.py +++ b/test/backends/test_openai_vllm.py @@ -83,7 +83,7 @@ def vllm_process(): "64", "--enable-prefix-caching", "--gpu-memory-utilization", - "0.65", + "0.4", "--max-num-seqs", "256", "--max-model-len", diff --git a/test/scripts/run_tests_with_ollama_and_vllm.sh b/test/scripts/run_tests_with_ollama_and_vllm.sh index 28898a3ee..76565d7da 100755 --- a/test/scripts/run_tests_with_ollama_and_vllm.sh +++ b/test/scripts/run_tests_with_ollama_and_vllm.sh @@ -12,6 +12,7 @@ # ./run_tests_with_ollama_and_vllm.sh --group-by-backend -v -s # custom pytest args # WITH_VLLM=1 ./run_tests_with_ollama_and_vllm.sh # force-enable vLLM # WITH_VLLM=0 ./run_tests_with_ollama_and_vllm.sh # force-disable vLLM +# SKIP_WARMUP=1 ./run_tests_with_ollama_and_vllm.sh # skip ollama model warmup # WITH_VLLM=1 VLLM_MODEL=ibm-granite/granite-3.3-8b-instruct \ # ./run_tests_with_ollama_and_vllm.sh --group-by-backend -v -s # @@ -55,7 +56,7 @@ if [[ -z "${WITH_VLLM:-}" ]]; then fi VLLM_PORT="${VLLM_PORT:-8100}" VLLM_MODEL="${VLLM_MODEL:-ibm-granite/granite-4.0-micro}" -VLLM_GPU_MEM="${VLLM_GPU_MEM:-0.65}" +VLLM_GPU_MEM="${VLLM_GPU_MEM:-0.4}" VLLM_MAX_MODEL_LEN="${VLLM_MAX_MODEL_LEN:-4096}" VLLM_MAX_NUM_SEQS="${VLLM_MAX_NUM_SEQS:-256}" VLLM_VENV="${CACHE_DIR:+${CACHE_DIR}/.vllm-venv}" @@ -183,8 +184,10 @@ done log "All ollama models ready." # --- Warm up models (first load into memory is slow) --- -if [[ "${OLLAMA_SKIP_WARMUP:-0}" == "1" ]]; then - log "Skipping model warmup (OLLAMA_SKIP_WARMUP=1)" +# Disable with SKIP_WARMUP=1 (covers all backends) or OLLAMA_SKIP_WARMUP=1 (ollama only). +# Note: vLLM has no warmup step — it serves immediately after the readiness check. +if [[ "${SKIP_WARMUP:-0}" == "1" || "${OLLAMA_SKIP_WARMUP:-0}" == "1" ]]; then + log "Skipping model warmup" else log "Warming up models..." for model in "${OLLAMA_MODELS[@]}"; do