From 21a57efa9df0f0bef4cd6492835f9bc21129edd6 Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Tue, 6 Jan 2026 12:31:32 -0500 Subject: [PATCH 1/9] refactor: removed outlines from vllm/hf backend, use latest vllm, V1 API, use llguidance --- conda/environment.yml | 2 - mellea/backends/huggingface.py | 86 ++++++++++++++++++++-------------- mellea/backends/vllm.py | 78 +++++++++--------------------- pyproject.toml | 20 +------- uv.lock | 20 +++----- 5 files changed, 82 insertions(+), 124 deletions(-) diff --git a/conda/environment.yml b/conda/environment.yml index 7f6deeaa..553bd011 100644 --- a/conda/environment.yml +++ b/conda/environment.yml @@ -5,5 +5,3 @@ channels: dependencies: - python=3.12 # note: at the time of writing, xformer (< vllm) has a broken wheel for 3.13. https://github.com/facebookresearch/xformers/issues/740#issuecomment-2753869337 - uv -variables: - VLLM_USE_V1: 0 # need this to make outlines work diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index ff1e92da..ec2a5865 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -18,8 +18,9 @@ from typing import TYPE_CHECKING, Any, cast import granite_common -import outlines -import outlines_core +import llguidance +import llguidance.hf +import llguidance.torch import peft import torch from transformers import ( @@ -27,6 +28,7 @@ AutoModelForCausalLM, AutoTokenizer, DynamicCache, + LogitsProcessorList, PreTrainedModel, PreTrainedTokenizer, set_seed, @@ -69,8 +71,6 @@ from mellea.stdlib.intrinsics.intrinsic import Intrinsic from mellea.stdlib.requirement import ALoraRequirement, LLMaJRequirement, Requirement -assert outlines, "outlines needs to be present to make outlines_core work" - """A configuration type for the unhappy path: Tokenizer * Model * torch device string Huggingface backends can initialize themselves from a model string if the transformers `Auto*` classes can be used. Therefore, a TransformersTorchConfig usually isn't required. However, sometimes a model needs special care to instantiate properly, or a custom device type needs to bse used. Instead of trying to do a lot of partial magic, we basically have two modaliites: either the constructor can figure out everything from the model_id, or the user has to provide an entire config. @@ -90,6 +90,38 @@ class HFAloraCacheInfo: q_end: int = -1 +# modified from VLLM v0.9.2 code base +# https://github.com/vllm-project/vllm/blob/v0.9.2/vllm/model_executor/guided_decoding/guidance_logits_processors.py +class _GuidanceLogitsProcessor: + def __init__(self, grammar: str, ll_tokenizer: llguidance.LLTokenizer) -> None: + self.grammar = grammar + self.ll_tokenizer = ll_tokenizer + self.ll_matcher = llguidance.LLMatcher(self.ll_tokenizer, self.grammar) + self.bitmask = llguidance.torch.allocate_token_bitmask( + 1, self.ll_tokenizer.vocab_size + ) # type: ignore[attr-defined] + self.new_sampling = False + self.initialized = False + + def __call__(self, input_ids: list[int], scores: torch.Tensor) -> torch.Tensor: + if self.new_sampling and len(input_ids) > 0: + self.ll_matcher.consume_token( # type: ignore[attr-defined] + input_ids[-1] + ) + err = self.ll_matcher.get_error() # type: ignore[attr-defined] + if err: + FancyLogger.get_logger().warning("Error in LLMatcher: %s", err) + + llguidance.torch.fill_next_token_bitmask(self.ll_matcher, self.bitmask, 0) + llguidance.torch.apply_token_bitmask_inplace( + scores, self.bitmask.to(scores.device) + ) # type: ignore[attr-defined] + + self.new_sampling = True + + return scores + + class LocalHFBackend(FormatterBackend, AdapterMixin): """The LocalHFBackend uses Huggingface's transformers library for inference, and uses a Formatter to convert `Component`s into prompts. This backend also supports Activated LoRAs (ALoras)](https://arxiv.org/pdf/2504.12397). @@ -178,6 +210,10 @@ def __init__( case _: self._tokenizer, self._model, self._device = custom_config + self._llguidance_tokenizer: llguidance.LLTokenizer = ( + llguidance.hf.from_tokenizer(self._tokenizer) # type:ignore + ) + self._use_caches = use_caches self._cache = cache if cache is not None else SimpleLRUCache(3) @@ -763,24 +799,15 @@ async def _generate_from_context_standard( format_kwargs = {} if _format: - # outlines.generate.json always parses the resulting json into a python dict. - # We however want to keep it as a json string for later storing it in ModelOutputThunk schema: dict[str, Any] = _format.model_json_schema() - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json + grammar: str = llguidance.LLMatcher.grammar_from_json_schema( + schema, defaults={"whitespace_flexible": False} + ) + logits_processor = _GuidanceLogitsProcessor( + grammar, self._llguidance_tokenizer ) - - from outlines.models.transformers import TransformerTokenizer - from outlines.processors.structured import RegexLogitsProcessor - from transformers import LogitsProcessorList - format_kwargs["logits_processor"] = LogitsProcessorList( - [ - RegexLogitsProcessor( - regex_str, tokenizer=TransformerTokenizer(self._tokenizer) - ) - ] + [logits_processor] ) streaming_kwargs = {} @@ -990,25 +1017,14 @@ async def generate_from_raw( format_kwargs = {} if format: - # outlines.generate.json always parses the resulting json into a python dict. - # We however want to keep it as a json string for later storing it in ModelOutputThunk schema: dict[str, Any] = format.model_json_schema() - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json + grammar: str = llguidance.LLMatcher.grammar_from_json_schema( + schema, defaults={"whitespace_flexible": False} ) - - from outlines.models.transformers import TransformerTokenizer - from outlines.processors.structured import RegexLogitsProcessor - from transformers import LogitsProcessorList - - format_kwargs["logits_processor"] = LogitsProcessorList( - [ - RegexLogitsProcessor( - regex_str, tokenizer=TransformerTokenizer(self._tokenizer) - ) - ] + logits_processor = _GuidanceLogitsProcessor( + grammar, self._llguidance_tokenizer ) + format_kwargs["logits_processor"] = LogitsProcessorList([logits_processor]) outputs = await asyncio.to_thread( self._generate_with_adapter_lock, diff --git a/mellea/backends/vllm.py b/mellea/backends/vllm.py index 6d3fc228..2747ff59 100644 --- a/mellea/backends/vllm.py +++ b/mellea/backends/vllm.py @@ -19,8 +19,6 @@ from typing import TYPE_CHECKING, Any, Optional import msgspec # type:ignore -import outlines -import outlines_core import torch import vllm # type:ignore from transformers import AutoTokenizer, PreTrainedTokenizerBase @@ -50,8 +48,6 @@ from mellea.stdlib.chat import Message from mellea.stdlib.requirement import LLMaJRequirement, Requirement -assert outlines, "outlines needs to be present to make outlines_core work" - format: None = None # typing this variable in order to shadow the global format function and ensure mypy checks for errors @@ -210,30 +206,24 @@ def __init__( self._hf_model_id ) # type:ignore - # See the notes in outlines.models.vllm.adapt_tokenizer for why this is needed. - # Note: there is a module named outlines.models.vllm and a function named outlines.models.vllm.vllm . - # However, outlines.models import outlines.models.vllm.vllm as vllm, - # thus the module outlines.models.vllm becomes inaccessible, - # hence the use of importlib to get the module. - self._tokenizer_for_outlines: PreTrainedTokenizerBase = importlib.import_module( - "outlines.models.vllm" - ).adapt_tokenizer(self._tokenizer) - @property def _model(self) -> vllm.AsyncLLMEngine: """Use model when making generation requests.""" - 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: - self._underlying_model.shutdown_background_loop() - self._underlying_model.start_background_loop() - self._event_loop = el + # 2026/01/06 Masa: Temporarily canceling the mechanism below. + # After vllm 0.11.0, start/shutdown_background_loop is gone. + + # 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: + # self._underlying_model.shutdown_background_loop() + # self._underlying_model.start_background_loop() + # self._event_loop = el return self._underlying_model @@ -320,22 +310,10 @@ async def _generate_from_context_standard( ) if _format is not None: - # outlines.generate.json always parses the resulting json into a python dict. - # We however want to keep it as a json string for later storing it in ModelOutputThunk - schema: dict[str, Any] = _format.model_json_schema() - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json # type: ignore - ) # type: ignore - - from outlines.processors import RegexLogitsProcessor - - logits_processor = RegexLogitsProcessor( - regex_str, - tokenizer=self._tokenizer_for_outlines, # type: ignore - ) - sampling_params.logits_processors = ( - [logits_processor] if logits_processor is not None else [] + sampling_params.structured_outputs = ( + vllm.sampling_params.StructuredOutputsParams( + json=_format.model_json_schema() + ) ) # stream = model_options.get(ModelOption.STREAM, False) @@ -458,20 +436,10 @@ async def generate_from_raw( ) if format is not None: - schema: dict[str, Any] = format.model_json_schema() - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json # type: ignore - ) # type: ignore - - from outlines.processors import RegexLogitsProcessor - - logits_processor = RegexLogitsProcessor( - regex_str, - tokenizer=self._tokenizer_for_outlines, # type: ignore - ) - sampling_params.logits_processors = ( - [logits_processor] if logits_processor is not None else [] + sampling_params.structured_outputs = ( + vllm.sampling_params.StructuredOutputsParams( + json=format.model_json_schema() + ) ) async def generate(prompt, request_id): diff --git a/pyproject.toml b/pyproject.toml index 1f6cdb67..49cad6ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,34 +57,18 @@ m = "cli.m:cli" # uv pip install -e .[hf, watsonx] # if you want to install all dependencies, use uv sync --all-extras - -# note on outlines versions: -# outlines>=1.2.0 requires outlines-core==0.2.11 -# outlines<=1.1.* requires outlines-core==0.1.26 -# vllm==0.10.0 requires outlines-core==0.2.10 -# vllm==0.9.* requires outlines-core==0.1.26 -# -# thus the following version combination allows installing vllm and outlines -# (main library) at the same time. - hf = [ "accelerate>=1.9.0", "alora==0.2.0", "datasets>=4.0.0", - "outlines-core==0.1.26", - "outlines", # intentionally un-versioned, expecting a minor update. coutlines-core version should be enough to specify it + "llguidance", "peft>=0.18.0", # aLoRA support was added in Peft 0.18.0 "transformers>=4.53.2", "trl==0.19.1", ] vllm = [ - "transformers<4.54.0", - # see https://github.com/vllm-project/vllm-ascend/issues/2046 - "numpy<2.0.0", # patching incorrect dependencies in vllm and outlines. - # see https://github.com/vllm-project/vllm/issues/5587 - "outlines-core==0.1.26", - "vllm>=0.9.1", + "vllm", ] litellm = [ diff --git a/uv.lock b/uv.lock index b49e0bd1..b7b4273c 100644 --- a/uv.lock +++ b/uv.lock @@ -2861,7 +2861,9 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b3/e1/694c89986fcae7777184fc8b22baa0976eba15a6847221763f6ad211fc1f/llguidance-0.7.30-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:c80af02c118d2b0526bcecaab389af2ed094537a069b0fc724cd2a2f2ba3990f", size = 3327974, upload-time = "2025-06-23T00:23:47.556Z" }, { url = "https://files.pythonhosted.org/packages/fd/77/ab7a548ae189dc23900fdd37803c115c2339b1223af9e8eb1f4329b5935a/llguidance-0.7.30-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:00a256d532911d2cf5ba4ef63e182944e767dd2402f38d63002016bc37755958", size = 3210709, upload-time = "2025-06-23T00:23:45.872Z" }, { url = "https://files.pythonhosted.org/packages/9c/5b/6a166564b14f9f805f0ea01ec233a84f55789cb7eeffe1d6224ccd0e6cdd/llguidance-0.7.30-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:af8741c867e4bc7e42f7cdc68350c076b4edd0ca10ecefbde75f15a9f6bc25d0", size = 14867038, upload-time = "2025-06-23T00:23:39.571Z" }, + { url = "https://files.pythonhosted.org/packages/17/ec/69507bdb36767f9b6ff2e290660a9b5afdda0fb8a7903faa37f37c6c2a72/llguidance-0.7.30-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f4a327a30dd37d86dd6347861ac8de3521fc1dbef9475296c06744e5b40ffc54", size = 15142936, upload-time = "2025-06-23T00:23:41.944Z" }, { url = "https://files.pythonhosted.org/packages/af/80/5a40b9689f17612434b820854cba9b8cabd5142072c491b5280fe5f7a35e/llguidance-0.7.30-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9edc409b9decd6cffba5f5bf3b4fbd7541f95daa8cbc9510cbf96c6ab1ffc153", size = 15004926, upload-time = "2025-06-23T00:23:43.965Z" }, + { url = "https://files.pythonhosted.org/packages/bb/bc/2d2f9b446bb3e51e4dd4db290590afee03ae29163f417168569f0361204c/llguidance-0.7.30-cp39-abi3-win32.whl", hash = "sha256:a0d52b8d1b2d3b0e661e3f953ecccfa16644f302026b3067a4815c1baa2ae643", size = 2585627, upload-time = "2025-06-23T00:23:52.39Z" }, { url = "https://files.pythonhosted.org/packages/99/47/58e49a118b514855b245f8a962c6aaf9a5cc95a0f61eac7e230e691c7b7e/llguidance-0.7.30-cp39-abi3-win_amd64.whl", hash = "sha256:05234ecceea7c9c6ff13b9739112043173a3bcb88cae860249b20335a07b3075", size = 2796878, upload-time = "2025-06-23T00:23:51Z" }, ] @@ -3245,9 +3247,7 @@ all = [ { name = "ibm-watsonx-ai", version = "1.3.42", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "ibm-watsonx-ai", version = "1.4.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "litellm" }, - { name = "numpy" }, - { name = "outlines" }, - { name = "outlines-core" }, + { name = "llguidance" }, { name = "peft" }, { name = "transformers" }, { name = "trl" }, @@ -3260,8 +3260,7 @@ hf = [ { name = "accelerate" }, { name = "alora" }, { name = "datasets" }, - { name = "outlines" }, - { name = "outlines-core" }, + { name = "llguidance" }, { name = "peft" }, { name = "transformers" }, { name = "trl" }, @@ -3270,9 +3269,6 @@ litellm = [ { name = "litellm" }, ] vllm = [ - { name = "numpy" }, - { name = "outlines-core" }, - { name = "transformers" }, { name = "vllm" }, ] watsonx = [ @@ -3320,29 +3316,25 @@ requires-dist = [ { name = "jinja2" }, { name = "json5" }, { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.76" }, + { name = "llguidance", marker = "extra == 'hf'" }, { name = "llm-sandbox", extras = ["docker"], specifier = ">=0.3.23" }, { name = "math-verify" }, { name = "mellea", extras = ["watsonx", "docling", "hf", "vllm", "litellm"], marker = "extra == 'all'" }, { name = "mistletoe", specifier = ">=1.4.0" }, - { name = "numpy", marker = "extra == 'vllm'", specifier = "<2.0.0" }, { name = "ollama", specifier = ">=0.5.1" }, { name = "openai" }, - { name = "outlines", marker = "extra == 'hf'" }, - { name = "outlines-core", marker = "extra == 'hf'", specifier = "==0.1.26" }, - { name = "outlines-core", marker = "extra == 'vllm'", specifier = "==0.1.26" }, { name = "peft", marker = "extra == 'hf'", specifier = ">=0.18.0" }, { name = "pillow" }, { name = "pydantic" }, { name = "requests", specifier = ">=2.32.3" }, { name = "rouge-score" }, { name = "transformers", marker = "extra == 'hf'", specifier = ">=4.53.2" }, - { name = "transformers", marker = "extra == 'vllm'", specifier = "<4.54.0" }, { name = "trl", marker = "extra == 'hf'", specifier = "==0.19.1" }, { name = "typer" }, { name = "types-requests" }, { name = "types-tqdm" }, { name = "uvicorn" }, - { name = "vllm", marker = "extra == 'vllm'", specifier = ">=0.9.1" }, + { name = "vllm", marker = "extra == 'vllm'" }, ] provides-extras = ["hf", "vllm", "litellm", "watsonx", "docling", "all"] From b6c1eb3667c4f95cb21c0095795e49279fceb06d Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Tue, 6 Jan 2026 12:33:32 -0500 Subject: [PATCH 2/9] fix: conda should not always supersede mamba --- conda/install.sh | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/conda/install.sh b/conda/install.sh index 6df570e7..5547029b 100755 --- a/conda/install.sh +++ b/conda/install.sh @@ -4,8 +4,7 @@ CONDA="" if which mamba > /dev/null then CONDA=$(which mamba) -fi -if which conda > /dev/null +elif which conda > /dev/null then CONDA=$(which conda) fi From 35d9521ffe281f2e2c9eaee64dd741b2196082ef Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Tue, 6 Jan 2026 15:47:29 -0500 Subject: [PATCH 3/9] fix(test): hf test uses a smaller model --- test/backends/test_huggingface.py | 2 +- test/backends/test_huggingface_tools.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/test/backends/test_huggingface.py b/test/backends/test_huggingface.py index 328b6068..84d21368 100644 --- a/test/backends/test_huggingface.py +++ b/test/backends/test_huggingface.py @@ -39,7 +39,7 @@ def backend(): """Shared HuggingFace backend for all tests in this module.""" backend = LocalHFBackend( - model_id="ibm-granite/granite-3.3-8b-instruct", + model_id="ibm-granite/granite-3.3-2b-instruct", formatter=TemplateFormatter(model_id="ibm-granite/granite-4.0-tiny-preview"), cache=SimpleLRUCache(5), ) diff --git a/test/backends/test_huggingface_tools.py b/test/backends/test_huggingface_tools.py index 0df5f3dc..7bcd0caf 100644 --- a/test/backends/test_huggingface_tools.py +++ b/test/backends/test_huggingface_tools.py @@ -22,7 +22,7 @@ def backend(): """Shared HuggingFace backend for all tests in this module.""" backend = LocalHFBackend( - model_id=model_ids.MISTRALAI_MISTRAL_0_3_7B, cache=SimpleLRUCache(5) + model_id="ibm-granite/granite-3.3-2b-instruct", cache=SimpleLRUCache(5) ) # add_granite_aloras(backend) return backend From fbd37f1fb44a6c06079b65a06cb8e8e56543d76f Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Wed, 7 Jan 2026 13:23:36 -0500 Subject: [PATCH 4/9] fixup! refactor: removed outlines from vllm/hf backend, use latest vllm, V1 API, use llguidance --- mellea/backends/huggingface.py | 63 ++++++++++++++++++++++------------ 1 file changed, 42 insertions(+), 21 deletions(-) diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index ec2a5865..4e6fe3f4 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -95,27 +95,48 @@ class HFAloraCacheInfo: class _GuidanceLogitsProcessor: def __init__(self, grammar: str, ll_tokenizer: llguidance.LLTokenizer) -> None: self.grammar = grammar - self.ll_tokenizer = ll_tokenizer - self.ll_matcher = llguidance.LLMatcher(self.ll_tokenizer, self.grammar) - self.bitmask = llguidance.torch.allocate_token_bitmask( - 1, self.ll_tokenizer.vocab_size - ) # type: ignore[attr-defined] - self.new_sampling = False - self.initialized = False - - def __call__(self, input_ids: list[int], scores: torch.Tensor) -> torch.Tensor: - if self.new_sampling and len(input_ids) > 0: - self.ll_matcher.consume_token( # type: ignore[attr-defined] - input_ids[-1] - ) - err = self.ll_matcher.get_error() # type: ignore[attr-defined] - if err: - FancyLogger.get_logger().warning("Error in LLMatcher: %s", err) - - llguidance.torch.fill_next_token_bitmask(self.ll_matcher, self.bitmask, 0) - llguidance.torch.apply_token_bitmask_inplace( - scores, self.bitmask.to(scores.device) - ) # type: ignore[attr-defined] + self.vocab_size: int = ll_tokenizer.vocab_size + self.ll_tokenizer: llguidance.LLTokenizer = ll_tokenizer + self.ll_matchers: list[llguidance.LLMatcher] = [] + self.bitmasks: list[torch.Tensor] = [] + self.new_sampling: bool = False + self.batch_size: int = -1 + + def __call__( + self, batch_input_ids: torch.Tensor, batch_scores: torch.Tensor + ) -> torch.Tensor: + i_batch, i_seqlen = batch_input_ids.shape + s_batch, s_vocab = batch_scores.shape + assert i_batch == s_batch + assert s_vocab == self.vocab_size + + if self.batch_size != i_batch: + self.batch_size = i_batch + self.bitmasks = [ + llguidance.torch.allocate_token_bitmask(1, self.vocab_size) # type: ignore[attr-defined] + for _ in range(self.batch_size) + ] + + self.ll_matchers = [ + llguidance.LLMatcher(self.ll_tokenizer, self.grammar) + for _ in range(self.batch_size) + ] + + for input_ids, scores, ll_matcher, bitmask in zip( + batch_input_ids, batch_scores, self.ll_matchers, self.bitmasks + ): + if self.new_sampling and len(input_ids) > 0: + ll_matcher.consume_token( # type: ignore[attr-defined] + input_ids.tolist()[-1] + ) + err = ll_matcher.get_error() # type: ignore[attr-defined] + if err: + FancyLogger.get_logger().warning("Error in LLMatcher: %s", err) + + llguidance.torch.fill_next_token_bitmask(ll_matcher, bitmask, 0) + llguidance.torch.apply_token_bitmask_inplace( + scores, bitmask.to(scores.device) + ) # type: ignore[attr-defined] self.new_sampling = True From f3b459106bc744e610ff42c2d1f6d0555868246d Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Wed, 7 Jan 2026 13:46:47 -0500 Subject: [PATCH 5/9] fixup! refactor: removed outlines from vllm/hf backend, use latest vllm, V1 API, use llguidance --- mellea/backends/vllm.py | 8 -------- test/backends/test_vllm.py | 3 --- test/backends/test_vllm_tools.py | 2 -- 3 files changed, 13 deletions(-) diff --git a/mellea/backends/vllm.py b/mellea/backends/vllm.py index 2747ff59..5bcb710c 100644 --- a/mellea/backends/vllm.py +++ b/mellea/backends/vllm.py @@ -79,14 +79,6 @@ def __init__( formatter (Formatter): A mechanism for turning `stdlib` stuff into strings. Experimental Span-based models should use `mellea.backends.span.*` backends. model_options (Optional[dict]): Default model options. """ - if os.environ.get("VLLM_USE_V1", -1) != "0": - FancyLogger.get_logger().error( - "Mellea LocalVLLMBackend doesn't support VLLM V1. Must `export VLLM_USE_V1=0`." - ) - raise ValueError( - "Mellea LocalVLLMBackend doesn't support VLLM V1. Must `export VLLM_USE_V1=0`." - ) - formatter = ( formatter if formatter is not None else TemplateFormatter(model_id=model_id) ) diff --git a/test/backends/test_vllm.py b/test/backends/test_vllm.py index cfcda8c2..fc8ffa86 100644 --- a/test/backends/test_vllm.py +++ b/test/backends/test_vllm.py @@ -20,9 +20,6 @@ @pytest.fixture(scope="module") def backend(): """Shared vllm backend for all tests in this module.""" - if os.environ.get("VLLM_USE_V1", -1) != "0": - pytest.skip("skipping vllm tests; tests require `export VLLM_USE_V1=0`") - backend = LocalVLLMBackend( model_id=model_ids.QWEN3_0_6B, # formatter=TemplateFormatter(model_id="ibm-granite/granite-4.0-tiny-preview"), diff --git a/test/backends/test_vllm_tools.py b/test/backends/test_vllm_tools.py index 69c824b2..b80c8037 100644 --- a/test/backends/test_vllm_tools.py +++ b/test/backends/test_vllm_tools.py @@ -19,8 +19,6 @@ @pytest.fixture(scope="module") def backend(): """Shared vllm backend for all tests in this module.""" - if os.environ.get("VLLM_USE_V1", -1) != "0": - pytest.skip("skipping vllm tests; tests require `export VLLM_USE_V1=0`") backend = LocalVLLMBackend( model_id=model_ids.MISTRALAI_MISTRAL_0_3_7B, From 20ae052cdf57da0cb084d7f8618c03a940de2e73 Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Wed, 7 Jan 2026 14:26:36 -0500 Subject: [PATCH 6/9] fixup! refactor: removed outlines from vllm/hf backend, use latest vllm, V1 API, use llguidance --- mellea/backends/vllm.py | 34 +++++++++++++++++++++------------- 1 file changed, 21 insertions(+), 13 deletions(-) diff --git a/mellea/backends/vllm.py b/mellea/backends/vllm.py index 5bcb710c..c33d2999 100644 --- a/mellea/backends/vllm.py +++ b/mellea/backends/vllm.py @@ -193,6 +193,8 @@ def __init__( # 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 self._tokenizer: PreTrainedTokenizerBase = AutoTokenizer.from_pretrained( self._hf_model_id @@ -203,19 +205,25 @@ 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. - - # 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: - # self._underlying_model.shutdown_background_loop() - # self._underlying_model.start_background_loop() - # self._event_loop = el + # 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._event_loop = el return self._underlying_model From 198711575354e5b53d6b72773cf92bfc0bf7c26d Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Wed, 7 Jan 2026 15:17:31 -0500 Subject: [PATCH 7/9] fix: force vllm >= 0.13.0 and updated lock --- pyproject.toml | 2 +- uv.lock | 1488 +++++++++++++++++++++++++++++++++--------------- 2 files changed, 1031 insertions(+), 459 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 49cad6ab..b1f99dda 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -68,7 +68,7 @@ hf = [ ] vllm = [ 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Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Wed, 7 Jan 2026 15:23:05 -0500 Subject: [PATCH 8/9] fixup! refactor: removed outlines from vllm/hf backend, use latest vllm, V1 API, use llguidance --- mellea/backends/huggingface.py | 21 ++++++--------------- 1 file changed, 6 insertions(+), 15 deletions(-) diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index 4e6fe3f4..a64bf175 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -653,24 +653,15 @@ async def _generate_from_context_with_kv_cache( format_kwargs = {} if _format: - # outlines.generate.json always parses the resulting json into a python dict. - # We however want to keep it as a json string for later storing it in ModelOutputThunk schema: dict[str, Any] = _format.model_json_schema() - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json + grammar: str = llguidance.LLMatcher.grammar_from_json_schema( + schema, defaults={"whitespace_flexible": False} + ) + logits_processor = _GuidanceLogitsProcessor( + grammar, self._llguidance_tokenizer ) - - from outlines.models.transformers import TransformerTokenizer - from outlines.processors.structured import RegexLogitsProcessor - from transformers import LogitsProcessorList - format_kwargs["logits_processor"] = LogitsProcessorList( - [ - RegexLogitsProcessor( - regex_str, tokenizer=TransformerTokenizer(self._tokenizer) - ) - ] + [logits_processor] ) streaming_kwargs = {} From 29588fb9b8f3e53a2cc7915fc1499b16992106d1 Mon Sep 17 00:00:00 2001 From: Masataro Asai Date: Mon, 12 Jan 2026 16:13:52 -0500 Subject: [PATCH 9/9] fix: disable vllm on darwin --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 09769323..2875e30e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -68,7 +68,7 @@ hf = [ ] vllm = [ - "vllm>=0.13.0", + "vllm>=0.13.0; sys_platform != 'darwin'", ] litellm = [