diff --git a/plugins/huggingface/modelgauge/suts/huggingface_chat_completion.py b/plugins/huggingface/modelgauge/suts/huggingface_chat_completion.py index b151fd7c3..f4777cce1 100644 --- a/plugins/huggingface/modelgauge/suts/huggingface_chat_completion.py +++ b/plugins/huggingface/modelgauge/suts/huggingface_chat_completion.py @@ -1,3 +1,4 @@ +from abc import ABC, abstractmethod from dataclasses import asdict from typing import Dict, List, Optional @@ -22,6 +23,7 @@ class ChatMessage(BaseModel): class HuggingFaceChatCompletionRequest(BaseModel): + model: Optional[str] = None messages: List[ChatMessage] logprobs: bool top_logprobs: Optional[int] = None @@ -39,18 +41,65 @@ class HuggingFaceChatCompletionOutput(BaseModel): usage: Optional[Dict] = None -@modelgauge_sut(capabilities=[AcceptsTextPrompt, ProducesPerTokenLogProbabilities]) -class HuggingFaceChatCompletionSUT( - PromptResponseSUT[HuggingFaceChatCompletionRequest, HuggingFaceChatCompletionOutput] +class BaseHuggingFaceChatCompletionSUT( + PromptResponseSUT[HuggingFaceChatCompletionRequest, HuggingFaceChatCompletionOutput], ABC ): - """A Hugging Face SUT that is hosted on a dedicated inference endpoint and uses the chat_completion API.""" + """A Huggingface SUT that uses the chat_completion API.""" - def __init__(self, uid: str, inference_endpoint: str, token: HuggingFaceInferenceToken): + def __init__(self, uid: str, token: HuggingFaceInferenceToken): super().__init__(uid) self.token = token - self.inference_endpoint = inference_endpoint self.client = None + @abstractmethod + def _create_client(self) -> InferenceClient: + """Create the InferenceClient for the SUT. Must be implemented by subclasses.""" + pass + + def evaluate(self, request: HuggingFaceChatCompletionRequest) -> HuggingFaceChatCompletionOutput: + if self.client is None: + self.client = self._create_client() + + request_dict = request.model_dump(exclude_none=True) + response = self.client.chat_completion(**request_dict) # type: ignore + # Convert to cacheable pydantic object. + return HuggingFaceChatCompletionOutput( + choices=[asdict(choice) for choice in response.choices], + created=response.created, + id=response.id, + model=response.model, + system_fingerprint=response.system_fingerprint, + usage=asdict(response.usage), + ) + + def translate_response( + self, request: HuggingFaceChatCompletionRequest, response: HuggingFaceChatCompletionOutput + ) -> SUTResponse: + assert len(response.choices) == 1, f"Expected a single response message, got {len(response.choices)}." + choice = response.choices[0] + text = choice["message"]["content"] + assert text is not None + logprobs: Optional[List[TopTokens]] = None + if request.logprobs: + logprobs = [] + assert choice["logprobs"] is not None, "Expected logprobs, but not returned." + lobprobs_sequence = choice["logprobs"]["content"] + for token in lobprobs_sequence: + top_tokens = [] + for top_logprob in token["top_logprobs"]: + top_tokens.append(TokenProbability(token=top_logprob["token"], logprob=top_logprob["logprob"])) + logprobs.append(TopTokens(top_tokens=top_tokens)) + return SUTResponse(text=text, top_logprobs=logprobs) + + +@modelgauge_sut(capabilities=[AcceptsTextPrompt, ProducesPerTokenLogProbabilities]) +class HuggingFaceChatCompletionDedicatedSUT(BaseHuggingFaceChatCompletionSUT): + """A Hugging Face SUT that is hosted on a dedicated inference endpoint and uses the chat_completion API.""" + + def __init__(self, uid: str, inference_endpoint: str, token: HuggingFaceInferenceToken): + super().__init__(uid, token) + self.inference_endpoint = inference_endpoint + def _create_client(self): endpoint = get_inference_endpoint(self.inference_endpoint, token=self.token.value) @@ -74,7 +123,7 @@ def _create_client(self): f"Endpoint is not running: Please contact admin to ensure endpoint is starting or running (status: {endpoint.status})" ) - self.client = InferenceClient(base_url=endpoint.url, token=self.token.value) + return InferenceClient(base_url=endpoint.url, token=self.token.value) def translate_text_prompt(self, prompt: TextPrompt, options: SUTOptions) -> HuggingFaceChatCompletionRequest: logprobs = False @@ -86,60 +135,52 @@ def translate_text_prompt(self, prompt: TextPrompt, options: SUTOptions) -> Hugg **options.model_dump(), ) - def evaluate(self, request: HuggingFaceChatCompletionRequest) -> HuggingFaceChatCompletionOutput: - if self.client is None: - self._create_client() - request_dict = request.model_dump(exclude_none=True) - response = self.client.chat_completion(**request_dict) # type: ignore - # Convert to cacheable pydantic object. - return HuggingFaceChatCompletionOutput( - choices=[asdict(choice) for choice in response.choices], - created=response.created, - id=response.id, - model=response.model, - system_fingerprint=response.system_fingerprint, - usage=asdict(response.usage), +@modelgauge_sut(capabilities=[AcceptsTextPrompt, ProducesPerTokenLogProbabilities]) +class HuggingFaceChatCompletionServerlessSUT(BaseHuggingFaceChatCompletionSUT): + """A SUT hosted by an inference provider on huggingface.""" + + def __init__(self, uid: str, model: str, provider: str, token: HuggingFaceInferenceToken): + super().__init__(uid, token) + self.model = model + self.provider = provider + + def _create_client(self): + return InferenceClient( + provider=self.provider, + api_key=self.token.value, ) - def translate_response( - self, request: HuggingFaceChatCompletionRequest, response: HuggingFaceChatCompletionOutput - ) -> SUTResponse: - assert len(response.choices) == 1, f"Expected a single response message, got {len(response.choices)}." - choice = response.choices[0] - text = choice["message"]["content"] - assert text is not None - logprobs: Optional[List[TopTokens]] = None - if request.logprobs: - logprobs = [] - assert choice["logprobs"] is not None, "Expected logprobs, but not returned." - lobprobs_sequence = choice["logprobs"]["content"] - for token in lobprobs_sequence: - top_tokens = [] - for top_logprob in token["top_logprobs"]: - top_tokens.append(TokenProbability(token=top_logprob["token"], logprob=top_logprob["logprob"])) - logprobs.append(TopTokens(top_tokens=top_tokens)) - return SUTResponse(text=text, top_logprobs=logprobs) + def translate_text_prompt(self, prompt: TextPrompt, options: SUTOptions) -> HuggingFaceChatCompletionRequest: + logprobs = False + if options.top_logprobs is not None: + logprobs = True + return HuggingFaceChatCompletionRequest( + model=self.model, + messages=[ChatMessage(role="user", content=prompt.text)], + logprobs=logprobs, + **options.model_dump(), + ) HF_SECRET = InjectSecret(HuggingFaceInferenceToken) SUTS.register( - HuggingFaceChatCompletionSUT, + HuggingFaceChatCompletionDedicatedSUT, "gemma-2-9b-it-hf", "gemma-2-9b-it-plf", HF_SECRET, ) SUTS.register( - HuggingFaceChatCompletionSUT, + HuggingFaceChatCompletionDedicatedSUT, "mistral-nemo-instruct-2407-hf", "mistral-nemo-instruct-2407-mgt", HF_SECRET, ) SUTS.register( - HuggingFaceChatCompletionSUT, + HuggingFaceChatCompletionDedicatedSUT, "nvidia-llama-3-1-nemotron-nano-8b-v1", "llama-3-1-nemotron-nano-8b-v-uhu", HF_SECRET, @@ -147,15 +188,23 @@ def translate_response( SUTS.register( - HuggingFaceChatCompletionSUT, + HuggingFaceChatCompletionDedicatedSUT, "qwen2-5-7b-instruct-hf", "qwen2-5-7b-instruct-hgy", HF_SECRET, ) SUTS.register( - HuggingFaceChatCompletionSUT, + HuggingFaceChatCompletionDedicatedSUT, "olmo-2-0325-32b-instruct-hf", "olmo-2-0325-32b-instruct-yft", HF_SECRET, ) + +SUTS.register( + HuggingFaceChatCompletionServerlessSUT, + "google-gemma-3-27b-it-hf-nebius", + "google/gemma-3-27b-it", + "nebius", + HF_SECRET, +) diff --git a/plugins/huggingface/pyproject.toml b/plugins/huggingface/pyproject.toml index d05180aef..178700b52 100644 --- a/plugins/huggingface/pyproject.toml +++ b/plugins/huggingface/pyproject.toml @@ -8,7 +8,7 @@ packages = [{include = "modelgauge"}] [tool.poetry.dependencies] python = "^3.10" -huggingface-hub = "^0.26.3" +huggingface-hub = "^0.29.0" [build-system] requires = ["poetry-core"] diff --git a/plugins/huggingface/tests/test_huggingface_chat_completion.py b/plugins/huggingface/tests/test_huggingface_chat_completion.py index 18a50ef3d..9ad43ec75 100644 --- a/plugins/huggingface/tests/test_huggingface_chat_completion.py +++ b/plugins/huggingface/tests/test_huggingface_chat_completion.py @@ -20,7 +20,7 @@ ChatMessage, HuggingFaceChatCompletionOutput, HuggingFaceChatCompletionRequest, - HuggingFaceChatCompletionSUT, + HuggingFaceChatCompletionDedicatedSUT, ) @@ -37,7 +37,7 @@ def mock_endpoint(): def fake_sut(mock_get_inference_endpoint, mock_endpoint): mock_get_inference_endpoint.return_value = mock_endpoint - sut = HuggingFaceChatCompletionSUT("fake_uid", "fake_endpoint", HuggingFaceInferenceToken("fake_token")) + sut = HuggingFaceChatCompletionDedicatedSUT("fake_uid", "fake_endpoint", HuggingFaceInferenceToken("fake_token")) return sut diff --git a/poetry.lock b/poetry.lock index 61074d376..7c0cfc164 100644 --- a/poetry.lock +++ b/poetry.lock @@ -7,8 +7,8 @@ description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.9" files = [ - { file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8" }, - { file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558" }, + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, + {file = 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