mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-05 10:13:05 +00:00
fixes and linting
This commit is contained in:
Hardik Shah
parent
021dd0d35d
commit
5251d2422d
8 changed files with 149 additions and 345 deletions
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@ -56,9 +56,7 @@ from .models import MODEL_ENTRIES
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logger = get_logger(name=__name__, category="inference")
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class FireworksInferenceAdapter(
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ModelRegistryHelper, Inference, NeedsRequestProviderData
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):
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class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
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def __init__(self, config: FireworksImplConfig) -> None:
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ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
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self.config = config
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@ -70,9 +68,7 @@ class FireworksInferenceAdapter(
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pass
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def _get_api_key(self) -> str:
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config_api_key = (
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self.config.api_key.get_secret_value() if self.config.api_key else None
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)
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config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
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if config_api_key:
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return config_api_key
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else:
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@ -112,9 +108,7 @@ class FireworksInferenceAdapter(
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else:
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return await self._nonstream_completion(request)
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async def _nonstream_completion(
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self, request: CompletionRequest
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) -> CompletionResponse:
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async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
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params = await self._get_params(request)
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r = await self._get_client().completion.acreate(**params)
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return process_completion_response(r)
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@ -194,9 +188,7 @@ class FireworksInferenceAdapter(
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else:
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return await self._nonstream_chat_completion(request)
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest
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) -> ChatCompletionResponse:
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async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
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params = await self._get_params(request)
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if "messages" in params:
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r = await self._get_client().chat.completions.acreate(**params)
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@ -204,9 +196,7 @@ class FireworksInferenceAdapter(
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r = await self._get_client().completion.acreate(**params)
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return process_chat_completion_response(r, request)
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async def _stream_chat_completion(
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self, request: ChatCompletionRequest
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) -> AsyncGenerator:
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async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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async def _to_async_generator():
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@ -221,9 +211,7 @@ class FireworksInferenceAdapter(
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async for chunk in process_chat_completion_stream_response(stream, request):
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yield chunk
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async def _get_params(
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self, request: Union[ChatCompletionRequest, CompletionRequest]
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) -> dict:
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async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict:
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input_dict = {}
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media_present = request_has_media(request)
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@ -231,17 +219,12 @@ class FireworksInferenceAdapter(
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if isinstance(request, ChatCompletionRequest):
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if media_present or not llama_model:
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input_dict["messages"] = [
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await convert_message_to_openai_dict(m, download=True)
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for m in request.messages
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await convert_message_to_openai_dict(m, download=True) for m in request.messages
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]
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else:
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input_dict["prompt"] = await chat_completion_request_to_prompt(
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request, llama_model
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)
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input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
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else:
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assert (
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not media_present
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), "Fireworks does not support media for Completion requests"
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assert not media_present, "Fireworks does not support media for Completion requests"
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input_dict["prompt"] = await completion_request_to_prompt(request)
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# Fireworks always prepends with BOS
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@ -253,9 +236,7 @@ class FireworksInferenceAdapter(
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"model": request.model,
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**input_dict,
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"stream": request.stream,
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**self._build_options(
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request.sampling_params, request.response_format, request.logprobs
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),
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**self._build_options(request.sampling_params, request.response_format, request.logprobs),
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}
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logger.debug(f"params to fireworks: {params}")
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@ -274,9 +255,9 @@ class FireworksInferenceAdapter(
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kwargs = {}
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if model.metadata.get("embedding_dimension"):
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kwargs["dimensions"] = model.metadata.get("embedding_dimension")
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assert all(
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not content_has_media(content) for content in contents
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), "Fireworks does not support media for embeddings"
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assert all(not content_has_media(content) for content in contents), (
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"Fireworks does not support media for embeddings"
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)
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response = self._get_client().embeddings.create(
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model=model.provider_resource_id,
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input=[interleaved_content_as_str(content) for content in contents],
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@ -5,10 +5,9 @@
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# the root directory of this source tree.
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import logging
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from typing import AsyncGenerator, List, Optional
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from huggingface_hub import AsyncInferenceClient, HfApi, InferenceClient
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from huggingface_hub import AsyncInferenceClient, HfApi
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from llama_stack.apis.common.content_types import (
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InterleavedContent,
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@ -16,12 +15,10 @@ from llama_stack.apis.common.content_types import (
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)
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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CompletionRequest,
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EmbeddingsResponse,
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EmbeddingTaskType,
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Inference,
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JsonSchemaResponseFormat,
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LogProbConfig,
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Message,
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ResponseFormat,
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@ -38,26 +35,20 @@ from llama_stack.log import get_logger
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from llama_stack.models.llama.sku_list import all_registered_models
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from llama_stack.providers.datatypes import ModelsProtocolPrivate
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from llama_stack.providers.utils.inference.model_registry import (
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build_hf_repo_model_entry,
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ModelRegistryHelper,
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build_hf_repo_model_entry,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_chat_completion_request_to_openai_params,
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convert_completion_request_to_openai_params,
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convert_message_to_openai_dict_new,
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convert_openai_chat_completion_choice,
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convert_openai_chat_completion_stream,
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convert_tooldef_to_openai_tool,
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get_sampling_options,
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OpenAICompatCompletionChoice,
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OpenAICompatCompletionResponse,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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convert_chat_completion_request_to_openai_params,
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convert_openai_chat_completion_choice,
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convert_openai_chat_completion_stream,
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get_sampling_options,
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process_completion_response,
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process_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_model_input_info,
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completion_request_to_prompt_model_input_info,
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)
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@ -85,9 +76,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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def __init__(self) -> None:
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self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
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self.huggingface_repo_to_llama_model_id = {
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model.huggingface_repo: model.descriptor()
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for model in all_registered_models()
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if model.huggingface_repo
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model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo
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}
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async def shutdown(self) -> None:
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@ -114,7 +103,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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if response_format:
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raise ValueError(f"TGI does not support Response Format for completions.")
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raise ValueError("TGI does not support Response Format for completions.")
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if sampling_params is None:
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sampling_params = SamplingParams()
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@ -166,17 +155,13 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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return options
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async def _get_params_for_completion(self, request: CompletionRequest) -> dict:
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prompt, input_tokens = await completion_request_to_prompt_model_input_info(
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request
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)
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prompt, input_tokens = await completion_request_to_prompt_model_input_info(request)
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return dict(
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prompt=prompt,
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stream=request.stream,
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details=True,
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max_new_tokens=self._get_max_new_tokens(
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request.sampling_params, input_tokens
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),
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max_new_tokens=self._get_max_new_tokens(request.sampling_params, input_tokens),
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**self._build_options(request.sampling_params, request.response_format),
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)
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@ -185,16 +170,14 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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params = await self._get_params_for_completion(request)
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async def _generate_and_convert_to_openai_compat():
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s = self.client.text_generation(**params)
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for chunk in s:
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s = await self.client.text_generation(**params)
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async for chunk in s:
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token_result = chunk.token
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finish_reason = None
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if chunk.details:
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finish_reason = chunk.details.finish_reason
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choice = OpenAICompatCompletionChoice(
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text=token_result.text, finish_reason=finish_reason
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)
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choice = OpenAICompatCompletionChoice(text=token_result.text, finish_reason=finish_reason)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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@ -205,7 +188,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
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params = await self._get_params_for_completion(request)
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r = self.client.text_generation(**params)
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r = await self.client.text_generation(**params)
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choice = OpenAICompatCompletionChoice(
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finish_reason=r.details.finish_reason,
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@ -234,9 +217,6 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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if sampling_params is None:
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sampling_params = SamplingParams()
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model = await self.model_store.get_model(model_id)
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from rich.pretty import pprint
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pprint(messages)
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request = ChatCompletionRequest(
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model=model.provider_resource_id,
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messages=messages,
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@ -250,18 +230,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
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params = await convert_chat_completion_request_to_openai_params(request)
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import json
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# print(json.dumps(params, indent=2))
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pprint(params)
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response = self.client.chat.completions.create(**params)
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response = await self.client.chat.completions.create(**params)
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if stream:
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return convert_openai_chat_completion_stream(
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response, enable_incremental_tool_calls=True
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)
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return convert_openai_chat_completion_stream(response, enable_incremental_tool_calls=True)
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else:
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return convert_openai_chat_completion_choice(response.choices[0])
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@ -281,18 +252,16 @@ class TGIAdapter(_HfAdapter):
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logger.info(f"Initializing TGI client with url={config.url}")
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# unfortunately, the TGI async client does not work well with proxies
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# so using sync client for now instead
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self.client = InferenceClient(model=f"{config.url}")
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self.client = AsyncInferenceClient(model=f"{config.url}")
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endpoint_info = self.client.get_endpoint_info()
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endpoint_info = await self.client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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class InferenceAPIAdapter(_HfAdapter):
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async def initialize(self, config: InferenceAPIImplConfig) -> None:
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self.client = AsyncInferenceClient(
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model=config.huggingface_repo, token=config.api_token.get_secret_value()
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)
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self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
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endpoint_info = await self.client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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@ -310,6 +279,4 @@ class InferenceEndpointAdapter(_HfAdapter):
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# Initialize the adapter
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self.client = endpoint.async_client
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self.model_id = endpoint.repository
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self.max_tokens = int(
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endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
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)
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self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"])
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@ -19,7 +19,6 @@ from llama_stack.apis.inference import (
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EmbeddingsResponse,
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EmbeddingTaskType,
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Inference,
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JsonSchemaResponseFormat,
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LogProbConfig,
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Message,
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ResponseFormat,
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@ -36,11 +35,8 @@ from llama_stack.log import get_logger
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_chat_completion_request_to_openai_params,
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convert_message_to_openai_dict_new,
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convert_openai_chat_completion_choice,
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convert_openai_chat_completion_stream,
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convert_tooldef_to_openai_tool,
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get_sampling_options,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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interleaved_content_as_str,
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@ -54,9 +50,7 @@ class LiteLLMOpenAIMixin(
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Inference,
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NeedsRequestProviderData,
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):
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def __init__(
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self, model_entries, api_key_from_config: str, provider_data_api_key_field: str
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):
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def __init__(self, model_entries, api_key_from_config: str, provider_data_api_key_field: str):
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ModelRegistryHelper.__init__(self, model_entries)
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self.api_key_from_config = api_key_from_config
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self.provider_data_api_key_field = provider_data_api_key_field
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@ -96,9 +90,7 @@ class LiteLLMOpenAIMixin(
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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tool_config: Optional[ToolConfig] = None,
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) -> Union[
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ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
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]:
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) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
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if sampling_params is None:
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sampling_params = SamplingParams()
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model = await self.model_store.get_model(model_id)
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|
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@ -6,7 +6,49 @@
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import json
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import logging
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import warnings
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from typing import AsyncGenerator, Dict, Generator, Iterable, List, Optional, Union
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from typing import AsyncGenerator, Dict, Iterable, List, Optional, Union
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from openai import AsyncStream
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from openai.types.chat import (
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ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage,
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)
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from openai.types.chat import (
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ChatCompletionChunk as OpenAIChatCompletionChunk,
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)
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from openai.types.chat import (
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ChatCompletionContentPartImageParam as OpenAIChatCompletionContentPartImageParam,
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)
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from openai.types.chat import (
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ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam,
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)
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from openai.types.chat import (
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ChatCompletionContentPartTextParam as OpenAIChatCompletionContentPartTextParam,
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)
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from openai.types.chat import (
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ChatCompletionMessageParam as OpenAIChatCompletionMessage,
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)
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from openai.types.chat import (
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ChatCompletionMessageToolCall as OpenAIChatCompletionMessageToolCall,
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)
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from openai.types.chat import (
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ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage,
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)
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from openai.types.chat import (
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ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage,
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)
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from openai.types.chat import (
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ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage,
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)
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from openai.types.chat.chat_completion import (
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Choice as OpenAIChoice,
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)
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from openai.types.chat.chat_completion import (
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ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs
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)
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from openai.types.chat.chat_completion_content_part_image_param import (
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ImageURL as OpenAIImageURL,
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)
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from pydantic import BaseModel
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from llama_stack.apis.common.content_types import (
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ImageContentItem,
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@ -23,7 +65,6 @@ from llama_stack.apis.inference import (
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ChatCompletionResponseEventType,
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ChatCompletionResponseStreamChunk,
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CompletionMessage,
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CompletionRequest,
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CompletionResponse,
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CompletionResponseStreamChunk,
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JsonSchemaResponseFormat,
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|
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@ -49,32 +90,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
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decode_assistant_message,
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)
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from openai import AsyncStream, Stream
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from openai.types.chat import (
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ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage,
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ChatCompletionChunk as OpenAIChatCompletionChunk,
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ChatCompletionContentPartImageParam as OpenAIChatCompletionContentPartImageParam,
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ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam,
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ChatCompletionContentPartTextParam as OpenAIChatCompletionContentPartTextParam,
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ChatCompletionMessageParam as OpenAIChatCompletionMessage,
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ChatCompletionMessageToolCall as OpenAIChatCompletionMessageToolCall,
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ChatCompletionMessageToolCallParam as OpenAIChatCompletionMessageToolCallParam,
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ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage,
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ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage,
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ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage,
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)
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||||
from openai.types.chat.chat_completion import (
|
||||
Choice as OpenAIChoice,
|
||||
ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs
|
||||
)
|
||||
from openai.types.chat.chat_completion_content_part_image_param import (
|
||||
ImageURL as OpenAIImageURL,
|
||||
)
|
||||
from openai.types.chat.chat_completion_message_tool_call_param import (
|
||||
Function as OpenAIFunction,
|
||||
)
|
||||
from pydantic import BaseModel
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
|
|
@ -169,16 +184,12 @@ def convert_openai_completion_logprobs(
|
|||
if logprobs.tokens and logprobs.token_logprobs:
|
||||
return [
|
||||
TokenLogProbs(logprobs_by_token={token: token_lp})
|
||||
for token, token_lp in zip(
|
||||
logprobs.tokens, logprobs.token_logprobs, strict=False
|
||||
)
|
||||
for token, token_lp in zip(logprobs.tokens, logprobs.token_logprobs, strict=False)
|
||||
]
|
||||
return None
|
||||
|
||||
|
||||
def convert_openai_completion_logprobs_stream(
|
||||
text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]
|
||||
):
|
||||
def convert_openai_completion_logprobs_stream(text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]):
|
||||
if logprobs is None:
|
||||
return None
|
||||
if isinstance(logprobs, float):
|
||||
|
|
@ -223,9 +234,7 @@ def process_chat_completion_response(
|
|||
if not choice.message or not choice.message.tool_calls:
|
||||
raise ValueError("Tool calls are not present in the response")
|
||||
|
||||
tool_calls = [
|
||||
convert_tool_call(tool_call) for tool_call in choice.message.tool_calls
|
||||
]
|
||||
tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls]
|
||||
if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
|
||||
# If we couldn't parse a tool call, jsonify the tool calls and return them
|
||||
return ChatCompletionResponse(
|
||||
|
|
@ -249,9 +258,7 @@ def process_chat_completion_response(
|
|||
|
||||
# TODO: This does not work well with tool calls for vLLM remote provider
|
||||
# Ref: https://github.com/meta-llama/llama-stack/issues/1058
|
||||
raw_message = decode_assistant_message(
|
||||
text_from_choice(choice), get_stop_reason(choice.finish_reason)
|
||||
)
|
||||
raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason))
|
||||
|
||||
# NOTE: If we do not set tools in chat-completion request, we should not
|
||||
# expect the ToolCall in the response. Instead, we should return the raw
|
||||
|
|
@ -452,17 +459,13 @@ async def process_chat_completion_stream_response(
|
|||
)
|
||||
|
||||
|
||||
async def convert_message_to_openai_dict(
|
||||
message: Message, download: bool = False
|
||||
) -> dict:
|
||||
async def convert_message_to_openai_dict(message: Message, download: bool = False) -> dict:
|
||||
async def _convert_content(content) -> dict:
|
||||
if isinstance(content, ImageContentItem):
|
||||
return {
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": await convert_image_content_to_url(
|
||||
content, download=download
|
||||
),
|
||||
"url": await convert_image_content_to_url(content, download=download),
|
||||
},
|
||||
}
|
||||
else:
|
||||
|
|
@ -541,9 +544,7 @@ async def convert_message_to_openai_dict_new(
|
|||
elif isinstance(content_, ImageContentItem):
|
||||
return OpenAIChatCompletionContentPartImageParam(
|
||||
type="image_url",
|
||||
image_url=OpenAIImageURL(
|
||||
url=await convert_image_content_to_url(content_)
|
||||
),
|
||||
image_url=OpenAIImageURL(url=await convert_image_content_to_url(content_)),
|
||||
)
|
||||
elif isinstance(content_, list):
|
||||
return [await impl(item) for item in content_]
|
||||
|
|
@ -587,9 +588,7 @@ async def convert_message_to_openai_dict_new(
|
|||
"id": tool.call_id,
|
||||
"function": {
|
||||
"name": (
|
||||
tool.tool_name
|
||||
if not isinstance(tool.tool_name, BuiltinTool)
|
||||
else tool.tool_name.value
|
||||
tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value
|
||||
),
|
||||
"arguments": json.dumps(tool.arguments),
|
||||
},
|
||||
|
|
@ -709,11 +708,7 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
|
|||
properties = parameters["properties"]
|
||||
required = []
|
||||
for param_name, param in tool.parameters.items():
|
||||
properties[param_name] = {
|
||||
"type": PYTHON_TYPE_TO_LITELLM_TYPE.get(
|
||||
param.param_type, param.param_type
|
||||
)
|
||||
}
|
||||
properties[param_name] = {"type": PYTHON_TYPE_TO_LITELLM_TYPE.get(param.param_type, param.param_type)}
|
||||
if param.description:
|
||||
properties[param_name].update(description=param.description)
|
||||
if param.default:
|
||||
|
|
@ -834,11 +829,7 @@ def _convert_openai_logprobs(
|
|||
return None
|
||||
|
||||
return [
|
||||
TokenLogProbs(
|
||||
logprobs_by_token={
|
||||
logprobs.token: logprobs.logprob for logprobs in content.top_logprobs
|
||||
}
|
||||
)
|
||||
TokenLogProbs(logprobs_by_token={logprobs.token: logprobs.logprob for logprobs in content.top_logprobs})
|
||||
for content in logprobs.content
|
||||
]
|
||||
|
||||
|
|
@ -876,17 +867,14 @@ def convert_openai_chat_completion_choice(
|
|||
end_of_message = "end_of_message"
|
||||
out_of_tokens = "out_of_tokens"
|
||||
"""
|
||||
assert (
|
||||
hasattr(choice, "message") and choice.message
|
||||
), "error in server response: message not found"
|
||||
assert (
|
||||
hasattr(choice, "finish_reason") and choice.finish_reason
|
||||
), "error in server response: finish_reason not found"
|
||||
assert hasattr(choice, "message") and choice.message, "error in server response: message not found"
|
||||
assert hasattr(choice, "finish_reason") and choice.finish_reason, (
|
||||
"error in server response: finish_reason not found"
|
||||
)
|
||||
|
||||
return ChatCompletionResponse(
|
||||
completion_message=CompletionMessage(
|
||||
content=choice.message.content
|
||||
or "", # CompletionMessage content is not optional
|
||||
content=choice.message.content or "", # CompletionMessage content is not optional
|
||||
stop_reason=_convert_openai_finish_reason(choice.finish_reason),
|
||||
tool_calls=_convert_openai_tool_calls(choice.message.tool_calls),
|
||||
),
|
||||
|
|
@ -895,9 +883,7 @@ def convert_openai_chat_completion_choice(
|
|||
|
||||
|
||||
async def convert_openai_chat_completion_stream(
|
||||
stream: Union[
|
||||
AsyncStream[OpenAIChatCompletionChunk], Stream[OpenAIChatCompletionChunk]
|
||||
],
|
||||
stream: AsyncStream[OpenAIChatCompletionChunk],
|
||||
enable_incremental_tool_calls: bool,
|
||||
) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
|
||||
"""
|
||||
|
|
@ -905,14 +891,6 @@ async def convert_openai_chat_completion_stream(
|
|||
of ChatCompletionResponseStreamChunk.
|
||||
"""
|
||||
|
||||
async def yield_from_stream(stream):
|
||||
if isinstance(stream, AsyncGenerator):
|
||||
async for chunk in stream:
|
||||
yield chunk
|
||||
elif isinstance(stream, Generator):
|
||||
for chunk in stream:
|
||||
yield chunk
|
||||
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.start,
|
||||
|
|
@ -924,7 +902,7 @@ async def convert_openai_chat_completion_stream(
|
|||
stop_reason = None
|
||||
tool_call_idx_to_buffer = {}
|
||||
|
||||
async for chunk in yield_from_stream(stream):
|
||||
async for chunk in stream:
|
||||
choice = chunk.choices[0] # assuming only one choice per chunk
|
||||
|
||||
# we assume there's only one finish_reason in the stream
|
||||
|
|
@ -1092,7 +1070,7 @@ async def convert_openai_chat_completion_stream(
|
|||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
except json.JSONDecodeError as e:
|
||||
except json.JSONDecodeError:
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
|
|
@ -1137,7 +1115,7 @@ async def convert_openai_chat_completion_stream(
|
|||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
except (KeyError, json.JSONDecodeError) as e:
|
||||
except (KeyError, json.JSONDecodeError):
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
|
|
@ -1158,26 +1136,6 @@ async def convert_openai_chat_completion_stream(
|
|||
)
|
||||
|
||||
|
||||
async def convert_completion_request_to_openai_params(
|
||||
request: CompletionRequest,
|
||||
) -> dict:
|
||||
"""
|
||||
Convert a ChatCompletionRequest to an OpenAI API-compatible dictionary.
|
||||
"""
|
||||
input_dict = {}
|
||||
if request.logprobs:
|
||||
input_dict["logprobs"] = request.logprobs.top_k
|
||||
|
||||
return {
|
||||
"model": request.model,
|
||||
"prompt": request.content,
|
||||
**input_dict,
|
||||
"stream": request.stream,
|
||||
**get_sampling_options(request.sampling_params),
|
||||
"n": 1,
|
||||
}
|
||||
|
||||
|
||||
async def convert_chat_completion_request_to_openai_params(
|
||||
request: ChatCompletionRequest,
|
||||
) -> dict:
|
||||
|
|
@ -1186,14 +1144,10 @@ async def convert_chat_completion_request_to_openai_params(
|
|||
"""
|
||||
input_dict = {}
|
||||
|
||||
input_dict["messages"] = [
|
||||
await convert_message_to_openai_dict_new(m) for m in request.messages
|
||||
]
|
||||
input_dict["messages"] = [await convert_message_to_openai_dict_new(m) for m in request.messages]
|
||||
if fmt := request.response_format:
|
||||
if not isinstance(fmt, JsonSchemaResponseFormat):
|
||||
raise ValueError(
|
||||
f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
|
||||
)
|
||||
raise ValueError(f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported.")
|
||||
|
||||
fmt = fmt.json_schema
|
||||
name = fmt["title"]
|
||||
|
|
@ -1217,9 +1171,7 @@ async def convert_chat_completion_request_to_openai_params(
|
|||
}
|
||||
|
||||
if request.tools:
|
||||
input_dict["tools"] = [
|
||||
convert_tooldef_to_openai_tool(tool) for tool in request.tools
|
||||
]
|
||||
input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
|
||||
if request.tool_config.tool_choice:
|
||||
input_dict["tool_choice"] = (
|
||||
request.tool_config.tool_choice.value
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ import re
|
|||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import httpx
|
||||
from PIL import Image as PIL_Image
|
||||
|
||||
from llama_stack.apis.common.content_types import (
|
||||
ImageContentItem,
|
||||
|
|
@ -33,7 +34,6 @@ from llama_stack.apis.inference import (
|
|||
)
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.models.llama.datatypes import (
|
||||
is_multimodal,
|
||||
ModelFamily,
|
||||
RawContent,
|
||||
RawContentItem,
|
||||
|
|
@ -43,6 +43,7 @@ from llama_stack.models.llama.datatypes import (
|
|||
Role,
|
||||
StopReason,
|
||||
ToolPromptFormat,
|
||||
is_multimodal,
|
||||
)
|
||||
from llama_stack.models.llama.llama3.chat_format import ChatFormat
|
||||
from llama_stack.models.llama.llama3.prompt_templates import (
|
||||
|
|
@ -55,7 +56,6 @@ from llama_stack.models.llama.llama3.prompt_templates import (
|
|||
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
|
||||
from llama_stack.models.llama.sku_list import resolve_model
|
||||
from llama_stack.providers.utils.inference import supported_inference_models
|
||||
from PIL import Image as PIL_Image
|
||||
|
||||
log = get_logger(name=__name__, category="inference")
|
||||
|
||||
|
|
@ -129,9 +129,7 @@ async def interleaved_content_convert_to_raw(
|
|||
if image.url.uri.startswith("data"):
|
||||
match = re.match(r"data:image/(\w+);base64,(.+)", image.url.uri)
|
||||
if not match:
|
||||
raise ValueError(
|
||||
f"Invalid data URL format, {image.url.uri[:40]}..."
|
||||
)
|
||||
raise ValueError(f"Invalid data URL format, {image.url.uri[:40]}...")
|
||||
_, image_data = match.groups()
|
||||
data = base64.b64decode(image_data)
|
||||
elif image.url.uri.startswith("file://"):
|
||||
|
|
@ -211,17 +209,13 @@ async def convert_image_content_to_url(
|
|||
|
||||
content, format = await localize_image_content(media)
|
||||
if include_format:
|
||||
return f"data:image/{format};base64," + base64.b64encode(content).decode(
|
||||
"utf-8"
|
||||
)
|
||||
return f"data:image/{format};base64," + base64.b64encode(content).decode("utf-8")
|
||||
else:
|
||||
return base64.b64encode(content).decode("utf-8")
|
||||
|
||||
|
||||
async def completion_request_to_prompt(request: CompletionRequest) -> str:
|
||||
content = augment_content_with_response_format_prompt(
|
||||
request.response_format, request.content
|
||||
)
|
||||
content = augment_content_with_response_format_prompt(request.response_format, request.content)
|
||||
request.content = content
|
||||
request = await convert_request_to_raw(request)
|
||||
|
||||
|
|
@ -233,9 +227,7 @@ async def completion_request_to_prompt(request: CompletionRequest) -> str:
|
|||
async def completion_request_to_prompt_model_input_info(
|
||||
request: CompletionRequest,
|
||||
) -> Tuple[str, int]:
|
||||
content = augment_content_with_response_format_prompt(
|
||||
request.response_format, request.content
|
||||
)
|
||||
content = augment_content_with_response_format_prompt(request.response_format, request.content)
|
||||
request.content = content
|
||||
request = await convert_request_to_raw(request)
|
||||
|
||||
|
|
@ -256,9 +248,7 @@ def augment_content_with_response_format_prompt(response_format, content):
|
|||
return content
|
||||
|
||||
|
||||
async def chat_completion_request_to_prompt(
|
||||
request: ChatCompletionRequest, llama_model: str
|
||||
) -> str:
|
||||
async def chat_completion_request_to_prompt(request: ChatCompletionRequest, llama_model: str) -> str:
|
||||
messages = chat_completion_request_to_messages(request, llama_model)
|
||||
request.messages = messages
|
||||
request = await convert_request_to_raw(request)
|
||||
|
|
@ -266,8 +256,7 @@ async def chat_completion_request_to_prompt(
|
|||
formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
|
||||
model_input = formatter.encode_dialog_prompt(
|
||||
request.messages,
|
||||
tool_prompt_format=request.tool_config.tool_prompt_format
|
||||
or get_default_tool_prompt_format(llama_model),
|
||||
tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
|
||||
)
|
||||
return formatter.tokenizer.decode(model_input.tokens)
|
||||
|
||||
|
|
@ -282,8 +271,7 @@ async def chat_completion_request_to_model_input_info(
|
|||
formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
|
||||
model_input = formatter.encode_dialog_prompt(
|
||||
request.messages,
|
||||
tool_prompt_format=request.tool_config.tool_prompt_format
|
||||
or get_default_tool_prompt_format(llama_model),
|
||||
tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
|
||||
)
|
||||
tokens = []
|
||||
for t in model_input.tokens:
|
||||
|
|
@ -318,8 +306,7 @@ def chat_completion_request_to_messages(
|
|||
return request.messages
|
||||
|
||||
if model.model_family == ModelFamily.llama3_1 or (
|
||||
model.model_family == ModelFamily.llama3_2
|
||||
and is_multimodal(model.core_model_id)
|
||||
model.model_family == ModelFamily.llama3_2 and is_multimodal(model.core_model_id)
|
||||
):
|
||||
# llama3.1 and llama3.2 multimodal models follow the same tool prompt format
|
||||
messages = augment_messages_for_tools_llama_3_1(request)
|
||||
|
|
@ -355,9 +342,7 @@ def augment_messages_for_tools_llama_3_1(
|
|||
if existing_messages[0].role == Role.system.value:
|
||||
existing_system_message = existing_messages.pop(0)
|
||||
|
||||
assert (
|
||||
existing_messages[0].role != Role.system.value
|
||||
), "Should only have 1 system message"
|
||||
assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
|
||||
|
||||
messages = []
|
||||
|
||||
|
|
@ -389,13 +374,9 @@ def augment_messages_for_tools_llama_3_1(
|
|||
if isinstance(existing_system_message.content, str):
|
||||
sys_content += _process(existing_system_message.content)
|
||||
elif isinstance(existing_system_message.content, list):
|
||||
sys_content += "\n".join(
|
||||
[_process(c) for c in existing_system_message.content]
|
||||
)
|
||||
sys_content += "\n".join([_process(c) for c in existing_system_message.content])
|
||||
|
||||
tool_choice_prompt = _get_tool_choice_prompt(
|
||||
request.tool_config.tool_choice, request.tools
|
||||
)
|
||||
tool_choice_prompt = _get_tool_choice_prompt(request.tool_config.tool_choice, request.tools)
|
||||
if tool_choice_prompt:
|
||||
sys_content += "\n" + tool_choice_prompt
|
||||
|
||||
|
|
@ -429,9 +410,7 @@ def augment_messages_for_tools_llama_3_2(
|
|||
if existing_messages[0].role == Role.system.value:
|
||||
existing_system_message = existing_messages.pop(0)
|
||||
|
||||
assert (
|
||||
existing_messages[0].role != Role.system.value
|
||||
), "Should only have 1 system message"
|
||||
assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
|
||||
|
||||
sys_content = ""
|
||||
custom_tools, builtin_tools = [], []
|
||||
|
|
@ -452,16 +431,10 @@ def augment_messages_for_tools_llama_3_2(
|
|||
if custom_tools:
|
||||
fmt = request.tool_config.tool_prompt_format or ToolPromptFormat.python_list
|
||||
if fmt != ToolPromptFormat.python_list:
|
||||
raise ValueError(
|
||||
f"Non supported ToolPromptFormat {request.tool_config.tool_prompt_format}"
|
||||
)
|
||||
raise ValueError(f"Non supported ToolPromptFormat {request.tool_config.tool_prompt_format}")
|
||||
|
||||
system_prompt = None
|
||||
if (
|
||||
existing_system_message
|
||||
and request.tool_config.system_message_behavior
|
||||
== SystemMessageBehavior.replace
|
||||
):
|
||||
if existing_system_message and request.tool_config.system_message_behavior == SystemMessageBehavior.replace:
|
||||
system_prompt = existing_system_message.content
|
||||
|
||||
tool_template = PythonListCustomToolGenerator().gen(custom_tools, system_prompt)
|
||||
|
|
@ -470,16 +443,11 @@ def augment_messages_for_tools_llama_3_2(
|
|||
sys_content += "\n"
|
||||
|
||||
if existing_system_message and (
|
||||
request.tool_config.system_message_behavior == SystemMessageBehavior.append
|
||||
or not custom_tools
|
||||
request.tool_config.system_message_behavior == SystemMessageBehavior.append or not custom_tools
|
||||
):
|
||||
sys_content += interleaved_content_as_str(
|
||||
existing_system_message.content, sep="\n"
|
||||
)
|
||||
sys_content += interleaved_content_as_str(existing_system_message.content, sep="\n")
|
||||
|
||||
tool_choice_prompt = _get_tool_choice_prompt(
|
||||
request.tool_config.tool_choice, request.tools
|
||||
)
|
||||
tool_choice_prompt = _get_tool_choice_prompt(request.tool_config.tool_choice, request.tools)
|
||||
if tool_choice_prompt:
|
||||
sys_content += "\n" + tool_choice_prompt
|
||||
|
||||
|
|
@ -487,15 +455,11 @@ def augment_messages_for_tools_llama_3_2(
|
|||
return messages
|
||||
|
||||
|
||||
def _get_tool_choice_prompt(
|
||||
tool_choice: ToolChoice | str, tools: List[ToolDefinition]
|
||||
) -> str:
|
||||
def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: List[ToolDefinition]) -> str:
|
||||
if tool_choice == ToolChoice.auto:
|
||||
return ""
|
||||
elif tool_choice == ToolChoice.required:
|
||||
return (
|
||||
"You MUST use one of the provided functions/tools to answer the user query."
|
||||
)
|
||||
return "You MUST use one of the provided functions/tools to answer the user query."
|
||||
elif tool_choice == ToolChoice.none:
|
||||
# tools are already not passed in
|
||||
return ""
|
||||
|
|
@ -507,14 +471,11 @@ def _get_tool_choice_prompt(
|
|||
def get_default_tool_prompt_format(model: str) -> ToolPromptFormat:
|
||||
llama_model = resolve_model(model)
|
||||
if llama_model is None:
|
||||
log.warning(
|
||||
f"Could not resolve model {model}, defaulting to json tool prompt format"
|
||||
)
|
||||
log.warning(f"Could not resolve model {model}, defaulting to json tool prompt format")
|
||||
return ToolPromptFormat.json
|
||||
|
||||
if llama_model.model_family == ModelFamily.llama3_1 or (
|
||||
llama_model.model_family == ModelFamily.llama3_2
|
||||
and is_multimodal(llama_model.core_model_id)
|
||||
llama_model.model_family == ModelFamily.llama3_2 and is_multimodal(llama_model.core_model_id)
|
||||
):
|
||||
# llama3.1 and llama3.2 multimodal models follow the same tool prompt format
|
||||
return ToolPromptFormat.json
|
||||
|
|
|
|||
|
|
@ -120,16 +120,13 @@ def client_with_models(
|
|||
judge_model_id,
|
||||
):
|
||||
client = llama_stack_client
|
||||
from rich.pretty import pprint
|
||||
|
||||
providers = [p for p in client.providers.list() if p.api == "inference"]
|
||||
pprint(providers)
|
||||
assert len(providers) > 0, "No inference providers found"
|
||||
inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]
|
||||
|
||||
model_ids = {m.identifier for m in client.models.list()}
|
||||
model_ids.update(m.provider_resource_id for m in client.models.list())
|
||||
pprint(model_ids)
|
||||
|
||||
if text_model_id and text_model_id not in model_ids:
|
||||
client.models.register(model_id=text_model_id, provider_id=inference_providers[0])
|
||||
|
|
|
|||
|
|
@ -8,9 +8,9 @@
|
|||
import os
|
||||
|
||||
import pytest
|
||||
from pydantic import BaseModel
|
||||
|
||||
from llama_stack.models.llama.sku_list import resolve_model
|
||||
from pydantic import BaseModel
|
||||
|
||||
from ..test_cases.test_case import TestCase
|
||||
|
||||
|
|
@ -29,9 +29,7 @@ def skip_if_model_doesnt_support_completion(client_with_models, model_id):
|
|||
"remote::gemini",
|
||||
"remote::groq",
|
||||
):
|
||||
pytest.skip(
|
||||
f"Model {model_id} hosted by {provider.provider_type} doesn't support completion"
|
||||
)
|
||||
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support completion")
|
||||
|
||||
|
||||
def get_llama_model(client_with_models, model_id):
|
||||
|
|
@ -112,9 +110,7 @@ def test_text_completion_streaming(client_with_models, text_model_id, test_case)
|
|||
"inference:completion:stop_sequence",
|
||||
],
|
||||
)
|
||||
def test_text_completion_stop_sequence(
|
||||
client_with_models, text_model_id, inference_provider_type, test_case
|
||||
):
|
||||
def test_text_completion_stop_sequence(client_with_models, text_model_id, inference_provider_type, test_case):
|
||||
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
|
||||
# This is only supported/tested for remote vLLM: https://github.com/meta-llama/llama-stack/issues/1771
|
||||
if inference_provider_type != "remote::vllm":
|
||||
|
|
@ -141,9 +137,7 @@ def test_text_completion_stop_sequence(
|
|||
"inference:completion:log_probs",
|
||||
],
|
||||
)
|
||||
def test_text_completion_log_probs_non_streaming(
|
||||
client_with_models, text_model_id, inference_provider_type, test_case
|
||||
):
|
||||
def test_text_completion_log_probs_non_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
|
||||
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
|
||||
if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
|
||||
pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
|
||||
|
|
@ -162,9 +156,7 @@ def test_text_completion_log_probs_non_streaming(
|
|||
},
|
||||
)
|
||||
assert response.logprobs, "Logprobs should not be empty"
|
||||
assert (
|
||||
1 <= len(response.logprobs) <= 5
|
||||
) # each token has 1 logprob and here max_tokens=5
|
||||
assert 1 <= len(response.logprobs) <= 5 # each token has 1 logprob and here max_tokens=5
|
||||
assert all(len(logprob.logprobs_by_token) == 1 for logprob in response.logprobs)
|
||||
|
||||
|
||||
|
|
@ -174,9 +166,7 @@ def test_text_completion_log_probs_non_streaming(
|
|||
"inference:completion:log_probs",
|
||||
],
|
||||
)
|
||||
def test_text_completion_log_probs_streaming(
|
||||
client_with_models, text_model_id, inference_provider_type, test_case
|
||||
):
|
||||
def test_text_completion_log_probs_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
|
||||
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
|
||||
if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
|
||||
pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
|
||||
|
|
@ -198,9 +188,7 @@ def test_text_completion_log_probs_streaming(
|
|||
for chunk in streamed_content:
|
||||
if chunk.delta: # if there's a token, we expect logprobs
|
||||
assert chunk.logprobs, "Logprobs should not be empty"
|
||||
assert all(
|
||||
len(logprob.logprobs_by_token) == 1 for logprob in chunk.logprobs
|
||||
)
|
||||
assert all(len(logprob.logprobs_by_token) == 1 for logprob in chunk.logprobs)
|
||||
else: # no token, no logprobs
|
||||
assert not chunk.logprobs, "Logprobs should be empty"
|
||||
|
||||
|
|
@ -211,13 +199,9 @@ def test_text_completion_log_probs_streaming(
|
|||
"inference:completion:structured_output",
|
||||
],
|
||||
)
|
||||
def test_text_completion_structured_output(
|
||||
client_with_models, text_model_id, test_case, inference_provider_type
|
||||
):
|
||||
def test_text_completion_structured_output(client_with_models, text_model_id, test_case, inference_provider_type):
|
||||
if inference_provider_type == "remote::tgi":
|
||||
pytest.xfail(
|
||||
f"{inference_provider_type} doesn't support structured outputs yet"
|
||||
)
|
||||
pytest.xfail(f"{inference_provider_type} doesn't support structured outputs yet")
|
||||
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
|
||||
|
||||
class AnswerFormat(BaseModel):
|
||||
|
|
@ -254,9 +238,7 @@ def test_text_completion_structured_output(
|
|||
"inference:chat_completion:non_streaming_02",
|
||||
],
|
||||
)
|
||||
def test_text_chat_completion_non_streaming(
|
||||
client_with_models, text_model_id, test_case
|
||||
):
|
||||
def test_text_chat_completion_non_streaming(client_with_models, text_model_id, test_case):
|
||||
tc = TestCase(test_case)
|
||||
question = tc["question"]
|
||||
expected = tc["expected"]
|
||||
|
|
@ -282,18 +264,15 @@ def test_text_chat_completion_non_streaming(
|
|||
"inference:chat_completion:ttft",
|
||||
],
|
||||
)
|
||||
def test_text_chat_completion_first_token_profiling(
|
||||
client_with_models, text_model_id, test_case
|
||||
):
|
||||
def test_text_chat_completion_first_token_profiling(client_with_models, text_model_id, test_case):
|
||||
tc = TestCase(test_case)
|
||||
|
||||
messages = tc["messages"]
|
||||
if os.environ.get(
|
||||
"DEBUG_TTFT"
|
||||
): # debugging print number of tokens in input, ideally around 800
|
||||
from llama_stack.apis.inference import Message
|
||||
if os.environ.get("DEBUG_TTFT"): # debugging print number of tokens in input, ideally around 800
|
||||
from pydantic import TypeAdapter
|
||||
|
||||
from llama_stack.apis.inference import Message
|
||||
|
||||
tokenizer, formatter = get_llama_tokenizer()
|
||||
typed_messages = [TypeAdapter(Message).validate_python(m) for m in messages]
|
||||
encoded = formatter.encode_dialog_prompt(typed_messages, None)
|
||||
|
|
@ -307,9 +286,7 @@ def test_text_chat_completion_first_token_profiling(
|
|||
message_content = response.completion_message.content.lower().strip()
|
||||
assert len(message_content) > 0
|
||||
|
||||
if os.environ.get(
|
||||
"DEBUG_TTFT"
|
||||
): # debugging print number of tokens in response, ideally around 150
|
||||
if os.environ.get("DEBUG_TTFT"): # debugging print number of tokens in response, ideally around 150
|
||||
tokenizer, formatter = get_llama_tokenizer()
|
||||
encoded = formatter.encode_content(message_content)
|
||||
raise ValueError(len(encoded.tokens) if encoded and encoded.tokens else 0)
|
||||
|
|
@ -332,9 +309,7 @@ def test_text_chat_completion_streaming(client_with_models, text_model_id, test_
|
|||
messages=[{"role": "user", "content": question}],
|
||||
stream=True,
|
||||
)
|
||||
streamed_content = [
|
||||
str(chunk.event.delta.text.lower().strip()) for chunk in response
|
||||
]
|
||||
streamed_content = [str(chunk.event.delta.text.lower().strip()) for chunk in response]
|
||||
assert len(streamed_content) > 0
|
||||
assert expected.lower() in "".join(streamed_content)
|
||||
|
||||
|
|
@ -345,9 +320,7 @@ def test_text_chat_completion_streaming(client_with_models, text_model_id, test_
|
|||
"inference:chat_completion:tool_calling",
|
||||
],
|
||||
)
|
||||
def test_text_chat_completion_with_tool_calling_and_non_streaming(
|
||||
client_with_models, text_model_id, test_case
|
||||
):
|
||||
def test_text_chat_completion_with_tool_calling_and_non_streaming(client_with_models, text_model_id, test_case):
|
||||
tc = TestCase(test_case)
|
||||
|
||||
response = client_with_models.inference.chat_completion(
|
||||
|
|
@ -361,10 +334,7 @@ def test_text_chat_completion_with_tool_calling_and_non_streaming(
|
|||
assert response.completion_message.role == "assistant"
|
||||
|
||||
assert len(response.completion_message.tool_calls) == 1
|
||||
assert (
|
||||
response.completion_message.tool_calls[0].tool_name
|
||||
== tc["tools"][0]["tool_name"]
|
||||
)
|
||||
assert response.completion_message.tool_calls[0].tool_name == tc["tools"][0]["tool_name"]
|
||||
assert response.completion_message.tool_calls[0].arguments == tc["expected"]
|
||||
|
||||
|
||||
|
|
@ -387,9 +357,7 @@ def extract_tool_invocation_content(response):
|
|||
"inference:chat_completion:tool_calling",
|
||||
],
|
||||
)
|
||||
def test_text_chat_completion_with_tool_calling_and_streaming(
|
||||
client_with_models, text_model_id, test_case
|
||||
):
|
||||
def test_text_chat_completion_with_tool_calling_and_streaming(client_with_models, text_model_id, test_case):
|
||||
tc = TestCase(test_case)
|
||||
|
||||
response = client_with_models.inference.chat_completion(
|
||||
|
|
@ -415,9 +383,7 @@ def test_text_chat_completion_with_tool_choice_required(
|
|||
client_with_models, text_model_id, test_case, inference_provider_type
|
||||
):
|
||||
if inference_provider_type == "remote::tgi":
|
||||
pytest.xfail(
|
||||
f"{inference_provider_type} doesn't support tool_choice 'required' parameter yet"
|
||||
)
|
||||
pytest.xfail(f"{inference_provider_type} doesn't support tool_choice 'required' parameter yet")
|
||||
|
||||
tc = TestCase(test_case)
|
||||
|
||||
|
|
@ -442,9 +408,7 @@ def test_text_chat_completion_with_tool_choice_required(
|
|||
"inference:chat_completion:tool_calling",
|
||||
],
|
||||
)
|
||||
def test_text_chat_completion_with_tool_choice_none(
|
||||
client_with_models, text_model_id, test_case
|
||||
):
|
||||
def test_text_chat_completion_with_tool_choice_none(client_with_models, text_model_id, test_case):
|
||||
tc = TestCase(test_case)
|
||||
|
||||
response = client_with_models.inference.chat_completion(
|
||||
|
|
@ -464,13 +428,9 @@ def test_text_chat_completion_with_tool_choice_none(
|
|||
"inference:chat_completion:structured_output",
|
||||
],
|
||||
)
|
||||
def test_text_chat_completion_structured_output(
|
||||
client_with_models, text_model_id, test_case, inference_provider_type
|
||||
):
|
||||
def test_text_chat_completion_structured_output(client_with_models, text_model_id, test_case, inference_provider_type):
|
||||
if inference_provider_type == "remote::tgi":
|
||||
pytest.xfail(
|
||||
f"{inference_provider_type} doesn't support structured outputs yet"
|
||||
)
|
||||
pytest.xfail(f"{inference_provider_type} doesn't support structured outputs yet")
|
||||
|
||||
class NBAStats(BaseModel):
|
||||
year_for_draft: int
|
||||
|
|
|
|||
|
|
@ -27,9 +27,7 @@ def base64_image_url(base64_image_data, image_path):
|
|||
return f"data:image/{image_path.suffix[1:]};base64,{base64_image_data}"
|
||||
|
||||
|
||||
# @pytest.mark.xfail(
|
||||
# reason="This test is failing because the image is not being downloaded correctly."
|
||||
# )
|
||||
@pytest.mark.xfail(reason="This test is failing because the image is not being downloaded correctly.")
|
||||
def test_image_chat_completion_non_streaming(client_with_models, vision_model_id):
|
||||
message = {
|
||||
"role": "user",
|
||||
|
|
@ -58,9 +56,7 @@ def test_image_chat_completion_non_streaming(client_with_models, vision_model_id
|
|||
assert any(expected in message_content for expected in {"dog", "puppy", "pup"})
|
||||
|
||||
|
||||
# @pytest.mark.xfail(
|
||||
# reason="This test is failing because the image is not being downloaded correctly."
|
||||
# )
|
||||
@pytest.mark.xfail(reason="This test is failing because the image is not being downloaded correctly.")
|
||||
def test_image_chat_completion_streaming(client_with_models, vision_model_id):
|
||||
message = {
|
||||
"role": "user",
|
||||
|
|
@ -92,9 +88,7 @@ def test_image_chat_completion_streaming(client_with_models, vision_model_id):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("type_", ["url"])
|
||||
def test_image_chat_completion_base64(
|
||||
client_with_models, vision_model_id, base64_image_data, base64_image_url, type_
|
||||
):
|
||||
def test_image_chat_completion_base64(client_with_models, vision_model_id, base64_image_data, base64_image_url, type_):
|
||||
image_spec = {
|
||||
"url": {
|
||||
"type": "image",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue