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fix model provider validation and inference params

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Dinesh Yeduguru 2024-11-12 10:13:43 -08:00 • committed by Dinesh Yeduguru
parent 95b7f57d92
commit d69f4f8635
5 changed files with 34 additions and 25 deletions
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12
llama_stack/providers/inline/inference/meta_reference/inference.py
View file

@ -46,7 +46,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
self.generator = Llama.build(self.config)
async def register_model(self, model: Model) -> None:
if model.identifier != self.model.descriptor():
if model.provider_resource_id != self.model.descriptor():
raise ValueError(
f"Model mismatch: {model.identifier} != {self.model.descriptor()}"
)
@ -68,7 +68,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -79,7 +79,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
request = CompletionRequest(
model=model,
model=model_id,
content=content,
sampling_params=sampling_params,
response_format=response_format,
@ -186,7 +186,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -201,7 +201,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
# wrapper request to make it easier to pass around (internal only, not exposed to API)
request = ChatCompletionRequest(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -386,7 +386,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

10
llama_stack/providers/inline/inference/vllm/vllm.py
View file

@ -110,7 +110,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -120,7 +120,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
log.info("vLLM completion")
messages = [UserMessage(content=content)]
return self.chat_completion(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
stream=stream,
@ -129,7 +129,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
tools: Optional[List[ToolDefinition]] = None,
@ -144,7 +144,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
assert self.engine is not None
request = ChatCompletionRequest(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -215,7 +215,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
yield chunk
async def embeddings(
self, model: str, contents: list[InterleavedTextMedia]
self, model_id: str, contents: list[InterleavedTextMedia]
) -> EmbeddingsResponse:
log.info("vLLM embeddings")
# TODO

16
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -66,8 +66,10 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
pass
async def register_model(self, model: Model) -> None:
if model.identifier not in OLLAMA_SUPPORTED_MODELS:
raise ValueError(f"Model {model.identifier} is not supported by Ollama")
if model.provider_resource_id not in OLLAMA_SUPPORTED_MODELS:
raise ValueError(
f"Model {model.provider_resource_id} is not supported by Ollama"
)
async def list_models(self) -> List[Model]:
ollama_to_llama = {v: k for k, v in OLLAMA_SUPPORTED_MODELS.items()}
@ -94,7 +96,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -102,7 +104,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
request = CompletionRequest(
model=model,
model=model_id,
content=content,
sampling_params=sampling_params,
stream=stream,
@ -148,7 +150,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -159,7 +161,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
request = ChatCompletionRequest(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -271,7 +273,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

19
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -45,8 +45,15 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
self.client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
async def register_model(self, model: Model) -> None:
for running_model in self.client.models.list():
repo = running_model.id
pass
async def shutdown(self) -> None:
pass
async def list_models(self) -> List[Model]:
models = []
for model in self.client.models.list():
repo = model.id
if repo not in self.huggingface_repo_to_llama_model_id:
print(f"Unknown model served by vllm: {repo}")
continue
@ -67,7 +74,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -78,7 +85,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -89,7 +96,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
request = ChatCompletionRequest(
model=model,
model=model_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -173,7 +180,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

2
llama_stack/providers/tests/inference/fixtures.py
View file

@ -49,7 +49,7 @@ def inference_meta_reference(inference_model) -> ProviderFixture:
providers=[
Provider(
provider_id=f"meta-reference-{i}",
provider_type="meta-reference",
provider_type="inline::meta-reference",
config=MetaReferenceInferenceConfig(
model=m,
max_seq_len=4096,