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Inference to use provider resource id to register and validate (#428)

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This PR changes the way model id gets translated to the final model name
that gets passed through the provider.
Major changes include:
1) Providers are responsible for registering an object and as part of
the registration returning the object with the correct provider specific
name of the model provider_resource_id
2) To help with the common look ups different names a new ModelLookup
class is created.



Tested all inference providers including together, fireworks, vllm,
ollama, meta reference and bedrock
This commit is contained in:
Dinesh Yeduguru 2024-11-12 20:02:00 -08:00 • committed by GitHub
parent e51107e019
commit fdff24e77a
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
21 changed files with 460 additions and 290 deletions
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40
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -7,11 +7,15 @@
from typing import * # noqa: F403
from botocore.client import BaseClient
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.model_registry import (
build_model_alias,
ModelRegistryHelper,
)
from llama_stack.apis.inference import * # noqa: F403
@ -19,19 +23,26 @@ from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
from llama_stack.providers.utils.bedrock.client import create_bedrock_client
BEDROCK_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
"Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
"Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
}
model_aliases = [
build_model_alias(
"meta.llama3-1-8b-instruct-v1:0",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_alias(
"meta.llama3-1-70b-instruct-v1:0",
CoreModelId.llama3_1_70b_instruct.value,
),
build_model_alias(
"meta.llama3-1-405b-instruct-v1:0",
CoreModelId.llama3_1_405b_instruct.value,
),
]
# NOTE: this is not quite tested after the recent refactors
class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=BEDROCK_SUPPORTED_MODELS
)
ModelRegistryHelper.__init__(self, model_aliases)
self._config = config
self._client = create_bedrock_client(config)
@ -49,7 +60,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -286,7 +297,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -298,8 +309,9 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
) -> Union[
ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
]:
model = await self.model_store.get_model(model_id)
request = ChatCompletionRequest(
model=model,
model=model.provider_resource_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -404,7 +416,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
pass
def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> Dict:
bedrock_model = self.map_to_provider_model(request.model)
bedrock_model = request.model
inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
request.sampling_params
)
@ -433,7 +445,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

30
llama_stack/providers/remote/inference/databricks/databricks.py
View file

@ -6,6 +6,8 @@
from typing import AsyncGenerator
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message
@ -15,7 +17,10 @@ from openai import OpenAI
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.model_registry import (
build_model_alias,
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
@ -28,16 +33,23 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import DatabricksImplConfig
DATABRICKS_SUPPORTED_MODELS = {
"Llama3.1-70B-Instruct": "databricks-meta-llama-3-1-70b-instruct",
"Llama3.1-405B-Instruct": "databricks-meta-llama-3-1-405b-instruct",
}
model_aliases = [
build_model_alias(
"databricks-meta-llama-3-1-70b-instruct",
CoreModelId.llama3_1_70b_instruct.value,
),
build_model_alias(
"databricks-meta-llama-3-1-405b-instruct",
CoreModelId.llama3_1_405b_instruct.value,
),
]
class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
def __init__(self, config: DatabricksImplConfig) -> None:
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=DATABRICKS_SUPPORTED_MODELS
self,
model_aliases=model_aliases,
)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())
@ -113,8 +125,10 @@ class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
def _get_params(self, request: ChatCompletionRequest) -> dict:
return {
"model": self.map_to_provider_model(request.model),
"prompt": chat_completion_request_to_prompt(request, self.formatter),
"model": request.model,
"prompt": chat_completion_request_to_prompt(
request, self.get_llama_model(request.model), self.formatter
),
"stream": request.stream,
**get_sampling_options(request.sampling_params),
}

76
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -7,14 +7,17 @@
from typing import AsyncGenerator
from fireworks.client import Fireworks
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.model_registry import (
build_model_alias,
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
@ -31,25 +34,52 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import FireworksImplConfig
FIREWORKS_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
"Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
"Llama3.1-405B-Instruct": "fireworks/llama-v3p1-405b-instruct",
"Llama3.2-1B-Instruct": "fireworks/llama-v3p2-1b-instruct",
"Llama3.2-3B-Instruct": "fireworks/llama-v3p2-3b-instruct",
"Llama3.2-11B-Vision-Instruct": "fireworks/llama-v3p2-11b-vision-instruct",
"Llama3.2-90B-Vision-Instruct": "fireworks/llama-v3p2-90b-vision-instruct",
"Llama-Guard-3-8B": "fireworks/llama-guard-3-8b",
}
model_aliases = [
build_model_alias(
"fireworks/llama-v3p1-8b-instruct",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_alias(
"fireworks/llama-v3p1-70b-instruct",
CoreModelId.llama3_1_70b_instruct.value,
),
build_model_alias(
"fireworks/llama-v3p1-405b-instruct",
CoreModelId.llama3_1_405b_instruct.value,
),
build_model_alias(
"fireworks/llama-v3p2-1b-instruct",
CoreModelId.llama3_2_3b_instruct.value,
),
build_model_alias(
"fireworks/llama-v3p2-3b-instruct",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_model_alias(
"fireworks/llama-v3p2-11b-vision-instruct",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_model_alias(
"fireworks/llama-v3p2-90b-vision-instruct",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
build_model_alias(
"fireworks/llama-guard-3-8b",
CoreModelId.llama_guard_3_8b.value,
),
build_model_alias(
"fireworks/llama-guard-3-11b-vision",
CoreModelId.llama_guard_3_11b_vision.value,
),
]
class FireworksInferenceAdapter(
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: FireworksImplConfig) -> None:
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=FIREWORKS_SUPPORTED_MODELS
)
ModelRegistryHelper.__init__(self, model_aliases)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())
@ -74,15 +104,16 @@ class FireworksInferenceAdapter(
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = CompletionRequest(
model=model,
model=model.provider_resource_id,
content=content,
sampling_params=sampling_params,
response_format=response_format,
@ -138,7 +169,7 @@ class FireworksInferenceAdapter(
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
tools: Optional[List[ToolDefinition]] = None,
@ -148,8 +179,9 @@ class FireworksInferenceAdapter(
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = ChatCompletionRequest(
model=model,
model=model.provider_resource_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -207,7 +239,7 @@ class FireworksInferenceAdapter(
]
else:
input_dict["prompt"] = chat_completion_request_to_prompt(
request, self.formatter
request, self.get_llama_model(request.model), self.formatter
)
else:
assert (
@ -221,7 +253,7 @@ class FireworksInferenceAdapter(
input_dict["prompt"] = input_dict["prompt"][len("<|begin_of_text|>") :]
return {
"model": self.map_to_provider_model(request.model),
"model": request.model,
**input_dict,
"stream": request.stream,
**self._build_options(request.sampling_params, request.response_format),
@ -229,7 +261,7 @@ class FireworksInferenceAdapter(
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

99
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -7,15 +7,20 @@
from typing import AsyncGenerator
import httpx
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from ollama import AsyncClient
from llama_stack.providers.utils.inference.model_registry import (
build_model_alias,
ModelRegistryHelper,
)
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
@ -33,19 +38,45 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
request_has_media,
)
OLLAMA_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
"Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
"Llama3.2-1B-Instruct": "llama3.2:1b-instruct-fp16",
"Llama3.2-3B-Instruct": "llama3.2:3b-instruct-fp16",
"Llama-Guard-3-8B": "llama-guard3:8b",
"Llama-Guard-3-1B": "llama-guard3:1b",
"Llama3.2-11B-Vision-Instruct": "x/llama3.2-vision:11b-instruct-fp16",
}
model_aliases = [
build_model_alias(
"llama3.1:8b-instruct-fp16",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_alias(
"llama3.1:70b-instruct-fp16",
CoreModelId.llama3_1_70b_instruct.value,
),
build_model_alias(
"llama3.2:1b-instruct-fp16",
CoreModelId.llama3_2_1b_instruct.value,
),
build_model_alias(
"llama3.2:3b-instruct-fp16",
CoreModelId.llama3_2_3b_instruct.value,
),
build_model_alias(
"llama-guard3:8b",
CoreModelId.llama_guard_3_8b.value,
),
build_model_alias(
"llama-guard3:1b",
CoreModelId.llama_guard_3_1b.value,
),
build_model_alias(
"x/llama3.2-vision:11b-instruct-fp16",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
]
class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
class OllamaInferenceAdapter(Inference, ModelRegistryHelper, ModelsProtocolPrivate):
def __init__(self, url: str) -> None:
ModelRegistryHelper.__init__(
self,
model_aliases=model_aliases,
)
self.url = url
self.formatter = ChatFormat(Tokenizer.get_instance())
@ -65,44 +96,18 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def shutdown(self) -> None:
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")
async def list_models(self) -> List[Model]:
ollama_to_llama = {v: k for k, v in OLLAMA_SUPPORTED_MODELS.items()}
ret = []
res = await self.client.ps()
for r in res["models"]:
if r["model"] not in ollama_to_llama:
print(f"Ollama is running a model unknown to Llama Stack: {r['model']}")
continue
llama_model = ollama_to_llama[r["model"]]
print(f"Found model {llama_model} in Ollama")
ret.append(
Model(
identifier=llama_model,
metadata={
"ollama_model": r["model"],
},
)
)
return ret
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = CompletionRequest(
model=model,
model=model.provider_resource_id,
content=content,
sampling_params=sampling_params,
stream=stream,
@ -148,7 +153,7 @@ 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,
@ -158,8 +163,10 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
print(f"model={model}")
request = ChatCompletionRequest(
model=model,
model=model.provider_resource_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -197,7 +204,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
else:
input_dict["raw"] = True
input_dict["prompt"] = chat_completion_request_to_prompt(
request, self.formatter
request, self.get_llama_model(request.model), self.formatter
)
else:
assert (
@ -207,7 +214,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
input_dict["raw"] = True
return {
"model": OLLAMA_SUPPORTED_MODELS[request.model],
"model": request.model,
**input_dict,
"options": sampling_options,
"stream": request.stream,
@ -271,7 +278,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

71
llama_stack/providers/remote/inference/together/together.py
View file

@ -6,6 +6,8 @@
from typing import AsyncGenerator
from llama_models.datatypes import CoreModelId
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message
@ -15,7 +17,10 @@ from together import Together
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.model_registry import (
build_model_alias,
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
@ -33,25 +38,47 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import TogetherImplConfig
TOGETHER_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
"Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
"Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
"Llama3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct-Turbo",
"Llama3.2-11B-Vision-Instruct": "meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
"Llama3.2-90B-Vision-Instruct": "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
"Llama-Guard-3-8B": "meta-llama/Meta-Llama-Guard-3-8B",
"Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision-Turbo",
}
model_aliases = [
build_model_alias(
"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_alias(
"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
CoreModelId.llama3_1_70b_instruct.value,
),
build_model_alias(
"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
CoreModelId.llama3_1_405b_instruct.value,
),
build_model_alias(
"meta-llama/Llama-3.2-3B-Instruct-Turbo",
CoreModelId.llama3_2_3b_instruct.value,
),
build_model_alias(
"meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_model_alias(
"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
build_model_alias(
"meta-llama/Meta-Llama-Guard-3-8B",
CoreModelId.llama_guard_3_8b.value,
),
build_model_alias(
"meta-llama/Llama-Guard-3-11B-Vision-Turbo",
CoreModelId.llama_guard_3_11b_vision.value,
),
]
class TogetherInferenceAdapter(
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: TogetherImplConfig) -> None:
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=TOGETHER_SUPPORTED_MODELS
)
ModelRegistryHelper.__init__(self, model_aliases)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())
@ -63,15 +90,16 @@ class TogetherInferenceAdapter(
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = CompletionRequest(
model=model,
model=model.provider_resource_id,
content=content,
sampling_params=sampling_params,
response_format=response_format,
@ -135,7 +163,7 @@ class TogetherInferenceAdapter(
async def chat_completion(
self,
model: str,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
tools: Optional[List[ToolDefinition]] = None,
@ -145,8 +173,9 @@ class TogetherInferenceAdapter(
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = ChatCompletionRequest(
model=model,
model=model.provider_resource_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -204,7 +233,7 @@ class TogetherInferenceAdapter(
]
else:
input_dict["prompt"] = chat_completion_request_to_prompt(
request, self.formatter
request, self.get_llama_model(request.model), self.formatter
)
else:
assert (
@ -213,7 +242,7 @@ class TogetherInferenceAdapter(
input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
return {
"model": self.map_to_provider_model(request.model),
"model": request.model,
**input_dict,
"stream": request.stream,
**self._build_options(request.sampling_params, request.response_format),
@ -221,7 +250,7 @@ class TogetherInferenceAdapter(
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

71
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -8,13 +8,17 @@ from typing import AsyncGenerator
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import all_registered_models, resolve_model
from llama_models.sku_list import all_registered_models
from openai import OpenAI
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
from llama_stack.providers.datatypes import ModelsProtocolPrivate
from llama_stack.providers.utils.inference.model_registry import (
build_model_alias,
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
@ -30,44 +34,36 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import VLLMInferenceAdapterConfig
class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
def build_model_aliases():
return [
build_model_alias(
model.huggingface_repo,
model.descriptor(),
)
for model in all_registered_models()
if model.huggingface_repo
]
class VLLMInferenceAdapter(Inference, ModelRegistryHelper, ModelsProtocolPrivate):
def __init__(self, config: VLLMInferenceAdapterConfig) -> None:
ModelRegistryHelper.__init__(
self,
model_aliases=build_model_aliases(),
)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())
self.client = None
self.huggingface_repo_to_llama_model_id = {
model.huggingface_repo: model.descriptor()
for model in all_registered_models()
if model.huggingface_repo
}
async def initialize(self) -> None:
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
if repo not in self.huggingface_repo_to_llama_model_id:
print(f"Unknown model served by vllm: {repo}")
continue
identifier = self.huggingface_repo_to_llama_model_id[repo]
if identifier == model.provider_resource_id:
print(
f"Verified that model {model.provider_resource_id} is being served by vLLM"
)
return
raise ValueError(
f"Model {model.provider_resource_id} is not being served by vLLM"
)
async def shutdown(self) -> None:
pass
async def completion(
self,
model: str,
model_id: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
@ -78,7 +74,7 @@ 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,
@ -88,8 +84,9 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
model = await self.model_store.get_model(model_id)
request = ChatCompletionRequest(
model=model,
model=model.provider_resource_id,
messages=messages,
sampling_params=sampling_params,
tools=tools or [],
@ -141,10 +138,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
if "max_tokens" not in options:
options["max_tokens"] = self.config.max_tokens
model = resolve_model(request.model)
if model is None:
raise ValueError(f"Unknown model: {request.model}")
input_dict = {}
media_present = request_has_media(request)
if isinstance(request, ChatCompletionRequest):
@ -156,16 +149,20 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
]
else:
input_dict["prompt"] = chat_completion_request_to_prompt(
request, self.formatter
request, self.get_llama_model(request.model), self.formatter
)
else:
assert (
not media_present
), "Together does not support media for Completion requests"
input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
input_dict["prompt"] = completion_request_to_prompt(
request,
self.get_llama_model(request.model),
self.formatter,
)
return {
"model": model.huggingface_repo,
"model": request.model,
**input_dict,
"stream": request.stream,
**options,
@ -173,7 +170,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
async def embeddings(
self,
model: str,
model_id: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()