mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-07-29 07:14:20 +00:00
add deepinfra inference provider
This commit is contained in:
Iskren Chernev
parent
6b094b72d3
commit
4a336ca8ba
5 changed files with 321 additions and 0 deletions
|
|
@ -0,0 +1,10 @@
|
|||
name: local-deepinfra
|
||||
distribution_spec:
|
||||
description: Use DeepInfra for running LLM inference
|
||||
providers:
|
||||
inference: remote::deepinfra
|
||||
memory: meta-reference
|
||||
safety: meta-reference
|
||||
agents: meta-reference
|
||||
telemetry: meta-reference
|
||||
image_type: conda
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from .config import DeepInfraImplConfig
|
||||
from .deepinfra import DeepInfraInferenceAdapter
|
||||
|
||||
async def get_adapter_impl(config: DeepInfraImplConfig, _deps):
|
||||
assert isinstance(
|
||||
config, DeepInfraImplConfig
|
||||
), f"Unexpected config type: {type(config)}"
|
||||
impl = DeepInfraInferenceAdapter(config)
|
||||
await impl.initialize()
|
||||
return impl
|
||||
22
llama_stack/providers/adapters/inference/deepinfra/config.py
Normal file
22
llama_stack/providers/adapters/inference/deepinfra/config.py
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from llama_models.schema_utils import json_schema_type
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class DeepInfraImplConfig(BaseModel):
|
||||
url: str = Field(
|
||||
default="https://api.deepinfra.com/v1/openai",
|
||||
description="The URL for the DeepInfra model serving endpoint",
|
||||
)
|
||||
api_token: str = Field(
|
||||
default=None,
|
||||
description="The DeepInfra API token",
|
||||
)
|
||||
262
llama_stack/providers/adapters/inference/deepinfra/deepinfra.py
Normal file
262
llama_stack/providers/adapters/inference/deepinfra/deepinfra.py
Normal file
|
|
@ -0,0 +1,262 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import AsyncGenerator
|
||||
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
from llama_models.llama3.api.chat_format import ChatFormat
|
||||
|
||||
from llama_models.llama3.api.datatypes import Message, StopReason
|
||||
from llama_models.llama3.api.tokenizer import Tokenizer
|
||||
from llama_models.sku_list import resolve_model
|
||||
|
||||
from llama_stack.apis.inference import * # noqa: F403
|
||||
from llama_stack.providers.utils.inference.augment_messages import (
|
||||
augment_messages_for_tools,
|
||||
)
|
||||
|
||||
from .config import DeepInfraImplConfig
|
||||
|
||||
DEEPINFRA_SUPPORTED_MODELS = {
|
||||
"Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
|
||||
"Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
|
||||
"Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
|
||||
"Llama3.2-1B-Instruct": "meta-llama/Llama-3.2-1B-Instruct",
|
||||
"Llama3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct",
|
||||
"Llama3.2-11B-Instruct": "meta-llama/Llama-3.2-11B-Vision-Instruct",
|
||||
"Llama3.2-90B-Instruct": "meta-llama/Llama-3.2-90B-Vision-Instruct",
|
||||
}
|
||||
|
||||
|
||||
class DeepInfraInferenceAdapter(Inference):
|
||||
def __init__(self, config: DeepInfraImplConfig) -> None:
|
||||
self.config = config
|
||||
tokenizer = Tokenizer.get_instance()
|
||||
self.formatter = ChatFormat(tokenizer)
|
||||
|
||||
@property
|
||||
def client(self) -> AsyncOpenAI:
|
||||
return AsyncOpenAI(
|
||||
base_url=self.config.url,
|
||||
api_key=self.config.api_token
|
||||
)
|
||||
|
||||
async def initialize(self) -> None:
|
||||
return
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
pass
|
||||
|
||||
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
|
||||
# these are the model names the Llama Stack will use to route requests to this provider
|
||||
# perform validation here if necessary
|
||||
pass
|
||||
|
||||
async def completion(self, request: CompletionRequest) -> AsyncGenerator:
|
||||
raise NotImplementedError()
|
||||
|
||||
def _messages_to_deepinfra_messages(self, messages: list[Message]) -> list:
|
||||
deepinfra_messages = []
|
||||
for message in messages:
|
||||
if message.role == "ipython":
|
||||
role = "tool"
|
||||
else:
|
||||
role = message.role
|
||||
deepinfra_messages.append({"role": role, "content": message.content})
|
||||
|
||||
return deepinfra_messages
|
||||
|
||||
def resolve_deepinfra_model(self, model_name: str) -> str:
|
||||
model = resolve_model(model_name)
|
||||
assert (
|
||||
model is not None
|
||||
and model.descriptor(shorten_default_variant=True)
|
||||
in DEEPINFRA_SUPPORTED_MODELS
|
||||
), f"Unsupported model: {model_name}, use one of the supported models: {','.join(DEEPINFRA_SUPPORTED_MODELS.keys())}"
|
||||
|
||||
return DEEPINFRA_SUPPORTED_MODELS.get(
|
||||
model.descriptor(shorten_default_variant=True)
|
||||
)
|
||||
|
||||
def get_deepinfra_chat_options(self, request: ChatCompletionRequest) -> dict:
|
||||
options = {}
|
||||
if request.sampling_params is not None:
|
||||
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
|
||||
if getattr(request.sampling_params, attr):
|
||||
options[attr] = getattr(request.sampling_params, attr)
|
||||
|
||||
return options
|
||||
|
||||
async def chat_completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[Message],
|
||||
sampling_params: Optional[SamplingParams] = SamplingParams(),
|
||||
tools: Optional[List[ToolDefinition]] = None,
|
||||
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
|
||||
tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
|
||||
stream: Optional[bool] = False,
|
||||
logprobs: Optional[LogProbConfig] = None,
|
||||
) -> AsyncGenerator:
|
||||
request = ChatCompletionRequest(
|
||||
model=model,
|
||||
messages=messages,
|
||||
sampling_params=sampling_params,
|
||||
tools=tools or [],
|
||||
tool_choice=tool_choice,
|
||||
tool_prompt_format=tool_prompt_format,
|
||||
stream=stream,
|
||||
logprobs=logprobs,
|
||||
)
|
||||
|
||||
messages = augment_messages_for_tools(request)
|
||||
options = self.get_deepinfra_chat_options(request)
|
||||
deepinfra_model = self.resolve_deepinfra_model(request.model)
|
||||
|
||||
if not request.stream:
|
||||
|
||||
r = await self.client.chat.completions.create(
|
||||
model=deepinfra_model,
|
||||
messages=self._messages_to_deepinfra_messages(messages),
|
||||
stream=False,
|
||||
**options,
|
||||
)
|
||||
|
||||
stop_reason = None
|
||||
if r.choices[0].finish_reason:
|
||||
if r.choices[0].finish_reason == "stop":
|
||||
stop_reason = StopReason.end_of_turn
|
||||
elif r.choices[0].finish_reason == "length":
|
||||
stop_reason = StopReason.out_of_tokens
|
||||
|
||||
completion_message = self.formatter.decode_assistant_message_from_content(
|
||||
r.choices[0].message.content, stop_reason
|
||||
)
|
||||
yield ChatCompletionResponse(
|
||||
completion_message=completion_message,
|
||||
logprobs=None,
|
||||
)
|
||||
else:
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.start,
|
||||
delta="",
|
||||
)
|
||||
)
|
||||
|
||||
buffer = ""
|
||||
ipython = False
|
||||
stop_reason = None
|
||||
|
||||
async for chunk in self.client.chat.completions.create(
|
||||
model=deepinfra_model,
|
||||
messages=self._messages_to_deepinfra_messages(messages),
|
||||
stream=True,
|
||||
**options,
|
||||
):
|
||||
if chunk.choices[0].finish_reason:
|
||||
if (
|
||||
stop_reason is None
|
||||
and chunk.choices[0].finish_reason == "stop"
|
||||
):
|
||||
stop_reason = StopReason.end_of_turn
|
||||
elif (
|
||||
stop_reason is None
|
||||
and chunk.choices[0].finish_reason == "length"
|
||||
):
|
||||
stop_reason = StopReason.out_of_tokens
|
||||
break
|
||||
|
||||
text = chunk.choices[0].delta.content
|
||||
|
||||
if text is None:
|
||||
continue
|
||||
|
||||
# check if its a tool call ( aka starts with <|python_tag|> )
|
||||
if not ipython and text.startswith("<|python_tag|>"):
|
||||
ipython = True
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=ToolCallDelta(
|
||||
content="",
|
||||
parse_status=ToolCallParseStatus.started,
|
||||
),
|
||||
)
|
||||
)
|
||||
buffer += text
|
||||
continue
|
||||
|
||||
if ipython:
|
||||
if text == "<|eot_id|>":
|
||||
stop_reason = StopReason.end_of_turn
|
||||
text = ""
|
||||
continue
|
||||
elif text == "<|eom_id|>":
|
||||
stop_reason = StopReason.end_of_message
|
||||
text = ""
|
||||
continue
|
||||
|
||||
buffer += text
|
||||
delta = ToolCallDelta(
|
||||
content=text,
|
||||
parse_status=ToolCallParseStatus.in_progress,
|
||||
)
|
||||
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=delta,
|
||||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
else:
|
||||
buffer += text
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=text,
|
||||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
|
||||
# parse tool calls and report errors
|
||||
message = self.formatter.decode_assistant_message_from_content(
|
||||
buffer, stop_reason
|
||||
)
|
||||
parsed_tool_calls = len(message.tool_calls) > 0
|
||||
if ipython and not parsed_tool_calls:
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=ToolCallDelta(
|
||||
content="",
|
||||
parse_status=ToolCallParseStatus.failure,
|
||||
),
|
||||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
|
||||
for tool_call in message.tool_calls:
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=ToolCallDelta(
|
||||
content=tool_call,
|
||||
parse_status=ToolCallParseStatus.success,
|
||||
),
|
||||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.complete,
|
||||
delta="",
|
||||
stop_reason=stop_reason,
|
||||
)
|
||||
)
|
||||
|
|
@ -115,6 +115,17 @@ def available_providers() -> List[ProviderSpec]:
|
|||
config_class="llama_stack.providers.adapters.inference.databricks.DatabricksImplConfig",
|
||||
),
|
||||
),
|
||||
remote_provider_spec(
|
||||
api=Api.inference,
|
||||
adapter=AdapterSpec(
|
||||
adapter_type="deepinfra",
|
||||
pip_packages=[
|
||||
"openai",
|
||||
],
|
||||
module="llama_stack.providers.adapters.inference.deepinfra",
|
||||
config_class="llama_stack.providers.adapters.inference.deepinfra.DeepInfraImplConfig",
|
||||
),
|
||||
),
|
||||
InlineProviderSpec(
|
||||
api=Api.inference,
|
||||
provider_type="vllm",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue