forked from phoenix-oss/llama-stack-mirror
314ee09ae3
llama-models should have extremely minimal cruft. Its sole purpose should be didactic -- show the simplest implementation of the llama models and document the prompt formats, etc. This PR is the complement to https://github.com/meta-llama/llama-models/pull/279 ## Test Plan Ensure all `llama` CLI `model` sub-commands work: ```bash llama model list llama model download --model-id ... llama model prompt-format -m ... ``` Ran tests: ```bash cd tests/client-sdk LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/ LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/ LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/ ``` Create a fresh venv `uv venv && source .venv/bin/activate` and run `llama stack build --template fireworks --image-type venv` followed by `llama stack run together --image-type venv` <-- the server runs Also checked that the OpenAPI generator can run and there is no change in the generated files as a result. ```bash cd docs/openapi_generator sh run_openapi_generator.sh ```
391 lines
14 KiB
Python
391 lines
14 KiB
Python
# 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.
|
|
|
|
import logging
|
|
from typing import AsyncGenerator, List, Optional, Union
|
|
|
|
import httpx
|
|
from llama_models.llama3.api.chat_format import ChatFormat
|
|
from llama_models.llama3.api.tokenizer import Tokenizer
|
|
from ollama import AsyncClient
|
|
|
|
from llama_stack.apis.common.content_types import (
|
|
ImageContentItem,
|
|
InterleavedContent,
|
|
TextContentItem,
|
|
)
|
|
from llama_stack.apis.inference import (
|
|
ChatCompletionRequest,
|
|
ChatCompletionResponse,
|
|
CompletionRequest,
|
|
EmbeddingsResponse,
|
|
Inference,
|
|
LogProbConfig,
|
|
Message,
|
|
ResponseFormat,
|
|
SamplingParams,
|
|
ToolChoice,
|
|
ToolConfig,
|
|
ToolDefinition,
|
|
ToolPromptFormat,
|
|
)
|
|
from llama_stack.apis.models import Model, ModelType
|
|
from llama_stack.models.llama.datatypes import CoreModelId
|
|
from llama_stack.providers.datatypes import ModelsProtocolPrivate
|
|
from llama_stack.providers.utils.inference.model_registry import (
|
|
ModelRegistryHelper,
|
|
build_model_alias,
|
|
build_model_alias_with_just_provider_model_id,
|
|
)
|
|
from llama_stack.providers.utils.inference.openai_compat import (
|
|
OpenAICompatCompletionChoice,
|
|
OpenAICompatCompletionResponse,
|
|
get_sampling_options,
|
|
process_chat_completion_response,
|
|
process_chat_completion_stream_response,
|
|
process_completion_response,
|
|
process_completion_stream_response,
|
|
)
|
|
from llama_stack.providers.utils.inference.prompt_adapter import (
|
|
chat_completion_request_to_prompt,
|
|
completion_request_to_prompt,
|
|
content_has_media,
|
|
convert_image_content_to_url,
|
|
interleaved_content_as_str,
|
|
request_has_media,
|
|
)
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
model_aliases = [
|
|
build_model_alias(
|
|
"llama3.1:8b-instruct-fp16",
|
|
CoreModelId.llama3_1_8b_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.1:8b",
|
|
CoreModelId.llama3_1_8b_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.1:70b-instruct-fp16",
|
|
CoreModelId.llama3_1_70b_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.1:70b",
|
|
CoreModelId.llama3_1_70b_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.1:405b-instruct-fp16",
|
|
CoreModelId.llama3_1_405b_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.1:405b",
|
|
CoreModelId.llama3_1_405b_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.2:1b-instruct-fp16",
|
|
CoreModelId.llama3_2_1b_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.2:1b",
|
|
CoreModelId.llama3_2_1b_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.2:3b-instruct-fp16",
|
|
CoreModelId.llama3_2_3b_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.2:3b",
|
|
CoreModelId.llama3_2_3b_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.2-vision:11b-instruct-fp16",
|
|
CoreModelId.llama3_2_11b_vision_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.2-vision:latest",
|
|
CoreModelId.llama3_2_11b_vision_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.2-vision:90b-instruct-fp16",
|
|
CoreModelId.llama3_2_90b_vision_instruct.value,
|
|
),
|
|
build_model_alias_with_just_provider_model_id(
|
|
"llama3.2-vision:90b",
|
|
CoreModelId.llama3_2_90b_vision_instruct.value,
|
|
),
|
|
build_model_alias(
|
|
"llama3.3:70b",
|
|
CoreModelId.llama3_3_70b_instruct.value,
|
|
),
|
|
# The Llama Guard models don't have their full fp16 versions
|
|
# so we are going to alias their default version to the canonical SKU
|
|
build_model_alias(
|
|
"llama-guard3:8b",
|
|
CoreModelId.llama_guard_3_8b.value,
|
|
),
|
|
build_model_alias(
|
|
"llama-guard3:1b",
|
|
CoreModelId.llama_guard_3_1b.value,
|
|
),
|
|
]
|
|
|
|
|
|
class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|
def __init__(self, url: str) -> None:
|
|
self.register_helper = ModelRegistryHelper(model_aliases)
|
|
self.url = url
|
|
self.formatter = ChatFormat(Tokenizer.get_instance())
|
|
|
|
@property
|
|
def client(self) -> AsyncClient:
|
|
return AsyncClient(host=self.url)
|
|
|
|
async def initialize(self) -> None:
|
|
log.info(f"checking connectivity to Ollama at `{self.url}`...")
|
|
try:
|
|
await self.client.ps()
|
|
except httpx.ConnectError as e:
|
|
raise RuntimeError(
|
|
"Ollama Server is not running, start it using `ollama serve` in a separate terminal"
|
|
) from e
|
|
|
|
async def shutdown(self) -> None:
|
|
pass
|
|
|
|
async def unregister_model(self, model_id: str) -> None:
|
|
pass
|
|
|
|
async def completion(
|
|
self,
|
|
model_id: str,
|
|
content: InterleavedContent,
|
|
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.provider_resource_id,
|
|
content=content,
|
|
sampling_params=sampling_params,
|
|
response_format=response_format,
|
|
stream=stream,
|
|
logprobs=logprobs,
|
|
)
|
|
if stream:
|
|
return self._stream_completion(request)
|
|
else:
|
|
return await self._nonstream_completion(request)
|
|
|
|
async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
|
|
params = await self._get_params(request)
|
|
|
|
async def _generate_and_convert_to_openai_compat():
|
|
s = await self.client.generate(**params)
|
|
async for chunk in s:
|
|
choice = OpenAICompatCompletionChoice(
|
|
finish_reason=chunk["done_reason"] if chunk["done"] else None,
|
|
text=chunk["response"],
|
|
)
|
|
yield OpenAICompatCompletionResponse(
|
|
choices=[choice],
|
|
)
|
|
|
|
stream = _generate_and_convert_to_openai_compat()
|
|
async for chunk in process_completion_stream_response(stream, self.formatter):
|
|
yield chunk
|
|
|
|
async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
|
|
params = await self._get_params(request)
|
|
r = await self.client.generate(**params)
|
|
|
|
choice = OpenAICompatCompletionChoice(
|
|
finish_reason=r["done_reason"] if r["done"] else None,
|
|
text=r["response"],
|
|
)
|
|
response = OpenAICompatCompletionResponse(
|
|
choices=[choice],
|
|
)
|
|
|
|
return process_completion_response(response, self.formatter)
|
|
|
|
async def chat_completion(
|
|
self,
|
|
model_id: str,
|
|
messages: List[Message],
|
|
sampling_params: Optional[SamplingParams] = SamplingParams(),
|
|
response_format: Optional[ResponseFormat] = None,
|
|
tools: Optional[List[ToolDefinition]] = None,
|
|
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
|
|
tool_prompt_format: Optional[ToolPromptFormat] = None,
|
|
stream: Optional[bool] = False,
|
|
logprobs: Optional[LogProbConfig] = None,
|
|
tool_config: Optional[ToolConfig] = None,
|
|
) -> AsyncGenerator:
|
|
model = await self.model_store.get_model(model_id)
|
|
request = ChatCompletionRequest(
|
|
model=model.provider_resource_id,
|
|
messages=messages,
|
|
sampling_params=sampling_params,
|
|
tools=tools or [],
|
|
stream=stream,
|
|
logprobs=logprobs,
|
|
response_format=response_format,
|
|
tool_config=tool_config,
|
|
)
|
|
if stream:
|
|
return self._stream_chat_completion(request)
|
|
else:
|
|
return await self._nonstream_chat_completion(request)
|
|
|
|
async def _get_params(self, request: Union[ChatCompletionRequest, CompletionRequest]) -> dict:
|
|
sampling_options = get_sampling_options(request.sampling_params)
|
|
# This is needed since the Ollama API expects num_predict to be set
|
|
# for early truncation instead of max_tokens.
|
|
if sampling_options.get("max_tokens") is not None:
|
|
sampling_options["num_predict"] = sampling_options["max_tokens"]
|
|
|
|
input_dict = {}
|
|
media_present = request_has_media(request)
|
|
if isinstance(request, ChatCompletionRequest):
|
|
if media_present:
|
|
contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages]
|
|
# flatten the list of lists
|
|
input_dict["messages"] = [item for sublist in contents for item in sublist]
|
|
else:
|
|
input_dict["raw"] = True
|
|
input_dict["prompt"] = await chat_completion_request_to_prompt(
|
|
request,
|
|
self.register_helper.get_llama_model(request.model),
|
|
self.formatter,
|
|
)
|
|
else:
|
|
assert not media_present, "Ollama does not support media for Completion requests"
|
|
input_dict["prompt"] = await completion_request_to_prompt(request, self.formatter)
|
|
input_dict["raw"] = True
|
|
|
|
if fmt := request.response_format:
|
|
if fmt.type == "json_schema":
|
|
input_dict["format"] = fmt.json_schema
|
|
elif fmt.type == "grammar":
|
|
raise NotImplementedError("Grammar response format is not supported")
|
|
else:
|
|
raise ValueError(f"Unknown response format type: {fmt.type}")
|
|
|
|
return {
|
|
"model": request.model,
|
|
**input_dict,
|
|
"options": sampling_options,
|
|
"stream": request.stream,
|
|
}
|
|
|
|
async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
|
|
params = await self._get_params(request)
|
|
if "messages" in params:
|
|
r = await self.client.chat(**params)
|
|
else:
|
|
r = await self.client.generate(**params)
|
|
|
|
if "message" in r:
|
|
choice = OpenAICompatCompletionChoice(
|
|
finish_reason=r["done_reason"] if r["done"] else None,
|
|
text=r["message"]["content"],
|
|
)
|
|
else:
|
|
choice = OpenAICompatCompletionChoice(
|
|
finish_reason=r["done_reason"] if r["done"] else None,
|
|
text=r["response"],
|
|
)
|
|
response = OpenAICompatCompletionResponse(
|
|
choices=[choice],
|
|
)
|
|
return process_chat_completion_response(response, self.formatter, request)
|
|
|
|
async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
|
|
params = await self._get_params(request)
|
|
|
|
async def _generate_and_convert_to_openai_compat():
|
|
if "messages" in params:
|
|
s = await self.client.chat(**params)
|
|
else:
|
|
s = await self.client.generate(**params)
|
|
async for chunk in s:
|
|
if "message" in chunk:
|
|
choice = OpenAICompatCompletionChoice(
|
|
finish_reason=chunk["done_reason"] if chunk["done"] else None,
|
|
text=chunk["message"]["content"],
|
|
)
|
|
else:
|
|
choice = OpenAICompatCompletionChoice(
|
|
finish_reason=chunk["done_reason"] if chunk["done"] else None,
|
|
text=chunk["response"],
|
|
)
|
|
yield OpenAICompatCompletionResponse(
|
|
choices=[choice],
|
|
)
|
|
|
|
stream = _generate_and_convert_to_openai_compat()
|
|
async for chunk in process_chat_completion_stream_response(stream, self.formatter, request):
|
|
yield chunk
|
|
|
|
async def embeddings(
|
|
self,
|
|
model_id: str,
|
|
contents: List[InterleavedContent],
|
|
) -> EmbeddingsResponse:
|
|
model = await self.model_store.get_model(model_id)
|
|
|
|
assert all(not content_has_media(content) for content in contents), (
|
|
"Ollama does not support media for embeddings"
|
|
)
|
|
response = await self.client.embed(
|
|
model=model.provider_resource_id,
|
|
input=[interleaved_content_as_str(content) for content in contents],
|
|
)
|
|
embeddings = response["embeddings"]
|
|
|
|
return EmbeddingsResponse(embeddings=embeddings)
|
|
|
|
async def register_model(self, model: Model) -> Model:
|
|
async def check_model_availability(model_id: str):
|
|
response = await self.client.ps()
|
|
available_models = [m["model"] for m in response["models"]]
|
|
if model_id not in available_models:
|
|
raise ValueError(
|
|
f"Model '{model_id}' is not available in Ollama. Available models: {', '.join(available_models)}"
|
|
)
|
|
|
|
if model.model_type == ModelType.embedding:
|
|
await check_model_availability(model.provider_resource_id)
|
|
return model
|
|
|
|
model = await self.register_helper.register_model(model)
|
|
await check_model_availability(model.provider_resource_id)
|
|
|
|
return model
|
|
|
|
|
|
async def convert_message_to_openai_dict_for_ollama(message: Message) -> List[dict]:
|
|
async def _convert_content(content) -> dict:
|
|
if isinstance(content, ImageContentItem):
|
|
return {
|
|
"role": message.role,
|
|
"images": [await convert_image_content_to_url(content, download=True, include_format=False)],
|
|
}
|
|
else:
|
|
text = content.text if isinstance(content, TextContentItem) else content
|
|
assert isinstance(text, str)
|
|
return {
|
|
"role": message.role,
|
|
"content": text,
|
|
}
|
|
|
|
if isinstance(message.content, list):
|
|
return [await _convert_content(c) for c in message.content]
|
|
else:
|
|
return [await _convert_content(message.content)]
|