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 ```
276 lines
10 KiB
Python
276 lines
10 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import AsyncGenerator, List, Optional, Union
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.tokenizer import Tokenizer
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from together import Together
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from llama_stack.apis.common.content_types import InterleavedContent
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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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Inference,
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LogProbConfig,
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Message,
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ResponseFormat,
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ResponseFormatType,
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SamplingParams,
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ToolChoice,
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ToolConfig,
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.distribution.request_headers import NeedsRequestProviderData
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from llama_stack.models.llama.datatypes import CoreModelId
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from llama_stack.providers.utils.inference.model_registry import (
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ModelRegistryHelper,
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build_model_alias,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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convert_message_to_openai_dict,
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get_sampling_options,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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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_prompt,
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completion_request_to_prompt,
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content_has_media,
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interleaved_content_as_str,
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request_has_media,
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)
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from .config import TogetherImplConfig
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MODEL_ALIASES = [
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build_model_alias(
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"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
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CoreModelId.llama3_1_8b_instruct.value,
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),
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build_model_alias(
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"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
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CoreModelId.llama3_1_70b_instruct.value,
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),
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build_model_alias(
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"meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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CoreModelId.llama3_1_405b_instruct.value,
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),
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build_model_alias(
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"meta-llama/Llama-3.2-3B-Instruct-Turbo",
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CoreModelId.llama3_2_3b_instruct.value,
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),
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build_model_alias(
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"meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
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CoreModelId.llama3_2_11b_vision_instruct.value,
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),
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build_model_alias(
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"meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
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CoreModelId.llama3_2_90b_vision_instruct.value,
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),
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build_model_alias(
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"meta-llama/Llama-3.3-70B-Instruct-Turbo",
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CoreModelId.llama3_3_70b_instruct.value,
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),
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build_model_alias(
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"meta-llama/Meta-Llama-Guard-3-8B",
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CoreModelId.llama_guard_3_8b.value,
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),
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build_model_alias(
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"meta-llama/Llama-Guard-3-11B-Vision-Turbo",
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CoreModelId.llama_guard_3_11b_vision.value,
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),
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]
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class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
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def __init__(self, config: TogetherImplConfig) -> None:
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ModelRegistryHelper.__init__(self, MODEL_ALIASES)
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self.config = config
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self.formatter = ChatFormat(Tokenizer.get_instance())
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def completion(
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self,
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model_id: str,
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content: InterleavedContent,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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model = await self.model_store.get_model(model_id)
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request = CompletionRequest(
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model=model.provider_resource_id,
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content=content,
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sampling_params=sampling_params,
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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)
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if stream:
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return self._stream_completion(request)
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else:
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return await self._nonstream_completion(request)
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def _get_client(self) -> Together:
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together_api_key = None
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if self.config.api_key is not None:
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together_api_key = self.config.api_key.get_secret_value()
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else:
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provider_data = self.get_request_provider_data()
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if provider_data is None or not provider_data.together_api_key:
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raise ValueError(
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'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": <your api key>}'
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)
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together_api_key = provider_data.together_api_key
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return Together(api_key=together_api_key)
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async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse:
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params = await self._get_params(request)
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r = self._get_client().completions.create(**params)
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return process_completion_response(r, self.formatter)
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async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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# if we shift to TogetherAsyncClient, we won't need this wrapper
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async def _to_async_generator():
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s = self._get_client().completions.create(**params)
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for chunk in s:
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yield chunk
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stream = _to_async_generator()
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async for chunk in process_completion_stream_response(stream, self.formatter):
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yield chunk
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def _build_options(
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self,
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sampling_params: Optional[SamplingParams],
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logprobs: Optional[LogProbConfig],
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fmt: ResponseFormat,
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) -> dict:
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options = get_sampling_options(sampling_params)
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if fmt:
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if fmt.type == ResponseFormatType.json_schema.value:
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options["response_format"] = {
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"type": "json_object",
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"schema": fmt.json_schema,
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}
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elif fmt.type == ResponseFormatType.grammar.value:
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raise NotImplementedError("Grammar response format not supported yet")
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else:
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raise ValueError(f"Unknown response format {fmt.type}")
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if logprobs and logprobs.top_k:
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if logprobs.top_k != 1:
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raise ValueError(
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f"Unsupported value: Together only supports logprobs top_k=1. {logprobs.top_k} was provided",
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)
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options["logprobs"] = 1
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return options
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async def chat_completion(
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self,
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model_id: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = None,
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response_format: Optional[ResponseFormat] = None,
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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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) -> AsyncGenerator:
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model = await self.model_store.get_model(model_id)
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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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sampling_params=sampling_params,
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tools=tools or [],
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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tool_config=tool_config,
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)
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if stream:
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return self._stream_chat_completion(request)
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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(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 = self._get_client().chat.completions.create(**params)
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else:
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r = self._get_client().completions.create(**params)
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return process_chat_completion_response(r, self.formatter, request)
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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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# if we shift to TogetherAsyncClient, we won't need this wrapper
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async def _to_async_generator():
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if "messages" in params:
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s = self._get_client().chat.completions.create(**params)
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else:
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s = self._get_client().completions.create(**params)
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for chunk in s:
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yield chunk
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stream = _to_async_generator()
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async for chunk in process_chat_completion_stream_response(stream, self.formatter, request):
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yield chunk
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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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if isinstance(request, ChatCompletionRequest):
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if media_present:
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input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages]
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else:
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input_dict["prompt"] = await chat_completion_request_to_prompt(
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request, self.get_llama_model(request.model), self.formatter
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)
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else:
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assert not media_present, "Together does not support media for Completion requests"
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input_dict["prompt"] = await completion_request_to_prompt(request, self.formatter)
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return {
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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(request.sampling_params, request.logprobs, request.response_format),
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}
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async def embeddings(
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self,
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model_id: str,
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contents: List[InterleavedContent],
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) -> EmbeddingsResponse:
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model = await self.model_store.get_model(model_id)
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assert all(not content_has_media(content) for content in contents), (
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"Together does not support media for embeddings"
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)
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r = 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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)
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embeddings = [item.embedding for item in r.data]
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return EmbeddingsResponse(embeddings=embeddings)
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