From f2e18826b6f9c0122eb714f3bda404da19d2355b Mon Sep 17 00:00:00 2001 From: Hassan El Mghari Date: Wed, 28 Aug 2024 16:07:13 -0700 Subject: [PATCH] Together AI basic integration (#43) * working! * accounting for eos --- llama_toolchain/distribution/registry.py | 9 + llama_toolchain/inference/providers.py | 9 + .../inference/together/__init__.py | 8 + llama_toolchain/inference/together/config.py | 20 ++ .../inference/together/together.py | 318 ++++++++++++++++++ 5 files changed, 364 insertions(+) create mode 100644 llama_toolchain/inference/together/__init__.py create mode 100644 llama_toolchain/inference/together/config.py create mode 100644 llama_toolchain/inference/together/together.py diff --git a/llama_toolchain/distribution/registry.py b/llama_toolchain/distribution/registry.py index 7db320076..466b02f7b 100644 --- a/llama_toolchain/distribution/registry.py +++ b/llama_toolchain/distribution/registry.py @@ -59,6 +59,15 @@ def available_distribution_specs() -> List[DistributionSpec]: Api.agentic_system: providers[Api.agentic_system]["meta-reference"], }, ), + DistributionSpec( + spec_id="remote-together", + description="Use Together.ai for running LLM inference", + provider_specs={ + Api.inference: providers[Api.inference]["together"], + Api.safety: providers[Api.safety]["meta-reference"], + Api.agentic_system: providers[Api.agentic_system]["meta-reference"], + }, + ), ] diff --git a/llama_toolchain/inference/providers.py b/llama_toolchain/inference/providers.py index 757335495..6a8b97e81 100644 --- a/llama_toolchain/inference/providers.py +++ b/llama_toolchain/inference/providers.py @@ -45,4 +45,13 @@ def available_inference_providers() -> List[ProviderSpec]: module="llama_toolchain.inference.fireworks", config_class="llama_toolchain.inference.fireworks.FireworksImplConfig", ), + InlineProviderSpec( + api=Api.inference, + provider_id="together", + pip_packages=[ + "together", + ], + module="llama_toolchain.inference.together", + config_class="llama_toolchain.inference.together.TogetherImplConfig", + ), ] diff --git a/llama_toolchain/inference/together/__init__.py b/llama_toolchain/inference/together/__init__.py new file mode 100644 index 000000000..5be75efcc --- /dev/null +++ b/llama_toolchain/inference/together/__init__.py @@ -0,0 +1,8 @@ +# 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 TogetherImplConfig # noqa +from .together import get_provider_impl # noqa diff --git a/llama_toolchain/inference/together/config.py b/llama_toolchain/inference/together/config.py new file mode 100644 index 000000000..03ee047d2 --- /dev/null +++ b/llama_toolchain/inference/together/config.py @@ -0,0 +1,20 @@ +# 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 llama_models.schema_utils import json_schema_type +from pydantic import BaseModel, Field + + +@json_schema_type +class TogetherImplConfig(BaseModel): + url: str = Field( + default="https://api.together.xyz/v1", + description="The URL for the Together AI server", + ) + api_key: str = Field( + default="", + description="The Together AI API Key", + ) diff --git a/llama_toolchain/inference/together/together.py b/llama_toolchain/inference/together/together.py new file mode 100644 index 000000000..e7ccf623e --- /dev/null +++ b/llama_toolchain/inference/together/together.py @@ -0,0 +1,318 @@ +# 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 uuid +from typing import AsyncGenerator, Dict + +from llama_models.llama3.api.datatypes import ( + BuiltinTool, + CompletionMessage, + Message, + StopReason, + ToolCall, +) +from llama_models.llama3.api.tool_utils import ToolUtils +from llama_models.sku_list import resolve_model +from together import Together + +from llama_toolchain.distribution.datatypes import Api, ProviderSpec +from llama_toolchain.inference.api import ( + ChatCompletionRequest, + ChatCompletionResponse, + ChatCompletionResponseEvent, + ChatCompletionResponseEventType, + ChatCompletionResponseStreamChunk, + CompletionRequest, + Inference, + ToolCallDelta, + ToolCallParseStatus, +) + +from .config import TogetherImplConfig + +TOGETHER_SUPPORTED_MODELS = { + "Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", + "Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", + "Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", +} + + +async def get_provider_impl( + config: TogetherImplConfig, _deps: Dict[Api, ProviderSpec] +) -> Inference: + assert isinstance( + config, TogetherImplConfig + ), f"Unexpected config type: {type(config)}" + impl = TogetherInference(config) + await impl.initialize() + return impl + + +class TogetherInference(Inference): + def __init__(self, config: TogetherImplConfig) -> None: + self.config = config + + @property + def client(self) -> Together: + return Together(api_key=self.config.api_key) + + async def initialize(self) -> None: + return + + async def shutdown(self) -> None: + pass + + async def completion(self, request: CompletionRequest) -> AsyncGenerator: + raise NotImplementedError() + + def _messages_to_together_messages(self, messages: list[Message]) -> list: + together_messages = [] + for message in messages: + if message.role == "ipython": + role = "tool" + else: + role = message.role + together_messages.append({"role": role, "content": message.content}) + + return together_messages + + def resolve_together_model(self, model_name: str) -> str: + model = resolve_model(model_name) + assert ( + model is not None + and model.descriptor(shorten_default_variant=True) + in TOGETHER_SUPPORTED_MODELS + ), f"Unsupported model: {model_name}, use one of the supported models: {','.join(TOGETHER_SUPPORTED_MODELS.keys())}" + + return TOGETHER_SUPPORTED_MODELS.get( + model.descriptor(shorten_default_variant=True) + ) + + def get_together_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, request: ChatCompletionRequest) -> AsyncGenerator: + # accumulate sampling params and other options to pass to together + options = self.get_together_chat_options(request) + together_model = self.resolve_together_model(request.model) + + if not request.stream: + # TODO: might need to add back an async here + r = self.client.chat.completions.create( + model=together_model, + messages=self._messages_to_together_messages(request.messages), + stream=False, + **options, + ) + stop_reason = None + if r.choices[0].finish_reason: + if ( + r.choices[0].finish_reason == "stop" + or r.choices[0].finish_reason == "eos" + ): + stop_reason = StopReason.end_of_turn + elif r.choices[0].finish_reason == "length": + stop_reason = StopReason.out_of_tokens + + completion_message = 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 + + for chunk in self.client.chat.completions.create( + model=together_model, + messages=self._messages_to_together_messages(request.messages), + stream=True, + **options, + ): + if chunk.choices[0].finish_reason: + if ( + stop_reason is None and chunk.choices[0].finish_reason == "stop" + ) or ( + stop_reason is None and chunk.choices[0].finish_reason == "eos" + ): + 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 = 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, + ) + ) + + +# TODO: Consolidate this with impl in llama-models +def decode_assistant_message_from_content( + content: str, + stop_reason: StopReason, +) -> CompletionMessage: + ipython = content.startswith("<|python_tag|>") + if ipython: + content = content[len("<|python_tag|>") :] + + if content.endswith("<|eot_id|>"): + content = content[: -len("<|eot_id|>")] + stop_reason = StopReason.end_of_turn + elif content.endswith("<|eom_id|>"): + content = content[: -len("<|eom_id|>")] + stop_reason = StopReason.end_of_message + + tool_name = None + tool_arguments = {} + + custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content) + if custom_tool_info is not None: + tool_name, tool_arguments = custom_tool_info + # Sometimes when agent has custom tools alongside builin tools + # Agent responds for builtin tool calls in the format of the custom tools + # This code tries to handle that case + if tool_name in BuiltinTool.__members__: + tool_name = BuiltinTool[tool_name] + tool_arguments = { + "query": list(tool_arguments.values())[0], + } + else: + builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content) + if builtin_tool_info is not None: + tool_name, query = builtin_tool_info + tool_arguments = { + "query": query, + } + if tool_name in BuiltinTool.__members__: + tool_name = BuiltinTool[tool_name] + elif ipython: + tool_name = BuiltinTool.code_interpreter + tool_arguments = { + "code": content, + } + + tool_calls = [] + if tool_name is not None and tool_arguments is not None: + call_id = str(uuid.uuid4()) + tool_calls.append( + ToolCall( + call_id=call_id, + tool_name=tool_name, + arguments=tool_arguments, + ) + ) + content = "" + + if stop_reason is None: + stop_reason = StopReason.out_of_tokens + + return CompletionMessage( + content=content, + stop_reason=stop_reason, + tool_calls=tool_calls, + )