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Introduce Llama stack distributions (#22)
* Add distribution CLI scaffolding * More progress towards `llama distribution install` * getting closer to a distro definition, distro install + configure works * Distribution server now functioning * read existing configuration, save enums properly * Remove inference uvicorn server entrypoint and llama inference CLI command * updated dependency and client model name * Improved exception handling * local imports for faster cli * undo a typo, add a passthrough distribution * implement full-passthrough in the server * add safety adapters, configuration handling, server + clients * cleanup, moving stuff to common, nuke utils * Add a Path() wrapper at the earliest place * fixes * Bring agentic system api to toolchain Add adapter dependencies and resolve adapters using a topological sort * refactor to reduce size of `agentic_system` * move straggler files and fix some important existing bugs * ApiSurface -> Api * refactor a method out * Adapter -> Provider * Make each inference provider into its own subdirectory * installation fixes * Rename Distribution -> DistributionSpec, simplify RemoteProviders * dict key instead of attr * update inference config to take model and not model_dir * Fix passthrough streaming, send headers properly not part of body :facepalm * update safety to use model sku ids and not model dirs * Update cli_reference.md * minor fixes * add DistributionConfig, fix a bug in model download * Make install + start scripts do proper configuration automatically * Update CLI_reference * Nuke fp8_requirements, fold fbgemm into common requirements * Update README, add newline between API surface configurations * Refactor download functionality out of the Command so can be reused * Add `llama model download` alias for `llama download` * Show message about checksum file so users can check themselves * Simpler intro statements * get ollama working * Reduce a bunch of dependencies from toolchain Some improvements to the distribution install script * Avoid using `conda run` since it buffers everything * update dependencies and rely on LLAMA_TOOLCHAIN_DIR for dev purposes * add validation for configuration input * resort imports * make optional subclasses default to yes for configuration * Remove additional_pip_packages; move deps to providers * for inline make 8b model the default * Add scripts to MANIFEST * allow installing from test.pypi.org * Fix #2 to help with testing packages * Must install llama-models at that same version first * fix PIP_ARGS --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Hardik Shah <hjshah@meta.com>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
da4645a27a
commit
e830814399
115 changed files with 5839 additions and 1120 deletions
204
llama_toolchain/inference/meta_reference/inference.py
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204
llama_toolchain/inference/meta_reference/inference.py
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# 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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import asyncio
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from typing import AsyncIterator, Dict, Union
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from llama_models.llama3_1.api.datatypes import StopReason
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from llama_models.sku_list import resolve_model
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from llama_toolchain.distribution.datatypes import Api, ProviderSpec
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from llama_toolchain.inference.api import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseEvent,
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ChatCompletionResponseEventType,
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ChatCompletionResponseStreamChunk,
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Inference,
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ToolCallDelta,
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ToolCallParseStatus,
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)
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from .config import MetaReferenceImplConfig
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from .model_parallel import LlamaModelParallelGenerator
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async def get_provider_impl(
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config: MetaReferenceImplConfig, _deps: Dict[Api, ProviderSpec]
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):
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assert isinstance(
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config, MetaReferenceImplConfig
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), f"Unexpected config type: {type(config)}"
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impl = MetaReferenceInferenceImpl(config)
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await impl.initialize()
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return impl
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# there's a single model parallel process running serving the model. for now,
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# we don't support multiple concurrent requests to this process.
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SEMAPHORE = asyncio.Semaphore(1)
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class MetaReferenceInferenceImpl(Inference):
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def __init__(self, config: MetaReferenceImplConfig) -> None:
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self.config = config
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model = resolve_model(config.model)
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if model is None:
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raise RuntimeError(f"Unknown model: {config.model}, Run `llama model list`")
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self.model = model
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# verify that the checkpoint actually is for this model lol
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async def initialize(self) -> None:
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self.generator = LlamaModelParallelGenerator(self.config)
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self.generator.start()
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async def shutdown(self) -> None:
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self.generator.stop()
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# hm, when stream=False, we should not be doing SSE :/ which is what the
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# top-level server is going to do. make the typing more specific here
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async def chat_completion(
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self, request: ChatCompletionRequest
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) -> AsyncIterator[
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Union[ChatCompletionResponseStreamChunk, ChatCompletionResponse]
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]:
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model = resolve_model(request.model)
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if model is None:
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raise RuntimeError(
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f"Unknown model: {request.model}, Run `llama model list`"
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)
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elif model.descriptor() != self.model.descriptor():
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raise RuntimeError(
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f"Model mismatch: {request.model} != {self.model.descriptor()}"
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)
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if SEMAPHORE.locked():
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raise RuntimeError("Only one concurrent request is supported")
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async with SEMAPHORE:
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if request.stream:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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tokens = []
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logprobs = []
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stop_reason = None
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buffer = ""
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ipython = False
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for token_result in self.generator.chat_completion(
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messages=request.messages,
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temperature=request.sampling_params.temperature,
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top_p=request.sampling_params.top_p,
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max_gen_len=request.sampling_params.max_tokens,
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logprobs=request.logprobs,
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):
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buffer += token_result.text
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tokens.append(token_result.token)
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if not ipython and buffer.startswith("<|python_tag|>"):
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ipython = True
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.started,
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),
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)
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)
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buffer = buffer[len("<|python_tag|>") :]
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continue
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if not request.stream:
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if request.logprobs:
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logprobs.append(token_result.logprob)
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continue
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if token_result.text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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elif token_result.text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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else:
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text = token_result.text
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if ipython:
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delta = ToolCallDelta(
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content=text,
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parse_status=ToolCallParseStatus.in_progress,
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)
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else:
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delta = text
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if stop_reason is None:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=delta,
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stop_reason=stop_reason,
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)
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)
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if stop_reason is None:
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stop_reason = StopReason.out_of_tokens
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# TODO(ashwin): parse tool calls separately here and report errors?
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# if someone breaks the iteration before coming here we are toast
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message = self.generator.formatter.decode_assistant_message(
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tokens, stop_reason
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)
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if request.stream:
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.failure,
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),
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stop_reason=stop_reason,
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)
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)
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for tool_call in message.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content=tool_call,
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parse_status=ToolCallParseStatus.success,
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),
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stop_reason=stop_reason,
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta="",
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stop_reason=stop_reason,
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)
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)
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# TODO(ashwin): what else do we need to send out here when everything finishes?
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else:
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yield ChatCompletionResponse(
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completion_message=message,
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logprobs=logprobs if request.logprobs else None,
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)
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