forked from phoenix-oss/llama-stack-mirror
API Updates (#73)
* API Keys passed from Client instead of distro configuration * delete distribution registry * Rename the "package" word away * Introduce a "Router" layer for providers Some providers need to be factorized and considered as thin routing layers on top of other providers. Consider two examples: - The inference API should be a routing layer over inference providers, routed using the "model" key - The memory banks API is another instance where various memory bank types will be provided by independent providers (e.g., a vector store is served by Chroma while a keyvalue memory can be served by Redis or PGVector) This commit introduces a generalized routing layer for this purpose. * update `apis_to_serve` * llama_toolchain -> llama_stack * Codemod from llama_toolchain -> llama_stack - added providers/registry - cleaned up api/ subdirectories and moved impls away - restructured api/api.py - from llama_stack.apis.<api> import foo should work now - update imports to do llama_stack.apis.<api> - update many other imports - added __init__, fixed some registry imports - updated registry imports - create_agentic_system -> create_agent - AgenticSystem -> Agent * Moved some stuff out of common/; re-generated OpenAPI spec * llama-toolchain -> llama-stack (hyphens) * add control plane API * add redis adapter + sqlite provider * move core -> distribution * Some more toolchain -> stack changes * small naming shenanigans * Removing custom tool and agent utilities and moving them client side * Move control plane to distribution server for now * Remove control plane from API list * no codeshield dependency randomly plzzzzz * Add "fire" as a dependency * add back event loggers * stack configure fixes * use brave instead of bing in the example client * add init file so it gets packaged * add init files so it gets packaged * Update MANIFEST * bug fix --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Xi Yan <xiyan@meta.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
f294eac5f5
commit
9487ad8294
213 changed files with 1725 additions and 1204 deletions
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@ -1,215 +0,0 @@
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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, Union
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from llama_models.llama3.api.datatypes import StopReason
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from llama_models.sku_list import resolve_model
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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 llama_toolchain.inference.prepare_messages import prepare_messages
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from .config import MetaReferenceImplConfig
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from .model_parallel import LlamaModelParallelGenerator
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_toolchain.inference.api import * # noqa: F403
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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,
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model: 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]] = list(),
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncIterator[
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Union[ChatCompletionResponseStreamChunk, ChatCompletionResponse]
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]:
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# wrapper request to make it easier to pass around (internal only, not exposed to API)
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools,
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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messages = prepare_messages(request)
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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=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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tool_prompt_format=request.tool_prompt_format,
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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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