mirror of
https://github.com/meta-llama/llama-stack.git
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280 lines
10 KiB
Python
280 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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import asyncio
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from typing import AsyncGenerator, List
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from llama_models.sku_list import resolve_model
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.datatypes import ModelDef, ModelsProtocolPrivate
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_messages,
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)
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from .config import MetaReferenceInferenceConfig
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from .model_parallel import LlamaModelParallelGenerator
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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, ModelsProtocolPrivate):
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def __init__(self, config: MetaReferenceInferenceConfig) -> 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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print(f"Loading model `{self.model.descriptor()}`")
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self.generator = LlamaModelParallelGenerator(self.config)
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self.generator.start()
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async def register_model(self, model: ModelDef) -> None:
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raise ValueError("Dynamic model registration is not supported")
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async def list_models(self) -> List[ModelDef]:
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return [
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ModelDef(
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identifier=self.model.descriptor(),
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llama_model=self.model.descriptor(),
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)
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]
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async def shutdown(self) -> None:
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self.generator.stop()
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def completion(
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self,
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model: str,
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content: InterleavedTextMedia,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
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raise NotImplementedError()
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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]] = None,
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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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) -> AsyncGenerator:
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if logprobs:
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assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
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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 or [],
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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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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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if request.stream:
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return self._stream_chat_completion(request)
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else:
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return self._nonstream_chat_completion(request)
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest
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) -> ChatCompletionResponse:
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async with SEMAPHORE:
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messages = chat_completion_request_to_messages(request)
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tokens = []
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logprobs = []
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stop_reason = None
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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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tokens.append(token_result.token)
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if token_result.text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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elif token_result.text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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if request.logprobs:
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assert len(token_result.logprobs) == 1
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logprobs.append(
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TokenLogProbs(
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logprobs_by_token={
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token_result.text: token_result.logprobs[0]
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}
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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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message = self.generator.formatter.decode_assistant_message(
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tokens, stop_reason
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)
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return 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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async def _stream_chat_completion(
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self, request: ChatCompletionRequest
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) -> AsyncGenerator:
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async with SEMAPHORE:
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messages = chat_completion_request_to_messages(request)
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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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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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tokens.append(token_result.token)
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if not ipython and token_result.text.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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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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if request.logprobs:
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assert len(token_result.logprobs) == 1
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logprobs.append(
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TokenLogProbs(
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logprobs_by_token={
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token_result.text: token_result.logprobs[0]
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}
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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.progress,
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delta=delta,
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stop_reason=stop_reason,
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logprobs=logprobs if request.logprobs else None,
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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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message = self.generator.formatter.decode_assistant_message(
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tokens, stop_reason
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)
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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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async def embeddings(
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self,
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model: str,
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contents: List[InterleavedTextMedia],
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) -> EmbeddingsResponse:
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raise NotImplementedError()
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