llama-stack/llama_toolchain/inference/meta_reference/inference.py

# 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 asyncio

from typing import AsyncIterator, Union

from llama_models.llama3.api.datatypes import StopReason
from llama_models.sku_list import resolve_model

from llama_toolchain.inference.api import (
    ChatCompletionRequest,
    ChatCompletionResponse,
    ChatCompletionResponseEvent,
    ChatCompletionResponseEventType,
    ChatCompletionResponseStreamChunk,
    Inference,
    ToolCallDelta,
    ToolCallParseStatus,
)
from llama_toolchain.inference.prepare_messages import prepare_messages

from .config import MetaReferenceImplConfig
from .model_parallel import LlamaModelParallelGenerator

from llama_models.llama3.api.datatypes import *  # noqa: F403
from llama_toolchain.inference.api import *  # noqa: F403

# there's a single model parallel process running serving the model. for now,
# we don't support multiple concurrent requests to this process.
SEMAPHORE = asyncio.Semaphore(1)


class MetaReferenceInferenceImpl(Inference):
    def __init__(self, config: MetaReferenceImplConfig) -> None:
        self.config = config
        model = resolve_model(config.model)
        if model is None:
            raise RuntimeError(f"Unknown model: {config.model}, Run `llama model list`")
        self.model = model
        # verify that the checkpoint actually is for this model lol

    async def initialize(self) -> None:
        self.generator = LlamaModelParallelGenerator(self.config)
        self.generator.start()

    async def shutdown(self) -> None:
        self.generator.stop()

    # hm, when stream=False, we should not be doing SSE :/ which is what the
    # top-level server is going to do. make the typing more specific here
    async def chat_completion(
        self,
        model: str,
        messages: List[Message],
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        tools: Optional[List[ToolDefinition]] = list(),
        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> AsyncIterator[
        Union[ChatCompletionResponseStreamChunk, ChatCompletionResponse]
    ]:
        # wrapper request to make it easier to pass around (internal only, not exposed to API)
        request = ChatCompletionRequest(
            model=model,
            messages=messages,
            sampling_params=sampling_params,
            tools=tools,
            tool_choice=tool_choice,
            tool_prompt_format=tool_prompt_format,
            stream=stream,
            logprobs=logprobs,
        )

        messages = prepare_messages(request)
        model = resolve_model(request.model)
        if model is None:
            raise RuntimeError(
                f"Unknown model: {request.model}, Run `llama model list`"
            )
        elif model.descriptor() != self.model.descriptor():
            raise RuntimeError(
                f"Model mismatch: {request.model} != {self.model.descriptor()}"
            )

        if SEMAPHORE.locked():
            raise RuntimeError("Only one concurrent request is supported")

        async with SEMAPHORE:
            if request.stream:
                yield ChatCompletionResponseStreamChunk(
                    event=ChatCompletionResponseEvent(
                        event_type=ChatCompletionResponseEventType.start,
                        delta="",
                    )
                )

            tokens = []
            logprobs = []

            stop_reason = None

            buffer = ""
            ipython = False

            for token_result in self.generator.chat_completion(
                messages=messages,
                temperature=request.sampling_params.temperature,
                top_p=request.sampling_params.top_p,
                max_gen_len=request.sampling_params.max_tokens,
                logprobs=request.logprobs,
                tool_prompt_format=request.tool_prompt_format,
            ):
                buffer += token_result.text
                tokens.append(token_result.token)

                if not ipython and buffer.startswith("<|python_tag|>"):
                    ipython = True
                    yield ChatCompletionResponseStreamChunk(
                        event=ChatCompletionResponseEvent(
                            event_type=ChatCompletionResponseEventType.progress,
                            delta=ToolCallDelta(
                                content="",
                                parse_status=ToolCallParseStatus.started,
                            ),
                        )
                    )
                    buffer = buffer[len("<|python_tag|>") :]
                    continue

                if not request.stream:
                    if request.logprobs:
                        logprobs.append(token_result.logprob)

                    continue

                if token_result.text == "<|eot_id|>":
                    stop_reason = StopReason.end_of_turn
                    text = ""
                elif token_result.text == "<|eom_id|>":
                    stop_reason = StopReason.end_of_message
                    text = ""
                else:
                    text = token_result.text

                if ipython:
                    delta = ToolCallDelta(
                        content=text,
                        parse_status=ToolCallParseStatus.in_progress,
                    )
                else:
                    delta = text

                if stop_reason is None:
                    yield ChatCompletionResponseStreamChunk(
                        event=ChatCompletionResponseEvent(
                            event_type=ChatCompletionResponseEventType.progress,
                            delta=delta,
                            stop_reason=stop_reason,
                        )
                    )

            if stop_reason is None:
                stop_reason = StopReason.out_of_tokens

            # TODO(ashwin): parse tool calls separately here and report errors?
            # if someone breaks the iteration before coming here we are toast
            message = self.generator.formatter.decode_assistant_message(
                tokens, stop_reason
            )
            if request.stream:
                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(ashwin): what else do we need to send out here when everything finishes?
            else:
                yield ChatCompletionResponse(
                    completion_message=message,
                    logprobs=logprobs if request.logprobs else None,
                )