lightseekorg--tokenspeed/python/tokenspeed/runtime/execution/factory.py

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"""Factory helpers for model runners and model executors."""

from __future__ import annotations

from typing import TYPE_CHECKING

import tokenspeed.runtime.layers.attention.backends  # noqa: F401  # trigger register_backend() calls
from tokenspeed.runtime.configs.model_config import ModelConfig
from tokenspeed.runtime.execution.model_executor import (
    ModelExecutor,
    ModelExecutorConfig,
)
from tokenspeed.runtime.execution.model_runner import ModelRunner
from tokenspeed.runtime.sampling.registry import create_sampling_backend
from tokenspeed.runtime.utils.nvtx import set_nvtx_enabled
from tokenspeed.runtime.utils.server_args import ServerArgs

if TYPE_CHECKING:
    from tokenspeed.runtime.layers.attention.backends.base import AttentionBackend
    from tokenspeed.runtime.layers.attention.kv_cache.base import BaseTokenToKVPool


def create_model_runner(
    server_args: ServerArgs,
    model_config: ModelConfig,
    draft_model_config: ModelConfig | None,
    gpu_id: int,
    global_rank: int,
):
    """Create the main model runner and optional draft model runner."""
    model_runner = ModelRunner(
        model_config=model_config,
        gpu_id=gpu_id,
        server_args=server_args,
        global_rank=global_rank,
    )

    draft_model_runner = None
    if draft_model_config is not None:
        draft_model_runner = ModelRunner(
            model_config=draft_model_config,
            gpu_id=gpu_id,
            server_args=server_args,
            global_rank=global_rank,
            is_draft_worker=True,
        )

    return model_runner, draft_model_runner


def create_model_executor(
    server_args: ServerArgs,
    config: ModelExecutorConfig,
    model_runner: ModelRunner,
    attn_backend: AttentionBackend,
    token_to_kv_pool: BaseTokenToKVPool,
    draft_model_runner: ModelRunner | None = None,
    draft_attn_backend: AttentionBackend | None = None,
    draft_token_to_kv_pool: BaseTokenToKVPool | None = None,
    mamba_pool: object | None = None,
) -> ModelExecutor:
    """Create the model executor with its sampler configuration."""
    if server_args.enable_nvtx:
        set_nvtx_enabled(True)

    max_bs = config.max_num_seqs // max(config.data_parallel_size, 1)

    max_draft_tokens_per_req = (
        config.spec_num_tokens if config.spec_algo is not None else 1
    )

    sampling_backend = create_sampling_backend(
        server_args,
        max_bs=max_bs,
        max_draft_tokens_per_req=max_draft_tokens_per_req,
        device=config.device,
        max_req_pool_size=config.max_req_pool_size,
        vocab_size=config.vocab_size,
        # Same TP group as LogitsProcessor.
        tp_group=model_runner.mapping.attn.tp_group,
    )

    return ModelExecutor(
        config=config,
        model_runner=model_runner,
        attn_backend=attn_backend,
        token_to_kv_pool=token_to_kv_pool,
        sampling_backend=sampling_backend,
        draft_model_runner=draft_model_runner,
        draft_attn_backend=draft_attn_backend,
        draft_token_to_kv_pool=draft_token_to_kv_pool,
        mamba_pool=mamba_pool,
    )