lightseekorg--tokenspeed/python/tokenspeed/runtime/model_loader/utils.py

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"""Utilities for selecting and loading models."""

import contextlib
from collections.abc import Iterator

import torch
from torch import nn

from tokenspeed.runtime.configs.model_config import ModelConfig
from tokenspeed.runtime.utils import get_colorful_logger

logger = get_colorful_logger(__name__)


@contextlib.contextmanager
def set_default_torch_dtype(dtype: torch.dtype) -> Iterator[None]:
    """Sets the default torch dtype to the given dtype."""
    old_dtype = torch.get_default_dtype()
    torch.set_default_dtype(dtype)
    yield
    torch.set_default_dtype(old_dtype)


def get_model_architecture(model_config: ModelConfig) -> tuple[type[nn.Module], str]:
    from tokenspeed.runtime.models.registry import ModelRegistry

    architectures = getattr(model_config.hf_config, "architectures", [])
    # Mixtral only supports the quantization backends listed here in the
    # current model registry and loader stack.
    mixtral_supported = ["fp8", "compressed-tensors"]

    if (
        model_config.quantization is not None
        and model_config.quantization not in mixtral_supported
        and "MixtralForCausalLM" in architectures
    ):
        architectures = ["QuantMixtralForCausalLM"]

    return ModelRegistry.resolve_model_cls(architectures)