models/llava/model/language_model/mpt/param_init_fns.py

import math
import warnings
from collections.abc import Sequence
from functools import partial
from typing import Optional, Tuple, Union

import torch
from torch import nn

from .norm import NORM_CLASS_REGISTRY


def torch_default_param_init_fn_(module: nn.Module, verbose: int = 0, **kwargs):
    del kwargs
    if verbose > 1:
        warnings.warn(f"Initializing network using module's reset_parameters attribute")
    if hasattr(module, "reset_parameters"):
        module.reset_parameters()


def fused_init_helper_(module: nn.Module, init_fn_):
    _fused = getattr(module, "_fused", None)
    if _fused is None:
        raise RuntimeError(f"Internal logic error")
    (dim, splits) = _fused
    splits = (0, *splits, module.weight.size(dim))
    for s, e in zip(splits[:-1], splits[1:]):
        slice_indices = [slice(None)] * module.weight.ndim
        slice_indices[dim] = slice(s, e)
        init_fn_(module.weight[slice_indices])


def generic_param_init_fn_(
    module: nn.Module,
    init_fn_,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    if verbose > 1:
        warnings.warn(f"If model has bias parameters they are initialized to 0.")
    init_div_is_residual = init_div_is_residual
    if init_div_is_residual is False:
        div_is_residual = 1.0
    elif init_div_is_residual is True:
        div_is_residual = math.sqrt(2 * n_layers)
    elif isinstance(init_div_is_residual, float) or isinstance(
        init_div_is_residual, int
    ):
        div_is_residual = init_div_is_residual
    elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric():
        div_is_residual = float(init_div_is_residual)
    else:
        div_is_residual = 1.0
        raise ValueError(
            f"Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}"
        )
    if init_div_is_residual is not False:
        if verbose > 1:
            warnings.warn(
                f"Initializing _is_residual layers then dividing them by {div_is_residual:.3f}. "
                + f"Set `init_div_is_residual: false` in init config to disable this."
            )
    if isinstance(module, nn.Linear):
        if hasattr(module, "_fused"):
            fused_init_helper_(module, init_fn_)
        else:
            init_fn_(module.weight)
        if module.bias is not None:
            torch.nn.init.zeros_(module.bias)
        if init_div_is_residual is not False and getattr(module, "_is_residual", False):
            with torch.no_grad():
                module.weight.div_(div_is_residual)
    elif isinstance(module, nn.Embedding):
        if emb_init_std is not None:
            std = emb_init_std
            if std == 0:
                warnings.warn(f"Embedding layer initialized to 0.")
            emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std)
            if verbose > 1:
                warnings.warn(
                    f"Embedding layer initialized using normal distribution with mean=0 and std={std!r}."
                )
        elif emb_init_uniform_lim is not None:
            lim = emb_init_uniform_lim
            if isinstance(lim, Sequence):
                if len(lim) > 2:
                    raise ValueError(
                        f"Uniform init requires a min and a max limit. User input: {lim}."
                    )
                if lim[0] == lim[1]:
                    warnings.warn(f"Embedding layer initialized to {lim[0]}.")
            else:
                if lim == 0:
                    warnings.warn(f"Embedding layer initialized to 0.")
                lim = [-lim, lim]
            (a, b) = lim
            emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b)
            if verbose > 1:
                warnings.warn(
                    f"Embedding layer initialized using uniform distribution in range {lim}."
                )
        else:
            emb_init_fn_ = init_fn_
        emb_init_fn_(module.weight)
    elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))):
        if verbose > 1:
            warnings.warn(
                f"Norm weights are set to 1. If norm layer has a bias it is initialized to 0."
            )
        if hasattr(module, "weight") and module.weight is not None:
            torch.nn.init.ones_(module.weight)
        if hasattr(module, "bias") and module.bias is not None:
            torch.nn.init.zeros_(module.bias)
    elif isinstance(module, nn.MultiheadAttention):
        if module._qkv_same_embed_dim:
            assert module.in_proj_weight is not None
            assert (
                module.q_proj_weight is None
                and module.k_proj_weight is None
                and (module.v_proj_weight is None)
            )
            assert d_model is not None
            _d = d_model
            splits = (0, _d, 2 * _d, 3 * _d)
            for s, e in zip(splits[:-1], splits[1:]):
                init_fn_(module.in_proj_weight[s:e])
        else:
            assert (
                module.q_proj_weight is not None
                and module.k_proj_weight is not None
                and (module.v_proj_weight is not None)
            )
            assert module.in_proj_weight is None
            init_fn_(module.q_proj_weight)
            init_fn_(module.k_proj_weight)
            init_fn_(module.v_proj_weight)
        if module.in_proj_bias is not None:
            torch.nn.init.zeros_(module.in_proj_bias)
        if module.bias_k is not None:
            torch.nn.init.zeros_(module.bias_k)
        if module.bias_v is not None:
            torch.nn.init.zeros_(module.bias_v)
        init_fn_(module.out_proj.weight)
        if init_div_is_residual is not False and getattr(
            module.out_proj, "_is_residual", False
        ):
            with torch.no_grad():
                module.out_proj.weight.div_(div_is_residual)
        if module.out_proj.bias is not None:
            torch.nn.init.zeros_(module.out_proj.bias)
    else:
        for _ in module.parameters(recurse=False):
            raise NotImplementedError(
                f"{module.__class__.__name__} parameters are not initialized by param_init_fn."
            )


def _normal_init_(std, mean=0.0):
    return partial(torch.nn.init.normal_, mean=mean, std=std)


def _normal_param_init_fn_(
    module: nn.Module,
    std: float,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    init_fn_ = _normal_init_(std=std)
    if verbose > 1:
        warnings.warn(f"Using torch.nn.init.normal_ init fn mean=0.0, std={std}")
    generic_param_init_fn_(
        module=module,
        init_fn_=init_fn_,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def baseline_param_init_fn_(
    module: nn.Module,
    init_std: float,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    if init_std is None:
        raise ValueError(
            "You must set model.init_config['init_std'] to a float value to use the default initialization scheme."
        )
    _normal_param_init_fn_(
        module=module,
        std=init_std,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def small_param_init_fn_(
    module: nn.Module,
    n_layers: int,
    d_model: int,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    std = math.sqrt(2 / (5 * d_model))
    _normal_param_init_fn_(
        module=module,
        std=std,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def neox_param_init_fn_(
    module: nn.Module,
    n_layers: int,
    d_model: int,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    verbose: int = 0,
    **kwargs,
):
    """From section 2.3.1 of GPT-NeoX-20B:

    An Open-Source AutoregressiveLanguage Model — Black et. al. (2022)
    see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151
    and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py
    """
    del kwargs
    residual_div = n_layers / math.sqrt(10)
    if verbose > 1:
        warnings.warn(f"setting init_div_is_residual to {residual_div}")
    small_param_init_fn_(
        module=module,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=residual_div,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def kaiming_uniform_param_init_fn_(
    module: nn.Module,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    init_gain: float = 0,
    fan_mode: str = "fan_in",
    init_nonlinearity: str = "leaky_relu",
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    if verbose > 1:
        warnings.warn(
            f"Using nn.init.kaiming_uniform_ init fn with parameters: "
            + f"a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}"
        )
    kaiming_uniform_ = partial(
        nn.init.kaiming_uniform_,
        a=init_gain,
        mode=fan_mode,
        nonlinearity=init_nonlinearity,
    )
    generic_param_init_fn_(
        module=module,
        init_fn_=kaiming_uniform_,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def kaiming_normal_param_init_fn_(
    module: nn.Module,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    init_gain: float = 0,
    fan_mode: str = "fan_in",
    init_nonlinearity: str = "leaky_relu",
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    if verbose > 1:
        warnings.warn(
            f"Using nn.init.kaiming_normal_ init fn with parameters: "
            + f"a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}"
        )
    kaiming_normal_ = partial(
        torch.nn.init.kaiming_normal_,
        a=init_gain,
        mode=fan_mode,
        nonlinearity=init_nonlinearity,
    )
    generic_param_init_fn_(
        module=module,
        init_fn_=kaiming_normal_,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def xavier_uniform_param_init_fn_(
    module: nn.Module,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    init_gain: float = 0,
    verbose: int = 0,
    **kwargs,
):
    del kwargs
    xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain)
    if verbose > 1:
        warnings.warn(
            f"Using torch.nn.init.xavier_uniform_ init fn with parameters: "
            + f"gain={init_gain}"
        )
    generic_param_init_fn_(
        module=module,
        init_fn_=xavier_uniform_,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


def xavier_normal_param_init_fn_(
    module: nn.Module,
    n_layers: int,
    d_model: Optional[int] = None,
    init_div_is_residual: Union[int, float, str, bool] = True,
    emb_init_std: Optional[float] = None,
    emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None,
    init_gain: float = 0,
    verbose: int = 0,
    **kwargs,
):
    xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain)
    if verbose > 1:
        warnings.warn(
            f"Using torch.nn.init.xavier_normal_ init fn with parameters: "
            + f"gain={init_gain}"
        )
    generic_param_init_fn_(
        module=module,
        init_fn_=xavier_normal_,
        d_model=d_model,
        n_layers=n_layers,
        init_div_is_residual=init_div_is_residual,
        emb_init_std=emb_init_std,
        emb_init_uniform_lim=emb_init_uniform_lim,
        verbose=verbose,
    )


MODEL_INIT_REGISTRY = {
    "default_": torch_default_param_init_fn_,
    "baseline_": baseline_param_init_fn_,
    "kaiming_uniform_": kaiming_uniform_param_init_fn_,
    "kaiming_normal_": kaiming_normal_param_init_fn_,
    "neox_init_": neox_param_init_fn_,
    "small_init_": small_param_init_fn_,
    "xavier_uniform_": xavier_uniform_param_init_fn_,
    "xavier_normal_": xavier_normal_param_init_fn_,
}