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import math
import warnings
from collections.abc import Sequence
from functools import partial
from typing import Any, Callable, Optional, Tuple, Union
import torch
from torch import nn
from .fc import FC_CLASS_REGISTRY
from .norm import NORM_CLASS_REGISTRY
try:
import transformer_engine.pytorch as te
except:
te = None
def torch_default_param_init_fn_(module: nn.Module, **kwargs: Any) -> None:
del kwargs
if hasattr(module, "reset_parameters") and isinstance(
module.reset_parameters, Callable
):
module.reset_parameters()
def fused_init_helper_(module: nn.Module, init_fn_: Callable) -> None:
_fused = getattr(module, "_fused", None)
if _fused is None:
raise RuntimeError(f"Internal logic error")
assert isinstance(module.weight, torch.Tensor)
(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_: Callable,
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,
**kwargs: Any,
) -> None:
del kwargs
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 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 isinstance(module, tuple(set(FC_CLASS_REGISTRY.values()))):
if hasattr(module, "_fused"):
fused_init_helper_(module, init_fn_)
else:
init_fn_(module.weight)
if module.bias is not None:
assert isinstance(module.bias, torch.Tensor)
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)
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)
else:
emb_init_fn_ = init_fn_
emb_init_fn_(module.weight)
elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))):
if hasattr(module, "weight") and isinstance(module.weight, torch.Tensor):
torch.nn.init.ones_(module.weight)
if hasattr(module, "bias") and isinstance(module.bias, torch.Tensor):
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)
elif te is not None and isinstance(module, te.LayerNormMLP):
if isinstance(module.layer_norm_weight, torch.Tensor):
torch.nn.init.ones_(module.layer_norm_weight)
if isinstance(module.layer_norm_bias, torch.Tensor):
torch.nn.init.zeros_(module.layer_norm_bias)
init_fn_(module.fc1_weight)
if module.fc1_bias is not None:
assert isinstance(module.fc1_bias, torch.Tensor)
torch.nn.init.zeros_(module.fc1_bias)
init_fn_(module.fc2_weight)
if module.fc2_bias is not None:
assert isinstance(module.fc2_bias, torch.Tensor)
torch.nn.init.zeros_(module.fc2_bias)
with torch.no_grad():
module.fc2_weight.div_(div_is_residual)
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: float, mean: float = 0.0) -> Callable:
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,
**kwargs: Any,
) -> None:
del kwargs
init_fn_ = _normal_init_(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,
)
def baseline_param_init_fn_(
module: nn.Module,
init_std: Optional[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,
**kwargs: Any,
) -> None:
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,
)
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,
**kwargs: Any,
) -> None:
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,
)
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,
**kwargs: Any,
) -> None:
"""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)
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,
)
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",
**kwargs: Any,
) -> None:
del kwargs
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,
)
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",
**kwargs: Any,
) -> None:
del kwargs
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,
)
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,
**kwargs: Any,
) -> None:
del kwargs
xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, 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,
)
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,
**kwargs: Any,
) -> None:
del kwargs
xavier_normal_ = partial(torch.nn.init.xavier_normal_, 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,
)
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_,
}
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