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mpt-1b-redpajama-200b-dolly / param_init_fns.py
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# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
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
def torch_default_param_init_fn_(
module: nn.Module,
verbose: int = 0,
**kwargs,
):
del kwargs # unused, just to capture any extra args from the config
if verbose > 1:
warnings.warn(
f"Initializing network using module's reset_parameters attribute")
if hasattr(module, 'reset_parameters'):
module.reset_parameters() # type: ignore
def fused_init_helper_(module: nn.Module, init_fn_):
# parameter initialization is often based on the parameters shape.
# If a layer is fused, initialization should be based on the shapes
# of the original tensor instead of the shape of the fused tensor.
# Layers which are fused should have the _fused attibute defined.
# The first element of _fused is the dimension along which the tensor is fused.
# This is followed by an iterable of split indices."
_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)) # type: ignore
for s, e in zip(splits[:-1], splits[1:]):
slice_indices = [slice(None)] * module.weight.ndim # type: ignore
slice_indices[dim] = slice(s, e)
init_fn_(module.weight[slice_indices]) # type: ignore
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 # unused, just to capture any extra args from the config
if verbose > 1:
warnings.warn(
f'If model has bias parameters they are initialized to 0.')
# enable user to divide _is_residual weights by
# a value which defaults to math.sqrt(2 * cfg.n_layers)
init_div_is_residual = init_div_is_residual
if init_div_is_residual is False:
# not used, for pyright
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():
# do not trust YAML parsing to always convert numbers to numbers
div_is_residual = float(init_div_is_residual)
else:
# not used, for pyright
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}.' +\
f'set `init_div_is_residual: false` in model config to disable this.'
)
if isinstance(module, nn.Linear):
# 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):
# 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=}.'
)
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, nn.LayerNorm):
# LayerNorm
if verbose > 1:
warnings.warn(
f'LayerNorm gamma weights are set to 1. If the layer has a bias it is initialized to 0.'
)
torch.nn.init.ones_(module.weight)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.MultiheadAttention):
# torch's 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
# in_proj_weight is actually 3 layers and should be split up for width based init
_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)
# bias
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)
# out proj
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 error if uninitialized module has any parameters
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 # unused, just to capture any extra args from the config
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 # unused, just to capture any extra args from the config
if init_std is None:
raise ValueError(
'You must set model.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 # unused, just to capture any extra args from the config
# very close to kaiming normal
# from Transformers without Tears (2019) - Nguyen & Salazar
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 # unused, just to capture any extra args from the config
residual_div = n_layers / math.sqrt(10) # small std / wang std
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 # unused, just to capture any extra args from the config
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 # unused, just to capture any extra args from the config
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 # unused, just to capture any extra args from the config
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_,
}