File size: 11,860 Bytes
c5ccdb7
 
 
 
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
c5e05a7
 
 
 
c5ccdb7
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
 
 
c5e05a7
c5ccdb7
 
 
 
 
 
 
c5e05a7
c5ccdb7
 
 
 
 
 
 
 
c5e05a7
c5ccdb7
 
 
 
c5e05a7
c5ccdb7
 
 
 
 
c5e05a7
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c5e05a7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c5ccdb7
 
 
 
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
 
 
 
 
 
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
c5e05a7
c5ccdb7
 
c5e05a7
c5ccdb7
c5e05a7
 
c5ccdb7
c5e05a7
c5ccdb7
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
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_}