Text Generation
Transformers
PyTorch
mosaic_gpt
custom_code
File size: 15,936 Bytes
0b68fcb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
# 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_,
}