File size: 18,111 Bytes
6fc683c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 |
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from apex.normalization import FusedLayerNorm as LayerNorm
from fairseq import utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import BaseFairseqModel, register_model, register_model_architecture
from fairseq.models.squad import SQuADHead
from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, Embedding
from fairseq.modules import PositionalEmbedding
from omegaconf import II
from torchscale.architecture.config import EncoderConfig
from .machine_translation import MTEncoder as Encoder
DEFAULT_MAX_SOURCE_POSITIONS = 1024
logger = logging.getLogger(__name__)
@dataclass
class BertConfig(FairseqDataclass):
activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
default="relu", metadata={"help": "activation function to use"}
)
dropout: float = field(default=0.1, metadata={"help": "dropout probability"})
attention_dropout: float = field(
default=0.0, metadata={"help": "dropout probability for attention weights"}
)
activation_dropout: float = field(
default=0.0, metadata={"help": "dropout probability after activation in FFN."}
)
encoder_embed_dim: int = field(
default=512, metadata={"help": "encoder embedding dimension"}
)
encoder_output_dim: int = field(
default=512, metadata={"help": "encoder output dimension"}
)
encoder_input_dim: int = field(
default=512, metadata={"help": "encoder input dimension"}
)
encoder_ffn_embed_dim: int = field(
default=2048, metadata={"help": "encoder embedding dimension for FFN"}
)
encoder_layers: int = field(default=6, metadata={"help": "num encoder layers"})
encoder_attention_heads: int = field(
default=8, metadata={"help": "num encoder attention heads"}
)
encoder_normalize_before: bool = field(
default=False, metadata={"help": "apply layernorm before each encoder block"}
)
no_encoder_final_norm: bool = field(
default=False,
metadata={"help": "don't add an extra layernorm after the last encoder block"},
)
no_token_positional_embeddings: bool = field(
default=False,
metadata={
"help": "if set, disables positional embeddings (outside self attention)"
},
)
share_encoder_input_output_embed: bool = field(
default=False, metadata={"help": "share encoder input and output embeddings"}
)
encoder_learned_pos: bool = field(
default=False,
metadata={"help": "use learned positional embeddings in the encoder"},
)
layernorm_embedding: bool = field(
default=False, metadata={"help": "add layernorm to embedding"}
)
no_scale_embedding: bool = field(
default=False, metadata={"help": "if True, dont scale embeddings"}
)
checkpoint_activations: bool = field(
default=False, metadata={"help": "checkpoint activations at each layer"}
)
offload_activations: bool = field(
default=False,
metadata={"help": "move checkpointed activations to CPU after they are used."},
)
# config for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
encoder_layerdrop: float = field(
default=0.0, metadata={"help": "LayerDrop probability for encoder"}
)
encoder_layers_to_keep: Optional[str] = field(
default=None,
metadata={
"help": "which layers to *keep* when pruning as a comma-separated list"
},
)
# config for Fully Sharded Data Parallel (FSDP) training
min_params_to_wrap: int = field(
default=DEFAULT_MIN_PARAMS_TO_WRAP,
metadata={
"help": (
"minimum number of params for a layer to be wrapped with FSDP() when "
"training with --ddp-backend=fully_sharded. Smaller values will "
"improve memory efficiency, but may make torch.distributed "
"communication less efficient due to smaller input sizes. This option "
"is set to 0 (i.e., always wrap) when --checkpoint-activations or "
"--offload-activations are passed."
)
},
)
max_source_positions: int = field(
default=1024, metadata={"help": "max source positions"}
)
pooler_activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
default="relu", metadata={"help": "activation function to use for pooler layer"}
)
pooler_dropout: float = field(
default=0.0,
metadata={"help": "dropout probability in the masked_lm pooler layers"},
)
# options from other parts of the config
# add_bos_token: bool = II("task.add_bos_token")
# tokens_per_sample: int = II("task.tokens_per_sample")
tpu: bool = II("common.tpu")
rel_pos_buckets: int = field(default=0, metadata={"help": ""})
max_rel_pos: int = field(default=0, metadata={"help": ""})
moe_freq: int = field(
default=0,
metadata={"help": "Frequency at which we insert MoE Transformer layers"},
)
moe_expert_count: int = field(
default=0, metadata={"help": "Number of experts in each MoE Layer"}
)
moe_gating_use_fp32: bool = field(
default=False,
metadata={"help": "Use FP32 computations in MoE top2 gating function"},
)
moe_second_expert_policy: str = field(
default="sampling",
metadata={"help": "policy for second expert, options: all/sampling/random"},
)
moe_normalize_gate_prob_before_dropping: bool = field(
default=False,
metadata={
"help": "whether to normalize gate probs before or after dropping experts for capacity and randomization"
},
)
moe_expert_ffn_dim: Optional[int] = field(
default=None, metadata={"help": "MoE expert FFN dimension"}
)
moe_top1_expert: Optional[bool] = field(
default=False, metadata={"help": "Use top1 gate instead of top2"}
)
moe_eval_capacity_token_fraction: Optional[float] = field(
default=0.25,
metadata={
"help": (
"Default: 0.25, Fraction of tokens as capacity during validation, "
"if set to negative, use same as training. range: (0.0, 1.0]."
)
},
)
moe_normalize_expert_grad: Optional[str] = field(
default="world_size",
metadata={
"help": "Divide expert gradients by (1) 'world_size' (2) 'sqrt_world_size'"
},
)
record_a2a_perf_stats: Optional[bool] = field(
default=False,
metadata={"help": "records all to all perf stats during distributed training"},
)
dummy_a2a: Optional[bool] = field(
default=False,
metadata={
"help": "By passes all to all during distributed training by returning the input buffer as output"
},
)
moe_batch_prioritized_routing: Optional[bool] = field(
default=False,
metadata={
"help": "if true orders token by the gate prob before capacity dropping."
},
)
ddp_rank: int = II("distributed_training.distributed_rank")
deepnorm: Optional[bool] = field(
default=False,
)
subln: Optional[bool] = field(
default=False,
)
@register_model("mlm", dataclass=BertConfig)
class BertModel(BaseFairseqModel):
def __init__(self, args, encoder):
super().__init__()
self.args = args
self.encoder = encoder
self.padding_idx = self.encoder.embed_tokens.padding_idx
self.classification_heads = nn.ModuleDict()
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
args.max_source_positions = getattr(
args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS
)
embed_tokens = cls.build_embedding(
args, task.dictionary, args.encoder_embed_dim
)
embed_positions = (
PositionalEmbedding(
args.max_source_positions,
args.encoder_embed_dim,
task.dictionary.pad(),
learned=args.encoder_learned_pos,
)
if not args.no_token_positional_embeddings
else None
)
lm_head = cls.build_lm_head(
args,
args.encoder_embed_dim,
len(task.dictionary),
args.activation_fn,
weight=embed_tokens.weight,
)
config = EncoderConfig()
config.override(args)
encoder = Encoder(
config,
embed_tokens=embed_tokens,
embed_positions=embed_positions,
output_projection=lm_head,
is_encoder_decoder=False,
dictionary=task.dictionary,
)
return cls(args, encoder)
@classmethod
def build_embedding(cls, args, dictionary, embed_dim, path=None):
embed_tokens = Embedding(len(dictionary), embed_dim, dictionary.pad())
return embed_tokens
@classmethod
def build_lm_head(cls, args, embed_dim, output_dim, activation_fn, weight):
return LMHead(embed_dim, output_dim, activation_fn, weight)
def output_layer(self, features, masked_tokens=None):
return self.encoder.output_projection(features, masked_tokens=masked_tokens)
def register_classification_head(
self, name, num_classes=None, inner_dim=None, **kwargs
):
"""Register a classification head."""
if name in self.classification_heads:
prev_num_classes = self.classification_heads[name].out_proj.out_features
prev_inner_dim = self.classification_heads[name].dense.out_features
if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
logger.warning(
're-registering head "{}" with num_classes {} (prev: {}) '
"and inner_dim {} (prev: {})".format(
name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
)
)
self.classification_heads[name] = ClassificationHead(
self.args.encoder_embed_dim,
inner_dim or self.args.encoder_embed_dim,
num_classes,
self.args.pooler_activation_fn,
self.args.pooler_dropout,
)
def register_question_answering_head(self, name, num_classes=None):
self.classification_heads[name] = SQuADHead(
self.args.encoder_embed_dim,
)
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + "." if name != "" else ""
# upgrade children modules
super().upgrade_state_dict_named(state_dict, name)
# Handle new classification heads present in the state dict.
current_head_names = (
[]
if not hasattr(self, "classification_heads")
else self.classification_heads.keys()
)
keys_to_delete = []
for k in state_dict.keys():
if not k.startswith(prefix + "classification_heads."):
continue
head_name = k[len(prefix + "classification_heads.") :].split(".")[0] # noqa: E203
num_classes = state_dict[
prefix + "classification_heads." + head_name + ".out_proj.weight"
].size(0)
inner_dim = state_dict[
prefix + "classification_heads." + head_name + ".dense.weight"
].size(0)
if getattr(self.args, "load_checkpoint_heads", False):
if head_name not in current_head_names:
self.register_classification_head(head_name, num_classes, inner_dim)
else:
if head_name not in current_head_names:
logger.warning(
"deleting classification head ({}) from checkpoint "
"not present in current model: {}".format(head_name, k)
)
keys_to_delete.append(k)
elif (
num_classes
!= self.classification_heads[head_name].out_proj.out_features
or inner_dim
!= self.classification_heads[head_name].dense.out_features
):
logger.warning(
"deleting classification head ({}) from checkpoint "
"with different dimensions than current model: {}".format(
head_name, k
)
)
keys_to_delete.append(k)
for k in keys_to_delete:
del state_dict[k]
# Copy any newly-added classification heads into the state dict
# with their current weights.
if hasattr(self, "classification_heads"):
cur_state = self.classification_heads.state_dict()
for k, v in cur_state.items():
if prefix + "classification_heads." + k not in state_dict:
logger.info("Overwriting " + prefix + "classification_heads." + k)
state_dict[prefix + "classification_heads." + k] = v
def forward(
self,
src_tokens=None,
features_only=False,
return_all_hiddens=False,
classification_head_name=None,
masked_tokens=None,
**kwargs
):
encoder_out = self.encoder(
src_tokens, features_only=True, return_all_hiddens=return_all_hiddens
)
x, extra = encoder_out["encoder_out"], encoder_out
x = x.transpose(0, 1)
if classification_head_name is not None:
x = self.classification_heads[classification_head_name](x)
elif not features_only:
x = self.output_layer(x, masked_tokens=masked_tokens)
return x, extra
class ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim,
inner_dim,
num_classes,
activation_fn,
pooler_dropout,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = self.activation_fn(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class LMHead(nn.Module):
"""Head for masked language modeling."""
def __init__(self, embed_dim, output_dim, activation_fn, weight=None):
super().__init__()
self.dense = nn.Linear(embed_dim, embed_dim)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.layer_norm = LayerNorm(embed_dim)
if weight is None:
weight = nn.Linear(embed_dim, output_dim, bias=False).weight
self.weight = weight
self.bias = nn.Parameter(torch.zeros(output_dim))
def forward(self, features, masked_tokens=None, **kwargs):
# Only project the masked tokens while training,
# saves both memory and computation
if masked_tokens is not None:
features = features[masked_tokens, :]
x = self.dense(features)
x = self.activation_fn(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = F.linear(x, self.weight) + self.bias
return x
@register_model_architecture("mlm", "mlm_base")
def base_unilm_architecture(args):
if hasattr(args, "encoder_final_norm"):
args.no_encoder_final_norm = not args.encoder_final_norm
args.dropout = getattr(args, "dropout", 0.1)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 3072)
args.encoder_layers = getattr(args, "encoder_layers", 12)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
args.activation_fn = getattr(args, "activation_fn", "gelu")
args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
# args.add_bos_token = getattr(args, "add_bos_token", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.share_encoder_input_output_embed = getattr(
args, "share_encoder_input_output_embed", True
)
args.encoder_output_dim = getattr(
args, "encoder_output_dim", args.encoder_embed_dim
)
args.encoder_input_dim = getattr(args, "encoder_input_dim", args.encoder_embed_dim)
# Model training is not stable without this
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.no_encoder_final_norm = getattr(args, "no_encoder_final_norm", False)
args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
args.offload_activations = getattr(args, "offload_activations", False)
if args.offload_activations:
args.checkpoint_activations = True
|