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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
from dataclasses import dataclass, field
from typing import Optional, Callable
from functools import partial
import numpy as np
from omegaconf import II
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from fairseq.modules import EMAModule, EMAModuleConfig
from fairseq.dataclass import FairseqDataclass
from fairseq.models import BaseFairseqModel, register_model
from examples.data2vec.data.modality import Modality
from examples.data2vec.models.modalities.base import (
MaskSeed,
D2vModalityConfig,
ModalitySpecificEncoder,
get_annealed_rate,
)
from examples.data2vec.models.modalities.modules import (
D2vDecoderConfig,
AltBlock,
Decoder1d,
)
from .modalities.audio import (
D2vAudioConfig,
AudioEncoder,
)
from examples.data2vec.models.modalities.images import (
D2vImageConfig,
ImageEncoder,
)
from examples.data2vec.models.modalities.text import (
D2vTextConfig,
TextEncoder,
)
logger = logging.getLogger(__name__)
@dataclass
class D2vModalitiesConfig(FairseqDataclass):
audio: D2vAudioConfig = D2vAudioConfig()
image: D2vImageConfig = D2vImageConfig()
text: D2vTextConfig = D2vTextConfig()
@dataclass
class Data2VecMultiConfig(FairseqDataclass):
loss_beta: float = field(
default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"}
)
loss_scale: Optional[float] = field(
default=None,
metadata={
"help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)"
},
)
input_feature_ndim: int = 40
depth: int = 8
start_drop_path_rate: float = 0
end_drop_path_rate: float = 0
num_heads: int = 12
norm_eps: float = 1e-6
norm_affine: bool = True
encoder_dropout: float = 0.1
post_mlp_drop: float = 0.1
attention_dropout: float = 0.1
activation_dropout: float = 0.0
dropout_input: float = 0.0
layerdrop: float = 0.0
embed_dim: int = 768
mlp_ratio: float = 4
layer_norm_first: bool = False
average_top_k_layers: int = field(
default=8, metadata={"help": "how many layers to average"}
)
end_of_block_targets: bool = False
clone_batch: int = 1
layer_norm_target_layer: bool = False
batch_norm_target_layer: bool = False
instance_norm_target_layer: bool = False
instance_norm_targets: bool = False
layer_norm_targets: bool = False
ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"})
ema_same_dtype: bool = True
log_norms: bool = True
ema_end_decay: float = field(
default=0.9999, metadata={"help": "final ema decay rate"}
)
# when to finish annealing ema decay rate
ema_anneal_end_step: int = II("optimization.max_update")
ema_encoder_only: bool = field(
default=True,
metadata={
"help": "whether to momentum update only the shared transformer encoder"
},
)
max_update: int = II("optimization.max_update")
modalities: D2vModalitiesConfig = D2vModalitiesConfig()
shared_decoder: Optional[D2vDecoderConfig] = None
min_target_var: float = field(
default=0.1, metadata={"help": "stop training if target var falls below this"}
)
min_pred_var: float = field(
default=0.01,
metadata={"help": "stop training if prediction var falls below this"},
)
supported_modality: Optional[Modality] = None
mae_init: bool = False
seed: int = II("common.seed")
skip_ema: bool = False
cls_loss: float = 0
recon_loss: float = 0
d2v_loss: float = 1
decoder_group: bool = False
@register_model("data2vec_multi", dataclass=Data2VecMultiConfig)
class Data2VecMultiModel(BaseFairseqModel):
def make_modality_encoder(
self,
cfg: D2vModalityConfig,
embed_dim: int,
make_block: Callable[[float], nn.ModuleList],
norm_layer: Callable[[int], nn.LayerNorm],
layer_norm_first: bool,
alibi_biases,
task,
) -> ModalitySpecificEncoder:
if cfg.type == Modality.AUDIO:
enc_cls = AudioEncoder
elif cfg.type == Modality.IMAGE:
enc_cls = ImageEncoder
elif cfg.type == Modality.TEXT:
enc_cls = TextEncoder
if hasattr(task, "text_task"):
task = task.text_task
else:
raise Exception(f"unsupported modality {cfg.type}")
return enc_cls(
cfg,
embed_dim,
make_block,
norm_layer,
layer_norm_first,
alibi_biases,
task,
)
def __init__(self, cfg: Data2VecMultiConfig, modalities, skip_ema=False, task=None):
super().__init__()
self.cfg = cfg
self.modalities = modalities
self.task = task
make_layer_norm = partial(
nn.LayerNorm, eps=cfg.norm_eps, elementwise_affine=cfg.norm_affine
)
def make_block(drop_path, dim=None, heads=None):
return AltBlock(
cfg.embed_dim if dim is None else dim,
cfg.num_heads if heads is None else heads,
cfg.mlp_ratio,
qkv_bias=True,
drop=cfg.encoder_dropout,
attn_drop=cfg.attention_dropout,
mlp_drop=cfg.activation_dropout,
post_mlp_drop=cfg.post_mlp_drop,
drop_path=drop_path,
norm_layer=make_layer_norm,
layer_norm_first=cfg.layer_norm_first,
ffn_targets=not cfg.end_of_block_targets,
)
self.alibi_biases = {}
self.modality_encoders = nn.ModuleDict()
for mod in self.modalities:
mod_cfg = getattr(cfg.modalities, mod.name.lower())
enc = self.make_modality_encoder(
mod_cfg,
cfg.embed_dim,
make_block,
make_layer_norm,
cfg.layer_norm_first,
self.alibi_biases,
task,
)
self.modality_encoders[mod.name] = enc
self.ema = None
self.average_top_k_layers = cfg.average_top_k_layers
self.loss_beta = cfg.loss_beta
self.loss_scale = cfg.loss_scale
self.dropout_input = nn.Dropout(cfg.dropout_input)
dpr = np.linspace(cfg.start_drop_path_rate, cfg.end_drop_path_rate, cfg.depth)
self.blocks = nn.ModuleList([make_block(dpr[i]) for i in range(cfg.depth)])
self.norm = None
if cfg.layer_norm_first:
self.norm = make_layer_norm(cfg.embed_dim)
if self.cfg.mae_init:
self.apply(self._init_weights)
else:
from fairseq.modules.transformer_sentence_encoder import init_bert_params
self.apply(init_bert_params)
for mod_enc in self.modality_encoders.values():
mod_enc.reset_parameters()
if not skip_ema:
self.ema = self.make_ema_teacher(cfg.ema_decay)
self.shared_decoder = (
Decoder1d(cfg.shared_decoder, cfg.embed_dim)
if self.cfg.shared_decoder is not None
else None
)
if self.shared_decoder is not None:
self.shared_decoder.apply(self._init_weights)
self.recon_proj = None
if cfg.recon_loss > 0:
self.recon_proj = nn.Linear(cfg.embed_dim, cfg.embed_dim)
for pn, p in self.named_parameters():
if len(p.shape) == 1 or pn.endswith(".bias") or "alibi_scale" in pn:
p.optim_overrides = {"optimizer": {"weight_decay_scale": 0}}
if cfg.decoder_group and "decoder" in pn:
p.param_group = "decoder"
self.num_updates = 0
def _init_weights(self, m):
try:
from apex.normalization import FusedLayerNorm
fn = FusedLayerNorm
except:
fn = nn.LayerNorm
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm) or isinstance(m, fn):
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if m.weight is not None:
nn.init.constant_(m.weight, 1.0)
@torch.no_grad()
def make_ema_teacher(self, ema_decay):
ema_config = EMAModuleConfig(
ema_decay=ema_decay,
ema_fp32=True,
log_norms=self.cfg.log_norms,
add_missing_params=False,
)
model_copy = self.make_target_model()
return EMAModule(
model_copy,
ema_config,
copy_model=False,
)
def make_target_model(self):
logger.info("making target model")
model_copy = Data2VecMultiModel(
self.cfg, self.modalities, skip_ema=True, task=self.task
)
if self.cfg.ema_encoder_only:
model_copy = model_copy.blocks
for p_s, p_t in zip(self.blocks.parameters(), model_copy.parameters()):
p_t.data.copy_(p_s.data)
else:
for p_s, p_t in zip(self.parameters(), model_copy.parameters()):
p_t.data.copy_(p_s.data)
for mod_enc in model_copy.modality_encoders.values():
mod_enc.decoder = None
if not mod_enc.modality_cfg.ema_local_encoder:
mod_enc.local_encoder = None
mod_enc.project_features = None
model_copy.requires_grad_(False)
return model_copy
def set_num_updates(self, num_updates):
super().set_num_updates(num_updates)
if self.ema is not None and (
(self.num_updates == 0 and num_updates > 1)
or self.num_updates >= num_updates
):
pass
elif self.training and self.ema is not None:
ema_weight_decay = None
if self.cfg.ema_decay != self.cfg.ema_end_decay:
if num_updates >= self.cfg.ema_anneal_end_step:
decay = self.cfg.ema_end_decay
else:
decay = get_annealed_rate(
self.cfg.ema_decay,
self.cfg.ema_end_decay,
num_updates,
self.cfg.ema_anneal_end_step,
)
self.ema.set_decay(decay, weight_decay=ema_weight_decay)
if self.ema.get_decay() < 1:
self.ema.step(self.blocks if self.cfg.ema_encoder_only else self)
self.num_updates = num_updates
def state_dict(self, destination=None, prefix="", keep_vars=False):
state = super().state_dict(destination, prefix, keep_vars)
if self.ema is not None:
state[prefix + "_ema"] = self.ema.fp32_params
return state
def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
k = prefix + "_ema"
if self.ema is not None:
assert k in state_dict
self.ema.restore(state_dict[k], True)
del state_dict[k]
elif k in state_dict:
del state_dict[k]
return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
@classmethod
def build_model(cls, cfg: Data2VecMultiConfig, task=None):
"""Build a new model instance."""
if task is None or not hasattr(task, "supported_modalities"):
modalities = (
[cfg.supported_modality]
if cfg.supported_modality is not None
else [
Modality.AUDIO,
Modality.IMAGE,
Modality.TEXT,
]
)
else:
modalities = task.supported_modalities
return cls(cfg, modalities, task=task, skip_ema=cfg.skip_ema)
def forward(
self,
source,
target=None,
id=None,
mode=None,
padding_mask=None,
mask=True,
features_only=False,
force_remove_masked=False,
remove_extra_tokens=True,
precomputed_mask=None,
corpus_key=None, # for config compatiblity
):
if mode is None:
assert self.cfg.supported_modality is not None
mode = self.cfg.supported_modality
if isinstance(mode, Modality):
mode = mode.name
feature_extractor = self.modality_encoders[mode]
mask_seeds = None
if id is not None:
mask_seeds = MaskSeed(seed=self.cfg.seed, update=self.num_updates, ids=id)
extractor_out = feature_extractor(
source,
padding_mask,
mask,
remove_masked=not features_only or force_remove_masked,
clone_batch=self.cfg.clone_batch if not features_only else 1,
mask_seeds=mask_seeds,
precomputed_mask=precomputed_mask,
)
x = extractor_out["x"]
encoder_mask = extractor_out["encoder_mask"]
masked_padding_mask = extractor_out["padding_mask"]
masked_alibi_bias = extractor_out.get("alibi_bias", None)
alibi_scale = extractor_out.get("alibi_scale", None)
if self.dropout_input is not None:
x = self.dropout_input(x)
layer_results = []
for i, blk in enumerate(self.blocks):
if (
not self.training
or self.cfg.layerdrop == 0
or (np.random.random() > self.cfg.layerdrop)
):
ab = masked_alibi_bias
if ab is not None and alibi_scale is not None:
scale = (
alibi_scale[i]
if alibi_scale.size(0) > 1
else alibi_scale.squeeze(0)
)
ab = ab * scale.type_as(ab)
x, lr = blk(
x,
padding_mask=masked_padding_mask,
alibi_bias=ab,
)
if features_only:
layer_results.append((x, lr))
if self.norm is not None:
x = self.norm(x)
if features_only:
if remove_extra_tokens:
x = x[:, feature_extractor.modality_cfg.num_extra_tokens :]
if masked_padding_mask is not None:
masked_padding_mask = masked_padding_mask[
:, feature_extractor.modality_cfg.num_extra_tokens :
]
return {
"x": x,
"padding_mask": masked_padding_mask,
"layer_results": layer_results,
"mask": encoder_mask,
}
xs = []
if self.shared_decoder is not None:
dx = self.forward_decoder(
x,
feature_extractor,
self.shared_decoder,
encoder_mask,
)
xs.append(dx)
if feature_extractor.decoder is not None:
dx = self.forward_decoder(
x,
feature_extractor,
feature_extractor.decoder,
encoder_mask,
)
xs.append(dx)
orig_x = x
assert len(xs) > 0
p = next(self.ema.model.parameters())
device = x.device
dtype = x.dtype
ema_device = p.device
ema_dtype = p.dtype
if not self.cfg.ema_same_dtype:
dtype = ema_dtype
if ema_device != device or ema_dtype != dtype:
logger.info(f"adjusting ema dtype to {dtype} and device to {device}")
self.ema.model = self.ema.model.to(dtype=dtype, device=device)
ema_dtype = dtype
def to_device(d):
for k, p in d.items():
if isinstance(d[k], dict):
to_device(d[k])
else:
d[k] = p.to(device=device)
to_device(self.ema.fp32_params)
tm = self.ema.model
with torch.no_grad():
tm.eval()
if self.cfg.ema_encoder_only:
assert target is None
ema_input = extractor_out["local_features"]
ema_input = feature_extractor.contextualized_features(
ema_input.to(dtype=ema_dtype),
padding_mask,
mask=False,
remove_masked=False,
)
ema_blocks = tm
else:
ema_blocks = tm.blocks
if feature_extractor.modality_cfg.ema_local_encoder:
inp = (
target.to(dtype=ema_dtype)
if target is not None
else source.to(dtype=ema_dtype)
)
ema_input = tm.modality_encoders[mode](
inp,
padding_mask,
mask=False,
remove_masked=False,
)
else:
assert target is None
ema_input = extractor_out["local_features"]
ema_feature_enc = tm.modality_encoders[mode]
ema_input = ema_feature_enc.contextualized_features(
ema_input.to(dtype=ema_dtype),
padding_mask,
mask=False,
remove_masked=False,
)
ema_padding_mask = ema_input["padding_mask"]
ema_alibi_bias = ema_input.get("alibi_bias", None)
ema_alibi_scale = ema_input.get("alibi_scale", None)
ema_input = ema_input["x"]
y = []
ema_x = []
extra_tokens = feature_extractor.modality_cfg.num_extra_tokens
for i, blk in enumerate(ema_blocks):
ab = ema_alibi_bias
if ab is not None and alibi_scale is not None:
scale = (
ema_alibi_scale[i]
if ema_alibi_scale.size(0) > 1
else ema_alibi_scale.squeeze(0)
)
ab = ab * scale.type_as(ab)
ema_input, lr = blk(
ema_input,
padding_mask=ema_padding_mask,
alibi_bias=ab,
)
y.append(lr[:, extra_tokens:])
ema_x.append(ema_input[:, extra_tokens:])
y = self.make_targets(y, self.average_top_k_layers)
orig_targets = y
if self.cfg.clone_batch > 1:
y = y.repeat_interleave(self.cfg.clone_batch, 0)
masked = encoder_mask.mask.unsqueeze(-1)
masked_b = encoder_mask.mask.bool()
y = y[masked_b]
if xs[0].size(1) == masked_b.size(1):
xs = [x[masked_b] for x in xs]
else:
xs = [x.reshape(-1, x.size(-1)) for x in xs]
sample_size = masked.sum().long()
result = {
"losses": {},
"sample_size": sample_size,
}
sample_size = result["sample_size"]
if self.cfg.cls_loss > 0:
assert extra_tokens > 0
cls_target = orig_targets.mean(dim=1)
if self.cfg.clone_batch > 1:
cls_target = cls_target.repeat_interleave(self.cfg.clone_batch, 0)
cls_pred = x[:, extra_tokens - 1]
result["losses"]["cls"] = self.d2v_loss(cls_pred, cls_target) * (
self.cfg.cls_loss * sample_size
)
if self.cfg.recon_loss > 0:
with torch.no_grad():
target = feature_extractor.patchify(source)
mean = target.mean(dim=-1, keepdim=True)
var = target.var(dim=-1, keepdim=True)
target = (target - mean) / (var + 1.0e-6) ** 0.5
if self.cfg.clone_batch > 1:
target = target.repeat_interleave(self.cfg.clone_batch, 0)
if masked_b is not None:
target = target[masked_b]
recon = xs[0]
if self.recon_proj is not None:
recon = self.recon_proj(recon)
result["losses"]["recon"] = (
self.d2v_loss(recon, target.float()) * self.cfg.recon_loss
)
if self.cfg.d2v_loss > 0:
for i, x in enumerate(xs):
reg_loss = self.d2v_loss(x, y)
n = f"{mode}_regression_{i}" if len(xs) > 1 else f"{mode}_regression"
result["losses"][n] = reg_loss * self.cfg.d2v_loss
suffix = "" if len(self.modalities) == 1 else f"_{mode}"
with torch.no_grad():
if encoder_mask is not None:
result["masked_pct"] = 1 - (
encoder_mask.ids_keep.size(1) / encoder_mask.ids_restore.size(1)
)
for i, x in enumerate(xs):
n = f"pred_var{suffix}_{i}" if len(xs) > 1 else f"pred_var{suffix}"
result[n] = self.compute_var(x.float())
if self.ema is not None:
for k, v in self.ema.logs.items():
result[k] = v
y = y.float()
result[f"target_var{suffix}"] = self.compute_var(y)
if self.num_updates > 5000:
if result[f"target_var{suffix}"] < self.cfg.min_target_var:
logger.error(
f"target var is {result[f'target_var{suffix}'].item()} < {self.cfg.min_target_var}, exiting ({mode})"
)
raise Exception(
f"target var is {result[f'target_var{suffix}'].item()} < {self.cfg.min_target_var}, exiting ({mode})"
)
for k in result.keys():
if k.startswith("pred_var") and result[k] < self.cfg.min_pred_var:
logger.error(
f"{k} is {result[k].item()} < {self.cfg.min_pred_var}, exiting ({mode})"
)
raise Exception(
f"{k} is {result[k].item()} < {self.cfg.min_pred_var}, exiting ({mode})"
)
result["ema_decay"] = self.ema.get_decay() * 1000
return result
def forward_decoder(
self,
x,
feature_extractor,
decoder,
mask_info,
):
x = feature_extractor.decoder_input(x, mask_info)
x = decoder(*x)
return x
def d2v_loss(self, x, y):
x = x.view(-1, x.size(-1)).float()
y = y.view(-1, x.size(-1))
if self.loss_beta == 0:
loss = F.mse_loss(x, y, reduction="none")
else:
loss = F.smooth_l1_loss(x, y, reduction="none", beta=self.loss_beta)
if self.loss_scale is not None:
scale = self.loss_scale
else:
scale = 1 / math.sqrt(x.size(-1))
reg_loss = loss * scale
return reg_loss
def make_targets(self, y, num_layers):
with torch.no_grad():
target_layer_results = y[-num_layers:]
permuted = False
if self.cfg.instance_norm_target_layer or self.cfg.batch_norm_target_layer:
target_layer_results = [
tl.transpose(1, 2) for tl in target_layer_results # BTC -> BCT
]
permuted = True
if self.cfg.batch_norm_target_layer:
target_layer_results = [
F.batch_norm(
tl.float(), running_mean=None, running_var=None, training=True
)
for tl in target_layer_results
]
if self.cfg.instance_norm_target_layer:
target_layer_results = [
F.instance_norm(tl.float()) for tl in target_layer_results
]
if permuted:
target_layer_results = [
tl.transpose(1, 2) for tl in target_layer_results # BCT -> BTC
]
if self.cfg.layer_norm_target_layer:
target_layer_results = [
F.layer_norm(tl.float(), tl.shape[-1:])
for tl in target_layer_results
]
y = target_layer_results[0].float()
for tl in target_layer_results[1:]:
y.add_(tl.float())
y = y.div_(len(target_layer_results))
if self.cfg.layer_norm_targets:
y = F.layer_norm(y, y.shape[-1:])
if self.cfg.instance_norm_targets:
y = F.instance_norm(y.transpose(1, 2)).transpose(1, 2)
return y
@staticmethod
def compute_var(y):
y = y.view(-1, y.size(-1))
if dist.is_initialized():
zc = torch.tensor(y.size(0)).cuda()
zs = y.sum(dim=0)
zss = (y**2).sum(dim=0)
dist.all_reduce(zc)
dist.all_reduce(zs)
dist.all_reduce(zss)
var = zss / (zc - 1) - (zs**2) / (zc * (zc - 1))
return torch.sqrt(var + 1e-6).mean()
else:
return torch.sqrt(y.var(dim=0) + 1e-6).mean()
def extract_features(
self, source, mode=None, padding_mask=None, mask=False, remove_extra_tokens=True
):
res = self.forward(
source,
mode=mode,
padding_mask=padding_mask,
mask=mask,
features_only=True,
remove_extra_tokens=remove_extra_tokens,
)
return res
def remove_pretraining_modules(self, modality=None, keep_decoder=False):
self.ema = None
self.cfg.clone_batch = 1
self.recon_proj = None
if not keep_decoder:
self.shared_decoder = None
modality = modality.lower() if modality is not None else None
for k in list(self.modality_encoders.keys()):
if modality is not None and k.lower() != modality:
del self.modality_encoders[k]
else:
self.modality_encoders[k].remove_pretraining_modules(
keep_decoder=keep_decoder
)
if not keep_decoder:
self.modality_encoders[k].decoder = None
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