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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 math
import re
from dataclasses import dataclass, field
from typing import List, Optional
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
@dataclass
class HubertCriterionConfig(FairseqDataclass):
pred_masked_weight: float = field(
default=1.0,
metadata={"help": "weight for predictive loss for masked frames"},
)
pred_nomask_weight: float = field(
default=0.0,
metadata={"help": "weight for predictive loss for unmasked frames"},
)
loss_weights: Optional[List[float]] = field(
default=None,
metadata={"help": "weights for additional loss terms (not first one)"},
)
log_keys: List[str] = field(
default_factory=lambda: [],
metadata={"help": "output keys to log"},
)
@register_criterion("hubert", dataclass=HubertCriterionConfig)
class HubertCriterion(FairseqCriterion):
def __init__(self, task, pred_masked_weight, pred_nomask_weight, loss_weights=None, log_keys=None):
super().__init__(task)
self.pred_masked_weight = pred_masked_weight
self.pred_nomask_weight = pred_nomask_weight
self.loss_weights = loss_weights
self.log_keys = [] if log_keys is None else log_keys
def forward(self, model, sample, reduce=True, log_pred=False):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
net_output = model(target_list=sample["target_list"], **sample["net_input"])
loss = 0.
sample_size = 0
logging_output = {}
reduction = "sum" if reduce else "none"
loss_m_list = []
logp_m_list = model.get_logits(net_output, True)
targ_m_list = model.get_targets(net_output, True)
assert self.pred_masked_weight == 0 or len(logp_m_list) > 0
for i, (logp_m, targ_m) in enumerate(zip(logp_m_list, targ_m_list)):
loss_m = F.cross_entropy(logp_m, targ_m, reduction=reduction)
loss_m_list.append(loss_m)
logging_output[f"loss_m_{i}"] = loss_m.detach().item()
if self.pred_masked_weight > 0:
loss += self.pred_masked_weight * sum(loss_m_list)
sample_size += targ_m_list[0].numel()
loss_u_list = []
logp_u_list = model.get_logits(net_output, False)
targ_u_list = model.get_targets(net_output, False)
assert self.pred_nomask_weight == 0 or len(logp_u_list) > 0
for i, (logp_u, targ_u) in enumerate(zip(logp_u_list, targ_u_list)):
loss_u = F.cross_entropy(logp_u, targ_u, reduction=reduction)
loss_u_list.append(loss_u)
logging_output[f"loss_u_{i}"] = loss_u.detach().item()
if self.pred_nomask_weight > 0:
loss += self.pred_nomask_weight * sum(loss_u_list)
sample_size += targ_u_list[0].numel()
if self.loss_weights is not None:
assert hasattr(model, "get_extra_losses")
extra_losses, names = model.get_extra_losses(net_output)
if torch.is_tensor(extra_losses):
extra_losses = [extra_losses]
names = [names]
if len(self.loss_weights) == 1 and len(extra_losses) != 1:
self.loss_weights = [self.loss_weights[0]] * len(extra_losses)
assert len(extra_losses) == len(self.loss_weights), f"{len(extra_losses)}, {len(self.loss_weights)}"
for p, n, coef in zip(extra_losses, names, self.loss_weights):
if coef != 0 and p is not None:
p = coef * p.float() * sample_size
loss += p
logging_output[f"loss_{n}"] = p.item()
logging_output = {
"loss": loss.item() if reduce else loss,
"ntokens": sample_size,
"nsentences": sample["id"].numel(),
"sample_size": sample_size,
**logging_output,
}
for lk in self.log_keys:
if lk in net_output:
logging_output[lk] = float((net_output[lk]))
def compute_correct(logits):
if logits.numel() == 0:
return 0, 0
else:
assert logits.dim() > 1, logits.shape
max = logits.argmax(-1) == 0
min = logits.argmin(-1) == 0
both = max & min
corr = max.long().sum().item() - both.long().sum().item()
count = max.numel()
return corr, count
with torch.no_grad():
for i, logp_m in enumerate(logp_m_list):
corr_m, count_m = compute_correct(logp_m)
logging_output[f"correct_m_{i}"] = corr_m
logging_output[f"count_m_{i}"] = count_m
for i, logp_u in enumerate(logp_u_list):
corr_u, count_u = compute_correct(logp_u)
logging_output[f"correct_u_{i}"] = corr_u
logging_output[f"count_u_{i}"] = count_u
return loss, sample_size, logging_output
@staticmethod
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training (copied from normal cross entropy)."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
metrics.log_scalar("loss", loss_sum / sample_size / math.log(2), sample_size, round=3)
if sample_size != ntokens:
metrics.log_scalar("nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3)
metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg))
else:
metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["loss"].avg))
counts = {}
for lk in logging_outputs[0].keys():
if lk.startswith("count_"):
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val)
counts[lk] = val
for lk in logging_outputs[0].keys():
if lk.startswith("loss_"):
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val / sample_size / math.log(2), round=3)
elif lk.startswith("correct_"):
val = sum(log[lk] for log in logging_outputs)
metrics.log_scalar(lk, val / counts[re.sub("correct", "count", lk)])
@staticmethod
def aggregate_logging_outputs(logging_outputs):
"""Aggregate logging outputs from data parallel training."""
raise NotImplementedError()
@staticmethod
def logging_outputs_can_be_summed() -> bool:
"""
Whether the logging outputs returned by `forward` can be summed
across workers prior to calling `reduce_metrics`. Setting this
to True will improves distributed training speed.
"""
return False
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