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# --------------------------------------------------------
# Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data (https://arxiv.org/abs/2203.17113)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/Speech2C
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on fairseq code bases
# https://github.com/pytorch/fairseq
# --------------------------------------------------------
import math
import re
from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.criterions.label_smoothed_cross_entropy import label_smoothed_nll_loss
from fairseq.criterions.hubert_criterion import HubertCriterionConfig
@dataclass
class Speech2cCriterionConfig(HubertCriterionConfig):
dec_weight: float = field(
default=1.0,
metadata={"help": "weights for decoder CE Loss, loss will be (hubert_loss + dec_weight * CE_Loss)"},
)
report_accuracy: bool = field(
default=True,
metadata={"help": "report decoder accuracy metric"},
)
ignore_prefix_size: int = field(
default=0,
metadata={"help": "Ignore first N tokens"},
)
label_smoothing: float = field(
default=0.0,
metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
)
@register_criterion("speech2c", dataclass=Speech2cCriterionConfig)
class Speech2cCriterion(FairseqCriterion):
def __init__(self, task, pred_masked_weight, pred_nomask_weight, loss_weights=None, log_keys=None, dec_weight=1.0, report_accuracy=False, ignore_prefix_size=0, label_smoothing=0.0):
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
self.dec_weight = dec_weight
self.report_accuracy = report_accuracy
self.ignore_prefix_size = ignore_prefix_size
self.eps = label_smoothing
self.padding_idx = task.dictionaries[0].pad()
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 = 0.0
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()
if "decoder_target" in sample:
dec_sample_size = sample["dec_ntokens"]
dec_loss, dec_nll_loss = self.compute_ce_loss(model, net_output["decoder_out"], sample, reduce=reduce)
loss = loss + (self.dec_weight * dec_loss * sample_size / dec_sample_size)
logging_output["dec_loss"] = dec_loss.item()
logging_output["dec_nll_loss"] = dec_nll_loss.item()
logging_output["dec_sample_size"] = dec_sample_size
if self.report_accuracy:
n_correct, total = self.compute_accuracy(model, net_output["decoder_out"], sample)
logging_output["dec_n_correct"] = utils.item(n_correct.data)
logging_output["total"] = utils.item(total.data)
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
def compute_ce_loss(self, model, net_output, sample, reduce=True):
lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
loss, nll_loss = label_smoothed_nll_loss(
lprobs,
target,
self.eps,
ignore_index=self.padding_idx,
reduce=reduce,
)
return loss, nll_loss
def compute_accuracy(self, model, net_output, sample):
lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
mask = target.ne(self.padding_idx)
n_correct = torch.sum(
lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
)
total = torch.sum(mask)
return n_correct, total
def get_lprobs_and_target(self, model, net_output, sample):
lprobs = model.get_normalized_probs(net_output, log_probs=True)
target = sample["decoder_target"]
if self.ignore_prefix_size > 0:
if getattr(lprobs, "batch_first", False):
lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
target = target[:, self.ignore_prefix_size :].contiguous()
else:
lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
target = target[self.ignore_prefix_size :, :].contiguous()
return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
@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)])
if "dec_loss" in logging_outputs[0]:
dec_loss_sum = sum(log.get("dec_loss", 0) for log in logging_outputs)
dec_nll_loss_sum = sum(log.get("dec_nll_loss", 0) for log in logging_outputs)
dec_sample_size = sum(log.get("dec_sample_size", 0) for log in logging_outputs)
metrics.log_scalar(
"dec_loss", dec_loss_sum / dec_sample_size / math.log(2), dec_sample_size, round=3
)
metrics.log_scalar(
"dec_nll_loss", dec_nll_loss_sum / dec_sample_size / math.log(2), dec_sample_size, round=3
)
metrics.log_derived(
"dec_ppl", lambda meters: utils.get_perplexity(meters["dec_nll_loss"].avg)
)
total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
if total > 0:
metrics.log_scalar("total", total)
n_correct = utils.item(
sum(log.get("dec_n_correct", 0) for log in logging_outputs)
)
metrics.log_scalar("dec_n_correct", n_correct)
metrics.log_derived(
"dec_accuracy",
lambda meters: round(
meters["dec_n_correct"].sum * 100.0 / meters["total"].sum, 3
)
if meters["total"].sum > 0
else float("nan"),
)
@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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