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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
from dataclasses import dataclass, field
from typing import Optional
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
@dataclass
class SpeechDLMCriterionConfig(FairseqDataclass):
sentence_avg: bool = II("optimization.sentence_avg")
main_and_cross_weights: Optional[str] = field(
default="1,0",
metadata={
"help": "Comma-separated list of weights of Main-channel vs Cross-channel Prediction Losses"
"(default: 1,0)"
},
)
general_unit_loss_weight: float = field(
default=0,
metadata={
"help": "The weight of the General Prediction Loss (Next-step Unit Prediction Loss)"
"(default: 0)"
},
)
edge_unit_loss_weight: float = field(
default=1,
metadata={"help": "The weight of the Edge Unit Prediction Loss" "(default: 1)"},
)
duration_loss_weight: float = field(
default=1,
metadata={
"help": "The weight of the Edge Unit Duration Prediction Loss"
"(default: 1)"
},
)
@register_criterion("speech_dlm_criterion", dataclass=SpeechDLMCriterionConfig)
class SpeechDLMCriterion(FairseqCriterion):
"""Criteron for the SpeechDLM model as described in the paper:
https://arxiv.org/pdf/2203.16502.pdf
There are 3 possible losses depending on the targets of the model:
- general_unit_loss : The next unit prediction loss, corresponding to
'next' target
- edge_unit_loss : The edge unit prediction loss, corresponding to
'edge' target
- duration_loss : The duration prediction loss, corresponding to
'duration' target
"""
def __init__(
self,
task,
sentence_avg,
main_and_cross_weights,
general_unit_loss_weight,
edge_unit_loss_weight,
duration_loss_weight,
):
super().__init__(task)
self.sentence_avg = sentence_avg
self.channels = task.channels
self.targets = task.targets
self.delayed_duration_target = task.delayed_duration_target
self.main_channel_weight = float(main_and_cross_weights.split(",")[0])
self.cross_channel_weight = float(main_and_cross_weights.split(",")[1])
assert self.main_channel_weight >= 0 and self.cross_channel_weight >= 0
self.channel_weights = {
channel: weight
for channel, weight in zip(self.channels, task.channel_weights)
}
self.target_weights = {}
for t in self.targets:
if t == "next":
self.target_weights[t] = general_unit_loss_weight
assert (
general_unit_loss_weight > 0
), "Expect a positive --general-unit-loss-weight for next unit prediction"
elif t == "edge":
self.target_weights[t] = edge_unit_loss_weight
assert (
edge_unit_loss_weight > 0
), "Expect a positive --edge-unit-loss-weight for edge unit prediction"
elif t == "duration":
self.target_weights[t] = duration_loss_weight
assert (
duration_loss_weight > 0
), "Expect a positive --duration-loss-weight for duration prediction"
def forward(self, model, sample, reduce=True):
"""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(**sample["net_input"])
loss_dict, stats_dict = self.compute_loss(
model, net_output, sample, reduce=reduce
)
nsentences = sample["net_input"]["src_tokens"][self.channels[0]].size(0)
logging_output = {
"nsentences": nsentences,
}
logging_output["nsentences"] = nsentences
loss_all = {t: 0 for t in self.targets}
correct_all = {t: 0 for t in self.targets}
count_all = {t: 0 for t in self.targets}
ntokens_all = 0
sample_size_all = 0
for channel in loss_dict:
for pred_channel in loss_dict[channel]:
# Get ntokens & sample_size
ntokens = sample["net_input"]["src_tokens"][channel].numel()
sample_size = nsentences if self.sentence_avg else ntokens
prefix = "[{}-{}]".format(channel, pred_channel)
log_keys = {
"next": "general_token",
"edge": "edge_token",
"duration": "edge_duration",
}
# Log & Update the sizes
logging_output["{}ntokens".format(prefix)] = ntokens
logging_output["{}sample_size".format(prefix)] = sample_size
ntokens_all += ntokens
sample_size_all += sample_size
for t in self.targets:
log_key = log_keys[t]
loss = loss_dict[channel][pred_channel][t]
correct, count = stats_dict[channel][pred_channel][t]
# Log the statistics
logging_output["{}{}_loss".format(prefix, log_key)] = loss.data
logging_output["{}{}_correct".format(prefix, log_key)] = correct
logging_output["{}{}_count".format(prefix, log_key)] = count
# Scale the training loss by weights
target_loss = loss * self.channel_weights[channel]
if pred_channel == channel:
target_loss = target_loss * self.main_channel_weight
else:
target_loss = target_loss * self.cross_channel_weight
# Normalize the losses in the training by the number of edges
if t in ["edge", "duration"]:
target_loss = target_loss / count * sample_size
# Update the statistics
loss_all[t] += target_loss
correct_all[t] += correct
count_all[t] += count
# Logging the average statistics
logging_output["ntokens"] = ntokens_all
logging_output["sample_size"] = sample_size_all
for t in self.targets:
log_key = {
"next": "general_token",
"edge": "edge_token",
"duration": "edge_duration",
}[t]
logging_output["{}_loss".format(log_key)] = loss_all[t].data
logging_output["{}_correct".format(log_key)] = correct_all[t]
logging_output["{}_count".format(log_key)] = count_all[t]
# Define the training loss
training_loss = 0
for t in self.targets:
training_loss += loss_all[t] * self.target_weights[t]
logging_output["loss"] = training_loss.data
return training_loss, sample_size_all, logging_output
def compute_loss(self, model, net_output, sample, reduce=True):
# Get the model outputs and target
lprobs_dict = model.get_normalized_probs(net_output, log_probs=True)
target_dict = model.get_targets(sample, net_output)
# Init the dictionaries
loss_dict, stats_dict = {}, {}
for channel in lprobs_dict:
# Init the dictionaries
loss_dict[channel], stats_dict[channel] = {}, {}
for pred_channel in lprobs_dict[channel]:
# Init the dictionaries
loss_dict[channel][pred_channel] = {}
stats_dict[channel][pred_channel] = {}
# Get token & duration predictions
outputs = lprobs_dict[channel][pred_channel]
if not isinstance(outputs, dict):
token_lprobs = outputs
else:
token_lprobs = outputs["pred_token"]
dur_preds = outputs["pred_duration"]
dur_preds = dur_preds.view(-1)
token_lprobs = token_lprobs.view(-1, token_lprobs.size(-1))
token_preds = token_lprobs.argmax(dim=-1)
# Get edge indices
if "edge" in self.targets or "duration" in self.targets:
edge_indices = target_dict["edge_indices"][pred_channel]
# Compute loss and statistics
for t in self.targets:
if t in ["next", "edge"]:
if t == "next":
target = target_dict["next"][pred_channel].view(-1)
lprobs = token_lprobs
preds = token_preds
elif t == "edge":
target = target_dict["edge"][pred_channel]
lprobs = token_lprobs[edge_indices]
preds = token_preds[edge_indices]
loss = F.nll_loss(
lprobs,
target,
ignore_index=self.padding_idx,
reduction="sum" if reduce else "none",
)
elif t == "duration":
target = target_dict["duration"][pred_channel]
if self.delayed_duration_target:
duration_indices = edge_indices + 1
if duration_indices[-1] == len(dur_preds):
duration_indices = duration_indices[:-1]
target = target[:-1]
else:
duration_indices = edge_indices
preds = dur_preds[duration_indices]
loss = F.l1_loss(
preds,
target,
reduction="sum" if reduce else "none",
)
preds = preds.round()
correct = (preds == target).sum().float().cpu().item()
count = float(target.size(0))
loss_dict[channel][pred_channel][t] = loss
stats_dict[channel][pred_channel][t] = (correct, count)
return loss_dict, stats_dict
@staticmethod
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
logging_keys = next(iter(logging_outputs)).keys()
channels = [item[:-7] for item in logging_keys if item.endswith("ntokens")]
target_prefixes = set(
[
item[:-5].split("]")[-1]
for item in logging_keys
if item.endswith("_loss")
]
)
for channel_prefix in channels:
for target_prefix in target_prefixes:
prefix = "{}{}".format(channel_prefix, target_prefix)
count_sum = sum(
log.get("{}_count".format(prefix), 0) for log in logging_outputs
)
correct_sum = sum(
log.get("{}_correct".format(prefix), 0) for log in logging_outputs
)
loss_sum = sum(
log.get("{}_loss".format(prefix), 0) for log in logging_outputs
)
if "duration" not in target_prefix:
# we divide by log(2) to convert the loss from base e to base 2
metrics.log_scalar(
"{}_loss".format(prefix),
loss_sum / count_sum / math.log(2),
count_sum,
round=3,
)
metrics.log_derived(
"{}_ppl".format(prefix),
lambda meters, prefix=prefix: utils.get_perplexity(
meters["{}_loss".format(prefix)].avg
),
)
else:
# for duration we don't need to divide by log(2)
metrics.log_scalar(
"{}_loss".format(prefix),
loss_sum / count_sum,
count_sum,
round=3,
)
accuracy = 100 * correct_sum / count_sum
metrics.log_scalar("{}_pred_acc".format(prefix), accuracy, round=3)
# Logging training loss
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
# we divide by log(2) to convert the loss from base e to base 2
metrics.log_scalar(
"loss", loss_sum / sample_size / math.log(2), sample_size, round=3
)
@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 True
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