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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.
from dataclasses import dataclass, field
import torch
from fairseq import utils
from fairseq.logging import metrics
from fairseq.criterions import register_criterion
from fairseq.criterions.label_smoothed_cross_entropy import (
LabelSmoothedCrossEntropyCriterion,
LabelSmoothedCrossEntropyCriterionConfig,
)
try:
from simuleval.metrics.latency import (
AverageLagging,
AverageProportion,
DifferentiableAverageLagging,
)
LATENCY_METRICS = {
"average_lagging": AverageLagging,
"average_proportion": AverageProportion,
"differentiable_average_lagging": DifferentiableAverageLagging,
}
except ImportError:
LATENCY_METRICS = None
@dataclass
class LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig(
LabelSmoothedCrossEntropyCriterionConfig
):
latency_avg_weight: float = field(
default=0.0,
metadata={"help": "weight fot average latency loss."},
)
latency_var_weight: float = field(
default=0.0,
metadata={"help": "weight fot variance latency loss."},
)
latency_avg_type: str = field(
default="differentiable_average_lagging",
metadata={"help": "latency type for average loss"},
)
latency_var_type: str = field(
default="variance_delay",
metadata={"help": "latency typ for variance loss"},
)
latency_gather_method: str = field(
default="weighted_average",
metadata={"help": "method to gather latency loss for all heads"},
)
latency_update_after: int = field(
default=0,
metadata={"help": "Add latency loss after certain steps"},
)
@register_criterion(
"latency_augmented_label_smoothed_cross_entropy",
dataclass=LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig,
)
class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
LabelSmoothedCrossEntropyCriterion
):
def __init__(
self,
task,
sentence_avg,
label_smoothing,
ignore_prefix_size,
report_accuracy,
latency_avg_weight,
latency_var_weight,
latency_avg_type,
latency_var_type,
latency_gather_method,
latency_update_after,
):
super().__init__(
task, sentence_avg, label_smoothing, ignore_prefix_size, report_accuracy
)
assert LATENCY_METRICS is not None, "Please make sure SimulEval is installed."
self.latency_avg_weight = latency_avg_weight
self.latency_var_weight = latency_var_weight
self.latency_avg_type = latency_avg_type
self.latency_var_type = latency_var_type
self.latency_gather_method = latency_gather_method
self.latency_update_after = latency_update_after
def forward(self, model, sample, reduce=True):
net_output = model(**sample["net_input"])
# 1. Compute cross entropy loss
loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
# 2. Compute cross latency loss
latency_loss, expected_latency, expected_delays_var = self.compute_latency_loss(
model, sample, net_output
)
if self.latency_update_after > 0:
num_updates = getattr(model.decoder, "num_updates", None)
assert (
num_updates is not None
), "model.decoder doesn't have attribute 'num_updates'"
if num_updates <= self.latency_update_after:
latency_loss = 0
loss += latency_loss
sample_size = (
sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
)
logging_output = {
"loss": loss.data,
"nll_loss": nll_loss.data,
"ntokens": sample["ntokens"],
"nsentences": sample["target"].size(0),
"sample_size": sample_size,
"latency": expected_latency,
"delays_var": expected_delays_var,
"latency_loss": latency_loss,
}
if self.report_accuracy:
n_correct, total = self.compute_accuracy(model, net_output, sample)
logging_output["n_correct"] = utils.item(n_correct.data)
logging_output["total"] = utils.item(total.data)
return loss, sample_size, logging_output
def compute_latency_loss(self, model, sample, net_output):
assert (
net_output[-1].encoder_padding_mask is None
or not net_output[-1].encoder_padding_mask[:, 0].any()
), "Only right padding on source is supported."
# 1. Obtain the expected alignment
alpha_list = [item["alpha"] for item in net_output[1].attn_list]
num_layers = len(alpha_list)
bsz, num_heads, tgt_len, src_len = alpha_list[0].size()
# bsz * num_layers * num_heads, tgt_len, src_len
alpha_all = torch.cat(alpha_list, dim=1).view(-1, tgt_len, src_len)
# 2 compute expected delays
# bsz * num_heads * num_layers, tgt_len, src_len for MMA
steps = (
torch.arange(1, 1 + src_len)
.unsqueeze(0)
.unsqueeze(1)
.expand_as(alpha_all)
.type_as(alpha_all)
)
expected_delays = torch.sum(steps * alpha_all, dim=-1)
target_padding_mask = (
model.get_targets(sample, net_output)
.eq(self.padding_idx)
.unsqueeze(1)
.expand(bsz, num_layers * num_heads, tgt_len)
.contiguous()
.view(-1, tgt_len)
)
src_lengths = (
sample["net_input"]["src_lengths"]
.unsqueeze(1)
.expand(bsz, num_layers * num_heads)
.contiguous()
.view(-1)
)
expected_latency = LATENCY_METRICS[self.latency_avg_type](
expected_delays, src_lengths, None, target_padding_mask=target_padding_mask
)
# 2.1 average expected latency of heads
# bsz, num_layers * num_heads
expected_latency = expected_latency.view(bsz, -1)
if self.latency_gather_method == "average":
# bsz * tgt_len
expected_latency = expected_delays.mean(dim=1)
elif self.latency_gather_method == "weighted_average":
weights = torch.nn.functional.softmax(expected_latency, dim=1)
expected_latency = torch.sum(expected_latency * weights, dim=1)
elif self.latency_gather_method == "max":
expected_latency = expected_latency.max(dim=1)[0]
else:
raise NotImplementedError
expected_latency = expected_latency.sum()
avg_loss = self.latency_avg_weight * expected_latency
# 2.2 variance of expected delays
expected_delays_var = (
expected_delays.view(bsz, -1, tgt_len).var(dim=1).mean(dim=1)
)
expected_delays_var = expected_delays_var.sum()
var_loss = self.latency_avg_weight * expected_delays_var
# 3. Final loss
latency_loss = avg_loss + var_loss
return latency_loss, expected_latency, expected_delays_var
@classmethod
def reduce_metrics(cls, logging_outputs) -> None:
super().reduce_metrics(logging_outputs)
latency = sum(log.get("latency", 0) for log in logging_outputs)
delays_var = sum(log.get("delays_var", 0) for log in logging_outputs)
latency_loss = sum(log.get("latency_loss", 0) for log in logging_outputs)
nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
metrics.log_scalar("latency", latency.float() / nsentences, nsentences, round=3)
metrics.log_scalar("delays_var", delays_var / nsentences, nsentences, round=3)
metrics.log_scalar(
"latency_loss", latency_loss / nsentences, nsentences, round=3
)
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