OFA-OCR / criterions /label_smoothed_encouraging_loss.py
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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
import torch.nn.functional as F
import numpy as np
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II
@dataclass
class AdjustLabelSmoothedEncouragingLossConfig(FairseqDataclass):
label_smoothing: float = field(
default=0.0,
metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
)
report_accuracy: bool = field(
default=False,
metadata={"help": "report accuracy metric"},
)
ignore_prefix_size: int = field(
default=0,
metadata={"help": "Ignore first N tokens"},
)
ignore_eos: bool = field(
default=False,
metadata={"help": "Ignore eos token"},
)
sentence_avg: bool = II("optimization.sentence_avg")
drop_worst_ratio: float = field(
default=0.0,
metadata={"help": "ratio for discarding bad samples"},
)
drop_worst_after: int = field(
default=0,
metadata={"help": "steps for discarding bad samples"},
)
use_rdrop: bool = field(
default=False, metadata={"help": "use R-Drop"}
)
reg_alpha: float = field(
default=1.0, metadata={"help": "weight for R-Drop"}
)
sample_patch_num: int = field(
default=196, metadata={"help": "sample patchs for v1"}
)
constraint_range: Optional[str] = field(
default=None,
metadata={"help": "constraint range"}
)
log_end: float = field(
default=0.75,
metadata={"help": "higher log_end is for cases with higher performance,"
" we recommend 0.75 or 0.5 as your first try."}
)
drop_best_ratio: float = field(
default=0.0,
metadata={"help": "ratio for discarding best samples"},
)
drop_best_after: int = field(
default=0,
metadata={"help": "steps for discarding best samples"},
)
def construct_rdrop_sample(x):
if isinstance(x, dict):
for key in x:
x[key] = construct_rdrop_sample(x[key])
return x
elif isinstance(x, torch.Tensor):
return x.repeat(2, *([1] * (x.dim()-1)))
elif isinstance(x, int):
return x * 2
elif isinstance(x, np.ndarray):
return x.repeat(2)
else:
raise NotImplementedError
def kl_loss(p, q):
p_loss = F.kl_div(p, torch.exp(q), reduction='sum')
q_loss = F.kl_div(q, torch.exp(p), reduction='sum')
loss = (p_loss + q_loss) / 2
return loss
def label_smoothed_nll_loss(
lprobs, target, epsilon, update_num, reduce=True,
drop_worst_ratio=0.0, drop_worst_after=0, use_rdrop=False, reg_alpha=1.0,
constraint_masks=None, constraint_start=None, constraint_end=None, drop_best_ratio=0.0,
drop_best_after=0,
):
if target.dim() == lprobs.dim() - 1:
target = target.unsqueeze(-1)
nll_loss = -lprobs.gather(dim=-1, index=target).squeeze(-1)
if constraint_masks is not None:
smooth_loss = -lprobs.masked_fill(~constraint_masks, 0).sum(dim=-1, keepdim=True).squeeze(-1)
eps_i = epsilon / (constraint_masks.sum(1) - 1 + 1e-6)
elif constraint_start is not None and constraint_end is not None:
constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
smooth_loss = -lprobs[:, constraint_range].sum(dim=-1, keepdim=True).squeeze(-1)
eps_i = epsilon / (len(constraint_range) - 1 + 1e-6)
else:
smooth_loss = -lprobs.sum(dim=-1, keepdim=True).squeeze(-1)
eps_i = epsilon / (lprobs.size(-1) - 1)
loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
if drop_worst_ratio > 0 and update_num > drop_worst_after:
if use_rdrop:
true_batch_size = loss.size(0) // 2
_, indices = torch.topk(loss[:true_batch_size], k=int(true_batch_size * (1 - drop_worst_ratio)), largest=False)
loss = torch.cat([loss[indices], loss[indices+true_batch_size]])
nll_loss = torch.cat([nll_loss[indices], nll_loss[indices+true_batch_size]])
lprobs = torch.cat([lprobs[indices], lprobs[indices+true_batch_size]])
else:
loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_worst_ratio)), largest=False)
nll_loss = nll_loss[indices]
lprobs = lprobs[indices]
target = target[indices]
if update_num > drop_best_after:
loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_best_ratio)), largest=True)
nll_loss = nll_loss[indices]
lprobs = lprobs[indices]
target = target[indices]
ntokens = loss.numel()
nll_loss = nll_loss.sum()
loss = loss.sum()
if use_rdrop:
true_batch_size = lprobs.size(0) // 2
p = lprobs[:true_batch_size]
q = lprobs[true_batch_size:]
if constraint_start is not None and constraint_end is not None:
constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
p = p[:, constraint_range]
q = q[:, constraint_range]
loss += kl_loss(p, q) * reg_alpha
return loss, nll_loss, ntokens,lprobs,target
@register_criterion(
"adjust_label_smoothed_encouraging_loss", dataclass=AdjustLabelSmoothedEncouragingLossConfig
)
class AdjustLabelSmoothedEncouragingLossCriterion(FairseqCriterion):
def __init__(
self,
task,
sentence_avg,
label_smoothing,
ignore_prefix_size=0,
ignore_eos=False,
report_accuracy=False,
drop_worst_ratio=0,
drop_worst_after=0,
use_rdrop=False,
reg_alpha=1.0,
sample_patch_num=196,
constraint_range=None,
log_end=0.75,
drop_best_ratio=0.0,
drop_best_after=0,
):
super().__init__(task)
self.sentence_avg = sentence_avg
self.eps = label_smoothing
self.ignore_prefix_size = ignore_prefix_size
self.ignore_eos = ignore_eos
self.report_accuracy = report_accuracy
self.drop_worst_ratio = drop_worst_ratio
self.drop_worst_after = drop_worst_after
self.use_rdrop = use_rdrop
self.reg_alpha = reg_alpha
self.sample_patch_num = sample_patch_num
self.constraint_start = None
self.constraint_end = None
if constraint_range is not None:
constraint_start, constraint_end = constraint_range.split(',')
self.constraint_start = int(constraint_start)
self.constraint_end = int(constraint_end)
self.log_end = log_end
self.drop_best_ratio = drop_best_ratio
self.drop_best_after = drop_best_after
print('el, self.log_end=', self.log_end)
# @staticmethod
# def add_args(parser):
# """Add criterion-specific arguments to the parser."""
# # fmt: off
# parser.add_argument('--log_end', type=float, default=1.0)
def forward(self, model, sample, update_num=0, 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
"""
if isinstance(sample, list):
if self.sample_patch_num > 0:
sample[0]['net_input']['sample_patch_num'] = self.sample_patch_num
loss_v1, sample_size_v1, logging_output_v1 = self.forward(model, sample[0], update_num, reduce)
loss_v2, sample_size_v2, logging_output_v2 = self.forward(model, sample[1], update_num, reduce)
loss = loss_v1 / sample_size_v1 + loss_v2 / sample_size_v2
sample_size = 1
logging_output = {
"loss": loss.data,
"loss_v1": loss_v1.data,
"loss_v2": loss_v2.data,
"nll_loss": logging_output_v1["nll_loss"].data / sample_size_v1 + logging_output_v2["nll_loss"].data / sample_size_v2,
"ntokens": logging_output_v1["ntokens"] + logging_output_v2["ntokens"],
"nsentences": logging_output_v1["nsentences"] + logging_output_v2["nsentences"],
"sample_size": 1,
"sample_size_v1": sample_size_v1,
"sample_size_v2": sample_size_v2,
}
return loss, sample_size, logging_output
if self.use_rdrop:
construct_rdrop_sample(sample)
net_output = model(**sample["net_input"])
loss, nll_loss, ntokens = self.compute_loss(model, net_output, sample, update_num, reduce=reduce)
sample_size = (
sample["target"].size(0) if self.sentence_avg else ntokens
)
logging_output = {
"loss": loss.data,
"nll_loss": nll_loss.data,
"ntokens": sample["ntokens"],
"nsentences": sample["nsentences"],
"sample_size": sample_size,
}
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 get_lprobs_and_target(self, model, net_output, sample):
conf = sample['conf'][:, None, None] if 'conf' in sample and sample['conf'] is not None else 1
constraint_masks = None
if "constraint_masks" in sample and sample["constraint_masks"] is not None:
constraint_masks = sample["constraint_masks"]
net_output[0].masked_fill_(~constraint_masks, -math.inf)
if self.constraint_start is not None and self.constraint_end is not None:
net_output[0][:, :, 4:self.constraint_start] = -math.inf
net_output[0][:, :, self.constraint_end:] = -math.inf
lprobs = model.get_normalized_probs(net_output, log_probs=True) * conf
target = model.get_targets(sample, net_output)
if self.ignore_prefix_size > 0:
lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
target = target[:, self.ignore_prefix_size :].contiguous()
if constraint_masks is not None:
constraint_masks = constraint_masks[:, self.ignore_prefix_size :, :].contiguous()
if self.ignore_eos:
bsz, seq_len, embed_dim = lprobs.size()
eos_indices = target.eq(self.task.tgt_dict.eos())
lprobs = lprobs[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
target = target[~eos_indices].reshape(bsz, seq_len-1)
if constraint_masks is not None:
constraint_masks = constraint_masks[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
if constraint_masks is not None:
constraint_masks = constraint_masks.view(-1, constraint_masks.size(-1))
return lprobs.view(-1, lprobs.size(-1)), target.view(-1), constraint_masks
def compute_loss(self, model, net_output, sample, update_num, reduce=True):
lprobs, target, constraint_masks = self.get_lprobs_and_target(model, net_output, sample)
if constraint_masks is not None:
constraint_masks = constraint_masks[target != self.padding_idx]
lprobs = lprobs[target != self.padding_idx]
target = target[target != self.padding_idx]
loss, nll_loss, ntokens, lprobs, target = label_smoothed_nll_loss(
lprobs,
target,
self.eps,
update_num,
reduce=reduce,
drop_worst_ratio=self.drop_worst_ratio,
drop_worst_after=self.drop_worst_after,
use_rdrop=self.use_rdrop,
reg_alpha=self.reg_alpha,
constraint_masks=constraint_masks,
constraint_start=self.constraint_start,
constraint_end=self.constraint_end
)
# for encouraging loss
probs = torch.exp(lprobs)
bonus = torch.log(torch.clamp((torch.ones_like(probs) - probs), min=1e-5)) # likelihood bonus
log_end = self.log_end
if log_end != 1.0: # e.g. 0.9
y_log_end = torch.log(torch.ones_like(probs) - log_end)
bonus_after_log_end = 1 / (log_end - torch.ones_like(probs)) * (probs - log_end) + y_log_end
# x:log_end, y torch.log(torch.clamp((torch.ones_like(probs) - probs), min=self.cl_eps))
bonus = torch.where(probs > log_end, bonus_after_log_end, bonus)
c_loss = F.nll_loss(
-bonus,
target.view(-1),
reduction='sum',
)
smoothing_c_loss = bonus.sum(dim=-1)
smoothing_c_loss = smoothing_c_loss.sum()
c_loss = c_loss * (1 - self.eps) + (self.eps / lprobs.size(-1)) * smoothing_c_loss
loss = loss + c_loss
# end for encouraging loss
return loss, nll_loss, ntokens
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
@classmethod
def reduce_metrics(cls, logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
loss_sum_v1 = sum(log.get("loss_v1", 0) for log in logging_outputs)
loss_sum_v2 = sum(log.get("loss_v2", 0) for log in logging_outputs)
nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
sample_size_v1 = sum(log.get("sample_size_v1", 0) for log in logging_outputs)
sample_size_v2 = sum(log.get("sample_size_v2", 0) for log in logging_outputs)
metrics.log_scalar(
"loss", loss_sum / sample_size, sample_size, round=3
)
metrics.log_scalar(
"loss_v1", loss_sum_v1 / max(sample_size_v1, 1), max(sample_size_v1, 1), round=3
)
metrics.log_scalar(
"loss_v2", loss_sum_v2 / max(sample_size_v2, 1), max(sample_size_v2, 1), round=3
)
metrics.log_scalar(
"nll_loss", nll_loss_sum / sample_size, ntokens, round=3
)
metrics.log_derived(
"ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
)
metrics.log_scalar(
"ntokens", ntokens, 1, round=3
)
metrics.log_scalar(
"nsentences", nsentences, 1, round=3
)
metrics.log_scalar(
"sample_size", sample_size, 1, round=3
)
metrics.log_scalar(
"sample_size_v1", sample_size_v1, 1, round=3
)
metrics.log_scalar(
"sample_size_v2", sample_size_v2, 1, round=3
)
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("n_correct", 0) for log in logging_outputs)
)
metrics.log_scalar("n_correct", n_correct)
metrics.log_derived(
"accuracy",
lambda meters: round(
meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
)
if meters["total"].sum > 0
else float("nan"),
)
@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