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import logging |
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from dataclasses import dataclass, field |
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from typing import Dict, List |
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from fairseq import metrics, utils |
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from fairseq.criterions import FairseqCriterion, register_criterion |
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from fairseq.dataclass import FairseqDataclass |
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logger = logging.getLogger(__name__) |
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@dataclass |
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class ModelCriterionConfig(FairseqDataclass): |
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loss_weights: Dict[str, float] = field( |
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default_factory=dict, |
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metadata={"help": "weights for the loss terms"}, |
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) |
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log_keys: List[str] = field( |
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default_factory=list, |
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metadata={"help": "additional output keys to log"}, |
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) |
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@register_criterion("model", dataclass=ModelCriterionConfig) |
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class ModelCriterion(FairseqCriterion): |
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""" |
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This criterion relies on the model to supply losses. |
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The losses should be a dictionary of name -> scalar returned by |
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the model either by including it in the net_output dict or by |
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implementing a get_losses(net_output, sample) method. The final loss is |
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a scaled sum of all losses according to weights in loss_weights. |
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If no weights are provided, then all losses are scaled by 1.0. |
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The losses will be automatically logged. Additional keys from |
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net_output dict can be logged via the log_keys parameter. |
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""" |
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def __init__(self, task, loss_weights=None, log_keys=None): |
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super().__init__(task) |
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self.loss_weights = loss_weights |
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self.log_keys = log_keys |
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def forward(self, model, sample, reduce=True): |
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net_output = model(**sample["net_input"]) |
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sample_size = net_output["sample_size"] |
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scaled_losses = {} |
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if hasattr(model, "get_losses"): |
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losses = model.get_losses(net_output, sample) |
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elif isinstance(net_output, dict) and "losses" in net_output: |
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losses = net_output["losses"] |
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else: |
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raise Exception("Could not retrieve losses") |
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for lk, p in losses.items(): |
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try: |
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coef = 1.0 if len(self.loss_weights) == 0 else self.loss_weights[lk] |
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except KeyError: |
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logger.error( |
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f"weight for loss {lk} is not in loss_weights ({self.loss_weights})" |
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) |
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raise |
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if coef != 0 and p is not None: |
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scaled_losses[lk] = coef * p.float() |
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loss = sum(scaled_losses.values()) |
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if reduce and loss.numel() > 1: |
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loss = loss.sum() |
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logging_output = { |
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"loss": loss.data, |
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"ntokens": sample_size, |
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"nsentences": sample["id"].numel(), |
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"sample_size": sample_size, |
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"_world_size": 1, |
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} |
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for lk in self.log_keys: |
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if lk in net_output and net_output[lk] is not None: |
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logging_output[lk] = float(net_output[lk]) |
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if len(scaled_losses) > 1: |
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for lk, l in scaled_losses.items(): |
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logging_output[f"loss_{lk}"] = l.item() |
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return loss, sample_size, logging_output |
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@staticmethod |
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def reduce_metrics(logging_outputs) -> None: |
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"""Aggregate logging outputs from data parallel training.""" |
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loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs)) |
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ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs)) |
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nsentences = utils.item( |
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sum(log.get("nsentences", 0) for log in logging_outputs) |
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) |
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sample_size = utils.item( |
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sum(log.get("sample_size", 0) for log in logging_outputs) |
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) |
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metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3) |
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metrics.log_scalar("ntokens", ntokens) |
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metrics.log_scalar("nsentences", nsentences) |
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builtin_keys = { |
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"loss", |
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"ntokens", |
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"nsentences", |
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"sample_size", |
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"_world_size", |
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} |
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world_size = utils.item( |
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sum(log.get("_world_size", 0) for log in logging_outputs) |
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) |
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for k in logging_outputs[0]: |
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if k not in builtin_keys: |
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val = sum(log.get(k, 0) for log in logging_outputs) |
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if k.startswith("loss_"): |
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metrics.log_scalar(k, val / sample_size, sample_size, round=3) |
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else: |
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metrics.log_scalar(k, val / world_size, round=3) |
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@staticmethod |
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def logging_outputs_can_be_summed() -> bool: |
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""" |
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Whether the logging outputs returned by `forward` can be summed |
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across workers prior to calling `reduce_metrics`. Setting this |
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to True will improves distributed training speed. |
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""" |
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return True |
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