Source code for nemo.collections.common.metrics.perplexity

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import torch
from torch.distributions.categorical import Categorical
from torchmetrics import Metric

__all__ = ['Perplexity']


[docs]class Perplexity(Metric): """ This class computes mean perplexity of distributions in the last dimension of inputs. It is a wrapper around :doc:`torch.distributions.Categorical.perplexity<pytorch:distributions>` method. You have to provide either ``probs`` or ``logits`` to the :meth:`update` method. The class computes perplexities for distributions passed to :meth:`update` method in ``probs`` or ``logits`` arguments and averages the perplexities. Reducing results between all workers is done via SUM operations. See :doc:`PyTorch Lightning Metrics<pytorch-lightning:metrics>` for the metric usage instructions. Args: compute_on_step: Forward only calls ``update()`` and returns ``None`` if this is set to ``False``. default: ``True`` dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the value at the step. process_group: Specify the process group on which synchronization is called. default: ``None`` (which selects the entire world) validate_args: If ``True`` values of :meth:`update` method parameters are checked. ``logits`` has to not contain NaNs and ``probs`` last dim has to be valid probability distribution. """ def __init__(self, compute_on_step=True, dist_sync_on_step=False, process_group=None, validate_args=True): super().__init__( compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group ) self.validate_args = validate_args self.add_state('perplexities_sum', torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum') # Total number of distributions seen since last reset self.add_state('num_distributions', torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum')
[docs] def update(self, probs=None, logits=None): """ Updates :attr:`perplexities_sum` and :attr:`num_distributions`. Args: probs: A ``torch.Tensor`` which innermost dimension is valid probability distribution. logits: A ``torch.Tensor`` without NaNs. """ d = Categorical( None if probs is None else probs.detach(), None if logits is None else logits.detach(), validate_args=self.validate_args, ) ppl = d.perplexity() self.num_distributions += ppl.numel() self.perplexities_sum += ppl.sum()
[docs] def compute(self): """ Returns perplexity across all workers and resets to 0 :attr:`perplexities_sum` and :attr:`num_distributions`. """ if self.num_distributions.eq(0): return None return self.perplexities_sum / self.num_distributions