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# Copyright 2023 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluator for perplexity of a model."""
from big_vision.evaluators import mean
import big_vision.utils as u
import jax.numpy as jnp
# Temporary global flag to facilitate backwards compatability. Will be removed
# by the end of year 2023.
API = 'jit'
def perplexity(predict_fn, normalize_by_seqlen):
"""Returns a function that computes perplexity."""
def _perplexity_fn(train_state, batch, pad_token=0, **kw):
logits, _ = predict_fn(train_state, batch, **kw)
# Ignore perplexity on the padding label.
weights = jnp.where(batch['labels'] != pad_token, 1, 0).astype(jnp.float32)
if batch.get('label_masks') is not None:
weights = weights * batch['label_masks']
losses = u.weighted_softmax_xent(
logits=logits, labels=batch['labels'],
weights=weights, label_smoothing=0.0,
reduction=False, normalize=normalize_by_seqlen)
return {'perplexity': losses}
return _perplexity_fn
class Evaluator(mean.Evaluator):
"""Perplexity evaluator."""
def __init__(self, predict_fn, *a, normalize_by_seqlen=False, **kw):
super().__init__(perplexity(predict_fn, normalize_by_seqlen), *a, **kw)
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