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# Copyright 2022 The T5X 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.
"""Tests for t5x.losses."""
from absl.testing import absltest
import jax
import jax.numpy as jnp
import numpy as np
from t5x import losses
class LossTest(absltest.TestCase):
def test_xent(self):
def lossfn(logits, targets, weights):
loss, z_loss, weight_sum = losses.compute_weighted_cross_entropy(
logits,
targets,
weights,
label_smoothing=0.1,
z_loss=0.1,
loss_normalizing_factor=0.1)
return loss, (z_loss, weight_sum)
batch_size = 2
length = 4
vocab_size = 8
logits = np.random.normal(size=(batch_size, length,
vocab_size)).astype(np.float32)
targets = np.random.randint(0, vocab_size, size=(batch_size, length))
weights = np.ones_like(targets)
out = jax.jit(jax.value_and_grad(lossfn, has_aux=True))(logits, targets,
weights)
(loss, (z_loss, weight_sum)), dlogits = out
# Just a smoke test for now
# TODO(t5x): Expand test
print(jax.device_get(((loss, (z_loss, weight_sum)), dlogits)))
class SpecialLossNormalizingFactorTest(absltest.TestCase):
def test_num_real_target_tokens(self):
batch = {
'decoder_target_tokens':
jnp.asarray([[1, 2, 3, 4, 0], [5, 6, 0, 0, 0]], jnp.int32)
}
(output_lnf,
output_loss_weights) = losses.get_loss_normalizing_factor_and_weights(
loss_normalizing_factor=losses.SpecialLossNormalizingFactor
.NUM_REAL_TARGET_TOKENS,
batch=batch)
np.testing.assert_allclose(output_lnf, 6.0, rtol=1e-3)
np.testing.assert_allclose(
output_loss_weights,
np.array([[1.0, 1.0, 1.0, 1.0, 0.0], [1.0, 1.0, 0.0, 0.0, 0.0]],
dtype=np.float32),
rtol=1e-3)
def test_num_total_target_tokens(self):
batch = {
'decoder_target_tokens':
jnp.asarray([[1, 2, 3, 4, 0], [5, 6, 0, 0, 0]], jnp.int32)
}
(output_lnf,
output_loss_weights) = losses.get_loss_normalizing_factor_and_weights(
loss_normalizing_factor=losses.SpecialLossNormalizingFactor
.NUM_TOTAL_TARGET_TOKENS,
batch=batch)
np.testing.assert_allclose(output_lnf, 10.0, rtol=1e-3)
np.testing.assert_allclose(
output_loss_weights,
np.array([[1.0, 1.0, 1.0, 1.0, 0.0], [1.0, 1.0, 0.0, 0.0, 0.0]],
dtype=np.float32),
rtol=1e-3)
def test_average_per_sequence(self):
batch = {
'decoder_target_tokens':
jnp.asarray([[1, 2, 3, 4, 0], [5, 6, 0, 0, 0]], jnp.int32)
}
(output_lnf,
output_loss_weights) = losses.get_loss_normalizing_factor_and_weights(
loss_normalizing_factor=losses.SpecialLossNormalizingFactor
.AVERAGE_PER_SEQUENCE,
batch=batch)
np.testing.assert_allclose(output_lnf, 2.0, rtol=1e-3)
np.testing.assert_allclose(
output_loss_weights,
jnp.asarray([[0.25, 0.25, 0.25, 0.25, 0.0], [0.5, 0.5, 0.0, 0.0, 0.0]],
jnp.float32),
rtol=1e-3)
def test_average_per_sequence_with_weights(self):
batch = {
'decoder_target_tokens':
jnp.asarray([[1, 2, 3, 4, 0], [5, 6, 0, 0, 0]], jnp.int32),
'decoder_loss_weights':
jnp.asarray([[0.5, 1.0, 0.25, 2.0, 0.0], [1.0, 1.0, 0.0, 0.0, 0.0]],
jnp.float32)
}
(output_lnf,
output_loss_weights) = losses.get_loss_normalizing_factor_and_weights(
loss_normalizing_factor=losses.SpecialLossNormalizingFactor
.AVERAGE_PER_SEQUENCE,
batch=batch)
np.testing.assert_allclose(output_lnf, 2.0, rtol=1e-3)
np.testing.assert_allclose(
output_loss_weights,
jnp.asarray(
[[0.1333, 0.2666, 0.0666, 0.5333, 0.0], [0.5, 0.5, 0.0, 0.0, 0.0]],
jnp.float32),
rtol=1e-3)
if __name__ == '__main__':
absltest.main()
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