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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() | |