code: # 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()
  
apis: numpy.random.normal numpy.ones_like numpy.testing.assert_allclose numpy.array numpy.random.randint