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BasicDecoder fails with masked input tensor #534

kazemnejad opened this issue Sep 20, 2019 · 0 comments · Fixed by #546

BasicDecoder fails with masked input tensor #534

kazemnejad opened this issue Sep 20, 2019 · 0 comments · Fixed by #546
bug Something isn't working seq2seq


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kazemnejad commented Sep 20, 2019

System information

  • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04
  • TensorFlow version and how it was installed (source or binary): binary(2.0.0-dev20190920)
  • TensorFlow-Addons version and how it was installed (source or binary): binary(0.6.0-dev)
  • Python version: 3.6
  • Is GPU used? (yes/no): yes

Describe the bug

When the BasicDecoder receives a tensor equipped with masking meta-data as the input, it will fail due to the unhandled mask argument in TrainingSampler.initialize(..). This is a common scenario in which the input tensor is computed using the embedding layer.

Code to reproduce the issue

units = 32
vocab_size = 1000
embedding = tf.keras.layers.Embedding(
    input_dim=vocab_size, output_dim=units, mask_zero=True)
attention_mechanism = attention_wrapper.LuongAttention(units)
cell = tf.keras.layers.LSTMCell(units)
cell = tfa.seq2seq.AttentionWrapper(cell, attention_mechanism)
output_layer = tf.keras.layers.Dense(vocab_size)
sampler = tfa.seq2seq.sampler.TrainingSampler()
decoder = tfa.seq2seq.BasicDecoder(
    cell, sampler, output_layer=output_layer)

# setup the attention mechanism's memory with a random tensor
fake_mem = tf.random.normal((2, 3, units))
fake_mem_mask = tf.ones((2,3), dtype=tf.bool)
attention_mechanism(fake_mem, mask=fake_mem_mask, setup_memory=True)

word_ids = tf.random.uniform((2, 5), 0, vocab_size, dtype=tf.int64) \
            * tf.constant([[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]], dtype=tf.int64)
word_embeds = embedding(word_ids)
mask = embedding.compute_mask(word_ids)

initial_state = cell.get_initial_state(batch_size=2, dtype=tf.float32)
outputs = decoder(
    sequence_length=tf.math.reduce_sum(tf.cast(mask, tf.int32), axis=1),

It will raise the following error:

  File "tensorflow_addons/seq2seq/", line 72, in initialize
    return self.sampler.initialize(inputs, **kwargs) + (initial_state,)
TypeError: initialize() got an unexpected keyword argument 'mask'

Other info / logs
This issue happens because we capture the mask argument on the, **kwargs) and propagate it all the way through TrainingSample.initialize(...) except that the initialize method doesn't accept the mask argument.
One can temporarily fix this error by adding delattr(word_embeds, '_keras_mask') just before calling the decoder instance.

I encountered this issue when i was working on #335

Ideas to fix this issue:

  • Fix the BasicDecoder to only pass the appropriate argument when it calls the sampler. or even fix the TrainingSampler to capture all other arguments using the kwargs
  • Add the appropriate support for the mask argument in the TrainingSampler in additon to sequence_length (similar to AttentionMechanism)

I think there is no harm in the latter option as it provides a more Keras-friendly way to work with the framework.

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