def test_encdec_block(self):
batch_size = 2
from_seq_length = 5
to_seq_length = 3
d_model = 4
l = t5.EncDecoderBlock(d_model=4,
d_kv=3,
num_heads=2,
d_ff=8,
name="foo")
pos_embed = t5.RelativePositionEmbedding(
num_heads=2,
bidirectional=True,
embeddings_initializer=tf.keras.initializers.Ones(),
name="bar")
encoder_decoder_mask = t5.make_attention_mask(
tf.ones((batch_size, from_seq_length)),
tf.ones((batch_size, to_seq_length)))
position_bias = pos_embed(from_seq_length, from_seq_length)
inputs = tf.ones((batch_size, from_seq_length, d_model),
dtype=tf.float32)
encoder_hidden_states = tf.ones((batch_size, to_seq_length, d_model),
dtype=tf.float32)
outputs = l(inputs,
encoder_hidden_states,
encoder_decoder_mask=encoder_decoder_mask,
position_bias=position_bias)
self.assertEqual(outputs[0].shape,
(batch_size, from_seq_length, d_model))
def test_relative_position(self, dtype):
l = t5.RelativePositionEmbedding(
num_heads=4,
bidirectional=False,
embeddings_initializer=tf.keras.initializers.Ones(),
compute_dtype=dtype,
name="foo")
self.assertEqual(l(4, 2).shape, (1, 4, 4, 2))
l = t5.RelativePositionEmbedding(
num_heads=4,
bidirectional=True,
embeddings_initializer=tf.keras.initializers.Ones(),
compute_dtype=dtype,
name="bar")
outputs = l(4, 2)
self.assertEqual(outputs.shape, (1, 4, 4, 2))
self.assertEqual(outputs.dtype, dtype)
def test_attention(self, distribution):
num_heads, head_size = 2, 4
from_seq_length, to_seq_length = 4, 6
batch_size = 2
pos_embed = t5.RelativePositionEmbedding(
num_heads=4,
bidirectional=False,
embeddings_initializer=tf.keras.initializers.Ones(),
name="pos_embed")
position_bias = pos_embed(from_seq_length, from_seq_length)
l = t5.MultiHeadAttention(d_model=4,
d_kv=2,
num_heads=4,
dropout_rate=0.1)
query = tf.convert_to_tensor(
np.ones((batch_size, from_seq_length, 4), dtype=np.float32))
self.assertEqual(
l(query, position_bias=position_bias)["context"].shape,
query.shape)
kv = tf.convert_to_tensor(
np.ones((batch_size, to_seq_length, 4), dtype=np.float32))
position_bias = pos_embed(from_seq_length, to_seq_length)
outputs = l(query, kv=kv, position_bias=position_bias)
self.assertEqual(outputs["context"].shape, query.shape)
with distribution.scope():
l = t5.MultiHeadAttention(d_model=4,
d_kv=head_size,
num_heads=num_heads,
dropout_rate=0.1)
@tf.function
def step(inputs):
def _step_fn(inputs):
cache = _create_cache(batch_size, from_seq_length,
num_heads, head_size)
mask = t5.make_causal_mask(tf.ones((batch_size, 1)))
return l(query=inputs,
mask=mask,
cache=cache,
decode_position=decode_position)
outputs = distribution.run(_step_fn, args=(inputs, ))
return tf.nest.map_structure(
distribution.experimental_local_results, outputs)
decode_position = 2
query = tf.convert_to_tensor(np.ones((2, 1, 4), dtype=np.float32))
local_outputs = step(query)
self.assertEqual(local_outputs["context"][0].shape, (2, 1, 4))
self.assertNotEqual(
np.sum(local_outputs["cache"]["key"][0][:, decode_position,
...].numpy()), 0.0)
def test_attention_layers(self, distribution):
num_heads, head_size = 2, 2
from_seq_length = 4
# TPU decoding should pre-allocate the entire sequence.
batch_size = 2
with distribution.scope():
pos_embed = t5.RelativePositionEmbedding(
num_heads=head_size,
bidirectional=False,
embeddings_initializer=tf.keras.initializers.Ones(),
name="pos_embed")
l = t5.SelfAttention(d_model=4,
d_kv=head_size,
num_heads=num_heads,
dropout_rate=0.1)
decode_position = 2
@tf.function
def step(inputs):
def _step_fn(inputs):
cache = _create_cache(batch_size, from_seq_length,
num_heads, head_size)
mask = t5.make_causal_mask(tf.ones((batch_size, 1)))
position_bias = pos_embed(from_seq_length, from_seq_length)
return l(hidden_states=inputs,
cache=cache,
attention_mask=mask,
decode_position=decode_position,
position_bias=position_bias)
outputs = distribution.run(_step_fn, args=(inputs, ))
return tf.nest.map_structure(
distribution.experimental_local_results, outputs)
query = tf.convert_to_tensor(np.ones((2, 1, 4), dtype=np.float32))
local_outputs = step(query)
self.assertEqual(local_outputs["layer_output"][0].shape, (2, 1, 4))
self.assertNotEqual(
np.sum(local_outputs["cache"]["key"][0]
[:, decode_position, :, :].numpy()), 0.0)
l = t5.CrossAttention(d_model=4,
d_kv=head_size,
num_heads=num_heads,
dropout_rate=0.1)
to_seq_length = 6
query = tf.convert_to_tensor(
np.ones((2, from_seq_length, 4), dtype=np.float32))
kv = tf.convert_to_tensor(
np.ones((2, to_seq_length, 4), dtype=np.float32))
@tf.function
def step_cross_attn(inputs):
def _step_fn(inputs):
query, kv = inputs
mask = t5.make_attention_mask(
tf.ones((batch_size, from_seq_length)),
tf.ones((batch_size, to_seq_length)))
return l(hidden_states=query, kv=kv, attention_mask=mask)
outputs = distribution.run(_step_fn, args=(inputs, ))
return tf.nest.map_structure(
distribution.experimental_local_results, outputs)
local_outputs = step_cross_attn((query, kv))
self.assertEqual(local_outputs["layer_output"][0].shape,
(2, from_seq_length, 4))
