def test_time_bin_causal_attention_n_bins(self):
qkv_shape = ShapeDtype((3, 57, 8))
input_signature = (qkv_shape, qkv_shape, qkv_shape)
layer = attention.TimeBinCausalAttention(
n_bins=4, dropout=0.1, mode='train')
final_shape = base.check_shape_agreement(layer, input_signature)
self.assertEqual((3, 57, 8), final_shape)
def test_time_bin_causal_attention_bin_length(self):
qkv_shape = (3, 57, 8)
input_shape = (qkv_shape, qkv_shape, qkv_shape)
layer = attention.TimeBinCausalAttention(
bin_length=16, dropout=0.1, mode='train')
final_shape = base.check_shape_agreement(layer, input_shape)
self.assertEqual((3, 57, 8), final_shape)
def test_time_bin_and_dot_product_causal_attention_are_consistent(self):
dot_product_layer = attention.DotProductCausalAttention(
dropout=0.0, mode='train')
time_bin_layer = attention.TimeBinCausalAttention(
bin_length=4, dropout=0.0, mode='train')
# Exactly 2 bins.
input_shape = (3, 8, 8)
inputs = [onp.random.uniform(size=input_shape) for _ in range(3)]
dot_product_output = dot_product_layer(inputs)
time_bin_output = time_bin_layer(inputs)
onp.testing.assert_array_almost_equal(dot_product_output, time_bin_output)