def test_shapes_single(self, seq_len, embed_size, d_key, num_heads,
d_value, d_out):
query = key = value = jnp.zeros((seq_len, embed_size))
mha = attention.MultiHeadAttention(key_size=d_key,
num_heads=num_heads,
value_size=d_value,
model_size=d_out,
w_init_scale=1.0)(query, key, value)
self.assertEqual(mha.shape, (seq_len, d_out))
def test_different_seq_lengths(self):
query = jnp.zeros((2, 3))
key = value = jnp.zeros((5, 3))
mha = attention.MultiHeadAttention(key_size=7,
num_heads=11,
value_size=13,
model_size=15,
w_init_scale=1.0)(query, key, value)
self.assertEqual(mha.shape, (2, 15))
def test_mask_arg(self):
seq_len = 3
embed_size = 2
model_size = 15
query = key = value = jnp.zeros((seq_len, embed_size))
causal_mask = jnp.tril(jnp.ones((seq_len, seq_len)))
causal_mask = causal_mask[None, :, :]
mha = attention.MultiHeadAttention(key_size=7,
num_heads=11,
value_size=13,
model_size=model_size,
w_init_scale=1.0)(query,
key,
value,
mask=causal_mask)
self.assertEqual(mha.shape, (seq_len, model_size))
def f(query, key, value):
return attention.MultiHeadAttention(key_size=3,
num_heads=5,
w_init_scale=1.0)(query, key,
value)
def test_default_sizes(self):
mha = attention.MultiHeadAttention(key_size=3,
num_heads=5,
w_init_scale=1.0)
self.assertEqual(mha.value_size, mha.key_size)
self.assertEqual(mha.model_size, mha.key_size * mha.num_heads)
def test_shapes(self, batch_size, seq_len, embed_size, d_key, num_heads):
query = key = value = np.zeros((batch_size, seq_len, embed_size))
mha = attention.MultiHeadAttention(d_key, num_heads, 1.0)(query, key,
value)
self.assertEqual(mha.shape, (batch_size, seq_len, d_key * num_heads))
