def test_lsh_and_pure_lsh_self_attention_equivalence(self):
# Given the same weight matrices and random numbers, do these produce the
# same output.
with fastmath.use_backend(fastmath.Backend.JAX):
n_heads = 4
d_head = 4
d_model = n_heads * d_head
pure_lsh_layer = efficient_attention.PureLSHSelfAttention(
n_heads=n_heads,
d_qk=d_head,
d_v=d_head,
causal=True,
masked=False,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
n_hashes=4,
n_buckets=8,
use_reference_code=False,
attention_dropout=0.0,
use_python_loop=True,
bias=False,
mode='train')
lsh_layer = efficient_attention.LSHSelfAttention(
n_heads=n_heads,
d_qk=d_head,
d_v=d_head,
causal=True,
masked=False,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
n_hashes=4,
n_buckets=8,
use_reference_code=False,
attention_dropout=0.0,
use_python_loop=True,
mode='train')
batch, seqlen = 3, 32
input_shape = (batch, seqlen, d_model)
x = jax.random.uniform(jax.random.PRNGKey(0),
input_shape,
dtype=jnp.float32)
lsh_layer_input = x
call_rng = jax.random.PRNGKey(42)
lsh_layer_weights, lsh_layer_state = lsh_layer.init(
shapes.signature(lsh_layer_input))
lsh_layer.rng = call_rng
lsh_layer_output = lsh_layer(lsh_layer_input)
# Shapes are: (n_heads, d_model, d_head), (n_heads, d_model, d_head),
# (n_heads, d_head, d_model)
# Abbreviated as - hmn, hmn, hnm
w_qk, w_v, w_o = lsh_layer_weights
qk = jnp.einsum('blm,hmn->bhln', x, w_qk)
qk = qk.reshape((-1, qk.shape[2], qk.shape[3]))
v = jnp.einsum('blm,hmn->bhln', x, w_v)
v = v.reshape((-1, v.shape[2], v.shape[3]))
pure_lsh_layer_input = (qk, v)
_, _ = pure_lsh_layer.init(shapes.signature(pure_lsh_layer_input))
pure_lsh_layer.rng = call_rng
pure_lsh_layer.state = lsh_layer_state
pure_lsh_layer_output = pure_lsh_layer(pure_lsh_layer_input)
# b*h,l,n
pure_lsh_layer_output = pure_lsh_layer_output.reshape(
(batch, -1) + pure_lsh_layer_output.shape[1:])
pure_lsh_layer_output_projected = (jnp.einsum(
'bhld,hdm->blm', pure_lsh_layer_output, w_o))
diff = pure_lsh_layer_output_projected - lsh_layer_output
avg_diff = jnp.sum(jnp.abs(diff)) / jnp.sum(jnp.ones_like(diff))
self.assertLess(avg_diff, 1e-5)
def init(self, weights):
return jnp.ones_like(weights)