def test_reformer2_deterministic_eval(self):
with fastmath.use_backend(fastmath.Backend.JAX):
vocab_size = 16
d_model = 4
batch_size = 2
length = 5
model_fn = functools.partial(
reformer.Reformer2,
vocab_size,
d_model=d_model,
d_ff=16,
n_encoder_layers=0,
n_decoder_layers=1,
n_heads=2,
dropout=0.0,
max_len=length * 2,
pos_type=None,
encoder_attention_type=tl.Attention,
encoder_decoder_attention_type=tl.CausalAttention,
)
inp = np.random.randint(vocab_size, size=(batch_size, length))
out = np.zeros((batch_size, length), dtype=np.int32)
test_utils.test_eval_is_deterministic((inp, out), model_fn)
def test_deterministic_eval(self):
d_model = 32
seq_len = 3
x_shape = (1, seq_len, d_model)
inp = np.ones(x_shape).astype(np.float32)
model_fn = functools.partial(
tl.CausalAttention,
d_feature=d_model,
n_heads=4,
)
test_utils.test_eval_is_deterministic(inp, model_fn)
