def test_autoregressive_sample_transformerlm(self):
model = models.TransformerLM(10,
d_model=32,
d_ff=64,
n_layers=1,
n_heads=2,
mode='predict')
model.init(shapes.ShapeDtype((1, 1), dtype=jnp.int32))
s1 = trainer_lib.autoregressive_sample(model,
batch_size=1,
eos_id=-1,
max_length=10)
self.assertEqual(s1.shape[0], 1)
self.assertEqual(s1.shape[1], 10)
batch_per_device = 2 // fastmath.device_count()
model.init(shapes.ShapeDtype((batch_per_device, 1), dtype=jnp.int32))
s2 = trainer_lib.autoregressive_sample(model,
batch_size=2,
max_length=10)
self.assertEqual(s2.shape[0], 2)
self.assertLess(s2.shape[1], 11)
model.init(shapes.ShapeDtype((1, 1), dtype=jnp.int32))
prefix = jnp.array([[1, 2, 3]])
s3 = trainer_lib.autoregressive_sample(model,
eos_id=-1,
max_length=10,
batch_size=1,
prefix=prefix)
self.assertEqual(s3.shape[0], 1)
self.assertEqual(int(s3[0][0]), 1)
self.assertEqual(int(s3[0][1]), 2)
self.assertEqual(int(s3[0][2]), 3)
def test_autoregressive_sample_transformerlm_tfnp(self):
with fastmath.use_backend(fastmath.Backend.TFNP):
model = models.TransformerLM(10,
d_model=32,
d_ff=64,
n_layers=1,
n_heads=2,
mode='predict')
model.init(shapes.ShapeDtype((1, 1), dtype=np.int32))
s1 = decoding.autoregressive_sample(model,
batch_size=1,
eos_id=-1,
max_length=10)
self.assertEqual(s1.shape[0], 1)
self.assertEqual(s1.shape[1], 10)
batch_per_device = 2 // fastmath.device_count()
model.init(shapes.ShapeDtype((batch_per_device, 1),
dtype=np.int32))
s2 = decoding.autoregressive_sample(model,
batch_size=2,
max_length=10)
self.assertEqual(s2.shape[0], 2)
self.assertLess(s2.shape[1], 11)
model.init(shapes.ShapeDtype((1, 1), dtype=np.int32))
prefix = np.array([[1, 2, 3]])
s3 = decoding.autoregressive_sample(model,
prefix,
eos_id=-1,
max_length=10,
batch_size=1)
self.assertEqual(s3.shape[0], 1)
self.assertEqual(s3.shape[1], 10)
def test_training_loop_cartpole_serialized_init_from_world_model(
self, two_towers):
gin.bind_parameter('BoxSpaceSerializer.precision', 1)
transformer_kwargs = {
'd_model': 1,
'd_ff': 1,
'n_layers': 1,
'n_heads': 1,
'max_len': 128,
}
obs_serializer = space_serializer.create(gym.spaces.MultiDiscrete(
[2, 2]),
vocab_size=4)
act_serializer = space_serializer.create(gym.spaces.Discrete(2),
vocab_size=4)
model_fn = lambda mode: serialization_utils.SerializedModel( # pylint: disable=g-long-lambda
seq_model=models.TransformerLM(
mode=mode, vocab_size=4, **transformer_kwargs),
observation_serializer=obs_serializer,
action_serializer=act_serializer,
significance_decay=0.9,
)
with self.tmp_dir() as output_dir:
model_dir = os.path.join(output_dir, 'model')
def dummy_stream(_):
while True:
obs = np.zeros((1, 2, 2), dtype=np.int32)
act = np.zeros((1, 1), dtype=np.int32)
mask = np.ones_like(obs)
yield (obs, act, obs, mask)
inputs = trax_inputs.Inputs(train_stream=dummy_stream,
eval_stream=dummy_stream)
inputs._input_shape = ((2, 2), (1, )) # pylint: disable=protected-access
inputs._input_dtype = (np.int32, np.int32) # pylint: disable=protected-access
# Initialize a world model checkpoint by running the trainer.
trainer_lib.train(
model_dir,
model=model_fn,
inputs=inputs,
steps=1,
eval_steps=1,
has_weights=True,
)
policy_dir = os.path.join(output_dir, 'policy')
trainer = self._make_trainer(
train_env=self.get_wrapped_env('CartPole-v0', 2),
eval_env=self.get_wrapped_env('CartPole-v0', 2),
output_dir=policy_dir,
model=functools.partial(models.TransformerDecoder,
**transformer_kwargs),
policy_and_value_vocab_size=4,
init_policy_from_world_model_output_dir=model_dir,
policy_and_value_two_towers=two_towers,
)
trainer.training_loop(n_epochs=2)
def inner_model(mode):
return models.TransformerLM(
mode=mode,
vocab_size=vocab_size,
d_model=2,
d_ff=4,
n_layers=1,
n_heads=1,
)
def test_autoregressive_sample_transformerlm_quality(self):
pred_model = models.TransformerLM(
d_model=64, d_ff=128, dropout=0.05, max_len=256, n_heads=2,
n_layers=2, vocab_size=13, mode='predict')
shape11 = shapes.ShapeDtype((1, 1), dtype=np.int32)
model_path = os.path.join(_TESTDATA, 'transformerlm_copy.pkl.gz')
pred_model.init_from_file(model_path, weights_only=True,
input_signature=(shape11, shape11))
inputs = np.array([[0, 3, 7, 5, 3, 2, 4, 0]], dtype=np.int32)
s = decoding.autoregressive_sample(pred_model, inputs,
max_length=6, temperature=0.0)
self.assertEqual(str(s[0]), '[3 7 5 3 2 4]')
def model(mode):
return serialization_utils.SerializedModel(
trax_models.TransformerLM(mode=mode,
vocab_size=vocab_size,
d_model=16,
d_ff=8,
n_layers=1,
n_heads=1),
observation_serializer=srl,
action_serializer=srl,
significance_decay=0.9,
)
def test_autoregressive_sample_transformerlm_quality_eval(self):
eval_model = models.TransformerLM(d_model=64,
d_ff=128,
dropout=0.05,
max_len=256,
n_heads=2,
n_layers=2,
vocab_size=13,
mode='eval')
model_path = os.path.join(_TESTDATA, 'transformerlm_copy.pkl.gz')
eval_model.init_from_file(model_path)
inputs = np.array([[0, 3, 7, 5, 3, 2, 4, 0]], dtype=np.int32)
s = decoding.autoregressive_sample(eval_model,
inputs,
eval_mode=True,
max_length=6,
temperature=0.0)
self.assertEqual(str(s[0]), '[3 7 5 3 2 4]')