def test_train_restart(self, backend_name):
if xla_bridge.device_count() > 1 and backend_name == 'tf':
self.skipTest(
"tf-numpy backend doesn't support multi-devices yet.")
with backend.use_backend(backend_name), self.tmp_dir() as output_dir:
# Prepare model and inputs
n_classes = 4
train_steps = 2
eval_steps = 2
model_fn = functools.partial(models.MLP,
d_hidden=16,
n_output_classes=n_classes)
inputs = lambda _: test_inputs(n_classes)
# Train and evaluate
trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
train_steps=train_steps,
eval_steps=eval_steps)
# Restart training
state = trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
train_steps=(2 * train_steps),
eval_steps=eval_steps)
# Assert total train steps
self.assertEqual(state.step, 2 * train_steps)
def _test_fast_inference(self, attention_type, length):
with backend.use_backend('jax'):
vocab_size = 16
model_fn = functools.partial(
transformer.TransformerLM,
vocab_size=vocab_size,
d_model=4,
d_ff=8,
n_layers=2,
n_heads=2,
attention_type=attention_type,
)
model_slow = model_fn(mode='eval')
model_fast = model_fn(mode='predict')
rng = backend.random.get_prng(0)
batch_size = 2
# Given the same rng, both models initialize with the same parameters.
model_slow.initialize_once((batch_size, 1), np.int32, rng)
model_fast.initialize_once((batch_size, 1), np.int32, rng)
buf = onp.zeros((batch_size, length), dtype=np.int32)
next_sym = onp.zeros((batch_size, 1), dtype=onp.int32)
for index in range(length):
logits_slow = model_slow(buf, rng=rng)
logits_fast = model_fast(next_sym, rng=rng)
onp.testing.assert_array_almost_equal(logits_slow[:, index, :],
logits_fast[:, 0, :])
next_sym = onp.random.randint(vocab_size, size=(batch_size, 1))
buf[:, index] = next_sym[:, 0]
def nontrainable_params(self):
# TODO(afrozm): Give further thought to this name.
# TODO(lukaszkaiser): it makes no sense to use an accelerator (e.g. TPU)
# in op-by-op mode just to compute the learning rate. However, there
# should be a cleaner approach that forceably swapping out the backend.
with backend.use_backend('numpy'):
return self._lr_fn(self._step)
def test_train_eval_predict_sm3(self, backend_name):
if xla_bridge.device_count() > 1 and backend_name == 'tf':
self.skipTest(
"tf-numpy backend doesn't support multi-devices yet.")
with backend.use_backend(backend_name), self.tmp_dir() as output_dir:
# Prepare model and inputs
n_classes = 4
train_steps = 2
eval_steps = 2
model_fn = functools.partial(models.MLP,
d_hidden=16,
n_output_classes=n_classes)
inputs = lambda _: test_inputs(n_classes)
# Train and evaluate
state = trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
train_steps=train_steps,
eval_steps=eval_steps,
optimizer=trax_opt.SM3)
# Assert total train steps
self.assertEqual(train_steps, state.step)
# Assert 2 evaluations ran
train_acc = state.history.get('train', 'metrics/accuracy')
eval_acc = state.history.get('eval', 'metrics/accuracy')
self.assertEqual(len(train_acc), len(eval_acc))
self.assertLen(eval_acc, 2)
# Predict with final params
inputs = inputs(1).train_stream()
model = layers.Serial(model_fn())
model(next(inputs)[0], params=state.opt_state.params)
def test_reformer_rng_consistency(self):
with backend.use_backend('jax'):
vocab_size = 16
batch_size = 1
input_sd = ShapeDtype((batch_size, 8), np.int32)
input_signature = (input_sd, input_sd)
model = reformer.ReformerLM(
vocab_size,
d_model=32,
d_ff=64,
d_attention_key=16,
d_attention_value=16,
n_layers=1,
n_heads=2,
max_len=16,
n_chunks=2,
n_attention_chunks=1,
mode='train',
attention_type=PoisonOnRNGMismatchAttention)
rng = backend.random.get_prng(0)
weights, state = model.initialize_once(input_signature)
def dummy_loss_fn(weights):
inputs = (np.zeros(input_sd.shape, dtype=np.int32), ) * 2
output = model(inputs, weights=weights, state=state, rng=rng)
dummy_loss = backend.numpy.sum(output[0])
return dummy_loss
grad_fn = backend.grad(dummy_loss_fn)
grads = grad_fn(weights)
# PoisonOnRNGMismatchAttention uses NaNs to signal an rng mismatch.
for grad in jax.tree_util.tree_leaves(grads):
assert onp.all(onp.isfinite(grad))
def test_no_int32_or_uint32_returned(self):
"""Tests that Trainer._jit_update_fn doesn't return int32 or uint32.
TF pins int32/uint32 tensors to CPU, which will cause XLA-forced-compiled
computation to copy int32/uint32 outputs to CPU. This test makes sure that
won't happen.
"""
if xla_bridge.device_count() > 1:
self.skipTest("tf-numpy backend doesn't support multi-devices yet.")
with backend.use_backend('tf'), self.tmp_dir() as output_dir:
n_classes = 1001
model_fn = functools.partial(models.Resnet50,
n_output_classes=n_classes)
inputs = lambda _: test_inputs(n_classes, input_shape=(224, 224, 3))
trainer = trainer_lib.Trainer(
model=model_fn,
loss_fn=layers.CrossEntropyLossScalar,
optimizer=trax_opt.SM3,
lr_schedule=lr.MultifactorSchedule,
inputs=inputs,
)
trainer.reset(output_dir)
trainer.train_epoch(1, 0)
# Those are the things returned by Trainer._jit_update_fn
arrays = (trainer._opt_state.weights, trainer._opt_state.slots,
trainer._model_state, trainer._rngs)
arrays = tf.nest.flatten(arrays)
for x in arrays:
if isinstance(x, np.ndarray) and (x.dtype == np.int32 or
x.dtype == np.uint32):
raise ValueError('Found an array of int32 or uint32: %s' % x)
def test_fails_to_evaluate_model_with_matrix_observation_space(self):
with backend.use_backend('numpy'):
env = self._make_env( # pylint: disable=no-value-for-parameter
observation_space=gym.spaces.Box(shape=(2, 2), low=0, high=1),
action_space=gym.spaces.Discrete(n=1),
max_trajectory_length=2,
batch_size=1,
)
trajectories = [
self._make_trajectory(np.array([[0, 1], [2, 3]]), np.array([0]))]
metrics = simple.evaluate_model(env, trajectories, plt)
self.assertIsNone(metrics)
def test_takes_new_history(self):
histories = np.array([[[0, 1, 2]], [[3, 4, 5]]])
with backend.use_backend('numpy'):
env = self._create_env( # pylint: disable=no-value-for-parameter
model=mock.MagicMock(),
histories=histories,
trajectory_length=2,
)
env.reset()
observation = env.reset()
np.testing.assert_array_equal(observation, [5])
def test_communicates_with_model(self):
# Mock model increasing the observation by action, reward is the parity of
# the new observation.
def mock_transition(inputs, *args, **kwargs):
del args
del kwargs
(observations, actions) = inputs
new_observations = observations[:, -1] + actions
rewards = np.array([[int(new_observations % 2 == 0)]])
return (new_observations, rewards)
mock_model_fn = mock.MagicMock()
mock_model_fn.return_value.side_effect = mock_transition
mock_model = mock_model_fn.return_value
actions_to_take = np.array([[1], [3]])
histories = np.array([[[0, 1, 2, 3]]])
expected_observations = np.array([[3], [4], [7]])
expected_rewards = np.array([[1], [0]])
expected_dones = np.array([[False], [True]])
expected_histories = np.array([[[0, 1, 2, 3]], [[1, 2, 3, 4]]])
expected_actions = actions_to_take
with backend.use_backend('numpy'):
env = self._create_env( # pylint: disable=no-value-for-parameter
model=mock_model_fn,
histories=histories,
trajectory_length=len(actions_to_take),
)
actual_observations = [env.reset()]
actual_rewards = []
actual_dones = []
actual_histories = []
actual_actions = []
for action in actions_to_take:
(observation, reward, done, _) = env.step(action)
actual_observations.append(observation)
actual_rewards.append(reward)
actual_dones.append(done)
# Mock call is a tuple (args, kwargs). There is one positional argument,
# which is a tuple (history, action).
(((history, action), ), _) = mock_model.call_args
actual_actions.append(action)
actual_histories.append(history)
np.testing.assert_array_equal(actual_observations,
expected_observations)
np.testing.assert_array_equal(actual_rewards, expected_rewards)
np.testing.assert_array_equal(actual_dones, expected_dones)
np.testing.assert_array_equal(actual_histories, expected_histories)
np.testing.assert_array_equal(actual_actions, expected_actions)
def test_evaluates_model_with_vector_observation_space(self):
with backend.use_backend('numpy'):
env = self._make_env( # pylint: disable=no-value-for-parameter
observation_space=gym.spaces.Box(shape=(2,), low=0, high=1),
action_space=gym.spaces.Discrete(n=1),
max_trajectory_length=2,
batch_size=3,
)
trajectories = [
self._make_trajectory(observations, actions) # pylint: disable=g-complex-comprehension
for (observations, actions) in [
(np.array([[0, 1]]), np.array([0])),
(np.array([[1, 2], [3, 4]]), np.array([0, 0])),
(np.array([[1, 2], [3, 4], [5, 6]]), np.array([0, 0, 0])),
]
]
metrics = simple.evaluate_model(env, trajectories, plt)
self.assertIsNotNone(metrics)
self.assertEqual(len(metrics), 2)
def _test_train_eval_predict(self, backend_name):
if xla_bridge.device_count() > 1 and backend_name == 'tf':
self.skipTest("tf-numpy backend does't support multi-devices yet.")
with backend.use_backend(backend_name), self.tmp_dir() as output_dir:
# Prepare model and inputs
n_classes = 4
train_steps = 2
eval_steps = 2
# Adds Dropout and BatchNorm to test state handling.
def model_fn(mode='train'):
return layers.Serial(
layers.Dropout(mode=mode, rate=0.1),
layers.BatchNorm(mode=mode),
models.MLP(d_hidden=16,
n_output_classes=n_classes,
mode=mode))
inputs = lambda _: test_inputs(n_classes)
# Train and evaluate
state = trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
train_steps=train_steps,
eval_steps=eval_steps)
# Assert total train steps
self.assertEqual(train_steps, state.step)
# Assert 2 evaluations ran
train_acc = state.history.get('train', 'metrics/accuracy')
eval_acc = state.history.get('eval', 'metrics/accuracy')
self.assertEqual(len(train_acc), len(eval_acc))
self.assertLen(eval_acc, 2)
# Predict with final params
inputs = inputs(1).train_stream()
model = layers.Serial(model_fn())
model(next(inputs)[0], params=state.opt_state.params)
def test_reset_twice(self, backend_name):
if xla_bridge.device_count() > 1 and backend_name == 'tf':
self.skipTest("tf-numpy backend doesn't support multi-devices yet.")
with backend.use_backend(backend_name), self.tmp_dir() as output_dir1, \
self.tmp_dir() as output_dir2:
n_classes = 4
model_fn = functools.partial(
models.MLP, d_hidden=16, n_output_classes=n_classes)
inputs = lambda _: test_inputs(n_classes)
trainer = trainer_lib.Trainer(
model=model_fn,
loss_fn=layers.CrossEntropyLossScalar,
optimizer=trax_opt.SM3,
lr_schedule=lr.MultifactorSchedule,
inputs=inputs,
)
trainer.reset(output_dir1)
trainer.evaluate(1)
trainer.reset(output_dir2)
trainer.evaluate(1)
def test_communicates_with_model(self, mock_restore_state):
gin.bind_parameter('BoxSpaceSerializer.precision', 1)
vocab_size = 16
# Mock model predicting a fixed sequence of symbols. It is made such that
# the first two observations are different and the last one is equal to the
# first.
symbols = [
1,
1,
2,
2,
0,
0, # obs1 act1
1,
2,
2,
1,
0,
0, # obs2 act2
1,
1,
2,
2, # obs3
]
def make_prediction(symbol):
one_hot = np.eye(vocab_size)[symbol]
log_probs = (1 - one_hot) * -100.0 # Virtually deterministic.
# (4 obs symbols + 1 action symbol) * 3 timesteps = 15.
return np.array([[log_probs]])
mock_predict_fn = mock.MagicMock()
mock_predict_fn.side_effect = map(make_prediction, symbols)
with backend.use_backend('numpy'):
# (model_params, opt_state)
mock_restore_state.return_value.params = (None, None)
env = self._make_env(
predict_fn=mock_predict_fn,
reward_fn=(lambda _1, _2: np.array([0.5])),
done_fn=(lambda _1, _2: np.array([False])),
vocab_size=vocab_size,
batch_size=1,
max_trajectory_length=3,
observation_space=gym.spaces.Box(low=0, high=5, shape=(4, )),
action_space=gym.spaces.MultiDiscrete(nvec=[2, 2]),
)
def assert_input_suffix(expected_symbols):
actual_symbols = np.array([
symbol.item() for ((symbol, ), _) in
mock_predict_fn.call_args_list[-len(expected_symbols):]
])
np.testing.assert_array_equal(actual_symbols, expected_symbols)
actions = [[0, 1], [1, 0]]
obs1 = env.reset()
assert_input_suffix(symbols[:3])
(obs2, reward, done, _) = env.step(np.array([actions[0]]))
# Symbols going into the decoder when predicting the next observation are:
# the last symbol of the previous observation, all action symbols, all
# symbols but the last one of the next observation.
assert_input_suffix([symbols[3]] + actions[0] + symbols[6:9])
self.assertFalse(np.array_equal(obs1, obs2))
np.testing.assert_array_equal(reward, [0.5])
np.testing.assert_array_equal(done, [False])
(obs3, reward, done, _) = env.step(np.array([actions[1]]))
assert_input_suffix([symbols[9]] + actions[1] + symbols[12:15])
np.testing.assert_array_equal(obs1, obs3)
np.testing.assert_array_equal(reward, [0.5])
np.testing.assert_array_equal(done, [True])