def test_forward_dtype(self, backend, dtype):
with math.use_backend(backend):
layer = tl.BatchNorm()
x = np.ones((3, 2, 7)).astype(dtype)
_, _ = layer.init(shapes.signature(x))
y = layer(x)
self.assertEqual(y.dtype, dtype)
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 math.use_backend('tf'), self.tmp_dir() as output_dir:
n_classes = 1001
model_fn = functools.partial(models.Resnet50,
n_output_classes=n_classes)
inputs = test_inputs(n_classes, input_shape=(224, 224, 3))
trainer = trainer_lib.Trainer(
model=model_fn,
loss_fn=layers.CrossEntropyLoss,
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_batching_lsh_self_attention(self):
with math.use_backend('jax'):
common_kwargs = dict(
n_heads=6,
d_qk=7,
d_v=17,
causal=True,
chunk_len=5,
n_chunks_before=1,
n_chunks_after=0,
n_hashes=2,
n_buckets=4,
attention_dropout=0.2,
output_dropout=0.1,
mode='train',
)
test_kwargs = []
for n_parallel_heads in [1, 3, 6, 12]:
for use_python_loop in [True, False]:
test_kwargs.append(
dict(n_parallel_heads=n_parallel_heads,
use_python_loop=use_python_loop))
x = jax.random.uniform(jax.random.PRNGKey(0), (2, 10, 13),
dtype=jnp.float32)
input_signature = shapes.signature(x)
self._test_equivalence_to_reference_code(
efficient_attention.LSHSelfAttention, x, input_signature,
common_kwargs, *test_kwargs)
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 math.use_backend(backend_name), self.tmp_dir() as output_dir:
# Prepare model and inputs
n_classes = 4
steps = 2
eval_steps = 2
model_fn = functools.partial(models.MLP,
d_hidden=16,
n_output_classes=n_classes)
inputs = test_inputs(n_classes)
# Train and evaluate
trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
steps=steps,
eval_steps=eval_steps)
# Restart training
state = trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
steps=(2 * steps),
eval_steps=eval_steps)
# Assert total train steps
self.assertEqual(state.step, 2 * steps)
def test_batching_self_attention(self):
with math.use_backend('jax'):
common_kwargs = dict(
n_heads=6,
d_qk=7,
d_v=17,
share_qk=False,
causal=True,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
attention_dropout=0.0,
mode='train',
)
test_kwargs = []
for n_parallel_heads in [1, 3, 6, 12]:
for use_python_loop in [True, False]:
test_kwargs.append(
dict(n_parallel_heads=n_parallel_heads,
use_python_loop=use_python_loop))
inp = jax.random.uniform(jax.random.PRNGKey(0), (2, 16, 64),
dtype=np.float32)
input_signature = ShapeDtype((2, 16, 64), dtype=np.float32)
self._test_equivalence_to_reference_code(
efficient_attention_v2.SelfAttention, inp, input_signature,
common_kwargs, *test_kwargs)
def test_fast_inference_self_attention(self):
with math.use_backend('jax'):
common_kwargs = dict(
n_heads=6,
d_qk=7,
d_v=17,
share_qk=False,
causal=True,
chunk_len=5,
n_chunks_before=1,
n_chunks_after=0,
attention_dropout=0.0,
output_dropout=0.0,
)
test_kwargs = []
for n_parallel_heads in [1, 3, 6, 12]:
for use_python_loop in [True, False]:
test_kwargs.append(
dict(n_parallel_heads=n_parallel_heads,
use_python_loop=use_python_loop))
x = jax.random.uniform(jax.random.PRNGKey(0), (2, 10, 13),
dtype=jnp.float32)
input_signature = shapes.signature(x)
self._test_fast_inference(efficient_attention.SelfAttention, x,
input_signature, common_kwargs,
*test_kwargs)
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 math.use_backend(backend_name), self.tmp_dir() as output_dir:
# Prepare model and inputs
n_classes = 4
steps = 2
eval_steps = 2
model_fn = functools.partial(
models.MLP, d_hidden=16, n_output_classes=n_classes)
inputs = test_inputs(n_classes)
# Train and evaluate
state = trainer_lib.train(
output_dir,
model=model_fn,
inputs=inputs,
steps=steps,
eval_steps=eval_steps,
optimizer=trax_opt.SM3)
# Assert total train steps
self.assertEqual(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 weights loaded from file.
inputs = inputs.train_stream(1)
model = model_fn()
model.init_from_file(os.path.join(output_dir, 'model.pkl'))
model(next(inputs)[0])
def _test_lsh_self_attention_deterministic_given_seed(self, causal=False):
# Once the initialization and the call seeds are pinned down we have
# deterministic output.
with math.use_backend('jax'):
layer = efficient_attention.LSHSelfAttention(
n_heads=5,
d_qk=7,
d_v=17,
causal=causal,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
n_hashes=2,
n_buckets=4,
use_reference_code=True,
attention_dropout=0.0,
mode='train')
x = np.ones((3, 32, 8)).astype(np.float32)
def get_output():
_, _ = layer.init(shapes.signature(x), jax.random.PRNGKey(0))
return layer(x, rng=jax.random.PRNGKey(1))
ys = [get_output() for _ in range(10)]
self.assertEqual(ys[0].shape, x.shape)
for y in ys[1:]:
np.testing.assert_array_almost_equal(ys[0], y, decimal=6)
def test_reformer_rng_consistency(self):
with math.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 = math.random.get_prng(0)
weights, state = model.init(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 = math.numpy.sum(output[0])
return dummy_loss
grad_fn = math.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_custom_zero_grad(self, backend_name):
class IdWithZeroGrad(base.Layer):
def forward(self, x, weights):
return x
@property
def has_backward(self):
return True
def backward(self, inputs, output, grad, weights, state, new_state,
rng):
return (jnp.zeros_like(grad), ())
with math.use_backend(backend_name):
layer = IdWithZeroGrad()
rng = math.random.get_prng(0)
input_signature = shapes.ShapeDtype((9, 17))
random_input = math.random.uniform(rng,
input_signature.shape,
minval=-1.0,
maxval=1.0)
layer.init(input_signature)
f = lambda x: jnp.mean(layer(x))
grad = math.grad(f)(random_input)
self.assertEqual(grad.shape, (9, 17)) # Gradient for each input.
self.assertEqual(sum(sum(grad * grad)), 0.0) # Each one is 0.
def _test_fast_inference(self, attention_type, length):
with math.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 = math.random.get_prng(0)
batch_size = 2
input_signature = ShapeDtype((batch_size, 1), np.int32)
# Given the same rng, both models initialize with the same parameters.
model_slow.init(input_signature)
model_fast.init(input_signature)
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, :],
decimal=5,
)
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 math.use_backend('numpy'):
return self._lr_fn(self._step)
def test_lsh_self_attention_masked_non_causal(self):
# Test that when the input that is in the masked area changes the attention
# for the un-masked outputs doesn't change, but the masked region does
# change.
with math.use_backend('jax'):
layer = efficient_attention.LSHSelfAttention(
n_heads=5,
d_qk=7,
d_v=17,
causal=False,
masked=True,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
n_hashes=2,
n_buckets=4,
use_reference_code=True,
attention_dropout=0.0,
mode='train')
batch = 5
max_len = 32
hidden = 8
x = np.random.uniform(size=(batch, max_len, hidden))
mask = np.ones((batch, max_len)).astype(np.bool)
rngs = jax.random.randint(jax.random.PRNGKey(0), (batch, ),
minval=1,
maxval=max_len - 1)
# Set some suffix of each mask[b] to 0.
for i in range(batch):
mask[i, rngs[i]:] = 0
# Fix rngs and get the output for the LSH layer.
def get_output(x, mask):
xs = [x, mask]
_, _ = layer.init(shapes.signature(xs), jax.random.PRNGKey(0))
return layer(xs, rng=jax.random.PRNGKey(1))
# Get the attention output for masked x.
y = get_output(x, mask)
# Change x, but only in the masked regions.
for i in range(batch):
x[i, rngs[i]:] = np.random.uniform(size=(max_len - rngs[i],
hidden))
y2 = get_output(x, mask)
for i in range(batch):
# y and y2 should be identical in the non-masked part.
np.testing.assert_array_almost_equal(y[i, :rngs[i]],
y2[i, :rngs[i]],
decimal=6)
# In the masked out part, they should be different.
self.assertGreater(
np.mean(np.abs(y[i, rngs[i]:] - y2[i, rngs[i]:])), 1e-5)
def test_layer_norm_dtype(self, backend, dtype):
with use_backend(backend):
input_shape = (2, 3, 4)
input_signature = ShapeDtype(input_shape, dtype)
layer = normalization.LayerNorm()
layer.init(input_signature)
out = layer(onp.empty(input_shape, dtype=dtype))
self.assertEqual(out.dtype, dtype)
def test_takes_new_history(self):
histories = np.array([[[0, 1, 2]], [[3, 4, 5]]])
with math.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_fails_to_evaluate_model_with_matrix_observation_space(self):
with math.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_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_fn.return_value.init.return_value = (None, None)
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 math.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 __init__(self,
trax_layer,
batch_size=None,
initializer_rng=None,
rng=None,
rng_updater=None,
dtype=None):
"""Creates a Keras layer wrapping around a Trax layer.
Args:
trax_layer: an object of class `trax.layers.Layer`, the trax layer to
wrap.
batch_size: (optional) an integer, the batch size that this Keras layer
will be used on. Keras sometimes needs to generate a TF graph for a
layer (e.g. for acceleration or checkpointing). The inputs used to trace
the graph will have `None` as the length of their batch dimensions, so
as to generate a graph that can handle any batch size. Some Trax layers
can't handle tensors whose shapes contain `None`. If `batch_size` is set
to an integer, the graph will be traced with `batch_size` as the batch
size instead of `None`. Note that in this case the graph (and the Keras
layer) can only be used on a specific batch size. If you want to use a
different batch size, you need to create another `TraxKerasLayer` object
with a different `batch_size`.
initializer_rng: (optional) an RNG key used to create the weights and
state if `trax_layer` doesn't have them. If `None`,
`trax.math.random.get_prng(0)` will be used.
rng: (optional) an RNG key for the forward function (aka the "forward
key"). If `None`, `trax.math.random.get_prng(0)` will be used.
rng_updater: (optional) a function of type rng_key -> rng_key, used to
update the forward key after each forward pass. If `None`, the function
`lambda x: trax.math.random.split(x, 1)[0]` will be used, which advances
the RNG key.
dtype: (optional) the dtype of the inputs. See the `dtype` argument of
`tf.keras.layers.Layer.__init__` for details.
"""
super(TraxKerasLayer, self).__init__(dtype=dtype)
with math_lib.use_backend("tf"):
if initializer_rng is None:
initializer_rng = math_lib.random.get_prng(0)
if rng is None:
rng = math_lib.random.get_prng(0)
if rng_updater is None:
rng_updater = lambda x: math_lib.random.split(x, 1)[0]
self._trax_layer = trax_layer
self._batch_size = batch_size
self._initializer_rng = initializer_rng
self._forward_rng_init = rng
self._rng_updater = rng_updater
def test_self_attention(self):
with math.use_backend('jax'):
input_signature = ShapeDtype((3, 32, 8))
layer = efficient_attention.SelfAttention(n_heads=5,
d_qk=7,
d_v=17,
share_qk=False,
causal=True,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
use_reference_code=True,
attention_dropout=0.0,
mode='train')
final_shape = base.check_shape_agreement(layer, input_signature)
self.assertEqual((3, 32, 8), final_shape)
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 math.use_backend(backend_name), self.tmp_dir() as output_dir:
# Prepare model and inputs
n_classes = 4
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 = test_inputs(n_classes)
# Train and evaluate
state = trainer_lib.train(output_dir,
model=model_fn,
inputs=inputs,
steps=steps,
eval_steps=eval_steps)
# Assert total train steps
self.assertEqual(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 weights
inputs = inputs.train_stream(1)
model = model_fn()
weights = state.opt_state.weights[0]
state = state.model_state[0]
if xla_bridge.device_count() > 1:
unreplicate = lambda x: x[0]
weights = math.nested_map(unreplicate, weights)
state = math.nested_map(unreplicate, state)
model(next(inputs)[0], weights=weights, state=state)
def test_self_attention_tf(self):
with math.use_backend('tf'):
layer = efficient_attention.SelfAttention(n_heads=5,
d_qk=7,
d_v=17,
share_qk=False,
causal=True,
chunk_len=8,
n_chunks_before=1,
n_chunks_after=0,
use_reference_code=True,
attention_dropout=0.0,
mode='train')
x = np.ones((3, 32, 8)).astype(np.float32)
_, _ = layer.init(shapes.signature(x))
y = layer(x)
self.assertEqual(y.shape, x.shape)
def call(self, inputs):
with math_lib.use_backend("tf"):
inputs = math_lib.nested_map(
functools.partial(_replace_none_batch,
batch_size=self._batch_size), inputs)
weights, state, rng = read_values(
[self._weights, self._state, self._rng])
inputs, weights, state, rng = to_arrays(
[inputs, weights, state, rng])
outputs, new_state = self._trax_layer.pure_fn(inputs,
weights=weights,
state=state,
rng=rng)
tf.nest.map_structure(lambda v, t: v.assign(t), self._state,
new_state)
self._rng.assign(self._rng_updater(rng))
outputs = to_tensors(outputs)
return outputs
def test_evaluates_model_with_vector_observation_space(self):
with math.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_reformer_lm_forward_shape_tf(self):
with math.use_backend('tf'):
vocab_size = 16
timebin_attn = self._timebin_self_attention_fn(
use_reference_code=True)
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=64,
attention_type=timebin_attn)
xs = [
np.ones((1, 64)).astype(np.int32),
np.ones((1, 64)).astype(np.int32)
]
_, _ = model.init(shapes.signature(xs))
ys = model(xs)
self.assertEqual([y.shape for y in ys], [(1, 64, 16), (1, 64)])
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 math.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 = test_inputs(n_classes)
trainer = trainer_lib.Trainer(
model=model_fn,
loss_fn=layers.CrossEntropyLoss(),
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 build(self, input_shape):
with math_lib.use_backend("tf"):
# Using `is` instead of `==` following Trax's practice
if self._trax_layer.weights is base.EMPTY_WEIGHTS:
sanitized_input_shape = math_lib.nested_map(
functools.partial(_replace_none_batch,
batch_size=self._batch_size),
input_shape)
weights, state = self._trax_layer.init(
tensor_shapes_to_shape_dtypes(sanitized_input_shape,
self.dtype),
rng=self._initializer_rng)
else:
weights = self._trax_layer.weights
state = self._trax_layer.state
# Note: `weights` may contain `EMPTY_WEIGHTS`
self._weights = math_lib.nested_map(
functools.partial(tf.Variable, trainable=True), weights)
self._state = math_lib.nested_map(
functools.partial(tf.Variable, trainable=False), state)
self._rng = tf.Variable(self._forward_rng_init, trainable=False)
super(TraxKerasLayer, self).build(input_shape)
def testTrain(self, layer_id, rng_updater_id, batch_size, trax_has_weights,
explicit_build, use_model):
"""Tests training (forward and backward pass) for TraxKerasLayer.
Args:
layer_id: an integer, the index into `_LAYERS`.
rng_updater_id: an integer, the index into `_RNG_UPDATERS`.
batch_size: an integer or `None`, the value for the `batch_size` argument
in `TraxKerasLayer.__init__`.
trax_has_weights: bool, whether to make the trax layer contain weights at
the time when `TraxKerasLayer.build` is called.
explicit_build: bool, whether to explicitly call `TraxKerasLayer.build`.
use_model: bool, whether to build a `tf.keras.Model` out of the
`TraxKerasLayer` layer and use the model to do the training instead of
the bare layer. If `True`, we will also test checkpointing and restoring
using the model.
"""
with math_lib.use_backend("tf"):
make_trax_layer, input_shapes_no_batch, dtype, allow_none_batch = (
_LAYERS[layer_id])
# We make a fresh trax layer for each test case, so that different test
# cases won't interfere with each other.
trax_layer = make_trax_layer()
if not allow_none_batch and batch_size is None:
self.skipTest("This Trax layer can't handle None batch size.")
rng_updater = _RNG_UPDATERS[rng_updater_id]
input_shapes = math_lib.nested_map(lambda s: [batch_size] + s,
input_shapes_no_batch)
input_sig = trax2keras.tensor_shapes_to_shape_dtypes(
input_shapes, dtype)
initializer_rng = math_lib.random.get_prng(765)
weights, state = trax_layer.init(input_sig, rng=initializer_rng)
generator = tf.random.Generator.from_seed(567)
def get_inputs():
return dummy_inputs(generator, input_sig)
if trax_has_weights:
trax_layer(to_arrays(get_inputs()),
weights=weights,
state=state)
rng = math_lib.random.get_prng(1234)
keras_layer = trax2keras.TraxKerasLayer(
trax_layer,
batch_size=batch_size,
initializer_rng=initializer_rng,
rng=rng,
rng_updater=rng_updater)
if explicit_build:
keras_layer.build(input_shapes)
if use_model:
x = tf.keras.Input(shape=input_shapes_no_batch, dtype=dtype)
y = keras_layer(x)
keras_model = tf.keras.Model(inputs=x, outputs=y)
lr = 0.1 # learning rate
for _ in range(3):
inputs = get_inputs()
with tf.GradientTape() as trax_tape:
trax_tape.watch([x.data for x in tf.nest.flatten(weights)])
trax_outputs, state = trax_layer.pure_fn(to_arrays(inputs),
weights=weights,
state=state,
rng=rng)
trax_grads = trax_tape.gradient(
*to_tensors([trax_outputs, weights]))
# `g` may be `tf.IndexedSlices`, so we need to `convert_to_tensor`
# before multiplication.
weights = tf.nest.map_structure(
lambda w, g: w + np.asarray(lr * tf.convert_to_tensor(g), w
.dtype), weights, trax_grads)
rng = rng_updater(rng)
with tf.GradientTape() as keras_tape:
if use_model:
keras_outputs = keras_model(inputs)
else:
keras_outputs = keras_layer(inputs)
if isinstance(keras_outputs,
tuple) and len(keras_outputs) == 1:
keras_outputs = keras_outputs[0]
self.assertAllClose(to_tensors(trax_outputs), keras_outputs)
keras_grads = keras_tape.gradient(
keras_outputs, keras_layer.trainable_variables)
tf.nest.map_structure(
lambda v, g: v.assign_add( # pylint: disable=g-long-lambda
tf.cast(lr * tf.convert_to_tensor(g), v.dtype)),
keras_layer.trainable_variables,
keras_grads)
self.assertAllClose(to_tensors(weights),
read_values(keras_layer._weights),
rtol=2e-6,
atol=5e-5)
self.assertAllClose(to_tensors(state),
read_values(keras_layer._state))
self.assertAllClose(to_tensors(rng),
read_values(keras_layer._rng))
if use_model:
fname = os.path.join(self.get_temp_dir(), "checkpoint")
keras_model.save(fname)
loaded_model = tf.keras.models.load_model(fname)
for _ in range(2):
inputs = get_inputs()
self.assertAllClose(keras_model(inputs),
loaded_model(inputs))
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 math.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])
def test_reversible_swap(self, backend_name):
with math.use_backend(backend_name):
layer = tl.ReversibleSwap()
xs = [np.array([1, 2]), np.array([10, 20])]
ys = layer(xs)
self.assertEqual(tl.to_list(ys), [[10, 20], [1, 2]])
