def forward(self, inputs):
state = self.state
depth = inputs.shape[-1]
if self._mode == 'predict':
emb = self._get_embeddings(t=state)
emb = emb[:, jnp.newaxis, :]
state = state + 1
else:
input_len = inputs.shape[-2]
emb = self._get_embeddings(
t=jnp.arange(input_len, dtype=jnp.int32))
# Leave batch axis as 1 for broadcasting:
emb = emb[jnp.newaxis, :, :]
emb = jnp.broadcast_to(emb, inputs.shape[:-1] + (3, ))
# Replace the last num_features channels of input.
inputs = jnp.concatenate([inputs[..., :-self.num_features], emb], -1)
if inputs.shape[-1] > depth:
logging.warning('dropping feature(s): %d down to %d',
inputs.shape[-1], depth)
inputs = inputs[..., -depth:]
assert inputs.shape[-1] == depth, inputs.shape
self.state = state
return inputs
def f(x):
if n_devices > 1 and fastmath.is_backend(fastmath.Backend.JAX):
return _multi_device_put(x)
elif n_devices > 1:
return jnp.broadcast_to(x, (n_devices,) + jnp.asarray(x).shape)
else:
return x
def f(x):
if n_devices > 1 and fastmath.is_backend(fastmath.Backend.JAX):
return jax.device_put_replicated(x, jax.local_devices())
elif n_devices > 1:
return jnp.broadcast_to(x, (n_devices,) + jnp.asarray(x).shape)
else:
return x
def forward(self, inputs):
rng, state = self.rng, self.state
embs = []
for ax_emb in self.weights:
ax_emb = jnp.broadcast_to(
ax_emb, (inputs.shape[0],) + self._shape + (ax_emb.shape[-1],))
embs.append(ax_emb)
if self._mode == 'predict':
assert self._dropout == 0.0
emb = jnp.concatenate(embs, -1)
emb = jnp.reshape(emb, (inputs.shape[0], -1, emb.shape[-1]))
emb = jax.lax.dynamic_slice_in_dim(emb, state, inputs.shape[1], axis=1)
self.state = state + inputs.shape[1]
return inputs + emb
elif self._dropout == 0:
# TODO(kitaev): concat-then-reshape (as is the case with dropout enabled)
# leads to memory blow-up on TPU.
# emb = jnp.concatenate(embs, -1)
# return inputs + jnp.reshape(emb, inputs.shape), state
return inputs + jnp.concatenate(
[jnp.reshape(emb, inputs.shape[:-1] + (emb.shape[-1],))
for emb in embs
], -1)
else:
emb = jnp.concatenate(embs, -1)
noise_shape = list(emb.shape)
for dim in self._dropout_broadcast_dims:
noise_shape[dim] = 1
keep_prob = 1.0 - self._dropout
keep = fastmath.random.bernoulli(rng, keep_prob, tuple(noise_shape))
multiplier = keep.astype(inputs.dtype) / keep_prob
return inputs + jnp.reshape(emb * multiplier, inputs.shape)
def f(x):
if n_devices > 1 and fastmath.backend_name() == 'jax':
return _multi_device_put(x)
elif n_devices > 1:
return jnp.broadcast_to(x, (n_devices, ) + x.shape)
else:
return x
def _params(self, inputs):
"""Extracts the mean and std parameters from the inputs."""
assert inputs.shape[-1] == self.n_inputs
n_dims = self._n_dims
# Split the distribution inputs into two parts: mean and std.
mean = inputs[..., :n_dims]
if self._learn_std is not None:
std = inputs[..., n_dims:]
# Std is non-negative, so let's softplus it.
std = tl.Softplus()(std + self._std)
else:
std = self._std
# In case of constant or shared std, upsample it to the same dimensionality
# as the means.
std = jnp.broadcast_to(std, mean.shape)
return (mean, std)
def _params(self, inputs):
"""Extracts the mean and std parameters from the inputs."""
if inputs.shape[-1] != self.n_inputs:
raise ValueError(
'Invalid distribution parametrization - expected {} parameters, '
'got {}. Input shape: {}.'.format(self.n_inputs,
inputs.shape[-1],
inputs.shape))
n_dims = self._n_dims
# Split the distribution inputs into two parts: mean and std.
mean = inputs[..., :n_dims]
if self._learn_std is not None:
std = inputs[..., n_dims:]
# Std is non-negative, so let's softplus it.
std = tl.Softplus()(std + self._std)
else:
std = self._std
# In case of constant or shared std, upsample it to the same dimensionality
# as the means.
std = jnp.broadcast_to(std, mean.shape)
return (mean, std)
def forward(self, x):
"""Executes this layer as part of a forward pass through the model.
Args:
x: Tensor of same shape and dtype as the input signature used to
initialize this layer.
Returns:
Tensor of same shape and dtype as the input.
"""
m1, w1, w2, b2 = self.weights
x_shape = x.shape
x = jnp.reshape(x,
[-1, x_shape[-1]]) # Easier to operate on flattened x.
# Q: check if we need bias and/or put relu after the m1 dot?
mask_logits = jnp.dot(x, m1)
# Softmax.
mask_logsumexp = fastmath.logsumexp(mask_logits,
axis=-1,
keepdims=True)
log_mask = mask_logits - mask_logsumexp
mask = jnp.exp(log_mask)
# Gumbel-softmax with straight-through discretization.
# TODO(lukaszkaiser, chowdhery): Extract this block and share
rng1, rng2 = fastmath.random.split(self.rng, 2)
u = fastmath.random.uniform(rng1, mask.shape, jnp.float32, 1e-6,
1.0 - 1e-6)
g = -jnp.log(-jnp.log(u))
selected_experts = jnp.argmax(log_mask + g * self._temperature,
axis=-1)
if self._mode == 'train':
# Tricks from Section 2.1 in https://arxiv.org/abs/1801.09797
quant_mask = tl.one_hot(selected_experts, self._num_experts)
quant_mask = fastmath.stop_gradient(quant_mask)
quant_mask += mask - fastmath.stop_gradient(
mask) # straight-through
# We will sometimes (50% of the batches) use the soft-mask instead of
# the quantized mask to improve training stability (see the paper above).
# Q: is selecting 50% of batches the best? Other %? Mixed in-batch?
select = fastmath.random.uniform(rng2, (), jnp.float32, -1.0, 1.0)
quant_mask = jnp.where(select > 0.0, quant_mask, mask)
else:
quant_mask = tl.one_hot(selected_experts, self._num_experts)
quant_mask = jnp.reshape(quant_mask, [-1, self._num_experts, 1])
quant_mask_shape = quant_mask.shape
batch_size = quant_mask.shape[0]
if self._mode == 'predict' and batch_size == 1:
# This implementation mimicks inference for batch_size 1.
start_idx = selected_experts[0] * self._n_elements_in_block
# w1 is [d_model, d_ff], w is [d_model, n_elements_in_block]
w = fastmath.dynamic_slice(
w1, [0, start_idx], [w1.shape[0], self._n_elements_in_block])
mid = jnp.dot(x, w)
relu = jnp.where(mid <= 0, jnp.zeros_like(mid), mid)
# w2 is [d_ff, d_model], v is [n_elements_in_block, d_model]
v = fastmath.dynamic_slice(
w2, [start_idx, 0], [self._n_elements_in_block, w2.shape[-1]])
v = jnp.reshape(v, [self._n_elements_in_block, -1])
res = jnp.dot(relu, v) + b2
else:
expanded_mask = jnp.broadcast_to(
quant_mask, (quant_mask_shape[0], quant_mask.shape[1],
self._n_elements_in_block))
expanded_mask = jnp.reshape(expanded_mask, (-1, self._d_ff))
mid = jnp.dot(x, w1) * expanded_mask # [joint_batch, d_ff]
relu = jnp.where(mid <= 0, jnp.zeros_like(mid), mid)
res = jnp.dot(relu, w2) + b2
return jnp.reshape(res, x_shape) # un-flatten if needed
def representation_mask(mask):
mask = jnp.amax(mask, axis=tuple(range(2, mask.ndim)))
return jnp.broadcast_to(
mask[:, :, None], mask.shape + (serializer.representation_length,)
)
def significance_weights(mask): # (repr,) -> (batch, length, repr) significance = serializer.significance_map[None, None] return mask * decay ** jnp.broadcast_to(significance, mask.shape)
