def apply(self,
inputs,
mlp_dim,
dtype=jnp.float32,
out_dim=None,
dropout_rate=0.1,
deterministic=False,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6),
num_partitions=2):
"""Applies Transformer MlpBlock module."""
actual_out_dim = inputs.shape[-1] if out_dim is None else out_dim
inputs_shape = inputs.shape
inputs = inputs.reshape((-1, inputs_shape[-1]))
x = nn.Dense(inputs,
mlp_dim,
dtype=dtype,
kernel_init=kernel_init,
bias_init=bias_init)
x = nn.relu(x)
if num_partitions > 1:
x = with_sharding_constraint(x, P(1, num_partitions))
x = nn.dropout(x, rate=dropout_rate, deterministic=deterministic)
output = nn.Dense(x,
actual_out_dim,
dtype=dtype,
kernel_init=kernel_init,
bias_init=bias_init)
output = nn.dropout(output,
rate=dropout_rate,
deterministic=deterministic)
output = output.reshape(inputs_shape[:-1] + (actual_out_dim, ))
return output
def apply(self,
num_embeddings,
features,
embedding_init=default_embed_init,
dtype=jnp.float32,
num_partitions=2):
"""Layer that returns an embedding matrix.
Args:
num_embeddings: number of embeddings.
features: Number of feature dimensions for each embedding.
embedding_init: embedding initializer.
dtype: dtype to use for activations.
num_partitions: number of ways to partition (i.e. how many devices to run
across).
Returns:
An embedding matrix suitable for embedding[inputs].
"""
embedding = self.param('embedding', (num_embeddings, features),
embedding_init)
embedding = jnp.asarray(embedding, dtype)
if num_partitions > 1:
embedding = with_sharding_constraint(embedding,
P(num_partitions, 1))
return embedding
def f(x):
y = jnp.dot(x, x)
y = with_sharding_constraint(y, P(2, 1))
return y * 2
def f(x):
y = x + 1
y = with_sharding_constraint(y, P(2, 1))
return y * 2
def f(x):
y = x + 1
p, vjp_f = vjp(
lambda z: jnp.sin(with_sharding_constraint(z, P(2, 2))), y)
return vjp_f(p)
def f(x):
return lax.while_loop(
lambda i: i[0, 0] < 10.,
lambda i: with_sharding_constraint(i + 1., P(2, 1)), x)
def apply(self,
inputs_q,
inputs_kv,
num_heads,
dtype=jnp.float32,
qkv_features=None,
out_features=None,
attention_axis=None,
causal_mask=False,
padding_mask=None,
key_padding_mask=None,
segmentation=None,
key_segmentation=None,
cache=None,
broadcast_dropout=True,
dropout_rng=None,
dropout_rate=0.,
deterministic=False,
precision=None,
kernel_init=nn.linear.default_kernel_init,
bias_init=nn.initializers.zeros,
bias=True,
num_partitions=2):
"""Applies multi-head dot product attention on the input data.
Projects the inputs into multi-headed query, key, and value vectors,
applies dot-product attention and project the results to an output vector.
This can be used for encoder-decoder attention by specifying both `inputs_q`
and `inputs_kv` orfor self-attention by only specifying `inputs_q` and
setting `inputs_kv` to None.
Args:
inputs_q: input queries of shape `[bs, dim1, dim2, ..., dimN, features]`.
inputs_kv: key/values of shape `[bs, dim1, dim2, ..., dimN, features]`
or None for self-attention, inn which case key/values will be derived
from inputs_q.
num_heads: number of attention heads. Features (i.e. inputs_q.shape[-1])
should be divisible by the number of heads.
dtype: the dtype of the computation (default: float32)
qkv_features: dimension of the key, query, and value.
out_features: dimension of the last projection
attention_axis: axes over which the attention is applied ( 'None' means
attention over all axes, but batch, heads, and features).
causal_mask: boolean specifying whether to apply a causal mask on the
attention weights. If True, the output at timestep `t` will not depend
on inputs at timesteps strictly greater than `t`.
padding_mask: boolean specifying query tokens that are pad token.
key_padding_mask: boolean specifying key-value tokens that are pad token.
segmentation: segment indices for packed inputs_q data.
key_segmentation: segment indices for packed inputs_kv data.
cache: an instance of `flax.nn.attention.Cache` used for efficient
autoregressive decoding.
broadcast_dropout: bool: use a broadcasted dropout along batch dims.
dropout_rng: JAX PRNGKey: to be used for dropout
dropout_rate: dropout rate
deterministic: bool, deterministic or not (to apply dropout)
precision: numerical precision of the computation see `jax.lax.Precision`
for details.
kernel_init: initializer for the kernel of the Dense layers.
bias_init: initializer for the bias of the Dense layers.
bias: bool: whether pointwise QKVO dense transforms use bias.
num_partitions: number of ways to partition (i.e. how many devices to run
across).
Returns:
output of shape `[bs, dim1, dim2, ..., dimN, features]`.
"""
assert causal_mask or not cache, (
'Caching is only support for causal attention.')
if inputs_kv is None:
inputs_kv = inputs_q
if attention_axis is None:
attention_axis = tuple(range(1, inputs_q.ndim - 1))
features = out_features or inputs_q.shape[-1]
qkv_features = qkv_features or inputs_q.shape[-1]
assert qkv_features % num_heads == 0, (
'Memory dimension must be divisible by number of heads.')
head_dim = qkv_features // num_heads
dense = nn.DenseGeneral.partial(axis=-1,
features=(num_heads, head_dim),
kernel_init=kernel_init,
bias_init=bias_init,
bias=bias,
precision=precision)
# project inputs_q to multi-headed q/k/v
# dimensions are then [bs, dims..., n_heads, n_features_per_head]
query, key, value = (dense(inputs_q, dtype=dtype, name='query'),
dense(inputs_kv, dtype=dtype, name='key'),
dense(inputs_kv, dtype=dtype, name='value'))
if num_partitions > 1:
partitions = P(1, 1, num_partitions, 1)
query = with_sharding_constraint(query, partitions)
key = with_sharding_constraint(key, partitions)
value = with_sharding_constraint(value, partitions)
if cache:
assert isinstance(cache,
Cache), 'cache must be an instance of Cache'
if self.is_initializing():
cache.store(lambda: (key.ndim, key.shape[-2:]))
else:
cache_entry = cache.retrieve(None)
expected_shape = list(cache_entry.key.shape[:-2])
for attn_dim in attention_axis:
expected_shape[attn_dim] = 1
expected_shape = tuple(expected_shape) + inputs_q.shape[-1:]
if expected_shape != inputs_q.shape:
raise ValueError('Invalid shape provided, '
'expected shape %s instead got %s.' %
(expected_shape, inputs_q.shape))
if not isinstance(cache_entry, _CacheEntry):
raise ValueError('Cache is not initialized.')
cshape = cache_entry.key.shape
i = cache_entry.i
one_hot_indices = jax.nn.one_hot(i, cshape[3],
dtype=key.dtype).reshape(
(1, 1, 1, cshape[3]))
key = key.transpose((0, 2, 3, 1))
key = cache_entry.key + key * one_hot_indices
value = value.transpose((0, 2, 3, 1))
value = cache_entry.value + value * one_hot_indices
one = jnp.array(1, jnp.uint32)
cache_entry = cache_entry.replace(i=cache_entry.i + one,
key=key,
value=value)
cache.store(cache_entry)
key = key.transpose((0, 3, 1, 2))
value = value.transpose((0, 3, 1, 2))
cshape = (cshape[0], cshape[3], cshape[1], cshape[2])
# TODO(levskaya): verify this is still needed in translation decoding.
key_padding_mask = jnp.broadcast_to(
(jnp.arange(cshape[1]) < cache_entry.i), cshape[:2])
key_padding_mask = key_padding_mask.astype(jnp.float32)[...,
None]
# create attention masks
mask_components = []
if causal_mask:
if cache and not self.is_initializing():
bias_pre_shape = (1, ) * (key.ndim - 1)
attn_shape = tuple(np.take(key.shape, attention_axis))
attn_size = np.prod(attn_shape)
ii = jnp.arange(attn_size, dtype=jnp.uint32)
mask = ii < cache_entry.i
mask_components.append(
mask.reshape(bias_pre_shape + attn_shape))
else:
mask_components.append(_make_causal_mask(key, attention_axis))
if padding_mask is not None:
if key_padding_mask is None:
key_padding_mask = padding_mask
padding_mask = make_padding_mask(padding_mask_query=padding_mask,
padding_mask_key=key_padding_mask,
query_shape=query.shape,
key_shape=key.shape,
attention_axis=attention_axis)
mask_components.append(padding_mask)
if segmentation is not None:
if key_segmentation is None:
key_segmentation = segmentation
segmentation_mask = make_padding_mask(
padding_mask_query=segmentation,
padding_mask_key=key_segmentation,
query_shape=query.shape,
key_shape=key.shape,
attention_axis=attention_axis,
segmentation_mask=True)
mask_components.append(segmentation_mask)
if mask_components:
attention_mask = mask_components[0]
for component in mask_components[1:]:
attention_mask = jnp.logical_and(attention_mask, component)
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.).astype(dtype),
jnp.full(attention_mask.shape, -1e10).astype(dtype))
else:
attention_bias = None
# apply attention
x = dot_product_attention(query,
key,
value,
dtype=dtype,
axis=attention_axis,
bias=attention_bias,
precision=precision,
dropout_rng=dropout_rng,
dropout_rate=dropout_rate,
broadcast_dropout=broadcast_dropout,
deterministic=deterministic)
# back to the original inputs dimensions
out = nn.DenseGeneral(x,
features=features,
axis=(-2, -1),
kernel_init=kernel_init,
bias_init=bias_init,
bias=bias,
dtype=dtype,
precision=precision,
name='out')
if num_partitions > 1:
x = with_sharding_constraint(x, None)
return out
def jax_func(x, y):
logits1 = jnp.dot(x, y)
return jnp.sin(
sharded_jit.with_sharding_constraint(logits1, P(2, 1)))
