def test_call_and_grad(self):
layer_partial = tl.Serial(
tl.Branch(tl.Embedding(3, 4), tl.PaddingMask()),
sparsity.Favor(d_feature=4, n_heads=2),
tl.Select([0], n_in=2),
)
layer = tl.Serial(
tl.Branch(tl.Embedding(3, 4), tl.PaddingMask()),
sparsity.Favor(d_feature=4, n_heads=2),
tl.Select([0], n_in=2),
tl.WeightedCategoryCrossEntropy(),
)
x = np.ones((1, 2), dtype=np.int32)
w = np.ones_like(x).astype(np.float32)
x_sig = shapes.signature(x)
w_sig = shapes.signature(w)
layer_partial.init(x_sig)
y = layer_partial(x)
self.assertEqual(y.shape, (1, 2, 4))
layer.init((x_sig, x_sig, w_sig))
y = layer((x, x, w))
self.assertEqual(y.shape, ())
state = layer.state
rng = fastmath.random.get_prng(0)
fwd = lambda weights, inp: layer.pure_fn(inp, weights, state, rng=rng)[
0]
g = fastmath.grad(fwd)(layer.weights, (x, x, w))
self.assertEqual(g[0][1][0].shape, (3, 4))
def test_funnel_block_forward_shape(self):
n_even = 4
d_model = 8
x = np.ones((1, n_even, d_model), dtype=np.float)
mask = np.ones((1, n_even), dtype=np.int32)
masker = tl.PaddingMask()
mask = masker(mask)
block = tl.Serial(
ft._FunnelBlock(d_model,
8,
2,
0.1,
None,
'train',
tl.Relu,
tl.AvgPool, (2, ), (2, ),
separate_cls=True))
xs = [x, mask]
_, _ = block.init(shapes.signature(xs))
y, _ = block(xs)
self.assertEqual(y.shape, (1, n_even // 2, d_model))
def TransformerEncoder(vocab_size=vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
dropout=0.1,
dropout_shared_axes=None,
max_len=2048,
mode='train',
ff_activation=tl.Relu,
EncoderBlock=EncoderBlock):
"""
Returns a Transformer encoder model.
The input to the model is a tensor of tokens.
Args:
vocab_size (int): vocab size. Defaults to vocab_size.
n_classes (int): how many classes on output. Defaults to 10.
d_model (int): depth of embedding. Defaults to 512.
d_ff (int): depth of feed-forward layer. Defaults to 2048.
n_layers (int): number of encoder/decoder layers. Defaults to 6.
n_heads (int): number of attention heads. Defaults to 8.
dropout (float): dropout rate (how much to drop out). Defaults to 0.1.
dropout_shared_axes (int): axes on which to share dropout mask. Defaults to None.
max_len (int): maximum symbol length for positional encoding. Defaults to 2048.
mode (str): 'train' or 'eval'. Defaults to 'train'.
ff_activation (function): the non-linearity in feed-forward layer. Defaults to tl.Relu.
EncoderBlock (function): Returns the encoder block. Defaults to EncoderBlock.
Returns:
trax.layers.combinators.Serial: A Transformer model as a layer that maps
from a tensor of tokens to activations over a set of output classes.
"""
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)
]
# repeatation of Encoder block upto number of layers
encoder_blocks = [
EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation) for _ in range(n_layers)
]
# Encoder Model
return tl.Serial(
tl.Branch(
positional_encoder,
tl.PaddingMask(),
),
encoder_blocks,
tl.Select([0], n_in=2),
tl.LayerNorm(),
tl.Mean(axis=1),
tl.Dense(n_classes),
tl.LogSoftmax(),
)
def TransformerEncoder(vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
dropout=0.1,
dropout_shared_axes=None,
max_len=2048,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer encoder model.
The input to the model is a tensor of tokens.
Args:
vocab_size: int: vocab size
n_classes: how many classes on output
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_layers: int: number of encoder/decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
dropout_shared_axes: axes on which to share dropout mask
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
ff_activation: the non-linearity in feed-forward layer
Returns:
A Transformer model as a layer that maps from a tensor of tokens to
activations over a set of output classes.
"""
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)
]
encoder_blocks = [
_EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation) for i in range(n_layers)
]
# Assemble and return the model.
return tl.Serial( # toks
# Encode.
tl.Branch(positional_encoder, tl.PaddingMask()), # vecs masks
encoder_blocks, # vecs masks
tl.Select([0], n_in=2), # vecs
tl.LayerNorm(), # vecs
# Map to output categories.
tl.Mean(axis=1), # vecs
tl.Dense(n_classes), # vecs
tl.LogSoftmax(), # vecs
)
def test_padding_mask(self):
layer = tl.PaddingMask()
x = np.array([
[1., 2., 3., 4., 0.],
[1., 2., 3., 0., 0.],
[1., 2., 0., 0., 0.],
])
y = layer(x)
self.assertEqual(x.shape, (3, 5))
self.assertEqual(y.shape, (3, 1, 1, 5))
np.testing.assert_equal(y, [[[[True, True, True, True, False]]],
[[[True, True, True, False, False]]],
[[[True, True, False, False, False]]]])
def TransformerEncoder(vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer encoder model.
The input to the model is a tensor of tokens.
Args:
vocab_size: int: vocab size
n_classes: how many classes on output
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_layers: int: number of encoder/decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
ff_activation: the non-linearity in feed-forward layer
Returns:
A Transformer model as a layer that maps from a tensor of tokens to
activations over a set of output classes.
"""
embedder = [
tl.Embedding(d_model, vocab_size),
tl.Dropout(rate=dropout, name='emb_dropout', mode=mode),
tl.PositionalEncoding(max_len=max_len),
]
return tl.Serial( # tokens
tl.Dup(), # toks toks
tl.Parallel(embedder, tl.PaddingMask()), # vecs mask
[
EncoderBlock(d_model, d_ff, n_heads, dropout, i, mode,
ff_activation) for i in range(n_layers)
], # vecs mask
tl.Parallel([], tl.Drop()), # ____ 0
tl.LayerNorm(), # vecs
tl.Mean(axis=1), # Average on length. # vecs
tl.Dense(n_classes), # vecs
tl.LogSoftmax(), # vecs
)
def _inp_layers():
if input_vocab_size is not None:
return tl.AssertFunction(
'bl,br->bld,bl,bl,br', # b: batch, l/r: enc/dec length, d: vec depth
tl.Serial( # tok_e tok_d
tl.Select([0, 0, 0, 1]),
tl.Parallel(
in_encoder,
[tl.PaddingMask(), _RemoveAxes12()
]))) # vec_e mask_e tok_e tok_d
else:
# Input in this case is vec_e, mask_e, tok_d. Where all downstream
# operations expect tok_e, we give it instead mask_e, expecting that
# downstream ops only are looking for padding/not padding.
return tl.AssertFunction(
'blf,bl,br->bld,bl,bl,br', # f: in-feature depth, d: out-vector depth
tl.Serial( # vec_e mask_e tok_d
tl.Select([0, 1, 1, 2]),
tl.Parallel(in_encoder, [],
_AsTokenIDs()))) # vec_e mask_e tok_e tok_d
def ConfigurableTransformerEncoder(vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
max_len=2048,
dropout=0.1,
dropout_shared_axes=None,
mode='train',
ff_activation=tl.Relu,
ff_dropout=0.1,
ff_chunk_size=0,
ff_use_sru=0,
ff_sparsity=0,
ff_sparsity_type='1inN',
attention_chunk_size=0,
attention_type=tl.Attention,
pos_type=None,
pos_axial_shape=None,
pos_d_axial_embs=None):
"""Returns a Transformer encoder merged with an N-way categorization head.
This model performs text categorization:
- input: rank 2 tensor representing a batch of text strings via token IDs
plus padding markers; shape is (batch_size, sequence_length). The tensor
elements are integers in `range(vocab_size)`, and `0` values mark padding
positions.
- output: rank 2 tensor representing a batch of log-probability
distributions over N categories; shape is (batch_size, `n_classes`).
Args:
vocab_size: Input vocabulary size -- each element of the input tensor should
be an integer in `range(vocab_size)`. These integers typically represent
token IDs from a vocabulary-based tokenizer.
n_classes: Final dimension of the output tensors, representing N-way
classification.
d_model: Final dimension of tensors at most points in the model, including
the initial embedding output.
d_ff: Size of special dense layer in the feed-forward part of each encoder
block.
n_layers: Number of encoder blocks. Each block includes attention, dropout,
residual, feed-forward (`Dense`), and activation layers.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value when
applying dropout within an encoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask. Sharing
along batch and sequence axes (`dropout_shared_axes=(0,1)`) is a useful
way to save memory and apply consistent masks to activation vectors at
different sequence positions.
mode: If `'train'`, each encoder block will include dropout; else, it will
pass all values through unaltered.
ff_activation: Type of activation function at the end of each encoder block;
must be an activation-type subclass of `Layer`.
ff_dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout after the FF dense layer.
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_use_sru: int or pair of ints; if > 0, we use this many SRU layers
in addition to the feed-forward block (second int specifies sru size)
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
ff_sparsity_type: string, if ff_sparsity >0,
use SparseFF if ff_sparsity_type=`'1inN'` and
use BlockSparseFF if ff_sparsity_type=`'Block'`
attention_chunk_size: int, if > 0 run attention chunked at this size
attention_type: The attention layer to use for the encoder part.
pos_type: string, the type of positional embeddings to use.
pos_axial_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
pos_d_axial_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match pos_axial_shape, and values must sum to d_model.
Returns:
A Transformer model that maps strings (conveyed via token IDs) to
probability-like activations over a range of output classes.
"""
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
PositionalEncoder(mode, dropout, max_len, pos_type, pos_axial_shape,
pos_d_axial_embs)
]
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation, ff_dropout, ff_chunk_size,
ff_use_sru, ff_sparsity, ff_sparsity_type,
attention_chunk_size, attention_type)
for i in range(n_layers)
]
# pylint: enable=g-complex-comprehension
# Assemble and return the model.
return tl.Serial( # toks
# Encode.
tl.Branch(positional_encoder, tl.PaddingMask()), # vecs masks
encoder_blocks, # vecs masks
tl.Select([0], n_in=2), # vecs
tl.LayerNorm(), # vecs
# Map to output categories.
tl.Mean(axis=1), # vecs
tl.Dense(n_classes), # vecs
)
def ConfigurableTransformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
max_len=2048,
dropout=0.1,
dropout_shared_axes=None,
mode='train',
ff_activation=tl.Relu,
ff_dropout=0.1,
ff_chunk_size=0,
ff_use_sru=0,
ff_sparsity=0,
ff_sparsity_type='1inN',
loss_sparsity_type='mult',
loss_sparsity=0,
loss_d_lowrank=0,
loss_sparsity_prob=None,
attention_chunk_size=0,
encoder_attention_type=tl.Attention,
encoder_decoder_attention_type=tl.CausalAttention,
pos_type=None,
pos_axial_shape=None,
pos_d_axial_embs=None,
enc_dec_attention_sparsity=0):
"""Returns a full Transformer model.
This model is an encoder-decoder that performs tokenized string-to-string
("source"-to-"target") transduction:
- inputs (2):
- source: rank 2 tensor representing a batch of text strings via token
IDs plus padding markers; shape is (batch_size, sequence_length). The
tensor elements are integers in `range(input_vocab_size)`, and `0`
values mark padding positions.
- target: rank 2 tensor representing a batch of text strings via token
IDs plus padding markers; shape is (batch_size, sequence_length). The
tensor elements are integers in `range(output_vocab_size)`, and `0`
values mark padding positions.
- output: rank 3 tensor representing a batch of log-probability
distributions for each sequence position over possible token IDs;
shape is (batch_size, sequence_length, `vocab_size`).
An example use would be to translate (tokenized) sentences from English to
German.
Args:
input_vocab_size: Input vocabulary size -- each element of the input tensor
should be an integer in `range(vocab_size)`. These integers typically
represent token IDs from a vocabulary-based tokenizer.
output_vocab_size: If specified, gives the vocabulary size for the targets;
if None, then input and target integers (token IDs) are assumed to come
from the same vocabulary.
d_model: Final dimension of tensors at most points in the model, including
the initial embedding output.
d_ff: Size of special dense layer in the feed-forward part of each encoder
and decoder block.
n_encoder_layers: Number of encoder blocks.
n_decoder_layers: Number of decoder blocks.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value when
applying dropout within an encoder/decoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask. Sharing
along batch and sequence axes (`dropout_shared_axes=(0,1)`) is a useful
way to save memory and apply consistent masks to activation vectors at
different sequence positions.
mode: If `'predict'`, use fast inference. If `'train'`, each encoder/decoder
block will include dropout; else, it will pass all values through
unaltered.
ff_activation: Type of activation function at the end of each
encoder/decoder block; must be an activation-type subclass of `Layer`.
ff_dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout after the FF dense layer.
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_use_sru: int or pair of ints; if > 0, we use this many SRU layers
in addition to the feed-forward block (second int specifies sru size)
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
ff_sparsity_type: string, if ff_sparsity >0,
use SparseFF if ff_sparsity_type=`'1inN'` and
use BlockSparseFF if ff_sparsity_type=`'Block'`
loss_sparsity_type: str, type of sparsity to used in loss layer. See
SparseDenseWithOptions for options. None if no sparsity should be used.
loss_sparsity: int, the sparsity for loss layer (if used)
loss_d_lowrank: int, the dimensions for intermediate layer (if used)
loss_sparsity_prob: float, the probability for sparse version of loss to be
used. If None, only sparse version is used.
attention_chunk_size: int, if > 0 run attention chunked at this size
encoder_attention_type: The attention layer to use for the encoder part.
encoder_decoder_attention_type: The attention layer to use for the
encoder-decoder attention.
pos_type: string, the type of positional embeddings to use.
pos_axial_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
pos_d_axial_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match pos_axial_shape, and values must sum to d_model.
enc_dec_attention_sparsity: int, if > 0 use this sparsity in attention.
Returns:
A Transformer model as a layer that maps from a source-target tokenized
text pair to activations over a vocab set.
"""
in_encoder, out_encoder, output_vocab_size = (
EmbeddingAndPositionalEncodings(input_vocab_size,
d_model,
mode,
dropout,
dropout_shared_axes,
max_len,
output_vocab_size=output_vocab_size,
pos_type=pos_type,
pos_axial_shape=pos_axial_shape,
pos_d_axial_embs=pos_d_axial_embs))
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation, ff_dropout, ff_chunk_size,
ff_use_sru, ff_sparsity, ff_sparsity_type,
attention_chunk_size, encoder_attention_type)
for i in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = tl.Serial(in_encoder, encoder_blocks, tl.LayerNorm())
if mode == 'predict':
encoder = tl.Cache(encoder)
# pylint: disable=g-complex-comprehension
encoder_decoder_blocks = [
EncoderDecoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation,
ff_dropout, ff_chunk_size, ff_use_sru, ff_sparsity,
ff_sparsity_type, attention_chunk_size,
encoder_decoder_attention_type,
enc_dec_attention_sparsity)
for i in range(n_decoder_layers)
]
# pylint: enable=g-complex-comprehension
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
# Encode.
tl.Branch([], tl.PaddingMask()), # tok_e masks ..... .....
encoder, # vec_e ..... ..... .....
# Decode.
tl.Select([2, 1, 0]), # tok_d masks vec_e .....
tl.ShiftRight(mode=mode), # tok_d ..... ..... .....
out_encoder, # vec_d ..... ..... .....
tl.Branch([], tl.EncoderDecoderMask()), # vec_d masks ..... .....
encoder_decoder_blocks, # vec_d masks ..... .....
tl.LayerNorm(), # vec_d ..... ..... .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d tok_d
tl.SparseDenseWithOptions( # vec_d .....
output_vocab_size,
d_input=d_model,
sparsity_type=loss_sparsity_type,
sparsity=loss_sparsity,
d_lowrank=loss_d_lowrank,
prob_sparse=loss_sparsity_prob,
mode=mode),
)
def Reformer2(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
d_attention_key=None,
d_attention_value=None,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
encoder_attention_type=tl.SelfAttention,
encoder_decoder_attention_type=tl.SelfAttention,
axial_pos_shape='fixed-base',
d_axial_pos_embs=None,
ff_activation=tl.Relu,
ff_use_sru=0,
ff_chunk_size=0,
ff_dropout=None,
ff_sparsity=0,
loss_sparsity_type='mult',
loss_sparsity=0,
loss_d_lowrank=0,
loss_sparsity_prob=None,
attention_chunk_size=0,
n_layers_forget=0,
n_decoder_attention_layers=2,
use_bfloat16=False,
reversible_encoder=False,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: source, target.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
d_attention_key: int: depth of key vector for each attention head
d_attention_value: int: depth of value vector for each attention head
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
encoder_attention_type: class: attention class to use, such as SelfAttention
encoder_decoder_attention_type: class: attention class to use, such as
SelfAttention
axial_pos_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
d_axial_pos_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match axial_pos_shape, and values must sum to d_model.
ff_activation: the non-linearity in feed-forward layer
ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
loss_sparsity_type: str, type of sparsity to used in loss layer. See
SparseDenseWithOptions for options. None if no sparsity should be used.
loss_sparsity: int, the sparsity for loss layer (if used)
loss_d_lowrank: int, the dimensions for intermediate layer (if used)
loss_sparsity_prob: float, the probability for sparse version of loss to be
used. If None, only sparse version is used.
attention_chunk_size: int, if > 0 run attention chunked at this size
n_layers_forget: how often to have a forgetting block between layers
n_decoder_attention_layers: how many attention layers in a decoder block
use_bfloat16: whether to use bfloat16 for weights (default: False)
reversible_encoder: whether to be reversible through the encoder
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
# Set default dimensions for attention head key and value sizes.
if d_attention_key is None:
if d_model % n_heads != 0:
raise ValueError(
f'n_heads ({n_heads}) must divide d_model ({d_model})')
d_attention_key = d_model // n_heads
if d_attention_value is None:
if d_model % n_heads != 0:
raise ValueError(
f'n_heads ({n_heads}) must divide d_model ({d_model})')
d_attention_value = d_model // n_heads
# Vector embeddings.
in_encoder, out_encoder, output_vocab_size = (
ct.EmbeddingAndPositionalEncodings(
input_vocab_size,
d_model,
mode,
dropout,
[-2], # dropout_shared_axes
max_len,
output_vocab_size=output_vocab_size,
axial_pos_shape=axial_pos_shape,
d_axial_pos_embs=d_axial_pos_embs,
use_bfloat16=use_bfloat16))
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model,
d_ff,
n_heads,
encoder_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_dropout=ff_dropout,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity,
attention_chunk_size=attention_chunk_size,
use_bfloat16=use_bfloat16,
mode=mode) for _ in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = [ # vec_e mask_e tok_e tok_d tok_d
tl.ReversibleSelect([0, 0]), # vec_e1 vec_e2 mask_e tok_e tok_d tok_d
_ReversibleSerialForget(encoder_blocks, d_model, n_layers_forget)
]
if not reversible_encoder:
encoder += [
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0),
tl.Dense(d_model, use_bfloat16=use_bfloat16),
tl.LayerNorm(),
]
encoder = tl.Serial(encoder)
if mode == 'predict':
encoder = tl.Cache(encoder)
decoder_blocks = []
if isinstance(encoder_decoder_attention_type, (tuple, list)):
assert n_decoder_layers % len(encoder_decoder_attention_type) == 0
else:
encoder_decoder_attention_type = [encoder_decoder_attention_type]
for layer_idx in range(n_decoder_layers):
layer_attention_type = encoder_decoder_attention_type[
layer_idx % len(encoder_decoder_attention_type)]
decoder_block = DecoderBlock(
d_model,
d_ff,
d_attention_key,
d_attention_value,
n_heads,
attention_type=layer_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_dropout=ff_dropout,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity,
attention_chunk_size=attention_chunk_size,
n_attention_layers=n_decoder_attention_layers,
use_bfloat16=use_bfloat16,
mode=mode)
decoder_blocks.append(decoder_block)
dense_loss_layer = tl.SparseDenseWithOptions(
output_vocab_size,
d_input=d_model,
sparsity_type=loss_sparsity_type,
sparsity=loss_sparsity,
d_lowrank=loss_d_lowrank,
prob_sparse=loss_sparsity_prob,
use_bfloat16=use_bfloat16,
mode=mode)
# Layers to merge encoder and decoder, see below for details.
if reversible_encoder:
encdec_layers = [
tl.ReversibleSelect([0, 1, 4, 2,
3]), # vec_e vec_d mask_e tok_e tok_d
t2.ConcatWithPadding2(mode=mode), # vec_ed vec_ed tok_e tok_d
]
else:
encdec_layers = [
tl.ReversibleSelect([0, 3, 1,
2]), # vec_e vec_d mask_e tok_e tok_d
t2.ConcatWithPadding(mode=mode), # vec_ed tok_e tok_d
tl.ReversibleSelect([0, 0]), # vec_ed vec_ed tok_e tok_d
]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 0, 0, 1, 1]), # tok_e tok_e tok_e tok_d tok_d
# Embed in and out tokens; done together as weights may be shared.
tl.Parallel(
in_encoder,
[],
[], # vec_e tok_e tok_e vec_d tok_d
[tl.ShiftRight(mode=mode), out_encoder]),
tl.Parallel([], [
tl.PaddingMask(),
tl.Fn('Squeeze', lambda x: jnp.squeeze(x, (1, 2)), n_out=1)
]),
# # vec_e mask_e tok_e vec_d tok_d
# Encode.
encoder, # vec_e mask_e tok_e vec_d tok_d
# Concat encoder and decoder, given encoder mask.
encdec_layers,
# Run decoder blocks.
_ReversibleSerialForget(
decoder_blocks, d_model,
n_layers_forget), # vec_ed1 vec_ed2 tok_e tok_d
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0), # vec_ed tok_e tok_d
tl.LayerNorm(), # vec_ed tok_e tok_d
# Separate out the encoder part from the concatenated vector.
tl.Select([0, 1, 2, 2]), # vec_ed tok_e tok_d tok_d
t2.StripFromConcatenateWithPadding(mode=mode), # vec_d tok_d
# Map to output vocab.
dense_loss_layer, # vec_d tok_d
)
def TransformerEncoder(vocab_size=vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
dropout=0.1,
dropout_shared_axes=None,
max_len=2048,
mode='train',
ff_activation=tl.Relu,
EncoderBlock=EncoderBlock):
"""
Returns a Transformer encoder model.
The input to the model is a tensor of tokens.
Args:
vocab_size (int): vocab size. Defaults to vocab_size.
n_classes (int): how many classes on output. Defaults to 10.
d_model (int): depth of embedding. Defaults to 512.
d_ff (int): depth of feed-forward layer. Defaults to 2048.
n_layers (int): number of encoder/decoder layers. Defaults to 6.
n_heads (int): number of attention heads. Defaults to 8.
dropout (float): dropout rate (how much to drop out). Defaults to 0.1.
dropout_shared_axes (int): axes on which to share dropout mask. Defaults to None.
max_len (int): maximum symbol length for positional encoding. Defaults to 2048.
mode (str): 'train' or 'eval'. Defaults to 'train'.
ff_activation (function): the non-linearity in feed-forward layer. Defaults to tl.Relu.
EncoderBlock (function): Returns the encoder block. Defaults to EncoderBlock.
Returns:
trax.layers.combinators.Serial: A Transformer model as a layer that maps
from a tensor of tokens to activations over a set of output classes.
"""
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)
]
### START CODE HERE (REPLACE INSTANCES OF 'None' WITH YOUR CODE) ###
# Use the function `EncoderBlock` (implemented above) and pass in the parameters over `n_layers`
encoder_blocks = [EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes, mode, ff_activation) for _ in range(n_layers)]
# Assemble and return the model.
return tl.Serial(
# Encode
tl.Branch(
# Use `positional_encoder`
positional_encoder,
# Use trax padding mask
tl.PaddingMask(),
),
# Use `encoder_blocks`
encoder_blocks,
# Use select layer
tl.Select([0], n_in=2),
# Use trax layer normalization
tl.LayerNorm(),
# Map to output categories.
# Use trax mean. set axis to 1
tl.Mean(axis=1),
# Use trax Dense using `n_classes`
tl.Dense(n_classes),
# Use trax log softmax
tl.LogSoftmax(),
)
def Reformer2(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
d_attention_key=None,
d_attention_value=None,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
encoder_attention_type=tl.SelfAttention,
encoder_decoder_attention_type=tl.SelfAttention,
pos_type='fixed-base',
pos_axial_shape=(),
pos_d_axial_embs=None,
pos_start_from_zero_prob=1.0,
pos_max_offset_to_add=0,
ff_activation=tl.Relu,
ff_use_sru=0,
ff_chunk_size=0,
ff_dropout=None,
ff_sparsity=0,
loss_sparsity_type='mult',
loss_sparsity=0,
loss_d_lowrank=0,
loss_sparsity_prob=None,
attention_chunk_size=0,
n_layers_forget=0,
forget_dense=True,
n_decoder_attention_layers=2,
use_bfloat16=False,
reversible_encoder=False,
use_two_swaps_per_encoder_block=True,
center_layernorm=True,
half_before_layer=None,
double_after_layer=None,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: source, target.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
d_attention_key: int: depth of key vector for each attention head
d_attention_value: int: depth of value vector for each attention head
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
encoder_attention_type: class: attention class to use, such as SelfAttention
encoder_decoder_attention_type: class: attention class to use, such as
SelfAttention
pos_type: string, the type of positional embeddings to use.
pos_axial_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
pos_d_axial_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match pos_axial_shape, and values must sum to d_model.
pos_start_from_zero_prob: how often to start from 0 during training,
(if 1.0, we always start from position 0, if less, we randomize).
pos_max_offset_to_add: maximum offset to add to positions during training
when randomizing; this offset plus input length must still be less than
max_len for all training examples.
ff_activation: the non-linearity in feed-forward layer
ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
loss_sparsity_type: str, type of sparsity to used in loss layer. See
SparseDenseWithOptions for options. None if no sparsity should be used.
loss_sparsity: int, the sparsity for loss layer (if used)
loss_d_lowrank: int, the dimensions for intermediate layer (if used)
loss_sparsity_prob: float, the probability for sparse version of loss to be
used. If None, only sparse version is used.
attention_chunk_size: int, if > 0 run attention chunked at this size
n_layers_forget: how often to have a forgetting block between layers
forget_dense: whether to use Dense or no-op (Serial) as a forget layer.
n_decoder_attention_layers: how many attention layers in a decoder block
use_bfloat16: whether to use bfloat16 for weights (default: False)
reversible_encoder: whether to be reversible through the encoder
use_two_swaps_per_encoder_block: whether to allow even number of swaps in
the encoder
center_layernorm: whether to use centering in LayerNorm (default) or if
to skip it, which is known as RMS normalization.
half_before_layer: int, half d_model and d_ff before that layer
double_after_layer: int, double d_model and d_ff after that layer
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
# Set default dimensions for attention head key and value sizes.
if (d_model / 2) % n_heads != 0:
raise ValueError(
f'n_heads ({n_heads}) must divide d_model/2 ({d_model/2})')
if d_attention_key is None:
d_attention_key = d_model // n_heads
if d_attention_value is None:
d_attention_value = d_model // n_heads
# Set values of d_model, d_ff and d_qkv for the first stage.
d_model1, d_ff1 = d_model, d_ff
d_attention_key1, d_attention_value1 = d_attention_key, d_attention_value
if half_before_layer:
d_model1, d_ff1 = d_model / 2, d_ff / 2
d_attention_key1 = d_attention_key / 2
d_attention_value1 = d_attention_value / 2
# Set values of d_model, d_ff and d_qkv for the final stage.
d_model2, d_ff2 = d_model, d_ff
d_attention_key2, d_attention_value2 = d_attention_key, d_attention_value
if double_after_layer:
d_model2, d_ff2 = d_model * 2, d_ff * 2
d_attention_key2 = d_attention_key * 2
d_attention_value2 = d_attention_value * 2
# Vector embeddings.
in_encoder, out_encoder, output_vocab_size = (
ct.EmbeddingAndPositionalEncodings(
input_vocab_size,
d_model1,
mode,
dropout,
[-2], # dropout_shared_axes
max_len,
output_vocab_size=output_vocab_size,
pos_type=pos_type,
pos_axial_shape=pos_axial_shape,
pos_d_axial_embs=pos_d_axial_embs,
pos_start_from_zero_prob=pos_start_from_zero_prob,
pos_max_offset_to_add=pos_max_offset_to_add,
use_bfloat16=use_bfloat16))
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model1,
d_ff1,
n_heads,
encoder_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_dropout=ff_dropout,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity,
attention_chunk_size=attention_chunk_size,
center_layernorm=center_layernorm,
use_bfloat16=use_bfloat16,
use_two_swaps_per_block=use_two_swaps_per_encoder_block,
mode=mode) for _ in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = [ # vec_e mask_e tok_e tok_d tok_d
tl.ReversibleSelect([0, 0]), # vec_e1 vec_e2 mask_e tok_e tok_d tok_d
_ReversibleSerialForget(encoder_blocks, d_model1, n_layers_forget,
forget_dense)
]
if not reversible_encoder:
encoder += [
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0),
tl.Dense(d_model1, use_bfloat16=use_bfloat16),
tl.LayerNorm(),
]
encoder = tl.Serial(encoder)
if mode == 'predict':
encoder = tl.Cache(encoder)
decoder_blocks = []
if isinstance(encoder_decoder_attention_type, (tuple, list)):
assert n_decoder_layers % len(encoder_decoder_attention_type) == 0
else:
encoder_decoder_attention_type = [encoder_decoder_attention_type]
for layer_idx in range(n_decoder_layers):
layer_attention_type = encoder_decoder_attention_type[
layer_idx % len(encoder_decoder_attention_type)]
# Grow d_model, d_ff, and d_qkv if requested.
d_m, d_f, d_k, d_v = d_model1, d_ff1, d_attention_key1, d_attention_value1
if half_before_layer and layer_idx >= half_before_layer:
d_m, d_f, d_k, d_v = d_model, d_ff, d_attention_key, d_attention_value
if double_after_layer and layer_idx > double_after_layer:
d_m, d_f, d_k, d_v = d_model2, d_ff2, d_attention_key2, d_attention_value2
decoder_block = DecoderBlock(
d_m,
d_f,
d_k,
d_v,
n_heads,
attention_type=layer_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_dropout=ff_dropout,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity,
attention_chunk_size=attention_chunk_size,
n_attention_layers=n_decoder_attention_layers,
center_layernorm=center_layernorm,
use_bfloat16=use_bfloat16,
mode=mode)
decoder_blocks.append(decoder_block)
if half_before_layer and layer_idx == half_before_layer - 1:
decoder_blocks.append(tl.ReversibleConcatenatePair())
if double_after_layer and layer_idx == double_after_layer:
decoder_blocks.append(tl.ReversibleConcatenatePair())
dense_loss_layer = tl.SparseDenseWithOptions(
output_vocab_size,
d_input=d_model2,
sparsity_type=loss_sparsity_type,
sparsity=loss_sparsity,
d_lowrank=loss_d_lowrank,
prob_sparse=loss_sparsity_prob,
use_bfloat16=use_bfloat16,
mode=mode)
# Layers to merge encoder and decoder, see below for details.
if reversible_encoder:
encdec_layers = [
tl.ReversibleSelect([0, 1, 4, 2,
3]), # vec_e vec_d mask_e tok_e tok_d
t2.ConcatWithPadding2(mode=mode), # vec_ed vec_ed tok_e tok_d
]
else:
encdec_layers = [
tl.ReversibleSelect([0, 3, 1,
2]), # vec_e vec_d mask_e tok_e tok_d
t2.ConcatWithPadding(mode=mode), # vec_ed tok_e tok_d
tl.ReversibleSelect([0, 0]), # vec_ed vec_ed tok_e tok_d
]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 0, 0, 1, 1]), # tok_e tok_e tok_e tok_d tok_d
# Embed in and out tokens; done together as weights may be shared.
tl.Parallel(
in_encoder,
[],
[], # vec_e tok_e tok_e vec_d tok_d
[tl.ShiftRight(mode=mode), out_encoder]),
# Predict mode doesn't work with padding in encoder. Raising an exception
# in jitted function isn't possible, so the second next best thing is
# to convert every embedding to NaNs, so the user will not get subtly
# wrong results, but clearly wrong results.
(_ConvertToNaNsOnAnyZero() if mode == 'predict' else []),
tl.Parallel([], [
tl.PaddingMask(),
tl.Fn('Squeeze', lambda x: jnp.squeeze(x, (1, 2)), n_out=1)
]),
# # vec_e mask_e tok_e vec_d tok_d
# Encode.
encoder, # vec_e mask_e tok_e vec_d tok_d
# Concat encoder and decoder, given encoder mask.
encdec_layers,
# Run decoder blocks.
_ReversibleSerialForget(decoder_blocks, d_model2, n_layers_forget,
forget_dense), # vec_ed1 vec_ed2 tok_e tok_d
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0), # vec_ed tok_e tok_d
tl.LayerNorm(), # vec_ed tok_e tok_d
# Separate out the encoder part from the concatenated vector.
tl.Select([0, 1, 2, 2]), # vec_ed tok_e tok_d tok_d
t2.StripFromConcatenateWithPadding(mode=mode), # vec_d tok_d
# Map to output vocab.
dense_loss_layer, # vec_d tok_d
)
def Reformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
ff_activation=tl.Relu,
ff_dropout=None,
mode='train',
axial_pos_shape=None,
d_axial_pos_embs=None,
ff_use_sru=0,
ff_chunk_size=0,
ff_sparsity=0):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: target, source.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
ff_activation: the non-linearity in feed-forward layer
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
mode: str: 'train' or 'eval'
axial_pos_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
d_axial_pos_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match axial_pos_shape, and values must sum to d_model.
ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
in_encoder, out_encoder, output_vocab_size = (
ct.EmbeddingAndPositionalEncodings(
input_vocab_size,
d_model,
mode,
dropout,
[-2], # dropout_shared_axes
max_len,
output_vocab_size=output_vocab_size,
axial_pos_shape=axial_pos_shape,
d_axial_pos_embs=d_axial_pos_embs))
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model,
d_ff,
n_heads,
tl.SelfAttention,
dropout,
ff_activation,
ff_dropout,
mode=mode,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity) for _ in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = tl.Serial([
in_encoder,
tl.Dup(),
tl.ReversibleSerial(encoder_blocks),
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0),
tl.LayerNorm(),
])
if mode == 'predict':
encoder = tl.Cache(encoder)
# pylint: disable=g-complex-comprehension
encoder_decoder_blocks = [
EncoderDecoderBlock(d_model,
d_ff,
n_heads,
dropout,
ff_activation,
ff_dropout,
mode,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity)
for _ in range(n_decoder_layers)
]
# pylint: enable=g-complex-comprehension
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
tl.Branch([], [
tl.PaddingMask(),
tl.Fn('Squeeze', lambda x: jnp.squeeze(x, (1, 2)), n_out=1)
]),
# # tok_e mask tok_d .....
# Encode.
encoder, # vec_e mask tok_d .....
# Decode.
tl.Select([2, 0, 1]), # tok_d vec_e mask .....
tl.ShiftRight(mode=mode), # tok_d vec_e mask .....
out_encoder, # vec_d vec_e mask .....
tl.Dup(), # vec_d1 vec_d2 vec_e mask .....
tl.ReversibleSerial(encoder_decoder_blocks),
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0), # vec_d vec_e mask .....
tl.LayerNorm(), # vec_d vec_e mask .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d .....
tl.Dense(output_vocab_size), # vec_d .....
)
def TransformerNoEncDecAttention(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
dropout_shared_axes=None,
max_len=2048,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer model.
This model expects an input pair: target, source.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
dropout_shared_axes: axes on which to share dropout mask
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
ff_activation: the non-linearity in feed-forward layer
Returns:
A Transformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
def PositionalEncoder(vocab_size): # tokens --> vectors
return [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len),
]
in_encoder = PositionalEncoder(input_vocab_size)
out_encoder = (in_encoder if output_vocab_size is None
else PositionalEncoder(output_vocab_size))
if output_vocab_size is None:
output_vocab_size = input_vocab_size
encoder_blocks = [
transformer._EncoderBlock(d_model, d_ff, n_heads, dropout, # pylint: disable=protected-access
dropout_shared_axes, mode, ff_activation)
for i in range(n_encoder_layers)]
encoder = tl.Serial(
in_encoder,
encoder_blocks,
tl.LayerNorm()
)
if mode == 'predict':
encoder = tl.Cache(encoder)
decoder_blocks = [
transformer._DecoderBlock(d_model, d_ff, n_heads, dropout, # pylint: disable=protected-access
dropout_shared_axes, mode, ff_activation)
for i in range(n_decoder_layers)]
# pylint: disable=protected-access
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 0, 1, 1]), # tok_e tok_e tok_d tok_d
# Encode.
tl.Branch([], tl.PaddingMask()), # tok_e mask_e tok_e tok_d tok_d
encoder, # vec_e mask_e tok_e tok_d tok_d
# Simple encoder mask, doesn't contain extra dims.
tl.Select([2, 0, 2], n_in=3), # tok_e vec_e tok_e tok_d tok_d
transformer._MaskOfRightShiftedArray(
n_positions=0), # mask_e vec_e tok_e tok_d tok_d
# Decode.
tl.Select([3, 1, 0, 2]), # tok_d vec_e mask_e tok_e tok_d
tl.ShiftRight(mode=mode), # stok_d vec_e mask_e tok_e tok_d
tl.Branch(
[],
transformer._MaskOfRightShiftedArray()
), # stok_d mask_d vec_e mask_e tok_e tok_d
out_encoder, # svec_d mask_d vec_e mask_e tok_e tok_d
# Concat encoder and decoder.
tl.Select([2, 0, 3, 1]), # vec_e svec_d mask_e mask_d tok_e tok_d
transformer._ConcatWithPadding(), # vec_ed tok_e tok_d
# Decoder blocks with causal attention
decoder_blocks, # vec_ed tok_e tok_d
tl.LayerNorm(), # vec_ed tok_e tok_d
# Separate out the encoder part from the concatenated vector.
tl.Select([0, 1, 2, 2]), # vec_ed tok_e tok_d tok_d
transformer._StripFromConcatenateWithPadding(), # vec_d tok_d
# Map to output vocab.
tl.Dense(output_vocab_size), # vec_d tok_d
tl.LogSoftmax(), # vec_d tok_d
)
def ReformerNoEncDecAttention(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
d_attention_key=64,
d_attention_value=64,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
encoder_attention_type=tl.SelfAttention,
encoder_decoder_attention_type=tl.SelfAttention,
axial_pos_shape=(),
d_axial_pos_embs=None,
ff_activation=tl.Relu,
ff_use_sru=0,
ff_chunk_size=0,
ff_dropout=None,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: source, target.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
d_attention_key: int: depth of key vector for each attention head
d_attention_value: int: depth of value vector for each attention head
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
encoder_attention_type: class: attention class to use, such as SelfAttention
encoder_decoder_attention_type: class: attention class to use, such as
SelfAttention
axial_pos_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
d_axial_pos_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match axial_pos_shape, and values must sum to d_model.
ff_activation: the non-linearity in feed-forward layer
ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
# The current API for custom gradients assumes that a layer must be
# differentiable wrt all of its inputs, but the Transformer puts bool-dtype
# masks on the stack. This causes jax to error, even though the so-called
# "gradient" wrt the masks is never actually computed.
# TODO(kitaev): remove this hack.
if fastmath.backend_name() == 'jax':
jax.api._check_inexact_input_vjp = lambda x: None # pylint: disable=protected-access
def PositionalEncoder(vocab_size, mode): # tokens --> vectors
if not axial_pos_shape:
positional_encoding = tl.PositionalEncoding(
max_len=max_len, dropout=dropout, mode=mode)
else:
assert d_axial_pos_embs is not None
positional_encoding = tl.AxialPositionalEncoding(
shape=axial_pos_shape, d_embs=d_axial_pos_embs,
dropout_broadcast_dims=tuple(range(1, len(axial_pos_shape) + 1)),
dropout=dropout, mode=mode)
return [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=[-2], mode=mode),
positional_encoding,
]
# TODO(kitaev): The regular trax Transformer shares vocab embeddings and
# position embeddings between the encoder and decoder if output_vocab_size is
# None. This isn't supported here because (a) Trax shares weights by sharing
# layer instances, but we need two separate instances to have mode == 'eval'
# for the encoder but mode == 'predict' for the decoder; and (b) tl.Cache does
# not work if its sublayers participate in any weight sharing.
# Mode 'predict' means that the decoder should be run one token at a time.
# The encoder only ever runs over full sequences, which is why it's switched
# to 'eval' mode instead.
in_encoder = PositionalEncoder(
input_vocab_size, mode='eval' if mode == 'predict' else mode)
if output_vocab_size is None:
output_vocab_size = input_vocab_size
out_encoder = PositionalEncoder(output_vocab_size, mode)
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(
d_model, d_ff, n_heads, encoder_attention_type, dropout,
ff_activation, ff_dropout, mode)
for _ in range(n_encoder_layers)]
# pylint: enable=g-complex-comprehension
encoder = tl.Serial([ # tok_e mask_e tok_e tok_d tok_d
in_encoder, # vec_e mask_e tok_e tok_d tok_d
tl.Dup(), # vec_e1 vec_e2 mask_e tok_e tok_d tok_d
tl.ReversibleSerial(encoder_blocks),
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0),
tl.LayerNorm(),
])
if mode == 'predict':
encoder = tl.Cache(encoder)
decoder_blocks = []
if isinstance(encoder_decoder_attention_type, (tuple, list)):
assert n_decoder_layers % len(encoder_decoder_attention_type) == 0
else:
encoder_decoder_attention_type = [encoder_decoder_attention_type]
for layer_idx in range(n_decoder_layers):
layer_attention_type = encoder_decoder_attention_type[
layer_idx % len(encoder_decoder_attention_type)]
decoder_block = DecoderBlock(
d_model, d_ff, d_attention_key, d_attention_value, n_heads,
attention_type=layer_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
mode=mode)
decoder_blocks.append(decoder_block)
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 0, 1, 1]), # tok_e tok_e tok_d tok_d
tl.Branch([], [tl.PaddingMask(),
tl.Fn('Squeeze',
lambda x: jnp.squeeze(x, (1, 2)), n_out=1)]),
# # tok_e mask_e tok_e tok_d tok_d
# Encode.
encoder, # vec_e mask_e tok_e tok_d tok_d
# Decode.
tl.Select([3, 0, 1, 2]), # tok_d vec_e mask_e tok_e tok_d
tl.ShiftRight(mode=mode), # stok_d vec_e mask_e tok_e tok_d
tl.Branch(
[],
_MaskOfRightShiftedArray()
), # stok_d mask_d vec_e mask_e tok_e tok_d
out_encoder, # svec_d mask_d vec_e mask_e tok_e tok_d
# Concat encoder and decoder, given their masks.
tl.Select([2, 0, 3, 1]), # svec_d mask_d vec_e mask_e tok_e tok_d
_ConcatWithPadding(), # vec_ed tok_e tok_d
# Run (encoder and) decoder blocks.
tl.Dup(), # vec_ed1 vec_ed2 tok_e tok_d
tl.ReversibleSerial(decoder_blocks), # vec_ed1 vec_ed2 tok_e tok_d
tl.Fn('XYAvg',
lambda x, y: (x + y) / 2.0), # vec_ed tok_e tok_d
tl.LayerNorm(), # vec_ed tok_e tok_d
# Separate out the encoder part from the concatenated vector.
tl.Select([0, 1, 2, 2]), # vec_ed tok_e tok_d tok_d
_StripFromConcatenateWithPadding(), # vec_d tok_d
# Map to output vocab.
tl.Dense(output_vocab_size), # vec_d tok_d
tl.LogSoftmax(), # vec_d tok_d
)
def Reformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
ff_activation=tl.Relu,
ff_dropout=None,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: target, source.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
ff_activation: the non-linearity in feed-forward layer
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
def PositionalEncoder(vocab_size, mode): # tokens --> vectors
# TODO(kitaev): axial positional encoding is better for very long sequences.
positional_encoding = tl.PositionalEncoding(max_len=max_len,
dropout=dropout,
mode=mode)
return [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=[-2], mode=mode),
positional_encoding,
]
# Mode 'predict' means that the decoder should be run one token at a time.
# The encoder only ever runs over full sequences, which is why it's switched
# to 'eval' mode instead.
in_encoder = PositionalEncoder(input_vocab_size,
mode='eval' if mode == 'predict' else mode)
if output_vocab_size is None:
output_vocab_size = input_vocab_size
out_encoder = PositionalEncoder(output_vocab_size, mode)
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model,
d_ff,
n_heads,
tl.SelfAttention,
dropout,
ff_activation,
ff_dropout,
mode=mode) for _ in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = tl.Serial([
in_encoder,
tl.Dup(),
tl.ReversibleSerial(encoder_blocks),
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0),
tl.LayerNorm(),
])
if mode == 'predict':
encoder = tl.Cache(encoder)
encoder_decoder_blocks = [
EncoderDecoderBlock(d_model, d_ff, n_heads, dropout, ff_activation,
ff_dropout, mode) for _ in range(n_decoder_layers)
]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
tl.Branch([], [
tl.PaddingMask(),
tl.Fn('Squeeze', lambda x: jnp.squeeze(x, (1, 2)), n_out=1)
]),
# # tok_e mask tok_d .....
# Encode.
encoder, # vec_e mask tok_d .....
# Decode.
tl.Select([2, 0, 1]), # tok_d vec_e mask .....
tl.ShiftRight(mode=mode), # tok_d vec_e mask .....
out_encoder, # vec_d vec_e mask .....
tl.Dup(), # vec_d1 vec_d2 vec_e mask .....
tl.ReversibleSerial(encoder_decoder_blocks),
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0), # vec_d vec_e mask .....
tl.LayerNorm(), # vec_d vec_e mask .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d .....
tl.Dense(output_vocab_size), # vec_d .....
tl.LogSoftmax(), # vec_d .....
)
def Reformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
ff_activation=tl.Relu,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: target, source.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
ff_activation: the non-linearity in feed-forward layer
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
# The current API for custom gradients assumes that a layer must be
# differentiable wrt all of its inputs, but the Transformer puts bool-dtype
# masks on the stack. This causes jax to error, even though the so-called
# "gradient" wrt the masks is never actually computed.
# TODO(kitaev): remove this hack.
jax.api._check_inexact_input_vjp = lambda x: None # pylint: disable=protected-access
def PositionalEncoder(vocab_size): # tokens --> vectors
# TODO(kitaev): axial positional encoding is better for very long sequences.
# TODO(kitaev): dropout=0.0 for tl.PositionalEncoding matches trax
# Transformer, but may not be the right option in general.
positional_encoding = tl.PositionalEncoding(
max_len=max_len, dropout=0.0, mode=mode)
return [
tl.Embedding(d_model, vocab_size),
# TODO(kitaev): BroadcastedDropout?
tl.Dropout(rate=dropout, mode=mode),
positional_encoding,
]
in_encoder = PositionalEncoder(input_vocab_size)
out_encoder = (in_encoder if output_vocab_size is None
else PositionalEncoder(output_vocab_size))
if output_vocab_size is None:
output_vocab_size = input_vocab_size
encoder_blocks = [
EncoderBlock(
d_model, d_ff, n_heads, dropout, ff_activation, mode)
for _ in range(n_encoder_layers)]
encoder_decoder_blocks = [
EncoderDecoderBlock(
d_model, d_ff, n_heads, dropout, ff_activation, mode)
for _ in range(n_decoder_layers)]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
# Encode.
tl.Branch(
in_encoder, [tl.PaddingMask(),
tl.Fn(lambda x: np.squeeze(x, (1, 2)), n_out=1)]
), # vec_e mask tok_d .....
tl.Dup(), # vec_e1 vec_e2 mask tok_d .....
tl.ReversibleSerial(encoder_blocks), # vec_e1 vec_e2 mask tok_d .....
# The two sets of activations need to be reduced to one, in this case by
# averaging them. Note that ReformerLM concatenates instead. Various
# options (concat, average, add, keep only one, etc.) seem to perform
# similarly. We don't concatenate here because we want exact parameter
# parity with the standard Transformer.
tl.Fn(lambda x, y: (x+y)/2.0), # vec_e mask tok_d .....
tl.LayerNorm(), # vec_e mask tok_d .....
# Decode.
tl.Select([2, 0, 1]), # tok_d vec_e mask .....
tl.ShiftRight(), # tok_d vec_e mask .....
out_encoder, # vec_d vec_e mask .....
tl.Dup(), # vec_d1 vec_d2 vec_e mask .....
tl.ReversibleSerial(encoder_decoder_blocks),
tl.Fn(lambda x, y: (x+y)/2.0), # vec_d vec_e mask .....
tl.LayerNorm(), # vec_d vec_e mask .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d .....
tl.Dense(output_vocab_size), # vec_d .....
tl.LogSoftmax(), # vec_d .....
)
def Transformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer model.
This model expects an input pair: source, target.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
ff_activation: the non-linearity in feed-forward layer
Returns:
A Transformer model as a layer that maps from a source, target pair to
activations over a vocab set.
"""
def PositionalEncoder(vocab_size): # tokens --> vectors
return [
tl.Embedding(d_model, vocab_size),
tl.Dropout(rate=dropout, mode=mode),
tl.PositionalEncoding(max_len=max_len),
]
in_encoder = PositionalEncoder(input_vocab_size)
out_encoder = (in_encoder if output_vocab_size is None
else PositionalEncoder(output_vocab_size))
if output_vocab_size is None:
output_vocab_size = input_vocab_size
encoder_blocks = [
_EncoderBlock(
d_model, d_ff, n_heads, dropout, i, mode, ff_activation)
for i in range(n_encoder_layers)]
encoder = tl.Serial(
in_encoder,
encoder_blocks,
tl.LayerNorm()
)
if mode == 'predict':
encoder = tl.Cache(encoder)
encoder_decoder_blocks = [
_EncoderDecoderBlock(
d_model, d_ff, n_heads, dropout, i, mode, ff_activation)
for i in range(n_decoder_layers)]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
# Encode.
tl.Branch([], tl.PaddingMask()), # tok_e masks ..... .....
encoder, # vec_e ..... ..... .....
# Decode.
tl.Select([2, 1, 0]), # tok_d masks vec_e .....
tl.ShiftRight(), # tok_d ..... ..... .....
out_encoder, # vec_d ..... ..... .....
tl.Branch(
[], tl.EncoderDecoderMask()), # vec_d masks ..... .....
encoder_decoder_blocks, # vec_d masks ..... .....
tl.LayerNorm(), # vec_d ..... ..... .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d tok_d
tl.Dense(output_vocab_size), # vec_d .....
tl.LogSoftmax(), # vec_d .....
)
def Transformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
max_len=2048,
dropout=0.1,
dropout_shared_axes=None,
mode='train',
ff_activation=tl.Relu):
"""Returns a full Transformer model.
This model is an encoder-decoder that performs tokenized string-to-string
("source"-to-"target") transduction:
- inputs (2):
- source: rank 2 tensor representing a batch of text strings via token
IDs plus padding markers; shape is (batch_size, sequence_length). The
tensor elements are integers in `range(input_vocab_size)`, and `0`
values mark padding positions.
- target: rank 2 tensor representing a batch of text strings via token
IDs plus padding markers; shape is (batch_size, sequence_length). The
tensor elements are integers in `range(output_vocab_size)`, and `0`
values mark padding positions.
- output: rank 3 tensor representing a batch of log-probability
distributions for each sequence position over possible token IDs;
shape is (batch_size, sequence_length, `vocab_size`).
An example use would be to translate (tokenized) sentences from English to
German.
Args:
input_vocab_size: Input vocabulary size -- each element of the input tensor
should be an integer in `range(vocab_size)`. These integers typically
represent token IDs from a vocabulary-based tokenizer.
output_vocab_size: If specified, gives the vocabulary size for the targets;
if None, then input and target integers (token IDs) are assumed to come
from the same vocabulary.
d_model: Final dimension of tensors at most points in the model, including
the initial embedding output.
d_ff: Size of special dense layer in the feed-forward part of each encoder
and decoder block.
n_encoder_layers: Number of encoder blocks.
n_decoder_layers: Number of decoder blocks.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within an encoder/decoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
Sharing along batch and sequence axes (`dropout_shared_axes=(0,1)`) is
a useful way to save memory and apply consistent masks to activation
vectors at different sequence positions.
mode: If `'predict'`, use fast inference. If `'train'`, each encoder/decoder
block will include dropout; else, it will pass all values through
unaltered.
ff_activation: Type of activation function at the end of each
encoder/decoder block; must be an activation-type subclass of `Layer`.
Returns:
A Transformer model as a layer that maps from a source-target tokenized
text pair to activations over a vocab set.
"""
def Embedder(vocab_size): # tokens --> vectors
return [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout,
shared_axes=dropout_shared_axes,
mode=mode),
]
in_embedder = Embedder(input_vocab_size)
out_embedder = (in_embedder if output_vocab_size is None else
Embedder(output_vocab_size))
# Positional encodings are not shared between encoder and decoder.
# Since encoder doesn't run stepwise, we do not use predict mode there.
encoder_mode = 'eval' if mode == 'predict' else mode
in_encoder = in_embedder + [
tl.PositionalEncoding(max_len=max_len, mode=encoder_mode)
]
out_encoder = out_embedder + [
tl.PositionalEncoding(max_len=max_len, mode=mode)
]
if output_vocab_size is None:
output_vocab_size = input_vocab_size
encoder_blocks = [
_EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation) for i in range(n_encoder_layers)
]
encoder = tl.Serial(in_encoder, encoder_blocks, tl.LayerNorm())
if mode == 'predict':
encoder = tl.Cache(encoder)
encoder_decoder_blocks = [
_EncoderDecoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation)
for i in range(n_decoder_layers)
]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
# Encode.
tl.Branch([], tl.PaddingMask()), # tok_e masks ..... .....
encoder, # vec_e ..... ..... .....
# Decode.
tl.Select([2, 1, 0]), # tok_d masks vec_e .....
tl.ShiftRight(mode=mode), # tok_d ..... ..... .....
out_encoder, # vec_d ..... ..... .....
tl.Branch([], tl.EncoderDecoderMask()), # vec_d masks ..... .....
encoder_decoder_blocks, # vec_d masks ..... .....
tl.LayerNorm(), # vec_d ..... ..... .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d tok_d
tl.Dense(output_vocab_size), # vec_d .....
)
def TransformerEncoder(vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
max_len=2048,
dropout=0.1,
dropout_shared_axes=None,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer encoder merged with an N-way categorization head.
This model performs text categorization:
- input: rank 2 tensor representing a batch of text strings via token IDs
plus padding markers; shape is (batch_size, sequence_length). The tensor
elements are integers in `range(vocab_size)`, and `0` values mark padding
positions.
- output: rank 2 tensor representing a batch of log-probability
distributions over N categories; shape is (batch_size, `n_classes`).
Args:
vocab_size: Input vocabulary size -- each element of the input tensor
should be an integer in `range(vocab_size)`. These integers typically
represent token IDs from a vocabulary-based tokenizer.
n_classes: Final dimension of the output tensors, representing N-way
classification.
d_model: Final dimension of tensors at most points in the model, including
the initial embedding output.
d_ff: Size of special dense layer in the feed-forward part of each encoder
block.
n_layers: Number of encoder blocks. Each block includes attention, dropout,
residual, feed-forward (`Dense`), and activation layers.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within an encoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
Sharing along batch and sequence axes (`dropout_shared_axes=(0,1)`) is
a useful way to save memory and apply consistent masks to activation
vectors at different sequence positions.
mode: If `'train'`, each encoder block will include dropout; else, it will
pass all values through unaltered.
ff_activation: Type of activation function at the end of each encoder
block; must be an activation-type subclass of `Layer`.
Returns:
A Transformer model that maps strings (conveyed via token IDs) to
probability-like activations over a range of output classes.
"""
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)
]
encoder_blocks = [
_EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation) for i in range(n_layers)
]
# Assemble and return the model.
return tl.Serial( # toks
# Encode.
tl.Branch(positional_encoder, tl.PaddingMask()), # vecs masks
encoder_blocks, # vecs masks
tl.Select([0], n_in=2), # vecs
tl.LayerNorm(), # vecs
# Map to output categories.
tl.Mean(axis=1), # vecs
tl.Dense(n_classes), # vecs
)
def FunnelTransformerEncoder(vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
encoder_segment_lengths=(2, 2, 2),
n_heads=8,
max_len=2048,
dropout=0.1,
dropout_shared_axes=None,
mode='train',
ff_activation=tl.Relu,
pool_layer=tl.AvgPool,
pool_size=(2,),
strides=(2,),
separate_cls=True):
"""Returns a Funnel Encoder.
This model performs text categorization:
- input: rank 2 tensor representing a batch of text strings via token IDs
plus padding markers; shape is (batch_size, sequence_length). The tensor
elements are integers in `range(vocab_size)`, and `0` values mark padding
positions.
- output: rank 2 tensor representing a batch of log-probability
distributions over N categories; shape is (batch_size, `n_classes`).
Args:
vocab_size: Input vocabulary size -- each element of the input tensor
should be an integer in `range(vocab_size)`. These integers typically
represent token IDs from a vocabulary-based tokenizer.
n_classes: Final dimension of the output tensors, representing N-way
classification.
d_model: Final dimension of tensors at most points in the model, including
the initial embedding output.
d_ff: Size of special dense layer in the feed-forward part of each encoder
block.
encoder_segment_lengths: Tuple, where each element denotes the number of
transformer encoder blocks preceding a funnel transformer block.
There is no funnel block after the last sequence of encoder blocks,
therefore the total number of blocks in the model is equal to
`sum(encoder_segment_lengths) + len(encoder_segment_lengths) - 1`.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within an encoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
Sharing along batch and sequence axes (`dropout_shared_axes=(0,1)`) is
a useful way to save memory and apply consistent masks to activation
vectors at different sequence positions.
mode: If `'train'`, each encoder block will include dropout; else, it will
pass all values through unaltered.
ff_activation: Type of activation function at the end of each encoder
block; must be an activation-type subclass of `Layer`.
pool_layer: Type of pooling layer used for downsampling in each of the
funnel blocks; should be `tl.AvgPool` or `tl.MaxPool`.
pool_size: Shape of window that gets reduced to a single vector value.
If the layer inputs are :math:`n`-dimensional arrays, then `pool_size`
must be a tuple of length :math:`n-2`.
strides: Offsets from the location of one window to the locations of
neighboring windows along each axis. If specified, must be a tuple of
the same length as `pool_size`. If None, then offsets of 1 along each
window axis, :math:`(1, ..., 1)`, will be used.
separate_cls: If `True`, pooling in funnel blocks is not applied to
embeddings of the first token (`cls` from BERT paper) and only final
embedding of this token is used for categorization - the rest are
discarded. If `False`, each token from the beginning is pooled and
all embeddings are averaged and mapped to output categories like in
original `TransformerEncoder` model.
Returns:
A Transformer model that maps strings (conveyed via token IDs) to
probability-like activations over a range of output classes.
"""
assert encoder_segment_lengths
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)]
encoder_blocks = []
n_encoder_segments = len(encoder_segment_lengths)
for i in range(n_encoder_segments):
# Building i'th segment
for _ in range(encoder_segment_lengths[i]):
# Create segment_size encoder blocks
encoder_blocks.append(
_EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation))
# If not last segment, add funnel block
if i != n_encoder_segments - 1:
encoder_blocks.append(
_FunnelBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode,
ff_activation, pool_layer, pool_size,
strides, separate_cls))
cls_pooling = SelectFirst() if separate_cls else tl.Mean(axis=1)
# Assemble and return the model.
return tl.Serial( # toks
# Encode.
tl.Branch(
positional_encoder, tl.PaddingMask()), # vecs masks
encoder_blocks, # vecs masks
tl.Select([0], n_in=2), # vecs
tl.LayerNorm(), # vecs
# Map to output categories.
cls_pooling, # cls
tl.Dense(n_classes), # cls
)
def TransformerEncoder(vocab_size,
n_classes=10,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
dropout=0.1,
dropout_shared_axes=None,
max_len=2048,
mode='train',
ff_activation=tl.Relu):
"""Returns a Transformer-style encoder.
For each item in a batch, this model performs a sequence-to-sequence mapping:
- input: sequence of integers, usually token id's from a fixed-size
vocabulary -- integers in `range(M)`, where `M` is the vocabulary
size.
- output: same-length sequence of N-dimensional vectors, where each vector
can be interpreted as a log-probability distribution over N discrete
categories.
Args:
vocab_size: "Vocabulary size" -- input integer id's must be in
`range(vocab_size)`. Id's typically come from preprocessing text data
with a vocabulary-based tokenizer.
n_classes: Size/depth of the output vectors, intended for an N-way
classification task.
d_model: The basic embedding size (vector depth) of the model. This is the
vector size used by the initial embedding layer and at many intermediate
points in the model.
d_ff: Vector depth (typically greater than `d_model`) used in the
feed-forward (`Dense`) layer of each encoder block.
n_layers: Number of encoder blocks. Each encoder block includes attention,
dropout, residual, feed-forward (`Dense`), and activation layers.
n_heads: Number of attention heads.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within an encoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
max_len: Maximum symbol length for positional encoding.
mode: If `'train'`, each encoder block will include dropout; else, it will
pass all values through unaltered.
ff_activation: The activation function (layer) at the end of each encoder
block.
Returns:
A Transformer model as a layer that maps from token id's to activations
over a set of output classes.
"""
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)
]
encoder_blocks = [
_EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation) for i in range(n_layers)
]
# Assemble and return the model.
return tl.Serial( # toks
# Encode.
tl.Branch(positional_encoder, tl.PaddingMask()), # vecs masks
encoder_blocks, # vecs masks
tl.Select([0], n_in=2), # vecs
tl.LayerNorm(), # vecs
# Map to output categories.
tl.Mean(axis=1), # vecs
tl.Dense(n_classes), # vecs
tl.LogSoftmax(), # vecs
)
def FunnelTransformer(vocab_size,
d_model=512,
d_ff=2048,
encoder_segment_lengths=(2, 2, 2),
n_decoder_blocks=2,
n_heads=8,
max_len=2048,
dropout=0.1,
dropout_shared_axes=None,
mode='train',
ff_activation=tl.Relu,
pool_layer=tl.AvgPool,
pool_size=(2,),
separate_cls=True):
"""Returns a Full Funnel Transformer, that can be used for example for BERT.
This model outputs token-level categorical distributions over all vocab:
- input: rank 2 tensor representing a batch of text strings via token IDs
plus padding markers; shape is (batch_size, sequence_length). The tensor
elements are integers in `range(vocab_size)`, and `0` values mark padding
positions.
- output: rank 3 tensor representing a batch of log-probability
distributions over `vocab_size` categories for each token; shape is
(batch_size, sequence_length, vocab_size).
Args:
vocab_size: Input vocabulary size -- each element of the input tensor
should be an integer in `range(vocab_size)`. These integers typically
represent token IDs from a vocabulary-based tokenizer.
d_model: Final dimension of tensors at most points in the model, including
the initial embedding output.
d_ff: Size of special dense layer in the feed-forward part of each encoder
block.
encoder_segment_lengths: Tuple, where each element denotes the number of
transformer encoder blocks preceding a funnel transformer block.
There is no funnel block after the last sequence of encoder blocks,
therefore the total number of blocks in the model is equal to
`sum(encoder_segment_lengths) + len(encoder_segment_lengths) - 1`.
n_decoder_blocks: Number of transformer blocks in the upsampling decoder.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within an encoder block.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
Sharing along batch and sequence axes (`dropout_shared_axes=(0,1)`) is
a useful way to save memory and apply consistent masks to activation
vectors at different sequence positions.
mode: If `'train'`, each encoder block will include dropout; else, it will
pass all values through unaltered.
ff_activation: Type of activation function at the end of each encoder
block; must be an activation-type subclass of `Layer`.
pool_layer: Type of pooling layer used for downsampling in each of the
funnel blocks; should be `tl.AvgPool` or `tl.MaxPool`.
pool_size: Shape of window that gets reduced to a single vector value.
If the layer inputs are :math:`n`-dimensional arrays, then `pool_size`
must be a tuple of length :math:`n-2`.
separate_cls: If `True`, pooling in funnel blocks is not applied to
embeddings of the first token (`cls` from BERT paper) and only final
embedding of this token is used for categorization - the rest are
discarded. If `False`, each token from the beginning is pooled and
all embeddings are averaged and mapped to output categories like in
original `TransformerEncoder` model.
"""
assert encoder_segment_lengths
positional_encoder = [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode),
tl.PositionalEncoding(max_len=max_len)]
n_encoder_segments = len(encoder_segment_lengths)
encoder_blocks_before_first_pooling = [
_EncoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation)
for _ in range(encoder_segment_lengths[0])]
encoder_blocks_from_first_pooling = []
for i in range(1, n_encoder_segments):
# Building i'th segment
# Add funnel block between segments
encoder_blocks_from_first_pooling.append(
_FunnelBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode,
ff_activation, pool_layer,
pool_size=pool_size, strides=pool_size,
separate_cls=separate_cls))
for _ in range(encoder_segment_lengths[i]):
# Create segment_size encoder blocks
encoder_blocks_from_first_pooling.append(
_EncoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation))
decoder_blocks = [_EncoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation)
for _ in range(n_decoder_blocks)]
total_pool_size = pool_size[0] ** (len(encoder_segment_lengths) - 1)
# Assemble and return the model.
return tl.Serial( # toks
tl.Branch(
positional_encoder, tl.PaddingMask()), # vecs masks
encoder_blocks_before_first_pooling, # vecs masks
tl.Select([0, 1, 0, 1]),
# vecs masks residual = vecs old_masks
encoder_blocks_from_first_pooling, # vecs masks residual masks
tl.Select([0, 2, 3]), # vecs residual masks
tl.Parallel(
# residual from first segment is taken before
# normalization, so apply it now
None, tl.LayerNorm(), None), # vecs norm(residual) masks
_Upsampler(total_pool_size, separate_cls), # vecs masks
decoder_blocks,
tl.Select([0], n_in=2), # vecs
tl.LayerNorm(),
tl.Dense(vocab_size),
)
def Transformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
mode='train'):
"""Returns a Transformer model.
This model expects an input pair: target, source.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
Returns:
A Transformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
in_embed = [ # tokens
tl.Embedding(d_model, input_vocab_size), # vecs
tl.Dropout(rate=dropout, mode=mode), # vecs
tl.PositionalEncoding(max_len=max_len), # vecs
]
if output_vocab_size is None:
output_vocab_size = input_vocab_size
out_embed = in_embed
else:
out_embed = [ # tokens
tl.Embedding(d_model, output_vocab_size), # vecs
tl.Dropout(rate=dropout, mode=mode), # vecs
tl.PositionalEncoding(max_len=max_len), # vecs
]
encoder_stack = ( # masks vectors --> masks vectors
[
EncoderBlock(d_model, d_ff, n_heads, dropout, i, mode)
for i in range(n_encoder_layers)
])
encoder_decoder_stack = ( # vecs_d masks vecs_e --> vecs_d masks vecs_e
[
EncoderDecoder(d_model, d_ff, n_heads, dropout, i, mode)
for i in range(n_decoder_layers)
])
# Input: encoder_side_tokens, decoder_side_tokens
return tl.Serial( # tokens_e tokens_d
tl.Parallel([], tl.Dup()), # toks_e toks_d toks_d (for loss)
tl.Swap(), # toks_d toks_e ....
# Encode.
tl.Parallel( # toks_d toks_e
[],
[
tl.Dup(), # ______ toks_e toks_e
tl.Parallel(in_embed, tl.PaddingMask()), # ______ vecs_e masks
encoder_stack, # ______ vecs_e masks
tl.LayerNorm(), # ______ vecs_e .....
tl.Swap()
]), # ______ masks vecs_e
# Decode. # toks_d masks vecs_e
tl.ShiftRight(), # toks_d ..... ......
out_embed, # vecs_d ..... ......
tl.Dup(), # vecs_d vecs_d ..... ......
tl.Parallel([], tl.EncoderDecoderMask()), # ______ masks ......
encoder_decoder_stack, # vecs_d masks vecs_e
tl.Parallel([], tl.Drop(), tl.Drop()), # vecs_d
tl.LayerNorm(), # vecs_d
tl.Dense(output_vocab_size), # vecs_d
tl.LogSoftmax(), # vecs_d
)
def Reformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
ff_activation=tl.Relu,
ff_dropout=None,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: target, source.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
ff_activation: the non-linearity in feed-forward layer
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
# The current API for custom gradients assumes that a layer must be
# differentiable wrt all of its inputs, but the Transformer puts bool-dtype
# masks on the stack. This causes jax to error, even though the so-called
# "gradient" wrt the masks is never actually computed.
# TODO(kitaev): remove this hack.
jax.api._check_inexact_input_vjp = lambda x: None # pylint: disable=protected-access
def PositionalEncoder(vocab_size, mode): # tokens --> vectors
# TODO(kitaev): axial positional encoding is better for very long sequences.
positional_encoding = tl.PositionalEncoding(
max_len=max_len, dropout=dropout, mode=mode)
return [
tl.Embedding(d_model, vocab_size),
BroadcastedDropout(rate=dropout, mode=mode),
positional_encoding,
]
# TODO(kitaev): The regular trax Transformer shares vocab embeddings and
# position embeddings between the encoder and decoder if output_vocab_size is
# None. This isn't supported here because (a) Trax shares weights by sharing
# layer instances, but we need two separate instances to have mode == 'eval'
# for the encoder but mode == 'predict' for the decoder; and (b) tl.Cache does
# not work if its sublayers participate in any weight sharing.
# Mode 'predict' means that the decoder should be run one token at a time.
# The encoder only ever runs over full sequences, which is why it's switched
# to 'eval' mode instead.
in_encoder = PositionalEncoder(
input_vocab_size, mode='eval' if mode == 'predict' else mode)
if output_vocab_size is None:
output_vocab_size = input_vocab_size
out_encoder = PositionalEncoder(output_vocab_size, mode)
encoder_blocks = [
EncoderBlock(
d_model, d_ff, n_heads, dropout, ff_activation, ff_dropout, mode)
for _ in range(n_encoder_layers)]
encoder = tl.Serial([
in_encoder,
tl.Dup(),
tl.ReversibleSerial(encoder_blocks),
tl.Fn(lambda x, y: (x+y)/2.0),
tl.LayerNorm(),
])
if mode == 'predict':
encoder = tl.Cache(encoder)
encoder_decoder_blocks = [
EncoderDecoderBlock(
d_model, d_ff, n_heads, dropout, ff_activation, ff_dropout, mode)
for _ in range(n_decoder_layers)]
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 1, 1]), # tok_e tok_d tok_d
tl.Branch([], [ # tok_e mask tok_d .....
tl.PaddingMask(),
tl.Fn(lambda x: np.squeeze(x, (1, 2)), n_out=1)]),
# Encode.
encoder, # vec_e mask tok_d .....
# Decode.
tl.Select([2, 0, 1]), # tok_d vec_e mask .....
tl.ShiftRight(mode=mode), # tok_d vec_e mask .....
out_encoder, # vec_d vec_e mask .....
tl.Dup(), # vec_d1 vec_d2 vec_e mask .....
tl.ReversibleSerial(encoder_decoder_blocks),
tl.Fn(lambda x, y: (x+y)/2.0), # vec_d vec_e mask .....
tl.LayerNorm(), # vec_d vec_e mask .....
# Map to output vocab.
tl.Select([0], n_in=3), # vec_d .....
tl.Dense(output_vocab_size), # vec_d .....
tl.LogSoftmax(), # vec_d .....
)
def Reformer2(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
d_attention_key=None,
d_attention_value=None,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
encoder_attention_type=tl.SelfAttention,
encoder_decoder_attention_type=tl.SelfAttention,
axial_pos_shape='fixed-base',
d_axial_pos_embs=None,
ff_activation=tl.Relu,
ff_use_sru=0,
ff_chunk_size=0,
ff_dropout=None,
ff_sparsity=0,
n_layers_forget=0,
mode='train'):
"""Reversible transformer encoder-decoder model.
This model expects an input pair: source, target.
At the moment, this model supports dot-product attention only. For the
attention types in the Reformer paper, see ReformerLM.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
d_attention_key: int: depth of key vector for each attention head
d_attention_value: int: depth of value vector for each attention head
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
max_len: int: maximum symbol length for positional encoding
encoder_attention_type: class: attention class to use, such as SelfAttention
encoder_decoder_attention_type: class: attention class to use, such as
SelfAttention
axial_pos_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
d_axial_pos_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match axial_pos_shape, and values must sum to d_model.
ff_activation: the non-linearity in feed-forward layer
ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_dropout: float: (optional) separate dropout rate at feed-forward
nonlinearity. This is called relu_dropout in T2T.
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
n_layers_forget: how often to have a forgetting block between layers
mode: str: 'train' or 'eval'
Returns:
A Reformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
# Set default dimensions for attention head key and value sizes.
if d_attention_key is None:
if d_model % n_heads != 0:
raise ValueError(
f'n_heads ({n_heads}) must divide d_model ({d_model})')
d_attention_key = d_model // n_heads
if d_attention_value is None:
if d_model % n_heads != 0:
raise ValueError(
f'n_heads ({n_heads}) must divide d_model ({d_model})')
d_attention_value = d_model // n_heads
# Vector embeddings.
def Embedder(vocab_size): # tokens --> vectors
return [
tl.Embedding(vocab_size, d_model),
tl.Dropout(rate=dropout, shared_axes=[-2], mode=mode),
]
in_embedder = Embedder(input_vocab_size)
out_embedder = (in_embedder if output_vocab_size is None else
Embedder(output_vocab_size))
def PositionalEnc(mode):
return PositionalEncoding(mode, dropout, max_len, axial_pos_shape,
d_axial_pos_embs)
# Mode 'predict' means that the decoder should be run one token at a time.
# The encoder only ever runs over full sequences, which is why it's switched
# to 'eval' mode instead.
encoder_mode = 'eval' if mode == 'predict' else mode
in_encoder = in_embedder + [PositionalEnc(encoder_mode)]
out_encoder = out_embedder + [PositionalEnc(mode)]
if output_vocab_size is None:
output_vocab_size = input_vocab_size
# pylint: disable=g-complex-comprehension
encoder_blocks = [
EncoderBlock(d_model,
d_ff,
n_heads,
encoder_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_dropout=ff_dropout,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity,
mode=mode) for _ in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = tl.Serial([ # vec_e mask_e tok_e tok_d tok_d
tl.Dup(), # vec_e1 vec_e2 mask_e tok_e tok_d tok_d
_ReversibleSerialForget(encoder_blocks, d_model, n_layers_forget),
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0),
tl.Dense(d_model),
tl.LayerNorm(),
])
if mode == 'predict':
encoder = tl.Cache(encoder)
decoder_blocks = []
if isinstance(encoder_decoder_attention_type, (tuple, list)):
assert n_decoder_layers % len(encoder_decoder_attention_type) == 0
else:
encoder_decoder_attention_type = [encoder_decoder_attention_type]
for layer_idx in range(n_decoder_layers):
layer_attention_type = encoder_decoder_attention_type[
layer_idx % len(encoder_decoder_attention_type)]
decoder_block = DecoderBlock(d_model,
d_ff,
d_attention_key,
d_attention_value,
n_heads,
attention_type=layer_attention_type,
dropout=dropout,
ff_activation=ff_activation,
ff_dropout=ff_dropout,
ff_use_sru=ff_use_sru,
ff_chunk_size=ff_chunk_size,
ff_sparsity=ff_sparsity,
mode=mode)
decoder_blocks.append(decoder_block)
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 0, 0, 1, 1]), # tok_e tok_e tok_e tok_d tok_d
# Embed in and out tokens; done together as weights may be shared.
tl.Parallel(
in_encoder,
[],
[], # vec_e tok_e tok_e vec_d tok_d
[tl.ShiftRight(mode=mode), out_encoder]),
tl.Parallel([], [
tl.PaddingMask(),
tl.Fn('Squeeze', lambda x: jnp.squeeze(x, (1, 2)), n_out=1)
]),
# # vec_e mask_e tok_e vec_d tok_d
# Encode.
encoder, # vec_e mask_e tok_e vec_d tok_d
# Decode.
tl.Select([3, 0, 1, 2]), # vec_d vec_e mask_e tok_e tok_d
# Concat encoder and decoder, given encoder mask.
tl.Select([1, 0]), # vec_e vec_d mask_e tok_e tok_d
t2.ConcatWithPadding(mode=mode), # vec_ed tok_e tok_d
# Run (encoder and) decoder blocks.
tl.Dup(), # vec_ed1 vec_ed2 tok_e tok_d
_ReversibleSerialForget(
decoder_blocks, d_model,
n_layers_forget), # vec_ed1 vec_ed2 tok_e tok_d
tl.Fn('XYAvg', lambda x, y: (x + y) / 2.0), # vec_ed tok_e tok_d
tl.LayerNorm(), # vec_ed tok_e tok_d
# Separate out the encoder part from the concatenated vector.
tl.Select([0, 1, 2, 2]), # vec_ed tok_e tok_d tok_d
t2.StripFromConcatenateWithPadding(mode=mode), # vec_d tok_d
# Map to output vocab.
tl.Dense(output_vocab_size), # vec_d tok_d
tl.LogSoftmax(), # vec_d tok_d
)
def Transformer2(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_encoder_layers=6,
n_decoder_layers=6,
n_heads=8,
dropout=0.1,
dropout_shared_axes=None,
max_len=2048,
mode='train',
ff_activation=tl.Relu,
ff_dropout=0.1,
ff_chunk_size=0,
ff_use_sru=0,
ff_sparsity=0,
ff_sparsity_type='1inN',
attention_chunk_size=0,
encoder_attention_type=tl.Attention,
n_encoder_attention_layers=1,
decoder_attention_type=tl.CausalAttention,
n_decoder_attention_layers=2,
axial_pos_shape=None,
d_axial_pos_embs=None):
"""Returns a Transformer model.
This model expects an input pair: target, source.
Args:
input_vocab_size: int: vocab size of the source.
output_vocab_size: int (optional): vocab size of the target. If None, the
source and target are assumed to have the same vocab.
d_model: int: depth of embedding
d_ff: int: depth of feed-forward layer
n_encoder_layers: int: number of encoder layers
n_decoder_layers: int: number of decoder layers
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
dropout_shared_axes: axes on which to share dropout mask
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
ff_activation: the non-linearity in feed-forward layer
ff_dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout after the FF dense layer.
ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
ff_sparsity_type: string, if ff_sparsity >0,
use SparseFF if ff_sparsity_type=`'1inN'` and
use BlockSparseFF if ff_sparsity_type=`'Block'`
attention_chunk_size: int, if > 0 run attention chunked at this size
encoder_attention_type: The attention layer to use for the encoder part.
n_encoder_attention_layers: int, within each encoder block, how many
attention layers to have.
decoder_attention_type: The attention layer to use for the
encoder-decoder attention.
n_decoder_attention_layers: int, within each decoder block, how many
attention layers to have.
axial_pos_shape: tuple of ints: input shape to use for the axial position
encoding. If unset, axial position encoding is disabled.
d_axial_pos_embs: tuple of ints: depth of position embedding for each axis.
Tuple length must match axial_pos_shape, and values must sum to d_model.
Returns:
A Transformer model as a layer that maps from a target, source pair to
activations over a vocab set.
"""
in_encoder, out_encoder, output_vocab_size = (
ct.EmbeddingAndPositionalEncodings(input_vocab_size,
d_model,
mode,
dropout,
dropout_shared_axes,
max_len,
output_vocab_size=output_vocab_size,
axial_pos_shape=axial_pos_shape,
d_axial_pos_embs=d_axial_pos_embs))
# pylint: disable=g-complex-comprehension
encoder_blocks = [
ct.EncoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation, ff_dropout, ff_chunk_size,
ff_use_sru, ff_sparsity, ff_sparsity_type,
attention_chunk_size, encoder_attention_type,
n_encoder_attention_layers)
for i in range(n_encoder_layers)
]
# pylint: enable=g-complex-comprehension
encoder = tl.Serial(in_encoder, encoder_blocks, tl.LayerNorm())
if mode == 'predict':
encoder = tl.Cache(encoder)
# pylint: disable=g-complex-comprehension
decoder_blocks = [
ct.DecoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes,
mode, ff_activation, ff_dropout, ff_chunk_size,
ff_use_sru, ff_sparsity, ff_sparsity_type,
attention_chunk_size, decoder_attention_type,
n_decoder_attention_layers)
for i in range(n_decoder_layers)
]
# pylint: enable=g-complex-comprehension
# Assemble and return the model.
return tl.Serial(
# Input: encoder_side_tokens, decoder_side_tokens
# Copy decoder tokens for use in loss.
tl.Select([0, 0, 1, 1]), # tok_e tok_e tok_d tok_d
# Encode.
tl.Branch([], tl.PaddingMask()), # tok_e mask_e tok_e tok_d tok_d
encoder, # vec_e mask_e tok_e tok_d tok_d
# Simple encoder mask, doesn't contain extra dims.
tl.Select([2, 0, 2], n_in=3), # tok_e vec_e tok_e tok_d tok_d
tl.Fn(
'EncoderMask', # mask_e vec_e tok_e tok_d tok_d
lambda x: x != 0,
n_out=1),
# Decode.
tl.Select([3, 1, 0, 2]), # tok_d vec_e mask_e tok_e tok_d
tl.ShiftRight(mode=mode), # stok_d vec_e mask_e tok_e tok_d
out_encoder, # svec_d vec_e mask_e tok_e tok_d
# Concat encoder and decoder.
tl.Select([1, 0]), # vec_e svec_d mask_e tok_e tok_d
ConcatWithPadding(mode=mode), # vec_ed tok_e tok_d
# Decoder blocks with causal attention
decoder_blocks, # vec_ed tok_e tok_d
tl.LayerNorm(), # vec_ed tok_e tok_d
# Separate out the encoder part from the concatenated vector.
tl.Select([0, 1, 2, 2]), # vec_ed tok_e tok_d tok_d
StripFromConcatenateWithPadding(mode=mode), # vec_d tok_d
# Map to output vocab.
tl.Dense(output_vocab_size), # vec_d tok_d
)
def Transformer(input_vocab_size,
output_vocab_size=None,
d_model=D_MODEL,
d_ff=D_FF,
n_encoder_layers=N_LAYERS,
n_decoder_layers=N_LAYERS,
n_heads=N_HEADS,
max_len=MAX_SEQUENCE_LENGTH,
dropout=DROPOUT_RATE,
dropout_shared_axes=DROPOUT_SHARED_AXES,
mode=MODE,
ff_activation=FF_ACTIVATION_TYPE):
"""Returns a full Transformer model.
This model is an encoder-decoder that performs tokenized string-to-string
("source"-to-"target") transduction:
- inputs (2):
- source: Array representing a batch of text strings via token
IDs plus padding markers; shape is (batch_size, sequence_length),
where sequence_length <= ``max_len``. Array elements are integers in
``range(input_vocab_size)``, and 0 values mark padding positions.
- target: Array representing a batch of text strings via token
IDs plus padding markers; shape is (batch_size, sequence_length),
where sequence_length <= ``max_len``. Array elements are integers in
``range(output_vocab_size)``, and 0 values mark padding positions.
- output: 3-D array of raw activations with last/innermost dimension of
``output_vocab_size``, suitable for decoding into a batch of token
strings; shape is (batch_size, sequence_length, ``vocab_size``).
An example use would be to translate (tokenized) sentences from English to
German.
Args:
input_vocab_size: Input vocabulary size -- each element of the input tensor
should be an integer in ``range(vocab_size)``. These integers typically
represent token IDs from a vocabulary-based tokenizer.
output_vocab_size: If specified, gives the vocabulary size for the targets;
if ``None``, then input and target integers (token IDs) are assumed to
come from the same vocabulary.
d_model: Last/innermost dimension of activation arrays at most points in
the model, including the initial embedding output.
d_ff: Last/innermost dimension of special (typically wider)
:py:class:`Dense` layer in the feedforward part of each encoder block.
n_encoder_layers: Number of encoder blocks.
n_decoder_layers: Number of decoder blocks.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within encoder/decoder blocks. The same rate is
also used for attention dropout in encoder/decoder blocks.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
Sharing along batch and sequence axes (``dropout_shared_axes=(0,1)``)
is a useful way to save memory and apply consistent masks to activation
vectors at different sequence positions.
mode: If ``'predict'``, use fast inference. If ``'train'``, each
encoder/decoder block will include dropout; else, it will pass all
values through unaltered.
ff_activation: Type of activation function at the end of each
encoder/decoder block; must be an activation-type subclass of
:py:class:`Layer`.
Returns:
A Transformer model as a layer that maps from a source-target tokenized
text pair to activations over a vocab set.
"""
# Avoid 'predict' mode in encoder, since encoder doesn't run stepwise.
encoder_mode = 'eval' if mode == 'predict' else mode
# Share embedding weights if no separate output vocab size.
in_embedder = tl.Embedding(input_vocab_size, d_model)
if output_vocab_size is None:
out_embedder = in_embedder
output_vocab_size = input_vocab_size
else:
out_embedder = tl.Embedding(output_vocab_size, d_model)
def _Dropout():
return tl.Dropout(rate=dropout,
shared_axes=dropout_shared_axes,
mode=mode)
def _EncBlock():
return _EncoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation)
def _Encoder():
encoder = tl.Serial(
in_embedder,
_Dropout(),
tl.PositionalEncoding(max_len=max_len, mode=encoder_mode),
[_EncBlock() for _ in range(n_encoder_layers)],
tl.LayerNorm(),
)
return tl.Cache(encoder) if mode == 'predict' else encoder
def _EncDecBlock():
return _EncoderDecoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation)
# Input to model is encoder-side tokens and decoder-side tokens: tok_d, tok_e
# Model output is decoder-side vectors and decoder-side tokens: vec_d tok_d
return tl.Serial(
tl.Select([0, 1, 1]), # Copies decoder tokens for use in loss.
# Encode.
tl.Branch([], tl.PaddingMask()), # tok_e masks tok_d tok_d
_Encoder(),
# Decode.
tl.Select([2, 1, 0]), # Re-orders inputs: tok_d masks vec_e .....
tl.ShiftRight(mode=mode),
out_embedder,
_Dropout(),
tl.PositionalEncoding(max_len=max_len, mode=mode),
tl.Branch([], tl.EncoderDecoderMask()), # vec_d masks ..... .....
[_EncDecBlock() for _ in range(n_decoder_layers)],
tl.LayerNorm(),
tl.Select([0], n_in=3), # Drops masks and encoding vectors.
# Map vectors to match output vocab size.
tl.Dense(output_vocab_size),
)
def BERT(d_model=768,
vocab_size=30522,
max_len=512,
type_vocab_size=2,
n_heads=12,
d_ff=3072,
n_layers=12,
head=None,
init_checkpoint=None,
mode='eval',
):
"""BERT (default hparams are for bert-base-uncased)."""
layer_norm_eps = 1e-12
d_head = d_model // n_heads
word_embeddings = tl.Embedding(d_model, vocab_size)
type_embeddings = tl.Embedding(d_model, type_vocab_size)
position_embeddings = tl.PositionalEncoding(max_len, mode=mode)
embeddings = [
tl.Select([0, 1, 0], n_in=3), # Drops 'idx' input.
tl.Parallel(
word_embeddings,
type_embeddings,
[tl.PaddingMask(),
tl.Fn('Squeeze', lambda x: np.squeeze(x, (1, 2)), n_out=1)]
),
tl.Add(),
position_embeddings,
tl.LayerNorm(epsilon=layer_norm_eps),
]
encoder = []
for _ in range(n_layers):
attn = tl.SelfAttention(n_heads=n_heads, d_qk=d_head, d_v=d_head,
bias=True, masked=True, mode=mode)
feed_forward = [
tl.Dense(d_ff),
tl.Gelu(),
tl.Dense(d_model)
]
encoder += [
tl.Select([0, 1, 1]), # Save a copy of the mask
tl.Residual(attn, AddBias()), # pylint: disable=no-value-for-parameter
tl.LayerNorm(epsilon=layer_norm_eps),
tl.Residual(*feed_forward),
tl.LayerNorm(epsilon=layer_norm_eps),
]
encoder += [tl.Select([0], n_in=2)] # Drop the mask
pooler = [
tl.Fn('', lambda x: (x[:, 0, :], x), n_out=2),
tl.Dense(d_model),
tl.Tanh(),
]
init_checkpoint = init_checkpoint if mode == 'train' else None
bert = PretrainedBERT(
embeddings + encoder + pooler, init_checkpoint=init_checkpoint)
if head is not None:
bert = tl.Serial(bert, head())
return bert
def TransformerEncoder(vocab_size,
n_classes=10,
d_model=D_MODEL,
d_ff=D_FF,
n_layers=N_LAYERS,
n_heads=N_HEADS,
max_len=MAX_SEQUENCE_LENGTH,
dropout=DROPOUT_RATE,
dropout_shared_axes=DROPOUT_SHARED_AXES,
mode=MODE,
ff_activation=FF_ACTIVATION_TYPE):
"""Returns a Transformer encoder suitable for N-way classification.
This model maps tokenized text to N-way (``n_classes``) activations:
- input: Array representing a batch of text strings via token IDs plus
padding markers; shape is (batch_size, sequence_length), where
sequence_length <= ``max_len``. Array elements are integers in
``range(vocab_size)``, and 0 values mark padding positions.
- output: Array representing a batch of raw (non-normalized) activations
over ``n_classes`` categories; shape is (batch_size, ``n_classes``).
Args:
vocab_size: Input vocabulary size -- each element of the input array
should be an integer in ``range(vocab_size)``. These integers typically
represent token IDs from a vocabulary-based tokenizer.
n_classes: Last/innermost dimension of output arrays, suitable for N-way
classification.
d_model: Last/innermost dimension of activation arrays at most points in
the model, including the initial embedding output.
d_ff: Last/innermost dimension of special (typically wider)
:py:class:`Dense` layer in the feedforward part of each encoder block.
n_layers: Number of encoder blocks. Each block includes attention, dropout,
residual, layer-norm, feedforward (:py:class:`Dense`), and activation
layers.
n_heads: Number of attention heads.
max_len: Maximum symbol length for positional encoding.
dropout: Stochastic rate (probability) for dropping an activation value
when applying dropout within encoder blocks. The same rate is also
used for attention dropout in encoder blocks.
dropout_shared_axes: Tensor axes on which to share a dropout mask.
Sharing along batch and sequence axes (``dropout_shared_axes=(0,1)``)
is a useful way to save memory and apply consistent masks to activation
vectors at different sequence positions.
mode: If ``'train'``, each encoder block will include dropout; else, it
will pass all values through unaltered.
ff_activation: Type of activation function at the end of each encoder
block; must be an activation-type subclass of :py:class:`Layer`.
Returns:
A Transformer model that maps strings (conveyed by token IDs) to
raw (non-normalized) activations over a range of output classes.
"""
def _Dropout():
tl.Dropout(rate=dropout, shared_axes=dropout_shared_axes, mode=mode)
def _EncBlock():
return _EncoderBlock(d_model, d_ff, n_heads, dropout,
dropout_shared_axes, mode, ff_activation)
return tl.Serial(
tl.Branch([],
tl.PaddingMask()), # Creates masks from copy of the tokens.
tl.Embedding(vocab_size, d_model),
_Dropout(),
tl.PositionalEncoding(max_len=max_len),
[_EncBlock() for _ in range(n_layers)],
tl.Select([0], n_in=2), # Drops the masks.
tl.LayerNorm(),
tl.Mean(axis=1),
tl.Dense(n_classes),
)
