def policy_and_value_net(n_actions, n_controls, vocab_size, bottom_layers_fn,
two_towers):
"""A policy and value net function."""
# Layers.
# Now, with the current logits, one head computes action probabilities and the
# other computes the value function.
# NOTE: The LogSoftmax instead of the Softmax because of numerical stability.
@tl.layer()
def FlattenControlsIntoTime(x, **unused_kwargs): # pylint: disable=invalid-name
"""Splits logits for actions in different controls and flattens controls."""
return np.reshape(x, (x.shape[0], -1, n_actions))
if vocab_size is None:
# In continuous policies every element of the output sequence corresponds to
# an observation.
n_preds_per_input = n_controls
kwargs = {}
else:
# In discrete policies every element of the output sequence corresponds to
# a symbol in the discrete representation, and each control takes 1 symbol.
n_preds_per_input = 1
kwargs = {"vocab_size": vocab_size}
if two_towers:
layers = [
tl.Dup(),
tl.Parallel(
[
bottom_layers_fn(**kwargs),
tl.Dense(n_preds_per_input * n_actions),
FlattenControlsIntoTime(), # pylint: disable=no-value-for-parameter
tl.LogSoftmax()
],
[
bottom_layers_fn(**kwargs),
tl.Dense(n_preds_per_input),
tl.Flatten()
],
)
]
else:
layers = [
bottom_layers_fn(**kwargs),
tl.Dup(),
tl.Parallel(
[
tl.Dense(n_preds_per_input * n_actions),
FlattenControlsIntoTime(), # pylint: disable=no-value-for-parameter
tl.LogSoftmax()
],
[tl.Dense(n_preds_per_input),
tl.Flatten()],
)
]
return tl.Model(layers)
def TransformerDecoder(vocab_size=None,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
d_attention_key=None,
d_attention_value=None,
attention_type=tl.DotProductCausalAttention,
dropout=0.1,
share_qk=False,
max_len=2048,
mode='train'):
"""Returns a Transformer decoder model.
The input to the model is either continuous or discrete - controlled by
vocab_size. Does not shift the input to the right, i.e. the output for
timestep t is based on inputs up to timestep t inclusively.
Args:
vocab_size: int or None: vocab size if running on discrete input, None
otherwise.
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
d_attention_key: int: depth of key vector for each attention head
(default is d_model // n_heads)
d_attention_value: int: depth of value vector for each attention head
(default is d_model // n_heads)
attention_type: subclass of tl.BaseCausalAttention: attention class to use
dropout: float: dropout rate (how much to drop out)
share_qk: bool, whether to share queries and keys in decoder attention
max_len: int: maximum symbol length for positional encoding
mode: str: 'train' or 'eval'
Returns:
A Transformer decoder as a layer that maps from a continuous or discrete
tensor to a continuous tensor.
"""
if vocab_size is None:
input_layer = tl.Dense
else:
input_layer = functools.partial(tl.Embedding, vocab_size=vocab_size)
return tl.Model( # vecs
input_layer(d_model), # vecs
tl.Dropout(rate=dropout, mode=mode),
tl.PositionalEncoding(max_len=max_len),
[
DecoderBlock( # pylint: disable=g-complex-comprehension
d_model, d_ff, n_heads, d_attention_key, d_attention_value,
attention_type, dropout, share_qk, i, mode)
for i in range(n_layers)
], # vecs
tl.LayerNorm(), # vecs
)
def FrameStackMLP(n_frames=4,
hidden_sizes=(64, ),
output_size=64,
mode='train'):
"""MLP operating on a fixed number of last frames."""
del mode
return tl.Model(
FrameStack(n_frames=n_frames),
[[tl.Dense(d_hidden), tl.Relu()] for d_hidden in hidden_sizes],
tl.Dense(output_size),
)
def Resnet50(d_hidden=64, n_output_classes=1001, mode='train'):
"""ResNet.
Args:
d_hidden: Dimensionality of the first hidden layer (multiplied later).
n_output_classes: Number of distinct output classes.
mode: Whether we are training or evaluating or doing inference.
Returns:
The list of layers comprising a ResNet model with the given parameters.
"""
return tl.Model(
tl.ToFloat(),
tl.Conv(d_hidden, (7, 7), (2, 2), 'SAME'),
tl.BatchNorm(mode=mode),
tl.Relu(),
tl.MaxPool(pool_size=(3, 3), strides=(2, 2)),
ConvBlock(3, [d_hidden, d_hidden, 4 * d_hidden], (1, 1), mode=mode),
IdentityBlock(3, [d_hidden, d_hidden, 4 * d_hidden], mode=mode),
IdentityBlock(3, [d_hidden, d_hidden, 4 * d_hidden], mode=mode),
ConvBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden], (2, 2),
mode=mode),
IdentityBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden],
mode=mode),
IdentityBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden],
mode=mode),
IdentityBlock(3, [2 * d_hidden, 2 * d_hidden, 8 * d_hidden],
mode=mode),
ConvBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden], (2, 2),
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
IdentityBlock(3, [4 * d_hidden, 4 * d_hidden, 16 * d_hidden],
mode=mode),
ConvBlock(3, [8 * d_hidden, 8 * d_hidden, 32 * d_hidden], (2, 2),
mode=mode),
IdentityBlock(3, [8 * d_hidden, 8 * d_hidden, 32 * d_hidden],
mode=mode),
IdentityBlock(3, [8 * d_hidden, 8 * d_hidden, 32 * d_hidden],
mode=mode),
tl.AvgPool(pool_size=(7, 7)),
tl.Flatten(),
tl.Dense(n_output_classes),
tl.LogSoftmax(),
)
def MLP(n_hidden_layers=2,
d_hidden=512,
activation_fn=tl.Relu,
n_output_classes=10,
mode="train"):
"""A multi-layer feedforward (perceptron) network."""
del mode
return tl.Model(
tl.Flatten(),
[[tl.Dense(d_hidden), activation_fn()]
for _ in range(n_hidden_layers)],
tl.Dense(n_output_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,
max_len=2048,
mode='train'):
"""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'
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.Model([ # tokens
tl.Dup(), # toks toks
tl.Parallel(embedder, tl.PaddingMask()), # vecs mask
[
EncoderBlock(d_model, d_ff, n_heads, dropout, i, mode)
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 NeuralGPU(d_feature=96, steps=16, vocab_size=2, mode='train'):
"""Implementation of Neural GPU: https://arxiv.org/abs/1702.08727.
Args:
d_feature: Number of memory channels (dimensionality of feature embedding).
steps: Number of times depthwise recurrence steps.
vocab_size: Vocabulary size.
mode: Whether we are training or evaluating or doing inference.
Returns:
A NeuralGPU Stax model.
"""
del mode
core = ConvDiagonalGRU(units=d_feature)
return tl.Model(
tl.Embedding(d_feature=d_feature, vocab_size=vocab_size),
[core] * steps,
tl.Dense(vocab_size),
tl.LogSoftmax(),
)
def AtariCnn(n_frames=4, hidden_sizes=(32, 32), output_size=128, mode='train'):
"""An Atari CNN."""
del mode
# TODO(jonni): Include link to paper?
# Input shape: (B, T, H, W, C)
# Output shape: (B, T, output_size)
return tl.Model(
tl.ToFloat(),
tl.Div(divisor=255.0),
# Set up n_frames successive game frames, concatenated on the last axis.
FrameStack(n_frames=n_frames), # (B, T, H, W, 4C)
tl.Conv(hidden_sizes[0], (5, 5), (2, 2), 'SAME'),
tl.Relu(),
tl.Conv(hidden_sizes[1], (5, 5), (2, 2), 'SAME'),
tl.Relu(),
tl.Flatten(n_axes_to_keep=2), # B, T and rest.
tl.Dense(output_size),
tl.Relu(),
)
def WideResnet(n_blocks=3,
widen_factor=1,
n_output_classes=10,
bn_momentum=0.9,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
n_blocks: int, number of blocks in a group. total layers = 6n + 4.
widen_factor: int, widening factor of each group. k=1 is vanilla resnet.
n_output_classes: int, number of distinct output classes.
bn_momentum: float, momentum in BatchNorm.
mode: Whether we are training or evaluating or doing inference.
Returns:
The list of layers comprising a WideResnet model with the given parameters.
"""
return tl.Model(
tl.ToFloat(),
tl.Conv(16, (3, 3), padding='SAME'),
WideResnetGroup(n_blocks,
16 * widen_factor,
bn_momentum=bn_momentum,
mode=mode),
WideResnetGroup(n_blocks,
32 * widen_factor, (2, 2),
bn_momentum=bn_momentum,
mode=mode),
WideResnetGroup(n_blocks,
64 * widen_factor, (2, 2),
bn_momentum=bn_momentum,
mode=mode),
tl.BatchNorm(momentum=bn_momentum, mode=mode),
tl.Relu(),
tl.AvgPool(pool_size=(8, 8)),
tl.Flatten(),
tl.Dense(n_output_classes),
tl.LogSoftmax(),
)
def ReformerLM(vocab_size,
d_model=512,
d_ff=2048,
d_attention_key=64,
d_attention_value=64,
n_layers=6,
n_heads=8,
dropout=0.1,
max_len=2048,
n_chunks=0,
n_attention_chunks=1,
attention_type=tl.DotProductCausalAttention,
share_qk=False,
mode='train'):
"""Reversible transformer language model (only uses a decoder, no encoder).
Args:
vocab_size: int: vocab size
d_model: int: depth of *each half* of the two-part features
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_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
n_chunks: int: number of chunks (must match input pipeline)
n_attention_chunks: int: number of chunks for attention
attention_type: class: attention class to use, such as DotProductAttention.
share_qk: bool, whether to share queries and keys.
mode: str: 'train' or 'eval'
Returns:
the layer.
"""
if n_chunks == 0:
n_chunks = 1
concatenate_input_chunks = []
concatenate_output_chunks = tl.Concatenate(n_items=n_chunks, axis=-2)
else:
concatenate_input_chunks = tl.Concatenate(n_items=n_chunks)
concatenate_output_chunks = []
positional_embedder = [
tl.Embedding(d_model, vocab_size),
BroadcastedDropout(rate=dropout, mode=mode), # pylint: disable=no-value-for-parameter
tl.PositionalEncoding(max_len=max_len),
]
return tl.Model(
concatenate_input_chunks,
tl.ShiftRight(),
positional_embedder,
tl.Dup(),
tl.ReversibleSerial([
# pylint: disable=g-complex-comprehension
DecoderBlock(d_model, d_ff,
d_attention_key, d_attention_value, n_heads,
n_attention_chunks, attention_type,
dropout, share_qk, mode)
for _ in range(n_layers)
] + [
SplitForOutput(n_sections=n_chunks, axis=-2), # pylint: disable=no-value-for-parameter
]),
Map([
# TODO(kitaev): Test whether dropout should go before or after the
# LayerNorm, and whether dropout broadcasting is needed here.
tl.LayerNorm(),
BroadcastedDropout(rate=dropout, mode=mode), # pylint: disable=no-value-for-parameter
tl.Dense(vocab_size),
tl.LogSoftmax(),
], n_sections=n_chunks),
concatenate_output_chunks,
)
def model_fn(mode="train"):
return layers.Model(
layers.Dropout(mode=mode, rate=0.1), layers.BatchNorm(mode=mode),
models.MLP(d_hidden=16, n_output_classes=n_classes, mode=mode))
def Transformer(input_vocab_size,
output_vocab_size=None,
d_model=512,
d_ff=2048,
n_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_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'
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_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_layers)
])
# Input: encoder_side_tokens, decoder_side_tokens
return tl.Model( # 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 TransformerLM(vocab_size,
d_model=512,
d_ff=2048,
n_layers=6,
n_heads=8,
d_attention_key=None,
d_attention_value=None,
attention_type=tl.DotProductCausalAttention,
dropout=0.1,
share_qk=False,
max_len=2048,
n_chunks=0,
mode='train'):
"""Returns a Transformer language model.
The input to the model is a tensor of tokens. (This model uses only the
decoder part of the overall Transformer.)
Args:
vocab_size: int: vocab size
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
d_attention_key: int: depth of key vector for each attention head
(default is d_model // n_heads)
d_attention_value: int: depth of value vector for each attention head
(default is d_model // n_heads)
attention_type: subclass of tl.BaseCausalAttention: attention class to use
dropout: float: dropout rate (how much to drop out)
share_qk: bool, whether to share queries and keys in decoder attention
max_len: int: maximum symbol length for positional encoding
n_chunks: int: number of chunks (must match input pipeline)
mode: str: 'train', 'eval' or 'predict', predict mode is for fast inference
Returns:
A Transformer language model as a layer that maps from a tensor of tokens
to activations over a vocab set.
"""
if n_chunks == 0:
concatenate_chunks = split_chunks = []
else:
concatenate_chunks = tl.Concatenate(n_items=n_chunks)
split_chunks = tl.Split(n_sections=n_chunks, axis=-2)
embedder = [
tl.Embedding(d_model, vocab_size),
tl.Dropout(rate=dropout, name='embedding', mode=mode),
tl.PositionalEncoding(max_len=max_len, mode=mode),
]
return tl.Model( # tokens (or chunked tuple of tokens)
concatenate_chunks, # tokens
tl.ShiftRight(mode=mode), # toks
embedder, # vecs
[
DecoderBlock( # pylint: disable=g-complex-comprehension
d_model, d_ff, n_heads, d_attention_key, d_attention_value,
attention_type, dropout, share_qk, i, mode)
for i in range(n_layers)
], # vecs
tl.LayerNorm(), # vecs
tl.Dense(vocab_size), # vecs
tl.LogSoftmax(), # vecs
split_chunks, # vecs (or chunked tuple of vecs)
)