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import jax
import jax.numpy as np
from flax import linen as nn
from .layers import SequenceLayer
class StackedEncoderModel(nn.Module):
""" Defines a stack of S5 layers to be used as an encoder.
Args:
ssm (nn.Module): the SSM to be used (i.e. S5 ssm)
d_model (int32): this is the feature size of the layer inputs and outputs
we usually refer to this size as H
n_layers (int32): the number of S5 layers to stack
activation (string): Type of activation function to use
dropout (float32): dropout rate
training (bool): whether in training mode or not
prenorm (bool): apply prenorm if true or postnorm if false
batchnorm (bool): apply batchnorm if true or layernorm if false
bn_momentum (float32): the batchnorm momentum if batchnorm is used
step_rescale (float32): allows for uniformly changing the timescale parameter,
e.g. after training on a different resolution for
the speech commands benchmark
"""
ssm: nn.Module
d_model: int
n_layers: int
activation: str = "gelu"
dropout: float = 0.0
training: bool = True
prenorm: bool = False
batchnorm: bool = False
bn_momentum: float = 0.9
step_rescale: float = 1.0
def setup(self):
"""
Initializes a linear encoder and the stack of S5 layers.
"""
self.encoder = nn.Dense(self.d_model)
self.layers = [
SequenceLayer(
ssm=self.ssm,
dropout=self.dropout,
d_model=self.d_model,
activation=self.activation,
training=self.training,
prenorm=self.prenorm,
batchnorm=self.batchnorm,
bn_momentum=self.bn_momentum,
step_rescale=self.step_rescale,
)
for _ in range(self.n_layers)
]
def __call__(self, x, integration_timesteps):
"""
Compute the LxH output of the stacked encoder given an Lxd_input
input sequence.
Args:
x (float32): input sequence (L, d_input)
Returns:
output sequence (float32): (L, d_model)
"""
x = self.encoder(x)
for layer in self.layers:
x = layer(x)
return x
def masked_meanpool(x, lengths):
"""
Helper function to perform mean pooling across the sequence length
when sequences have variable lengths. We only want to pool across
the prepadded sequence length.
Args:
x (float32): input sequence (L, d_model)
lengths (int32): the original length of the sequence before padding
Returns:
mean pooled output sequence (float32): (d_model)
"""
L = x.shape[0]
mask = np.arange(L) < lengths
return np.sum(mask[..., None]*x, axis=0)/lengths
# Here we call vmap to parallelize across a batch of input sequences
batch_masked_meanpool = jax.vmap(masked_meanpool)
class ClassificationModel(nn.Module):
""" S5 classificaton sequence model. This consists of the stacked encoder
(which consists of a linear encoder and stack of S5 layers), mean pooling
across the sequence length, a linear decoder, and a softmax operation.
Args:
ssm (nn.Module): the SSM to be used (i.e. S5 ssm)
d_output (int32): the output dimension, i.e. the number of classes
d_model (int32): this is the feature size of the layer inputs and outputs
we usually refer to this size as H
n_layers (int32): the number of S5 layers to stack
padded: (bool): if true: padding was used
activation (string): Type of activation function to use
dropout (float32): dropout rate
training (bool): whether in training mode or not
mode (str): Options: [pool: use mean pooling, last: just take
the last state]
prenorm (bool): apply prenorm if true or postnorm if false
batchnorm (bool): apply batchnorm if true or layernorm if false
bn_momentum (float32): the batchnorm momentum if batchnorm is used
step_rescale (float32): allows for uniformly changing the timescale parameter,
e.g. after training on a different resolution for
the speech commands benchmark
"""
ssm: nn.Module
d_output: int
d_model: int
n_layers: int
padded: bool
activation: str = "gelu"
dropout: float = 0.2
training: bool = True
mode: str = "pool"
prenorm: bool = False
batchnorm: bool = False
bn_momentum: float = 0.9
step_rescale: float = 1.0
def setup(self):
"""
Initializes the S5 stacked encoder and a linear decoder.
"""
self.encoder = StackedEncoderModel(
ssm=self.ssm,
d_model=self.d_model,
n_layers=self.n_layers,
activation=self.activation,
dropout=self.dropout,
training=self.training,
prenorm=self.prenorm,
batchnorm=self.batchnorm,
bn_momentum=self.bn_momentum,
step_rescale=self.step_rescale,
)
self.decoder = nn.Dense(self.d_output)
def __call__(self, x, integration_timesteps):
"""
Compute the size d_output log softmax output given a
Lxd_input input sequence.
Args:
x (float32): input sequence (L, d_input)
Returns:
output (float32): (d_output)
"""
if self.padded:
x, length = x # input consists of data and prepadded seq lens
x = self.encoder(x, integration_timesteps)
if self.mode in ["pool"]:
# Perform mean pooling across time
if self.padded:
x = masked_meanpool(x, length)
else:
x = np.mean(x, axis=0)
elif self.mode in ["last"]:
# Just take the last state
if self.padded:
raise NotImplementedError("Mode must be in ['pool'] for self.padded=True (for now...)")
else:
x = x[-1]
else:
raise NotImplementedError("Mode must be in ['pool', 'last]")
x = self.decoder(x)
return nn.log_softmax(x, axis=-1)
# Here we call vmap to parallelize across a batch of input sequences
BatchClassificationModel = nn.vmap(
ClassificationModel,
in_axes=(0, 0),
out_axes=0,
variable_axes={"params": None, "dropout": None, 'batch_stats': None, "cache": 0, "prime": None},
split_rngs={"params": False, "dropout": True}, axis_name='batch')
# For Document matching task (e.g. AAN)
class RetrievalDecoder(nn.Module):
"""
Defines the decoder to be used for document matching tasks,
e.g. the AAN task. This is defined as in the S4 paper where we apply
an MLP to a set of 4 features. The features are computed as described in
Tay et al 2020 https://arxiv.org/pdf/2011.04006.pdf.
Args:
d_output (int32): the output dimension, i.e. the number of classes
d_model (int32): this is the feature size of the layer inputs and outputs
we usually refer to this size as H
"""
d_model: int
d_output: int
def setup(self):
"""
Initializes 2 dense layers to be used for the MLP.
"""
self.layer1 = nn.Dense(self.d_model)
self.layer2 = nn.Dense(self.d_output)
def __call__(self, x):
"""
Computes the input to be used for the softmax function given a set of
4 features. Note this function operates directly on the batch size.
Args:
x (float32): features (bsz, 4*d_model)
Returns:
output (float32): (bsz, d_output)
"""
x = self.layer1(x)
x = nn.gelu(x)
return self.layer2(x)
class RetrievalModel(nn.Module):
""" S5 Retrieval classification model. This consists of the stacked encoder
(which consists of a linear encoder and stack of S5 layers), mean pooling
across the sequence length, constructing 4 features which are fed into a MLP,
and a softmax operation. Note that unlike the standard classification model above,
the apply function of this model operates directly on the batch of data (instead of calling
vmap on this model).
Args:
ssm (nn.Module): the SSM to be used (i.e. S5 ssm)
d_output (int32): the output dimension, i.e. the number of classes
d_model (int32): this is the feature size of the layer inputs and outputs
we usually refer to this size as H
n_layers (int32): the number of S5 layers to stack
padded: (bool): if true: padding was used
activation (string): Type of activation function to use
dropout (float32): dropout rate
training (bool): whether in training mode or not
prenorm (bool): apply prenorm if true or postnorm if false
batchnorm (bool): apply batchnorm if true or layernorm if false
bn_momentum (float32): the batchnorm momentum if batchnorm is used
"""
ssm: nn.Module
d_output: int
d_model: int
n_layers: int
padded: bool
activation: str = "gelu"
dropout: float = 0.2
training: bool = True
prenorm: bool = False
batchnorm: bool = False
bn_momentum: float = 0.9
step_rescale: float = 1.0
def setup(self):
"""
Initializes the S5 stacked encoder and the retrieval decoder. Note that here we
vmap over the stacked encoder model to work well with the retrieval decoder that
operates directly on the batch.
"""
BatchEncoderModel = nn.vmap(
StackedEncoderModel,
in_axes=(0, 0),
out_axes=0,
variable_axes={"params": None, "dropout": None, 'batch_stats': None, "cache": 0, "prime": None},
split_rngs={"params": False, "dropout": True}, axis_name='batch'
)
self.encoder = BatchEncoderModel(
ssm=self.ssm,
d_model=self.d_model,
n_layers=self.n_layers,
activation=self.activation,
dropout=self.dropout,
training=self.training,
prenorm=self.prenorm,
batchnorm=self.batchnorm,
bn_momentum=self.bn_momentum,
step_rescale=self.step_rescale,
)
BatchRetrievalDecoder = nn.vmap(
RetrievalDecoder,
in_axes=0,
out_axes=0,
variable_axes={"params": None},
split_rngs={"params": False},
)
self.decoder = BatchRetrievalDecoder(
d_model=self.d_model,
d_output=self.d_output
)
def __call__(self, input, integration_timesteps): # input is a tuple of x and lengths
"""
Compute the size d_output log softmax output given a
Lxd_input input sequence. The encoded features are constructed as in
Tay et al 2020 https://arxiv.org/pdf/2011.04006.pdf.
Args:
input (float32, int32): tuple of input sequence and prepadded sequence lengths
input sequence is of shape (2*bsz, L, d_input) (includes both documents) and
lengths is (2*bsz,)
Returns:
output (float32): (d_output)
"""
x, lengths = input # x is 2*bsz*seq_len*in_dim, lengths is: (2*bsz,)
x = self.encoder(x, integration_timesteps) # The output is: 2*bszxseq_lenxd_model
outs = batch_masked_meanpool(x, lengths) # Avg non-padded values: 2*bszxd_model
outs0, outs1 = np.split(outs, 2) # each encoded_i is bszxd_model
features = np.concatenate([outs0, outs1, outs0-outs1, outs0*outs1], axis=-1) # bszx4*d_model
out = self.decoder(features)
return nn.log_softmax(out, axis=-1)
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