Gated Recurrent Units (GRU)⚓︎
:label:sec_gru
As RNNs and particularly the LSTM architecture (:numref:sec_lstm)
rapidly gained popularity during the 2010s,
a number of researchers began to experiment
with simplified architectures in hopes
of retaining the key idea of incorporating
an internal state and multiplicative gating mechanisms
but with the aim of speeding up computation.
The gated recurrent unit (GRU) :cite:Cho.Van-Merrienboer.Bahdanau.ea.2014
offered a streamlined version of the LSTM memory cell
that often achieves comparable performance
but with the advantage of being faster
to compute :cite:Chung.Gulcehre.Cho.ea.2014.
%load_ext d2lbook.tab
tab.interact_select(['mxnet', 'pytorch', 'tensorflow', 'jax'])
%%tab mxnet
from d2l import mxnet as d2l
from mxnet import np, npx
from mxnet.gluon import rnn
npx.set_np()
%%tab pytorch
from d2l import torch as d2l
import torch
from torch import nn
%%tab tensorflow
from d2l import tensorflow as d2l
import tensorflow as tf
%%tab jax
from d2l import jax as d2l
from flax import linen as nn
import jax
from jax import numpy as jnp
Reset Gate and Update Gate⚓︎
Here, the LSTM's three gates are replaced by two:
the reset gate and the update gate.
As with LSTMs, these gates are given sigmoid activations,
forcing their values to lie in the interval \((0, 1)\).
Intuitively, the reset gate controls how much of the previous state
we might still want to remember.
Likewise, an update gate would allow us to control
how much of the new state is just a copy of the old one.
:numref:fig_gru_1 illustrates the inputs for both
the reset and update gates in a GRU,
given the input of the current time step
and the hidden state of the previous time step.
The outputs of the gates are given
by two fully connected layers
with a sigmoid activation function.
:label:fig_gru_1
Mathematically, for a given time step \(t\), suppose that the input is a minibatch \(\mathbf{X}_t \in \mathbb{R}^{n \times d}\) (number of examples \(=n\); number of inputs \(=d\)) and the hidden state of the previous time step is \(\mathbf{H}_{t-1} \in \mathbb{R}^{n \times h}\) (number of hidden units \(=h\)). Then the reset gate \(\mathbf{R}_t \in \mathbb{R}^{n \times h}\) and update gate \(\mathbf{Z}_t \in \mathbb{R}^{n \times h}\) are computed as follows:
where \(\mathbf{W}_{\textrm{xr}}, \mathbf{W}_{\textrm{xz}} \in \mathbb{R}^{d \times h}\) and \(\mathbf{W}_{\textrm{hr}}, \mathbf{W}_{\textrm{hz}} \in \mathbb{R}^{h \times h}\) are weight parameters and \(\mathbf{b}_\textrm{r}, \mathbf{b}_\textrm{z} \in \mathbb{R}^{1 \times h}\) are bias parameters.
Candidate Hidden State⚓︎
Next, we integrate the reset gate \(\mathbf{R}_t\)
with the regular updating mechanism
in :eqref:rnn_h_with_state,
leading to the following
candidate hidden state
\(\tilde{\mathbf{H}}_t \in \mathbb{R}^{n \times h}\) at time step \(t\):
\(\(\tilde{\mathbf{H}}_t = \tanh(\mathbf{X}_t \mathbf{W}_{\textrm{xh}} + \left(\mathbf{R}_t \odot \mathbf{H}_{t-1}\right) \mathbf{W}_{\textrm{hh}} + \mathbf{b}_\textrm{h}),\)\)
:eqlabel:gru_tilde_H
where \(\mathbf{W}_{\textrm{xh}} \in \mathbb{R}^{d \times h}\) and \(\mathbf{W}_{\textrm{hh}} \in \mathbb{R}^{h \times h}\) are weight parameters, \(\mathbf{b}_\textrm{h} \in \mathbb{R}^{1 \times h}\) is the bias, and the symbol \(\odot\) is the Hadamard (elementwise) product operator. Here we use a tanh activation function.
The result is a candidate, since we still need
to incorporate the action of the update gate.
Comparing with :eqref:rnn_h_with_state,
the influence of the previous states
can now be reduced with the
elementwise multiplication of
\(\mathbf{R}_t\) and \(\mathbf{H}_{t-1}\)
in :eqref:gru_tilde_H.
Whenever the entries in the reset gate \(\mathbf{R}_t\) are close to 1,
we recover a vanilla RNN such as that in :eqref:rnn_h_with_state.
For all entries of the reset gate \(\mathbf{R}_t\) that are close to 0,
the candidate hidden state is the result of an MLP with \(\mathbf{X}_t\) as input.
Any pre-existing hidden state is thus reset to defaults.
:numref:fig_gru_2 illustrates the computational flow after applying the reset gate.
:label:fig_gru_2
Hidden State⚓︎
Finally, we need to incorporate the effect of the update gate \(\mathbf{Z}_t\). This determines the extent to which the new hidden state \(\mathbf{H}_t \in \mathbb{R}^{n \times h}\) matches the old state \(\mathbf{H}_{t-1}\) compared with how much it resembles the new candidate state \(\tilde{\mathbf{H}}_t\). The update gate \(\mathbf{Z}_t\) can be used for this purpose, simply by taking elementwise convex combinations of \(\mathbf{H}_{t-1}\) and \(\tilde{\mathbf{H}}_t\). This leads to the final update equation for the GRU:
Whenever the update gate \(\mathbf{Z}_t\) is close to 1,
we simply retain the old state.
In this case the information from \(\mathbf{X}_t\) is ignored,
effectively skipping time step \(t\) in the dependency chain.
By contrast, whenever \(\mathbf{Z}_t\) is close to 0,
the new latent state \(\mathbf{H}_t\) approaches the candidate latent state \(\tilde{\mathbf{H}}_t\).
:numref:fig_gru_3 shows the computational flow after the update gate is in action.
:label:fig_gru_3
In summary, GRUs have the following two distinguishing features:
- Reset gates help capture short-term dependencies in sequences.
- Update gates help capture long-term dependencies in sequences.
Implementation from Scratch⚓︎
To gain a better understanding of the GRU model, let's implement it from scratch.
(Initializing Model Parameters)⚓︎
The first step is to initialize the model parameters.
We draw the weights from a Gaussian distribution
with standard deviation to be sigma and set the bias to 0.
The hyperparameter num_hiddens defines the number of hidden units.
We instantiate all weights and biases relating to the update gate,
the reset gate, and the candidate hidden state.
%%tab pytorch, mxnet, tensorflow
class GRUScratch(d2l.Module):
def __init__(self, num_inputs, num_hiddens, sigma=0.01):
super().__init__()
self.save_hyperparameters()
if tab.selected('mxnet'):
init_weight = lambda *shape: d2l.randn(*shape) * sigma
triple = lambda: (init_weight(num_inputs, num_hiddens),
init_weight(num_hiddens, num_hiddens),
d2l.zeros(num_hiddens))
if tab.selected('pytorch'):
init_weight = lambda *shape: nn.Parameter(d2l.randn(*shape) * sigma)
triple = lambda: (init_weight(num_inputs, num_hiddens),
init_weight(num_hiddens, num_hiddens),
nn.Parameter(d2l.zeros(num_hiddens)))
if tab.selected('tensorflow'):
init_weight = lambda *shape: tf.Variable(d2l.normal(shape) * sigma)
triple = lambda: (init_weight(num_inputs, num_hiddens),
init_weight(num_hiddens, num_hiddens),
tf.Variable(d2l.zeros(num_hiddens)))
self.W_xz, self.W_hz, self.b_z = triple() # Update gate
self.W_xr, self.W_hr, self.b_r = triple() # Reset gate
self.W_xh, self.W_hh, self.b_h = triple() # Candidate hidden state
%%tab jax
class GRUScratch(d2l.Module):
num_inputs: int
num_hiddens: int
sigma: float = 0.01
def setup(self):
init_weight = lambda name, shape: self.param(name,
nn.initializers.normal(self.sigma),
shape)
triple = lambda name : (
init_weight(f'W_x{name}', (self.num_inputs, self.num_hiddens)),
init_weight(f'W_h{name}', (self.num_hiddens, self.num_hiddens)),
self.param(f'b_{name}', nn.initializers.zeros, (self.num_hiddens)))
self.W_xz, self.W_hz, self.b_z = triple('z') # Update gate
self.W_xr, self.W_hr, self.b_r = triple('r') # Reset gate
self.W_xh, self.W_hh, self.b_h = triple('h') # Candidate hidden state
Defining the Model⚓︎
Now we are ready to [define the GRU forward computation]. Its structure is the same as that of the basic RNN cell, except that the update equations are more complex.
%%tab pytorch, mxnet, tensorflow
@d2l.add_to_class(GRUScratch)
def forward(self, inputs, H=None):
if H is None:
# Initial state with shape: (batch_size, num_hiddens)
if tab.selected('mxnet'):
H = d2l.zeros((inputs.shape[1], self.num_hiddens),
ctx=inputs.ctx)
if tab.selected('pytorch'):
H = d2l.zeros((inputs.shape[1], self.num_hiddens),
device=inputs.device)
if tab.selected('tensorflow'):
H = d2l.zeros((inputs.shape[1], self.num_hiddens))
outputs = []
for X in inputs:
Z = d2l.sigmoid(d2l.matmul(X, self.W_xz) +
d2l.matmul(H, self.W_hz) + self.b_z)
R = d2l.sigmoid(d2l.matmul(X, self.W_xr) +
d2l.matmul(H, self.W_hr) + self.b_r)
H_tilde = d2l.tanh(d2l.matmul(X, self.W_xh) +
d2l.matmul(R * H, self.W_hh) + self.b_h)
H = Z * H + (1 - Z) * H_tilde
outputs.append(H)
return outputs, H
%%tab jax
@d2l.add_to_class(GRUScratch)
def forward(self, inputs, H=None):
# Use lax.scan primitive instead of looping over the
# inputs, since scan saves time in jit compilation
def scan_fn(H, X):
Z = d2l.sigmoid(d2l.matmul(X, self.W_xz) + d2l.matmul(H, self.W_hz) +
self.b_z)
R = d2l.sigmoid(d2l.matmul(X, self.W_xr) +
d2l.matmul(H, self.W_hr) + self.b_r)
H_tilde = d2l.tanh(d2l.matmul(X, self.W_xh) +
d2l.matmul(R * H, self.W_hh) + self.b_h)
H = Z * H + (1 - Z) * H_tilde
return H, H # return carry, y
if H is None:
batch_size = inputs.shape[1]
carry = jnp.zeros((batch_size, self.num_hiddens))
else:
carry = H
# scan takes the scan_fn, initial carry state, xs with leading axis to be scanned
carry, outputs = jax.lax.scan(scan_fn, carry, inputs)
return outputs, carry
Training⚓︎
[Training] a language model on The Time Machine dataset
works in exactly the same manner as in :numref:sec_rnn-scratch.
%%tab all
data = d2l.TimeMachine(batch_size=1024, num_steps=32)
if tab.selected('mxnet', 'pytorch', 'jax'):
gru = GRUScratch(num_inputs=len(data.vocab), num_hiddens=32)
model = d2l.RNNLMScratch(gru, vocab_size=len(data.vocab), lr=4)
trainer = d2l.Trainer(max_epochs=50, gradient_clip_val=1, num_gpus=1)
if tab.selected('tensorflow'):
with d2l.try_gpu():
gru = GRUScratch(num_inputs=len(data.vocab), num_hiddens=32)
model = d2l.RNNLMScratch(gru, vocab_size=len(data.vocab), lr=4)
trainer = d2l.Trainer(max_epochs=50, gradient_clip_val=1)
trainer.fit(model, data)
[Concise Implementation]⚓︎
In high-level APIs, we can directly instantiate a GRU model. This encapsulates all the configuration detail that we made explicit above.
%%tab pytorch, mxnet, tensorflow
class GRU(d2l.RNN):
def __init__(self, num_inputs, num_hiddens):
d2l.Module.__init__(self)
self.save_hyperparameters()
if tab.selected('mxnet'):
self.rnn = rnn.GRU(num_hiddens)
if tab.selected('pytorch'):
self.rnn = nn.GRU(num_inputs, num_hiddens)
if tab.selected('tensorflow'):
self.rnn = tf.keras.layers.GRU(num_hiddens, return_sequences=True,
return_state=True)
%%tab jax
class GRU(d2l.RNN):
num_hiddens: int
@nn.compact
def __call__(self, inputs, H=None, training=False):
if H is None:
batch_size = inputs.shape[1]
H = nn.GRUCell.initialize_carry(jax.random.PRNGKey(0),
(batch_size,), self.num_hiddens)
GRU = nn.scan(nn.GRUCell, variable_broadcast="params",
in_axes=0, out_axes=0, split_rngs={"params": False})
H, outputs = GRU()(H, inputs)
return outputs, H
The code is significantly faster in training as it uses compiled operators rather than Python.
%%tab all
if tab.selected('mxnet', 'pytorch', 'tensorflow'):
gru = GRU(num_inputs=len(data.vocab), num_hiddens=32)
if tab.selected('jax'):
gru = GRU(num_hiddens=32)
if tab.selected('mxnet', 'pytorch', 'jax'):
model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=4)
if tab.selected('tensorflow'):
with d2l.try_gpu():
model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=4)
trainer.fit(model, data)
After training, we print out the perplexity on the training set and the predicted sequence following the provided prefix.
%%tab mxnet, pytorch
model.predict('it has', 20, data.vocab, d2l.try_gpu())
%%tab tensorflow
model.predict('it has', 20, data.vocab)
%%tab jax
model.predict('it has', 20, data.vocab, trainer.state.params)
Summary⚓︎
Compared with LSTMs, GRUs achieve similar performance but tend to be lighter computationally. Generally, compared with simple RNNs, gated RNNS, just like LSTMs and GRUs, can better capture dependencies for sequences with large time step distances. GRUs contain basic RNNs as their extreme case whenever the reset gate is switched on. They can also skip subsequences by turning on the update gate.
Exercises⚓︎
- Assume that we only want to use the input at time step \(t'\) to predict the output at time step \(t > t'\). What are the best values for the reset and update gates for each time step?
- Adjust the hyperparameters and analyze their influence on running time, perplexity, and the output sequence.
- Compare runtime, perplexity, and the output strings for
rnn.RNNandrnn.GRUimplementations with each other. - What happens if you implement only parts of a GRU, e.g., with only a reset gate or only an update gate?
:begin_tab:mxnet
Discussions
:end_tab:
:begin_tab:pytorch
Discussions
:end_tab:
:begin_tab:tensorflow
Discussions
:end_tab:
:begin_tab:jax
Discussions
:end_tab:
创建日期: November 25, 2023