"""
Benchmark networks and utilities for evaluating NengoDL's performance.
"""
import inspect
import random
import timeit
import click
import nengo
from nengo.utils.filter_design import cont2discrete
import numpy as np
import tensorflow as tf
import nengo_dl
[docs]def cconv(dimensions, neurons_per_d, neuron_type):
"""
Circular convolution (EnsembleArray) benchmark.
Parameters
----------
dimensions : int
Number of dimensions for vector values
neurons_per_d : int
Number of neurons to use per vector dimension
neuron_type : `~nengo.neurons.NeuronType`
Simulation neuron type
Returns
-------
net : `nengo.Network`
benchmark network
"""
with nengo.Network(label="cconv", seed=0) as net:
net.config[nengo.Ensemble].neuron_type = neuron_type
net.config[nengo.Ensemble].gain = nengo.dists.Choice([1, -1])
net.config[nengo.Ensemble].bias = nengo.dists.Uniform(-1, 1)
net.cconv = nengo.networks.CircularConvolution(neurons_per_d, dimensions)
net.inp_a = nengo.Node([0] * dimensions)
net.inp_b = nengo.Node([1] * dimensions)
nengo.Connection(net.inp_a, net.cconv.input_a)
nengo.Connection(net.inp_b, net.cconv.input_b)
net.p = nengo.Probe(net.cconv.output)
return net
[docs]def integrator(dimensions, neurons_per_d, neuron_type):
"""
Single integrator ensemble benchmark.
Parameters
----------
dimensions : int
Number of dimensions for vector values
neurons_per_d : int
Number of neurons to use per vector dimension
neuron_type : `~nengo.neurons.NeuronType`
Simulation neuron type
Returns
-------
net : `nengo.Network`
benchmark network
"""
with nengo.Network(label="integrator", seed=0) as net:
net.config[nengo.Ensemble].neuron_type = neuron_type
net.config[nengo.Ensemble].gain = nengo.dists.Choice([1, -1])
net.config[nengo.Ensemble].bias = nengo.dists.Uniform(-1, 1)
net.integ = nengo.networks.EnsembleArray(neurons_per_d, dimensions)
nengo.Connection(net.integ.output, net.integ.input, synapse=0.01)
net.inp = nengo.Node([0] * dimensions)
nengo.Connection(net.inp, net.integ.input, transform=0.01)
net.p = nengo.Probe(net.integ.output)
return net
[docs]def pes(dimensions, neurons_per_d, neuron_type):
"""
PES learning rule benchmark.
Parameters
----------
dimensions : int
Number of dimensions for vector values
neurons_per_d : int
Number of neurons to use per vector dimension
neuron_type : `~nengo.neurons.NeuronType`
Simulation neuron type
Returns
-------
net : `nengo.Network`
benchmark network
"""
with nengo.Network(label="pes", seed=0) as net:
net.config[nengo.Ensemble].neuron_type = neuron_type
net.config[nengo.Ensemble].gain = nengo.dists.Choice([1, -1])
net.config[nengo.Ensemble].bias = nengo.dists.Uniform(-1, 1)
net.inp = nengo.Node([1] * dimensions)
net.pre = nengo.Ensemble(neurons_per_d * dimensions, dimensions)
net.post = nengo.Node(size_in=dimensions)
nengo.Connection(net.inp, net.pre)
conn = nengo.Connection(net.pre, net.post, learning_rule_type=nengo.PES())
nengo.Connection(net.post, conn.learning_rule, transform=-1)
nengo.Connection(net.inp, conn.learning_rule)
net.p = nengo.Probe(net.post)
return net
[docs]def basal_ganglia(dimensions, neurons_per_d, neuron_type):
"""
Basal ganglia network benchmark.
Parameters
----------
dimensions : int
Number of dimensions for vector values
neurons_per_d : int
Number of neurons to use per vector dimension
neuron_type : `~nengo.neurons.NeuronType`
Simulation neuron type
Returns
-------
net : `nengo.Network`
benchmark network
"""
with nengo.Network(label="basal_ganglia", seed=0) as net:
net.config[nengo.Ensemble].neuron_type = neuron_type
net.inp = nengo.Node([1] * dimensions)
net.bg = nengo.networks.BasalGanglia(dimensions, neurons_per_d)
nengo.Connection(net.inp, net.bg.input)
net.p = nengo.Probe(net.bg.output)
return net
[docs]def mnist(use_tensor_layer=True):
"""
A network designed to stress-test tensor layers (based on mnist net).
Parameters
----------
use_tensor_layer : bool
If True, use individual tensor_layers to build the network, as opposed
to a single TensorNode containing all layers.
Returns
-------
net : `nengo.Network`
benchmark network
"""
with nengo.Network() as net:
# create node to feed in images
net.inp = nengo.Node(np.ones(28 * 28))
if use_tensor_layer:
nengo_nl = nengo.RectifiedLinear()
ensemble_params = dict(
max_rates=nengo.dists.Choice([100]), intercepts=nengo.dists.Choice([0])
)
amplitude = 1
synapse = None
x = nengo_dl.Layer(tf.keras.layers.Conv2D(filters=32, kernel_size=3))(
net.inp, shape_in=(28, 28, 1)
)
x = nengo_dl.Layer(nengo_nl)(x, **ensemble_params)
x = nengo_dl.Layer(tf.keras.layers.Conv2D(filters=32, kernel_size=3))(
x, shape_in=(26, 26, 32), transform=amplitude
)
x = nengo_dl.Layer(nengo_nl)(x, **ensemble_params)
x = nengo_dl.Layer(
tf.keras.layers.AveragePooling2D(pool_size=2, strides=2)
)(x, shape_in=(24, 24, 32), synapse=synapse, transform=amplitude)
x = nengo_dl.Layer(tf.keras.layers.Dense(units=128))(x)
x = nengo_dl.Layer(nengo_nl)(x, **ensemble_params)
x = nengo_dl.Layer(tf.keras.layers.Dropout(rate=0.4))(
x, transform=amplitude
)
x = nengo_dl.Layer(tf.keras.layers.Dense(units=10))(x)
else:
nl = tf.nn.relu
# def softlif_layer(x, sigma=1, tau_ref=0.002, tau_rc=0.02,
# amplitude=1):
# # x -= 1
# z = tf.nn.softplus(x / sigma) * sigma
# z += 1e-10
# rates = amplitude / (tau_ref + tau_rc * tf.log1p(1 / z))
# return rates
def mnist_node(x): # pragma: no cover (runs in TF)
x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=nl)(x)
x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=nl)(x)
x = tf.keras.layers.AveragePooling2D(pool_size=2, strides=2)(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(128, activation=nl)(x)
x = tf.keras.layers.Dropout(rate=0.4)(x)
x = tf.keras.layers.Dense(10)(x)
return x
node = nengo_dl.TensorNode(
mnist_node, shape_in=(28, 28, 1), shape_out=(10,)
)
x = node
nengo.Connection(net.inp, node, synapse=None)
net.p = nengo.Probe(x)
return net
[docs]def spaun(dimensions):
"""
Builds the Spaun network.
See [1]_ for more details.
Parameters
----------
dimensions : int
Number of dimensions for vector values
Returns
-------
net : `nengo.Network`
benchmark network
References
----------
.. [1] Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay,
Travis DeWolf, Yichuan Tang, and Daniel Rasmussen (2012). A large-scale
model of the functioning brain. Science, 338:1202-1205.
Notes
-----
This network needs to be installed via
.. code-block:: bash
pip install git+https://github.com/drasmuss/spaun2.0.git
"""
# pylint: disable=import-outside-toplevel
from _spaun.configurator import cfg
from _spaun.vocabulator import vocab
from _spaun.experimenter import experiment
from _spaun.modules.stim import stim_data
from _spaun.modules.vision import vis_data
from _spaun.modules.motor import mtr_data
from _spaun.spaun_main import Spaun
vocab.sp_dim = dimensions
cfg.mtr_arm_type = None
cfg.set_seed(1)
experiment.initialize(
"A",
stim_data.get_image_ind,
stim_data.get_image_label,
cfg.mtr_est_digit_response_time,
"",
cfg.rng,
)
vocab.initialize(stim_data.stim_SP_labels, experiment.num_learn_actions, cfg.rng)
vocab.initialize_mtr_vocab(mtr_data.dimensions, mtr_data.sps)
vocab.initialize_vis_vocab(vis_data.dimensions, vis_data.sps)
return Spaun()
[docs]def random_network(
dimensions,
neurons_per_d,
neuron_type,
n_ensembles,
connections_per_ensemble,
seed=0,
):
"""
A randomly interconnected network of ensembles.
Parameters
----------
dimensions : int
Number of dimensions for vector values
neurons_per_d : int
Number of neurons to use per vector dimension
neuron_type : `~nengo.neurons.NeuronType`
Simulation neuron type
n_ensembles : int
Number of ensembles in the network
connections_per_ensemble : int
Outgoing connections from each ensemble
Returns
-------
net : `nengo.Network`
benchmark network
"""
random.seed(seed)
with nengo.Network(label="random", seed=seed) as net:
net.inp = nengo.Node([0] * dimensions)
net.out = nengo.Node(size_in=dimensions)
net.p = nengo.Probe(net.out)
ensembles = [
nengo.Ensemble(
neurons_per_d * dimensions, dimensions, neuron_type=neuron_type
)
for _ in range(n_ensembles)
]
dec = np.ones((neurons_per_d * dimensions, dimensions))
for ens in net.ensembles:
# add a connection to input and output node, so we never have
# any "orphan" ensembles
nengo.Connection(net.inp, ens)
nengo.Connection(ens, net.out, solver=nengo.solvers.NoSolver(dec))
posts = random.sample(ensembles, connections_per_ensemble)
for post in posts:
nengo.Connection(ens, post, solver=nengo.solvers.NoSolver(dec))
return net
[docs]def lmu(theta, input_d, native_nengo=False, dtype="float32"):
"""
A network containing a single Legendre Memory Unit cell and dense readout.
See [1]_ for more details.
Parameters
----------
theta : int
Time window parameter for LMU.
input_d : int
Dimensionality of input signal.
native_nengo : bool
If True, build the LMU out of Nengo objects. Otherwise, build the LMU
directly in TensorFlow, and use a `.TensorNode` to wrap the whole cell.
dtype : str
Float dtype to use for internal parameters of LMU when ``native_nengo=False``
(``native_nengo=True`` will use the dtype of the Simulator).
Returns
-------
net : `nengo.Network`
Benchmark network
References
----------
.. [1] Aaron R. Voelker, Ivana Kajić, and Chris Eliasmith. Legendre memory units:
continuous-time representation in recurrent neural networks.
In Advances in Neural Information Processing Systems. 2019.
https://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.
"""
if native_nengo:
# building LMU cell directly out of Nengo objects
class LMUCell(nengo.Network):
"""Implements an LMU cell as a Nengo network."""
def __init__(self, units, order, theta, input_d, **kwargs):
super().__init__(**kwargs)
# compute the A and B matrices according to the LMU's mathematical
# derivation (see the paper for details)
Q = np.arange(order, dtype=np.float64)
R = (2 * Q + 1)[:, None] / theta
j, i = np.meshgrid(Q, Q)
A = np.where(i < j, -1, (-1.0) ** (i - j + 1)) * R
B = (-1.0) ** Q[:, None] * R
C = np.ones((1, order))
D = np.zeros((1,))
A, B, _, _, _ = cont2discrete((A, B, C, D), dt=1.0, method="zoh")
with self:
nengo_dl.configure_settings(trainable=None)
# create objects corresponding to the x/u/m/h variables in LMU
self.x = nengo.Node(size_in=input_d)
self.u = nengo.Node(size_in=1)
self.m = nengo.Node(size_in=order)
self.h = nengo_dl.TensorNode(
tf.nn.tanh, shape_in=(units,), pass_time=False
)
# compute u_t
# note that setting synapse=0 (versus synapse=None) adds a
# one-timestep delay, so we can think of any connections with
# synapse=0 as representing value_{t-1}
nengo.Connection(
self.x, self.u, transform=np.ones((1, input_d)), synapse=None
)
nengo.Connection(
self.h, self.u, transform=np.zeros((1, units)), synapse=0
)
nengo.Connection(
self.m, self.u, transform=np.zeros((1, order)), synapse=0
)
# compute m_t
# in this implementation we'll make A and B non-trainable, but they
# could also be optimized in the same way as the other parameters
conn = nengo.Connection(self.m, self.m, transform=A, synapse=0)
self.config[conn].trainable = False
conn = nengo.Connection(self.u, self.m, transform=B, synapse=None)
self.config[conn].trainable = False
# compute h_t
nengo.Connection(
self.x,
self.h,
transform=np.zeros((units, input_d)),
synapse=None,
)
nengo.Connection(
self.h, self.h, transform=np.zeros((units, units)), synapse=0
)
nengo.Connection(
self.m,
self.h,
transform=nengo_dl.dists.Glorot(distribution="normal"),
synapse=None,
)
with nengo.Network(seed=0) as net:
# remove some unnecessary features to speed up the training
nengo_dl.configure_settings(
trainable=None, stateful=False, keep_history=False,
)
# input node
net.inp = nengo.Node(np.zeros(input_d))
# lmu cell
lmu_cell = LMUCell(units=212, order=256, theta=theta, input_d=input_d)
conn = nengo.Connection(net.inp, lmu_cell.x, synapse=None)
net.config[conn].trainable = False
# dense linear readout
out = nengo.Node(size_in=10)
nengo.Connection(
lmu_cell.h, out, transform=nengo_dl.dists.Glorot(), synapse=None
)
# record output. note that we set keep_history=False above, so this will
# only record the output on the last timestep (which is all we need
# on this task)
net.p = nengo.Probe(out)
else:
# putting everything in a tensornode
# define LMUCell
class LMUCell(tf.keras.layers.AbstractRNNCell):
"""Implement LMU as Keras RNN cell."""
def __init__(self, units, order, theta, **kwargs):
super().__init__(**kwargs)
self.units = units
self.order = order
self.theta = theta
Q = np.arange(order, dtype=np.float64)
R = (2 * Q + 1)[:, None] / theta
j, i = np.meshgrid(Q, Q)
A = np.where(i < j, -1, (-1.0) ** (i - j + 1)) * R
B = (-1.0) ** Q[:, None] * R
C = np.ones((1, order))
D = np.zeros((1,))
self._A, self._B, _, _, _ = cont2discrete(
(A, B, C, D), dt=1.0, method="zoh"
)
@property
def state_size(self):
"""Size of RNN state variables."""
return self.units, self.order
@property
def output_size(self):
"""Size of cell output."""
return self.units
def build(self, input_shape):
"""Set up all the weight matrices used inside the cell."""
super().build(input_shape)
input_dim = input_shape[-1]
self.input_encoders = self.add_weight(
shape=(input_dim, 1), initializer=tf.initializers.ones(),
)
self.hidden_encoders = self.add_weight(
shape=(self.units, 1), initializer=tf.initializers.zeros(),
)
self.memory_encoders = self.add_weight(
shape=(self.order, 1), initializer=tf.initializers.zeros(),
)
self.input_kernel = self.add_weight(
shape=(input_dim, self.units), initializer=tf.initializers.zeros(),
)
self.hidden_kernel = self.add_weight(
shape=(self.units, self.units), initializer=tf.initializers.zeros(),
)
self.memory_kernel = self.add_weight(
shape=(self.order, self.units),
initializer=tf.initializers.glorot_normal(),
)
self.AT = self.add_weight(
shape=(self.order, self.order),
initializer=tf.initializers.constant(self._A.T),
trainable=False,
)
self.BT = self.add_weight(
shape=(1, self.order),
initializer=tf.initializers.constant(self._B.T),
trainable=False,
)
def call(self, inputs, states):
"""Compute cell output and state updates."""
h_prev, m_prev = states
# compute u_t from the above diagram
u = (
tf.matmul(inputs, self.input_encoders)
+ tf.matmul(h_prev, self.hidden_encoders)
+ tf.matmul(m_prev, self.memory_encoders)
)
# compute updated memory state vector (m_t in diagram)
m = tf.matmul(m_prev, self.AT) + tf.matmul(u, self.BT)
# compute updated hidden state vector (h_t in diagram)
h = tf.nn.tanh(
tf.matmul(inputs, self.input_kernel)
+ tf.matmul(h_prev, self.hidden_kernel)
+ tf.matmul(m, self.memory_kernel)
)
return h, [h, m]
with nengo.Network(seed=0) as net:
# remove some unnecessary features to speed up the training
# we could set use_loop=False as well here, but leaving it for parity
# with native_nengo
nengo_dl.configure_settings(stateful=False)
net.inp = nengo.Node(np.zeros(theta))
rnn = nengo_dl.Layer(
tf.keras.layers.RNN(
LMUCell(units=212, order=256, theta=theta, dtype=dtype),
return_sequences=False,
)
)(net.inp, shape_in=(theta, input_d))
out = nengo.Node(size_in=10)
nengo.Connection(rnn, out, transform=nengo_dl.dists.Glorot(), synapse=None)
net.p = nengo.Probe(out)
return net
[docs]def run_profile(
net, train=False, n_steps=150, do_profile=True, reps=1, dtype="float32", **kwargs
):
"""
Run profiler on a benchmark network.
Parameters
----------
net : `~nengo.Network`
The nengo Network to be profiled.
train : bool
If True, profile the `.Simulator.fit` function. Otherwise, profile the
`.Simulator.run` function.
n_steps : int
The number of timesteps to run the simulation.
do_profile : bool
Whether or not to run profiling
reps : int
Repeat the run this many times (only profile data from the last
run will be kept).
dtype : str
Simulation dtype (e.g. "float32")
Returns
-------
exec_time : float
Time (in seconds) taken to run the benchmark, taking the minimum over
``reps``.
Notes
-----
kwargs will be passed on to `.Simulator`
"""
exec_time = 1e10
n_batches = 1
with net:
nengo_dl.configure_settings(inference_only=not train, dtype=dtype)
with nengo_dl.Simulator(net, **kwargs) as sim:
if hasattr(net, "inp"):
x = {
net.inp: np.random.randn(
sim.minibatch_size * n_batches, n_steps, net.inp.size_out
)
}
elif hasattr(net, "inp_a"):
x = {
net.inp_a: np.random.randn(
sim.minibatch_size * n_batches, n_steps, net.inp_a.size_out
),
net.inp_b: np.random.randn(
sim.minibatch_size * n_batches, n_steps, net.inp_b.size_out
),
}
else:
x = None
if train:
y = {
net.p: np.random.randn(
sim.minibatch_size * n_batches, n_steps, net.p.size_in
)
}
sim.compile(tf.optimizers.SGD(0.001), loss=tf.losses.mse)
# run once to eliminate startup overhead
start = timeit.default_timer()
sim.fit(x, y, epochs=1, n_steps=n_steps)
print("Warmup time:", timeit.default_timer() - start)
for _ in range(reps):
if do_profile:
tf.profiler.experimental.start("profile")
start = timeit.default_timer()
sim.fit(x, y, epochs=1, n_steps=n_steps)
exec_time = min(timeit.default_timer() - start, exec_time)
if do_profile:
tf.profiler.experimental.stop()
else:
# run once to eliminate startup overhead
start = timeit.default_timer()
sim.predict(x, n_steps=n_steps)
print("Warmup time:", timeit.default_timer() - start)
for _ in range(reps):
if do_profile:
tf.profiler.experimental.start("profile")
start = timeit.default_timer()
sim.predict(x, n_steps=n_steps)
exec_time = min(timeit.default_timer() - start, exec_time)
if do_profile:
tf.profiler.experimental.stop()
exec_time /= n_batches
print("Execution time:", exec_time)
return exec_time
@click.group(chain=True)
def main():
"""Command-line interface for benchmarks."""
@main.command()
@click.pass_obj
@click.option("--benchmark", default="cconv", help="Name of benchmark network")
@click.option("--dimensions", default=128, help="Number of dimensions")
@click.option("--neurons_per_d", default=64, help="Neurons per dimension")
@click.option("--neuron_type", default="RectifiedLinear", help="Nengo neuron model")
@click.option(
"--kwarg",
type=str,
multiple=True,
help="Arbitrary kwarg to pass to benchmark network (key=value)",
)
def build(obj, benchmark, dimensions, neurons_per_d, neuron_type, kwarg):
"""Builds one of the benchmark networks"""
# get benchmark network by name
benchmark = globals()[benchmark]
# get the neuron type object from string class name
try:
neuron_type = getattr(nengo, neuron_type)()
except AttributeError:
neuron_type = getattr(nengo_dl, neuron_type)()
# set up kwargs
kwargs = dict((k, int(v)) for k, v in [a.split("=") for a in kwarg])
# add the special cli kwargs if applicable; note we could just do
# everything through --kwarg, but it is convenient to have a
# direct option for the common arguments
params = inspect.signature(benchmark).parameters
for kw in ("benchmark", "dimensions", "neurons_per_d", "neuron_type"):
if kw in params:
kwargs[kw] = locals()[kw]
# build benchmark and add to context for chaining
print(
"Building %s with %s"
% (nengo_dl.utils.function_name(benchmark, sanitize=False), kwargs)
)
obj["net"] = benchmark(**kwargs)
@main.command()
@click.pass_obj
@click.option(
"--train/--no-train",
default=False,
help="Whether to profile training (as opposed to running) the network",
)
@click.option(
"--n_steps", default=150, help="Number of steps for which to run the simulation"
)
@click.option("--batch_size", default=1, help="Number of inputs to the model")
@click.option(
"--device",
default="/gpu:0",
help="TensorFlow device on which to run the simulation",
)
@click.option(
"--unroll", default=25, help="Number of steps for which to unroll the simulation"
)
@click.option(
"--time-only",
is_flag=True,
default=False,
help="Only count total time, rather than profiling internals",
)
def profile(obj, train, n_steps, batch_size, device, unroll, time_only):
"""Runs profiling on a network (call after 'build')"""
if "net" not in obj:
raise ValueError("Must call `build` before `profile`")
obj["time"] = run_profile(
obj["net"],
do_profile=not time_only,
train=train,
n_steps=n_steps,
minibatch_size=batch_size,
device=device,
unroll_simulation=unroll,
)
if __name__ == "__main__":
main(obj={}) # pragma: no cover
