from __future__ import print_function
from collections import OrderedDict, defaultdict
import itertools
import logging
import warnings
from nengo import Connection, Process, Ensemble
from nengo.builder.operator import TimeUpdate, SimPyFunc
from nengo.builder.processes import SimProcess
from nengo.config import ConfigError
from nengo.ensemble import Neurons
from nengo.exceptions import SimulationError
from nengo.neurons import Direct
import numpy as np
import tensorflow as tf
from nengo_dl import builder, graph_optimizer, signals, utils, tensor_node
logger = logging.getLogger(__name__)
[docs]def with_self(func):
"""A decorator that can be used to ensure that any ops created within the
wrapped method will be added to the TensorGraph object's graph."""
def func_with_self(self, *args, **kwargs):
with self.graph.as_default(), self.graph.device(self.device):
return func(self, *args, **kwargs)
return func_with_self
[docs]class TensorGraph(object):
"""Manages the construction of the TensorFlow symbolic computation graph.
Parameters
----------
model : :class:`~nengo:nengo.builder.Model`
Pre-built Nengo model describing the network to be simulated
dt : float
Length of a simulator timestep, in seconds
unroll_simulation : int
Unroll simulation loop by explicitly building ``unroll_simulation``
iterations into the computation graph
dtype : ``tf.DType``
Floating point precision to use for simulation
minibatch_size : int
The number of simultaneous inputs that will be passed through the
network
device : None or ``"/cpu:0"`` or ``"/gpu:[0-n]"``
Device on which to execute computations (if None then uses the
default device as determined by TensorFlow)
progress : :class:`.utils.ProgressBar`
Progress bar for optimization stage
"""
def __init__(self, model, dt, unroll_simulation, dtype, minibatch_size,
device, progress):
self.model = model
self.dt = dt
self.unroll = unroll_simulation
self.dtype = dtype
self.minibatch_size = minibatch_size
self.device = device
self.graph = tf.Graph()
# find invariant inputs (nodes that don't receive any input other
# than the simulation time). we'll compute these outside the simulation
# and feed in the result.
if self.model.toplevel is None:
self.invariant_inputs = []
else:
self.invariant_inputs = [n for n in self.model.toplevel.all_nodes
if n.size_in == 0 and
not isinstance(n, tensor_node.TensorNode)]
# filter unused operators
# remove TimeUpdate because it is executed as part of the simulation
# loop, not part of the step plan. remove input nodes because they
# are executed outside the simulation.
node_processes = [n.output for n in self.invariant_inputs
if isinstance(n.output, Process)]
operators = [
op for op in self.model.operators if not (
isinstance(op, TimeUpdate) or
(isinstance(op, SimPyFunc) and op.x is None) or
(isinstance(op, SimProcess) and op.input is None and
op.process in node_processes))]
# mark trainable signals
self.mark_signals()
logger.info("Initial plan length: %d", len(operators))
# apply graph simplification functions
try:
simplifications = model.toplevel.config[
model.toplevel].simplifications
except (ConfigError, AttributeError):
# TODO: verify the performance improvement of the simplifications
simplifications = [
graph_optimizer.remove_constant_copies,
graph_optimizer.remove_unmodified_resets,
graph_optimizer.remove_zero_incs,
graph_optimizer.remove_identity_muls,
]
with progress.sub("operator simplificaton", max_value=None):
old_operators = []
while len(old_operators) != len(operators) or any(
x is not y for x, y in zip(operators, old_operators)):
old_operators = operators
for simp in simplifications:
operators = simp(operators)
# group mergeable operators
try:
planner = model.toplevel.config[model.toplevel].planner
except (ConfigError, AttributeError):
planner = graph_optimizer.tree_planner
with progress.sub("merging operators", max_value=None):
plan = planner(operators)
# TODO: we could also merge operators sequentially (e.g., combine
# a copy and dotinc into one op), as long as the intermediate signal
# is only written to by one op and read by one op
# order signals/operators to promote contiguous reads
try:
sorter = model.toplevel.config[model.toplevel].sorter
except (ConfigError, AttributeError):
sorter = graph_optimizer.order_signals
with progress.sub("ordering signals", max_value=None):
sigs, self.plan = sorter(plan, n_passes=10)
# create base arrays and map Signals to TensorSignals (views on those
# base arrays)
with progress.sub("creating signals", max_value=None):
self.base_arrays_init, self.sig_map = \
graph_optimizer.create_signals(
sigs, self.plan, float_type=dtype.as_numpy_dtype,
minibatch_size=self.minibatch_size)
logger.info("Optimized plan length: %d", len(self.plan))
logger.info("Number of base arrays: %d", len(self.base_arrays_init))
@with_self
def build(self, progress):
"""Constructs a new graph to simulate the model.
progress : :class:`.utils.ProgressBar`
Progress bar for construction stage
"""
self.signals = signals.SignalDict(self.sig_map, self.dtype,
self.minibatch_size)
self.target_phs = {}
self.losses = {}
self.optimizers = {}
# make sure indices are loaded for all probe signals (they won't
# have been loaded if this signal is only accessed as part of a
# larger block during the simulation)
for p in self.model.probes:
probe_sig = self.model.sig[p]["in"]
if probe_sig in self.sig_map:
self.sig_map[probe_sig].load_indices()
# create this constant once here so we don't end up creating a new
# dt constant in each operator
self.signals.dt = tf.constant(self.dt, self.dtype)
self.signals.dt_val = self.dt # store the actual value as well
# variable to track training step
with tf.device("/cpu:0"):
with tf.variable_scope("misc_vars", reuse=False):
self.training_step = tf.get_variable(
"training_step", initializer=tf.constant_initializer(0),
dtype=tf.int64, shape=(), trainable=False)
self.training_step_inc = tf.assign_add(self.training_step, 1)
# create base arrays
sub = progress.sub("creating base arrays")
self.base_vars = OrderedDict()
unique_ids = defaultdict(int)
for k, (v, trainable) in sub(self.base_arrays_init.items()):
name = "%s_%s_%s_%d" % (
v.dtype, "_".join(str(x) for x in v.shape), trainable,
unique_ids[(v.dtype, v.shape, trainable)])
unique_ids[(v.dtype, v.shape, trainable)] += 1
# we initialize all the variables from placeholders, and then
# feed in the initial values when the init op is called. this
# prevents TensorFlow from storing large constants in the graph
# def, which can cause problems for large models
ph = tf.placeholder(v.dtype, v.shape)
if trainable:
with tf.variable_scope("trainable_vars", reuse=False):
var = tf.get_variable(name, initializer=ph, trainable=True)
else:
with tf.variable_scope("local_vars", reuse=False):
var = tf.get_local_variable(name, initializer=ph,
trainable=False)
self.base_vars[k] = (var, ph, v)
logger.debug("created base arrays")
logger.debug([str(x[0]) for x in self.base_vars.values()])
# set up invariant inputs
sub = progress.sub("building inputs")
self.build_inputs(sub)
# pre-build stage
sub = progress.sub("pre-build stage")
self.op_builds = {}
for ops in sub(self.plan):
with self.graph.name_scope(utils.sanitize_name(
builder.Builder.builders[type(ops[0])].__name__)):
builder.Builder.pre_build(ops, self.signals, self.op_builds)
# build stage
sub = progress.sub("unrolled step ops")
self.build_loop(sub)
# ops for initializing variables (will be called by simulator)
trainable_vars = tf.trainable_variables() + [self.training_step]
self.trainable_init_op = tf.variables_initializer(trainable_vars)
self.local_init_op = tf.local_variables_initializer()
self.global_init_op = tf.variables_initializer(
[v for v in tf.global_variables() if v not in trainable_vars])
self.constant_init_op = tf.variables_initializer(
tf.get_collection("constants"))
# logging
logger.info("Number of reads: %d" % sum(
x for x in self.signals.read_types.values()))
for x in self.signals.read_types.items():
logger.info(" %s: %d" % x)
logger.info("Number of writes: %d" % sum(
x for x in self.signals.write_types.values()))
for x in self.signals.write_types.items():
logger.info(" %s: %d" % x)
[docs] def build_step(self):
"""Build the operators that execute a single simulation timestep
into the graph.
Returns
-------
probe_tensors : list of ``tf.Tensor``
The Tensor objects representing the data required for each model
Probe
side_effects : list of ``tf.Tensor``
The output Tensors of computations that may have side-effects
(e.g., :class:`~nengo:nengo.Node` functions), meaning that they
must be executed each time step even if their output doesn't appear
to be used in the simulation
"""
# build operators
side_effects = []
# manually build TimeUpdate. we don't include this in the plan,
# because loop variables (`step`) are (semi?) pinned to the CPU, which
# causes the whole variable to get pinned to the CPU if we include
# `step` as part of the normal planning process.
self.signals.time = tf.cast(self.signals.step,
self.dtype) * self.signals.dt
# build operators
for ops in self.plan:
with self.graph.name_scope(utils.sanitize_name(
builder.Builder.builders[type(ops[0])].__name__)):
outputs = builder.Builder.build(ops, self.signals,
self.op_builds)
if outputs is not None:
side_effects += outputs
logger.debug("collecting probe tensors")
probe_tensors = []
for p in self.model.probes:
probe_sig = self.model.sig[p]["in"]
if probe_sig in self.sig_map:
# TODO: better solution to avoid the forced_copy
# we need to make sure that probe reads occur before the
# probe value is overwritten on the next timestep. however,
# just blocking on the sliced value (probe_tensor) doesn't
# work, because slices of variables don't perform a
# copy, so the slice can be "executed" and then the value
# overwritten before the tensorarray write occurs. what we
# really want to do is block until the probe_arrays.write
# happens, but you can't block on probe_arrays (and blocking on
# probe_array.flow doesn't work, although I think it should).
# so by adding the copy here and then blocking on the copy, we
# make sure that the probe value is read before it can be
# overwritten.
probe_tensors.append(self.signals.gather(
self.sig_map[probe_sig], force_copy=True))
else:
# if a probe signal isn't in sig_map, that means that it isn't
# involved in any simulator ops. so we know its value never
# changes, and we'll just return a constant containing the
# initial value.
if probe_sig.minibatched:
init_val = np.tile(probe_sig.initial_value[..., None],
(1, self.minibatch_size))
else:
init_val = probe_sig.initial_value
probe_tensors.append(tf.constant(init_val, dtype=self.dtype))
logger.debug("=" * 30)
logger.debug("build_step complete")
logger.debug("probe_tensors %s", [str(x) for x in probe_tensors])
logger.debug("side_effects %s", [str(x) for x in side_effects])
return probe_tensors, side_effects
[docs] def build_loop(self, progress):
"""Build simulation loop.
Parameters
----------
progress : :class:`.utils.ProgressBar`
Progress bar for loop construction
"""
def loop_condition(step, stop, *_):
return step < stop
def loop_body(step, stop, loop_i, probe_arrays, base_vars):
self.signals.bases = OrderedDict(
[(k, v) for k, v in zip(itertools.chain(
self.base_vars.keys(), self.signals.internal_vars.keys()),
base_vars)])
for iter in progress(range(self.unroll)):
logger.debug("BUILDING ITERATION %d", iter)
with self.graph.name_scope("iteration_%d" % iter):
# note: nengo step counter is incremented at the beginning
# of the timestep
step += 1
self.signals.step = step
# fill in invariant input data
for n in self.input_ph:
self.signals.scatter(
self.sig_map[self.model.sig[n]["out"]],
self.input_ph[n][loop_i])
# build the operators for a single step
# note: we tie things to the `loop_i` variable so that we
# can be sure the other things we're tying to the
# simulation step (side effects and probes) from the
# previous timestep are executed before the next step
# starts
with self.graph.control_dependencies([loop_i]):
# note: we use the variable scope to make sure that we
# aren't accidentally creating new variables for
# unrolled iterations (this is really only a concern
# with TensorNodes)
with tf.variable_scope("", reuse=iter > 0):
probe_tensors, side_effects = self.build_step()
# copy probe data to array
for i, p in enumerate(probe_tensors):
probe_arrays[i] = probe_arrays[i].write(loop_i, p)
# need to make sure that any operators that could have side
# effects run each timestep, so we tie them to the loop
# increment. we also need to make sure that all the probe
# reads happen before those values get overwritten on the
# next timestep
with self.graph.control_dependencies(side_effects +
probe_tensors):
loop_i += 1
base_vars = tuple(self.signals.bases.values())
return step, stop, loop_i, probe_arrays, base_vars
self.step_var = tf.placeholder(tf.int32, shape=(), name="step")
self.stop_var = tf.placeholder(tf.int32, shape=(), name="stop")
loop_i = tf.constant(0)
probe_arrays = [
tf.TensorArray(
self.signals.dtype, clear_after_read=True, size=0,
dynamic_size=True)
for _ in self.model.probes]
# build simulation loop
loop_vars = (
self.step_var, self.stop_var, loop_i, probe_arrays,
tuple(x[0]._ref() if isinstance(x[0], tf.Variable) else x[0]
for x in self.base_vars.values()) +
tuple(x._ref() for x in self.signals.internal_vars.values()))
loop_vars = tf.while_loop(
loop_condition, loop_body, loop_vars=loop_vars,
parallel_iterations=1, back_prop=True)
self.steps_run = loop_vars[2]
self.probe_arrays = OrderedDict()
for p, a in zip(self.model.probes, loop_vars[3]):
x = a.stack()
if self.model.sig[p]["in"].minibatched:
x = tf.transpose(x, np.roll(np.arange(x.get_shape().ndims), 1))
else:
x = tf.expand_dims(x, 0)
self.probe_arrays[p] = x
@with_self
def build_optimizer(self, optimizer, objective):
"""
Adds elements into the graph to execute the given optimizer.
Parameters
----------
optimizer : ``tf.train.Optimizer``
Instance of a TensorFlow optimizer class
objective : dict of {:class:`~nengo:nengo.Probe`: ``"mse"`` or \
callable or ``None``}
The objective to be minimized for each probe. Passing
``"mse"`` will train with mean squared error. A custom function
``f(output, target) -> loss`` can be passed that consumes the
actual output and target output for a probe in ``targets``
and returns a ``tf.Tensor`` representing the scalar loss value for
that Probe (loss will be summed across Probes). ``None``
indicates that the error gradient is being directly specified
by the user.
Returns
-------
``tf.Tensor``
Operator implementing the given optimizer update
``tf.Tensor`` or ``None``
Operator for initializing variables created by the optimizer
(``None`` if there is nothing to initialize, or if we're returning
a previously built optimizer that should already be initialized)
"""
loss = self.build_loss(objective)
key = (optimizer, frozenset(objective.items()))
try:
# return the cached optimizer if it exists
return self.optimizers[key], None
except KeyError:
pass
agg_method = tf.AggregationMethod.EXPERIMENTAL_TREE
# compute gradients wrt loss
grads = []
if loss is not None:
grads.append(tf.gradients(loss, tf.trainable_variables(),
aggregation_method=agg_method))
# add in any gradients where the user directly specified the output
# error grad
for p, g in objective.items():
if g is None:
grads.append(tf.gradients(
self.probe_arrays[p], tf.trainable_variables(),
grad_ys=self.target_phs[p], aggregation_method=agg_method))
if len(grads) == 1:
grads = grads[0]
else:
grads = [tf.reduce_sum(gs, axis=0) for gs in zip(*grads)]
with tf.variable_scope(optimizer.get_name()) as scope:
# create optimizer operator
opt_op = optimizer.apply_gradients(
zip(grads, tf.trainable_variables()))
# get any new variables created by the optimizer (so they
# can be initialized)
vars = scope.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
if len(vars) > 0:
opt_slots_init = tf.variables_initializer(vars)
else:
opt_slots_init = None
self.optimizers[key] = opt_op
return opt_op, opt_slots_init
@with_self
def build_loss(self, objective):
"""
Adds elements into the graph to compute the given objective.
Parameters
----------
objective : dict of {:class:`~nengo:nengo.Probe`: ``"mse"`` or \
callable or ``None``}
The objective used to compute loss for each probe. Passing
``"mse"`` will use mean squared error. A custom function
``f(output, target) -> loss`` can be passed that consumes the
actual output and target output for a probe in ``targets``
and returns a ``tf.Tensor`` representing the scalar loss value for
that Probe (loss will be summed across Probes).
Returns
-------
``tf.Tensor``
Tensor representing the sum of the given objectives applied to
target probes
"""
key = frozenset(objective.items())
try:
# return the cached loss tensor if it exists
return self.losses[key]
except KeyError:
pass
loss = []
for p, obj in objective.items():
# create a placeholder for the target values
if p not in self.target_phs:
self.target_phs[p] = tf.placeholder(
self.dtype, (self.minibatch_size, None, p.size_in),
name="targets")
# compute loss
if obj == "mse":
# note: nan targets converted to zero error
target = tf.where(tf.is_nan(self.target_phs[p]),
self.probe_arrays[p],
self.target_phs[p])
loss.append(tf.reduce_mean(
tf.square(target - self.probe_arrays[p])))
elif callable(obj):
# move minibatch dimension back to the front
loss.append(obj(self.probe_arrays[p], self.target_phs[p]))
elif obj is None:
# user is directly specifying error, not using objective
continue
else:
raise NotImplementedError
if len(loss) > 0:
# sum loss across probes (note: this will also sum across
# the output of `objective` if it doesn't return a scalar)
loss = tf.reduce_sum(loss)
else:
loss = None
self.losses[key] = loss
return loss
@with_self
def build_post(self, sess, rng):
"""Executes post-build processes for operators (after the graph has
been constructed and session/variables initialized).
Note that unlike other build functions, this is called every time
the simulator is reset.
Parameters
----------
sess : ``tf.Session``
The TensorFlow session for the simulator
rng : :class:`~numpy:numpy.random.RandomState`
Seeded random number generator
"""
for ops, built_ops in self.op_builds.items():
built_ops.build_post(ops, self.signals, sess, rng)
@with_self
def build_summaries(self, summaries):
"""Adds ops to collect summary data for the given objects.
Parameters
----------
summaries : list of dict or \
:class:`~nengo:nengo.Connection` or \
:class:`~nengo:nengo.Ensemble` or \
:class:`~nengo:nengo.ensemble.Neurons` or \
``tf.Tensor``}
List of objects for which we want to collect data. Object can be a
Connection (in which case data on weights will be collected),
Ensemble (encoders), Neurons (biases), a dict of
``{probe: objective}`` that indicates a loss function that will
be tracked, or a pre-built summary tensor.
Returns
-------
``tf.Tensor``
Merged summary op for the given summaries
"""
summary_ops = []
with tf.device("/cpu:0"):
for obj in summaries:
if isinstance(obj, dict):
# overall loss
loss = self.build_loss(obj)
summary_ops.append(tf.summary.scalar(
"loss", loss, family="loss"))
if len(obj) > 1:
# get loss for each probe
inputs = tf.unstack(loss.op.inputs[0])
for p, t in zip(obj, inputs):
summary_ops.append(tf.summary.scalar(
utils.sanitize_name("Probe_%s_loss" % p.label),
t, family="loss"))
elif isinstance(obj, (Ensemble, Neurons, Connection)):
if isinstance(obj, Ensemble):
param = "encoders"
name = "Ensemble_%s" % obj.label
elif isinstance(obj, Neurons):
param = "bias"
name = "Ensemble.neurons_%s" % obj.ensemble.label
elif isinstance(obj, Connection):
param = "weights"
name = "Connection_%s" % obj.label
summary_ops.append(tf.summary.histogram(
utils.sanitize_name("%s_%s" % (name, param)),
self.get_tensor(self.model.sig[obj][param])))
elif isinstance(obj, tf.Tensor):
# we assume that obj is a summary op
summary_ops.append(obj)
else:
raise SimulationError(
"Unknown summary object: %s" % obj)
return tf.summary.merge(summary_ops)
@with_self
def get_tensor(self, sig):
"""Returns a Tensor corresponding to the given Signal.
Parameters
----------
sig : :class:`~nengo:nengo.builder.Signal`
A signal in the model
Returns
-------
``tf.Tensor``
Tensor containing the value of the given Signal
"""
tensor_sig = self.sig_map[sig]
if tensor_sig.tf_indices is None:
tensor_sig.load_indices()
base = self.base_vars[tensor_sig.key][0]
return tf.gather(base, tensor_sig.tf_indices)
[docs] def mark_signals(self):
"""Mark all the signals in ``self.model`` according to whether they
represent trainable parameters of the model (parameters that can be
optimized by deep learning methods).
Trainable parameters include connection weights, ensemble encoders, and
neuron biases. Unless one of those signals is targeted by a Nengo
learning rule (otherwise the learning rule update conflicts with the
deep learning optimization).
Users can manually specify whether signals are trainable or not using
the config system (e.g.,
``net.config[nengo.Ensemble].trainable = False``)
"""
def get_trainable(config, obj, network_trainable):
"""Looks up the current value of ``obj.trainable``."""
try:
if obj in config.params:
# priority #1: instance config
trainable = config[obj].trainable
elif network_trainable is not 1:
# priority #2: network setting
trainable = network_trainable
else:
# priority #3: class config
trainable = config[obj].trainable
except (ConfigError, AttributeError):
trainable = network_trainable
# we return 1 if trainable isn't configured, since the default is
# for everything to be trainable but we want to be able to
# distinguish whether something was specifically set to be
# trainable (True) or just defaulting to trainable (1)
return 1 if trainable is None else trainable
def mark_network(config, net, network_trainable):
"""Recursively marks the signals for objects within each
subnetwork."""
for subnet in net.networks:
mark_network(config, subnet,
get_trainable(config, subnet, network_trainable))
# encoders and biases are trainable
for ens in net.ensembles:
ens_trainable = get_trainable(config, ens, network_trainable)
self.model.sig[ens]["encoders"].trainable = ens_trainable
self.model.sig[ens]["encoders"].minibatched = False
if not isinstance(ens.neuron_type, Direct):
neurons_trainable = get_trainable(config, ens.neurons,
network_trainable)
if neurons_trainable is 1:
neurons_trainable = ens_trainable
self.model.sig[ens.neurons]["bias"].trainable = (
neurons_trainable)
self.model.sig[ens.neurons]["bias"].minibatched = False
# connection weights are trainable
for conn in net.connections:
# note: this doesn't include probe connections, since they
# aren't added to the network
self.model.sig[conn]["weights"].trainable = get_trainable(
config, conn, network_trainable)
self.model.sig[conn]["weights"].minibatched = False
# parameters can't be modified by an online Nengo learning rule
# and offline training at the same time. (it is possible in
# theory, but it complicates things a lot and is probably not a
# common use case). we also make those signals minibatched
# (they wouldn't be normally), because we want to be able to
# learn independently in each minibatch
for conn in net.connections:
rule = conn.learning_rule
if rule is not None:
if isinstance(rule, dict):
rule = list(rule.values())
elif not isinstance(rule, list):
rule = [rule]
for r in rule:
if r.modifies in ("weights", "decoders"):
obj = conn
attr = "weights"
elif r.modifies == "encoders":
obj = conn.post_obj
attr = "encoders"
else:
raise NotImplementedError
if self.model.sig[obj][attr].trainable is True:
warnings.warn(
"%s has a learning rule and is also set "
"to be trainable; this is likely to "
"produce strange training behaviour." %
obj)
else:
self.model.sig[obj][attr].trainable = False
self.model.sig[obj][attr].minibatched = True
if self.model.toplevel is None:
warnings.warn(
"No top-level network in model; assuming no trainable "
"parameters", UserWarning)
else:
config = self.model.toplevel.config
mark_network(config, self.model.toplevel,
get_trainable(config, self.model.toplevel, 1))
# the connections to connection probes are not trainable, but
# also not minibatched
probe_seeds = [self.model.seeds[p] for p in self.model.probes]
for obj, seed in self.model.seeds.items():
if isinstance(obj, Connection) and seed in probe_seeds:
self.model.sig[obj]["weights"].trainable = False
self.model.sig[obj]["weights"].minibatched = False
# fill in defaults for all other signals
# signals are not trainable by default, and views take on the
# properties of their bases
for op in self.model.operators:
for sig in op.all_signals:
if not hasattr(sig.base, "trainable"):
sig.base.trainable = False
if not hasattr(sig.base, "minibatched"):
sig.base.minibatched = not sig.base.trainable
if not hasattr(sig, "trainable"):
sig.trainable = sig.base.trainable
if not hasattr(sig, "minibatched"):
sig.minibatched = sig.base.minibatched