Source code for nengo_dl.tensor_graph

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()"Initial plan length: %d", len(operators)) # apply graph simplification functions try: simplifications = model.toplevel.config[ model.toplevel].simplifications except (ConfigError, AttributeError): 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)"Optimized plan length: %d", len(self.plan))"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([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"))
[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([type(ops[0])].__name__)): outputs =, 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
[docs] def build_inputs(self, progress): """Sets up the inputs in the model (which will be computed outside of TensorFlow and fed in each simulation block). Parameters ---------- progress : :class:`.utils.ProgressBar` Progress bar for input construction """ self.input_ph = {} for n in progress(self.invariant_inputs): if self.model.sig[n]["out"] in self.sig_map: # make sure the indices for this input are loaded into # TensorFlow (they may not be, if the output of this node is # only read as part of a larger block during the simulation) self.sig_map[self.model.sig[n]["out"]].load_indices() # set up a placeholder input for this node self.input_ph[n] = tf.placeholder( self.dtype, (None, n.size_out, self.minibatch_size))
@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} 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 averaged across Probes). 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 with tf.variable_scope(optimizer.get_name()) as scope: # create optimizer operator agg_method = tf.AggregationMethod.EXPERIMENTAL_TREE opt_op = optimizer.minimize( loss, var_list=tf.trainable_variables(), aggregation_method=agg_method) # 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} 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 += [tf.reduce_mean( tf.square(target - self.probe_arrays[p]))] elif callable(obj): # move minibatch dimension back to the front loss += [obj(self.probe_arrays[p], self.target_phs[p])] else: raise NotImplementedError # 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) 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