def run_steps(self, N, progress_bar=True): if self.closed: raise SimulatorClosed("Simulator cannot run because it is closed.") if self.n_steps + N >= 2**24: # since n_steps is float32, point at which `n_steps == n_steps + 1` raise ValueError("Cannot handle more than 2**24 steps") if self._cl_probe_plan is not None: # -- precondition: the probe buffers have been drained bufpositions = self._cl_probe_plan.cl_bufpositions.get() assert np.all(bufpositions == 0) if progress_bar is None: progress_bar = self.progress_bar try: progress = ProgressTracker(N, progress_bar, "Simulating") except TypeError: progress = ProgressTracker(N, progress_bar) with progress: # -- we will go through N steps of the simulator # in groups of up to B at a time, draining # the probe buffers after each group of B while N: B = min(N, self._max_steps_between_probes) self._plans.call_n_times(B) self._probe() N -= B progress.step(n=B) if self.profiling > 1: self.print_profiling()
def run_steps(self, N, progress_bar=True): if self.closed: raise SimulatorClosed("Simulator cannot run because it is closed.") if self.n_steps + N >= 2**24: # since n_steps is float32, point at which `n_steps == n_steps + 1` raise ValueError("Cannot handle more than 2**24 steps") if self._cl_probe_plan is not None: # -- precondition: the probe buffers have been drained bufpositions = self._cl_probe_plan.cl_bufpositions.get() assert np.all(bufpositions == 0) if progress_bar is None: progress_bar = self.progress_bar try: progress = ProgressTracker(N, progress_bar, "Simulating") except TypeError: progress = ProgressTracker(N, progress_bar) with progress: # -- we will go through N steps of the simulator # in groups of up to B at a time, draining # the probe buffers after each group of B while N: B = min(N, self._max_steps_between_probes) self._plans.call_n_times(B) self._probe() N -= B progress.step(n=B) if self.profiling > 1: self.print_profiling()
def build_network(model, network, progress_bar=False): """Builds a `.Network` object into a model. The network builder does this by mapping each high-level object to its associated signals and operators one-by-one, in the following order: 1. Ensembles, nodes, neurons 2. Subnetworks (recursively) 3. Connections, learning rules 4. Probes Before calling any of the individual objects' build functions, random number seeds are assigned to objects that did not have a seed explicitly set by the user. Whether the seed was assigned manually or automatically is tracked, and the decoder cache is only used when the seed is assigned manually. Parameters ---------- model : Model The model to build into. network : Network The network to build. progress_bar : bool or `.ProgressBar` or `.ProgressUpdater`, optional \ (Default: False) Progress bar for displaying build progress. If True, the default progress bar will be used. If False, the progress bar will be disabled. For more control over the progress bar, pass in a `.ProgressBar` or `.ProgressUpdater` instance. Note that this will only affect top-level networks. Subnetworks cannot have progress bars displayed. Notes ----- Sets ``model.params[network]`` to ``None``. """ def get_seed(obj, rng): # Generate a seed no matter what, so that setting a seed or not on # one object doesn't affect the seeds of other objects. seed = rng.randint(npext.maxint) return (seed if not hasattr(obj, 'seed') or obj.seed is None else obj.seed) if model.toplevel is None: model.toplevel = network model.seeds[network] = get_seed(network, np.random) model.seeded[network] = getattr(network, 'seed', None) is not None else: progress_bar = False max_steps = max(1, len(network.all_objects)) progress = ProgressTracker(max_steps, progress_bar, task="Building") # Set config old_config = model.config model.config = network.config # assign seeds to children rng = np.random.RandomState(model.seeds[network]) sorted_types = sorted(network.objects, key=lambda t: t.__name__) for obj_type in sorted_types: for obj in network.objects[obj_type]: model.seeded[obj] = (model.seeded[network] or getattr(obj, 'seed', None) is not None) model.seeds[obj] = get_seed(obj, rng) # If this is the toplevel network, enter the decoder cache context = (model.decoder_cache if model.toplevel is network else nullcontext()) with context, progress: model.build_callback = lambda obj: progress.step() logger.debug("Network step 1: Building ensembles and nodes") for obj in network.ensembles + network.nodes: model.build(obj) logger.debug("Network step 2: Building subnetworks") for subnetwork in network.networks: model.build(subnetwork) logger.debug("Network step 3: Building connections") for conn in network.connections: # NB: we do these in the order in which they're defined, and build # the learning rule in the connection builder. Because learning # rules are attached to connections, the connection that contains # the learning rule (and the learning rule) are always built # *before* a connection that attaches to that learning rule. # Therefore, we don't have to worry about connection ordering here. # TODO: Except perhaps if the connection being learned # is in a subnetwork? model.build(conn) logger.debug("Network step 4: Building probes") for probe in network.probes: model.build(probe) if context is model.decoder_cache: model.decoder_cache.shrink() model.build_callback = None # Unset config model.config = old_config model.params[network] = None
def build_network(model, network, progress_bar=False): """Builds a `.Network` object into a model. The network builder does this by mapping each high-level object to its associated signals and operators one-by-one, in the following order: 1. Ensembles, nodes, neurons 2. Subnetworks (recursively) 3. Connections, learning rules 4. Probes Before calling any of the individual objects' build functions, random number seeds are assigned to objects that did not have a seed explicitly set by the user. Whether the seed was assigned manually or automatically is tracked, and the decoder cache is only used when the seed is assigned manually. Parameters ---------- model : Model The model to build into. network : Network The network to build. progress_bar : bool or `.ProgressBar` or `.ProgressUpdater`, optional \ (Default: False) Progress bar for displaying build progress. If True, the default progress bar will be used. If False, the progress bar will be disabled. For more control over the progress bar, pass in a `.ProgressBar` or `.ProgressUpdater` instance. Note that this will only affect top-level networks. Subnetworks cannot have progress bars displayed. Notes ----- Sets ``model.params[network]`` to ``None``. """ def get_seed(obj, rng): # Generate a seed no matter what, so that setting a seed or not on # one object doesn't affect the seeds of other objects. seed = rng.randint(npext.maxint) return (seed if not hasattr(obj, 'seed') or obj.seed is None else obj.seed) if model.toplevel is None: model.toplevel = network model.seeds[network] = get_seed(network, np.random) model.seeded[network] = getattr(network, 'seed', None) is not None else: progress_bar = False max_steps = len(network.all_objects) + 1 # +1 for top level network itself progress = ProgressTracker(max_steps, progress_bar, task="Building") # Set config old_config = model.config model.config = network.config # assign seeds to children rng = np.random.RandomState(model.seeds[network]) # Put probes last so that they don't influence other seeds sorted_types = (Connection, Ensemble, Network, Node, Probe) assert all(tp in sorted_types for tp in network.objects) for obj_type in sorted_types: for obj in network.objects[obj_type]: model.seeded[obj] = (model.seeded[network] or getattr(obj, 'seed', None) is not None) model.seeds[obj] = get_seed(obj, rng) # If this is the toplevel network, enter the decoder cache context = (model.decoder_cache if model.toplevel is network else nullcontext()) with context, progress: def build_callback(obj): if isinstance(obj, tuple(network.objects)): progress.step() model.build_callback = build_callback logger.debug("Network step 1: Building ensembles and nodes") for obj in network.ensembles + network.nodes: model.build(obj) logger.debug("Network step 2: Building subnetworks") for subnetwork in network.networks: model.build(subnetwork) logger.debug("Network step 3: Building connections") for conn in network.connections: # NB: we do these in the order in which they're defined, and build # the learning rule in the connection builder. Because learning # rules are attached to connections, the connection that contains # the learning rule (and the learning rule) are always built # *before* a connection that attaches to that learning rule. # Therefore, we don't have to worry about connection ordering here. # TODO: Except perhaps if the connection being learned # is in a subnetwork? model.build(conn) logger.debug("Network step 4: Building probes") for probe in network.probes: model.build(probe) if context is model.decoder_cache: model.decoder_cache.shrink() progress.step() model.build_callback = None # Unset config model.config = old_config model.params[network] = None