def test_validate_block():
# too many compartments
block = LoihiBlock(1200)
assert block.compartment.n_compartments > 1024
with pytest.raises(BuildError, match="Number of compartments"):
validate_block(block)
# too many input axons
block = LoihiBlock(410)
block.add_synapse(Synapse(5000))
with pytest.raises(BuildError, match="Input axons"):
validate_block(block)
# too many output axons
block = LoihiBlock(410)
synapse = Synapse(2500)
axon = Axon(5000)
axon.target = synapse
block.add_synapse(synapse)
block.add_axon(axon)
with pytest.raises(BuildError, match="Output axons"):
validate_block(block)
# too many synapse bits
block = LoihiBlock(600)
synapse = Synapse(500)
synapse.set_full_weights(np.ones((500, 600)))
axon = Axon(500)
axon.target = synapse
block.add_synapse(synapse)
block.add_axon(axon)
with pytest.raises(BuildError, match="synapse bits"):
validate_block(block)
def _basic_model():
model = Model()
block0 = LoihiBlock(1)
block0.compartment.configure_lif()
model.add_block(block0)
block1 = LoihiBlock(1)
block1.compartment.configure_lif()
model.add_block(block1)
axon1 = Axon(1)
block0.add_axon(axon1)
synapse1 = Synapse(1)
synapse1.set_full_weights([[1]])
axon1.target = synapse1
block1.add_synapse(synapse1)
axon0 = Axon(1)
input = LoihiInput()
input.add_axon(axon0)
model.add_input(input)
synapse0 = Synapse(1)
synapse0.set_full_weights([[1]])
axon0.target = synapse0
block0.add_synapse(synapse0)
discretize_model(model)
return model
def new_syn(tracing_mag=None):
syn = Synapse(n_axons=1)
syn.set_full_weights(np.array([[1]]))
if tracing_mag is not None:
syn.set_learning(tracing_mag=tracing_mag)
core.add_synapse(syn)
return syn
def build_decode_neuron_encoders(model, ens, kind='decode_neuron_encoders'):
"""Build encoders accepting decode neuron input."""
block = model.objs[ens.neurons]['in']
scaled_encoders = model.params[ens].scaled_encoders
if kind == 'node_encoders':
encoders = model.node_neurons.get_post_encoders(scaled_encoders)
elif kind == 'decode_neuron_encoders':
encoders = model.decode_neurons.get_post_encoders(scaled_encoders)
synapse = Synapse(encoders.shape[0], label=kind)
synapse.set_full_weights(encoders)
block.add_synapse(synapse, name=kind)
def test_builder_poptype_errors():
pytest.importorskip('nxsdk')
# Test error in build_synapse
model = Model()
block = LoihiBlock(1)
block.compartment.configure_lif()
model.add_block(block)
synapse = Synapse(1)
synapse.set_full_weights([1])
synapse.pop_type = 8
block.add_synapse(synapse)
discretize_model(model)
allocator = OneToOne() # one core per ensemble
board = allocator(model)
with pytest.raises(ValueError, match="[Ss]ynapse.*[Uu]nrec.*pop.*type"):
build_board(board)
# Test error in collect_axons
model = Model()
block0 = LoihiBlock(1)
block0.compartment.configure_lif()
model.add_block(block0)
block1 = LoihiBlock(1)
block1.compartment.configure_lif()
model.add_block(block1)
axon = Axon(1)
block0.add_axon(axon)
synapse = Synapse(1)
synapse.set_full_weights([1])
synapse.pop_type = 8
axon.target = synapse
block1.add_synapse(synapse)
discretize_model(model)
board = allocator(model)
with pytest.raises(ValueError, match="[Aa]xon.*[Uu]nrec.*pop.*type"):
build_board(board)
def get_block(self, weights, block_label=None, syn_label=None):
gain = self.gain * self.dt
bias = self.bias * self.dt
d, n = weights.shape
n_neurons = 2 * d * self.pairs_per_dim
block = LoihiBlock(n_neurons, label=block_label)
block.compartment.configure_relu(dt=self.dt)
block.compartment.bias[:] = bias.repeat(d)
syn = Synapse(n, label=syn_label)
weights2 = []
for ga, gb in gain.reshape(self.pairs_per_dim, 2):
weights2.extend([ga * weights.T, -gb * weights.T])
weights2 = np.hstack(weights2)
syn.set_full_weights(weights2)
block.add_synapse(syn)
return block, syn
def build_connection(model, conn):
if nengo_transforms is not None:
if isinstance(conn.transform, nengo_transforms.Convolution):
# TODO: integrate these into the same function
conv.build_conv2d_connection(model, conn)
return
elif not isinstance(conn.transform, nengo_transforms.Dense):
raise NotImplementedError(
"nengo-loihi does not yet support %s transforms"
% conn.transform)
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_cx = model.objs[conn.pre_obj]['out']
post_cx = model.objs[conn.post_obj]['in']
assert isinstance(pre_cx, (LoihiBlock, LoihiInput))
assert isinstance(post_cx, (LoihiBlock, Probe))
weights = None
eval_points = None
solver_info = None
neuron_type = None
post_slice = conn.post_slice
# sample transform (if using a distribution)
transform = sample_transform(conn, rng=rng)
tau_s = 0.0 # `synapse is None` gets mapped to `tau_s = 0.0`
if isinstance(conn.synapse, nengo.synapses.Lowpass):
tau_s = conn.synapse.tau
elif conn.synapse is not None:
raise NotImplementedError("Cannot handle non-Lowpass synapses")
needs_decode_neurons = False
target_encoders = None
if isinstance(conn.pre_obj, Node):
assert conn.pre_slice == slice(None)
if np.array_equal(transform, np.array(1.)):
# TODO: this identity transform may be avoidable
transform = np.eye(conn.pre.size_out)
else:
assert transform.ndim == 2, "transform shape not handled yet"
assert transform.shape[1] == conn.pre.size_out
assert transform.shape[1] == conn.pre.size_out
if isinstance(conn.pre_obj, ChipReceiveNeurons):
weights = transform / model.dt
neuron_type = conn.pre_obj.neuron_type
else:
# input is on-off neuron encoded, so double/flip transform
weights = np.column_stack([transform, -transform])
target_encoders = 'node_encoders'
elif (isinstance(conn.pre_obj, Ensemble)
and isinstance(conn.pre_obj.neuron_type, nengo.Direct)):
raise NotImplementedError()
elif isinstance(conn.pre_obj, Ensemble): # Normal decoded connection
eval_points, decoders, solver_info = model.build(
conn.solver, conn, rng, transform)
if conn.solver.weights and not conn.solver.compositional:
weights = decoders
else:
weights = multiply(transform, decoders)
# the decoder solver assumes a spike height of 1/dt; that isn't the
# case on loihi, so we need to undo that scaling
weights = weights / model.dt
neuron_type = conn.pre_obj.neuron_type
if conn.solver.weights:
# weight solvers only allowed on ensemble->ensemble connections
assert isinstance(conn.post_obj, Ensemble)
if conn.solver.compositional:
encoders = model.params[conn.post_obj].scaled_encoders.T
encoders = encoders[post_slice]
weights = multiply(encoders.T, weights)
# post slice already applied to encoders (either here or in
# `build_decoders`), so don't apply later
post_slice = None
else:
needs_decode_neurons = True
elif isinstance(conn.pre_obj, Neurons):
assert conn.pre_slice == slice(None)
assert transform.ndim == 2, "transform shape not handled yet"
weights = transform / model.dt
neuron_type = conn.pre_obj.ensemble.neuron_type
else:
raise NotImplementedError("Connection from type %r" % (
type(conn.pre_obj),))
if neuron_type is not None and hasattr(neuron_type, 'amplitude'):
weights = weights * neuron_type.amplitude
mid_cx = pre_cx
mid_axon_inds = None
post_tau = tau_s
if needs_decode_neurons and not isinstance(conn.post_obj, Neurons):
# --- add decode neurons
assert weights.ndim == 2
d, n = weights.shape
if isinstance(post_cx, Probe):
# use non-spiking decode neurons for voltage probing
assert post_cx.target is None
assert post_slice == slice(None)
# use the same scaling as the ensemble does, to get good
# decodes. Note that this assumes that the decoded value
# is in the range -radius to radius, which is usually true.
weights = weights / conn.pre_obj.radius
gain = 1
dec_cx = LoihiBlock(2 * d, label='%s' % conn)
dec_cx.compartment.configure_nonspiking(
dt=model.dt, vth=model.vth_nonspiking)
dec_cx.compartment.bias[:] = 0
model.add_block(dec_cx)
model.objs[conn]['decoded'] = dec_cx
dec_syn = Synapse(n, label="probe_decoders")
weights2 = gain * np.vstack([weights, -weights]).T
dec_syn.set_full_weights(weights2)
dec_cx.add_synapse(dec_syn)
model.objs[conn]['decoders'] = dec_syn
else:
# use spiking decode neurons for on-chip connection
if isinstance(conn.post_obj, Ensemble):
# loihi encoders don't include radius, so handle scaling here
weights = weights / conn.post_obj.radius
post_d = conn.post_obj.size_in
post_inds = np.arange(post_d, dtype=np.int32)[post_slice]
assert weights.shape[0] == len(post_inds) == conn.size_out == d
mid_axon_inds = model.decode_neurons.get_post_inds(
post_inds, post_d)
target_encoders = 'decode_neuron_encoders'
dec_cx, dec_syn = model.decode_neurons.get_block(
weights, block_label="%s" % conn, syn_label="decoders")
model.add_block(dec_cx)
model.objs[conn]['decoded'] = dec_cx
model.objs[conn]['decoders'] = dec_syn
# use tau_s for filter into decode neurons, decode_tau for filter out
dec_cx.compartment.configure_filter(tau_s, dt=model.dt)
post_tau = model.decode_tau
dec_ax0 = Axon(n, label="decoders")
dec_ax0.target = dec_syn
pre_cx.add_axon(dec_ax0)
model.objs[conn]['decode_axon'] = dec_ax0
if conn.learning_rule_type is not None:
rule_type = conn.learning_rule_type
if isinstance(rule_type, nengo.PES):
if not isinstance(rule_type.pre_synapse,
nengo.synapses.Lowpass):
raise ValidationError(
"Loihi only supports `Lowpass` pre-synapses for "
"learning rules", attr='pre_synapse', obj=rule_type)
tracing_tau = rule_type.pre_synapse.tau / model.dt
# Nengo builder scales PES learning rate by `dt / n_neurons`
n_neurons = (conn.pre_obj.n_neurons
if isinstance(conn.pre_obj, Ensemble)
else conn.pre_obj.size_in)
learning_rate = rule_type.learning_rate * model.dt / n_neurons
# Account for scaling to put integer error in range [-127, 127]
learning_rate /= model.pes_error_scale
# Tracing mag set so that the magnitude of the pre trace
# is independent of the pre tau. `dt` factor accounts for
# Nengo's `dt` spike scaling. Where is the second `dt` from?
# Maybe the fact that post decode neurons have `vth = 1/dt`?
tracing_mag = -np.expm1(-1. / tracing_tau) / model.dt**2
# learning weight exponent controls the maximum weight
# magnitude/weight resolution
wgt_exp = model.pes_wgt_exp
dec_syn.set_learning(
learning_rate=learning_rate,
tracing_mag=tracing_mag,
tracing_tau=tracing_tau,
wgt_exp=wgt_exp,
)
else:
raise NotImplementedError()
mid_cx = dec_cx
if isinstance(post_cx, Probe):
assert post_cx.target is None
assert post_slice == slice(None)
post_cx.target = mid_cx
mid_cx.add_probe(post_cx)
elif isinstance(conn.post_obj, Neurons):
assert isinstance(post_cx, LoihiBlock)
assert post_slice == slice(None)
if weights is None:
raise NotImplementedError("Need weights for connection to neurons")
else:
assert weights.ndim == 2
n2, n1 = weights.shape
assert post_cx.n_neurons == n2
syn = Synapse(n1, label="neuron_weights")
gain = model.params[conn.post_obj.ensemble].gain
syn.set_full_weights(weights.T * gain)
post_cx.add_synapse(syn)
model.objs[conn]['weights'] = syn
ax = Axon(mid_cx.n_neurons, label="neuron_weights")
ax.target = syn
mid_cx.add_axon(ax)
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble) and conn.solver.weights:
assert isinstance(post_cx, LoihiBlock)
assert weights.ndim == 2
n2, n1 = weights.shape
assert post_cx.n_neurons == n2
# loihi encoders don't include radius, so handle scaling here
weights = weights / conn.post_obj.radius
syn = Synapse(n1, label="%s::decoder_weights" % conn)
syn.set_full_weights(weights.T)
post_cx.add_synapse(syn)
model.objs[conn]['weights'] = syn
ax = Axon(n1, label="decoder_weights")
ax.target = syn
mid_cx.add_axon(ax)
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble):
assert target_encoders is not None
if target_encoders not in post_cx.named_synapses:
build_decode_neuron_encoders(
model, conn.post_obj, kind=target_encoders)
mid_ax = Axon(mid_cx.n_neurons, label="encoders")
mid_ax.target = post_cx.named_synapses[target_encoders]
mid_ax.set_axon_map(mid_axon_inds)
mid_cx.add_axon(mid_ax)
model.objs[conn]['mid_axon'] = mid_ax
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
else:
# This includes Node, since nodes can't be targets on-chip
raise NotImplementedError()
model.params[conn] = BuiltConnection(
eval_points=eval_points,
solver_info=solver_info,
transform=transform,
weights=weights)
def test_uv_overflow(n_axons, plt, allclose, monkeypatch):
# TODO: Currently this is not testing the V overflow, since it is higher
# and I haven't been able to figure out a way to make it overflow.
nt = 15
model = Model()
# n_axons controls number of input spikes and thus amount of overflow
input = SpikeInput(n_axons)
for t in np.arange(1, nt + 1):
input.add_spikes(t, np.arange(n_axons)) # send spikes to all axons
model.add_input(input)
block = LoihiBlock(1)
block.compartment.configure_relu()
block.compartment.configure_filter(0.1)
synapse = Synapse(n_axons)
synapse.set_full_weights(np.ones((n_axons, 1)))
block.add_synapse(synapse)
axon = Axon(n_axons)
axon.target = synapse
input.add_axon(axon)
probe_u = Probe(target=block, key='current')
block.add_probe(probe_u)
probe_v = Probe(target=block, key='voltage')
block.add_probe(probe_v)
probe_s = Probe(target=block, key='spiked')
block.add_probe(probe_s)
model.add_block(block)
discretize_model(model)
# must set these after `discretize` to specify discretized values
block.compartment.vmin = -2**22 + 1
block.compartment.vth[:] = VTH_MAX
assert EmulatorInterface.strict # Tests should be run in strict mode
monkeypatch.setattr(EmulatorInterface, "strict", False)
with EmulatorInterface(model) as emu:
with pytest.warns(UserWarning):
emu.run_steps(nt)
emu_u = emu.get_probe_output(probe_u)
emu_v = emu.get_probe_output(probe_v)
emu_s = emu.get_probe_output(probe_s)
with HardwareInterface(model, use_snips=False) as sim:
sim.run_steps(nt)
sim_u = sim.get_probe_output(probe_u)
sim_v = sim.get_probe_output(probe_v)
sim_s = sim.get_probe_output(probe_s)
sim_v[sim_s > 0] = 0 # since Loihi has placeholder voltage after spike
plt.subplot(311)
plt.plot(emu_u)
plt.plot(sim_u)
plt.subplot(312)
plt.plot(emu_v)
plt.plot(sim_v)
plt.subplot(313)
plt.plot(emu_s)
plt.plot(sim_s)
assert allclose(emu_u, sim_u)
assert allclose(emu_v, sim_v)
