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_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(n_blocks=2):
model = Model()
blocks = []
for _ in range(n_blocks):
block = LoihiBlock(1)
block.compartment.configure_lif()
model.add_block(block)
blocks.append(block)
for i in range(n_blocks - 1):
axon = Axon(1)
blocks[i].add_axon(axon)
synapse = Synapse(1)
synapse.set_weights([[1]])
axon.target = synapse
blocks[i + 1].add_synapse(synapse)
axon0 = Axon(1)
input = LoihiInput()
input.add_axon(axon0)
model.add_input(input)
synapse0 = Synapse(1)
synapse0.set_weights([[1]])
axon0.target = synapse0
blocks[0].add_synapse(synapse0)
discretize_model(model)
return model
def new_syn(tracing_mag=None):
syn = Synapse(n_axons=1)
syn.set_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_weights(encoders)
block.add_synapse(synapse, name=kind)
def test_multiple_get_probe_output():
n_steps = 15
n_axons = 3
model = Model()
# n_axons controls number of input spikes and thus amount of overflow
input = SpikeInput(n_axons)
for t in np.arange(1, n_steps + 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)
model.add_block(block)
synapse = Synapse(n_axons)
synapse.set_weights(np.ones((n_axons, 1)))
block.add_synapse(synapse)
axon = Axon(n_axons)
axon.target = synapse
input.add_axon(axon)
probe_u = LoihiProbe(target=block, key="current", synapse=Lowpass(0.005))
model.add_probe(probe_u)
probe_v = LoihiProbe(target=block, key="voltage", synapse=Lowpass(0.005))
model.add_probe(probe_v)
probe_s = LoihiProbe(target=block, key="spiked", synapse=Lowpass(0.005))
model.add_probe(probe_s)
discretize_model(model)
# must set these after `discretize` to specify discretized values
block.compartment.vmin = -(2**22) + 1
block.compartment.vth[:] = VTH_MAX
with EmulatorInterface(model) as emu:
emu.run_steps(n_steps)
first_u = emu.get_probe_output(probe_u)
first_v = emu.get_probe_output(probe_v)
first_s = emu.get_probe_output(probe_s)
second_u = emu.get_probe_output(probe_u)
second_v = emu.get_probe_output(probe_v)
second_s = emu.get_probe_output(probe_s)
assert np.all(first_u == second_u)
assert np.all(first_v == second_v)
assert np.all(first_s == second_s)
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_weights([[1]])
synapse.pop_type = 8
block.add_synapse(synapse)
discretize_model(model)
allocator = Greedy() # one core per ensemble
board = allocator(model, n_chips=1)
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_weights([[1]])
synapse.pop_type = 8
axon.target = synapse
block1.add_synapse(synapse)
discretize_model(model)
board = allocator(model, n_chips=1)
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
n, d = 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([scale_matrix(weights, ga), scale_matrix(weights, -gb)])
weights2 = stack_matrices(weights2, order="h")
syn.set_weights(weights2)
block.add_synapse(syn)
return block, syn
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):
# send spikes to all axons
input.add_spikes(t, np.arange(n_axons), permanent=True)
model.add_input(input)
block = LoihiBlock(1)
block.compartment.configure_relu()
block.compartment.configure_filter(0.1)
model.add_block(block)
synapse = Synapse(n_axons)
synapse.set_weights(np.ones((n_axons, 1)))
block.add_synapse(synapse)
axon = Axon(n_axons)
axon.target = synapse
input.add_axon(axon)
probe_u = LoihiProbe(target=block, key="current")
model.add_probe(probe_u)
probe_v = LoihiProbe(target=block, key="voltage")
model.add_probe(probe_v)
probe_s = LoihiProbe(target=block, key="spiked")
model.add_probe(probe_s)
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)
overflow_var = "q0" if n_axons == 1000 else "current"
with EmulatorInterface(model) as emu:
with pytest.warns(UserWarning, match=f"Overflow in {overflow_var}"):
emu.run_steps(nt)
emu_u = emu.collect_probe_output(probe_u)
emu_v = emu.collect_probe_output(probe_v)
emu_s = emu.collect_probe_output(probe_s)
with HardwareInterface(model, use_snips=False) as sim:
sim.run_steps(nt)
sim_u = sim.collect_probe_output(probe_u)
sim_v = sim.collect_probe_output(probe_v)
sim_s = sim.collect_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)
def build_full_chip_connection(model, conn): # noqa: C901
"""Build dense or sparse connections on-chip"""
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_obj = model.objs[conn.pre_obj]["out"]
post_obj = model.objs[conn.post_obj]["in"]
assert isinstance(pre_obj, (LoihiBlock, LoihiInput))
assert isinstance(post_obj, (LoihiBlock, LoihiProbe))
weights = None
eval_points = None
solver_info = None
neuron_type = None
pre_slice = conn.pre_slice
post_slice = conn.post_slice
# sample transform (if using a distribution), transform shape (out, in)
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
is_chip_process = isinstance(conn.pre_obj, Node) and isinstance(
conn.pre_obj.output, ChipProcess
)
if isinstance(conn.pre_obj, Node) and not (
isinstance(conn.pre_obj, ChipReceiveNeurons) or is_chip_process
):
assert conn.pre_slice == slice(None)
weights = expand_matrix(transform, shape=(conn.post.size_in, conn.pre.size_out))
# input is on-off neuron encoded, so double/flip transform
weights = stack_matrices([weights, scale_matrix(weights, -1)], order="h")
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
if isinstance(transform, scipy.sparse.spmatrix):
raise BuildError(
"Applying a sparse transform to a decoded connection is not supported"
)
eval_points, decoders, solver_info = model.build(
conn.solver, conn, rng, transform
)
pre_slice = slice(None) # taken care of in decoders
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 = scale_matrix(weights, 1.0 / 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
weights = multiply(encoders[:, post_slice], weights)
# post slice already applied to encoders (either here or in
# `build_decoders`), so don't apply later
post_slice = slice(None)
else:
needs_decode_neurons = True
elif isinstance(conn.pre_obj, (Neurons, ChipReceiveNeurons)) or is_chip_process:
weights = expand_matrix(transform, shape=(conn.post.size_in, conn.pre.size_out))
weights = scale_matrix(weights, 1.0 / model.dt)
neuron_type = (
None
if is_chip_process
else conn.pre_obj.neuron_type
if isinstance(conn.pre_obj, ChipReceiveNeurons)
else conn.pre_obj.ensemble.neuron_type
)
if isinstance(conn.post_obj, Ensemble):
needs_decode_neurons = True
else:
raise NotImplementedError("Connection from type %r" % (type(conn.pre_obj),))
if neuron_type is not None and hasattr(neuron_type, "amplitude"):
weights = scale_matrix(weights, neuron_type.amplitude)
# to proper dtype
transform = transform.astype(nengo.rc.float_dtype)
weights = weights.astype(nengo.rc.float_dtype)
# loihi_weights has shape (in, out), to match the shape by block.Synapses
loihi_weights = weights.T
mid_obj = pre_obj
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
n, d = loihi_weights.shape
if isinstance(post_obj, LoihiProbe):
# use non-spiking decode neurons for voltage probing
assert len(post_obj.target) == 0 or post_obj.target == [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.
gain = np.array(1.0 / conn.pre_obj.radius, dtype=nengo.rc.float_dtype)
decoder_block = LoihiBlock(2 * d, label="%s" % conn)
decoder_block.compartment.configure_nonspiking(
dt=model.dt, vth=model.vth_nonspiking
)
decoder_block.compartment.bias[:] = 0
dec_syn = Synapse(n, label="probe_decoders")
weights2 = stack_matrices(
[scale_matrix(loihi_weights, gain), scale_matrix(loihi_weights, -gain)],
order="h",
)
dec_syn.set_weights(weights2)
decoder_block.add_synapse(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
gain = np.array(1.0 / conn.post_obj.radius, dtype=nengo.rc.float_dtype)
loihi_weights = scale_matrix(loihi_weights, gain)
post_d = conn.post_obj.size_in
post_inds = np.arange(post_d, dtype=np.int32)[post_slice]
assert loihi_weights.shape[1] == len(post_inds) == conn.size_out
mid_axon_inds = model.decode_neurons.get_post_inds(post_inds, post_d)
target_encoders = "decode_neuron_encoders"
decoder_block, dec_syn = model.decode_neurons.get_block(
loihi_weights, block_label="%s" % conn, syn_label="decoders"
)
model.add_block(decoder_block)
model.objs[conn]["decoded"] = decoder_block
model.objs[conn]["decoders"] = dec_syn
model.connection_decode_neurons[conn] = decoder_block
# use tau_s for filter into decode neurons, decode_tau for filter out
decoder_block.compartment.configure_filter(tau_s, dt=model.dt)
post_tau = model.decode_tau
target_axons = -np.ones(pre_obj.n_neurons, dtype=np.int32)
target_axons[pre_slice] = np.arange(target_axons[pre_slice].size)
pre_slice = slice(None)
dec_ax0 = Axon(n, label="decoders")
dec_ax0.target = dec_syn
dec_ax0.set_compartment_axon_map(target_axons)
pre_obj.add_axon(dec_ax0)
model.objs[conn]["decode_axon"] = dec_ax0
loihi_weights = None # weights have now been handled
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,
)
pre_tau = rule_type.pre_synapse.tau
float_tracing_tau = pre_tau / model.dt
tracing_tau = int(round(float_tracing_tau))
if not np.allclose(float_tracing_tau, tracing_tau):
warnings.warn(
f"PES learning rule `pre_synapse.tau` ({pre_tau}) is not an "
f"integer multiple of `dt` ({model.dt}). Rounding."
)
# 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(-1.0 / 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_obj = decoder_block
if isinstance(post_obj, LoihiProbe):
assert post_obj.target == [None]
assert post_slice == slice(None)
post_obj.target[0] = mid_obj
model.add_probe(post_obj)
elif isinstance(conn.post_obj, Neurons):
assert isinstance(post_obj, LoihiBlock)
assert post_slice == slice(None)
if loihi_weights is None:
raise NotImplementedError("Need weights for connection to neurons")
assert loihi_weights.ndim == 2
n1, n2 = loihi_weights.shape
assert post_obj.n_neurons == n2
syn = Synapse(n1, label="neuron_weights")
gain = model.params[conn.post_obj.ensemble].gain
loihi_weights = scale_matrix(loihi_weights, gain)
syn.set_weights(loihi_weights)
post_obj.add_synapse(syn)
model.objs[conn]["weights"] = syn
target_axons = -np.ones(mid_obj.n_neurons, dtype=np.int32)
target_axons[pre_slice] = np.arange(target_axons[pre_slice].size)
assert target_axons[pre_slice].size == n1
ax = Axon(mid_obj.n_neurons, label="neuron_weights")
ax.target = syn
ax.set_compartment_axon_map(target_axons)
mid_obj.add_axon(ax)
post_obj.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_obj, LoihiBlock)
assert pre_slice == slice(None), "Not implemented"
assert post_slice == slice(None)
assert loihi_weights.ndim == 2
n1, n2 = loihi_weights.shape
assert post_obj.n_neurons == n2
# loihi encoders don't include radius, so handle scaling here
scale = np.array(1.0 / conn.post_obj.radius, dtype=nengo.rc.float_dtype)
loihi_weights = scale_matrix(loihi_weights, scale)
syn = Synapse(n1, label="%s::decoder_weights" % conn)
syn.set_weights(loihi_weights)
post_obj.add_synapse(syn)
model.objs[conn]["weights"] = syn
ax = Axon(n1, label="decoder_weights")
ax.target = syn
mid_obj.add_axon(ax)
post_obj.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 isinstance(post_obj, LoihiBlock)
assert pre_slice == slice(None), "Not implemented"
assert post_slice == slice(None)
assert target_encoders is not None
if target_encoders not in post_obj.named_synapses:
build_decode_neuron_encoders(model, conn.post_obj, kind=target_encoders)
mid_ax = Axon(mid_obj.n_neurons, label="encoders")
mid_ax.target = post_obj.named_synapses[target_encoders]
mid_ax.set_compartment_axon_map(mid_axon_inds)
mid_obj.add_axon(mid_ax)
model.objs[conn]["mid_axon"] = mid_ax
post_obj.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, # sampled transform
weights=weights, # scaled weights (including decoders)
)
