def test_lif_step(upsample, n_elements):
"""Test the lif nonlinearity, comparing one step with the Numpy version."""
dt = 1e-3
# n_neurons = [3, 3, 3]
n_neurons = [12345, 23456, 34567]
N = len(n_neurons)
J = RA([np.random.normal(scale=1.2, size=n) for n in n_neurons])
V = RA([np.random.uniform(low=0, high=1, size=n) for n in n_neurons])
W = RA([np.random.uniform(low=-5*dt, high=5*dt, size=n) for n in n_neurons])
OS = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
# tau = 20e-3
# refs = list(np.random.uniform(low=1.7e-3, high=4.2e-3, size=len(n_neurons)))
taus = list(np.random.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clV = CLRA(queue, V)
clW = CLRA(queue, W)
clOS = CLRA(queue, OS)
# clRef = CLRA(queue, RA(refs))
clTau = CLRA(queue, RA(taus))
### simulate host
nls = [LIF(n, tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)]
for i, nl in enumerate(nls):
if upsample <= 1:
nl.step_math(dt, J[i], V[i], W[i], OS[i])
else:
s = np.zeros_like(OS[i])
for j in xrange(upsample):
nl.step_math(dt/upsample, J[i], V[i], W[i], s)
OS[i] = (OS[i] > 0.5) | (s > 0.5)
### simulate device
plan = plan_lif(queue, clJ, clV, clW, clV, clW, clOS, ref, clTau, dt,
n_elements=n_elements, upsample=upsample)
plan()
if 1:
a, b = V, clV
for i in xrange(len(a)):
nc, _ = not_close(a[i], b[i]).nonzero()
if len(nc) > 0:
j = nc[0]
print "i", i, "j", j
print "J", J[i][j], clJ[i][j]
print "V", V[i][j], clV[i][j]
print "W", W[i][j], clW[i][j]
print "...", len(nc) - 1, "more"
n_spikes = np.sum([np.sum(os) for os in OS])
if n_spikes < 1.0:
logger.warn("LIF spiking mechanism was not tested!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(V, clV.to_host())
assert ra.allclose(W, clW.to_host())
assert ra.allclose(OS, clOS.to_host())
def test_lif_rate(n_elements):
"""Test the `lif_rate` nonlinearity"""
# n_neurons = [3, 3, 3]
n_neurons = [123459, 23456, 34567]
N = len(n_neurons)
J = RA([np.random.normal(loc=1, scale=10, size=n) for n in n_neurons])
R = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = list(np.random.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clR = CLRA(queue, R)
clTau = CLRA(queue, RA(taus))
### simulate host
nls = [LIF(n, tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)]
for i, nl in enumerate(nls):
nl.gain = 1
nl.bias = 0
R[i] = nl.rates(J[i].flatten()).reshape((-1,1))
### simulate device
plan = plan_lif_rate(queue, clJ, clR, ref, clTau, dt=1,
n_elements=n_elements)
plan()
rate_sum = np.sum([np.sum(r) for r in R])
if rate_sum < 1.0:
logger.warn("LIF rate was not tested above the firing threshold!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(R, clR.to_host())
def test_lif_rate(ctx, blockify):
"""Test the `lif_rate` nonlinearity"""
rng = np.random
dt = 1e-3
n_neurons = [123459, 23456, 34567]
J = RA([rng.normal(loc=1, scale=10, size=n) for n in n_neurons])
R = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = list(rng.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clR = CLRA(queue, R)
clTaus = CLRA(queue, RA([t * np.ones(n) for t, n in zip(taus, n_neurons)]))
# simulate host
nls = [nengo.LIFRate(tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)]
for i, nl in enumerate(nls):
nl.step_math(dt, J[i], R[i])
# simulate device
plan = plan_lif_rate(queue, dt, clJ, clR, ref, clTaus, blockify=blockify)
plan()
rate_sum = np.sum([np.sum(r) for r in R])
if rate_sum < 1.0:
logger.warn("LIF rate was not tested above the firing threshold!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(R, clR.to_host())
def test_lif_rate(n_elements):
"""Test the `lif_rate` nonlinearity"""
rng = np.random
dt = 1e-3
n_neurons = [123459, 23456, 34567]
J = RA([rng.normal(loc=1, scale=10, size=n) for n in n_neurons])
R = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = list(rng.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clR = CLRA(queue, R)
clTau = CLRA(queue, RA(taus))
# simulate host
nls = [LIFRate(tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)]
for i, nl in enumerate(nls):
nl.step_math(dt, J[i], R[i])
# simulate device
plan = plan_lif_rate(queue, clJ, clR, ref, clTau, dt=dt,
n_elements=n_elements)
plan()
rate_sum = np.sum([np.sum(r) for r in R])
if rate_sum < 1.0:
logger.warn("LIF rate was not tested above the firing threshold!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(R, clR.to_host())
def test_lif_step(ctx, upsample):
"""Test the lif nonlinearity, comparing one step with the Numpy version."""
rng = np.random
dt = 1e-3
n_neurons = [12345, 23456, 34567]
J = RA([rng.normal(scale=1.2, size=n) for n in n_neurons])
V = RA([rng.uniform(low=0, high=1, size=n) for n in n_neurons])
W = RA([rng.uniform(low=-5 * dt, high=5 * dt, size=n) for n in n_neurons])
OS = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = rng.uniform(low=15e-3, high=80e-3, size=len(n_neurons))
amp = 1.
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clV = CLRA(queue, V)
clW = CLRA(queue, W)
clOS = CLRA(queue, OS)
clTaus = CLRA(queue, RA([t * np.ones(n) for t, n in zip(taus, n_neurons)]))
# simulate host
nls = [nengo.LIF(tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)]
for i, nl in enumerate(nls):
if upsample <= 1:
nl.step_math(dt, J[i], OS[i], V[i], W[i])
else:
s = np.zeros_like(OS[i])
for j in range(upsample):
nl.step_math(dt / upsample, J[i], s, V[i], W[i])
OS[i] = (1./dt) * ((OS[i] > 0) | (s > 0))
# simulate device
plan = plan_lif(
queue, dt, clJ, clV, clW, clOS, ref, clTaus, amp, upsample=upsample)
plan()
if 1:
a, b = V, clV
for i in range(len(a)):
nc, _ = not_close(a[i], b[i]).nonzero()
if len(nc) > 0:
j = nc[0]
print("i", i, "j", j)
print("J", J[i][j], clJ[i][j])
print("V", V[i][j], clV[i][j])
print("W", W[i][j], clW[i][j])
print("...", len(nc) - 1, "more")
n_spikes = np.sum([np.sum(os) for os in OS])
if n_spikes < 1.0:
logger.warn("LIF spiking mechanism was not tested!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(V, clV.to_host())
assert ra.allclose(W, clW.to_host())
assert ra.allclose(OS, clOS.to_host())
def test_lif_step(upsample):
"""Test the lif nonlinearity, comparing one step with the Numpy version."""
rng = np.random
dt = 1e-3
n_neurons = [12345, 23456, 34567]
J = RA([rng.normal(scale=1.2, size=n) for n in n_neurons])
V = RA([rng.uniform(low=0, high=1, size=n) for n in n_neurons])
W = RA([rng.uniform(low=-5 * dt, high=5 * dt, size=n) for n in n_neurons])
OS = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = rng.uniform(low=15e-3, high=80e-3, size=len(n_neurons))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clV = CLRA(queue, V)
clW = CLRA(queue, W)
clOS = CLRA(queue, OS)
clTaus = CLRA(queue, RA([t * np.ones(n) for t, n in zip(taus, n_neurons)]))
# simulate host
nls = [nengo.LIF(tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)]
for i, nl in enumerate(nls):
if upsample <= 1:
nl.step_math(dt, J[i], OS[i], V[i], W[i])
else:
s = np.zeros_like(OS[i])
for j in range(upsample):
nl.step_math(dt / upsample, J[i], s, V[i], W[i])
OS[i] = (1./dt) * ((OS[i] > 0) | (s > 0))
# simulate device
plan = plan_lif(
queue, dt, clJ, clV, clW, clOS, ref, clTaus, upsample=upsample)
plan()
if 1:
a, b = V, clV
for i in range(len(a)):
nc, _ = not_close(a[i], b[i]).nonzero()
if len(nc) > 0:
j = nc[0]
print("i", i, "j", j)
print("J", J[i][j], clJ[i][j])
print("V", V[i][j], clV[i][j])
print("W", W[i][j], clW[i][j])
print("...", len(nc) - 1, "more")
n_spikes = np.sum([np.sum(os) for os in OS])
if n_spikes < 1.0:
logger.warn("LIF spiking mechanism was not tested!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(V, clV.to_host())
assert ra.allclose(W, clW.to_host())
assert ra.allclose(OS, clOS.to_host())
def test_discontiguous_setitem(ctx, rng):
A = make_random_ra(3, 2, rng=rng)
A0 = np.array(A[0])
a = A0[::3, ::2]
v = rng.uniform(-1, 1, size=a.shape)
assert a.size > 0
A.add_views(
[A.starts[0]],
[a.shape[0]],
[a.shape[1]],
[a.strides[0] / a.itemsize],
[a.strides[1] / a.itemsize],
)
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
a[...] = v
A[-1] = v
assert np.allclose(A[0], A0)
assert np.allclose(A[-1], v)
print(clA[0].shape)
print(clA[-1].shape)
clA[-1] = v
assert ra.allclose(A, clA.to_host())
assert np.allclose(clA[-1], v)
def test_small(rng):
sizes = [3] * 3
vals = [rng.normal(size=size) for size in sizes]
A = RA(vals)
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
assert ra.allclose(A, clA.to_host())
def test_small(ctx, rng):
sizes = [3] * 3
vals = [rng.normal(size=size).astype(np.float32) for size in sizes]
A = RA(vals)
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
assert ra.allclose(A, clA.to_host())
def test_small():
n = 3
sizes = [3] * 3
vals = [np.random.normal(size=size) for size in sizes]
A = RA(vals)
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
assert ra.allclose(A, clA.to_host())
def test_setitem(ctx, rng):
A, clA = make_random_pair(ctx, 3, 2, rng=rng)
v = rng.uniform(0, 1, size=A[0].shape)
A[0] = v.astype(A.dtype)
clA[0] = v.astype(clA.dtype)
A[1] = 3
clA[1] = 3
assert ra.allclose(A, clA.to_host())
def test_setitem(rng):
A, clA = make_random_pair(3, 2, rng=rng)
v = rng.uniform(0, 1, size=A[0].shape)
A[0] = v.astype(A.dtype)
clA[0] = v.astype(clA.dtype)
A[1] = 3
clA[1] = 3
assert ra.allclose(A, clA.to_host())
def test_lif_rate(self, n_elements=0):
"""Test the `lif_rate` nonlinearity"""
# n_neurons = [3, 3, 3]
n_neurons = [123459, 23456, 34567]
N = len(n_neurons)
J = RA([np.random.normal(loc=1, scale=10, size=n) for n in n_neurons])
R = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
taus = list(
np.random.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clR = CLRA(queue, R)
clTau = CLRA(queue, RA(taus))
### simulate host
nls = [
LIF(n, tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)
]
for i, nl in enumerate(nls):
R[i] = nl.rates(J[i].flatten()).reshape((-1, 1))
### simulate device
plan = plan_lif_rate(queue,
clJ,
clR,
ref,
clTau,
n_elements=n_elements)
plan()
rate_sum = np.sum([np.sum(r) for r in R])
if rate_sum < 1.0:
logger.warn("LIF rate was not tested above the firing threshold!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(R, clR.to_host())
def test_discontiguous_setitem(ctx, rng):
A = make_random_ra(3, 2, rng=rng)
A0 = np.array(A[0])
a = A0[::3, ::2]
v = rng.uniform(-1, 1, size=a.shape)
assert a.size > 0
A.add_views([A.starts[0]], [a.shape[0]], [a.shape[1]], [a.strides[0] / a.itemsize], [a.strides[1] / a.itemsize])
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
a[...] = v
A[-1] = v
assert np.allclose(A[0], A0)
assert np.allclose(A[-1], v)
print(clA[0].shape)
print(clA[-1].shape)
clA[-1] = v
assert ra.allclose(A, clA.to_host())
assert np.allclose(clA[-1], v)
def test_random_vectors(ctx, rng):
n = np.int32(rng.randint(low=5, high=10))
A, clA = make_random_pair(ctx, n, 1, low=3000, high=4000, rng=rng)
assert ra.allclose(A, clA.to_host())
def test_random_vectors():
n = np.random.randint(low=5, high=10)
A, clA = make_random_pair(n, 1, low=3000, high=4000)
assert ra.allclose(A, clA.to_host())
def test_getitems(rng):
"""Try getting multiple items using a list of indices"""
A, clA = make_random_pair(10, 2, rng=rng)
s = [1, 3, 7, 8]
assert ra.allclose(A[s], clA[s].to_host())
def test_random_matrices(rng):
n = rng.randint(low=5, high=10)
A, clA = make_random_pair(n, 2, low=20, high=40, rng=rng)
assert ra.allclose(A, clA.to_host())
def test_random_vectors(rng):
n = rng.randint(low=5, high=10)
A, clA = make_random_pair(n, 1, low=3000, high=4000, rng=rng)
assert ra.allclose(A, clA.to_host())
def test_random_matrices():
n = np.random.randint(low=5, high=10)
A, clA = make_random_pair(n, 2, low=20, high=40)
assert ra.allclose(A, clA.to_host())
def test_random_vectors(self):
n = np.random.randint(low=5, high=10)
A, clA = make_random_pair(n, 1, low=3000, high=4000)
assert ra.allclose(A, clA.to_host())
def test_getitems(ctx, rng):
"""Try getting multiple items using a list of indices"""
A, clA = make_random_pair(ctx, 10, 2, rng=rng)
s = [1, 3, 7, 8]
assert ra.allclose(A[s], clA[s].to_host())
def test_random_matrices(ctx, rng):
n = rng.randint(low=5, high=10)
A, clA = make_random_pair(ctx, n, 2, low=20, high=40, rng=rng)
assert ra.allclose(A, clA.to_host())
def test_random_matrices(self):
n = np.random.randint(low=5, high=10)
A, clA = make_random_pair(n, 2, low=20, high=40)
assert ra.allclose(A, clA.to_host())
def test_lif_step(self, upsample=1, n_elements=0):
"""Test the lif nonlinearity, comparing one step with the Numpy version."""
dt = 1e-3
# n_neurons = [3, 3, 3]
n_neurons = [12345, 23456, 34567]
N = len(n_neurons)
J = RA([np.random.normal(scale=1.2, size=n) for n in n_neurons])
V = RA([np.random.uniform(low=0, high=1, size=n) for n in n_neurons])
W = RA([
np.random.uniform(low=-5 * dt, high=5 * dt, size=n)
for n in n_neurons
])
OS = RA([np.zeros(n) for n in n_neurons])
ref = 2e-3
# tau = 20e-3
# refs = list(np.random.uniform(low=1.7e-3, high=4.2e-3, size=len(n_neurons)))
taus = list(
np.random.uniform(low=15e-3, high=80e-3, size=len(n_neurons)))
queue = cl.CommandQueue(ctx)
clJ = CLRA(queue, J)
clV = CLRA(queue, V)
clW = CLRA(queue, W)
clOS = CLRA(queue, OS)
# clRef = CLRA(queue, RA(refs))
clTau = CLRA(queue, RA(taus))
### simulate host
nls = [
LIF(n, tau_ref=ref, tau_rc=taus[i])
for i, n in enumerate(n_neurons)
]
for i, nl in enumerate(nls):
if upsample <= 1:
nl.step_math0(dt, J[i], V[i], W[i], OS[i])
else:
s = np.zeros_like(OS[i])
for j in xrange(upsample):
nl.step_math0(dt / upsample, J[i], V[i], W[i], s)
OS[i] = (OS[i] > 0.5) | (s > 0.5)
### simulate device
plan = plan_lif(queue,
clJ,
clV,
clW,
clV,
clW,
clOS,
ref,
clTau,
dt,
n_elements=n_elements,
upsample=upsample)
plan()
if 1:
a, b = V, clV
for i in xrange(len(a)):
nc, _ = not_close(a[i], b[i]).nonzero()
if len(nc) > 0:
j = nc[0]
print "i", i, "j", j
print "J", J[i][j], clJ[i][j]
print "V", V[i][j], clV[i][j]
print "W", W[i][j], clW[i][j]
print "...", len(nc) - 1, "more"
n_spikes = np.sum([np.sum(os) for os in OS])
if n_spikes < 1.0:
logger.warn("LIF spiking mechanism was not tested!")
assert ra.allclose(J, clJ.to_host())
assert ra.allclose(V, clV.to_host())
assert ra.allclose(W, clW.to_host())
assert ra.allclose(OS, clOS.to_host())