def _sig_gemv(self,
ops,
A_js_fn,
X_js_fn,
Y_fn,
Y_in_fn=None,
alpha=1.0,
beta=1.0,
gamma=0.0,
tag=None):
if len(ops) == 0:
return []
all_data, sidx = self.all_data, self.sidx
A_js = RaggedArray([[sidx[ss] for ss in A_js_fn(op)] for op in ops])
X_js = RaggedArray([[sidx[ss] for ss in X_js_fn(op)] for op in ops])
Y_sigs = [Y_fn(item) for item in ops]
Y_in_sigs = [Y_in_fn(item) for item in ops] if Y_in_fn else Y_sigs
Y = all_data[[sidx[sig] for sig in Y_sigs]]
Y_in = all_data[[sidx[sig] for sig in Y_in_sigs]]
if callable(beta):
beta = RaggedArray([sidx[beta(o)] for o in ops], dtype=np.float32)
rval = plan_block_gemv(self.queue,
alpha,
all_data,
A_js,
all_data,
X_js,
beta,
Y,
Y_in=Y_in,
gamma=gamma,
tag=tag)
return rval.plans
def to_host(self):
"""Copy the whole object to a host RaggedArray"""
return RaggedArray.from_buffer(self.buf,
self.starts,
self.shape0s,
self.shape1s,
self.stride0s,
self.stride1s,
names=self.names)
def to_host(self):
"""Copy the whole object to a host RaggedArray"""
rval = RaggedArray.__new__(RaggedArray)
rval.starts = self.starts.tolist()
rval.shape0s = self.shape0s.tolist()
rval.shape1s = self.shape1s.tolist()
rval.stride0s = self.stride0s.tolist()
rval.stride1s = self.stride1s.tolist()
rval.buf = self.buf
rval.names = self.names[:]
return rval
def _plan_LinearFilter(self, ops):
steps = [
op.process.make_step(op.input.shape,
op.output.shape,
self.model.dt,
rng=None) for op in ops
]
A = self.RaggedArray([f.den for f in steps], dtype=np.float32)
B = self.RaggedArray([f.num for f in steps], dtype=np.float32)
X = self.all_data[[self.sidx[op.input] for op in ops]]
Y = self.all_data[[self.sidx[op.output] for op in ops]]
Xbuf0 = RaggedArray(
[np.zeros(shape) for shape in zip(B.sizes, X.sizes)],
dtype=np.float32)
Ybuf0 = RaggedArray(
[np.zeros(shape) for shape in zip(A.sizes, Y.sizes)],
dtype=np.float32)
Xbuf = CLRaggedArray(self.queue, Xbuf0)
Ybuf = CLRaggedArray(self.queue, Ybuf0)
self._raggedarrays_to_reset[Xbuf] = Xbuf0
self._raggedarrays_to_reset[Ybuf] = Ybuf0
return plan_linearfilter(self.queue, X, Y, A, B, Xbuf, Ybuf)
def from_arrays(cls, queue, arrays, names=None, dtype=None, align=False):
return cls(queue,
RaggedArray(arrays, names=names, dtype=dtype, align=align))
def __init__(self,
network,
dt=0.001,
seed=None,
model=None,
context=None,
n_prealloc_probes=32,
profiling=None,
if_python_code='none',
planner=greedy_planner,
progress_bar=True):
# --- check version
if nengo.version.version_info in bad_nengo_versions:
raise ValueError(
"This simulator does not support Nengo version %s. Upgrade "
"with 'pip install --upgrade --no-deps nengo'." %
nengo.__version__)
elif nengo.version.version_info > latest_nengo_version_info:
warnings.warn("This version of `nengo_ocl` has not been tested "
"with your `nengo` version (%s). The latest fully "
"supported version is %s" %
(nengo.__version__, latest_nengo_version))
# --- create these first since they are used in __del__
self.closed = False
self.model = None
# --- arguments/attributes
if context is None and Simulator.some_context is None:
print('No context argument was provided to nengo_ocl.Simulator')
print("Calling pyopencl.create_some_context() for you now:")
Simulator.some_context = cl.create_some_context()
if profiling is None:
profiling = int(os.getenv("NENGO_OCL_PROFILING", 0))
self.context = Simulator.some_context if context is None else context
self.profiling = profiling
self.queue = cl.CommandQueue(
self.context, properties=PROFILING_ENABLE if self.profiling else 0)
if if_python_code not in ['none', 'warn', 'error']:
raise ValueError("%r not a valid value for `if_python_code`" %
if_python_code)
self.if_python_code = if_python_code
self.n_prealloc_probes = n_prealloc_probes
self.progress_bar = progress_bar
# --- Nengo build
with Timer() as nengo_timer:
if model is None:
self.model = Model(dt=float(dt),
label="%s, dt=%f" % (network, dt),
decoder_cache=get_default_decoder_cache())
else:
self.model = model
if network is not None:
# Build the network into the model
self.model.build(network)
logger.info("Nengo build in %0.3f s" % nengo_timer.duration)
# --- operators
with Timer() as planner_timer:
operators = list(self.model.operators)
# convert DotInc and Copy to MultiDotInc
operators = list(map(MultiDotInc.convert_to, operators))
operators = MultiDotInc.compress(operators)
# plan the order of operations, combining where appropriate
op_groups = planner(operators)
assert len([typ for typ, _ in op_groups if typ is Reset
]) < 2, ("All resets not planned together")
self.operators = operators
self.op_groups = op_groups
logger.info("Planning in %0.3f s" % planner_timer.duration)
with Timer() as signals_timer:
# Initialize signals
all_signals = stable_unique(sig for op in operators
for sig in op.all_signals)
all_bases = stable_unique(sig.base for sig in all_signals)
sigdict = SignalDict() # map from Signal.base -> ndarray
for op in operators:
op.init_signals(sigdict)
# Add built states to the probe dictionary
self._probe_outputs = dict(self.model.params)
# Provide a nicer interface to probe outputs
self.data = ProbeDict(self._probe_outputs)
# Create data on host and add views
self.all_data = RaggedArray(
[sigdict[sb] for sb in all_bases],
names=[getattr(sb, 'name', '') for sb in all_bases],
dtype=np.float32)
view_builder = ViewBuilder(all_bases, self.all_data)
view_builder.setup_views(operators)
for probe in self.model.probes:
view_builder.append_view(self.model.sig[probe]['in'])
view_builder.add_views_to(self.all_data)
self.all_bases = all_bases
self.sidx = {
k: np.int32(v)
for k, v in iteritems(view_builder.sidx)
}
self._A_views = view_builder._A_views
self._X_views = view_builder._X_views
self._YYB_views = view_builder._YYB_views
del view_builder
# Copy data to device
self.all_data = CLRaggedArray(self.queue, self.all_data)
logger.info("Signals in %0.3f s" % signals_timer.duration)
# --- set seed
self.seed = np.random.randint(npext.maxint) if seed is None else seed
self.rng = np.random.RandomState(self.seed)
# --- create list of plans
self._raggedarrays_to_reset = {}
self._cl_rngs = {}
self._python_rngs = {}
plans = []
with Timer() as plans_timer:
for op_type, op_list in op_groups:
plans.extend(self.plan_op_group(op_type, op_list))
plans.extend(self.plan_probes())
logger.info("Plans in %0.3f s" % plans_timer.duration)
# -- create object to execute list of plans
self._plans = Plans(plans, self.profiling)
self.rng = None # all randomness set, should no longer be used
self._reset_probes() # clears probes from previous model builds
def sig_gemv(self,
seq,
alpha,
A_js_fn,
X_js_fn,
beta,
Y_sig_fn,
Y_in_sig_fn=None,
gamma=None,
verbose=0,
tag=None):
if len(seq) == 0:
return []
sidx = self.sidx
if callable(beta):
beta_sigs = list(map(beta, seq))
beta = RaggedArray(list(map(sidx.__getitem__, beta_sigs)),
dtype=np.float32)
Y_sigs = [Y_sig_fn(item) for item in seq]
if Y_in_sig_fn is None:
Y_in_sigs = Y_sigs
else:
Y_in_sigs = [Y_in_sig_fn(item) for item in seq]
Y_idxs = [sidx[sig] for sig in Y_sigs]
Y_in_idxs = [sidx[sig] for sig in Y_in_sigs]
# -- The following lines illustrate what we'd *like* to see...
#
# A_js = RaggedArray(
# [[sidx[ss] for ss in A_js_fn(item)] for item in seq])
# X_js = RaggedArray(
# [[sidx[ss] for ss in X_js_fn(item)] for item in seq])
#
# -- ... but the simulator supports broadcasting. So in fact whenever
# a signal in X_js has shape (N, 1), the corresponding A_js signal
# can have shape (M, N) or (1, 1).
# Fortunately, scalar multiplication of X by A can be seen as
# Matrix multiplication of A by X, so we can still use gemv,
# we just need to reorder and transpose.
A_js = []
X_js = []
for ii, item in enumerate(seq):
A_js_i = []
X_js_i = []
A_sigs_i = A_js_fn(item)
X_sigs_i = X_js_fn(item)
assert len(A_sigs_i) == len(X_sigs_i)
for asig, xsig in zip(A_sigs_i, X_sigs_i):
A_js_i.append(sidx[asig])
X_js_i.append(sidx[xsig])
A_js.append(A_js_i)
X_js.append(X_js_i)
if verbose:
print("in sig_vemv")
print("print A", A_js)
print("print X", X_js)
A_js = RaggedArray(A_js, dtype=np.int32)
X_js = RaggedArray(X_js, dtype=np.int32)
Y = self.all_data[Y_idxs]
Y_in = self.all_data[Y_in_idxs]
rval = self.plan_ragged_gather_gemv(
alpha=alpha,
A=self.all_data,
A_js=A_js,
X=self.all_data,
X_js=X_js,
beta=beta,
Y=Y,
Y_in=Y_in,
tag=tag,
seq=seq,
gamma=gamma,
)
try:
return rval.plans
except AttributeError:
return [rval]
def __init__(self,
network,
dt=0.001,
seed=None,
model=None,
planner=greedy_planner):
with Timer() as nengo_timer:
if model is None:
self.model = Model(dt=float(dt),
label="%s, dt=%f" % (network, dt),
decoder_cache=get_default_decoder_cache())
else:
self.model = model
if network is not None:
# Build the network into the model
self.model.build(network)
logger.info("Nengo build in %0.3f s" % nengo_timer.duration)
# --- set seed
seed = np.random.randint(npext.maxint) if seed is None else seed
self.seed = seed
self.rng = np.random.RandomState(self.seed)
self._step = Signal(np.array(0.0, dtype=np.float64), name='step')
self._time = Signal(np.array(0.0, dtype=np.float64), name='time')
# --- operators
with Timer() as planner_timer:
operators = list(self.model.operators)
# convert DotInc, Reset, Copy, and ProdUpdate to MultiProdUpdate
operators = list(map(MultiProdUpdate.convert_to, operators))
operators = MultiProdUpdate.compress(operators)
# plan the order of operations, combining where appropriate
op_groups = planner(operators)
assert len([typ for typ, _ in op_groups if typ is Reset
]) < 2, ("All resets not planned together")
# add time operator after planning, to ensure it goes first
time_op = TimeUpdate(self._step, self._time)
operators.insert(0, time_op)
op_groups.insert(0, (type(time_op), [time_op]))
self.operators = operators
self.op_groups = op_groups
logger.info("Planning in %0.3f s" % planner_timer.duration)
with Timer() as signals_timer:
# Initialize signals
all_signals = signals_from_operators(operators)
all_bases = stable_unique([sig.base for sig in all_signals])
sigdict = SignalDict() # map from Signal.base -> ndarray
for op in operators:
op.init_signals(sigdict)
# Add built states to the probe dictionary
self._probe_outputs = self.model.params
# Provide a nicer interface to probe outputs
self.data = ProbeDict(self._probe_outputs)
self.all_data = RaggedArray(
[sigdict[sb] for sb in all_bases],
[getattr(sb, 'name', '') for sb in all_bases],
dtype=np.float32)
builder = ViewBuilder(all_bases, self.all_data)
self._AX_views = {}
self._YYB_views = {}
for op_type, op_list in op_groups:
self.setup_views(builder, op_type, op_list)
for probe in self.model.probes:
builder.append_view(self.model.sig[probe]['in'])
builder.add_views_to(self.all_data)
self.all_bases = all_bases
self.sidx = builder.sidx
self._prep_all_data()
logger.info("Signals in %0.3f s" % signals_timer.duration)
# --- create list of plans
with Timer() as plans_timer:
self._plan = []
for op_type, op_list in op_groups:
self._plan.extend(self.plan_op_group(op_type, op_list))
self._plan.extend(self.plan_probes())
logger.info("Plans in %0.3f s" % plans_timer.duration)
self.n_steps = 0
from nengo_ocl import raggedarray as ra
from nengo_ocl.clra_nonlinearities import (
plan_copy,
plan_elementwise_inc,
plan_lif,
plan_lif_rate,
plan_linearfilter,
plan_reset,
plan_slicedcopy,
)
from nengo_ocl.clraggedarray import CLRaggedArray as CLRA
from nengo_ocl.clraggedarray import to_device
from nengo_ocl.raggedarray import RaggedArray
logger = logging.getLogger(__name__)
RA = lambda arrays, dtype=np.float32: RaggedArray(arrays, dtype=dtype)
def not_close(a, b, rtol=1e-3, atol=1e-3):
return np.abs(a - b) > atol + rtol * np.abs(b)
@pytest.mark.parametrize("upsample", [1, 4])
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])
def to_host(self):
"""Copy the whole object to a host RaggedArray"""
return RaggedArray.from_buffer(
self.buf, self.starts, self.shape0s, self.shape1s,
self.stride0s, self.stride1s, names=self.names)