Switch spike tensors from uint8 to bool dtype

.gitignore

@@ -26,3 +26,4 @@ data/*
 .claude/*
 .claude
 .vscode/*
+.venv/*

bindsnet/encoding/encodings.py

@@ -30,7 +30,7 @@ def single(
     quantile = torch.quantile(datum, 1 - sparsity)
     s = torch.zeros([time, *shape], device=device)
     s[0] = torch.where(datum > quantile, torch.ones(shape), torch.zeros(shape))
-    return torch.Tensor(s).byte()
+    return s.bool()
 
 
 def repeat(datum: torch.Tensor, time: int, dt: float = 1.0, **kwargs) -> torch.Tensor:
@@ -93,7 +93,7 @@ def bernoulli(
         spikes = torch.bernoulli(max_prob * datum.repeat([time, 1]))
         spikes = spikes.view(time, *shape)
 
-    return spikes.byte()
+    return spikes.bool()
 
 
 def poisson(
@@ -131,7 +131,7 @@ def poisson(
         x = torch.pow(x, (datum * 0.11 + 5) / 50)
         y = torch.tensor(x < 0.6, dtype=torch.bool, device=device)
 
-        return y.view(time, *shape).byte()
+        return y.view(time, *shape).bool()
     else:
         # Compute firing rates in seconds as function of data intensity,
         # accounting for simulation time step.
@@ -149,7 +149,7 @@ def poisson(
         times[times >= time + 1] = 0
 
         # Create tensor of spikes.
-        spikes = torch.zeros(time + 1, size, device=device).byte()
+        spikes = torch.zeros(time + 1, size, device=device, dtype=torch.bool)
         spikes[times, torch.arange(size)] = 1
         spikes = spikes[1:]
 
@@ -184,7 +184,7 @@ def rank_order(
 
     # Create spike times tensor (one spike per neuron whose time lies in
     # ``(0, time)``; vectorised form of the per-neuron loop).
-    spikes = torch.zeros(time, size, device=device).byte()
+    spikes = torch.zeros(time, size, device=device, dtype=torch.bool)
     fire = (times > 0) & (times < time)
     idx = fire.nonzero(as_tuple=False).squeeze(1)
     spikes[(times[fire] - 1).to(device), idx.to(device)] = 1

bindsnet/network/nodes.py

@@ -68,7 +68,7 @@ def __init__(
         self.traces_additive = (
             traces_additive  # Whether to record spike traces additively.
         )
-        self.register_buffer("s", torch.ByteTensor())  # Spike occurrences.
+        self.register_buffer("s", torch.BoolTensor())  # Spike occurrences.
 
         self.sum_input = sum_input  # Whether to sum all inputs.
 
@@ -232,7 +232,7 @@ def forward(self, x: torch.Tensor) -> None:
         :param x: Inputs to the layer.
         """
         # Set spike occurrences to input values.
-        self.s = x
+        self.s = x.bool()
 
         super().forward(x)
 
@@ -1239,7 +1239,7 @@ def __init__(
             self.c = -65.0 + 15 * (self.r**2)
             self.d = 8 - 6 * (self.r**2)
             self.S = 0.5 * torch.rand(n, n)
-            self.excitatory = torch.ones(n).byte()
+            self.excitatory = torch.ones(n, dtype=torch.bool)
 
         elif excitatory == 0:
             self.r = torch.rand(n)
@@ -1249,10 +1249,10 @@ def __init__(
             self.d = 2 * torch.ones(n)
             self.S = -torch.rand(n, n)
 
-            self.excitatory = torch.zeros(n).byte()
+            self.excitatory = torch.zeros(n, dtype=torch.bool)
 
         else:
-            self.excitatory = torch.zeros(n).byte()
+            self.excitatory = torch.zeros(n, dtype=torch.bool)
 
             ex = int(n * excitatory)
             inh = n - ex
@@ -1430,9 +1430,7 @@ def __init__(
         )  # Set in compute_decays.
 
         self.register_buffer("v", torch.FloatTensor())  # Neuron voltages.
-        self.register_buffer(
-            "last_spikes", torch.ByteTensor()
-        )  # Previous spikes occurrences in time window
+        self.register_buffer("last_spikes", torch.BoolTensor())
         self.register_buffer("theta", torch.zeros(*self.shape))  # Adaptive thresholds.
         self.lbound = lbound  # Lower bound of voltage.
 

bindsnet/network/topology.py

@@ -117,7 +117,7 @@ def update(self, **kwargs) -> None:
         Keyword arguments:
 
         :param bool learning: Whether to allow connection updates.
-        :param ByteTensor mask: Boolean mask determining which weights to clamp to zero.
+        :param BoolTensor mask: Boolean mask determining which weights to clamp to zero.
         """
         learning = kwargs.get("learning", True)
 

docs/source/guide/guide_part_i.rst

@@ -406,7 +406,7 @@ two-layer, input-output spiking neural network.
     network.add_monitor(monitor=target_monitor, name="B")
 
     # Create input spike data, where each spike is distributed according to Bernoulli(0.1).
-    input_data = torch.bernoulli(0.1 * torch.ones(time, source_layer.n)).byte()
+    input_data = torch.bernoulli(0.1 * torch.ones(time, source_layer.n)).bool()
     inputs = {"A": input_data}
 
     # Simulate network on input data.
@@ -417,7 +417,6 @@ two-layer, input-output spiking neural network.
         "A": source_monitor.get("s"), "B": target_monitor.get("s")
     }
     voltages = {"B": target_monitor.get("v")}
-
     plt.ioff()
     plot_spikes(spikes)
     plot_voltages(voltages, plot_type="line")

test/encoding/test_encoding.py

@@ -2,7 +2,6 @@
 
 from bindsnet.encoding import *
 
-
 class TestEncodings:
     """
     Tests all stable encoding functions and generators.
@@ -16,6 +15,7 @@ def test_bernoulli(self):
                     spikes = bernoulli(datum, time=t, max_prob=m)
 
                     assert spikes.size() == torch.Size((t, n))
+                    assert spikes.dtype == torch.bool
 
     def test_multidim_bernoulli(self):
         for shape in [[5, 5], [10, 10], [25, 25]]:  # shape of nodes in layer
@@ -25,6 +25,7 @@ def test_multidim_bernoulli(self):
                     spikes = bernoulli(datum, time=t, max_prob=m)
 
                     assert spikes.size() == torch.Size((t, *shape))
+                    assert spikes.dtype == torch.bool
 
     def test_bernoulli_loader(self):
         for s in [1, 100]:  # number of data samples
@@ -67,6 +68,7 @@ def test_poisson(self):
                 spikes = poisson(datum, time=t)  # Encode as spikes.
 
                 assert spikes.size() == torch.Size((t, n))
+                assert spikes.dtype == torch.bool
 
     def test_poisson_loader(self):
         for s in [1, 10]:  # number of data samples
@@ -77,3 +79,4 @@ def test_poisson_loader(self):
 
                     for i, spikes in enumerate(spike_loader):
                         assert spikes.size() == torch.Size((t, n))
+                        assert spikes.dtype == torch.bool

test/network/test_connections.py

@@ -158,7 +158,7 @@ def check_weights(self, conn_type, shape_a, shape_b, shape_w, *args, **kwargs):
 
                     ### Run network ###
                     network.run(
-                        inputs={"input": torch.bernoulli(torch.rand(time, 100)).byte()},
+                        inputs={"input": torch.bernoulli(torch.rand(time, 100)).bool()},
                         time=time,
                         reward=1,
                     )

test/network/test_learning.py

@@ -39,7 +39,7 @@ def test_hebbian(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()}, time=250
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()}, time=250
         )
 
         # Conv2dConnection test
@@ -60,7 +60,7 @@ def test_hebbian(self):
         )
         # shape is [time, batch, channels, height, width]
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).bool()},
             time=250,
         )
 
@@ -80,7 +80,7 @@ def test_post_pre(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()}, time=250
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()}, time=250
         )
 
         network2 = Network(dt=1.0)
@@ -97,7 +97,7 @@ def test_post_pre(self):
             target="output",
         )
         network2.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()}, time=250
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()}, time=250
         )
 
         # Conv2dConnection test
@@ -117,7 +117,7 @@ def test_post_pre(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).bool()},
             time=250,
         )
 
@@ -139,7 +139,7 @@ def test_weight_dependent_post_pre(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()}, time=250
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()}, time=250
         )
 
         # Conv2dConnection test
@@ -161,7 +161,7 @@ def test_weight_dependent_post_pre(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).bool()},
             time=250,
         )
 
@@ -181,7 +181,7 @@ def test_mstdp(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()},
             time=250,
             reward=1.0,
         )
@@ -204,7 +204,7 @@ def test_mstdp(self):
         )
 
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).bool()},
             time=250,
             reward=1.0,
         )
@@ -225,7 +225,7 @@ def test_mstdpet(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()},
             time=250,
             reward=1.0,
         )
@@ -248,7 +248,7 @@ def test_mstdpet(self):
         )
 
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).bool()},
             time=250,
             reward=1.0,
         )
@@ -269,7 +269,7 @@ def test_rmax(self):
             target="output",
         )
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(250, 100)).bool()},
             time=250,
             reward=1.0,
         )
@@ -313,7 +313,7 @@ def _build(rule, n=8, **rule_kwargs):
     def _drive(network, n=8, time=100, seed=0):
         torch.manual_seed(seed)
         network.run(
-            inputs={"input": torch.bernoulli(torch.rand(time, n)).byte()},
+            inputs={"input": torch.bernoulli(torch.rand(time, n)).bool()},
             time=time,
             reward=1.0,
         )