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[math] Add get JIT weight matrix methods(Uniform & Normal) for brainpy.dnn.linear #673

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Jun 1, 2024
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[math] Add get JIT weight matrix methods(Uniform & Normal) for brainpy.dnn.linear #673

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dd084a7
[math] Add get JIT connect matrix methods for `brainpy.dnn.linear`
Routhleck May 27, 2024
11a6624
Update
Routhleck May 28, 2024
3f46507
Update linear.py
Routhleck May 28, 2024
0e6dbc2
[math] Add get JIT weight matrix methods(Uniform & Normal) for `brain…
Routhleck May 29, 2024
1aa42aa
Merge branch 'master' of https://github.com/brainpy/BrainPy into get-…
Routhleck May 29, 2024
452b2a3
Update linear.py
Routhleck May 30, 2024
8defda9
Update
Routhleck Jun 1, 2024
2f58029
Update linear.py
Routhleck Jun 1, 2024
dc57c69
Add test for get_conn_matrix` at `brainpy.dnn.linear` module
Routhleck Jun 1, 2024
939f030
Update linear.py
Routhleck Jun 1, 2024
3908afe
Update
Routhleck Jun 1, 2024
fae9b2a
Fix bugs
Routhleck Jun 1, 2024
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43 changes: 32 additions & 11 deletions brainpy/_src/dnn/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -980,15 +980,36 @@ def __init__(
self.sharding = sharding


class JitFPLinear(Layer):
class JitLinear(Layer):
def get_conn_matrix(self):
return bm.jitconn.get_conn_matrix(self.prob, self.seed,
shape=(self.num_out, self.num_in),
transpose=self.transpose,
outdim_parallel=not self.atomic)
pass


class JitFPHomoLayer(JitLinear):
def get_conn_matrix(self):
return bm.jitconn.get_homo_weight_matrix(self.weight, self.prob, self.seed,
shape=(self.num_out, self.num_in),
transpose=self.transpose,
outdim_parallel=not self.atomic)


class JitFPUniformLayer(JitLinear):
def get_conn_matrix(self):
return bm.jitconn.get_uniform_weight_matrix(self.w_low, self.w_high, self.prob, self.seed,
shape=(self.num_out, self.num_in),
transpose=self.transpose,
outdim_parallel=not self.atomic)


class JitFPNormalLayer(JitLinear):
def get_conn_matrix(self):
return bm.jitconn.get_normal_weight_matrix(self.w_mu, self.w_sigma, self.prob, self.seed,
shape=(self.num_out, self.num_in),
transpose=self.transpose,
outdim_parallel=not self.atomic)


class JitFPHomoLinear(JitFPLinear):
class JitFPHomoLinear(JitFPHomoLayer):
r"""Synaptic matrix multiplication with the just-in-time connectivity.

It performs the computation of:
Expand Down Expand Up @@ -1067,7 +1088,7 @@ def _batch_mv(self, x):
outdim_parallel=not self.atomic)


class JitFPUniformLinear(JitFPLinear):
class JitFPUniformLinear(JitFPUniformLayer):
r"""Synaptic matrix multiplication with the just-in-time connectivity.

It performs the computation of:
Expand Down Expand Up @@ -1147,7 +1168,7 @@ def _batch_mv(self, x):
outdim_parallel=not self.atomic)


class JitFPNormalLinear(JitFPLinear):
class JitFPNormalLinear(JitFPNormalLayer):
r"""Synaptic matrix multiplication with the just-in-time connectivity.

It performs the computation of:
Expand Down Expand Up @@ -1227,7 +1248,7 @@ def _batch_mv(self, x):
outdim_parallel=not self.atomic)


class EventJitFPHomoLinear(JitFPLinear):
class EventJitFPHomoLinear(JitFPHomoLayer):
r"""Synaptic matrix multiplication with the just-in-time connectivity.

It performs the computation of:
Expand Down Expand Up @@ -1306,7 +1327,7 @@ def _batch_mv(self, x):
outdim_parallel=not self.atomic)


class EventJitFPUniformLinear(JitFPLinear):
class EventJitFPUniformLinear(JitFPUniformLayer):
r"""Synaptic matrix multiplication with the just-in-time connectivity.

It performs the computation of:
Expand Down Expand Up @@ -1386,7 +1407,7 @@ def _batch_mv(self, x):
outdim_parallel=not self.atomic)


class EventJitFPNormalLinear(JitFPLinear):
class EventJitFPNormalLinear(JitFPNormalLayer):
r"""Synaptic matrix multiplication with the just-in-time connectivity.

It performs the computation of:
Expand Down
29 changes: 26 additions & 3 deletions brainpy/_src/dnn/tests/test_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,11 @@ def test_JitFPHomoLinear(self, prob, weight, shape):
x = bm.random.random(shape + (100,))
y = f(x)
self.assertTrue(y.shape == shape + (200,))

conn_matrix = f.get_conn_matrix()
self.assertTrue(bm.allclose(y, x @ conn_matrix.T))
# print(conn_matrix.shape)
# self.assertTrue(conn_matrix.shape == (200, 100))
bm.clear_buffer_memory()

@parameterized.product(
Expand All @@ -155,6 +160,9 @@ def test_JitFPUniformLinear(self, prob, w_low, w_high, shape):
x = bm.random.random(shape + (100,))
y = f(x)
self.assertTrue(y.shape == shape + (200,))

conn_matrix = f.get_conn_matrix()
self.assertTrue(bm.allclose(y, x @ conn_matrix.T))
bm.clear_buffer_memory()

@parameterized.product(
Expand All @@ -169,6 +177,9 @@ def test_JitFPNormalLinear(self, prob, w_mu, w_sigma, shape):
x = bm.random.random(shape + (100,))
y = f(x)
self.assertTrue(y.shape == shape + (200,))

conn_matrix = f.get_conn_matrix()
self.assertTrue(bm.allclose(y, x @ conn_matrix.T))
bm.clear_buffer_memory()

@parameterized.product(
Expand All @@ -179,11 +190,15 @@ def test_JitFPNormalLinear(self, prob, w_mu, w_sigma, shape):
def test_EventJitFPHomoLinear(self, prob, weight, shape):
bm.random.seed()
f = bp.dnn.EventJitFPHomoLinear(100, 200, prob, weight, seed=123)
y = f(bm.random.random(shape + (100,)) < 0.1)
x = bm.random.random(shape + (100,)) < 0.1
y = f(x)
self.assertTrue(y.shape == shape + (200,))

y2 = f(bm.as_jax(bm.random.random(shape + (100,)) < 0.1, dtype=float))
self.assertTrue(y2.shape == shape + (200,))

conn_matrix = f.get_conn_matrix()
self.assertTrue(bm.allclose(y, x @ conn_matrix.T))
bm.clear_buffer_memory()

@parameterized.product(
Expand All @@ -195,11 +210,15 @@ def test_EventJitFPHomoLinear(self, prob, weight, shape):
def test_EventJitFPUniformLinear(self, prob, w_low, w_high, shape):
bm.random.seed()
f = bp.dnn.EventJitFPUniformLinear(100, 200, prob, w_low, w_high, seed=123)
y = f(bm.random.random(shape + (100,)) < 0.1)
x = bm.random.random(shape + (100,)) < 0.1
y = f(x)
self.assertTrue(y.shape == shape + (200,))

y2 = f(bm.as_jax(bm.random.random(shape + (100,)) < 0.1, dtype=float))
self.assertTrue(y2.shape == shape + (200,))

conn_matrix = f.get_conn_matrix()
self.assertTrue(bm.allclose(y, x @ conn_matrix.T))
bm.clear_buffer_memory()

@parameterized.product(
Expand All @@ -211,11 +230,15 @@ def test_EventJitFPUniformLinear(self, prob, w_low, w_high, shape):
def test_EventJitFPNormalLinear(self, prob, w_mu, w_sigma, shape):
bm.random.seed()
f = bp.dnn.EventJitFPNormalLinear(100, 200, prob, w_mu, w_sigma, seed=123)
y = f(bm.random.random(shape + (100,)) < 0.1)
x = bm.random.random(shape + (100,)) < 0.1
y = f(x)
self.assertTrue(y.shape == shape + (200,))

y2 = f(bm.as_jax(bm.random.random(shape + (100,)) < 0.1, dtype=float))
self.assertTrue(y2.shape == shape + (200,))

conn_matrix = f.get_conn_matrix()
self.assertTrue(bm.allclose(y, x @ conn_matrix.T))
bm.clear_buffer_memory()


Expand Down
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