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Add complex hypervector types #81

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merged 6 commits into from
Jun 7, 2022
Merged

Add complex hypervector types #81

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7beda38
WIP implement complex hypervectors
mikeheddes Jun 7, 2022
0385fd4
Implement complex level and circular hypervector sets
mikeheddes Jun 7, 2022
5bbabc2
Add complex tests
mikeheddes Jun 7, 2022
2269751
Simplify plotting utility
mikeheddes Jun 7, 2022
00eb7e5
Use unbind function
mikeheddes Jun 7, 2022
6403caa
Add unbind to docs
mikeheddes Jun 7, 2022
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1 change: 1 addition & 0 deletions docs/functional.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ Operations
:template: function.rst

bind
unbind
bundle
permute
cleanup
Expand Down
2 changes: 2 additions & 0 deletions torchhd/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
level_hv,
circular_hv,
bind,
unbind,
bundle,
permute,
)
Expand All @@ -27,6 +28,7 @@
"level_hv",
"circular_hv",
"bind",
"unbind",
"bundle",
"permute",
]
310 changes: 238 additions & 72 deletions torchhd/functional.py
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28 changes: 14 additions & 14 deletions torchhd/structures.py
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Expand Up @@ -212,10 +212,10 @@ def contains(self, input: Tensor) -> Tensor:
Examples::

>>> M.contains(letters_hv[0])
tensor([0.4575])
tensor(0.4575)

"""
return functional.cosine_similarity(input, self.value.unsqueeze(0))
return functional.cosine_similarity(input, self.value)

def __len__(self) -> int:
"""Returns the size of the multiset.
Expand Down Expand Up @@ -363,7 +363,7 @@ def get(self, key: Tensor) -> Tensor:
tensor([ 1., -1., 1., ..., -1., 1., -1.])

"""
return functional.bind(self.value, key)
return functional.unbind(self.value, key)

def replace(self, key: Tensor, old: Tensor, new: Tensor) -> None:
"""Replace the value from key-value pair in the hash table.
Expand Down Expand Up @@ -711,7 +711,7 @@ def pop(self, input: Tensor) -> None:

"""
self.size -= 1
self.value = functional.bind(self.value, input)
self.value = functional.unbind(self.value, input)
self.value = functional.permute(self.value, shifts=-1)

def popleft(self, input: Tensor) -> None:
Expand All @@ -727,7 +727,7 @@ def popleft(self, input: Tensor) -> None:
"""
self.size -= 1
rotated_input = functional.permute(input, shifts=len(self))
self.value = functional.bind(self.value, rotated_input)
self.value = functional.unbind(self.value, rotated_input)

def replace(self, index: int, old: Tensor, new: Tensor) -> None:
"""Replace the old hypervector value from the given index, for the new hypervector value.
Expand All @@ -744,7 +744,7 @@ def replace(self, index: int, old: Tensor, new: Tensor) -> None:

"""
rotated_old = functional.permute(old, shifts=self.size - index - 1)
self.value = functional.bind(self.value, rotated_old)
self.value = functional.unbind(self.value, rotated_old)

rotated_new = functional.permute(new, shifts=self.size - index - 1)
self.value = functional.bind(self.value, rotated_new)
Expand Down Expand Up @@ -880,13 +880,13 @@ def node_neighbors(self, input: Tensor, outgoing=True) -> Tensor:
"""
if self.is_directed:
if outgoing:
permuted_neighbors = functional.bind(self.value, input)
permuted_neighbors = functional.unbind(self.value, input)
return functional.permute(permuted_neighbors, shifts=-1)
else:
permuted_node = functional.permute(input, shifts=1)
return functional.bind(self.value, permuted_node)
return functional.unbind(self.value, permuted_node)
else:
return functional.bind(self.value, input)
return functional.unbind(self.value, input)

def contains(self, input: Tensor) -> Tensor:
"""Returns the cosine similarity of the input vector against the graph.
Expand All @@ -898,9 +898,9 @@ def contains(self, input: Tensor) -> Tensor:

>>> e = G.encode_edge(letters_hv[0], letters_hv[1])
>>> G.contains(e)
tensor([1.])
tensor(1.)
"""
return functional.cosine_similarity(input, self.value.unsqueeze(0))
return functional.cosine_similarity(input, self.value)

def clear(self) -> None:
"""Empties the graph.
Expand Down Expand Up @@ -1012,7 +1012,7 @@ def get_leaf(self, path: List[str]) -> Tensor:
hv_path, functional.permute(self.right, shifts=idx)
)

return functional.bind(hv_path, self.value)
return functional.unbind(self.value, hv_path)

def clear(self) -> None:
"""Empties the tree.
Expand Down Expand Up @@ -1084,8 +1084,8 @@ def transition(self, state: Tensor, action: Tensor) -> Tensor:
tensor([ 1., 1., -1., ..., -1., -1., 1.])

"""
next_state = functional.bind(self.value, state)
next_state = functional.bind(next_state, action)
next_state = functional.unbind(self.value, state)
next_state = functional.unbind(next_state, action)
return functional.permute(next_state, shifts=-1)

def clear(self) -> None:
Expand Down
45 changes: 30 additions & 15 deletions torchhd/tests/basis_hv/test_circular_hv.py
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Expand Up @@ -47,30 +47,51 @@ def test_value(self, dtype):
assert torch.all(
(hv == True) | (hv == False)
).item(), "values are either 1 or 0"
elif dtype in torch_complex_dtypes:
magnitudes= hv.abs()
assert torch.allclose(magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)), "magnitude must be 1"
else:
assert torch.all(
(hv == -1) | (hv == 1)
).item(), "values are either -1 or +1"


hv = functional.circular_hv(8, 1000000, generator=generator, dtype=dtype)
sims = functional.hamming_similarity(hv[0], hv).float() / 1000000
sims_diff = sims[:-1] - sims[1:]
if dtype in torch_complex_dtypes:
sims = functional.cosine_similarity(hv[0], hv)
sims_diff = sims[:-1] - sims[1:]

assert torch.all(
sims_diff.sign() == torch.tensor([1, 1, 1, 1, -1, -1, -1])
), "second half must get more similar"
assert torch.all(
sims_diff.sign() == torch.tensor([1, 1, 1, 1, -1, -1, -1])
), "second half must get more similar"

abs_sims_diff = sims_diff.abs()
assert torch.all(
(0.124 < abs_sims_diff) & (abs_sims_diff < 0.126)
).item(), "similarity changes linearly"
abs_sims_diff = sims_diff.abs()
assert torch.all(
(0.248 < abs_sims_diff) & (abs_sims_diff < 0.252)
).item(), "similarity changes linearly"
else:
sims = functional.hamming_similarity(hv[0], hv).float() / 1000000
sims_diff = sims[:-1] - sims[1:]

assert torch.all(
sims_diff.sign() == torch.tensor([1, 1, 1, 1, -1, -1, -1])
), "second half must get more similar"

abs_sims_diff = sims_diff.abs()
assert torch.all(
(0.124 < abs_sims_diff) & (abs_sims_diff < 0.126)
).item(), "similarity changes linearly"

@pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.756, 1.0])
@pytest.mark.parametrize("dtype", torch_dtypes)
def test_sparsity(self, sparsity, dtype):
if not supported_dtype(dtype):
return

if dtype in torch_complex_dtypes:
# Complex hypervectors don't support sparsity.
return

generator = torch.Generator()
generator.manual_seed(seed)

Expand All @@ -96,12 +117,6 @@ def test_device(self, dtype):

@pytest.mark.parametrize("dtype", torch_dtypes)
def test_dtype(self, dtype):
if dtype in torch_complex_dtypes:
with pytest.raises(NotImplementedError):
functional.circular_hv(3, 26, dtype=dtype)

return

if dtype == torch.uint8:
with pytest.raises(ValueError):
functional.circular_hv(3, 26, dtype=dtype)
Expand Down
6 changes: 0 additions & 6 deletions torchhd/tests/basis_hv/test_identity_hv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,12 +47,6 @@ def test_device(self, dtype):

@pytest.mark.parametrize("dtype", torch_dtypes)
def test_dtype(self, dtype):
if dtype in torch_complex_dtypes:
with pytest.raises(NotImplementedError):
functional.identity_hv(3, 26, dtype=dtype)

return

if dtype == torch.uint8:
with pytest.raises(ValueError):
functional.identity_hv(3, 26, dtype=dtype)
Expand Down
43 changes: 28 additions & 15 deletions torchhd/tests/basis_hv/test_level_hv.py
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Expand Up @@ -47,29 +47,48 @@ def test_value(self, dtype):
assert torch.all(
(hv == True) | (hv == False)
).item(), "values are either 1 or 0"
elif dtype in torch_complex_dtypes:
magnitudes= hv.abs()
assert torch.allclose(magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)), "magnitude must be 1"
else:
assert torch.all(
(hv == -1) | (hv == 1)
).item(), "values are either -1 or +1"

# look at the similarity profile w.r.t. the first hypervector
sims = functional.hamming_similarity(hv[0], hv).float() / 10000
sims_diff = sims[:-1] - sims[1:]
assert torch.all(sims_diff > 0).item(), "similarity must be decreasing"
if dtype in torch_complex_dtypes:
sims = functional.cosine_similarity(hv[0], hv)
sims_diff = sims[:-1] - sims[1:]
assert torch.all(sims_diff > 0).item(), "similarity must be decreasing"

hv = functional.level_hv(5, 1000000, generator=generator, dtype=dtype)
sims = functional.hamming_similarity(hv[0], hv).float() / 1000000
sims_diff = sims[:-1] - sims[1:]
assert torch.all(
(0.124 < sims_diff) & (sims_diff < 0.126)
).item(), "similarity decreases linearly"
hv = functional.level_hv(5, 1000000, generator=generator, dtype=dtype)
sims = functional.cosine_similarity(hv[0], hv)
sims_diff = sims[:-1] - sims[1:]
assert torch.all(
(0.248 < sims_diff) & (sims_diff < 0.252)
).item(), "similarity decreases linearly"
else:
sims = functional.hamming_similarity(hv[0], hv).float() / 10000
sims_diff = sims[:-1] - sims[1:]
assert torch.all(sims_diff > 0).item(), "similarity must be decreasing"

hv = functional.level_hv(5, 1000000, generator=generator, dtype=dtype)
sims = functional.hamming_similarity(hv[0], hv).float() / 1000000
sims_diff = sims[:-1] - sims[1:]
assert torch.all(
(0.124 < sims_diff) & (sims_diff < 0.126)
).item(), "similarity decreases linearly"

@pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.756, 1.0])
@pytest.mark.parametrize("dtype", torch_dtypes)
def test_sparsity(self, sparsity, dtype):
if not supported_dtype(dtype):
return

if dtype in torch_complex_dtypes:
# Complex hypervectors don't support sparsity.
return

generator = torch.Generator()
generator.manual_seed(seed)

Expand All @@ -95,12 +114,6 @@ def test_device(self, dtype):

@pytest.mark.parametrize("dtype", torch_dtypes)
def test_dtype(self, dtype):
if dtype in torch_complex_dtypes:
with pytest.raises(NotImplementedError):
functional.level_hv(3, 26, dtype=dtype)

return

if dtype == torch.uint8:
with pytest.raises(ValueError):
functional.level_hv(3, 26, dtype=dtype)
Expand Down
49 changes: 29 additions & 20 deletions torchhd/tests/basis_hv/test_random_hv.py
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Expand Up @@ -42,21 +42,26 @@ def test_value(self, dtype):
generator = torch.Generator()
generator.manual_seed(seed)

hv = functional.random_hv(100, 10000, dtype=dtype, generator=generator)

if dtype == torch.bool:
hv = functional.random_hv(100, 10000, dtype=dtype, generator=generator)
assert torch.all((hv == False) | (hv == True)).item()

return

hv = functional.random_hv(100, 10000, dtype=dtype, generator=generator)
assert torch.all((hv == -1) | (hv == 1)).item()
elif dtype in torch_complex_dtypes:
magnitudes= hv.abs()
assert torch.allclose(magnitudes, torch.tensor(1.0, dtype=magnitudes.dtype)), "magnitude must be 1"
else:
assert torch.all((hv == -1) | (hv == 1)).item()

@pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.756, 1.0])
@pytest.mark.parametrize("dtype", torch_dtypes)
def test_sparsity(self, sparsity, dtype):
if not supported_dtype(dtype):
return

if dtype in torch_complex_dtypes:
# Complex hypervectors don't support sparsity.
return

generator = torch.Generator()
generator.manual_seed(seed)

Expand All @@ -83,14 +88,24 @@ def test_orthogonality(self, dtype):
generator = torch.Generator()
generator.manual_seed(seed)

sims = [None] * 100
for i in range(100):
hv = functional.random_hv(2, 10000, dtype=dtype, generator=generator)
sims[i] = functional.hamming_similarity(hv[0], hv[1].unsqueeze(0))

sims = torch.cat(sims).float() / 10000
assert within(sims.mean().item(), 0.5, 0.001)
assert sims.std().item() < 0.01
if dtype in torch_complex_dtypes:
sims = [None] * 100
for i in range(100):
hv = functional.random_hv(2, 10000, dtype=dtype, generator=generator)
sims[i] = functional.cosine_similarity(hv[0], hv[1])

sims = torch.stack(sims).float() / 10000
assert within(sims.mean().item(), 0.0, 0.001)
assert sims.std().item() < 0.01
else:
sims = [None] * 100
for i in range(100):
hv = functional.random_hv(2, 10000, dtype=dtype, generator=generator)
sims[i] = functional.hamming_similarity(hv[0], hv[1].unsqueeze(0))

sims = torch.stack(sims).float() / 10000
assert within(sims.mean().item(), 0.5, 0.001)
assert sims.std().item() < 0.01

@pytest.mark.parametrize("dtype", torch_dtypes)
def test_device(self, dtype):
Expand All @@ -103,12 +118,6 @@ def test_device(self, dtype):

@pytest.mark.parametrize("dtype", torch_dtypes)
def test_dtype(self, dtype):
if dtype in torch_complex_dtypes:
with pytest.raises(NotImplementedError):
functional.random_hv(3, 26, dtype=dtype)

return

if dtype == torch.uint8:
with pytest.raises(ValueError):
functional.random_hv(3, 26, dtype=dtype)
Expand Down
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