Add normalization function of hypervectors and deprecate hard_quantize

.github/workflows/test.yml

@@ -15,7 +15,7 @@ jobs:
     timeout-minutes: 20
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10']
+        python-version: ['3.10', '3.11', '3.12']
         os: [ubuntu-latest, windows-latest, macos-latest]
 
     steps:

docs/torchhd.rst

@@ -34,6 +34,7 @@ Operations
     permute
     inverse
     negative
+    normalize
     cleanup
     randsel
     multirandsel

torchhd/__init__.py

@@ -51,6 +51,7 @@
     permute,
     inverse,
     negative,
+    normalize,
     cleanup,
     create_random_permute,
     randsel,
@@ -109,6 +110,7 @@
     "permute",
     "inverse",
     "negative",
+    "normalize",
     "cleanup",
     "create_random_permute",
     "randsel",

torchhd/functional.py

@@ -26,6 +26,7 @@
 import torch
 from torch import LongTensor, FloatTensor, Tensor
 from collections import deque
+import warnings
 
 from torchhd.tensors.base import VSATensor
 from torchhd.tensors.bsc import BSCTensor
@@ -50,6 +51,7 @@
     "permute",
     "inverse",
     "negative",
+    "normalize",
     "cleanup",
     "create_random_permute",
     "hard_quantize",
@@ -673,6 +675,11 @@ def bundle(input: VSATensor, other: VSATensor) -> VSATensor:
 
         \oplus: \mathcal{H} \times \mathcal{H} \to \mathcal{H}
 
+    .. note::
+
+        This operation does not normalize the resulting hypervectors.
+        Normalized hypervectors can be obtained with :func:`~torchhd.normalize`.
+
     Args:
         input (VSATensor): input hypervector
         other (VSATensor): other input hypervector
@@ -885,6 +892,12 @@ def hard_quantize(input: Tensor):
         tensor([ 1., -1., -1., -1.,  1., -1.])
 
     """
+    warnings.warn(
+        "torchhd.hard_quantize is deprecated, consider using torchhd.normalize instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
     # Make sure that the output tensor has the same dtype and device
     # as the input tensor.
     positive = torch.tensor(1.0, dtype=input.dtype, device=input.device)
@@ -893,6 +906,35 @@ def hard_quantize(input: Tensor):
     return torch.where(input > 0, positive, negative)
 
 
+def normalize(input: VSATensor) -> VSATensor:
+    """Normalize the input hypervectors.
+
+    Args:
+        input (Tensor): input tensor
+
+    Shapes:
+        - Input: :math:`(*)`
+        - Output: :math:`(*)`
+
+    Examples::
+
+        >>> x = torchhd.random(4, 10, "MAP").multibundle()
+        >>> x
+        MAPTensor([ 0.,  0., -2., -2.,  2., -2.,  2.,  2.,  2.,  0.])
+        >>> torchhd.normalize(x)
+        MAPTensor([-1., -1., -1., -1.,  1., -1.,  1.,  1.,  1., -1.])
+
+        >>> x = torchhd.random(4, 10, "HRR").multibundle()
+        >>> x
+        HRRTensor([-0.2999,  0.4686,  0.1797, -0.4830,  0.2718, -0.3663,  0.3079, 0.2558, -1.5157, -0.5196])
+        >>> torchhd.normalize(x)
+        HRRTensor([-0.1601,  0.2501,  0.0959, -0.2578,  0.1451, -0.1955,  0.1643, 0.1365, -0.8089, -0.2773])
+
+    """
+    input = ensure_vsa_tensor(input)
+    return input.normalize()
+
+
 def dot_similarity(input: VSATensor, others: VSATensor, **kwargs) -> VSATensor:
     """Dot product between the input vector and each vector in others.
 
@@ -1037,6 +1079,11 @@ def multiset(input: VSATensor) -> VSATensor:
 
         \bigoplus_{i=0}^{n-1} V_i
 
+    .. note::
+
+        This operation does not normalize the resulting or intermediate hypervectors.
+        Normalized hypervectors can be obtained with :func:`~torchhd.normalize`.
+
     Args:
         input (VSATensor): input hypervector tensor
 

torchhd/memory.py

@@ -121,22 +121,22 @@ def read(self, query: Tensor) -> VSATensor:
 
         """
         # first dims from query, last dim from value
-        out_shape = (*query.shape[:-1], self.value_dim)
+        out_shape = tuple(query.shape[:-1]) + (self.value_dim,)
 
         if query.dim() == 1:
             query = query.unsqueeze(0)
 
-        # make sure to have at least two dimension for index_add_
-        intermediate_shape = (*query.shape[:-1], self.value_dim)
+        intermediate_shape = tuple(query.shape[:-1]) + (self.value_dim,)
 
         similarity = query @ self.keys.T
         is_active = similarity >= self.threshold
 
-        # sparse matrix-vector multiplication
-        r_indices, v_indices = is_active.nonzero().T
-        read = query.new_zeros(intermediate_shape)
-        read.index_add_(0, r_indices, self.values[v_indices])
-        return read.view(out_shape)
+        # Sparse matrix-vector multiplication.
+        to_indices, from_indices = is_active.nonzero().T
+
+        read = torch.zeros(intermediate_shape, dtype=query.dtype, device=query.device)
+        read.index_add_(0, to_indices, self.values[from_indices])
+        return read.view(out_shape).as_subclass(functional.MAPTensor)
 
     @torch.no_grad()
     def write(self, keys: Tensor, values: Tensor) -> None:
@@ -161,7 +161,7 @@ def write(self, keys: Tensor, values: Tensor) -> None:
         similarity = keys @ self.keys.T
         is_active = similarity >= self.threshold
 
-        # sparse outer product and addition
+        # Sparse outer product and addition.
         from_indices, to_indices = is_active.nonzero().T
         self.values.index_add_(0, to_indices, values[from_indices])
 

torchhd/tensors/base.py

@@ -21,7 +21,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 #
-from typing import List, Set, Any
+from typing import List, Set
 import torch
 from torch import Tensor
 
@@ -131,6 +131,10 @@ def permute(self, shifts: int = 1) -> "VSATensor":
         """Permute the hypervector"""
         raise NotImplementedError
 
+    def normalize(self) -> "VSATensor":
+        """Normalize the hypervector"""
+        raise NotImplementedError
+
     def dot_similarity(self, others: "VSATensor") -> Tensor:
         """Inner product with other hypervectors"""
         raise NotImplementedError

torchhd/tensors/bsbc.py

@@ -335,6 +335,26 @@ def permute(self, shifts: int = 1) -> "BSBCTensor":
         """
         return torch.roll(self, shifts=shifts, dims=-1)
 
+    def normalize(self) -> "BSBCTensor":
+        r"""Normalize the hypervector.
+
+        Each operation on BSBC hypervectors ensures it remains normalized, so this returns a copy of self.
+
+        Shapes:
+            - Self: :math:`(*)`
+            - Output: :math:`(*)`
+
+        Examples::
+
+            >>> x = torchhd.BSBCTensor.random(4, 6, block_size=64).multibundle()
+            >>> x
+            BSBCTensor([28, 27, 20, 44, 57, 18])
+            >>> x.normalize()
+            BSBCTensor([28, 27, 20, 44, 57, 18])
+
+        """
+        return self.clone()
+
     def dot_similarity(self, others: "BSBCTensor", *, dtype=None) -> Tensor:
         """Inner product with other hypervectors"""
         if dtype is None:

torchhd/tensors/bsc.py

@@ -426,6 +426,26 @@ def permute(self, shifts: int = 1) -> "BSCTensor":
         """
         return super().roll(shifts=shifts, dims=-1)
 
+    def normalize(self) -> "BSCTensor":
+        r"""Normalize the hypervector.
+
+        Each operation on BSC hypervectors ensures it remains normalized, so this returns a copy of self.
+
+        Shapes:
+            - Self: :math:`(*)`
+            - Output: :math:`(*)`
+
+        Examples::
+
+            >>> x = torchhd.BSCTensor.random(4, 6).multibundle()
+            >>> x
+            BSCTensor([ True, False, False, False, False, False])
+            >>> x.normalize()
+            BSCTensor([ True, False, False, False, False, False])
+
+        """
+        return self.clone()
+
     def dot_similarity(self, others: "BSCTensor", *, dtype=None) -> Tensor:
         """Inner product with other hypervectors."""
         device = self.device

torchhd/tensors/fhrr.py

@@ -375,6 +375,29 @@ def permute(self, shifts: int = 1) -> "FHRRTensor":
         """
         return torch.roll(self, shifts=shifts, dims=-1)
 
+    def normalize(self) -> "FHRRTensor":
+        r"""Normalize the hypervector.
+
+        The normalization preserves the element phase but sets the magnitude to one.
+
+        Shapes:
+            - Self: :math:`(*)`
+            - Output: :math:`(*)`
+
+        Examples::
+
+            >>> x = torchhd.FHRRTensor.random(4, 6).multibundle()
+            >>> x
+            FHRRTensor([ 1.0878+0.9382j,  2.0057-1.5603j, -2.2828-1.4410j,  1.9643-1.8269j,
+            -0.9710-0.0120j, -0.7432+0.6956j])
+            >>> x.normalize()
+            FHRRTensor([ 0.7572+0.6531j,  0.7893-0.6140j, -0.8456-0.5338j,  0.7322-0.6810j,
+            -0.9999-0.0124j, -0.7301+0.6833j])
+
+        """
+        angle = self.angle()
+        return torch.complex(angle.cos(), angle.sin())
+
     def dot_similarity(self, others: "FHRRTensor") -> Tensor:
         """Inner product with other hypervectors"""
         if others.dim() >= 2:

torchhd/tensors/hrr.py

@@ -25,6 +25,7 @@
 import torch
 from torch import Tensor
 from torch.fft import fft, ifft
+import torch.nn.functional as F
 import math
 
 from torchhd.tensors.base import VSATensor
@@ -155,7 +156,7 @@ def random(
     ) -> "HRRTensor":
         """Creates a set of random independent hypervectors.
 
-        The resulting hypervectors are sampled at random from a normal with mean 0 and standard deviation 1/dimensions.
+        The resulting hypervectors are sampled uniformly at random from the (dimensions - 1)-unit sphere.
 
         Args:
             num_vectors (int): the number of hypervectors to generate.
@@ -186,8 +187,8 @@ def random(
             raise ValueError(f"{name} vectors must be one of dtype {options}.")
 
         size = (num_vectors, dimensions)
-        result = torch.empty(size, dtype=dtype, device=device)
-        result.normal_(0, 1.0 / math.sqrt(dimensions), generator=generator)
+        result = torch.randn(size, dtype=dtype, device=device, generator=generator)
+        result = F.normalize(result, p=2, dim=-1)
 
         result.requires_grad = requires_grad
         return result.as_subclass(cls)
@@ -362,6 +363,27 @@ def permute(self, shifts: int = 1) -> "HRRTensor":
         """
         return torch.roll(self, shifts=shifts, dims=-1)
 
+    def normalize(self) -> "HRRTensor":
+        r"""Normalize the hypervector.
+
+        The normalization preserves the direction of the hypervector but makes it unit norm.
+        This means that it is mapped to the closest point on the unit sphere.
+
+        Shapes:
+            - Self: :math:`(*)`
+            - Output: :math:`(*)`
+
+        Examples::
+
+            >>> x = torchhd.HRRTensor.random(4, 6).multibundle()
+            >>> x
+            HRRTensor([-0.6150,  0.4260,  0.6975,  0.3110,  0.9387,  0.0696])
+            >>> x.normalize()
+            HRRTensor([-0.4317,  0.2990,  0.4897,  0.2184,  0.6590,  0.0489])
+
+        """
+        return F.normalize(self, p=2, dim=-1)
+
     def dot_similarity(self, others: "HRRTensor") -> Tensor:
         """Inner product with other hypervectors"""
         if others.dim() >= 2:

torchhd/tensors/map.py

@@ -23,7 +23,6 @@
 #
 import torch
 from torch import Tensor
-import torch.nn.functional as F
 from typing import Set
 
 from torchhd.tensors.base import VSATensor
@@ -38,8 +37,6 @@ class MAPTensor(VSATensor):
     supported_dtypes: Set[torch.dtype] = {
         torch.float32,
         torch.float64,
-        torch.complex64,
-        torch.complex128,
         torch.int8,
         torch.int16,
         torch.int32,
@@ -318,6 +315,30 @@ def permute(self, shifts: int = 1) -> "MAPTensor":
         """
         return torch.roll(self, shifts=shifts, dims=-1)
 
+    def normalize(self) -> "MAPTensor":
+        r"""Normalize the hypervector.
+
+        The normalization sets all positive entries to +1 and all other entries to -1.
+
+        Shapes:
+            - Self: :math:`(*)`
+            - Output: :math:`(*)`
+
+        Examples::
+
+            >>> x = torchhd.MAPTensor.random(4, 6).multibundle()
+            >>> x
+            MAPTensor([-2., -4.,  4.,  0.,  4., -2.])
+            >>> x.normalize()
+            MAPTensor([-1., -1.,  1., -1.,  1., -1.])
+
+        """
+        # Ensure that the output tensor has the same dtype and device as the self tensor.
+        positive = torch.tensor(1.0, dtype=self.dtype, device=self.device)
+        negative = torch.tensor(-1.0, dtype=self.dtype, device=self.device)
+
+        return torch.where(self > 0, positive, negative)
+
     def clipping(self, kappa) -> "MAPTensor":
         r"""Performs the clipping function that clips the lower and upper values.