Fix dtype validation error in the Fractional Power Encoding

.github/pull_request_template.md

@@ -0,0 +1,11 @@
+<!--- Provide a short summary of your changes in the Title above -->
+
+## Description
+<!--- Describe your changes in detail -->
+<!-- Link the issue (if any) that will be resolved by the changes -->
+
+
+
+## Checklist
+- [ ] I added/updated documentation for the changes.
+- [ ] I have thoroughly tested the changes.

torchhd/embeddings.py

@@ -32,7 +32,7 @@
 import torchhd.functional as functional
 from torchhd.tensors.base import VSATensor
 from torchhd.tensors.map import MAPTensor
-from torchhd.tensors.fhrr import FHRRTensor
+from torchhd.tensors.fhrr import FHRRTensor, type_conversion as fhrr_type_conversion
 from torchhd.tensors.hrr import HRRTensor
 from torchhd.types import VSAOptions
 
@@ -1017,7 +1017,6 @@ def __init__(
         dtype=None,
         requires_grad: bool = False,
     ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
         super(FractionalPower, self).__init__()
 
         self.in_features = in_features  # data dimensions
@@ -1032,9 +1031,16 @@ def __init__(
 
         self.vsa_tensor = functional.get_vsa_tensor_class(vsa)
 
-        if dtype not in self.vsa_tensor.supported_dtypes:
+        # If a specific dtype is specified make sure it is supported by the VSA model
+        if dtype != None and dtype not in self.vsa_tensor.supported_dtypes:
             raise ValueError(f"dtype {dtype} not supported by {vsa}")
 
+        # The internal weights/phases are stored as floats even if the output is a complex tensor
+        if dtype != None and vsa == "FHRR":
+            dtype = fhrr_type_conversion[dtype]
+
+        factory_kwargs = {"device": device, "dtype": dtype}
+
         # If the distribution is a string use the presets in predefined_kernels
         if isinstance(distribution, str):
             try:

torchhd/tests/test_embeddings.py

@@ -23,11 +23,13 @@
 #
 import pytest
 import torch
+import math
 
 import torchhd
 from torchhd import functional
 from torchhd import embeddings
 from torchhd.tensors.hrr import HRRTensor
+from torchhd.tensors.fhrr import type_conversion as fhrr_type_conversion
 
 
 from .utils import (
@@ -540,3 +542,111 @@ def test_value(self, vsa):
             )
             > 0.99
         )
+
+
+class TestFractionalPower:
+    @pytest.mark.parametrize("vsa", vsa_tensors)
+    def test_default_dtype(self, vsa):
+        dimensions = 1000
+        embedding = 10
+
+        if vsa not in {"HRR", "FHRR"}:
+            with pytest.raises(ValueError):
+                embeddings.FractionalPower(embedding, dimensions, vsa=vsa)
+
+            return
+
+        emb = embeddings.FractionalPower(embedding, dimensions, vsa=vsa)
+        x = torch.randn(2, embedding)
+        y = emb(x)
+        assert y.shape == (2, dimensions)
+
+        if vsa == "HRR":
+            assert y.dtype == torch.float32
+        elif vsa == "FHRR":
+            assert y.dtype == torch.complex64
+        else:
+            return
+
+    @pytest.mark.parametrize("dtype", torch_dtypes)
+    def test_dtype(self, dtype):
+        dimensions = 1456
+        embedding = 2
+
+        if dtype not in {torch.float32, torch.float64}:
+            with pytest.raises(ValueError):
+                embeddings.FractionalPower(
+                    embedding, dimensions, vsa="HRR", dtype=dtype
+                )
+        else:
+            emb = embeddings.FractionalPower(
+                embedding, dimensions, vsa="HRR", dtype=dtype
+            )
+
+            x = torch.randn(13, embedding, dtype=dtype)
+            y = emb(x)
+            assert y.shape == (13, dimensions)
+            assert y.dtype == dtype
+
+        if dtype not in {torch.complex64, torch.complex128}:
+            with pytest.raises(ValueError):
+                embeddings.FractionalPower(
+                    embedding, dimensions, vsa="FHRR", dtype=dtype
+                )
+        else:
+            emb = embeddings.FractionalPower(
+                embedding, dimensions, vsa="FHRR", dtype=dtype
+            )
+
+            x = torch.randn(13, embedding, dtype=fhrr_type_conversion[dtype])
+            y = emb(x)
+            assert y.shape == (13, dimensions)
+            assert y.dtype == dtype
+
+    def test_device(self):
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        emb = embeddings.FractionalPower(35, 1000, "gaussian", device=device)
+
+        x = torchhd.random(5, 35, device=device)
+        y = emb(x)
+        assert y.shape == (5, 1000)
+        assert y.device.type == device.type
+
+    def test_custom_dist_iid(self):
+        kernel_shape = torch.distributions.Normal(0, 1)
+        band = 3.0
+
+        emb = embeddings.FractionalPower(3, 1000, kernel_shape, band)
+        x = torch.randn(1, 3)
+        y = emb(x)
+        assert y.shape == (1, 1000)
+
+    def test_custom_dist_2d(self):
+        # Phase distribution for periodic Sinc kernel
+        class HexDisc(torch.distributions.Categorical):
+            def __init__(self):
+                super().__init__(torch.ones(6))
+                self.r = 1
+                self.side = self.r * math.sqrt(3) / 2
+                self.phases = torch.tensor(
+                    [
+                        [-self.r, 0.0],
+                        [-self.r / 2, self.side],
+                        [self.r / 2, self.side],
+                        [self.r, 0.0],
+                        [self.r / 2, -self.side],
+                        [-self.r / 2, -self.side],
+                    ]
+                )
+
+            def sample(self, sample_shape=torch.Size()):
+                return self.phases[super().sample(sample_shape), :]
+
+        kernel_shape = HexDisc()
+        band = 3.0
+
+        emb = embeddings.FractionalPower(2, 1000, kernel_shape, band)
+        x = torch.randn(5, 2)
+        y = emb(x)
+        assert y.shape == (5, 1000)