[DEL] Remove .from_inner2

singd/structures/base.py

@@ -251,23 +251,6 @@ def from_inner(self, X: Union[Tensor, None] = None) -> StructuredMatrix:
         S_dense = self.to_dense().T if X is None else self.rmatmat(X)
         return self.from_dense(supported_matmul(S_dense, S_dense.T))
 
-    # NOTE This operation should be removed long-term as implementing IF-KFAC
-    # with `from_inner` is more efficient. For now, it will exist as it makes
-    # integrating this interface into existing implementations of sparse IF-KFAC
-    # easier, as they have access to the input/gradient covariance matrices.
-    def from_inner2(self, XXT: Tensor) -> StructuredMatrix:
-        """Extract the represented structure from ``self.T @ XXT @ self``.
-
-        Args:
-            XXT: 2d square symmetric matrix.
-
-        Returns:
-            The structured matrix extracted from ``self.T @ XXT @ self``.
-        """
-        self._warn_naive_implementation("from_inner2")
-        dense = self.to_dense()
-        return self.from_dense(supported_matmul(dense.T, XXT, dense))
-
     def trace(self) -> Tensor:
         """Compute the trace of the represented matrix.
 

singd/structures/diagonal.py

@@ -133,18 +133,6 @@ def from_inner(self, X: Union[Tensor, None] = None) -> DiagonalMatrix:
             mat_diag *= (X**2).sum(1)
         return DiagonalMatrix(mat_diag)
 
-    def from_inner2(self, XXT: Tensor) -> StructuredMatrix:
-        """Represent the matrix diagonal of ``self.T @ XXT @ self``.
-
-        Args:
-            XXT: 2d square symmetric matrix.
-
-        Returns:
-            A ``DiagonalMatrix`` representing matrix diagonal of
-            ``self.T @ XXT @ self``.
-        """
-        return DiagonalMatrix(self._mat_diag**2 * XXT.diag())
-
     def trace(self) -> Tensor:
         """Compute the trace of the represented matrix.
 

singd/structures/triltoeplitz.py

@@ -26,7 +26,6 @@ class TrilToeplitzMatrix(StructuredMatrix):
 
     WARN_NAIVE_EXCEPTIONS = {  # hard to leverage structure for efficient implementation
         "from_inner",
-        "from_inner2",
     }
 
     def __init__(self, diag_consts: Tensor) -> None:

singd/structures/triutoeplitz.py

@@ -26,7 +26,6 @@ class TriuToeplitzMatrix(StructuredMatrix):
 
     WARN_NAIVE_EXCEPTIONS = {  # hard to leverage structure for efficient implementation
         "from_inner",
-        "from_inner2",
     }
 
     def __init__(self, diag_consts: Tensor) -> None:

test/structures/utils.py

@@ -209,33 +209,6 @@ def _test_from_inner(
     )
 
 
-def _test_from_inner2(
-    sym_mat: Tensor,
-    structured_matrix_cls: Type[StructuredMatrix],
-    project: Callable[[Tensor], Tensor],
-    XXT: Tensor,
-):
-    """Test `from_inner2` method of any child of `StructuredMatrix`.
-
-    Args:
-        sym_mat: A symmetric dense matrix which will be converted into a structured
-            matrix.
-        structured_matrix_cls: The class of the structured matrix into which `sym_mat`
-            will be converted.
-        project: A function which converts an arbitrary symmetric dense matrix into a
-            dense matrix of the tested structure. Used to establish the ground truth.
-        XXT: An symmetric PSD matrix that will be passed to `from_inner2`.
-    """
-    truth = project(supported_matmul(project(sym_mat).T, XXT, project(sym_mat)))
-    sym_mat_structured = structured_matrix_cls.from_dense(sym_mat)
-    report_nonclose(
-        truth,
-        sym_mat_structured.from_inner2(XXT).to_dense(),
-        rtol=1e-2 if is_half_precision(sym_mat.dtype) else 1e-5,
-        atol=1e-4 if is_half_precision(sym_mat.dtype) else 1e-7,
-    )
-
-
 def _test_zeros(
     structured_matrix_cls: Type[StructuredMatrix],
     dim: int,
@@ -454,23 +427,6 @@ def test_from_inner(self, dev: device, dtype: torch.dtype):
             X = rand((dim, 2 * dim), device=dev, dtype=dtype)
             _test_from_inner(sym_mat, self.STRUCTURED_MATRIX_CLS, self.project, X)
 
-    @mark.parametrize("dtype", DTYPES, ids=DTYPE_IDS)
-    @mark.parametrize("dev", DEVICES, ids=DEVICE_IDS)
-    def test_from_inner2(self, dev: device, dtype: torch.dtype):
-        """Test structure extraction after self-inner product w/ intermediate matrix.
-
-        Args:
-            dev: The device on which to run the test.
-            dtype: The data type of the matrices.
-        """
-        for dim in self.DIMS:
-            manual_seed(0)
-
-            sym_mat = symmetrize(rand((dim, dim), device=dev, dtype=dtype))
-            X = rand((dim, 2 * dim), device=dev, dtype=dtype)
-            XXT = supported_matmul(X, X.T)
-            _test_from_inner2(sym_mat, self.STRUCTURED_MATRIX_CLS, self.project, XXT)
-
     @mark.parametrize("dtype", DTYPES, ids=DTYPE_IDS)
     @mark.parametrize("dev", DEVICES, ids=DEVICE_IDS)
     def test_eye(self, dev: device, dtype: torch.dtype):