Add diagonal operator mapping base operator over an array axis

scico/linop/__init__.py

@@ -17,7 +17,7 @@
 from ._func import Crop, Pad, Reshape, Slice, Sum, Transpose, linop_from_function
 from ._linop import ComposedLinearOperator, LinearOperator
 from ._matrix import MatrixOperator
-from ._stack import DiagonalStack, VerticalStack, linop_over_axes
+from ._stack import DiagonalReplicated, DiagonalStack, VerticalStack, linop_over_axes
 from ._util import jacobian, operator_norm, power_iteration, valid_adjoint
 from .xray import Parallel2dProjector, XRayTransform
 
@@ -29,6 +29,7 @@
     "FiniteDifference",
     "SingleAxisFiniteDifference",
     "Identity",
+    "DiagonalReplicated",
     "VerticalStack",
     "DiagonalStack",
     "MatrixOperator",

scico/linop/_stack.py

@@ -14,6 +14,7 @@
 import scico.numpy as snp
 from scico.numpy import Array, BlockArray
 from scico.numpy.util import normalize_axes
+from scico.operator._stack import DiagonalReplicated as DReplicated
 from scico.operator._stack import DiagonalStack as DStack
 from scico.operator._stack import VerticalStack as VStack
 from scico.typing import Axes, Shape
@@ -146,6 +147,83 @@ def _adj(self, y: Union[Array, BlockArray]) -> Union[Array, BlockArray]:  # type
         return snp.blockarray(result)
 
 
+class DiagonalReplicated(DReplicated, LinearOperator):
+    r"""A diagonal stack constructed from a single linear operator.
+
+    Given linear operator :math:`A`, create the linear operator
+
+    .. math::
+       H =
+       \begin{pmatrix}
+            A & 0   & \ldots & 0\\
+            0   & A & \ldots & 0\\
+            \vdots & \vdots & \ddots & \vdots\\
+            0   & 0 & \ldots & A \\
+       \end{pmatrix} \qquad
+       \text{such that} \qquad
+       H
+       \begin{pmatrix}
+            \mb{x}_1 \\
+            \mb{x}_2 \\
+            \vdots \\
+            \mb{x}_N \\
+       \end{pmatrix}
+       =
+       \begin{pmatrix}
+            A(\mb{x}_1) \\
+            A(\mb{x}_2) \\
+            \vdots \\
+            A(\mb{x}_N) \\
+       \end{pmatrix} \;.
+
+    The application of :math:`A` to each component :math:`\mb{x}_k` is
+    computed using :func:`jax.pmap` or :func:`jax.vmap`. The input shape
+    for linear operator :math:`A` should exclude the array axis on which
+    :math:`A` is replicated to form :math:`H`. For example, if :math:`A`
+    has input shape `(3, 4)` and :math:`H` is constructed to replicate
+    on axis 0 with 2 replicates, the input shape of :math:`H` will be
+    `(2, 3, 4)`.
+
+    Linear operators taking :class:`.BlockArray` input are not supported.
+    """
+
+    def __init__(
+        self,
+        op: LinearOperator,
+        replicates: int,
+        input_axis: int = 0,
+        output_axis: Optional[int] = None,
+        map_type: str = "auto",
+        **kwargs,
+    ):
+        """
+        Args:
+            op: Linear operator to replicate.
+            replicates: Number of replicates of `op`.
+            input_axis: Input axis over which `op` should be replicated.
+            output_axis: Index of replication axis in output array.
+               If ``None``, the input replication axis is used.
+            map_type: If "pmap" or "vmap", apply replicated mapping using
+               :func:`jax.pmap` or :func:`jax.vmap` respectively. If
+               "auto", use :func:`jax.pmap` if sufficient devices are
+               available for the number of replicates, otherwise use
+               :func:`jax.vmap`.
+        """
+        if not isinstance(op, LinearOperator):
+            raise TypeError("Argument op must be of type LinearOperator.")
+
+        super().__init__(
+            op,
+            replicates,
+            input_axis=input_axis,
+            output_axis=output_axis,
+            map_type=map_type,
+            **kwargs,
+        )
+
+        self._adj = self.jaxmap(op.adj, in_axes=self.input_axis, out_axes=self.output_axis)
+
+
 def linop_over_axes(
     linop: type[LinearOperator],
     input_shape: Shape,

scico/operator/__init__.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2021-2023 by SCICO Developers
+# Copyright (C) 2021-2024 by SCICO Developers
 # All rights reserved. BSD 3-clause License.
 # This file is part of the SCICO package. Details of the copyright and
 # user license can be found in the 'LICENSE' file distributed with the
@@ -13,11 +13,12 @@
 from ._operator import Operator
 from .biconvolve import BiConvolve
 from ._func import operator_from_function, Abs, Angle, Exp
-from ._stack import DiagonalStack, VerticalStack
+from ._stack import DiagonalStack, VerticalStack, DiagonalReplicated
 
 __all__ = [
     "Operator",
     "BiConvolve",
+    "DiagonalReplicated",
     "DiagonalStack",
     "VerticalStack",
     "operator_from_function",

scico/operator/_stack.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2023 by SCICO Developers
+# Copyright (C) 2023-2024 by SCICO Developers
 # All rights reserved. BSD 3-clause License.
 # This file is part of the SCICO package. Details of the copyright and
 # user license can be found in the 'LICENSE' file distributed with the
@@ -13,6 +13,8 @@
 
 import numpy as np
 
+import jax
+
 from typing_extensions import TypeGuard
 
 import scico.numpy as snp
@@ -234,3 +236,107 @@ def _eval(self, x: Union[Array, BlockArray]) -> Union[Array, BlockArray]:
         if self.collapse_output:
             return snp.stack(result)
         return snp.blockarray(result)
+
+
+class DiagonalReplicated(Operator):
+    r"""A diagonal stack constructed from a single operator.
+
+    Given operator :math:`A`, create the operator :math:`H` such that
+
+    .. math::
+       H \left(
+       \begin{pmatrix}
+            \mb{x}_1 \\
+            \mb{x}_2 \\
+            \vdots \\
+            \mb{x}_N \\
+       \end{pmatrix} \right)
+       =
+       \begin{pmatrix}
+            A(\mb{x}_1) \\
+            A(\mb{x}_2) \\
+            \vdots \\
+            A(\mb{x}_N) \\
+       \end{pmatrix} \;.
+
+    The application of :math:`A` to each component :math:`\mb{x}_k` is
+    computed using :func:`jax.pmap` or :func:`jax.vmap`. The input shape
+    for operator :math:`A` should exclude the array axis on which
+    :math:`A` is replicated to form :math:`H`. For example, if :math:`A`
+    has input shape `(3, 4)` and :math:`H` is constructed to replicate
+    on axis 0 with 2 replicates, the input shape of :math:`H` will be
+    `(2, 3, 4)`.
+
+    Operators taking :class:`.BlockArray` input are not supported.
+    """
+
+    def __init__(
+        self,
+        op: Operator,
+        replicates: int,
+        input_axis: int = 0,
+        output_axis: Optional[int] = None,
+        map_type: str = "auto",
+        **kwargs,
+    ):
+        """
+        Args:
+            op: Operator to replicate.
+            replicates: Number of replicates of `op`.
+            input_axis: Input axis over which `op` should be replicated.
+            output_axis: Index of replication axis in output array.
+               If ``None``, the input replication axis is used.
+            map_type: If "pmap" or "vmap", apply replicated mapping using
+               :func:`jax.pmap` or :func:`jax.vmap` respectively. If
+               "auto", use :func:`jax.pmap` if sufficient devices are
+               available for the number of replicates, otherwise use
+               :func:`jax.vmap`.
+        """
+        if map_type not in ["auto", "pmap", "vmap"]:
+            raise ValueError("Argument map_type must be one of 'auto', 'pmap, or 'vmap'.")
+        if input_axis < 0:
+            input_axis = len(op.input_shape) + 1 + input_axis
+        if input_axis < 0 or input_axis > len(op.input_shape):
+            raise ValueError(
+                "Argument input_axis must be positive and less than the number of axes "
+                "in the input shape of op."
+            )
+        if is_nested(op.input_shape):
+            raise ValueError("Argument op may not be an Operator taking BlockArray input.")
+        if is_nested(op.output_shape):
+            raise ValueError("Argument op may not be an Operator with BlockArray output.")
+        self.op = op
+        self.replicates = replicates
+        self.input_axis = input_axis
+        self.output_axis = self.input_axis if output_axis is None else output_axis
+
+        if map_type == "auto":
+            self.jaxmap = jax.pmap if replicates <= jax.device_count() else jax.vmap
+        else:
+            if map_type == "pmap" and replicates > jax.device_count():
+                raise ValueError(
+                    "Requested pmap mapping but number of replicates exceeds device count."
+                )
+            else:
+                self.jaxmap = jax.pmap if map_type == "pmap" else jax.vmap
+
+        eval_fn = self.jaxmap(op.__call__, in_axes=self.input_axis, out_axes=self.output_axis)
+
+        input_shape = (
+            op.input_shape[0 : self.input_axis] + (replicates,) + op.input_shape[self.input_axis :]
+        )
+        output_shape = (
+            op.output_shape[0 : self.output_axis]
+            + (replicates,)
+            + op.output_shape[self.output_axis :]
+        )
+
+        super().__init__(
+            input_shape=input_shape,  # type: ignore
+            output_shape=output_shape,  # type: ignore
+            eval_fn=eval_fn,
+            input_dtype=op.input_dtype,
+            output_dtype=op.output_dtype,
+            jit=False,
+            **kwargs,
+        )

scico/test/linop/test_linop_stack.py

@@ -5,8 +5,16 @@
 import pytest
 
 import scico.numpy as snp
-from scico.linop import Convolve, DiagonalStack, Identity, Sum, VerticalStack
+from scico.linop import (
+    Convolve,
+    DiagonalReplicated,
+    DiagonalStack,
+    Identity,
+    Sum,
+    VerticalStack,
+)
 from scico.operator import Abs
+from scico.random import randn
 from scico.test.linop.test_linop import adjoint_test
 
 
@@ -166,3 +174,16 @@ def test_output_collapse(self):
 
         H = DiagonalStack((A1, A2), collapse_output=False)
         assert H.output_shape == (S1, S1)
+
+
+class TestDiagonalReplicated:
+    def setup_method(self, method):
+        self.key = jax.random.PRNGKey(12345)
+
+    def test_adjoint(self):
+        x, key = randn((2, 3, 4), key=self.key)
+        A = Sum(x.shape[1:], axis=-1)
+        D = DiagonalReplicated(A, x.shape[0])
+        y = D.T(D(x))
+        np.testing.assert_allclose(y[0], A.T(A(x[0])))
+        np.testing.assert_allclose(y[1], A.T(A(x[1])))

scico/test/operator/test_op_stack.py

@@ -5,7 +5,14 @@
 import pytest
 
 import scico.numpy as snp
-from scico.operator import Abs, DiagonalStack, Operator, VerticalStack
+from scico.operator import (
+    Abs,
+    DiagonalReplicated,
+    DiagonalStack,
+    Operator,
+    VerticalStack,
+)
+from scico.random import randn
 
 TestOpA = Operator(input_shape=(3, 4), output_shape=(2, 3, 4), eval_fn=lambda x: snp.stack((x, x)))
 TestOpB = Operator(
@@ -140,3 +147,48 @@ def test_output_collapse(self):
 
         H = DiagonalStack((A1, A2), collapse_output=False)
         assert H.output_shape == (A1.output_shape, A1.output_shape)
+
+
+class TestDiagonalReplicated:
+    def setup_method(self, method):
+        self.key = jax.random.PRNGKey(12345)
+
+    @pytest.mark.parametrize("map_type", ["auto", "vmap"])
+    @pytest.mark.parametrize("input_axis", [0, 1])
+    def test_map_auto_vmap(self, input_axis, map_type):
+        x, key = randn((2, 3, 4), key=self.key)
+        mapshape = (3, 4) if input_axis == 0 else (2, 4)
+        replicates = x.shape[input_axis]
+        A = Abs(mapshape)
+        D = DiagonalReplicated(A, replicates, input_axis=input_axis, map_type=map_type)
+        y = D(x)
+        assert y.shape[input_axis] == replicates
+
+    @pytest.mark.skipif(jax.device_count() < 2, reason="multiple devices required for test")
+    def test_map_auto_pmap(self):
+        x, key = randn((2, 3, 4), key=self.key)
+        A = Abs(x.shape[1:])
+        replicates = x.shape[0]
+        D = DiagonalReplicated(A, replicates, map_type="pmap")
+        y = D(x)
+        assert y.shape[0] == replicates
+
+    def test_input_axis(self):
+        # Ensure that operators can be stacked on final axis
+        x, key = randn((2, 3, 4), key=self.key)
+        A = Abs(x.shape[0:2])
+        replicates = x.shape[2]
+        D = DiagonalReplicated(A, replicates, input_axis=2)
+        y = D(x)
+        assert y.shape == (2, 3, 4)
+        D = DiagonalReplicated(A, replicates, input_axis=-1)
+        y = D(x)
+        assert y.shape == (2, 3, 4)
+
+    def test_output_axis(self):
+        x, key = randn((2, 3, 4), key=self.key)
+        A = Abs(x.shape[1:])
+        replicates = x.shape[0]
+        D = DiagonalReplicated(A, replicates, output_axis=1)
+        y = D(x)
+        assert y.shape == (3, 2, 4)