Rewrite ExtractDiagonal of AllocDiagonal

Author: ricardoV94

pytensor/tensor/rewriting/subtensor.py

@@ -19,13 +19,15 @@
 from pytensor.scalar import constant as scalar_constant
 from pytensor.tensor.basic import (
     Alloc,
+    ExtractDiag,
     Join,
     ScalarFromTensor,
     TensorFromScalar,
     alloc,
     cast,
     concatenate,
     expand_dims,
+    full,
     get_scalar_constant_value,
     get_underlying_scalar_constant_value,
     register_infer_shape,
@@ -1793,3 +1795,96 @@ def ravel_multidimensional_int_idx(fgraph, node):
     "numba",
     use_db_name_as_tag=False,  # Not included if only "specialize" is requested
 )
+
+
+@register_canonicalize
+@register_stabilize
+@register_specialize
+@node_rewriter([ExtractDiag])
+def extract_diag_of_diagonal_set_subtensor(fgraph, node):
+    """Undo extract diagonal from a set diagonal
+
+    This rewrites the following pattern:
+        y = write_diagonal*(x, x_diag, offset=k1)
+        z = extract_diag(y, offset=k2)
+
+    as:
+        z = diag_x, if k1 == k2
+        z = x if k1 != k2
+
+    * write_diagonal is not an atomic operation, but a sequence of Arange/SetSubtensor operations.
+
+    """
+
+    def is_contant_arange(var) -> bool:
+        if not (isinstance(var, TensorConstant) and var.type.ndim == 1):
+            return False
+
+        data = var.data
+        start, stop = data[0], data[-1] + 1
+        return data.size == (stop - start) and (data == np.arange(start, stop)).all()
+
+    [diag_x] = node.inputs
+    if not (
+        diag_x.owner is not None
+        and isinstance(diag_x.owner.op, AdvancedIncSubtensor)
+        and diag_x.owner.op.set_instead_of_inc
+    ):
+        return None
+
+    x, y, *idxs = diag_x.owner.inputs
+
+    if not (
+        x.type.ndim >= 2
+        and None not in x.type.shape[-2:]
+        and x.type.shape[-2] == x.type.shape[-1]
+    ):
+        # TODO: for now we only support rewrite with static square shape for x
+        return None
+
+    op = node.op
+    if op.axis2 > len(idxs):
+        return None
+
+    # Check all non-axis indices are full slices
+    axis = {op.axis1, op.axis2}
+    if not all(is_full_slice(idx) for i, idx in enumerate(idxs) if i not in axis):
+        return None
+
+    # Check axis indices are arange we would expect from setting on the diagonal
+    axis1_idx = idxs[op.axis1]
+    axis2_idx = idxs[op.axis2]
+    if not (is_contant_arange(axis1_idx) and is_contant_arange(axis2_idx)):
+        return None
+
+    dim_length = x.type.shape[-1]
+    offset = op.offset
+    start_stop1 = (axis1_idx.data[0], axis1_idx.data[-1] + 1)
+    start_stop2 = (axis2_idx.data[0], axis2_idx.data[-1] + 1)
+    orig_start1, orig_start2 = start_stop1[0], start_stop2[0]
+
+    if offset < 0:
+        # The logic for checking if we are selecting or not a diagonal for negative offset is the same
+        # as the one with positive offset but swapped axis
+        start_stop1, start_stop2 = start_stop2, start_stop1
+        offset = -offset
+
+    start1, stop1 = start_stop1
+    start2, stop2 = start_stop2
+    if (
+        start1 == 0
+        and start2 == offset
+        and stop1 == dim_length - offset
+        and stop2 == dim_length
+    ):
+        # We are extracting the just written diagonal
+        if y.type.ndim == 0 or y.type.shape[-1] == 1:
+            # We may need to broadcast y
+            y = full((*x.shape[:-2], dim_length - offset), y, dtype=x.type.dtype)
+        return [y]
+    elif (orig_start2 - orig_start1) != op.offset:
+        # Some other diagonal was written, ignore it
+        return [op(x)]
+    else:
+        # A portion, but no the whole diagonal was written, don't do anything
+        return None

tests/tensor/rewriting/test_subtensor.py

@@ -1,3 +1,5 @@
+import random
+
 import numpy as np
 import pytest
 
@@ -9,7 +11,7 @@
 from pytensor.compile.mode import Mode, get_default_mode, get_mode
 from pytensor.compile.ops import DeepCopyOp
 from pytensor.configdefaults import config
-from pytensor.graph import vectorize_graph
+from pytensor.graph import rewrite_graph, vectorize_graph
 from pytensor.graph.basic import Constant, Variable, ancestors, equal_computations
 from pytensor.graph.rewriting.basic import check_stack_trace
 from pytensor.raise_op import Assert
@@ -1956,3 +1958,37 @@ def test_unknown_step(self):
             f(test_x, -2),
             test_x[0:3:-2, -1:-6:2, ::],
         )
+
+
+def test_extract_diag_of_diagonal_set_subtensor():
+    A = pt.full((2, 6, 6), np.nan)
+    rows = pt.arange(A.shape[-2])
+    cols = pt.arange(A.shape[-1])
+    write_offsets = [-2, -1, 0, 1, 2]
+    # Randomize order of write operations, to make sure rewrite is not sensitive to it
+    random.shuffle(write_offsets)
+    for offset in write_offsets:
+        value = offset + 0.1 * offset
+        if offset == 0:
+            A = A[..., rows, cols].set(value)
+        elif offset > 0:
+            A = A[..., rows[:-offset], cols[offset:]].set(value)
+        else:
+            offset = -offset
+            A = A[..., rows[offset:], cols[:-offset]].set(value)
+    # Add a partial diagonal along offset 3
+    A = A[..., rows[1:-3], cols[4:]].set(np.pi)
+
+    read_offsets = [-2, -1, 0, 1, 2, 3]
+    outs = [A.diagonal(offset=offset, axis1=-2, axis2=-1) for offset in read_offsets]
+    rewritten_outs = rewrite_graph(outs, include=("ShapeOpt", "canonicalize"))
+
+    # Every output should just be an Alloc with value
+    expected_outs = []
+    for offset in read_offsets[:-1]:
+        value = np.asarray(offset + 0.1 * offset, dtype=A.type.dtype)
+        expected_outs.append(pt.full((np.int64(2), np.int8(6 - abs(offset))), value))
+    # The partial diagonal shouldn't be rewritten
+    expected_outs.append(outs[-1])
+
+    assert equal_computations(rewritten_outs, expected_outs)