working specialised rewrite + test

pytensor/tensor/rewriting/linalg.py

@@ -2,6 +2,8 @@
 from collections.abc import Callable
 from typing import cast
 
+import numpy as np
+
 from pytensor import Variable
 from pytensor import tensor as pt
 from pytensor.graph import Apply, FunctionGraph
@@ -967,23 +969,24 @@ def rewrite_cholesky_diag_to_sqrt_diag(fgraph, node):
     return [eye_input * (non_eye_input**0.5)]
 
 
-# SLogDet Rewrites
-def check_log_abs_det(fgraph, client):
+def _check_log_abs_det(fgraph, client):
     # First, we find abs
     if not (isinstance(client.op, Elemwise) and isinstance(client.op.scalar_op, Abs)):
         return False
 
     # Check whether log is a client of abs
     for client_2 in fgraph.clients[client.outputs[0]]:
         if not (
-            isinstance(client_2.op, Elemwise) and isinstance(client_2.op.scalar_op, Log)
+            isinstance(client_2[0].op, Elemwise)
+            and isinstance(client_2[0].op.scalar_op, Log)
         ):
             return False
 
     return True
 
 
-@node_rewriter(tracks=[det])
+@register_specialize
+@node_rewriter([det])
 def slogdet_specialization(fgraph, node):
     replacements = {}
     for client in fgraph.clients[node.outputs[0]]:
@@ -996,19 +999,22 @@ def slogdet_specialization(fgraph, node):
             replacements[client[0].outputs[0]] = sign_det_x
 
         # Check for log(abs(det))
-        elif check_log_abs_det(fgraph, client[0]):
+        elif _check_log_abs_det(fgraph, client[0]):
             x = node.inputs[0]
             sign_det_x, slog_det_x = SLogDet()(x)
             replacements[fgraph.clients[client[0].outputs[0]][0][0].outputs[0]] = (
                 slog_det_x
             )
 
         # Check for log(det)
-        # elif isinstance(client[0].op, Elemwise) and isinstance(
-        # client[0].op.scalar_op, Log
-        # ):
-        # pass
-        # replacements[client[0].owner.outputs[0]] = pt.where(pt.eq(sign_det_x, -1), np.nan, slog_det_x)
+        elif isinstance(client[0].op, Elemwise) and isinstance(
+            client[0].op.scalar_op, Log
+        ):
+            x = node.inputs[0]
+            sign_det_x, slog_det_x = SLogDet()(x)
+            replacements[client[0].outputs[0]] = pt.where(
+                pt.eq(sign_det_x, -1), np.nan, slog_det_x
+            )
 
         # Det is used directly for something else, don't rewrite to avoid computing two dets
         else:

tests/tensor/rewriting/test_linalg.py

@@ -21,6 +21,7 @@
     KroneckerProduct,
     MatrixInverse,
     MatrixPinv,
+    SLogDet,
     matrix_inverse,
     svd,
 )
@@ -900,3 +901,23 @@ def test_rewrite_cholesky_diag_to_sqrt_diag_not_applied():
     f_rewritten = function([x], z_cholesky, mode="FAST_RUN")
     nodes = f_rewritten.maker.fgraph.apply_nodes
     assert any(isinstance(node.op, Cholesky) for node in nodes)
+
+
+def test_slogdet_specialisation():
+    x = pt.dmatrix("x")
+    det_x = pt.linalg.det(x)
+    log_abs_det_x = pt.log(pt.abs(det_x))
+    sign_det_x = pt.sign(det_x)
+    exp_det_x = pt.exp(det_x)
+    # sign(det(x))
+    f = function([x], [sign_det_x], mode="FAST_RUN")
+    nodes = f.maker.fgraph.apply_nodes
+    assert any(isinstance(node.op, SLogDet) for node in nodes)
+    # log(abs(det(x)))
+    f = function([x], [log_abs_det_x], mode="FAST_RUN")
+    nodes = f.maker.fgraph.apply_nodes
+    assert any(isinstance(node.op, SLogDet) for node in nodes)
+    # other functions (rewrite shouldnt be applied to these)
+    f = function([x], [exp_det_x], mode="FAST_RUN")
+    nodes = f.maker.fgraph.apply_nodes
+    assert not any(isinstance(node.op, SLogDet) for node in nodes)