Add OpFromGraph wrapper around alloc_diag

pytensor/link/jax/dispatch/tensor_basic.py

@@ -3,11 +3,14 @@
 import jax.numpy as jnp
 import numpy as np
 
+import pytensor
+from pytensor.graph import node_rewriter
 from pytensor.graph.basic import Constant
 from pytensor.link.jax.dispatch.basic import jax_funcify
 from pytensor.tensor import get_vector_length
 from pytensor.tensor.basic import (
     Alloc,
+    AllocDiag2,
     AllocEmpty,
     ARange,
     ExtractDiag,
@@ -21,6 +24,7 @@
     get_underlying_scalar_constant_value,
 )
 from pytensor.tensor.exceptions import NotScalarConstantError
+from pytensor.tensor.rewriting.basic import register_specialize
 from pytensor.tensor.shape import Shape_i
 
 
@@ -205,3 +209,29 @@ def tri(*args):
         return jnp.tri(*args, dtype=op.dtype)
 
     return tri
+
+
+@register_specialize
+@node_rewriter([AllocDiag2])
+def eagerly_inline_alloc_diag(fgraph, node):
+    """
+    Inline `AllocDiag2` OpFromGraph into the graph so the component Ops can themselves be jaxified
+
+    Parameters
+    ----------
+    fgraph: FunctionGraph
+        The function graph being rewritten
+    node: Apply
+        Node of the function graph to be optimized
+
+    Returns
+    -------
+
+    """
+    [input] = node.inputs
+    [output] = node.op.inner_outputs
+    inner_input = output.owner.inputs[1]
+
+    inline = pytensor.clone_replace(output, {inner_input: input})
+
+    return [inline]

pytensor/tensor/basic.py

@@ -21,6 +21,7 @@
 import pytensor.scalar.sharedvar
 from pytensor import compile, config, printing
 from pytensor import scalar as ps
+from pytensor.compile.builders import OpFromGraph
 from pytensor.gradient import DisconnectedType, grad_undefined
 from pytensor.graph import RewriteDatabaseQuery
 from pytensor.graph.basic import Apply, Constant, Variable, equal_computations
@@ -3831,6 +3832,23 @@ def __setstate__(self, state):
             self.axis2 = 1
 
 
+class AllocDiag2(OpFromGraph):
+    """
+    Wrapper Op for alloc_diag graphs
+    """
+
+    __props__ = ("offset", "axis1", "axis2", "inline")
+
+    def __init__(self, *args, offset, axis1, axis2, **kwargs):
+        inline = kwargs.pop("inline", False)
+        self.offset = offset
+        self.axis1 = axis1
+        self.axis2 = axis2
+        self.inline = inline
+
+        super().__init__(*args, **kwargs, strict=True, inline=inline)
+
+
 def alloc_diag(diag, offset=0, axis1=0, axis2=1):
     """Insert a vector on the diagonal of a zero-ed matrix.
 
@@ -3865,7 +3883,9 @@ def alloc_diag(diag, offset=0, axis1=0, axis2=1):
         axes = axes[:axis2] + [last_idx + 2] + axes[axis2:]
         result = result.transpose(axes)
 
-    return result
+    return AllocDiag2(
+        inputs=[diag], outputs=[result], offset=offset, axis1=axis1, axis2=axis2
+    )(diag)
 
 
 def diag(v, k=0):

pytensor/tensor/rewriting/linalg.py

@@ -5,12 +5,16 @@
 from pytensor import Variable
 from pytensor.graph import Apply, FunctionGraph
 from pytensor.graph.rewriting.basic import (
-    PatternNodeRewriter,
     copy_stack_trace,
     node_rewriter,
 )
 from pytensor.scalar.basic import Mul
-from pytensor.tensor.basic import ARange, Eye, TensorVariable, alloc, diagonal
+from pytensor.tensor.basic import (
+    AllocDiag2,
+    Eye,
+    TensorVariable,
+    diagonal,
+)
 from pytensor.tensor.blas import Dot22
 from pytensor.tensor.blockwise import Blockwise
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
@@ -41,7 +45,6 @@
     solve,
     solve_triangular,
 )
-from pytensor.tensor.subtensor import advanced_set_subtensor
 
 
 logger = logging.getLogger(__name__)
@@ -401,30 +404,59 @@ def _find_diag_from_eye_mul(potential_mul_input):
     eye_input = [
         mul_input
         for mul_input in inputs_to_mul
-        if mul_input.owner and isinstance(mul_input.owner.op, Eye)
+        if mul_input.owner
+        and (
+            isinstance(mul_input.owner.op, Eye)
+            or
+            # This whole condition checks if there is an Eye hiding inside a DimShuffle.
+            # This arises from batched elementwise multiplication between a tensor and an eye, e.g.:
+            # tensor(shape=(None, 3, 3) * eye(3). This is still potentially valid for diag rewrites.
+            (
+                isinstance(mul_input.owner.op, DimShuffle)
+                and mul_input.owner.inputs[0].owner is not None
+                and isinstance(mul_input.owner.inputs[0].owner.op, Eye)
+            )
+        )
     ]
-
-    # Check if 1's are being put on the main diagonal only (k = 0)
-    if eye_input and getattr(eye_input[0].owner.inputs[-1], "data", -1).item() != 0:
+    if not eye_input:
         return None
 
-    # If the broadcast pattern of eye_input is not (False, False), we do not get a diagonal matrix and thus, dont need to apply the rewrite
-    if eye_input and eye_input[0].broadcastable[-2:] != (False, False):
+    eye_input = eye_input[0]
+
+    # If this multiplication came from a batched operation, it will be wrapped in a DimShuffle
+    if isinstance(eye_input.owner.op, DimShuffle):
+        inner_eye = eye_input.owner.inputs[0]
+        if not isinstance(inner_eye.owner.op, Eye):
+            return None
+        # Check if 1's are being put on the main diagonal only (k = 0)
+        # and if the identity matrix is degenerate (column or row matrix)
+        if getattr(
+            inner_eye.owner.inputs[-1], "data", -1
+        ).item() != 0 or inner_eye.broadcastable[-2:] != (False, False):
+            return None
+
+    elif getattr(
+        eye_input.owner.inputs[-1], "data", -1
+    ).item() != 0 or eye_input.broadcastable[-2:] != (False, False):
         return None
 
     # Get all non Eye inputs (scalars/matrices/vectors)
-    non_eye_inputs = list(set(inputs_to_mul) - set(eye_input))
+    non_eye_inputs = list(set(inputs_to_mul) - {eye_input})
     return eye_input, non_eye_inputs
 
 
 @register_canonicalize("shape_unsafe")
 @register_stabilize("shape_unsafe")
 @node_rewriter([det])
-def rewrite_det_diag_from_eye_mul(fgraph, node):
+def rewrite_det_diag_to_prod_diag(fgraph, node):
     """
-     This rewrite takes advantage of the fact that for a diagonal matrix, the determinant value is the product of its diagonal elements.
+     This rewrite takes advantage of the fact that for a diagonal matrix, the determinant value is the product of its
+     diagonal elements.
 
-    The presence of a diagonal matrix is detected by inspecting the graph. This rewrite can identify diagonal matrices that arise as the result of elementwise multiplication with an identity matrix. Specialized computation is used to make this rewrite as efficient as possible, depending on whether the multiplication was with a scalar, vector or a matrix.
+    The presence of a diagonal matrix is detected by inspecting the graph. This rewrite can identify diagonal matrices
+    that arise as the result of elementwise multiplication with an identity matrix. Specialized computation is used to
+    make this rewrite as efficient as possible, depending on whether the multiplication was with a scalar,
+    vector or a matrix.
 
     Parameters
     ----------
@@ -438,53 +470,46 @@ def rewrite_det_diag_from_eye_mul(fgraph, node):
     list of Variable, optional
         List of optimized variables, or None if no optimization was performed
     """
-    potential_mul_input = node.inputs[0]
-    eye_non_eye_inputs = _find_diag_from_eye_mul(potential_mul_input)
-    if eye_non_eye_inputs is None:
+    inputs = node.inputs[0]
+
+    # Check for use of pt.diag first
+    if (
+        inputs.owner
+        and isinstance(inputs.owner.op, AllocDiag2)
+        and inputs.owner.op.offset == 0
+    ):
+        diag_input = inputs.owner.inputs[0]
+        det_val = diag_input.prod(axis=-1)
+        return [det_val]
+
+    # Check if the input is an elemwise multiply with identity matrix -- this also results in a diagonal matrix
+    inputs_or_none = _find_diag_from_eye_mul(inputs)
+
+    if inputs_or_none is None:
         return None
-    eye_input, non_eye_inputs = eye_non_eye_inputs
+
+    eye_input, non_eye_inputs = inputs_or_none
 
     # Dealing with only one other input
     if len(non_eye_inputs) != 1:
         return None
 
-    useful_eye, useful_non_eye = eye_input[0], non_eye_inputs[0]
+    eye_input, non_eye_input = eye_input[0], non_eye_inputs[0]
 
     # Checking if original x was scalar/vector/matrix
-    if useful_non_eye.type.broadcastable[-2:] == (True, True):
+    if non_eye_input.type.broadcastable[-2:] == (True, True):
         # For scalar
-        det_val = useful_non_eye.squeeze(axis=(-1, -2)) ** (useful_eye.shape[0])
-    elif useful_non_eye.type.broadcastable[-2:] == (False, False):
+        det_val = non_eye_input.squeeze(axis=(-1, -2)) ** (eye_input.shape[0])
+    elif non_eye_input.type.broadcastable[-2:] == (False, False):
         # For Matrix
-        det_val = useful_non_eye.diagonal(axis1=-1, axis2=-2).prod(axis=-1)
+        det_val = non_eye_input.diagonal(axis1=-1, axis2=-2).prod(axis=-1)
     else:
         # For vector
-        det_val = useful_non_eye.prod(axis=(-1, -2))
+        det_val = non_eye_input.prod(axis=(-1, -2))
     det_val = det_val.astype(node.outputs[0].type.dtype)
     return [det_val]
 
 
-arange = ARange("int64")
-det_diag_from_diag = PatternNodeRewriter(
-    (
-        det,
-        (
-            advanced_set_subtensor,
-            (alloc, 0, "sh1", "sh2"),
-            "x",
-            (arange, 0, "stop", 1),
-            (arange, 0, "stop", 1),
-        ),
-    ),
-    (prod, "x"),
-    name="det_diag_from_diag",
-    allow_multiple_clients=True,
-)
-register_canonicalize(det_diag_from_diag)
-register_stabilize(det_diag_from_diag)
-register_specialize(det_diag_from_diag)
-
-
 @register_canonicalize
 @register_stabilize
 @register_specialize

tests/tensor/rewriting/test_linalg.py

@@ -403,13 +403,19 @@ def test_det_diag_from_eye_mul(shape):
     # Initializing x based on scalar/vector/matrix
     x = pt.tensor("x", shape=shape)
     y = pt.eye(7) * x
+
     # Calculating determinant value using pt.linalg.det
     z_det = pt.linalg.det(y)
 
     # REWRITE TEST
-    f_rewritten = function([x], z_det, mode="FAST_RUN")
+    with pytensor.config.change_flags(optimizer_verbose=True):
+        f_rewritten = function([x], z_det, mode="FAST_RUN")
     nodes = f_rewritten.maker.fgraph.apply_nodes
-    assert not any(isinstance(node.op, Det) for node in nodes)
+
+    assert not any(
+        isinstance(node.op, Det) or isinstance(getattr(node.op, "core_op", None), Det)
+        for node in nodes
+    )
 
     # NUMERIC VALUE TEST
     if len(shape) == 0:
@@ -418,6 +424,7 @@ def test_det_diag_from_eye_mul(shape):
         x_test = np.random.rand(*shape).astype(config.floatX)
     else:
         x_test = np.random.rand(*shape).astype(config.floatX)
+
     x_test_matrix = np.eye(7) * x_test
     det_val = np.linalg.det(x_test_matrix)
     rewritten_val = f_rewritten(x_test)