Add OpFromGraph wrapper around alloc_diag

pytensor/compile/builders.py

@@ -8,7 +8,6 @@
 
 from pytensor.compile.function import function
 from pytensor.compile.function.pfunc import rebuild_collect_shared
-from pytensor.compile.mode import optdb
 from pytensor.compile.sharedvalue import SharedVariable
 from pytensor.configdefaults import config
 from pytensor.gradient import DisconnectedType, Rop, grad
@@ -24,7 +23,6 @@
 from pytensor.graph.null_type import NullType
 from pytensor.graph.op import HasInnerGraph, Op
 from pytensor.graph.replace import clone_replace
-from pytensor.graph.rewriting.basic import in2out, node_rewriter
 from pytensor.graph.utils import MissingInputError
 
 
@@ -575,7 +573,7 @@ def lop_overrides(inps, grads):
             for inp_grad in input_grads
             if not isinstance(inp_grad.type, DisconnectedType | NullType)
         ]
-        lop_op = type(self)(
+        lop_op = OpFromGraph(
             inputs=inner_inputs + connected_inner_outputs + connected_output_grads,
             outputs=connected_input_grads,
             inline=self.is_inline,
@@ -669,7 +667,7 @@ def _build_and_cache_rop_op(self):
             for out_grad in output_grads
             if not isinstance(out_grad.type, DisconnectedType | NullType)
         ]
-        rop_op = type(self)(
+        rop_op = OpFromGraph(
             inputs=inner_inputs + eval_points,
             outputs=filtered_output_grads,
             inline=self.is_inline,
@@ -852,29 +850,3 @@ def perform(self, node, inputs, outputs):
         assert len(variables) == len(outputs)
         for output, variable in zip(outputs, variables):
             output[0] = variable
-
-
-@node_rewriter([OpFromGraph])
-def inline_ofg_expansion(fgraph, node):
-    """
-    This optimization expands internal graph of OpFromGraph.
-    Only performed if node.op.is_inline == True
-    Doing so can improve optimization at the cost of compilation speed.
-    """
-    op = node.op
-    if not isinstance(op, OpFromGraph):
-        return False
-    if not op.is_inline:
-        return False
-    return clone_replace(op.inner_outputs, dict(zip(op.inner_inputs, node.inputs)))
-
-
-# We want to run this before the first merge optimizer
-# and before the first scan optimizer.
-optdb.register(
-    "inline_ofg_expansion",
-    in2out(inline_ofg_expansion),
-    "fast_compile",
-    "fast_run",
-    position=-0.01,
-)

pytensor/link/jax/dispatch/basic.py

@@ -1,10 +1,13 @@
 import warnings
+from collections.abc import Callable
 from functools import singledispatch
 
 import jax
 import jax.numpy as jnp
 import numpy as np
 
+from pytensor.compile import JAX
+from pytensor.compile.builders import OpFromGraph
 from pytensor.compile.ops import DeepCopyOp, ViewOp
 from pytensor.configdefaults import config
 from pytensor.graph.fg import FunctionGraph
@@ -114,3 +117,24 @@ def viewop(x):
         return x
 
     return viewop
+
+
+@jax_funcify.register(OpFromGraph)
+def jax_funcify_OpFromGraph(ofg: OpFromGraph, node=None, **kwargs) -> Callable:
+    _ = kwargs.pop("storage_map", None)
+
+    # Apply inner rewrites
+    JAX.optimizer(ofg.fgraph)
+    fgraph_fn = jax_funcify(ofg.fgraph, **kwargs)
+
+    if len(ofg.fgraph.outputs) == 1:
+
+        def opfromgraph(*inputs):
+            return fgraph_fn(*inputs)[0]
+
+    else:
+
+        def opfromgraph(*inputs):
+            return fgraph_fn(*inputs)
+
+    return opfromgraph

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
@@ -1333,6 +1334,25 @@ def infer_shape(self, fgraph, node, in_shapes):
     def grad(self, inp, grads):
         return [grad_undefined(self, i, inp[i]) for i in range(3)]
 
+    @staticmethod
+    def is_offset_zero(node) -> bool:
+        """
+        Test if an Eye Op has a diagonal offset of zero
+
+        Parameters
+        ----------
+        node
+            Eye node to test
+
+        Returns
+        -------
+        is_offset_zero: bool
+            True if the offset is zero (``k = 0``).
+        """
+
+        offset = node.inputs[-1]
+        return isinstance(offset, Constant) and offset.data.item() == 0
+
 
 def eye(n, m=None, k=0, dtype=None):
     """Return a 2-D array with ones on the diagonal and zeros elsewhere.
@@ -3726,109 +3746,37 @@ def trace(a, offset=0, axis1=0, axis2=1):
     return diagonal(a, offset=offset, axis1=axis1, axis2=axis2).sum(-1)
 
 
-class AllocDiag(Op):
-    """An `Op` that copies a vector to the diagonal of a zero-ed matrix."""
+class AllocDiag(OpFromGraph):
+    """
+    Wrapper Op for alloc_diag graphs
+    """
 
-    __props__ = ("offset", "axis1", "axis2")
+    __props__ = ("axis1", "axis2")
 
-    def __init__(self, offset=0, axis1=0, axis2=1):
-        """
-        Parameters
-        ----------
-        offset: int
-            Offset of the diagonal from the main diagonal defined by `axis1`
-            and `axis2`. Can be positive or negative.  Defaults to main
-            diagonal (i.e. 0).
-        axis1: int
-            Axis to be used as the first axis of the 2-D sub-arrays to which
-            the diagonals will be allocated.  Defaults to first axis (i.e. 0).
-        axis2: int
-            Axis to be used as the second axis of the 2-D sub-arrays to which
-            the diagonals will be allocated.  Defaults to second axis (i.e. 1).
-        """
-        warnings.warn(
-            "AllocDiag is deprecated. Use `alloc_diag` instead",
-            FutureWarning,
-        )
-        self.offset = offset
-        if axis1 < 0 or axis2 < 0:
-            raise NotImplementedError("AllocDiag does not support negative axis")
-        if axis1 == axis2:
-            raise ValueError("axis1 and axis2 cannot be the same")
+    def __init__(self, *args, axis1, axis2, **kwargs):
         self.axis1 = axis1
         self.axis2 = axis2
 
-    def make_node(self, diag):
-        diag = as_tensor_variable(diag)
-        if diag.type.ndim < 1:
-            raise ValueError(
-                "AllocDiag needs an input with 1 or more dimensions", diag.type
-            )
-        return Apply(
-            self,
-            [diag],
-            [diag.type.clone(shape=(None,) * (diag.ndim + 1))()],
-        )
-
-    def perform(self, node, inputs, outputs):
-        (x,) = inputs
-        (z,) = outputs
-
-        axis1 = np.minimum(self.axis1, self.axis2)
-        axis2 = np.maximum(self.axis1, self.axis2)
-        offset = self.offset
-
-        # Create array with one extra dimension for resulting matrix
-        result_shape = x.shape[:-1] + (x.shape[-1] + abs(offset),) * 2
-        result = np.zeros(result_shape, dtype=x.dtype)
-
-        # Create slice for diagonal in final 2 axes
-        idxs = np.arange(x.shape[-1])
-        diagonal_slice = (len(result_shape) - 2) * [slice(None)] + [
-            idxs + np.maximum(0, -offset),
-            idxs + np.maximum(0, offset),
-        ]
-
-        # Fill in final 2 axes with x
-        result[tuple(diagonal_slice)] = x
+        super().__init__(*args, **kwargs, strict=True)
 
-        if len(x.shape) > 1:
-            # Re-order axes so they correspond to diagonals at axis1, axis2
-            axes = list(range(len(x.shape[:-1])))
-            last_idx = axes[-1]
-            axes = axes[:axis1] + [last_idx + 1] + axes[axis1:]
-            axes = axes[:axis2] + [last_idx + 2] + axes[axis2:]
-            result = result.transpose(axes)
-
-        z[0] = result
-
-    def grad(self, inputs, gout):
-        (gz,) = gout
-        return [diagonal(gz, offset=self.offset, axis1=self.axis1, axis2=self.axis2)]
-
-    def infer_shape(self, fgraph, nodes, shapes):
-        (x_shape,) = shapes
-        axis1 = np.minimum(self.axis1, self.axis2)
-        axis2 = np.maximum(self.axis1, self.axis2)
-
-        result_shape = list(x_shape[:-1])
-        diag_shape = x_shape[-1] + abs(self.offset)
-        result_shape = result_shape[:axis1] + [diag_shape] + result_shape[axis1:]
-        result_shape = result_shape[:axis2] + [diag_shape] + result_shape[axis2:]
-        return [tuple(result_shape)]
+    @staticmethod
+    def is_offset_zero(node) -> bool:
+        """
+        Test if an AllocDiag Op has a diagonal offset of zero
 
-    def __setstate__(self, state):
-        if "view_map" in state:
-            del state["view_map"]
+        Parameters
+        ----------
+        node
+            AllocDiag node to test
 
-        self.__dict__.update(state)
+        Returns
+        -------
+        is_offset_zero: bool
+            True if the offset is zero (``k = 0``).
+        """
 
-        if "offset" not in state:
-            self.offset = 0
-        if "axis1" not in state:
-            self.axis1 = 0
-        if "axis2" not in state:
-            self.axis2 = 1
+        offset = node.inputs[-1]
+        return isinstance(offset, Constant) and offset.data.item() == 0
 
 
 def alloc_diag(diag, offset=0, axis1=0, axis2=1):
@@ -3837,8 +3785,11 @@ def alloc_diag(diag, offset=0, axis1=0, axis2=1):
     diagonal(alloc_diag(x)) == x
     """
     from pytensor.tensor import set_subtensor
+    from pytensor.tensor.math import maximum
 
     diag = as_tensor_variable(diag)
+    offset = as_tensor_variable(offset)
+
     axis1, axis2 = normalize_axis_tuple((axis1, axis2), ndim=diag.type.ndim + 1)
     if axis1 > axis2:
         axis1, axis2 = axis2, axis1
@@ -3850,8 +3801,8 @@ def alloc_diag(diag, offset=0, axis1=0, axis2=1):
     # Create slice for diagonal in final 2 axes
     idxs = arange(diag.shape[-1])
     diagonal_slice = (slice(None),) * (len(result_shape) - 2) + (
-        idxs + np.maximum(0, -offset),
-        idxs + np.maximum(0, offset),
+        idxs + maximum(0, -offset),
+        idxs + maximum(0, offset),
     )
 
     # Fill in final 2 axes with diag
@@ -3865,7 +3816,12 @@ 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 AllocDiag(
+        inputs=[diag, offset],
+        outputs=[result],
+        axis1=axis1,
+        axis2=axis2,
+    )(diag, offset)
 
 
 def diag(v, k=0):

pytensor/tensor/elemwise.py

@@ -181,6 +181,16 @@ def __init__(self, input_broadcastable, new_order):
         self.augment = sorted([i for i, x in enumerate(new_order) if x == "x"])
         self.drop = drop
 
+        input_ndim = len(input_broadcastable)
+
+        self.is_left_expand_dims = "x" in new_order and (
+            input_ndim == 0 or new_order[:input_ndim] == list(range(input_ndim))
+        )
+
+        self.is_right_expand_dims = "x" in new_order and (
+            input_ndim == 0 or new_order[-input_ndim:] == list(range(input_ndim))
+        )
+
         if self.inplace:
             self.view_map = {0: [0]}
 

pytensor/tensor/rewriting/__init__.py

@@ -10,6 +10,7 @@
 import pytensor.tensor.rewriting.jax
 import pytensor.tensor.rewriting.linalg
 import pytensor.tensor.rewriting.math
+import pytensor.tensor.rewriting.ofg
 import pytensor.tensor.rewriting.shape
 import pytensor.tensor.rewriting.special
 import pytensor.tensor.rewriting.subtensor