Add exceptions for hot loops

pytensor/compile/builders.py

@@ -863,5 +863,6 @@ class OpFromGraph(Op, HasInnerGraph):
     def perform(self, node, inputs, outputs):
         variables = self.fn(*inputs)
         assert len(variables) == len(outputs)
-        for output, variable in zip(outputs, variables, strict=True):
+        # strict=False because asserted above
+        for output, variable in zip(outputs, variables, strict=False):
             output[0] = variable

pytensor/compile/function/types.py

@@ -1002,8 +1002,9 @@ class Function:
         # if we are allowing garbage collection, remove the
         # output reference from the internal storage cells
         if getattr(self.vm, "allow_gc", False):
+            # strict=False because we are in a hot loop
             for o_container, o_variable in zip(
-                self.output_storage, self.maker.fgraph.outputs, strict=True
+                self.output_storage, self.maker.fgraph.outputs, strict=False
             ):
                 if o_variable.owner is not None:
                     # this node is the variable of computation
@@ -1012,8 +1013,9 @@ class Function:
 
         if getattr(self.vm, "need_update_inputs", True):
             # Update the inputs that have an update function
+            # strict=False because we are in a hot loop
             for input, storage in reversed(
-                list(zip(self.maker.expanded_inputs, input_storage, strict=True))
+                list(zip(self.maker.expanded_inputs, input_storage, strict=False))
             ):
                 if input.update is not None:
                     storage.data = outputs.pop()
@@ -1044,7 +1046,8 @@ class Function:
                 assert len(self.output_keys) == len(outputs)
 
                 if output_subset is None:
-                    return dict(zip(self.output_keys, outputs, strict=True))
+                    # strict=False because we are in a hot loop
+                    return dict(zip(self.output_keys, outputs, strict=False))
                 else:
                     return {
                         self.output_keys[index]: outputs[index]
@@ -1111,8 +1114,9 @@ def _pickle_Function(f):
     ins = list(f.input_storage)
     input_storage = []
 
+    # strict=False because we are in a hot loop
     for (input, indices, inputs), (required, refeed, default) in zip(
-        f.indices, f.defaults, strict=True
+        f.indices, f.defaults, strict=False
     ):
         input_storage.append(ins[0])
         del ins[0]

pytensor/ifelse.py

@@ -305,7 +305,8 @@ class IfElse(_NoPythonOp):
                     if len(ls) > 0:
                         return ls
                     else:
-                        for out, t in zip(outputs, input_true_branch, strict=True):
+                        # strict=False because we are in a hot loop
+                        for out, t in zip(outputs, input_true_branch, strict=False):
                             compute_map[out][0] = 1
                             val = storage_map[t][0]
                             if self.as_view:
@@ -325,7 +326,8 @@ class IfElse(_NoPythonOp):
                     if len(ls) > 0:
                         return ls
                     else:
-                        for out, f in zip(outputs, inputs_false_branch, strict=True):
+                        # strict=False because we are in a hot loop
+                        for out, f in zip(outputs, inputs_false_branch, strict=False):
                             compute_map[out][0] = 1
                             # can't view both outputs unless destroyhandler
                             # improves

pytensor/link/basic.py

@@ -539,12 +539,14 @@ class WrapLinker(Linker):
 
         def f():
             for inputs in input_lists[1:]:
-                for input1, input2 in zip(inputs0, inputs, strict=True):
+                # strict=False because we are in a hot loop
+                for input1, input2 in zip(inputs0, inputs, strict=False):
                     input2.storage[0] = copy(input1.storage[0])
             for x in to_reset:
                 x[0] = None
             pre(self, [input.data for input in input_lists[0]], order, thunk_groups)
-            for i, (thunks, node) in enumerate(zip(thunk_groups, order, strict=True)):
+            # strict=False because we are in a hot loop
+            for i, (thunks, node) in enumerate(zip(thunk_groups, order, strict=False)):
                 try:
                     wrapper(self.fgraph, i, node, *thunks)
                 except Exception:
@@ -666,8 +668,9 @@ class JITLinker(PerformLinker):
         ):
             outputs = fgraph_jit(*[self.input_filter(x[0]) for x in thunk_inputs])
 
+            # strict=False because we are in a hot loop
             for o_var, o_storage, o_val in zip(
-                fgraph.outputs, thunk_outputs, outputs, strict=True
+                fgraph.outputs, thunk_outputs, outputs, strict=False
             ):
                 compute_map[o_var][0] = True
                 o_storage[0] = self.output_filter(o_var, o_val)

pytensor/link/c/basic.py

@@ -1993,25 +1993,26 @@ class DualLinker(Linker):
         )
 
         def f():
-            for input1, input2 in zip(i1, i2, strict=True):
+            # strict=False because we are in a hot loop
+            for input1, input2 in zip(i1, i2, strict=False):
                 # Set the inputs to be the same in both branches.
                 # The copy is necessary in order for inplace ops not to
                 # interfere.
                 input2.storage[0] = copy(input1.storage[0])
             for thunk1, thunk2, node1, node2 in zip(
-                thunks1, thunks2, order1, order2, strict=True
+                thunks1, thunks2, order1, order2, strict=False
             ):
-                for output, storage in zip(node1.outputs, thunk1.outputs, strict=True):
+                for output, storage in zip(node1.outputs, thunk1.outputs, strict=False):
                     if output in no_recycling:
                         storage[0] = None
-                for output, storage in zip(node2.outputs, thunk2.outputs, strict=True):
+                for output, storage in zip(node2.outputs, thunk2.outputs, strict=False):
                     if output in no_recycling:
                         storage[0] = None
                 try:
                     thunk1()
                     thunk2()
                     for output1, output2 in zip(
-                        thunk1.outputs, thunk2.outputs, strict=True
+                        thunk1.outputs, thunk2.outputs, strict=False
                     ):
                         self.checker(output1, output2)
                 except Exception:

pytensor/link/numba/dispatch/basic.py

@@ -401,9 +401,10 @@ def generate_fallback_impl(op, node=None, storage_map=None, **kwargs):
     else:
 
         def py_perform_return(inputs):
+            # strict=False because we are in a hot loop
             return tuple(
                 out_type.filter(out[0])
-                for out_type, out in zip(output_types, py_perform(inputs), strict=True)
+                for out_type, out in zip(output_types, py_perform(inputs), strict=False)
             )
 
     @numba_njit

pytensor/link/pytorch/dispatch/shape.py

@@ -34,7 +34,8 @@ def pytorch_funcify_Shape_i(op, **kwargs):
 def pytorch_funcify_SpecifyShape(op, node, **kwargs):
     def specifyshape(x, *shape):
         assert x.ndim == len(shape)
-        for actual, expected in zip(x.shape, shape, strict=True):
+        # strict=False because asserted above
+        for actual, expected in zip(x.shape, shape, strict=False):
             if expected is None:
                 continue
             if actual != expected:

pytensor/link/utils.py

@@ -190,8 +190,9 @@ def streamline(
             for x in no_recycling:
                 x[0] = None
             try:
+                # strict=False because we are in a hot loop
                 for thunk, node, old_storage in zip(
-                    thunks, order, post_thunk_old_storage, strict=True
+                    thunks, order, post_thunk_old_storage, strict=False
                 ):
                     thunk()
                     for old_s in old_storage:
@@ -206,7 +207,8 @@ def streamline(
             for x in no_recycling:
                 x[0] = None
             try:
-                for thunk, node in zip(thunks, order, strict=True):
+                # strict=False because we are in a hot loop
+                for thunk, node in zip(thunks, order, strict=False):
                     thunk()
             except Exception:
                 raise_with_op(fgraph, node, thunk)

pytensor/scalar/basic.py

@@ -1150,8 +1150,9 @@ class ScalarOp(COp):
         else:
             variables = from_return_values(self.impl(*inputs))
             assert len(variables) == len(output_storage)
+            # strict=False because we are in a hot loop
             for out, storage, variable in zip(
-                node.outputs, output_storage, variables, strict=True
+                node.outputs, output_storage, variables, strict=False
             ):
                 dtype = out.dtype
                 storage[0] = self._cast_scalar(variable, dtype)
@@ -4328,7 +4329,8 @@ class Composite(ScalarInnerGraphOp):
 
     def perform(self, node, inputs, output_storage):
         outputs = self.py_perform_fn(*inputs)
-        for storage, out_val in zip(output_storage, outputs, strict=True):
+        # strict=False because we are in a hot loop
+        for storage, out_val in zip(output_storage, outputs, strict=False):
             storage[0] = out_val
 
     def grad(self, inputs, output_grads):

pytensor/scalar/loop.py

@@ -93,7 +93,7 @@ class ScalarLoop(ScalarInnerGraphOp):
                 )
         else:
             update = outputs
-        for i, u in zip(init[: len(update)], update, strict=True):
+        for i, u in zip(init, update, strict=False):
             if i.type != u.type:
                 raise TypeError(
                     "Init and update types must be the same: "
@@ -207,7 +207,8 @@ class ScalarLoop(ScalarInnerGraphOp):
             for i in range(n_steps):
                 carry = inner_fn(*carry, *constant)
 
-        for storage, out_val in zip(output_storage, carry, strict=True):
+        # strict=False because we are in a hot loop
+        for storage, out_val in zip(output_storage, carry, strict=False):
             storage[0] = out_val
 
     @property

pytensor/scan/op.py

@@ -1278,8 +1278,9 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
         if len(self.inner_outputs) != len(other.inner_outputs):
             return False
 
+        # strict=False because length already compared above
         for self_in, other_in in zip(
-            self.inner_inputs, other.inner_inputs, strict=True
+            self.inner_inputs, other.inner_inputs, strict=False
         ):
             if self_in.type != other_in.type:
                 return False

pytensor/tensor/basic.py

@@ -3463,7 +3463,8 @@ class PermuteRowElements(Op):
 
         # Make sure the output is big enough
         out_s = []
-        for xdim, ydim in zip(x_s, y_s, strict=True):
+        # strict=False because we are in a hot loop
+        for xdim, ydim in zip(x_s, y_s, strict=False):
             if xdim == ydim:
                 outdim = xdim
             elif xdim == 1:

pytensor/tensor/blockwise.py

@@ -342,16 +342,17 @@ class Blockwise(Op):
     def _check_runtime_broadcast(self, node, inputs):
         batch_ndim = self.batch_ndim(node)
 
+        # strict=False because we are in a hot loop
         for dims_and_bcast in zip(
             *[
                 zip(
                     input.shape[:batch_ndim],
                     sinput.type.broadcastable[:batch_ndim],
-                    strict=True,
+                    strict=False,
                 )
-                for input, sinput in zip(inputs, node.inputs, strict=True)
+                for input, sinput in zip(inputs, node.inputs, strict=False)
             ],
-            strict=True,
+            strict=False,
         ):
             if any(d != 1 for d, _ in dims_and_bcast) and (1, False) in dims_and_bcast:
                 raise ValueError(
@@ -374,8 +375,9 @@ class Blockwise(Op):
         if not isinstance(res, tuple):
             res = (res,)
 
+        # strict=False because we are in a hot loop
         for node_out, out_storage, r in zip(
-            node.outputs, output_storage, res, strict=True
+            node.outputs, output_storage, res, strict=False
         ):
             out_dtype = getattr(node_out, "dtype", None)
             if out_dtype and out_dtype != r.dtype:

pytensor/tensor/elemwise.py

@@ -737,8 +737,9 @@ class Elemwise(OpenMPOp):
         if nout == 1:
             variables = [variables]
 
+        # strict=False because we are in a hot loop
         for i, (variable, storage, nout) in enumerate(
-            zip(variables, output_storage, node.outputs, strict=True)
+            zip(variables, output_storage, node.outputs, strict=False)
         ):
             storage[0] = variable = np.asarray(variable, dtype=nout.dtype)
 
@@ -753,12 +754,13 @@ class Elemwise(OpenMPOp):
 
     @staticmethod
     def _check_runtime_broadcast(node, inputs):
+        # strict=False because we are in a hot loop
         for dims_and_bcast in zip(
             *[
                 zip(input.shape, sinput.type.broadcastable, strict=False)
-                for input, sinput in zip(inputs, node.inputs, strict=True)
+                for input, sinput in zip(inputs, node.inputs, strict=False)
             ],
-            strict=True,
+            strict=False,
         ):
             if any(d != 1 for d, _ in dims_and_bcast) and (1, False) in dims_and_bcast:
                 raise ValueError(

pytensor/tensor/random/basic.py

@@ -1862,7 +1862,8 @@ class CategoricalRV(RandomVariable):
             # to `p.shape[:-1]` in the call to `vsearchsorted` below.
             if len(size) < (p.ndim - 1):
                 raise ValueError("`size` is incompatible with the shape of `p`")
-            for s, ps in zip(reversed(size), reversed(p.shape[:-1]), strict=True):
+            # strict=False because we are in a hot loop
+            for s, ps in zip(reversed(size), reversed(p.shape[:-1]), strict=False):
                 if s == 1 and ps != 1:
                     raise ValueError("`size` is incompatible with the shape of `p`")
 

pytensor/tensor/random/utils.py

@@ -44,7 +44,8 @@ def params_broadcast_shapes(
     max_fn = maximum if use_pytensor else max
 
     rev_extra_dims: list[int] = []
-    for ndim_param, param_shape in zip(ndims_params, param_shapes, strict=True):
+    # strict=False because we are in a hot loop
+    for ndim_param, param_shape in zip(ndims_params, param_shapes, strict=False):
         # We need this in order to use `len`
         param_shape = tuple(param_shape)
         extras = tuple(param_shape[: (len(param_shape) - ndim_param)])
@@ -63,11 +64,12 @@ def params_broadcast_shapes(
 
     extra_dims = tuple(reversed(rev_extra_dims))
 
+    # strict=False because we are in a hot loop
     bcast_shapes = [
         (extra_dims + tuple(param_shape)[-ndim_param:])
         if ndim_param > 0
         else extra_dims
-        for ndim_param, param_shape in zip(ndims_params, param_shapes, strict=True)
+        for ndim_param, param_shape in zip(ndims_params, param_shapes, strict=False)
     ]
 
     return bcast_shapes
@@ -110,10 +112,11 @@ def broadcast_params(
     use_pytensor = False
     param_shapes = []
     for p in params:
+        # strict=False because we are in a hot loop
         param_shape = tuple(
             1 if bcast else s
             for s, bcast in zip(
-                p.shape, getattr(p, "broadcastable", (False,) * p.ndim), strict=True
+                p.shape, getattr(p, "broadcastable", (False,) * p.ndim), strict=False
             )
         )
         use_pytensor |= isinstance(p, Variable)
@@ -124,9 +127,10 @@ def broadcast_params(
     )
     broadcast_to_fn = broadcast_to if use_pytensor else np.broadcast_to
 
+    # strict=False because we are in a hot loop
     bcast_params = [
         broadcast_to_fn(param, shape)
-        for shape, param in zip(shapes, params, strict=True)
+        for shape, param in zip(shapes, params, strict=False)
     ]
 
     return bcast_params

pytensor/tensor/rewriting/subtensor.py

@@ -683,7 +683,7 @@ def local_subtensor_of_alloc(fgraph, node):
     # Slices to take from val
     val_slices = []
 
-    for i, (sl, dim) in enumerate(zip(slices, dims[: len(slices)], strict=True)):
+    for i, (sl, dim) in enumerate(zip(slices, dims, strict=False)):
         # If val was not copied over that dim,
         # we need to take the appropriate subtensor on it.
         if i >= n_added_dims:

pytensor/tensor/shape.py

@@ -448,8 +448,9 @@ class SpecifyShape(COp):
             raise AssertionError(
                 f"SpecifyShape: Got {x.ndim} dimensions (shape {x.shape}), expected {ndim} dimensions with shape {tuple(shape)}."
             )
+        # strict=False because we are in a hot loop
         if not all(
-            xs == s for xs, s in zip(x.shape, shape, strict=True) if s is not None
+            xs == s for xs, s in zip(x.shape, shape, strict=False) if s is not None
         ):
             raise AssertionError(
                 f"SpecifyShape: Got shape {x.shape}, expected {tuple(int(s) if s is not None else None for s in shape)}."
@@ -578,15 +579,12 @@ def specify_shape(
     x = ptb.as_tensor_variable(x)  # type: ignore[arg-type,unused-ignore]
     # The above is a type error in Python 3.9 but not 3.12.
     # Thus we need to ignore unused-ignore on 3.12.
+    new_shape_info = any(
+        s != xts for (s, xts) in zip(shape, x.type.shape, strict=False) if s is not None
+    )
 
     # If shape does not match x.ndim, we rely on the `Op` to raise a ValueError
-    if len(shape) != x.type.ndim:
-        return _specify_shape(x, *shape)
-
-    new_shape_matches = all(
-        s == xts for (s, xts) in zip(shape, x.type.shape, strict=True) if s is not None
-    )
-    if new_shape_matches:
+    if not new_shape_info and len(shape) == x.type.ndim:
         return x
 
     return _specify_shape(x, *shape)

pytensor/tensor/type.py

@@ -248,9 +248,10 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
                 " PyTensor C code does not support that.",
             )
 
+        # strict=False because we are in a hot loop
         if not all(
             ds == ts if ts is not None else True
-            for ds, ts in zip(data.shape, self.shape, strict=True)
+            for ds, ts in zip(data.shape, self.shape, strict=False)
         ):
             raise TypeError(
                 f"The type's shape ({self.shape}) is not compatible with the data's ({data.shape})"
@@ -319,6 +320,7 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
         return False
 
     def is_super(self, otype):
+        # strict=False because we are in a hot loop
         if (
             isinstance(otype, type(self))
             and otype.dtype == self.dtype
@@ -327,7 +329,7 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
             # but not less
             and all(
                 sb == ob or sb is None
-                for sb, ob in zip(self.shape, otype.shape, strict=True)
+                for sb, ob in zip(self.shape, otype.shape, strict=False)
             )
         ):
             return True