Speedup Python implementation of Blockwise

pyproject.toml

@@ -130,7 +130,7 @@ exclude = ["doc/", "pytensor/_version.py"]
 docstring-code-format = true
 
 [tool.ruff.lint]
-select = ["B905", "C", "E", "F", "I", "UP", "W", "RUF", "PERF", "PTH", "ISC", "T20", "NPY201"]
+select = ["C", "E", "F", "I", "UP", "W", "RUF", "PERF", "PTH", "ISC", "T20", "NPY201"]
 ignore = ["C408", "C901", "E501", "E741", "RUF012", "PERF203", "ISC001"]
 unfixable = [
     # zip-strict: the auto-fix adds `strict=False` but we might want `strict=True` instead

pytensor/compile/builders.py

@@ -873,7 +873,6 @@ def clone(self):
 
     def perform(self, node, inputs, outputs):
         variables = self.fn(*inputs)
-        assert len(variables) == len(outputs)
-        # strict=False because asserted above
-        for output, variable in zip(outputs, variables, strict=False):
+        # zip strict not specified because we are in a hot loop
+        for output, variable in zip(outputs, variables):
             output[0] = variable

pytensor/compile/function/types.py

@@ -924,7 +924,8 @@ def __call__(self, *args, output_subset=None, **kwargs):
 
         # Reinitialize each container's 'provided' counter
         if trust_input:
-            for arg_container, arg in zip(input_storage, args, strict=False):
+            # zip strict not specified because we are in a hot loop
+            for arg_container, arg in zip(input_storage, args):
                 arg_container.storage[0] = arg
         else:
             for arg_container in input_storage:
@@ -934,7 +935,8 @@ def __call__(self, *args, output_subset=None, **kwargs):
                 raise TypeError("Too many parameter passed to pytensor function")
 
             # Set positional arguments
-            for arg_container, arg in zip(input_storage, args, strict=False):
+            # zip strict not specified because we are in a hot loop
+            for arg_container, arg in zip(input_storage, args):
                 # See discussion about None as input
                 # https://groups.google.com/group/theano-dev/browse_thread/thread/920a5e904e8a8525/4f1b311a28fc27e5
                 if arg is None:

pytensor/graph/op.py

@@ -502,7 +502,7 @@ def make_py_thunk(
         self,
         node: Apply,
         storage_map: StorageMapType,
-        compute_map: ComputeMapType,
+        compute_map: ComputeMapType | None,
         no_recycling: list[Variable],
         debug: bool = False,
     ) -> ThunkType:
@@ -513,25 +513,38 @@ def make_py_thunk(
         """
         node_input_storage = [storage_map[r] for r in node.inputs]
         node_output_storage = [storage_map[r] for r in node.outputs]
-        node_compute_map = [compute_map[r] for r in node.outputs]
 
         if debug and hasattr(self, "debug_perform"):
             p = node.op.debug_perform
         else:
             p = node.op.perform
 
-        @is_thunk_type
-        def rval(
-            p=p,
-            i=node_input_storage,
-            o=node_output_storage,
-            n=node,
-            cm=node_compute_map,
-        ):
-            r = p(n, [x[0] for x in i], o)
-            for entry in cm:
-                entry[0] = True
-            return r
+        if compute_map is None:
+
+            @is_thunk_type
+            def rval(
+                p=p,
+                i=node_input_storage,
+                o=node_output_storage,
+                n=node,
+            ):
+                return p(n, [x[0] for x in i], o)
+
+        else:
+            node_compute_map = [compute_map[r] for r in node.outputs]
+
+            @is_thunk_type
+            def rval(
+                p=p,
+                i=node_input_storage,
+                o=node_output_storage,
+                n=node,
+                cm=node_compute_map,
+            ):
+                r = p(n, [x[0] for x in i], o)
+                for entry in cm:
+                    entry[0] = True
+                return r
 
         rval.inputs = node_input_storage
         rval.outputs = node_output_storage

pytensor/ifelse.py

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

pytensor/link/basic.py

@@ -539,14 +539,14 @@ def make_thunk(self, **kwargs):
 
         def f():
             for inputs in input_lists[1:]:
-                # strict=False because we are in a hot loop
-                for input1, input2 in zip(inputs0, inputs, strict=False):
+                # zip strict not specified because we are in a hot loop
+                for input1, input2 in zip(inputs0, inputs):
                     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)
-            # strict=False because we are in a hot loop
-            for i, (thunks, node) in enumerate(zip(thunk_groups, order, strict=False)):
+            # zip strict not specified because we are in a hot loop
+            for i, (thunks, node) in enumerate(zip(thunk_groups, order)):
                 try:
                     wrapper(self.fgraph, i, node, *thunks)
                 except Exception:
@@ -668,8 +668,8 @@ def thunk(
                 #  since the error may come from any of them?
                 raise_with_op(self.fgraph, output_nodes[0], thunk)
 
-            # strict=False because we are in a hot loop
-            for o_storage, o_val in zip(thunk_outputs, outputs, strict=False):
+            # zip strict not specified because we are in a hot loop
+            for o_storage, o_val in zip(thunk_outputs, outputs):
                 o_storage[0] = o_val
 
         thunk.inputs = thunk_inputs

pytensor/link/c/basic.py

@@ -1988,27 +1988,23 @@ def make_thunk(self, **kwargs):
         )
 
         def f():
-            # strict=False because we are in a hot loop
-            for input1, input2 in zip(i1, i2, strict=False):
+            # zip strict not specified because we are in a hot loop
+            for input1, input2 in zip(i1, i2):
                 # 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=False
-            ):
-                for output, storage in zip(node1.outputs, thunk1.outputs, strict=False):
+            for thunk1, thunk2, node1, node2 in zip(thunks1, thunks2, order1, order2):
+                for output, storage in zip(node1.outputs, thunk1.outputs):
                     if output in no_recycling:
                         storage[0] = None
-                for output, storage in zip(node2.outputs, thunk2.outputs, strict=False):
+                for output, storage in zip(node2.outputs, thunk2.outputs):
                     if output in no_recycling:
                         storage[0] = None
                 try:
                     thunk1()
                     thunk2()
-                    for output1, output2 in zip(
-                        thunk1.outputs, thunk2.outputs, strict=False
-                    ):
+                    for output1, output2 in zip(thunk1.outputs, thunk2.outputs):
                         self.checker(output1, output2)
                 except Exception:
                     raise_with_op(fgraph, node1)

pytensor/link/c/op.py

@@ -39,7 +39,7 @@
         self,
         node: Apply,
         storage_map: StorageMapType,
-        compute_map: ComputeMapType,
+        compute_map: ComputeMapType | None,
         no_recycling: Collection[Variable],
     ) -> CThunkWrapperType:
         """Create a thunk for a C implementation.
@@ -86,11 +86,17 @@
         )
         thunk, node_input_filters, node_output_filters = outputs
 
-        @is_cthunk_wrapper_type
-        def rval():
-            thunk()
-            for o in node.outputs:
-                compute_map[o][0] = True
+        if compute_map is None:
+            rval = is_cthunk_wrapper_type(thunk)
+
+        else:
+            cm_entries = [compute_map[o] for o in node.outputs]
+
+            @is_cthunk_wrapper_type
+            def rval(thunk=thunk, cm_entries=cm_entries):
+                thunk()
+                for entry in cm_entries:
+                    entry[0] = True
 
         rval.thunk = thunk
         rval.cthunk = thunk.cthunk

pytensor/link/numba/dispatch/basic.py

@@ -312,10 +312,10 @@ def py_perform_return(inputs):
     else:
 
         def py_perform_return(inputs):
-            # strict=False because we are in a hot loop
+            # zip strict not specified 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=False)
+                for out_type, out in zip(output_types, py_perform(inputs))
             )
 
     @numba_njit

pytensor/link/numba/dispatch/cython_support.py

@@ -166,10 +166,7 @@ def __wrapper_address__(self):
     def __call__(self, *args, **kwargs):
         # no strict argument because of the JIT
         # TODO: check
-        args = [
-            dtype(arg)
-            for arg, dtype in zip(args, self._signature.arg_dtypes)  # noqa: B905
-        ]
+        args = [dtype(arg) for arg, dtype in zip(args, self._signature.arg_dtypes)]
         if self.has_pyx_skip_dispatch():
             output = self._pyfunc(*args[:-1], **kwargs)
         else:

pytensor/link/numba/dispatch/extra_ops.py

@@ -186,7 +186,7 @@ def ravelmultiindex(*inp):
             new_arr = arr.T.astype(np.float64).copy()
             for i, b in enumerate(new_arr):
                 # no strict argument to this zip because numba doesn't support it
-                for j, (d, v) in enumerate(zip(shape, b)):  # noqa: B905
+                for j, (d, v) in enumerate(zip(shape, b)):
                     if v < 0 or v >= d:
                         mode_fn(new_arr, i, j, v, d)
 

pytensor/link/numba/dispatch/slinalg.py

@@ -183,7 +183,7 @@ def block_diag(*arrs):
 
         r, c = 0, 0
         # no strict argument because it is incompatible with numba
-        for arr, shape in zip(arrs, shapes):  # noqa: B905
+        for arr, shape in zip(arrs, shapes):
             rr, cc = shape
             out[r : r + rr, c : c + cc] = arr
             r += rr

pytensor/link/numba/dispatch/subtensor.py

@@ -219,7 +219,7 @@ def advanced_subtensor_multiple_vector(x, *idxs):
             shape_aft = x_shape[after_last_axis:]
             out_shape = (*shape_bef, *idx_shape, *shape_aft)
             out_buffer = np.empty(out_shape, dtype=x.dtype)
-            for i, scalar_idxs in enumerate(zip(*vec_idxs)):  # noqa: B905
+            for i, scalar_idxs in enumerate(zip(*vec_idxs)):
                 out_buffer[(*none_slices, i)] = x[(*none_slices, *scalar_idxs)]
             return out_buffer
 
@@ -253,7 +253,7 @@ def advanced_set_subtensor_multiple_vector(x, y, *idxs):
                     y = np.broadcast_to(y, x_shape[:first_axis] + x_shape[last_axis:])
 
                 for outer in np.ndindex(x_shape[:first_axis]):
-                    for i, scalar_idxs in enumerate(zip(*vec_idxs)):  # noqa: B905
+                    for i, scalar_idxs in enumerate(zip(*vec_idxs)):
                         out[(*outer, *scalar_idxs)] = y[(*outer, i)]
                 return out
 
@@ -275,7 +275,7 @@ def advanced_inc_subtensor_multiple_vector(x, y, *idxs):
                     y = np.broadcast_to(y, x_shape[:first_axis] + x_shape[last_axis:])
 
                 for outer in np.ndindex(x_shape[:first_axis]):
-                    for i, scalar_idxs in enumerate(zip(*vec_idxs)):  # noqa: B905
+                    for i, scalar_idxs in enumerate(zip(*vec_idxs)):
                         out[(*outer, *scalar_idxs)] += y[(*outer, i)]
                 return out
 
@@ -314,7 +314,7 @@ def advancedincsubtensor1_inplace(x, vals, idxs):
                 if not len(idxs) == len(vals):
                     raise ValueError("The number of indices and values must match.")
                 # no strict argument because incompatible with numba
-                for idx, val in zip(idxs, vals):  # noqa: B905
+                for idx, val in zip(idxs, vals):
                     x[idx] = val
                 return x
     else:
@@ -342,7 +342,7 @@ def advancedincsubtensor1_inplace(x, vals, idxs):
                     raise ValueError("The number of indices and values must match.")
                 # no strict argument because unsupported by numba
                 # TODO: this doesn't come up in tests
-                for idx, val in zip(idxs, vals):  # noqa: B905
+                for idx, val in zip(idxs, vals):
                     x[idx] += val
                 return x
 

pytensor/link/utils.py

@@ -207,8 +207,8 @@ def streamline_nice_errors_f():
             for x in no_recycling:
                 x[0] = None
             try:
-                # strict=False because we are in a hot loop
-                for thunk, node in zip(thunks, order, strict=False):
+                # zip strict not specified because we are in a hot loop
+                for thunk, node in zip(thunks, order):
                     thunk()
             except Exception:
                 raise_with_op(fgraph, node, thunk)

pytensor/scalar/basic.py

@@ -4416,8 +4416,8 @@ def make_node(self, *inputs):
 
     def perform(self, node, inputs, output_storage):
         outputs = self.py_perform_fn(*inputs)
-        # strict=False because we are in a hot loop
-        for storage, out_val in zip(output_storage, outputs, strict=False):
+        # zip strict not specified because we are in a hot loop
+        for storage, out_val in zip(output_storage, outputs):
             storage[0] = out_val
 
     def grad(self, inputs, output_grads):

pytensor/scalar/loop.py

@@ -196,8 +196,8 @@ def perform(self, node, inputs, output_storage):
             for i in range(n_steps):
                 carry = inner_fn(*carry, *constant)
 
-        # strict=False because we are in a hot loop
-        for storage, out_val in zip(output_storage, carry, strict=False):
+        # zip strict not specified because we are in a hot loop
+        for storage, out_val in zip(output_storage, carry):
             storage[0] = out_val
 
     @property

pytensor/tensor/basic.py

@@ -3589,8 +3589,8 @@ def perform(self, node, inp, out):
 
         # Make sure the output is big enough
         out_s = []
-        # strict=False because we are in a hot loop
-        for xdim, ydim in zip(x_s, y_s, strict=False):
+        # zip strict not specified because we are in a hot loop
+        for xdim, ydim in zip(x_s, y_s):
             if xdim == ydim:
                 outdim = xdim
             elif xdim == 1: