Rewrite for/append as list comprehensions

pytensor/compile/debugmode.py

@@ -906,11 +906,10 @@ def _get_preallocated_maps(
                 name = f"strided{tuple(steps)}"
                 for r in considered_outputs:
                     if r in init_strided:
-                        strides = []
-                        shapes = []
-                        for i, size in enumerate(r_vals[r].shape):
-                            shapes.append(slice(None, size, None))
-                            strides.append(slice(None, None, steps[i]))
+                        shapes = [slice(None, size, None) for size in r_vals[r].shape]
+                        strides = [
+                            slice(None, None, steps[i]) for i in range(r_vals[r].ndim)
+                        ]
 
                         r_buf = init_strided[r]
 

pytensor/compile/function/__init__.py

@@ -247,18 +247,10 @@ def function(
 
     """
     if isinstance(outputs, dict):
-        output_items = list(outputs.items())
+        assert all(isinstance(k, str) for k in outputs)
 
-        for item_pair in output_items:
-            assert isinstance(item_pair[0], str)
-
-        output_items_sorted = sorted(output_items)
-
-        output_keys = []
-        outputs = []
-        for pair in output_items_sorted:
-            output_keys.append(pair[0])
-            outputs.append(pair[1])
+        output_keys = sorted(outputs)
+        outputs = [outputs[key] for key in output_keys]
 
     else:
         output_keys = None

pytensor/compile/function/types.py

@@ -212,18 +212,14 @@ def std_fgraph(
 
         found_updates.extend(map(SymbolicOutput, updates))
     elif fgraph is None:
-        input_vars = []
-
         # If one of the inputs is non-atomic (i.e. has a non-`None` `Variable.owner`),
         # then we need to create/clone the graph starting at these inputs.
         # The result will be atomic versions of the given inputs connected to
         # the same outputs.
         # Otherwise, when all the inputs are already atomic, there's no need to
         # clone the graph.
-        clone = force_clone
-        for spec in input_specs:
-            input_vars.append(spec.variable)
-            clone |= spec.variable.owner is not None
+        input_vars = [spec.variable for spec in input_specs]
+        clone = force_clone or any(var.owner is not None for var in input_vars)
 
         fgraph = FunctionGraph(
             input_vars,

pytensor/compile/profiling.py

@@ -1204,8 +1204,7 @@ class ProfileStats:
                         compute_map[var][0] = 0
 
                     for k_remove, v_remove in viewedby_remove.items():
-                        for i in v_remove:
-                            viewed_by[k_remove].append(i)
+                        viewed_by[k_remove].extend(v_remove)
 
                     for k_add, v_add in viewedby_add.items():
                         for i in v_add:
@@ -1215,15 +1214,16 @@ class ProfileStats:
                         del view_of[k]
 
             # two data structure used to mimic Python gc
-            viewed_by = {}  # {var1: [vars that view var1]}
+            # * {var1: [vars that view var1]}
             # The len of the list is the value of python ref
             # count. But we use a list, not just the ref count value.
             # This is more safe to help detect potential bug in the algo
-            for var in fgraph.variables:
-                viewed_by[var] = []
-            view_of = {}  # {var1: original var viewed by var1}
+            viewed_by = {var: [] for var in fgraph.variables}
+
+            # * {var1: original var viewed by var1}
             # The original mean that we don't keep track of all the intermediate
             # relationship in the view.
+            view_of = {}
 
             min_memory_generator(executable_nodes, viewed_by, view_of)
 

pytensor/graph/basic.py

@@ -1474,9 +1474,8 @@ def general_toposort(
 
     _clients: dict[T, list[T]] = {}
     sources: deque[T] = deque()
-    search_res_len: int = 0
+    search_res_len = len(search_res)
     for snode, children in search_res:
-        search_res_len += 1
         if children:
             for child in children:
                 _clients.setdefault(child, []).append(snode)

pytensor/graph/fg.py

@@ -270,8 +270,10 @@ class FunctionGraph(MetaObject):
 
                     self.execute_callbacks("on_prune", apply_node, reason)
 
-                    for i, in_var in enumerate(apply_node.inputs):
-                        removal_stack.append((in_var, (apply_node, i)))
+                    removal_stack.extend(
+                        (in_var, (apply_node, i))
+                        for i, in_var in enumerate(apply_node.inputs)
+                    )
 
                     if remove_if_empty:
                         del clients[var]

pytensor/graph/rewriting/basic.py

@@ -479,9 +479,9 @@ class SequentialGraphRewriter(GraphRewriter, UserList):
             new_sub_profile.append(p[6][idx])
 
         new_rewrite = SequentialGraphRewriter(*new_l)
-        new_nb_nodes = []
-        for p1, p2 in zip(prof1[8], prof2[8]):
-            new_nb_nodes.append((p1[0] + p2[0], p1[1] + p2[1]))
+        new_nb_nodes = [
+            (p1[0] + p2[0], p1[1] + p2[1]) for p1, p2 in zip(prof1[8], prof2[8])
+        ]
         new_nb_nodes.extend(prof1[8][len(new_nb_nodes) :])
         new_nb_nodes.extend(prof2[8][len(new_nb_nodes) :])
 
@@ -960,9 +960,9 @@ class MetaNodeRewriter(NodeRewriter):
 
         tracks = rewriter.tracks()
         if tracks:
+            self._tracks.extend(tracks)
             for c in tracks:
                 self.track_dict[c].append(rewriter)
-                self._tracks.append(c)
 
         for tag in tag_list:
             self.tag_dict[tag].append(rewriter)

pytensor/link/basic.py

@@ -524,12 +524,13 @@ class WrapLinker(Linker):
         thunk_groups = list(zip(*thunk_lists))
         order = [x[0] for x in zip(*order_lists)]
 
-        to_reset = []
-        for thunks, node in zip(thunk_groups, order):
-            for j, output in enumerate(node.outputs):
-                if output in no_recycling:
-                    for thunk in thunks:
-                        to_reset.append(thunk.outputs[j])
+        to_reset = [
+            thunk.outputs[j]
+            for thunks, node in zip(thunk_groups, order)
+            for j, output in enumerate(node.outputs)
+            if output in no_recycling
+            for thunk in thunks
+        ]
 
         wrapper = self.wrapper
         pre = self.pre
@@ -696,10 +697,7 @@ class JITLinker(PerformLinker):
         computed, last_user = gc_helper(nodes)
 
         if self.allow_gc:
-            post_thunk_old_storage = []
-
-            for node in nodes:
-                post_thunk_old_storage.append(
+            post_thunk_old_storage = [
                 [
                     storage_map[input]
                     for input in node.inputs
@@ -707,7 +705,8 @@ class JITLinker(PerformLinker):
                     and (input not in fgraph.outputs)
                     and (node == last_user[input])
                 ]
-                )
+                for node in nodes
+            ]
         else:
             post_thunk_old_storage = None
 

pytensor/link/c/basic.py

@@ -1129,19 +1129,18 @@ class CLinker(Linker):
         )
 
     def get_init_tasks(self):
-        init_tasks = []
-        tasks = []
+        vars = [v for v in self.variables if v not in self.consts]
         id = 1
-        for v in self.variables:
-            if v in self.consts:
-                continue
-            init_tasks.append((v, "init", id))
-            tasks.append((v, "get", id + 1))
-            id += 2
-        for node in self.node_order:
-            tasks.append((node, "code", id))
-            init_tasks.append((node, "init", id + 1))
-            id += 2
+        init_tasks = [(v, "init", id + 2 * i) for i, v in enumerate(vars)]
+        tasks = [(v, "get", id + 2 * i + 1) for i, v in enumerate(vars)]
+
+        id += 2 * len(vars)
+        tasks.extend(
+            (node, "code", id + 2 * i) for i, node in enumerate(self.node_order)
+        )
+        init_tasks.extend(
+            (node, "init", id + 2 * i + 1) for i, node in enumerate(self.node_order)
+        )
         return init_tasks, tasks
 
     def make_thunk(
@@ -1492,12 +1491,11 @@ class CLinker(Linker):
         # graph's information used to compute the key. If we mistakenly
         # pretend that inputs with clients don't have any, were are only using
         # those inputs more than once to compute the key.
-        for ipos, var in [
-            (i, var)
-            for i, var in enumerate(fgraph.inputs)
+        sig.extend(
+            (var.type, in_sig(var, -1, ipos))
+            for ipos, var in enumerate(fgraph.inputs)
             if not len(fgraph.clients[var])
-        ]:
-            sig.append((var.type, in_sig(var, -1, ipos)))
+        )
 
         # crystalize the signature and version
         sig = tuple(sig)

pytensor/link/c/op.py

@@ -220,12 +220,7 @@ int main( int argc, const char* argv[] )
 
 def lquote_macro(txt: str) -> str:
     """Turn the last line of text into a ``\\``-commented line."""
-    res = []
-    spl = txt.split("\n")
-    for l in spl[:-1]:
-        res.append(l + " \\")
-    res.append(spl[-1])
-    return "\n".join(res)
+    return " \\\n".join(txt.split("\n"))
 
 
 def get_sub_macros(sub: dict[str, str]) -> tuple[str, str]:
@@ -240,21 +235,17 @@ def get_sub_macros(sub: dict[str, str]) -> tuple[str, str]:
     return "\n".join(define_macros), "\n".join(undef_macros)
 
 
-def get_io_macros(
-    inputs: list[str], outputs: list[str]
-) -> tuple[list[str]] | tuple[str, str]:
-    define_macros = []
-    undef_macros = []
+def get_io_macros(inputs: list[str], outputs: list[str]) -> tuple[str, str]:
+    define_inputs = [f"#define INPUT_{int(i)} {inp}" for i, inp in enumerate(inputs)]
+    define_outputs = [f"#define OUTPUT_{int(i)} {out}" for i, out in enumerate(outputs)]
 
-    for i, inp in enumerate(inputs):
-        define_macros.append(f"#define INPUT_{int(i)} {inp}")
-        undef_macros.append(f"#undef INPUT_{int(i)}")
+    undef_inputs = [f"#undef INPUT_{int(i)}" for i in range(len(inputs))]
+    undef_outputs = [f"#undef OUTPUT_{int(i)}" for i in range(len(outputs))]
 
-    for i, out in enumerate(outputs):
-        define_macros.append(f"#define OUTPUT_{int(i)} {out}")
-        undef_macros.append(f"#undef OUTPUT_{int(i)}")
+    define_all = "\n".join(define_inputs + define_outputs)
+    undef_all = "\n".join(undef_inputs + undef_outputs)
 
-    return "\n".join(define_macros), "\n".join(undef_macros)
+    return define_all, undef_all
 
 
 class ExternalCOp(COp):
@@ -560,9 +551,10 @@ class ExternalCOp(COp):
         define_macros.append(define_template % ("APPLY_SPECIFIC(str)", f"str##_{name}"))
         undef_macros.append(undef_template % "APPLY_SPECIFIC")
 
-        for n, v in self.__get_op_params():
-            define_macros.append(define_template % (n, v))
-            undef_macros.append(undef_template % (n,))
+        define_macros.extend(
+            define_template % (n, v) for n, v in self.__get_op_params()
+        )
+        undef_macros.extend(undef_template % (n,) for n, _ in self.__get_op_params())
 
         return "\n".join(define_macros), "\n".join(undef_macros)
 

pytensor/link/numba/dispatch/basic.py

@@ -131,21 +131,19 @@ def create_numba_signature(
     reduce_to_scalar: bool = False,
 ) -> numba.types.Type:
     """Create a Numba type for the signature of an `Apply` node or `FunctionGraph`."""
-    input_types = []
-    for inp in node_or_fgraph.inputs:
-        input_types.append(
+    input_types = [
         get_numba_type(
             inp.type, force_scalar=force_scalar, reduce_to_scalar=reduce_to_scalar
         )
-        )
+        for inp in node_or_fgraph.inputs
+    ]
 
-    output_types = []
-    for out in node_or_fgraph.outputs:
-        output_types.append(
+    output_types = [
         get_numba_type(
             out.type, force_scalar=force_scalar, reduce_to_scalar=reduce_to_scalar
         )
-        )
+        for out in node_or_fgraph.outputs
+    ]
 
     if len(output_types) > 1:
         return numba.types.Tuple(output_types)(*input_types)

pytensor/link/numba/dispatch/elemwise.py

@@ -520,9 +520,7 @@ def numba_funcify_Elemwise(op, node, **kwargs):
                 if length == 1 and shape and iter_length != 1 and not allow_bc:
                     raise ValueError("Broadcast not allowed.")
 
-        outputs = []
-        for dtype in output_dtypes:
-            outputs.append(np.empty(shape, dtype=dtype))
+        outputs = [np.empty(shape, dtype=dtype) for dtype in output_dtypes]
 
         for idx in np.ndindex(shape):
             vals = [input[idx] for input in inputs_bc]

pytensor/link/numba/dispatch/scan.py

@@ -268,14 +268,14 @@ def numba_funcify_Scan(op, node, **kwargs):
                 output_taps = inner_in_names_to_output_taps.get(
                     outer_in_name, [tap_storage_size]
                 )
-                for out_tap in output_taps:
-                    inner_out_to_outer_in_stmts.append(
+                inner_out_to_outer_in_stmts.extend(
                     idx_to_str(
                         storage_name,
                         out_tap,
                         size=storage_size_name,
                         allow_scalar=True,
                     )
+                    for out_tap in output_taps
                 )
 
                 add_output_storage_post_proc_stmt(

pytensor/link/vm.py

@@ -1111,9 +1111,8 @@ class VMLinker(LocalLinker):
             # builds the list of prereqs induced by e.g. destroy_handler
             ords = self.fgraph.orderings()
             node_prereqs = []
-            node_output_size = []
+            node_output_size = [0] * len(nodes)
             for i, node in enumerate(nodes):
-                node_output_size.append(0)
                 prereq_var_idxs = []
                 for prereq_node in ords.get(node, []):
                     prereq_var_idxs.extend([vars_idx[v] for v in prereq_node.outputs])

pytensor/scalar/basic.py

@@ -1575,9 +1575,7 @@ class InRange(LogicalComparison):
     def L_op(self, inputs, outputs, gout):
         (x, low, hi) = inputs
         (gz,) = gout
-        grads = []
-        for elem in [x, low, hi]:
-            grads.append(self.get_grad(elem))
+        grads = [self.get_grad(elem) for elem in [x, low, hi]]
         return grads
 
 

pytensor/scan/basic.py

@@ -646,9 +646,7 @@ def scan(
 
     # Since we've added all sequences now we need to level them up based on
     # n_steps or their different shapes
-    lengths_vec = []
-    for seq in scan_seqs:
-        lengths_vec.append(seq.shape[0])
+    lengths_vec = [seq.shape[0] for seq in scan_seqs]
 
     if not isNaN_or_Inf_or_None(n_steps):
         # ^ N_steps should also be considered

pytensor/tensor/basic.py

@@ -1629,10 +1629,7 @@ class Alloc(COp):
         return [node.inputs[1:]]
 
     def connection_pattern(self, node):
-        rval = [[True]]
-
-        for ipt in node.inputs[1:]:
-            rval.append([False])
+        rval = [[True], *([False] for _ in node.inputs[1:])]
 
         return rval
 
@@ -1859,9 +1856,7 @@ class MakeVector(COp):
         if self.dtype in discrete_dtypes:
             return [ipt.zeros_like().astype(config.floatX) for ipt in inputs]
 
-        grads = []
-        for i, inp in enumerate(inputs):
-            grads.append(output_gradients[0][i])
+        grads = [output_gradients[0][i] for i in range(len(inputs))]
         return grads
 
     def R_op(self, inputs, eval_points):
@@ -2514,12 +2509,10 @@ class Join(COp):
         (out,) = outputs
         fail = sub["fail"]
         adtype = node.inputs[0].type.dtype_specs()[1]
-        copy_to_list = []
 
-        for i, inp in enumerate(tens):
-            copy_to_list.append(
-                f"""Py_INCREF({inp});
-                   PyList_SetItem(list, {i}, (PyObject*){inp});"""
+        copy_to_list = (
+            f"""Py_INCREF({inp}); PyList_SetItem(list, {i}, (PyObject*){inp});"""
+            for i, inp in enumerate(tens)
         )
 
         copy_inputs_to_list = "\n".join(copy_to_list)
@@ -3442,9 +3435,7 @@ class PermuteRowElements(Op):
         shp_x = in_shapes[0]
         shp_y = in_shapes[1]
         assert len(shp_x) == len(shp_y)
-        out_shape = []
-        for i in range(len(shp_x)):
-            out_shape.append(maximum(shp_x[i], shp_y[i]))
+        out_shape = [maximum(sx, sy) for sx, sy in zip(shp_x, shp_y, strict=True)]
         return [out_shape]
 
     def grad(self, inp, grads):

pytensor/tensor/blockwise.py

@@ -167,9 +167,8 @@ class Blockwise(Op):
 
         batch_ndims = self.batch_ndim(node)
         core_dims: dict[str, Any] = {}
-        batch_shapes = []
+        batch_shapes = [input_shape[:batch_ndims] for input_shape in input_shapes]
         for input_shape, sig in zip(input_shapes, self.inputs_sig):
-            batch_shapes.append(input_shape[:batch_ndims])
             core_shape = input_shape[batch_ndims:]
 
             for core_dim, dim_name in zip(core_shape, sig):

pytensor/tensor/elemwise.py

@@ -1161,8 +1161,10 @@ class Elemwise(OpenMPOp):
             ],
         )
         version.append(self.scalar_op.c_code_cache_version_apply(scalar_node))
-        for i in node.inputs + node.outputs:
-            version.append(get_scalar_type(dtype=i.type.dtype).c_code_cache_version())
+        version.extend(
+            get_scalar_type(dtype=i.type.dtype).c_code_cache_version()
+            for i in node.inputs + node.outputs
+        )
         version.append(("openmp", self.openmp))
         version.append(("openmp_elemwise_minsize", config.openmp_elemwise_minsize))
         if all(version):
@@ -1664,8 +1666,10 @@ class CAReduce(COp):
             ],
         )
         version.append(self.scalar_op.c_code_cache_version_apply(scalar_node))
-        for i in node.inputs + node.outputs:
-            version.append(get_scalar_type(dtype=i.type.dtype).c_code_cache_version())
+        version.extend(
+            get_scalar_type(dtype=i.type.dtype).c_code_cache_version()
+            for i in node.inputs + node.outputs
+        )
         if all(version):
             return tuple(version)
         else:

pytensor/tensor/subtensor.py

@@ -952,10 +952,7 @@ class Subtensor(COp):
         return [first] + [DisconnectedType()()] * len(rest)
 
     def connection_pattern(self, node):
-        rval = [[True]]
-
-        for ipt in node.inputs[1:]:
-            rval.append([False])
+        rval = [[True], *([False] for _ in node.inputs[1:])]
 
         return rval
 
@@ -1963,10 +1960,7 @@ class IncSubtensor(COp):
         return self(eval_points[0], eval_points[1], *inputs[2:], return_list=True)
 
     def connection_pattern(self, node):
-        rval = [[True], [True]]
-
-        for ipt in node.inputs[2:]:
-            rval.append([False])
+        rval = [[True], [True], *([False] for _ in node.inputs[2:])]
 
         return rval
 
@@ -2765,10 +2759,7 @@ class AdvancedSubtensor(Op):
         out[0] = rval
 
     def connection_pattern(self, node):
-        rval = [[True]]
-
-        for ipt in node.inputs[1:]:
-            rval.append([False])
+        rval = [[True], *([False] for _ in node.inputs[1:])]
 
         return rval
 
@@ -2912,10 +2903,7 @@ class AdvancedIncSubtensor(Op):
         return [ishapes[0]]
 
     def connection_pattern(self, node):
-        rval = [[True], [True]]
-
-        for ipt in node.inputs[2:]:
-            rval.append([False])
+        rval = [[True], [True], *([False] for _ in node.inputs[2:])]
 
         return rval
 

pytensor/typed_list/basic.py

@@ -238,8 +238,7 @@ class Extend(COp):
         # need to copy toAppend due to destroy_handler limitation
         if toAppend:
             o = out[0]
-            for i in toAppend:
-                o.append(_lessbroken_deepcopy(i))
+            o.extend(_lessbroken_deepcopy(i) for i in toAppend)
 
     def __str__(self):
         return self.__class__.__name__