Remove strict=False in hot loops

This is actually slower than just not specifying it

Author: ricardoV94

pyproject.toml

@@ -130,12 +130,8 @@ 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
-    "B905",
-]
 
 
 [tool.ruff.lint.isort]

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):
+        # strict=None because we are in a hot loop
+        for output, variable in zip(outputs, variables):
             output[0] = variable

pytensor/link/basic.py

@@ -373,7 +373,12 @@ def make_all(
 
         # The function that actually runs your program is one of the f's in streamline.
         f = streamline(
-            fgraph, thunks, order, post_thunk_old_storage, no_recycling=no_recycling
+            fgraph,
+            thunks,
+            order,
+            post_thunk_old_storage=post_thunk_old_storage,
+            no_recycling=no_recycling,
+            output_storage=output_storage,
         )
 
         f.allow_gc = (
@@ -539,14 +544,14 @@ 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):
+                # strict=None 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)):
+            # strict=None 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 +673,8 @@ 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):
+            # strict=None 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/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
+            # strict=None 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

@@ -190,9 +190,9 @@ def streamline_default_f():
             for x in no_recycling:
                 x[0] = None
             try:
-                # strict=False because we are in a hot loop
+                # strict=None because we are in a hot loop
                 for thunk, node, old_storage in zip(
-                    thunks, order, post_thunk_old_storage, strict=False
+                    thunks, order, post_thunk_old_storage
                 ):
                     thunk()
                     for old_s in old_storage:
@@ -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):
+                # strict=None 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):
+        # strict=None 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):
+        # strict=None because we are in a hot loop
+        for storage, out_val in zip(output_storage, carry):
             storage[0] = out_val
 
     @property

pytensor/tensor/random/basic.py

@@ -1865,8 +1865,8 @@ def rng_fn(cls, rng, p, size):
             # 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`")
-            # strict=False because we are in a hot loop
-            for s, ps in zip(reversed(size), reversed(p.shape[:-1]), strict=False):
+            # strict=None because we are in a hot loop
+            for s, ps in zip(reversed(size), reversed(p.shape[:-1])):
                 if s == 1 and ps != 1:
                     raise ValueError("`size` is incompatible with the shape of `p`")
 

pytensor/tensor/random/utils.py

@@ -44,8 +44,8 @@ def params_broadcast_shapes(
     max_fn = maximum if use_pytensor else max
 
     rev_extra_dims: list[int] = []
-    # strict=False because we are in a hot loop
-    for ndim_param, param_shape in zip(ndims_params, param_shapes, strict=False):
+    # strict=None because we are in a hot loop
+    for ndim_param, param_shape in zip(ndims_params, param_shapes):
         # We need this in order to use `len`
         param_shape = tuple(param_shape)
         extras = tuple(param_shape[: (len(param_shape) - ndim_param)])
@@ -69,7 +69,7 @@ def max_bcast(x, y):
         (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=False)
+        for ndim_param, param_shape in zip(ndims_params, param_shapes)
     ]
 
     return bcast_shapes
@@ -127,10 +127,9 @@ def broadcast_params(
     )
     broadcast_to_fn = broadcast_to if use_pytensor else np.broadcast_to
 
-    # strict=False because we are in a hot loop
+    # strict=None because we are in a hot loop
     bcast_params = [
-        broadcast_to_fn(param, shape)
-        for shape, param in zip(shapes, params, strict=False)
+        broadcast_to_fn(param, shape) for shape, param in zip(shapes, params)
     ]
 
     return bcast_params

pytensor/tensor/shape.py

@@ -447,10 +447,8 @@ def perform(self, node, inp, out_):
             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=False) if s is not None
-        ):
+        # strict=None because we are in a hot loop
+        if not all(xs == s for xs, s in zip(x.shape, shape) 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)}."
             )

pytensor/tensor/type.py

@@ -261,10 +261,10 @@ def filter(self, data, strict=False, allow_downcast=None) -> np.ndarray:
                 " PyTensor C code does not support that.",
             )
 
-        # strict=False because we are in a hot loop
+        # strict=None 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=False)
+            for ds, ts in zip(data.shape, self.shape)
         ):
             raise TypeError(
                 f"The type's shape ({self.shape}) is not compatible with the data's ({data.shape})"