Remove frozendict

Author: Armavica

doc/LICENSE.txt

@@ -18,8 +18,6 @@ theano/tensor/sharedvar.py: James Bergstra, (c) 2010, Universite de Montreal, 3-
 theano/gradient.py: James Bergstra, Razvan Pascanu, Arnaud Bergeron, Ian Goodfellow, PyMC Developers, PyTensor Developers, (c) 2011, Universite de Montreal, 3-clause BSD License
 theano/compile/monitormode.py: this code was initially copied from the 'pyutools' package by its original author, and re-licensed under Theano's license.
 
-Contains frozendict code from slezica’s python-frozendict(https://github.com/slezica/python-frozendict/blob/master/frozendict/__init__.py), Copyright (c) 2012 Santiago Lezica. All rights reserved.
-
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
 

pytensor/link/numba/dispatch/elemwise.py

@@ -488,7 +488,7 @@ def numba_funcify_Elemwise(op, node, **kwargs):
     input_bc_patterns = tuple([inp.type.broadcastable for inp in node.inputs])
     output_bc_patterns = tuple([out.type.broadcastable for out in node.outputs])
     output_dtypes = tuple(out.type.dtype for out in node.outputs)
-    inplace_pattern = tuple(op.inplace_pattern.items())
+    inplace_pattern = op.inplace_pattern
     core_output_shapes = tuple(() for _ in range(nout))
 
     # numba doesn't support nested literals right now...

pytensor/misc/frozendict.py

@@ -13,7 +13,6 @@
 from pytensor.link.c.basic import failure_code
 from pytensor.link.c.op import COp, ExternalCOp, OpenMPOp
 from pytensor.link.c.params_type import ParamsType
-from pytensor.misc.frozendict import frozendict
 from pytensor.misc.safe_asarray import _asarray
 from pytensor.printing import Printer, pprint
 from pytensor.scalar import get_scalar_type
@@ -374,11 +373,11 @@ def __init__(
         """
         assert not isinstance(scalar_op, type(self))
         if inplace_pattern is None:
-            inplace_pattern = frozendict({})
+            inplace_pattern = {}
         self.name = name
         self.scalar_op = scalar_op
-        self.inplace_pattern = inplace_pattern
-        self.destroy_map = {o: [i] for o, i in self.inplace_pattern.items()}
+        self.inplace_pattern = tuple(inplace_pattern.items())
+        self.destroy_map = {o: [i] for o, i in self.inplace_pattern}
 
         if nfunc_spec is None:
             nfunc_spec = getattr(scalar_op, "nfunc_spec", None)
@@ -397,7 +396,6 @@ def __setstate__(self, d):
         super().__setstate__(d)
         self.ufunc = None
         self.nfunc = None
-        self.inplace_pattern = frozendict(self.inplace_pattern)
 
     def get_output_info(self, dim_shuffle, *inputs):
         """Return the outputs dtype and broadcastable pattern and the
@@ -446,27 +444,23 @@ def get_output_info(self, dim_shuffle, *inputs):
             )
 
         # inplace_pattern maps output idx -> input idx
-        inplace_pattern = self.inplace_pattern
-        if inplace_pattern:
-            for overwriter, overwritten in inplace_pattern.items():
-                for out_s, in_s in zip(
-                    out_shapes[overwriter],
-                    inputs[overwritten].type.shape,
-                ):
-                    if in_s == 1 and out_s != 1:
-                        raise ValueError(
-                            "Operation cannot be done inplace on an input "
-                            "with broadcasted dimensions."
-                        )
+        for overwriter, overwritten in self.inplace_pattern:
+            for out_s, in_s in zip(
+                out_shapes[overwriter],
+                inputs[overwritten].type.shape,
+            ):
+                if in_s == 1 and out_s != 1:
+                    raise ValueError(
+                        "Operation cannot be done inplace on an input "
+                        "with broadcasted dimensions."
+                    )
 
         out_dtypes = [o.type.dtype for o in shadow.outputs]
-        if any(
-            inputs[i].type.dtype != out_dtypes[o] for o, i in inplace_pattern.items()
-        ):
+        if any(inputs[i].type.dtype != out_dtypes[o] for o, i in self.inplace_pattern):
             raise TypeError(
                 (
                     "Cannot do an inplace operation on incompatible data types.",
-                    ([i.type.dtype for i in inputs], out_dtypes, inplace_pattern),
+                    ([i.type.dtype for i in inputs], out_dtypes, self.inplace_pattern),
                 )
             )
         assert len(out_dtypes) == len(out_shapes)
@@ -755,6 +749,7 @@ def perform(self, node, inputs, output_storage):
         if nout == 1:
             variables = [variables]
 
+        inplace_pattern = dict(self.inplace_pattern)
         for i, (variable, storage, nout) in enumerate(
             zip(variables, output_storage, node.outputs)
         ):
@@ -763,8 +758,8 @@ def perform(self, node, inputs, output_storage):
                 # always return an ndarray with dtype object
                 variable = np.asarray(variable, dtype=nout.dtype)
 
-            if i in self.inplace_pattern:
-                odat = inputs[self.inplace_pattern[i]]
+            if i in inplace_pattern:
+                odat = inputs[inplace_pattern[i]]
                 odat[...] = variable
                 storage[0] = odat
 
@@ -832,9 +827,7 @@ def _c_all(self, node, nodename, inames, onames, sub):
         # The destroy map is a map of output indices to input indices
         # that overwrite them.  We just convert them to the actual
         # Variables.
-        dmap = {
-            node.outputs[o]: [node.inputs[i]] for o, i in self.inplace_pattern.items()
-        }
+        dmap = {node.outputs[o]: [node.inputs[i]] for o, i in self.inplace_pattern}
 
         # dtypes of the inputs
         idtypes = [input.type.dtype_specs()[1] for input in inputs]

pytensor/tensor/elemwise.py

@@ -173,7 +173,7 @@ def apply(self, fgraph):
                 # original node add already some inplace patter and we
                 # still try to add more pattern.
 
-                baseline = op.inplace_pattern
+                baseline = dict(op.inplace_pattern)
                 candidate_outputs = [
                     i for i in self.candidate_input_idxs(node) if i not in baseline
                 ]