def repl(m: Match[str]) -> str:
suffix: str = info['suffix'](m) if callable(info['suffix']) else info['suffix']
expr: str = info['expr'](name) if 'expr' in info else m.group(0)
saved.append(SavedAttribute(
nctype=info['nctype'](name + suffix),
expr=expr,
))
if 'res' in info:
replacement: str = info['res'](name)
return replacement
return name + suffix
def saved_variables(
formula: str,
nctypes: List[NamedCType],
var_names: Tuple[str, ...],
) -> Tuple[str, Tuple[SavedAttribute, ...]]:
def stride_expr(name: str) -> str:
assert var_names == (name,), (
'Replacement for ".strides()" is currently only supported for single derivatives of the same tensor '
'that ".strides()" is being called on.')
return f'strides_or_error({name}, "{name}")'
REPLACEMENTS: List[Tuple[str, Dict[str, Any]]] = [
# replace self.sizes() with self_sizes
(r'{}.sizes\(\)', {
'suffix': '_sizes',
'nctype': lambda name: NamedCType(name, BaseCType(intArrayRefT)),
}),
# replace self.options() with self_options
(r'{}.options\(\)', {
'suffix': '_options',
'nctype': lambda name: NamedCType(name, BaseCType(tensorOptionsT)),
}),
# replace zeros_like(self) with self_info
(r'zeros_like\({}\)', {
'suffix': '_info',
'nctype': lambda name: NamedCType(name, BaseCType(typeAndSizeT)),
'expr': lambda name: name, # at save-time
'res': lambda name: name + '_info.zeros()', # at eval-time
}),
# replace self.size(2) with self_size_2
(r'{}.size\((\w+)\)', {
'suffix': lambda m: '_argsize_{}'.format(*m.groups()),
'nctype': lambda name: NamedCType(name, BaseCType(intT)),
}),
# replace self.numel() with self_numel
(r'{}.numel\(\)', {
'suffix': '_numel',
'nctype': lambda name: NamedCType(name, BaseCType(intT)),
}),
# replace to_args_sizes(self) with self_args_sizes
(r'to_args_sizes\({}\)', {
'suffix': '_args_sizes',
'nctype': lambda name: NamedCType(name, VectorCType(VectorCType(BaseCType(intT)))),
}),
# replace to_args_scalartypes(self) with self_args_scalartypes
(r'to_args_scalartypes\({}\)', {
'suffix': '_args_scalartypes',
'nctype': lambda name: NamedCType(name, VectorCType(BaseCType(scalarTypeT))),
}),
# replace TensorGeometry(self) with self_geometry
(r'TensorGeometry\({}\)', {
'suffix': '_geometry',
'nctype': lambda name: NamedCType(name, BaseCType(tensorGeometryT)),
}),
(r'{}.scalar_type\(\)', {
'suffix': '_scalar_type',
'nctype': lambda name: NamedCType(name, BaseCType(scalarTypeT)),
}),
# replace self.dim() with self_dim
(r'{}.dim\(\)', {
'suffix': '_dim',
'nctype': lambda name: NamedCType(name, BaseCType(intT)),
}),
# replace self.strides() with self_strides
(r'{}.strides\(\)', {
'suffix': '_strides',
'nctype': lambda name: NamedCType(name, BaseCType(intArrayRefT)),
'expr': stride_expr,
}),
]
# find which arguments need to be saved
saved: List[SavedAttribute] = []
for nctype in nctypes:
name = nctype.name.name if isinstance(nctype.name, SpecialArgName) else nctype.name
# First search the formula for expressions which can be evaluated
# when the autograd Function is created to avoid saving variables
for regex, info in REPLACEMENTS:
def repl(m: Match[str]) -> str:
suffix: str = info['suffix'](m) if callable(info['suffix']) else info['suffix']
expr: str = info['expr'](name) if 'expr' in info else m.group(0)
saved.append(SavedAttribute(
nctype=info['nctype'](name + suffix),
expr=expr,
))
if 'res' in info:
replacement: str = info['res'](name)
return replacement
return name + suffix
formula = re.sub(regex.format(name), repl, formula)
# Find any variables which remain in the formula and save them
if re.search(IDENT_REGEX.format(name), formula):
saved.append(SavedAttribute(
nctype=nctype,
expr=name,
))
return formula, tuple(saved)