def to_backend_value(self, v, t):
"""Convert an intermediate value to a backend value."""
if isinstance(t, abstract.AbstractError) or v is abstract.DEAD:
return None
elif isinstance(t, abstract.AbstractType):
# Handle abstract types.
# Return None if type does not match any torch type.
myia_type = t.element.xtype()
return _type_map.get(myia_type, None)
elif isinstance(t, abstract.AbstractArray):
return self.from_numpy(v)
elif isinstance(t, abstract.AbstractScalar):
if issubclass(t.values[abstract.TYPE],
(xtype.Number, xtype.Bool, xtype.Nil)):
return self.from_scalar(v, t.values[abstract.TYPE])
elif issubclass(t.values[abstract.TYPE], xtype.EnvType):
assert len(v._contents) == 0
return ()
else:
raise NotImplementedError(f"to_backend_value for {t}")
elif isinstance(t, abstract.AbstractTuple):
return tuple(
self.to_backend_value(v, t) for v, t in zip(v, t.elements))
elif isinstance(t, abstract.AbstractTaggedUnion):
real_t = t.options.get(v.tag)
return TaggedValue(v.tag, self.to_backend_value(v.value, real_t))
elif isinstance(t, abstract.AbstractRandomState):
return self.from_numpy(v.state.copy())
else:
raise NotImplementedError(f"to_backend_value for {t}")
def convert_tagged(self, v, t):
tag = get_myia_tag(v.tag)
try:
conv_val = self(v[0], t.options.get(tag))
except TypeError:
conv_val = self(v.fields[0], t.options.get(tag))
return TaggedValue(tag, conv_val)
def from_backend_value(self, v, t):
"""Convert a backend value to an intermediate value."""
if isinstance(t, abstract.AbstractScalar):
return self.to_scalar(v)
elif isinstance(t, abstract.AbstractArray):
# Convert torch tensor to numpy tensor.
output = self.to_numpy(v)
# If possible and necessary, cast numpy tensor to expected tensor.
array_type = t.element.xtype()
if array_type and array_type not in _type_map:
# Probably u16, u32 or u64. Let's cast.
output = output.astype(type_to_np_dtype(array_type))
return output
elif isinstance(t, abstract.AbstractTuple):
return tuple(
self.from_backend_value(ve, te)
for ve, te in zip(v, t.elements))
elif isinstance(t, abstract.AbstractTaggedUnion):
return TaggedValue(
v.tag, self.from_backend_value(v.value, t.options.get(v.tag)))
elif isinstance(t, abstract.AbstractRandomState):
return RandomStateWrapper(self.to_numpy(v))
elif isinstance(t, abstract.AbstractType):
if isinstance(t.element, abstract.AbstractHandle):
return HandleInstance
else:
myia_type = t.element.xtype()
if myia_type in _type_map:
return getattr(np, type_to_np_dtype(myia_type))
else:
return v
else:
raise NotImplementedError(f"Don't know what to do for {t}")