def test_serialize_jax_with_int32_to_int32(self):
self.skipTest('HLO pattern matching broken by '
'https://github.com/google/jax/pull/10232')
def traced_fn(x):
return x + 10
param_type = computation_types.to_type(np.int32)
arg_fn = function_utils.create_argument_unpacking_fn(
traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '(int32 -> int32)')
xla_comp = xla_serialization.unpack_xla_computation(
comp_pb.xla.hlo_module)
self.assertIn('ROOT tuple.6 = (s32[]) tuple(add.5)',
xla_comp.as_hlo_text())
self.assertEqual(str(comp_pb.xla.result), str(comp_pb.xla.parameter))
self.assertEqual(str(comp_pb.xla.result), 'tensor {\n'
' index: 0\n'
'}\n')
def test_wrap_as_zero_or_one_arg_callable(self, fn, parameter_type, unpack,
arg, expected_result):
parameter_type = computation_types.to_type(parameter_type)
unpack_arguments = function_utils.create_argument_unpacking_fn(
fn, parameter_type, unpack)
args, kwargs = unpack_arguments(arg)
actual_result = fn(*args, **kwargs)
self.assertEqual(actual_result, expected_result)
def _federated_computation_serializer(fn, parameter_name, parameter_type):
unpack_arguments = function_utils.create_argument_unpacking_fn(
fn, parameter_type)
fn_gen = federated_computation_utils.federated_computation_serializer(
parameter_name, parameter_type, context_stack_impl.context_stack)
args, kwargs = unpack_arguments(next(fn_gen))
result = fn(*args, **kwargs)
return fn_gen.send(result)
def test_nested_structure_type_signature_roundtrip(self):
def traced_fn(x):
return x[0][0]
param_type = computation_types.to_type([(np.int32, )])
arg_fn = function_utils.create_argument_unpacking_fn(
traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '(<<int32>> -> int32)')
def test_tracing_with_float64_input(self):
self.skipTest('b/237566862')
param_type = computation_types.TensorType(np.int64)
identity_fn = lambda x: x
arg_fn = function_utils.create_argument_unpacking_fn(
identity_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
identity_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '(float64 -> float64)')
def test_serialize_jax_with_2xint32_to_2xint32(self):
self.skipTest('HLO pattern matching broken by '
'https://github.com/google/jax/pull/10232')
def traced_fn(x):
return collections.OrderedDict([('sum', x['foo'] + x['bar']),
('difference', x['bar'] - x['foo'])
])
param_type = computation_types.to_type(
collections.OrderedDict([('foo', np.int32), ('bar', np.int32)]))
arg_fn = function_utils.create_argument_unpacking_fn(
traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(
str(type_spec),
'(<foo=int32,bar=int32> -> <sum=int32,difference=int32>)')
xla_comp = xla_serialization.unpack_xla_computation(
comp_pb.xla.hlo_module)
self.assertIn(
# pylint: disable=line-too-long
' constant.4 = pred[] constant(false)\n'
' parameter.1 = (s32[], s32[]) parameter(0)\n'
' get-tuple-element.2 = s32[] get-tuple-element(parameter.1), index=0\n'
' get-tuple-element.3 = s32[] get-tuple-element(parameter.1), index=1\n'
' add.5 = s32[] add(get-tuple-element.2, get-tuple-element.3)\n'
' subtract.6 = s32[] subtract(get-tuple-element.3, get-tuple-element.2)\n'
' ROOT tuple.7 = (s32[], s32[]) tuple(add.5, subtract.6)\n',
xla_comp.as_hlo_text())
self.assertEqual(str(comp_pb.xla.result), str(comp_pb.xla.parameter))
self.assertEqual(
str(comp_pb.xla.parameter), 'struct {\n'
' element {\n'
' tensor {\n'
' index: 0\n'
' }\n'
' }\n'
' element {\n'
' tensor {\n'
' index: 1\n'
' }\n'
' }\n'
'}\n')
def test_arg_ordering(self):
param_type = computation_types.to_type(
(computation_types.TensorType(np.int32, 10),
computation_types.TensorType(np.int32)))
def traced_fn(b, a):
return jax.numpy.add(a, jax.numpy.sum(b))
arg_fn = function_utils.create_argument_unpacking_fn(traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '(<int32[10],int32> -> int32)')
def test_serialize_jax_with_nested_struct_arg(self):
def traced_fn(x, y):
return x[0] + y
param_type = computation_types.StructType([
(None, computation_types.StructType([(None, np.int32)])),
(None, np.int32)
])
arg_fn = function_utils.create_argument_unpacking_fn(
traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '(<<int32>,int32> -> int32)')
def test_serialize_jax_with_noarg_to_int32(self):
def traced_fn():
return 10
param_type = None
arg_fn = function_utils.create_argument_unpacking_fn(traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '( -> int32)')
xla_comp = xla_serialization.unpack_xla_computation(comp_pb.xla.hlo_module)
self.assertIn('ROOT tuple.4 = (s32[]) tuple(constant.3)',
xla_comp.as_hlo_text())
self.assertEqual(str(comp_pb.xla.parameter), '')
self.assertEqual(str(comp_pb.xla.result), 'tensor {\n' ' index: 0\n' '}\n')
def test_serialize_jax_with_two_args(self):
def traced_fn(x, y):
return x + y
param_type = computation_types.to_type(
collections.OrderedDict([('x', np.int32), ('y', np.int32)]))
arg_fn = function_utils.create_argument_unpacking_fn(
traced_fn, param_type)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
traced_fn, arg_fn, param_type, ctx_stack)
self.assertIsInstance(comp_pb, pb.Computation)
self.assertEqual(comp_pb.WhichOneof('computation'), 'xla')
type_spec = type_serialization.deserialize_type(comp_pb.type)
self.assertEqual(str(type_spec), '(<x=int32,y=int32> -> int32)')
xla_comp = xla_serialization.unpack_xla_computation(
comp_pb.xla.hlo_module)
self.assertIn(
# pylint: disable=line-too-long
' constant.4 = pred[] constant(false)\n'
' parameter.1 = (s32[], s32[]) parameter(0)\n'
' get-tuple-element.2 = s32[] get-tuple-element(parameter.1), index=0\n'
' get-tuple-element.3 = s32[] get-tuple-element(parameter.1), index=1\n'
' add.5 = s32[] add(get-tuple-element.2, get-tuple-element.3)\n'
' ROOT tuple.6 = (s32[]) tuple(add.5)\n',
xla_comp.as_hlo_text())
self.assertEqual(
str(comp_pb.xla.parameter), 'struct {\n'
' element {\n'
' tensor {\n'
' index: 0\n'
' }\n'
' }\n'
' element {\n'
' tensor {\n'
' index: 1\n'
' }\n'
' }\n'
'}\n')
self.assertEqual(str(comp_pb.xla.result), 'tensor {\n'
' index: 0\n'
'}\n')
def _jax_strategy_fn(fn_to_wrap, fn_name, parameter_type, unpack):
"""Serializes a Python function containing JAX code as a TFF computation.
Args:
fn_to_wrap: The Python function containing JAX code to be serialized as a
computation containing XLA.
fn_name: The name for the constructed computation (currently ignored).
parameter_type: An instance of `computation_types.Type` that represents the
TFF type of the computation parameter, or `None` if there's none.
unpack: See `unpack` in `function_utils.create_argument_unpacking_fn`.
Returns:
An instance of `computation_impl.ComputationImpl` with the constructed
computation.
"""
del fn_name # Unused.
unpack_arguments_fn = function_utils.create_argument_unpacking_fn(
fn_to_wrap, parameter_type, unpack=unpack)
ctx_stack = context_stack_impl.context_stack
comp_pb = jax_serialization.serialize_jax_computation(
fn_to_wrap, unpack_arguments_fn, parameter_type, ctx_stack)
return computation_impl.ComputationImpl(comp_pb, ctx_stack)
def __call__(self, fn_to_wrap, fn_name, parameter_type, unpack):
unpack_arguments_fn = function_utils.create_argument_unpacking_fn(
fn_to_wrap, parameter_type, unpack=unpack)
wrapped_fn_generator = _wrap_concrete(fn_name, self._wrapper_fn,
parameter_type)
packed_args = next(wrapped_fn_generator)
try:
args, kwargs = unpack_arguments_fn(packed_args)
result = fn_to_wrap(*args, **kwargs)
if result is None:
raise ComputationReturnedNoneError(fn_to_wrap)
except Exception:
# Give nested generators an opportunity to clean up, then
# re-raise the original error without extra context.
# We don't want to simply pass the error into the generators,
# as that would result in the whole generator stack being added
# to the error message.
try:
wrapped_fn_generator.throw(_TracingError())
except _TracingError:
pass
raise
return wrapped_fn_generator.send(result)
