mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 13:44:15 +08:00
4b44b1861d
Proper fix would be to do something like https://github.com/pytorch/pytorch/pull/107877, but since that depends on internal changes and it would take too long for diff train to land we will first just make OSS work using torch.save. Pull Request resolved: https://github.com/pytorch/pytorch/pull/107906 Approved by: https://github.com/gmagogsfm
620 lines
22 KiB
Python
620 lines
22 KiB
Python
# Owner(s): ["module: dynamo"]
|
|
import io
|
|
import pathlib
|
|
import tempfile
|
|
import unittest
|
|
import zipfile
|
|
|
|
import torch
|
|
import torch._dynamo as torchdynamo
|
|
from torch._export import dynamic_dim, export, save, load
|
|
from torch._export.constraints import constrain_as_size
|
|
from torch._export.db.case import ExportCase, normalize_inputs, SupportLevel
|
|
from torch._export.db.examples import all_examples
|
|
from torch._export.serde.serialize import (
|
|
ExportedProgramDeserializer,
|
|
ExportedProgramSerializer,
|
|
deserialize,
|
|
serialize,
|
|
SerializeError,
|
|
)
|
|
from torch._subclasses.fake_tensor import FakeTensor
|
|
from torch.fx.experimental.symbolic_shapes import is_concrete_int
|
|
import torch.utils._pytree as pytree
|
|
from torch.testing._internal.common_utils import (
|
|
instantiate_parametrized_tests,
|
|
parametrize,
|
|
run_tests,
|
|
TestCase,
|
|
TemporaryFileName,
|
|
IS_FBCODE,
|
|
IS_MACOS,
|
|
IS_SANDCASTLE,
|
|
IS_WINDOWS,
|
|
find_library_location,
|
|
)
|
|
|
|
|
|
def get_filtered_export_db_tests():
|
|
unsupported_test_names = {
|
|
"dynamic_shape_constructor", # 'NoneType' object has no attribute 'from_tensor'
|
|
"dictionary", # Graph output must be a tuple()
|
|
"fn_with_kwargs", # export doesn't support kwargs yet
|
|
"scalar_output", # Tracing through 'f' must produce a single graph
|
|
}
|
|
|
|
return [
|
|
(name, case)
|
|
for name, case in all_examples().items()
|
|
if (
|
|
case.support_level == SupportLevel.SUPPORTED and
|
|
name not in unsupported_test_names
|
|
)
|
|
]
|
|
|
|
|
|
@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
|
|
class TestSerialize(TestCase):
|
|
def test_serialize_multiple_returns_from_node(self) -> None:
|
|
class MyModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x, w, b):
|
|
return torch.nn.functional.layer_norm(
|
|
x,
|
|
x.size()[1:],
|
|
weight=w,
|
|
bias=b,
|
|
eps=1e-5,
|
|
)
|
|
|
|
exported_module = export(
|
|
MyModule(),
|
|
(
|
|
torch.ones([512, 512], requires_grad=True),
|
|
torch.ones([512]),
|
|
torch.ones([512]),
|
|
),
|
|
)
|
|
|
|
serialized, _ = ExportedProgramSerializer().serialize(exported_module)
|
|
node = serialized.graph_module.graph.nodes[-1]
|
|
self.assertEqual(node.target, "torch.ops.aten.native_layer_norm.default")
|
|
# aten::native_layer_norm returns 3 tensnors
|
|
self.assertEqual(len(node.outputs), 3)
|
|
|
|
# check the names are unique
|
|
seen = set()
|
|
for output in node.outputs:
|
|
name = output.as_tensor.name
|
|
self.assertNotIn(name, seen)
|
|
seen.add(name)
|
|
|
|
def test_serialize_list_returns(self) -> None:
|
|
class MyModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x):
|
|
return torch.split(x, 2)
|
|
|
|
input = torch.arange(10.0).reshape(5, 2)
|
|
input.requires_grad = True
|
|
exported_module = export(MyModule(), (input,))
|
|
|
|
serialized, _ = ExportedProgramSerializer().serialize(exported_module)
|
|
node = serialized.graph_module.graph.nodes[-1]
|
|
self.assertEqual(node.target, "torch.ops.aten.split.Tensor")
|
|
self.assertEqual(len(node.outputs), 1)
|
|
# Input looks like:
|
|
# tensor([[0, 1],
|
|
# [2, 3],
|
|
# [4, 5],
|
|
# [6, 7],
|
|
# [8, 9]])
|
|
# Output looks like:
|
|
# (tensor([[0, 1],
|
|
# [2, 3]]),
|
|
# tensor([[4, 5],
|
|
# [6, 7]]),
|
|
# tensor([[8, 9]]))
|
|
self.assertEqual(len(node.outputs[0].as_tensors), 3)
|
|
|
|
# check the names are unique
|
|
seen = set()
|
|
for output in node.outputs[0].as_tensors:
|
|
name = output.name
|
|
self.assertNotIn(name, seen)
|
|
seen.add(name)
|
|
|
|
def test_multi_return_some_unused(self) -> None:
|
|
"""
|
|
Make sure the serialized output matches the op schema, even if some of
|
|
the arguments are never used in the graph.
|
|
"""
|
|
|
|
class MyModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x):
|
|
return torch.ops.aten.var_mean.correction(x, [1])[0]
|
|
|
|
exported_module = export(
|
|
MyModule(),
|
|
(torch.ones([512, 512], requires_grad=True),),
|
|
)
|
|
|
|
serialized, _ = ExportedProgramSerializer().serialize(exported_module)
|
|
node = serialized.graph_module.graph.nodes[-1]
|
|
self.assertEqual(node.target, "torch.ops.aten.var_mean.correction")
|
|
self.assertEqual(len(node.outputs), 2)
|
|
|
|
# check the names are unique
|
|
seen = set()
|
|
for output in node.outputs:
|
|
name = output.as_tensor.name
|
|
self.assertNotIn(name, seen)
|
|
seen.add(name)
|
|
|
|
def test_kwargs_default(self) -> None:
|
|
"""
|
|
Tests that the kwargs default values are serialized even if they are not
|
|
specified
|
|
"""
|
|
|
|
def f(x: torch.Tensor) -> torch.Tensor:
|
|
values = torch.randn(3, 2)
|
|
return torch.searchsorted(x, values, side="right", right=True)
|
|
|
|
x, _ = torch.sort(torch.randn(3, 4))
|
|
exported_module = export(f, (x,))
|
|
serialized, _ = ExportedProgramSerializer().serialize(exported_module)
|
|
|
|
node = serialized.graph_module.graph.nodes[-1]
|
|
self.assertEqual(node.target, "torch.ops.aten.searchsorted.Tensor")
|
|
self.assertEqual(len(node.inputs), 6)
|
|
self.assertEqual(node.inputs[2].arg.as_bool, False)
|
|
self.assertEqual(node.inputs[3].arg.as_bool, True)
|
|
self.assertEqual(node.inputs[4].arg.as_string, "right")
|
|
self.assertEqual(node.inputs[5].arg.as_none, ())
|
|
|
|
|
|
@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
|
|
class TestDeserialize(TestCase):
|
|
def check_graph(self, fn, inputs, constraints=None, _check_meta=True) -> None:
|
|
"""Export a graph, serialize it, deserialize it, and compare the results."""
|
|
# TODO(angelayi): test better with some sort of wrapper
|
|
constraints = [] if constraints is None else constraints
|
|
ep = export(fn, inputs, {}, constraints)
|
|
ep.graph.eliminate_dead_code()
|
|
|
|
serialized_struct, state_dict = serialize(ep, opset_version={"aten": 0})
|
|
deserialized_ep = deserialize(serialized_struct, state_dict, expected_opset_version={"aten": 0})
|
|
deserialized_ep.graph.eliminate_dead_code()
|
|
|
|
orig_outputs = ep(*inputs)
|
|
loaded_outputs = deserialized_ep(*inputs)
|
|
|
|
flat_orig_outputs, _ = pytree.tree_flatten(orig_outputs)
|
|
flat_loaded_outputs, _ = pytree.tree_flatten(loaded_outputs)
|
|
|
|
for orig, loaded in zip(flat_orig_outputs, flat_loaded_outputs):
|
|
self.assertEqual(type(orig), type(loaded))
|
|
if isinstance(orig, torch.Tensor):
|
|
self.assertTrue(torch.allclose(orig, loaded))
|
|
else:
|
|
self.assertEqual(orig, loaded)
|
|
|
|
self.assertEqual(len(ep.original_traced_arguments), len(deserialized_ep.original_traced_arguments))
|
|
for arg1, arg2 in zip(ep.original_traced_arguments, deserialized_ep.original_traced_arguments):
|
|
if isinstance(arg1, torch.Tensor) and isinstance(arg2, torch.Tensor):
|
|
self.assertTrue(torch.allclose(arg1, arg2))
|
|
else:
|
|
self.assertEqual(type(arg1), type(arg2))
|
|
|
|
def _check_graph_nodes(gm1, gm2, _check_meta=True):
|
|
# TODO: The _check_meta flag bypasses checking for
|
|
# source_fn/nn_module_stack as there is an issue with
|
|
# roundtripping the source_fn value on torch.ops.map nodes
|
|
# original source_fn: <functorch.experimental._map.MapWrapper object at 0x7f80a0549930>
|
|
# deserialized source_fn: 'functorch.experimental._map.map'
|
|
|
|
self.assertEqual(len(gm1.graph.nodes), len(gm2.graph.nodes))
|
|
|
|
for node1, node2 in zip(gm1.graph.nodes, gm2.graph.nodes):
|
|
self.assertEqual(node1.op, node2.op)
|
|
if node1.op == "call_function":
|
|
# Check "val" metadata
|
|
val1 = node1.meta.get("val", None)
|
|
val2 = node2.meta.get("val", None)
|
|
if val1 is None or val2 is None:
|
|
# Either both are None
|
|
self.assertEqual(val1, val2)
|
|
elif isinstance(val1, FakeTensor) and isinstance(val2, FakeTensor):
|
|
# Or both are fake tensors with the same shape/dtype
|
|
self.assertEqual(len(val1.shape), len(val2.shape))
|
|
for s1, s2 in zip(val1.shape, val2.shape):
|
|
if is_concrete_int(s1) and is_concrete_int(s2):
|
|
self.assertEqual(s1, s2)
|
|
else:
|
|
self.assertEqual(str(s1), str(s2))
|
|
self.assertEqual(val1.dtype, val2.dtype)
|
|
elif isinstance(val1, list) and isinstance(val2, list):
|
|
# Or both are fake tensors lists with one element and with the
|
|
# same shape/dtype
|
|
self.assertTrue(len(val1) == 1 and len(val2) == 1)
|
|
self.assertEqual(val1[0].shape, val2[0].shape)
|
|
self.assertEqual(val1[0].dtype, val2[0].dtype)
|
|
else:
|
|
# For expressions like 's0 < 10' can only compare through string
|
|
self.assertEqual(str(val1), str(val2))
|
|
|
|
# Check "stack_trace" metadata
|
|
self.assertEqual(
|
|
node1.meta.get("stack_trace", None),
|
|
node2.meta.get("stack_trace", None),
|
|
)
|
|
|
|
if node1.target == torch.ops.higher_order.cond:
|
|
true_graph1 = getattr(gm1, node1.args[1].target)
|
|
true_graph2 = getattr(gm2, node2.args[1].target)
|
|
_check_graph_nodes(true_graph1, true_graph2)
|
|
|
|
false_graph1 = getattr(gm1, node1.args[2].target)
|
|
false_graph2 = getattr(gm2, node2.args[2].target)
|
|
_check_graph_nodes(false_graph1, false_graph2)
|
|
elif node1.target == torch.ops.map_impl:
|
|
map_graph1 = getattr(gm1, node1.args[0].target)
|
|
map_graph2 = getattr(gm2, node2.args[0].target)
|
|
_check_graph_nodes(map_graph1, map_graph2, False)
|
|
|
|
if (
|
|
_check_meta and
|
|
node1.op not in ("get_attr", "placeholder", "output")
|
|
):
|
|
# Check "nn_module_stack" metadata
|
|
self.assertEqual(
|
|
node1.meta.get("nn_module_stack", None),
|
|
node2.meta.get("nn_module_stack", None),
|
|
)
|
|
# Check "source_fn" metadata
|
|
self.assertEqual(
|
|
node1.meta.get("source_fn", None),
|
|
node2.meta.get("source_fn", None),
|
|
)
|
|
|
|
_check_graph_nodes(ep.graph_module, deserialized_ep.graph_module, _check_meta)
|
|
|
|
def test_multi_return(self) -> None:
|
|
"""
|
|
Test multiple return from a single node (ex. layer_norm has 2 outputs)
|
|
"""
|
|
class MyModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x, w, b):
|
|
return torch.nn.functional.layer_norm(
|
|
x,
|
|
x.size()[1:],
|
|
weight=w,
|
|
bias=b,
|
|
eps=1e-5,
|
|
)
|
|
|
|
inputs = (
|
|
torch.ones([512, 512], requires_grad=True),
|
|
torch.ones([512]),
|
|
torch.ones([512]),
|
|
)
|
|
self.check_graph(MyModule(), inputs)
|
|
|
|
def test_basic(self) -> None:
|
|
class MyModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x):
|
|
x = x + x
|
|
x = x * x
|
|
x = x / x
|
|
return x, x.clone()
|
|
|
|
inputs = (torch.ones([512], requires_grad=True),)
|
|
self.check_graph(MyModule(), inputs)
|
|
|
|
def test_dynamic(self) -> None:
|
|
class DynamicShapeSimpleModel(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, a, b, c) -> torch.Tensor:
|
|
d = (torch.matmul(a, b) + c) / 2
|
|
d_s0 = d.shape[0]
|
|
d_s1 = d.shape[1]
|
|
d_s3 = d_s0 * d_s1
|
|
e = d.view(d_s3)
|
|
return torch.cat([e, e])
|
|
|
|
|
|
inputs = (torch.randn(2, 4), torch.randn(4, 7), torch.randn(2, 7))
|
|
constraints = [
|
|
dynamic_dim(inputs[0], 0),
|
|
dynamic_dim(inputs[2], 0),
|
|
dynamic_dim(inputs[2], 0) == dynamic_dim(inputs[0], 0),
|
|
]
|
|
self.check_graph(DynamicShapeSimpleModel(), inputs, constraints)
|
|
|
|
def test_sym_bool(self):
|
|
def f(x, y):
|
|
return x.size(0) in y
|
|
|
|
self.check_graph(f, (torch.ones(2), torch.ones(3)))
|
|
|
|
def test_shape(self):
|
|
def f(x):
|
|
z, y = x.size()
|
|
return z + y + x[0], z
|
|
|
|
inputs = (torch.ones(2, 3),)
|
|
constraints = [
|
|
dynamic_dim(inputs[0], 0),
|
|
dynamic_dim(inputs[0], 1),
|
|
]
|
|
self.check_graph(f, inputs, constraints)
|
|
|
|
def test_module(self):
|
|
class M(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.linear1 = torch.nn.Linear(3, 3)
|
|
self.relu = torch.nn.ReLU()
|
|
self.linear2 = torch.nn.Linear(3, 5)
|
|
|
|
def forward(self, x):
|
|
x = self.linear1(x)
|
|
x = self.linear1(x)
|
|
x = torch.nn.functional.relu(x)
|
|
x = self.linear2(x)
|
|
return x
|
|
|
|
inputs = (torch.randn(3, 3),)
|
|
self.check_graph(M(), inputs)
|
|
|
|
def test_cond(self):
|
|
from functorch.experimental.control_flow import cond
|
|
inputs = torch.ones(4, 3), torch.zeros(4, 3)
|
|
|
|
class M(torch.nn.Module):
|
|
def forward(self, x, y):
|
|
def t(x, y):
|
|
return x + y
|
|
|
|
def f(x, y):
|
|
return x - y
|
|
return cond(x[0][0] > 4, t, f, [x, y])
|
|
|
|
self.check_graph(M(), inputs)
|
|
|
|
def test_map(self):
|
|
from functorch.experimental import control_flow
|
|
|
|
def f(x, y):
|
|
return x + y
|
|
|
|
def g(xs, y):
|
|
return control_flow.map(f, xs, y)
|
|
|
|
inputs = (torch.ones(3, 2, 2), torch.ones(2))
|
|
self.check_graph(g, inputs, _check_meta=False)
|
|
|
|
@parametrize(
|
|
"name,case",
|
|
get_filtered_export_db_tests(),
|
|
name_fn=lambda name, case: f"case_{name}",
|
|
)
|
|
def test_exportdb_supported(self, name: str, case: ExportCase) -> None:
|
|
model = case.model
|
|
inputs = normalize_inputs(case.example_inputs)
|
|
_check_meta = "map" not in name
|
|
self.check_graph(model, inputs.args, _check_meta=_check_meta)
|
|
|
|
def test_constraints(self):
|
|
def f(x, y):
|
|
n = x.item()
|
|
constrain_as_size(n, min=2)
|
|
return y.sum() + torch.ones(n, 5).sum()
|
|
|
|
self.check_graph(f, (torch.tensor(3), torch.randn(4, 5)))
|
|
|
|
|
|
instantiate_parametrized_tests(TestDeserialize)
|
|
|
|
@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
|
|
class TestSchemaVersioning(TestCase):
|
|
def test_error(self):
|
|
def f(x):
|
|
return x + x
|
|
|
|
ep = export(f, (torch.randn(1, 3),))
|
|
|
|
serialized_ep, serialized_state_dict = ExportedProgramSerializer().serialize(ep)
|
|
serialized_ep.schema_version = -1
|
|
with self.assertRaisesRegex(SerializeError, r"Serialized schema version -1 does not match our current"):
|
|
ExportedProgramDeserializer().deserialize(serialized_ep, serialized_state_dict)
|
|
|
|
|
|
class TestOpVersioning(TestCase):
|
|
"""Test if serializer/deserializer behaves correctly if version mismatch."""
|
|
|
|
def test_empty_model_opset_version_raises(self):
|
|
compiler_opset_version = {"aten": 4}
|
|
model_opset_version = None
|
|
deserializer = ExportedProgramDeserializer(compiler_opset_version)
|
|
with self.assertRaises(RuntimeError):
|
|
deserializer._validate_model_opset_version(model_opset_version)
|
|
|
|
def test_opset_mismatch_raises(self):
|
|
compiler_opset_version = {"aten": 4}
|
|
model_opset_version = {"aten": 3}
|
|
deserializer = ExportedProgramDeserializer(compiler_opset_version)
|
|
with self.assertRaises(NotImplementedError):
|
|
deserializer._validate_model_opset_version(model_opset_version)
|
|
|
|
def test_model_op_namespace_version_missing_from_deserializer_do_not_raises(self):
|
|
compiler_opset_version = {"aten": 3}
|
|
model_opset_version = {"aten": 3, "custom": 4}
|
|
deserializer = ExportedProgramDeserializer(compiler_opset_version)
|
|
with self.assertLogs(level='WARN') as log:
|
|
deserializer._validate_model_opset_version(model_opset_version)
|
|
self.assertIn("Compiler doesn't have a version table for op namespace", log.output[0])
|
|
|
|
unittest.expectedFailure(
|
|
TestDeserialize.test_exportdb_supported_case_tensor_setattr
|
|
)
|
|
unittest.expectedFailure(
|
|
TestDeserialize.test_exportdb_supported_case_pytree_flatten
|
|
)
|
|
# getattr node in the graph from a torch.tensor call
|
|
unittest.expectedFailure(
|
|
TestDeserialize.test_exportdb_supported_case_cond_branch_nonlocal_variables
|
|
)
|
|
|
|
|
|
@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
|
|
class TestSaveLoad(TestCase):
|
|
def test_save_buffer(self):
|
|
inp = (torch.tensor([0.1, 0.1]),)
|
|
linear = torch.nn.Linear(2, 2)
|
|
|
|
def f(x):
|
|
x = x + 1
|
|
y = x.t()
|
|
y = y.relu()
|
|
y = linear(y)
|
|
return y
|
|
|
|
ep = export(f, inp)
|
|
|
|
buffer = io.BytesIO()
|
|
save(ep, buffer)
|
|
buffer.seek(0)
|
|
loaded_ep = load(buffer)
|
|
|
|
self.assertTrue(torch.allclose(ep(*inp), loaded_ep(*inp)))
|
|
|
|
def test_save_file(self):
|
|
|
|
def f(x):
|
|
return x * x
|
|
|
|
inp = (torch.randn(2, 2),)
|
|
ep = export(f, inp)
|
|
|
|
with tempfile.NamedTemporaryFile() as f:
|
|
save(ep, f)
|
|
f.seek(0)
|
|
loaded_ep = load(f)
|
|
|
|
self.assertTrue(torch.allclose(ep(*inp), loaded_ep(*inp)))
|
|
|
|
def test_save_path(self):
|
|
def f(x, y):
|
|
return x + y
|
|
|
|
inp = (torch.tensor([6]), torch.tensor([7]))
|
|
ep = export(f, inp)
|
|
|
|
with TemporaryFileName() as fname:
|
|
path = pathlib.Path(fname)
|
|
save(ep, path)
|
|
loaded_ep = load(path)
|
|
|
|
self.assertTrue(torch.allclose(ep(*inp), loaded_ep(*inp)))
|
|
|
|
def test_save_extra(self):
|
|
inp = (torch.tensor([0.1, 0.1]),)
|
|
|
|
def f(x):
|
|
return x * x + x
|
|
|
|
ep = export(f, inp)
|
|
|
|
buffer = io.BytesIO()
|
|
save(ep, buffer, extra_files={"extra.txt": "moo"})
|
|
buffer.seek(0)
|
|
extra_files = {"extra.txt": ""}
|
|
loaded_ep = load(buffer, extra_files=extra_files)
|
|
|
|
self.assertTrue(torch.allclose(ep(*inp), loaded_ep(*inp)))
|
|
self.assertEqual(extra_files["extra.txt"], "moo")
|
|
|
|
def test_version_error(self):
|
|
def f(x):
|
|
return x + x
|
|
|
|
ep = export(f, (torch.randn(1, 3),))
|
|
|
|
with tempfile.NamedTemporaryFile() as f:
|
|
save(ep, f)
|
|
f.seek(0)
|
|
|
|
# Modify the version
|
|
with zipfile.ZipFile(f, 'a') as zipf:
|
|
zipf.writestr('version', "-1")
|
|
|
|
with self.assertRaisesRegex(RuntimeError, r"Serialized version -1 does not match our current"):
|
|
f.seek(0)
|
|
loaded_ep = load(f)
|
|
|
|
|
|
@unittest.skipIf(not torchdynamo.is_dynamo_supported(), "dynamo doesn't support")
|
|
class TestSerializeCustomClass(TestCase):
|
|
def setUp(self):
|
|
if IS_SANDCASTLE or IS_MACOS or IS_FBCODE:
|
|
raise unittest.SkipTest("non-portable load_library call used in test")
|
|
lib_file_path = find_library_location('libtorchbind_test.so')
|
|
if IS_WINDOWS:
|
|
lib_file_path = find_library_location('torchbind_test.dll')
|
|
torch.ops.load_library(str(lib_file_path))
|
|
|
|
def test_custom_class(self):
|
|
custom_obj = torch.classes._TorchScriptTesting._PickleTester([3, 4])
|
|
|
|
def f(x):
|
|
return x + x
|
|
|
|
inputs = (torch.zeros(4, 4),)
|
|
ep = export(f, inputs)
|
|
|
|
# Replace one of the values with an instance of our custom class
|
|
for node in ep.graph.nodes:
|
|
if node.op == "call_function" and node.target == torch.ops.aten.add.Tensor:
|
|
with ep.graph.inserting_before(node):
|
|
custom_node = ep.graph.call_function(
|
|
torch.ops._TorchScriptTesting.take_an_instance.default,
|
|
(custom_obj,),
|
|
)
|
|
custom_node.meta["val"] = torch.ones(4, 4)
|
|
arg0, _ = node.args
|
|
node.args = (arg0, custom_node)
|
|
|
|
serialized_vals = serialize(ep)
|
|
deserialized_ep = deserialize(*serialized_vals)
|
|
|
|
for node in deserialized_ep.graph.nodes:
|
|
if (
|
|
node.op == "call_function" and
|
|
node.target == torch.ops._TorchScriptTesting.take_an_instance.default
|
|
):
|
|
arg = node.args[0]
|
|
self.assertTrue(isinstance(arg, torch._C.ScriptObject))
|
|
self.assertEqual(arg.__getstate__(), custom_obj.__getstate__())
|
|
self.assertEqual(arg.top(), 7)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|