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# Owner(s): ["oncall: distributed"]
import os
import pickle
from io import BytesIO
from typing import cast
import torch
import torch.distributed as dist
from torch.distributed._serialization import _streaming_load, _streaming_save
from torch.distributed.tensor import DeviceMesh, distribute_tensor, DTensor
from torch.testing._internal.common_utils import requires_cuda, run_tests, TestCase
DEBUG_ENV = "TORCH_SERIALIZATION_DEBUG"
class MyClass:
def __init__(self, a: int) -> None:
self.a = a
def __eq__(self, other: "MyClass") -> bool:
return self.a == other.a
class TestSerialization(TestCase):
def setUp(self) -> None:
# disable debug asserts
self._old_debug = os.environ.get(DEBUG_ENV)
os.environ[DEBUG_ENV] = "0"
def tearDown(self):
if self._old_debug is not None:
os.environ[DEBUG_ENV] = self._old_debug
def test_scalar_tensor(self) -> None:
tensor = torch.tensor(42, dtype=torch.int32)
state_dict = {"scalar": tensor}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
torch.testing.assert_close(result, state_dict)
def test_strided_tensor(self) -> None:
base_tensor = torch.arange(16, dtype=torch.float32).reshape(4, 4)
strided_tensor = base_tensor[::2, ::2]
state_dict = {"strided": strided_tensor}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
torch.testing.assert_close(result, state_dict)
def test_tensor_with_offset(self) -> None:
state_dict = {
"offset": torch.arange(10, dtype=torch.float64)[2:],
"strided": torch.arange(10, dtype=torch.float64)[2::2],
}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
torch.testing.assert_close(result, state_dict)
def test_nested_tensors(self) -> None:
tensor1 = torch.tensor([1, 2, 3], dtype=torch.int32)
tensor2 = torch.tensor([[1.5, 2.5], [3.5, 4.5]], dtype=torch.float64)
state_dict = {"nested": {"tensor1": tensor1, "tensor2": tensor2}}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
torch.testing.assert_close(result, state_dict)
def test_various_data_types(self) -> None:
tensor_float32 = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)
tensor_int16 = torch.tensor([1, 2, 3], dtype=torch.int16)
tensor_bool = torch.tensor([True, False, True], dtype=torch.bool)
tensor_uint16 = torch.tensor([True, False, True], dtype=torch.uint16)
state_dict = {
"float32": tensor_float32,
"int16": tensor_int16,
"bool": tensor_bool,
"uint16": tensor_uint16,
}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
torch.testing.assert_close(result, state_dict)
def test_dtensor(self) -> None:
dist.init_process_group(
backend="gloo", rank=0, world_size=1, store=dist.HashStore()
)
device_mesh = DeviceMesh("cpu", 1)
tensor = torch.randn(4, 4)
dtensor = distribute_tensor(tensor, device_mesh, [])
state_dict = dtensor
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = cast(DTensor, _streaming_load(file))
torch.testing.assert_close(result.to_local(), state_dict.to_local())
self.assertEqual(result._spec, state_dict._spec)
def test_python_object(self) -> None:
state_dict = {
"obj": MyClass(42),
}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file, weights_only=False)
self.assertEqual(result, state_dict)
def test_str_utf8(self) -> None:
state_dict = {
"obj": "Ü",
}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
self.assertEqual(result, state_dict)
def test_weights_only(self) -> None:
state_dict = {
"obj": MyClass(42),
}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
with self.assertRaisesRegex(pickle.UnpicklingError, "not an allowed global"):
_streaming_load(file)
with self.assertRaisesRegex(RuntimeError, "explicit pickle_module"):
_streaming_load(file, weights_only=True, pickle_module=pickle)
@requires_cuda
def test_cuda(self) -> None:
device = torch.device("cuda:0")
tensor = torch.tensor(42, dtype=torch.float, device=device)
state_dict = {"scalar": tensor}
file = BytesIO()
_streaming_save(state_dict, file)
file.seek(0)
result = _streaming_load(file)
torch.testing.assert_close(result, state_dict)
self.assertEqual(result["scalar"].device, device)
if __name__ == "__main__":
run_tests()
|
