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# -*- coding: utf-8 -*-
# Owner(s): ["module: tests"]
import torch
from torch.testing import make_tensor
from torch.testing._internal.common_utils import TestCase, run_tests
from torch.testing._internal.common_device_type import (
instantiate_device_type_tests, onlyCUDA, dtypes, skipMeta,
onlyNativeDeviceTypes)
from torch.testing._internal.common_dtype import all_types_and_complex_and
from torch.utils.dlpack import from_dlpack, to_dlpack
class TestTorchDlPack(TestCase):
exact_dtype = True
@skipMeta
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_dlpack_capsule_conversion(self, device, dtype):
# DLpack does not explicitly support bool (xref dmlc/dlpack#75)
x = make_tensor((5,), dtype=dtype, device=device)
z = from_dlpack(to_dlpack(x))
self.assertEqual(z, x)
@skipMeta
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_dlpack_protocol_conversion(self, device, dtype):
x = make_tensor((5,), dtype=dtype, device=device)
z = from_dlpack(x)
self.assertEqual(z, x)
@skipMeta
@onlyNativeDeviceTypes
def test_dlpack_shared_storage(self, device):
x = make_tensor((5,), dtype=torch.float64, device=device)
z = from_dlpack(to_dlpack(x))
z[0] = z[0] + 20.0
self.assertEqual(z, x)
@skipMeta
@onlyCUDA
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_dlpack_conversion_with_streams(self, device, dtype):
# Create a stream where the tensor will reside
stream = torch.cuda.Stream()
with torch.cuda.stream(stream):
# Do an operation in the actual stream
x = make_tensor((5,), dtype=dtype, device=device) + 1
# DLPack protocol helps establish a correct stream order
# (hence data dependency) at the exchange boundary.
# DLPack manages this synchronization for us, so we don't need to
# explicitly wait until x is populated
stream = torch.cuda.Stream()
with torch.cuda.stream(stream):
z = from_dlpack(x)
stream.synchronize()
self.assertEqual(z, x)
@skipMeta
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_from_dlpack(self, device, dtype):
x = make_tensor((5,), dtype=dtype, device=device)
y = torch.from_dlpack(x)
self.assertEqual(x, y)
@skipMeta
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_from_dlpack_noncontinguous(self, device, dtype):
x = make_tensor((25,), dtype=dtype, device=device).reshape(5, 5)
y1 = x[0]
y1_dl = torch.from_dlpack(y1)
self.assertEqual(y1, y1_dl)
y2 = x[:, 0]
y2_dl = torch.from_dlpack(y2)
self.assertEqual(y2, y2_dl)
y3 = x[1, :]
y3_dl = torch.from_dlpack(y3)
self.assertEqual(y3, y3_dl)
y4 = x[1]
y4_dl = torch.from_dlpack(y4)
self.assertEqual(y4, y4_dl)
y5 = x.t()
y5_dl = torch.from_dlpack(y5)
self.assertEqual(y5, y5_dl)
@skipMeta
@onlyCUDA
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_dlpack_conversion_with_diff_streams(self, device, dtype):
stream_a = torch.cuda.Stream()
stream_b = torch.cuda.Stream()
# DLPack protocol helps establish a correct stream order
# (hence data dependency) at the exchange boundary.
# the `tensor.__dlpack__` method will insert a synchronization event
# in the current stream to make sure that it was correctly populated.
with torch.cuda.stream(stream_a):
x = make_tensor((5,), dtype=dtype, device=device) + 1
z = torch.from_dlpack(x.__dlpack__(stream_b.cuda_stream))
stream_a.synchronize()
stream_b.synchronize()
self.assertEqual(z, x)
@skipMeta
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_from_dlpack_dtype(self, device, dtype):
x = make_tensor((5,), dtype=dtype, device=device)
y = torch.from_dlpack(x)
assert x.dtype == y.dtype
@skipMeta
@onlyCUDA
def test_dlpack_default_stream(self, device):
class DLPackTensor:
def __init__(self, tensor):
self.tensor = tensor
def __dlpack_device__(self):
return self.tensor.__dlpack_device__()
def __dlpack__(self, stream=None):
if torch.version.hip is None:
assert stream == 1
else:
assert stream == 0
capsule = self.tensor.__dlpack__(stream)
return capsule
# CUDA-based tests runs on non-default streams
with torch.cuda.stream(torch.cuda.default_stream()):
x = DLPackTensor(make_tensor((5,), dtype=torch.float32, device=device))
from_dlpack(x)
@skipMeta
@onlyNativeDeviceTypes
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16))
def test_dlpack_tensor_invalid_stream(self, device, dtype):
with self.assertRaises(TypeError):
x = make_tensor((5,), dtype=dtype, device=device)
x.__dlpack__(stream=object())
@skipMeta
def test_dlpack_error_on_bool_tensor(self):
x = torch.tensor([True], dtype=torch.bool)
with self.assertRaises(RuntimeError):
to_dlpack(x)
# TODO: add interchange tests once NumPy 1.22 (dlpack support) is required
@skipMeta
def test_dlpack_export_requires_grad(self):
x = torch.zeros(10, dtype=torch.float32, requires_grad=True)
with self.assertRaisesRegex(RuntimeError, r"require gradient"):
x.__dlpack__()
@skipMeta
def test_dlpack_export_is_conj(self):
x = torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j])
y = torch.conj(x)
with self.assertRaisesRegex(RuntimeError, r"conjugate bit"):
y.__dlpack__()
@skipMeta
def test_dlpack_export_non_strided(self):
x = torch.sparse_coo_tensor([[0]], [1], size=(1,))
y = torch.conj(x)
with self.assertRaisesRegex(RuntimeError, r"strided"):
y.__dlpack__()
@skipMeta
def test_dlpack_normalize_strides(self):
x = torch.rand(16)
y = x[::3][:1]
self.assertEqual(y.shape, (1,))
self.assertEqual(y.stride(), (3,))
z = from_dlpack(y)
self.assertEqual(z.shape, (1,))
# gh-83069, make sure __dlpack__ normalizes strides
self.assertEqual(z.stride(), (1,))
instantiate_device_type_tests(TestTorchDlPack, globals())
if __name__ == '__main__':
run_tests()
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