def test_unsafe_flag(self, device, dtype):
length_type = dtype
lengths = torch.tensor([0, 2, 3, 0], device=device, dtype=length_type)
data = torch.arange(6, dtype=torch.float, device=device)
# test for error on 1-D lenghts
with self.assertRaisesRegex(RuntimeError,
"Expected all rows of lengths along axis"):
torch.segment_reduce(data,
'sum',
lengths=lengths,
axis=0,
unsafe=False)
# test for error on multi-D lengths
nd_lengths = torch.tensor([[0, 3, 3, 0], [2, 3, 0, 0]],
dtype=length_type,
device=device)
nd_data = torch.arange(12, dtype=torch.float,
device=device).reshape(2, 6)
with self.assertRaisesRegex(RuntimeError,
"Expected all rows of lengths along axis"):
torch.segment_reduce(nd_data,
'sum',
lengths=nd_lengths,
axis=1,
unsafe=False)
def test_max_simple_1d(self, device, dtype):
lengths = torch.tensor([1, 2, 3], device=device)
data = torch.tensor([1, float("nan"), 3, 4, 5, 6],
device=device,
dtype=dtype)
expected_result = torch.tensor([1, float("nan"), 6],
device=device,
dtype=dtype)
actual_result = torch.segment_reduce(data=data,
reduce="max",
lengths=lengths,
axis=0,
unsafe=False)
self.assertEqual(expected_result,
actual_result,
rtol=1e-03,
atol=1e-05,
equal_nan=True)
actual_result = torch.segment_reduce(data=data,
reduce="max",
lengths=lengths,
axis=-1,
unsafe=False)
self.assertEqual(expected_result,
actual_result,
rtol=1e-03,
atol=1e-05,
equal_nan=True)
def _test_max_simple_1d(self, device, dtype, unsafe, axis):
lengths = torch.tensor([1, 2, 3], device=device)
data = torch.tensor(
[1, float("nan"), 3, 4, 5, 5],
device=device,
dtype=dtype,
requires_grad=True,
)
expected_result = torch.tensor([1, float("nan"), 5],
device=device,
dtype=dtype)
actual_result = torch.segment_reduce(data=data,
reduce="max",
lengths=lengths,
axis=axis,
unsafe=unsafe)
self.assertEqual(expected_result,
actual_result,
rtol=1e-03,
atol=1e-05,
equal_nan=True)
# Backward is only supported for cpu tensors for now. Return early if cuda
if data.is_cuda:
return
# Test backward
expected_grad = torch.tensor([1, 1, 0, 0, 0.5, 0.5],
device=device,
dtype=dtype)
actual_result.sum().backward()
self.assertEqual(expected_grad,
data.grad,
rtol=1e-03,
atol=1e-05,
equal_nan=True)
# gradcheck does not work well with bfloat16 or fp16 cpu types
# also there is small numerical difference with fp32
if dtype not in [torch.half, torch.bfloat16, torch.float]:
# gradcheck does not like "nan" input
data = torch.tensor(
[1, 10, 3, 4, 5, 5],
device=device,
dtype=dtype,
requires_grad=True,
)
self.assertTrue(
gradcheck(
lambda x: torch.segment_reduce(data=x,
reduce="max",
lengths=lengths,
axis=axis,
unsafe=unsafe),
(data, ),
))
def fn(x):
initial = 1
# supply initial values to prevent gradcheck from failing for 0 length segments
# where nan/inf are reduction identities that produce nans when calculating the numerical jacobian
if reduce == 'min':
initial = 1000
elif reduce == 'max':
initial = -1000
return torch.segment_reduce(x, reduce, lengths=lengths, axis=dim, unsafe=True, initial=initial)
def fn(x, mode='lengths'):
initial = 1
# supply initial values to prevent gradcheck from failing for 0 length segments
# where nan/inf are reduction identities that produce nans when calculating the numerical jacobian
if reduce == 'min':
initial = 1000
elif reduce == 'max':
initial = -1000
segment_reduce_args = {x, reduce}
segment_reduce_kwargs = dict(axis=dim,
unsafe=True,
initial=initial)
if mode == 'lengths':
segment_reduce_kwargs[mode] = lengths
elif mode == 'offsets':
segment_reduce_kwargs[mode] = indptr
return torch.segment_reduce(*segment_reduce_args,
**segment_reduce_kwargs)
def _test_common(
self,
reduction,
device,
dtype,
unsafe,
axis,
initial_value,
data_arr,
lengths_arr,
expected_arr,
expected_grad_arr,
check_backward,
lengths_dtype=torch.int,
):
lengths = torch.tensor(lengths_arr, device=device, dtype=lengths_dtype)
data = torch.tensor(
data_arr,
device=device,
dtype=dtype,
requires_grad=True,
)
expected_result = torch.tensor(expected_arr,
device=device,
dtype=dtype)
expected_grad = torch.tensor(expected_grad_arr,
device=device,
dtype=dtype)
actual_result = torch.segment_reduce(
data=data,
reduce=reduction,
lengths=lengths,
axis=axis,
unsafe=unsafe,
initial=initial_value,
)
self.assertEqual(expected_result,
actual_result,
rtol=1e-02,
atol=1e-05,
equal_nan=True)
if not check_backward:
return
# Test backward
actual_result.sum().backward()
self.assertEqual(expected_grad,
data.grad,
rtol=1e-02,
atol=1e-05,
equal_nan=True)
# gradcheck does not work well with bfloat16 or fp16 cpu types
# also there is small numerical difference with fp32
if dtype not in [torch.half, torch.bfloat16, torch.float]:
# gradcheck does not like "nan" input, setting to random 10
d_non_nan = np.nan_to_num(data_arr, nan=10)
data = torch.tensor(
# [10 if v == float("nan") else v for v in data],
d_non_nan,
device=device,
dtype=dtype,
requires_grad=True,
)
self.assertTrue(
gradcheck(
lambda x: torch.segment_reduce(
data=x,
reduce=reduction,
lengths=lengths,
axis=axis,
unsafe=unsafe,
initial=initial_value,
),
(data, ),
))
def _test_common(
self,
reduction,
device,
dtype,
unsafe,
axis,
initial_value,
data_arr,
lengths_arr,
expected_arr,
expected_grad_arr,
check_backward,
lengths_dtype=torch.int,
):
lengths = torch.tensor(lengths_arr, device=device, dtype=lengths_dtype)
# generate offsets from lengths
zeros_shape = list(lengths.shape)
zeros_shape[-1] = 1
offsets = torch.cat((lengths.new_zeros(zeros_shape), lengths),
-1).cumsum_(-1)
data = torch.tensor(
data_arr,
device=device,
dtype=dtype,
requires_grad=True,
)
expected_result = torch.tensor(expected_arr,
device=device,
dtype=dtype)
expected_grad = torch.tensor(expected_grad_arr,
device=device,
dtype=dtype)
for mode in ['lengths', 'offsets']:
segment_reduce_kwargs = dict(axis=axis,
unsafe=unsafe,
initial=initial_value)
if (mode == 'lengths'):
segment_reduce_kwargs['lengths'] = lengths
else:
segment_reduce_kwargs['offsets'] = offsets
actual_result = torch.segment_reduce(data=data,
reduce=reduction,
**segment_reduce_kwargs)
self.assertEqual(expected_result,
actual_result,
rtol=1e-02,
atol=1e-05,
equal_nan=True)
if not check_backward:
return
# Test backward
actual_result.sum().backward()
self.assertEqual(expected_grad,
data.grad,
rtol=1e-02,
atol=1e-05,
equal_nan=True)
data = data.clone().detach().requires_grad_(True)
# gradcheck does not work well with bfloat16 or fp16 cpu types
# also there is small numerical difference with fp32
if dtype not in [torch.half, torch.bfloat16, torch.float]:
# gradcheck does not like "nan" input, setting to random 10
d_non_nan = np.nan_to_num(data_arr, nan=10)
new_data = torch.tensor(
# [10 if v == float("nan") else v for v in data],
d_non_nan,
device=device,
dtype=dtype,
requires_grad=True,
)
self.assertTrue(
gradcheck(
lambda x: torch.segment_reduce(
data=x, reduce=reduction, **segment_reduce_kwargs),
(new_data, ),
))
def test_pytorch_scatter_test_cases(self, device, dtypes, reduce):
val_dtype, length_dtype = dtypes
# zero-length segments are filled with reduction inits contrary to pytorch_scatter.
tests = [
{
'src': [1, 2, 3, 4, 5, 6],
'index': [0, 0, 1, 1, 1, 3],
'indptr': [0, 2, 5, 5, 6],
'sum': [3, 12, 0, 6],
'prod': [2, 60, 1, 6],
'mean': [1.5, 4, float('nan'), 6],
'min': [1, 3, float('inf'), 6],
'max': [2, 5, -float('inf'), 6],
},
{
'src': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]],
'index': [0, 0, 1, 1, 1, 3],
'indptr': [0, 2, 5, 5, 6],
'sum': [[4, 6], [21, 24], [0, 0], [11, 12]],
'prod': [[3, 8], [315, 480], [1, 1], [11, 12]],
'mean': [[2, 3], [7, 8], [float('nan'),
float('nan')], [11, 12]],
'min': [[1, 2], [5, 6], [float('inf'),
float('inf')], [11, 12]],
'max': [[3, 4], [9, 10], [-float('inf'), -float('inf')],
[11, 12]],
},
{
'src': [[1, 3, 5, 7, 9, 11], [2, 4, 6, 8, 10, 12]],
'index': [[0, 0, 1, 1, 1, 3], [0, 0, 0, 1, 1, 2]],
'indptr': [[0, 2, 5, 5, 6], [0, 3, 5, 6, 6]],
'sum': [[4, 21, 0, 11], [12, 18, 12, 0]],
'prod': [[3, 315, 1, 11], [48, 80, 12, 1]],
'mean': [[2, 7, float('nan'), 11], [4, 9, 12,
float('nan')]],
'min': [[1, 5, float('inf'), 11], [2, 8, 12,
float('inf')]],
'max': [[3, 9, -float('inf'), 11], [6, 10, 12, -float('inf')]],
},
{
'src': [[[1, 2], [3, 4], [5, 6]], [[7, 9], [10, 11], [12,
13]]],
'index': [[0, 0, 1], [0, 2, 2]],
'indptr': [[0, 2, 3, 3], [0, 1, 1, 3]],
'sum': [[[4, 6], [5, 6], [0, 0]], [[7, 9], [0, 0], [22, 24]]],
'prod': [[[3, 8], [5, 6], [1, 1]], [[7, 9], [1, 1], [120,
143]]],
'mean': [[[2, 3], [5, 6], [float('nan'),
float('nan')]],
[[7, 9], [float('nan'), float('nan')], [11, 12]]],
'min': [[[1, 2], [5, 6], [float('inf'),
float('inf')]],
[[7, 9], [float('inf'), float('inf')], [10, 11]]],
'max': [[[3, 4], [5, 6], [-float('inf'), -float('inf')]],
[[7, 9], [-float('inf'), -float('inf')], [12, 13]]],
},
{
'src': [[1, 3], [2, 4]],
'index': [[0, 0], [0, 0]],
'indptr': [[0, 2], [0, 2]],
'sum': [[4], [6]],
'prod': [[3], [8]],
'mean': [[2], [3]],
'min': [[1], [2]],
'max': [[3], [4]],
},
{
'src': [[[1, 1], [3, 3]], [[2, 2], [4, 4]]],
'index': [[0, 0], [0, 0]],
'indptr': [[0, 2], [0, 2]],
'sum': [[[4, 4]], [[6, 6]]],
'prod': [[[3, 3]], [[8, 8]]],
'mean': [[[2, 2]], [[3, 3]]],
'min': [[[1, 1]], [[2, 2]]],
'max': [[[3, 3]], [[4, 4]]],
},
]
for test in tests:
data = torch.tensor(test['src'],
dtype=val_dtype,
device=device,
requires_grad=True)
indptr = torch.tensor(test['indptr'],
dtype=length_dtype,
device=device)
dim = indptr.ndim - 1
# calculate lengths from indptr
lengths = torch.diff(indptr, dim=dim)
expected = torch.tensor(test[reduce],
dtype=val_dtype,
device=device)
actual_result = torch.segment_reduce(
data=data,
reduce=reduce,
lengths=lengths,
axis=dim,
unsafe=True,
)
self.assertEqual(actual_result, expected)
# test offsets
actual_result = torch.segment_reduce(
data=data,
reduce=reduce,
offsets=indptr,
axis=dim,
unsafe=True,
)
self.assertEqual(actual_result, expected)
if val_dtype == torch.float64:
def fn(x, mode='lengths'):
initial = 1
# supply initial values to prevent gradcheck from failing for 0 length segments
# where nan/inf are reduction identities that produce nans when calculating the numerical jacobian
if reduce == 'min':
initial = 1000
elif reduce == 'max':
initial = -1000
segment_reduce_args = {x, reduce}
segment_reduce_kwargs = dict(axis=dim,
unsafe=True,
initial=initial)
if mode == 'lengths':
segment_reduce_kwargs[mode] = lengths
elif mode == 'offsets':
segment_reduce_kwargs[mode] = indptr
return torch.segment_reduce(*segment_reduce_args,
**segment_reduce_kwargs)
self.assertTrue(
gradcheck(partial(fn, mode='lengths'),
(data.clone().detach().requires_grad_(True))))
self.assertTrue(
gradcheck(partial(fn, mode='offsets'),
(data.clone().detach().requires_grad_(True))))
def _test_simple_1d(self, reduction, device, dtype, unsafe, axis):
lengths = torch.tensor([1, 2, 3, 0], device=device)
data = torch.tensor(
[1, float("nan"), 3, 4, 5, 5],
device=device,
dtype=dtype,
requires_grad=True,
)
initial_value = 0
if reduction == "max":
expected_result = torch.tensor(
[1, float("nan"), 5, initial_value], device=device, dtype=dtype
)
expected_grad = torch.tensor(
[1, 1, 0, 0, 0.5, 0.5], device=device, dtype=dtype
)
elif reduction == "mean":
expected_result = torch.tensor(
[1, float("nan"), 4.666, initial_value], device=device, dtype=dtype
)
expected_grad = torch.tensor(
[1.0, 0.5, 0.5, 0.333, 0.333, 0.333], device=device, dtype=dtype
)
actual_result = torch.segment_reduce(
data=data,
reduce=reduction,
lengths=lengths,
axis=axis,
unsafe=unsafe,
initial=initial_value,
)
self.assertEqual(
expected_result, actual_result, rtol=1e-02, atol=1e-05, equal_nan=True
)
# TODO: Remove this check once cuda backward support is implemented
if data.is_cuda:
return
# Test backward
actual_result.sum().backward()
self.assertEqual(
expected_grad, data.grad, rtol=1e-02, atol=1e-05, equal_nan=True
)
# gradcheck does not work well with bfloat16 or fp16 cpu types
# also there is small numerical difference with fp32
if dtype not in [torch.half, torch.bfloat16, torch.float]:
# gradcheck does not like "nan" input
data = torch.tensor(
[1, 10, 3, 4, 5, 5],
device=device,
dtype=dtype,
requires_grad=True,
)
self.assertTrue(
gradcheck(
lambda x: torch.segment_reduce(
data=x,
reduce=reduction,
lengths=lengths,
axis=axis,
unsafe=unsafe,
initial=initial_value,
),
(data,),
)
)