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# Owner(s): ["module: scatter & gather ops"]
import random
import torch
from torch.testing import make_tensor
from torch.testing._internal.common_utils import \
(parametrize, run_tests, TestCase, DeterministicGuard)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, onlyCPU, dtypes, dtypesIfCUDA,
toleranceOverride, tol,)
from torch.testing._internal.common_dtype import \
(get_all_dtypes,)
# Protects against includes accidentally setting the default dtype
assert torch.get_default_dtype() is torch.float32
# Note: test_scatter_gather_ops.py
# This test file tests scatter and gather operations,
# like torch.scatter and torch.gather.
class TestScatterGather(TestCase):
# Fills an index tensor with valid indices
def _fill_indices(self, idx, dim, dim_size, elems_per_row, m, n, o, unique_indices=True):
for i in range(1 if dim == 0 else m):
for j in range(1 if dim == 1 else n):
for k in range(1 if dim == 2 else o):
ii = [i, j, k]
ii[dim] = slice(0, idx.size(dim) + 1)
if unique_indices:
idx[tuple(ii)] = torch.randperm(dim_size)[0:elems_per_row]
else:
idx[tuple(ii)] = torch.randint(dim_size, (elems_per_row,))
@dtypes(torch.float32, torch.complex64)
def test_gather(self, device, dtype):
m, n, o = random.randint(10, 20), random.randint(10, 20), random.randint(10, 20)
elems_per_row = random.randint(1, 10)
dim = random.randrange(3)
src = make_tensor((m, n, o), device=device, dtype=dtype)
idx_size = [m, n, o]
idx_size[dim] = elems_per_row
idx = make_tensor(idx_size, device=device, dtype=torch.long)
self._fill_indices(idx, dim, src.size(dim), elems_per_row, m, n, o)
actual = torch.gather(src, dim, idx)
expected = torch.zeros(idx_size, device=device, dtype=dtype)
for i in range(idx_size[0]):
for j in range(idx_size[1]):
for k in range(idx_size[2]):
ii = [i, j, k]
ii[dim] = idx[i, j, k]
expected[i, j, k] = src[tuple(ii)]
self.assertEqual(actual, expected, atol=0, rtol=0)
# Guarded because torch.max isn't defined for complex types
if not dtype.is_complex:
src = make_tensor((3, 4, 5), device=device, dtype=dtype)
expected, idx = src.max(2, True)
actual = torch.gather(src, 2, idx)
self.assertEqual(actual, expected, atol=0, rtol=0)
@dtypes(torch.bool)
def test_gather_bool(self, device, dtype):
src = torch.tensor(((False, True), (True, True)), device=device, dtype=dtype)
idx = torch.tensor(((0, 0), (1, 0)), device=device, dtype=torch.long)
actual = torch.gather(src, 1, idx)
expected = torch.tensor(((False, False), (True, True)), device=device, dtype=dtype)
self.assertEqual(actual, expected, atol=0, rtol=0)
@parametrize("sparse_grad", [False, True])
@dtypes(torch.float32, torch.float64)
def test_gather_backward_with_empty_index_tensor(self, device, dtype, sparse_grad):
dim = -1
input = torch.rand([10, 5], dtype=dtype, device=device, requires_grad=True)
index = torch.randint(0, 2, [3, 0], dtype=torch.int64, device=device)
res = torch.gather(input, dim, index, sparse_grad=sparse_grad)
res.sum().backward()
grad = input.grad.to_dense() if sparse_grad else input.grad
expected_grad = torch.zeros_like(input, requires_grad=False)
self.assertEqual(grad, expected_grad, atol=0, rtol=0)
def _test_scatter_base(self, fn, *, device, dtype, is_scalar, reduction,
unique_indices=True, include_self=True):
m, n, o = random.randint(10, 20), random.randint(10, 20), random.randint(10, 20)
elems_per_row = random.randint(1, 10)
dim = random.randrange(3)
idx_size = [m, n, o]
idx_size[dim] = elems_per_row
idx = torch.empty(tuple(idx_size), device=device, dtype=torch.long)
self._fill_indices(idx, dim, ([m, n, o])[dim], elems_per_row, m, n, o, unique_indices)
if is_scalar:
src = random.random()
else:
src_size = [random.randint(1, 5) + s for s in idx_size]
src = make_tensor(tuple(src_size), device=device, dtype=dtype)
base = make_tensor((m, n, o), device=device, dtype=dtype)
if reduction is not None:
if fn is torch.Tensor.scatter_reduce_:
actual = fn(base.clone(), dim, idx, src, reduce=reduction, include_self=include_self)
else:
actual = fn(base.clone(), dim, idx, src, reduce=reduction)
else:
actual = fn(base.clone(), dim, idx, src)
expected = base.clone()
counts = torch.zeros(base.shape, dtype=torch.long, device=device) + include_self
for i in range(idx_size[0]):
for j in range(idx_size[1]):
for k in range(idx_size[2]):
ii = [i, j, k]
ii[dim] = idx[i, j, k]
if fn is torch.Tensor.scatter_add_:
expected[tuple(ii)] += src[i, j, k]
else:
# method may be 'scatter_', 'scatter', 'scatter_reduce'
# or 'scatter_reduce_', the former two might have a reduction argument
# while the latter two always do
value = src if is_scalar else src[i, j, k]
if ((not include_self) and counts[tuple(ii)] == 0):
expected[tuple(ii)] = value
else:
if reduction == "add" or reduction == "sum":
expected[tuple(ii)] += value
elif reduction == "multiply" or reduction == "prod":
expected[tuple(ii)] *= value
elif reduction == "amax":
expected[tuple(ii)] = max(expected[tuple(ii)], value)
elif reduction == "amin":
expected[tuple(ii)] = min(expected[tuple(ii)], value)
elif reduction == "mean":
expected[tuple(ii)] += value
else:
expected[tuple(ii)] = value
counts[tuple(ii)] += 1
if (reduction == "mean"):
counts.masked_fill_(counts == 0, 1)
if (dtype.is_floating_point or dtype.is_complex):
expected /= counts
else:
expected.div_(counts, rounding_mode="floor")
if dtype == torch.float16 or dtype == torch.bfloat16:
# Some CUDA kernels (e.g. indexing_backward_kernel_stride_1) that are called during
# the test use fp32 for internal accumulation for improved accuracy. When using 16 bit
# precision types can be small differences
self.assertEqual(actual, expected, atol=0.04, rtol=0.05)
else:
self.assertEqual(actual, expected, atol=0, rtol=0)
# Tests empty index
dst = make_tensor((2, 2), device=device, dtype=dtype)
idx = torch.tensor((), device=device, dtype=torch.long)
src = make_tensor((2, 2), device=device, dtype=dtype)
if reduction is not None:
actual = fn(dst, 0, idx, src, reduce=reduction)
else:
actual = fn(dst, 0, idx, src)
self.assertEqual(actual, dst, atol=0, rtol=0)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter_(self, device, dtype):
for deterministic in [False, True]:
with DeterministicGuard(deterministic):
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=False, reduction=None)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter__scalar(self, device, dtype):
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=True, reduction=None)
# FIXME: RuntimeError: "cuda_scatter_gather_base_kernel_reduce_multiply" not implemented for 'ComplexFloat'
@toleranceOverride({torch.float16: tol(atol=1e-2, rtol=0)})
@dtypesIfCUDA(torch.float16, torch.float32)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter__reductions(self, device, dtype):
for reduction in ("add", "multiply"):
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=False, reduction=reduction)
self._test_scatter_base(torch.Tensor.scatter_, device=device, dtype=dtype,
is_scalar=True, reduction=reduction)
@dtypes(torch.float16, torch.float32, torch.complex64)
def test_scatter_add_(self, device, dtype):
for deterministic in [False, True]:
with DeterministicGuard(deterministic):
self._test_scatter_base(torch.Tensor.scatter_add_, device=device, dtype=dtype,
is_scalar=False, reduction=None)
@dtypes(torch.float32)
def test_scatter_add_mult_index_base(self, device, dtype):
for deterministic in [False, True]:
with DeterministicGuard(deterministic):
m, n = 30, 40
idx = torch.zeros(m, n, device=device, dtype=torch.long)
src = torch.ones(m, n, device=device, dtype=dtype)
res0 = torch.zeros(m, n, device=device, dtype=dtype).scatter_add_(0, idx, src)
res1 = torch.zeros(m, n, device=device, dtype=dtype).scatter_add_(1, idx, src)
self.assertEqual(res0[0, :], m * torch.ones(n, device=device, dtype=dtype), atol=0, rtol=0)
self.assertEqual(res1[:, 0], n * torch.ones(m, device=device, dtype=dtype), atol=0, rtol=0)
# FIXME: discrepancy between bool ReduceAdd on CUDA and CPU (a + b on CPU and buggy a && b on CUDA)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_bool=False))
def test_scatter_reduce_sum(self, device, dtype):
for include_self in (True, False):
for deterministic in [False, True]:
with DeterministicGuard(deterministic):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='sum', unique_indices=False,
include_self=include_self)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True))
@dtypesIfCUDA(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False, include_bool=False))
def test_scatter_reduce_prod(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='prod', unique_indices=False,
include_self=include_self)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_bool=False))
@dtypesIfCUDA(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False, include_bool=False))
def test_scatter_reduce_mean(self, device, dtype):
for include_self in (True, False):
for deterministic in [False, True]:
with DeterministicGuard(deterministic):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='mean', unique_indices=False,
include_self=include_self)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False))
@dtypesIfCUDA(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False, include_bool=False))
def test_scatter_reduce_amax(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='amax', unique_indices=False,
include_self=include_self)
# simple test for nan/inf propagation
if (dtype.is_floating_point):
input = torch.zeros(3, device=device, dtype=dtype)
src = torch.tensor([1, float('nan'), -float('inf'), -float('inf'), 2, float('inf')], device=device, dtype=dtype)
idx = torch.tensor([0, 0, 1, 1, 2, 2], device=device)
input.scatter_reduce_(0, idx, src, 'amax', include_self=include_self)
expected_result = torch.tensor([float('nan'), -float('inf'), float('inf')], device=device, dtype=dtype)
if (include_self):
expected_result[1] = 0
self.assertEqual(input, expected_result)
@dtypes(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False))
@dtypesIfCUDA(*get_all_dtypes(include_half=True, include_bfloat16=True, include_complex=False, include_bool=False))
def test_scatter_reduce_amin(self, device, dtype):
for include_self in (True, False):
self._test_scatter_base(torch.Tensor.scatter_reduce_, device=device, dtype=dtype,
is_scalar=False, reduction='amin', unique_indices=False,
include_self=include_self)
# simple test for nan/inf propagation
if (dtype.is_floating_point):
input = torch.zeros(3, device=device, dtype=dtype)
src = torch.tensor([1, float('nan'), -2, -float('inf'), float('inf'), float('inf')], device=device, dtype=dtype)
idx = torch.tensor([0, 0, 1, 1, 2, 2], device=device)
input.scatter_reduce_(0, idx, src, 'amin', include_self=include_self)
expected_result = torch.tensor([float('nan'), -float('inf'), float('inf')], device=device, dtype=dtype)
if (include_self):
expected_result[2] = 0
self.assertEqual(input, expected_result)
@onlyCPU
@dtypes(torch.float32, torch.float64, torch.bfloat16, torch.float16)
def test_scatter_expanded_index(self, device, dtype):
def helper(input_size, idx_size):
input = torch.randn(input_size, device=device).to(dtype=dtype)
input2 = input.clone()
shape = [1] * len(input_size)
shape[0] = idx_size
dim_size = input_size[0]
idx = torch.randint(0, dim_size, shape)
# The fast path on scatter when index is expanded
# will depend on sorted index where the collected src indice
# for each row in input will be mapped to rowptrs in a CSR format.
# Create some empty rows by masking:
mask = (idx > 1) * (idx < 4)
idx[mask] = 0
expanded_shape = input_size
expanded_shape[0] = idx_size
idx = idx.expand(expanded_shape)
idx2 = idx.contiguous()
src = torch.randn(expanded_shape, device=device).to(dtype=dtype)
out = input.scatter_add(0, idx, src)
out2 = input2.scatter_add(0, idx2, src)
self.assertEqual(out, out2)
for reduce in ["sum", "prod", "mean", "amax", "amin"]:
for include_self in [True, False]:
out = input.scatter_reduce(0, idx, src, reduce=reduce, include_self=include_self)
out2 = input2.scatter_reduce(0, idx2, src, reduce=reduce, include_self=include_self)
self.assertEqual(out, out2)
helper([50, 17], 100)
helper([50, 1], 100)
helper([50, 8, 7], 100)
helper([50, 3, 4, 5], 100)
@onlyCPU
@dtypes(torch.float32, torch.float64, torch.bfloat16)
def test_gather_expanded_index(self, device, dtype):
# Test when index is [N, 1], which would have stride [1, 0]
# should be excluded from the fast path when index ix expanded
input = torch.arange(25).view(5, 5)
input2 = input.to(dtype=dtype)
idx = torch.arange(5).view(5, 1)
out = torch.gather(input, 0, idx)
out2 = torch.gather(input2, 0, idx)
self.assertEqual(out.to(dtype=dtype), out2)
def helper(input_size, idx_size):
input = torch.randn(input_size, device=device).to(dtype=dtype)
input2 = input.clone()
shape = [1] * len(input_size)
shape[0] = idx_size
dim_size = input_size[0]
idx = torch.randint(0, dim_size, shape)
# Test the fast path on gather when index is expanded
expanded_shape = input_size
expanded_shape[0] = idx_size
idx = idx.expand(expanded_shape)
idx2 = idx.contiguous()
out = torch.gather(input, 0, idx)
out2 = torch.gather(input2, 0, idx2)
self.assertEqual(out, out2)
# test unsqueezed index
# expanded_index kernel can not handle the case:
# the size > 1 and stride == 1 at a dimension.
# for example: the index with size of [1, 8, 7], stride of [1, 1, 0].
# see https://github.com/pytorch/pytorch/issues/129093
def unsqueeze_helper(idx, dim):
if dim == 2:
return idx.unsqueeze(1).t()
else:
return unsqueeze_helper(idx, dim - 1).unsqueeze(dim - 1)
idx = torch.randint(0, dim_size, (input.shape[1],))
idx = unsqueeze_helper(idx, len(input_size))
expanded_shape[0] = 1
idx = idx.expand(expanded_shape)
idx2 = idx.contiguous()
out = torch.gather(input, 0, idx)
out2 = torch.gather(input2, 0, idx2)
self.assertEqual(out, out2)
helper([50, 17], 100)
helper([50, 1], 100)
helper([50, 8, 7], 100)
helper([50, 3, 4, 5], 100)
# Generic Device Test Framework instantation, see
# https://github.com/pytorch/pytorch/wiki/Running-and-writing-tests
# for details.
instantiate_device_type_tests(TestScatterGather, globals())
if __name__ == '__main__':
run_tests()
|
