[inductor] Enable test_torch (#90518)
Summary: Skipping failures in those tests so that CI can guard other
passing cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90518
Approved by: https://github.com/jansel
diff --git a/.jenkins/pytorch/test.sh b/.jenkins/pytorch/test.sh
index 4e52f31..61b5056 100755
--- a/.jenkins/pytorch/test.sh
+++ b/.jenkins/pytorch/test.sh
@@ -252,7 +252,7 @@
test_inductor() {
python tools/dynamo/verify_dynamo.py
- python test/run_test.py --include test_modules test_ops test_ops_gradients --verbose
+ python test/run_test.py --include test_modules test_ops test_ops_gradients test_torch --verbose
PYTORCH_TEST_WITH_INDUCTOR=0 python test/run_test.py --include inductor/test_torchinductor --include inductor/test_torchinductor_opinfo --verbose
}
diff --git a/test/test_torch.py b/test/test_torch.py
index 9796d39..887678c 100644
--- a/test/test_torch.py
+++ b/test/test_torch.py
@@ -1154,6 +1154,7 @@
# FIXME: update OpInfos to support "nondeterministic samples" and port these tests
# to that architecture
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_AvgPool3d(self, device):
module = torch.nn.AvgPool3d(3)
input = torch.randn(2, 3, 3, 3, requires_grad=True, device=device)
@@ -1166,6 +1167,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_AdaptiveAvgPool2d(self, device):
module = torch.nn.AdaptiveAvgPool2d(3)
input = torch.randn(2, 3, 3, requires_grad=True, device=device)
@@ -1178,6 +1180,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_AdaptiveAvgPool3d(self, device):
module = torch.nn.AdaptiveAvgPool3d(3)
input = torch.randn(2, 3, 3, 3, requires_grad=True, device=device)
@@ -1190,6 +1193,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_MaxPool3d(self, device):
module = torch.nn.MaxPool3d(3)
input = torch.randn(2, 3, 3, 3, requires_grad=True, device=device)
@@ -1202,6 +1206,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_AdaptiveMaxPool2d(self, device):
module = torch.nn.AdaptiveMaxPool2d(3)
input = torch.randn(2, 3, 3, requires_grad=True, device=device)
@@ -1214,6 +1219,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_FractionalMaxPool2d(self, device):
module = torch.nn.FractionalMaxPool2d(2, output_ratio=0.5)
input = torch.randn(2, 3, 3, 3, requires_grad=True, device=device)
@@ -1226,6 +1232,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_FractionalMaxPool3d(self, device):
module = torch.nn.FractionalMaxPool3d(2, output_ratio=0.5)
input = torch.randn(2, 3, 3, 3, 3, requires_grad=True, device=device)
@@ -1280,6 +1287,7 @@
'max_unpooling3d_forward_out')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_interpolate_linear(self, device):
input = torch.randn(1, 2, 4, device=device, requires_grad=True)
res = torch.nn.functional.interpolate(
@@ -1294,6 +1302,7 @@
'upsample_linear1d_backward_out_cuda',
torch.device(device).type == 'cuda')
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_interpolate_bilinear(self, device):
input = torch.randn(1, 2, 4, 4, device=device, requires_grad=True)
res = torch.nn.functional.interpolate(
@@ -1309,6 +1318,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_interpolate_bicubic(self, device):
input = torch.randn(1, 2, 4, 4, device=device, requires_grad=True)
res = torch.nn.functional.interpolate(
@@ -1324,6 +1334,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_interpolate_trilinear(self, device):
input = torch.randn(1, 2, 4, 4, 4, device=device, requires_grad=True)
res = torch.nn.functional.interpolate(
@@ -1339,6 +1350,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_ReflectionPad1d(self, device):
module = torch.nn.ReflectionPad1d((1, 2))
input = torch.randn(2, 3, 8, device=device, requires_grad=True)
@@ -1350,6 +1362,7 @@
'reflection_pad1d_backward_out_cuda',
torch.device(device).type == 'cuda')
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_ReflectionPad2d(self, device):
module = torch.nn.ReflectionPad2d((1, 2, 3, 4))
input = torch.randn(2, 3, 8, 8, device=device, requires_grad=True)
@@ -1362,6 +1375,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_ReflectionPad3d(self, device):
module = torch.nn.ReflectionPad3d((1, 2, 3, 4, 5, 6))
input = torch.randn(2, 3, 8, 8, 8, device=device, requires_grad=True)
@@ -1374,6 +1388,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_ReplicationPad1d(self, device):
module = torch.nn.ReplicationPad1d((1, 2))
input = torch.randn(2, 3, 4, device=device, requires_grad=True)
@@ -1385,6 +1400,7 @@
'replication_pad1d_backward_cuda',
torch.device(device).type == 'cuda')
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_ReplicationPad2d(self, device):
module = torch.nn.ReplicationPad2d((1, 2, 3, 4))
input = torch.randn(2, 3, 4, 4, device=device, requires_grad=True)
@@ -1397,6 +1413,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_ReplicationPad3d(self, device):
module = torch.nn.ReplicationPad3d((1, 2, 3, 4, 5, 6))
input = torch.randn(2, 3, 4, 4, 4, device=device, requires_grad=True)
@@ -1419,6 +1436,7 @@
'nll_loss2d_forward_out_cuda_template',
torch.device(device).type == 'cuda')
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_CTCLoss(self, device):
module = torch.nn.CTCLoss()
input = torch.randn(50, 3, 15, device=device, requires_grad=True)
@@ -1433,6 +1451,7 @@
'ctc_loss_backward_gpu',
torch.device(device).type == 'cuda')
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_EmbeddingBag_max(self, device):
module = torch.nn.EmbeddingBag(
4, 3, None, 2., False, 'max',
@@ -1447,6 +1466,7 @@
torch.device(device).type == 'cuda')
@dtypes(*all_types_and_complex_and(torch.bool))
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_cumsum(self, device, dtype):
input = make_tensor((10,), dtype=dtype, device=device, low=-9, high=9)
should_alert = torch.device(device).type == 'cuda' and (dtype.is_floating_point or dtype.is_complex)
@@ -1523,6 +1543,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_grid_sample_2d(self, device):
input = torch.empty(1, 1, 2, 2, device=device, requires_grad=True)
grid = torch.empty(1, 1, 1, 2, device=device)
@@ -1535,6 +1556,7 @@
torch.device(device).type == 'cuda')
@skipIfMps
+ @skipIfTorchInductor("aot-autograd issue")
def test_nondeterministic_alert_grid_sample_3d(self, device):
input = torch.empty(1, 1, 2, 2, 2, device=device, requires_grad=True)
grid = torch.empty(1, 1, 1, 2, 3, device=device)
@@ -1710,6 +1732,7 @@
self.assertEqual(result, original, atol=0, rtol=0)
@onlyCUDA
+ @skipIfTorchInductor("FIXME")
def test_sync_warning(self, device):
def _sync_raises_helper(f, level):
@@ -3046,6 +3069,7 @@
# FIXME: move to test indexing
@onlyCPU
+ @skipIfTorchInductor("FIXME")
def test_errors_index_copy(self, device):
# We do not test the GPU as the CUDA_ASSERT would break the CUDA context
idx_dim = 8
@@ -3428,6 +3452,7 @@
@dtypes(*floating_and_complex_types())
@dtypesIfCPU(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
@dtypesIfCUDA(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
+ @skipIfTorchInductor("FIXME")
def test_scatter_reduce_non_unique_index(self, device, dtype):
height = 2
width = 2
@@ -4002,6 +4027,7 @@
# FIXME: move to an elementwise ternary test suite and make this an OpInfo test
@dtypes(torch.double)
+ @skipIfTorchInductor("FIXME")
def test_ternary_op_mem_overlap(self, device, dtype):
ops = [
("addcmul", True, True, 'cpu'),
@@ -4209,6 +4235,7 @@
# FIXME: move to test distributions
@deviceCountAtLeast(2)
@onlyCUDA
+ @skipIfTorchInductor("FIXME")
def test_multinomial_gpu_device_constrain(self, devices):
x = torch.empty(0, device=devices[0])
y = torch.empty(0, device=devices[1])
@@ -4359,6 +4386,7 @@
y = ndhwc.permute(0, 1, 4, 3, 2).permute(0, 1, 4, 3, 2)
self.assertTrue(y.is_contiguous(memory_format=torch.channels_last_3d))
+ @skipIfTorchInductor("To be supported")
def test_memory_format_propagation_rules(self, device):
contiguous = torch.rand(10, 3, 5, 5, device=device)
@@ -4402,6 +4430,7 @@
self.assertEqual(ambiguous.stride(), result.stride())
@skipIfMps
+ @skipIfTorchInductor("To be supported")
def test_memory_format_empty_like(self, device):
def test_helper(x, memory_format):
xc = x.contiguous(memory_format=memory_format)
@@ -4444,6 +4473,7 @@
x.is_contiguous(memory_format=torch.channels_last_3d), x_rep.is_contiguous(memory_format=torch.channels_last_3d))
# FIXME: make this a elementwise unary and elementwise binary OpInfo test
+ @skipIfTorchInductor("To be supported")
def test_memory_format_operators(self, device):
def _chunk_op(x, y):
x1, x2 = x.chunk(2, dim=1)
@@ -4878,6 +4908,7 @@
x = x.permute(permutation)
self.assertEqual(x.stride(), transformation_fn(x, memory_format=torch.preserve_format).stride())
+ @skipIfTorchInductor("To be supported")
def test_memory_format_to(self, device):
def get_generator(memory_format, shape):
def input_generator_fn(device):
@@ -4895,6 +4926,7 @@
self._test_memory_format_transformations(
device, get_generator(mf, shape), transformation_fn, mf, default_is_preserve=True)
+ @skipIfTorchInductor("To be supported")
def test_memory_format_type(self, device):
def get_generator(memory_format, shape):
def input_generator_fn(device):
@@ -4912,6 +4944,7 @@
self._test_memory_format_transformations(
device, get_generator(mf, shape), transformation_fn, mf, default_is_preserve=True)
+ @skipIfTorchInductor("To be supported")
def test_memory_format_clone(self, device):
def get_generator(memory_format, shape):
def input_generator_fn(device):
@@ -4929,6 +4962,7 @@
self._test_memory_format_transformations(
device, get_generator(mf, shape), transformation_fn, mf, True, default_is_preserve=True)
+ @skipIfTorchInductor("To be supported")
def test_memory_format_factory_like_functions_preserve(self, device):
def get_generator(memory_format, shape):
def input_generator_fn(device):
@@ -4954,6 +4988,7 @@
self._test_memory_format_transformations(
device, get_generator(mf, shape), transformation_fn, mf, compare_data=False, default_is_preserve=True)
+ @skipIfTorchInductor("To be supported")
def test_memory_format_type_shortcuts(self, device):
def get_generator(memory_format, shape, dtype):
def input_generator_fn(device):
@@ -4987,6 +5022,7 @@
device, get_generator(mf, shape, torch.float64), get_fn('float'), mf, default_is_preserve=True)
@onlyCUDA
+ @skipIfTorchInductor("To be supported")
def test_memory_format_cpu_and_cuda_ops(self, device):
def get_generator(memory_format, shape):
def input_generator_fn(device):
@@ -5136,6 +5172,7 @@
check_equal(torch.tensor(True), y, x)
+ @skipIfTorchInductor("FIXME")
def test_hook_remove(self, device):
# Reference: https://github.com/pytorch/pytorch/issues/58354
def _test_helper(remove_hook):
@@ -5186,6 +5223,7 @@
# FIXME: get PyTorch/XLA to run test_testing
# This test should ideally be in test_testing.py,
# but since pytorch/xla runs tests from test_torch.py, we have it here.
+ @skipIfTorchInductor("random_.from needs to be renamed")
def test_assertRaisesRegex_ignore_msg_non_native_device(self, device):
# Verify that self.assertRaisesRegex only checks the Error and ignores
# message for non-native devices.
@@ -5326,6 +5364,7 @@
# FIXME: moved to indexing test suite
@deviceCountAtLeast(1)
+ @skipIfTorchInductor("FIXME")
def test_advancedindex_mixed_cpu_devices(self, devices) -> None:
def test(x: torch.Tensor, ia: torch.Tensor, ib: torch.Tensor) -> None:
# test getitem
@@ -5359,6 +5398,7 @@
test(x, ia, ib)
@deviceCountAtLeast(1)
+ @skipIfTorchInductor("FIXME")
def test_advancedindex_mixed_devices_error(self, devices) -> None:
def test(x: torch.Tensor, ia: torch.Tensor, ib: torch.Tensor) -> None:
# test getitem
@@ -6508,6 +6548,7 @@
# Test that public functions related to TypedStorage produce a deprecation
# warning
+ @skipIfTorchInductor("FIXME")
def test_typed_storage_deprecation_warning(self):
s0 = torch.FloatStorage(10)
funcs = [
@@ -7122,6 +7163,7 @@
weight), torch.tensor(bias), 1, epsilon, True)
torch.testing.assert_close(expected_norm, actual_norm)
+ @skipIfTorchInductor("To be supported")
def test_memory_format(self):
def test_helper(x, memory_format):
y = x.contiguous(memory_format=memory_format)
@@ -7645,6 +7687,7 @@
self.assertEqual(y[:, 40], range(4000, 4100))
# FIXME: Port to a more appropriate test suite
+ @skipIfTorchInductor("FIXME")
def test_copy_broadcast(self):
torch.zeros(5, 6).copy_(torch.zeros(6))
self.assertRaises(RuntimeError, lambda: torch.zeros(5, 6).copy_(torch.zeros(30)))
@@ -7655,6 +7698,7 @@
# storage to a single storage would cause RuntimeError to be thrown
self.assertRaises(RuntimeError, lambda: torch.zeros(1, 6).expand(5, 6).copy_(torch.zeros(5, 6)))
+ @skipIfTorchInductor("FIXME")
def test_copy_float16(self):
# Check that fbgemm code no longer reads memory out of bounds, see
# copy_impl and fbgemm::Float16ToFloat_ref.
@@ -7764,6 +7808,7 @@
self.assertIs(torch.int32, b.to(dtype=torch.int32).dtype)
self.assertEqual(b.device, b.to(dtype=torch.int32).device)
+ @skipIfTorchInductor("FIXME")
def test_to(self):
self._test_to_with_layout(torch.strided)
is_cuda10_2_or_higher = (
@@ -7837,6 +7882,7 @@
s0 = t0.as_subclass(BadSubTensor)
# FIXME: Port to a test suite that better fits slicing
+ @skipIfTorchInductor("FIXME")
def test_slice(self):
empty = torch.empty(0, 4)
x = torch.arange(0., 16).view(4, 4)
diff --git a/torch/testing/_internal/common_utils.py b/torch/testing/_internal/common_utils.py
index 8b24fef..e9dc294 100644
--- a/torch/testing/_internal/common_utils.py
+++ b/torch/testing/_internal/common_utils.py
@@ -942,6 +942,9 @@
# TODO: Remove this; this is grandfathered in because we suppressed errors
# on test suite previously
torch._dynamo.config.suppress_errors = True
+ if TEST_WITH_TORCHINDUCTOR:
+ import torch._inductor.config
+ torch._inductor.config.fallback_random = True
def skipIfTorchDynamo(msg="test doesn't currently work with dynamo"):
