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fix test warnings (#13114)

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* fix test warnings

* precommit passes

* ignore std_mean warning
This commit is contained in:
George Hotz
2025-11-05 15:06:29 -08:00
committed by GitHub
parent bcfe42937f
commit 4027eef264
3 changed files with 18 additions and 14 deletions
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2
.pre-commit-config.yaml
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@@ -28,7 +28,7 @@ repos:
pass_filenames: false pass_filenames: false
- id: tests - id: tests
name: subset of tests name: subset of tests
entry: env OMP_NUM_THREADS=1 PYTHONPATH="." python3 -m pytest -n=8 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py entry: env OMP_NUM_THREADS=1 PYTHONPATH="." python3 -m pytest -n=6 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
language: system language: system
always_run: true always_run: true
pass_filenames: false pass_filenames: false

5
pytest.ini
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@@ -1,5 +1,8 @@
[pytest] [pytest]
norecursedirs = extra norecursedirs =
extra
.hypothesis
.git
timeout = 300 timeout = 300
timeout_method = thread timeout_method = thread
timeout_func_only = true timeout_func_only = true

25
test/test_ops.py
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@@ -1551,8 +1551,10 @@ class TestOps(unittest.TestCase):
lambda x: Tensor.stack(*x.std_mean(axis=(1,2)))) lambda x: Tensor.stack(*x.std_mean(axis=(1,2))))
def test_std_mean_loaded_nan(self): def test_std_mean_loaded_nan(self):
helper_test_op([(1,0,3,0,5)], lambda x: torch.stack(torch.std_mean(x, axis=(1,3))), with warnings.catch_warnings():
lambda x: Tensor.stack(*x.std_mean(axis=(1,3)))) warnings.filterwarnings("ignore", message="std_mean\\(\\): degrees of freedom is <= 0")
helper_test_op([(1,0,3,0,5)], lambda x: torch.stack(torch.std_mean(x, axis=(1,3))),
lambda x: Tensor.stack(*x.std_mean(axis=(1,3))))
def test_softmax(self): def test_softmax(self):
helper_test_op([(45,65)], torch.nn.Softmax(dim=1), Tensor.softmax, atol=1e-7, grad_atol=1e-7) helper_test_op([(45,65)], torch.nn.Softmax(dim=1), Tensor.softmax, atol=1e-7, grad_atol=1e-7)
helper_test_op([(45)], torch.nn.Softmax(dim=0), Tensor.softmax, atol=1e-7, grad_atol=1e-7) helper_test_op([(45)], torch.nn.Softmax(dim=0), Tensor.softmax, atol=1e-7, grad_atol=1e-7)
@@ -2820,13 +2822,13 @@ class TestOps(unittest.TestCase):
@slow_test @slow_test
def test_slice_fancy_indexing_list_indices(self): def test_slice_fancy_indexing_list_indices(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms() a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[((0,),)])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[0],b,c,d,:], lambda x: x[[0],j,k,o,:]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[(0,),b,c,d,:], lambda x: x[(0,),j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[[0]]],b,c,d,[[1]]], lambda x: x[[[[0]]],j,k,o,[[1]]]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[[0]]],b,c,d,[[1]]], lambda x: x[[[[0]]],j,k,o,[[1]]])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[1,0,-1],b,c,d,:], lambda x: x[[1,0,-1],j,k,o,:]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[(1,0,-1),b,c,d,:], lambda x: x[(1,0,-1),j,k,o,:])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[1,2,3],...], lambda x: x[i,j,k,[1,2,3],...]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,(1,2,3),...], lambda x: x[i,j,k,(1,2,3),...])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[[1],[2],[3]],...], lambda x: x[i,j,k,[[1],[2],[3]],...]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[[1],[2],[3]],...], lambda x: x[i,j,k,[[1],[2],[3]],...])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,[2,1,0],c,[-2,1,0],e], lambda x: x[i,[2,1,0],k,[-2,1,0],p]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,(2,1,0),c,(-2,1,0),e], lambda x: x[i,(2,1,0),k,(-2,1,0),p])
@slow_test @slow_test
def test_slice_fancy_indexing_tuple_indices(self): def test_slice_fancy_indexing_tuple_indices(self):
@@ -2841,11 +2843,10 @@ class TestOps(unittest.TestCase):
@slow_test @slow_test
def test_slice_fancy_indexing_list_with_tensors(self): def test_slice_fancy_indexing_list_with_tensors(self):
a,b,c,d,e,i,j,k,o,p = self._get_index_randoms() a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a]], lambda x: x[[i]]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,)], lambda x: x[(i,)])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,1]], lambda x: x[[i,1]]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,1)], lambda x: x[(i,1)])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,[1,1]]], lambda x: x[[i,[1,1]]]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,(1,1))], lambda x: x[(i,(1,1))])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,(1,1)]], lambda x: x[[i,(1,1)]]) helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,b,c,d,e)], lambda x: x[(i,j,k,o,p)])
helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,b,c,d,e]], lambda x: x[[i,j,k,o,p]])
def test_slice_fancy_indexing_errors(self): def test_slice_fancy_indexing_errors(self):
a = Tensor.ones(10,11,12) a = Tensor.ones(10,11,12)