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merge two PYTHON backend ci job (#12143)

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* merge two PYTHON backend ci job

and mark anything that takes > 10 in test_ops slow

* two more
This commit is contained in:
chenyu
2025-09-12 17:36:46 -04:00
committed by GitHub
parent a12d0933c1
commit aac3dceaf6
2 changed files with 27 additions and 27 deletions
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13
test/test_ops.py
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@@ -1458,6 +1458,7 @@ class TestOps(unittest.TestCase):
def test_mean_zero_axis(self):
helper_test_op([(1,0,3,0,5)], lambda x: x.mean(axis=(1,3)))
@slow_test
def test_var(self):
helper_test_op([(15, 25, 35)], lambda x: x.var())
helper_test_op([(15, 25, 35)], lambda x: x.var(correction=0))
@@ -1493,6 +1494,7 @@ class TestOps(unittest.TestCase):
helper_test_op([(15, 25, 35)], lambda x: x.var(keepdim=True))
helper_test_op([(15, 25, 35)], lambda x: x.var(0, keepdim=True, correction=0))
@slow_test
def test_std(self):
helper_test_op([(15, 25, 35)], lambda x: x.std())
helper_test_op([(15, 25, 35)], lambda x: x.std(correction=0))
@@ -1525,6 +1527,7 @@ class TestOps(unittest.TestCase):
def test_std_keepdim(self):
helper_test_op([(15, 25, 35)], lambda x: x.std(keepdim=True))
helper_test_op([(15, 25, 35)], lambda x: x.std(0, keepdim=True, correction=0))
@slow_test
def test_std_mean(self):
helper_test_op([(15,25,35)], lambda x: torch.stack(torch.std_mean(x)),
lambda x: Tensor.stack(*x.std_mean()))
@@ -2040,12 +2043,14 @@ class TestOps(unittest.TestCase):
lambda x,w,b: torch.nn.functional.conv2d(x,w,b),
lambda x,w,b: Tensor.conv2d(x,w,b), grad_rtol=1e-5)
@slow_test
@unittest.skipIf(IMAGE>0, "no conv3d on images")
def test_simple_conv3d(self):
helper_test_op([(1,4,9,9,9), (4,4,3,3,3)],
lambda x,w: torch.nn.functional.conv3d(x,w),
lambda x,w: Tensor.conv2d(x,w), grad_rtol=1e-5)
@slow_test
@unittest.skipIf(IMAGE>0, "no conv3d on images")
def test_padded_conv3d(self):
helper_test_op([(1,4,5,5,5), (4,4,3,3,3)],
@@ -2102,6 +2107,7 @@ class TestOps(unittest.TestCase):
lambda x,w: torch.nn.functional.conv_transpose2d(x,w,groups=2),
lambda x,w: Tensor.conv_transpose2d(x,w,groups=2), grad_rtol=1e-5)
@slow_test
def test_padded_conv_transpose2d(self):
for padding in [(1,2), (2,1), 2, 1, 0]:
helper_test_op([(2,4,9,9), (4,4,3,3)],
@@ -2110,6 +2116,7 @@ class TestOps(unittest.TestCase):
self.helper_test_exception([(2,16,2,2), (32,16,3,3)], lambda x,w: torch.nn.functional.conv_transpose2d(x,w,padding=(1,1,1)),
lambda x,w: Tensor.conv_transpose2d(x,w,padding=(1,1,1)), expected=(RuntimeError, ValueError))
@slow_test
def test_dilated_conv_transpose2d(self):
for dilation in [(1,2), (2,1), 2, 1]:
helper_test_op([(2,4,9,9), (4,4,3,3)],
@@ -2122,6 +2129,7 @@ class TestOps(unittest.TestCase):
lambda x,w: torch.nn.functional.conv_transpose2d(x,w, stride=stride),
lambda x,w: Tensor.conv_transpose2d(x,w,stride=stride), atol=1e-5, grad_rtol=1e-5)
@slow_test
def test_output_padded_conv_transpose2d(self):
for output_padding, stride in [((1,1), (2,3)), ((2,1), (3,2))]:
helper_test_op([(2,4,6,5), (4,4,3,3),(4,)],
@@ -2183,8 +2191,10 @@ class TestOps(unittest.TestCase):
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups),
lambda x,w: Tensor.conv2d(x,w,groups=groups), grad_rtol=1e-5)
def test_conv2d(self): self._test_conv2d(bs=1, cin=3)
@slow_test
def test_conv2d_bs_4_cin_3(self): self._test_conv2d(bs=4, cin=3, cout=2)
def test_conv2d_bs_1_cin_1(self): self._test_conv2d(bs=1, cin=1)
@slow_test
def test_conv2d_bs_4_cin_1(self): self._test_conv2d(bs=4, cin=1)
def test_conv2d_errors(self):
@@ -2256,6 +2266,7 @@ class TestOps(unittest.TestCase):
lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups),
lambda x,w: Tensor.conv2d(x,w,groups=groups), grad_rtol=1e-5)
@slow_test
def test_strided_conv2d_simple(self):
bs,H,W = 2,3,1
helper_test_op([(bs,1,5,1), (1,1,H,W)],
@@ -2266,6 +2277,7 @@ class TestOps(unittest.TestCase):
def test_strided_conv2d_simple_vec(self):
with Context(DEVECTORIZE=0): self.test_strided_conv2d_simple()
@slow_test
def test_strided_conv2d(self):
bs = 4
cin = 3
@@ -2501,6 +2513,7 @@ class TestOps(unittest.TestCase):
),
forward_only=True)
@slow_test
def test_avg_pool2d(self):
shape = (32,2,111,28)
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]: