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remove explicit np.array and np.int32 in test_div_int [pr] (#8395)

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vals default loads as int32 now in test_ops
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chenyu
2024-12-24 13:09:30 -05:00
committed by GitHub
parent 5c2fe04bb6
commit 2c93f27652
1 changed files with 9 additions and 10 deletions
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19
test/test_ops.py
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@@ -533,12 +533,11 @@ class TestOps(unittest.TestCase):
helper_test_op([(45,65), (45,65)], lambda x,y: x/y)
helper_test_op([(), ()], lambda x,y: x/y)
def test_div_int(self):
helper_test_op(None, lambda x,y: x/y, Tensor.div, forward_only=True, vals=np.array([[5, 6, 7],[1, 2, 3]], dtype=np.int32))
helper_test_op(None, lambda x,y: x//y, forward_only=True, vals=np.array([[5, 6, 7],[1, 2, 3]], dtype=np.int32))
helper_test_op(None, lambda x: x/2, forward_only=True, vals=np.array([[3, 4, 5]], dtype=np.int32))
helper_test_op(None, lambda x: x//2, forward_only=True, vals=np.array([[3, 4, 5]], dtype=np.int32))
helper_test_op(None, functools.partial(torch.div, rounding_mode="trunc"), Tensor.idiv, forward_only=True,
vals=np.array([[5, -6, 7],[1, 2, 3]], dtype=np.int32))
helper_test_op(None, lambda x,y: x/y, Tensor.div, forward_only=True, vals=[[5, 6, 7],[1, 2, 3]])
helper_test_op(None, lambda x,y: x//y, forward_only=True, vals=[[5, 6, 7],[1, 2, 3]])
helper_test_op(None, lambda x: x/2, forward_only=True, vals=[[3, 4, 5]])
helper_test_op(None, lambda x: x//2, forward_only=True, vals=[[3, 4, 5]])
helper_test_op(None, functools.partial(torch.div, rounding_mode="trunc"), Tensor.idiv, forward_only=True, vals=[[5, -6, 7],[1, 2, 3]])
if is_dtype_supported(dtypes.uint64):
x = Tensor(2**64 - 1, dtype=dtypes.uint64).idiv(1)
np.testing.assert_equal(x.numpy(), 2**64 - 1)
@@ -919,8 +918,8 @@ class TestOps(unittest.TestCase):
# check if it returns the first index for multiple occurences
helper_test_op(None, lambda x: x.argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True, vals=[[2, 2]])
helper_test_op(None, lambda x: x.argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True, vals=[[1, 2, 2]])
np.testing.assert_equal(Tensor([2,2]).argmax().numpy(), np.array(0))
np.testing.assert_equal(Tensor([1,2,2]).argmax().numpy(), np.array(1))
np.testing.assert_equal(Tensor([2,2]).argmax().numpy(), 0)
np.testing.assert_equal(Tensor([1,2,2]).argmax().numpy(), 1)
helper_test_op([(10,20)], lambda x: x.argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmax(0, False).type(torch.int32), lambda x: x.argmax(0, False), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmax(1, False).type(torch.int32), lambda x: x.argmax(1, False), forward_only=True)
@@ -938,8 +937,8 @@ class TestOps(unittest.TestCase):
# check if it returns the first index for multiple occurences
helper_test_op(None, lambda x: x.argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True, vals=[[2, 2]])
helper_test_op(None, lambda x: x.argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True, vals=[[3, 2, 2]])
np.testing.assert_equal(Tensor([2,2]).argmin().numpy(), np.array(0))
np.testing.assert_equal(Tensor([3,2,2]).argmin().numpy(), np.array(1))
np.testing.assert_equal(Tensor([2,2]).argmin().numpy(), 0)
np.testing.assert_equal(Tensor([3,2,2]).argmin().numpy(), 1)
helper_test_op([(10,20)], lambda x: x.argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmin(0, False).type(torch.int32), lambda x: x.argmin(0, False), forward_only=True)
helper_test_op([(10,20)], lambda x: x.argmin(1, False).type(torch.int32), lambda x: x.argmin(1, False), forward_only=True)