enable tests in test_ops.py that are disabled but now work.
remove custom tolerances that don't appear to be needed.
MODIFY
test/test_ops.py
test/test_ops.py
@@ -77,16 +77,13 @@ class TestOps(unittest.TestCase):
def test_exp(self):
helper_test_op([(45,65)], lambda x: torch.exp(x), Tensor.exp)
def test_sign(self):
- pass
- #helper_test_op([(45,65)], lambda x: torch.sign(x), Tensor.sign) --> broken test
+ helper_test_op([(45,65)], lambda x: torch.sign(x), Tensor.sign)
def test_sigmoid(self):
helper_test_op([(45,65)], lambda x: x.sigmoid(), Tensor.sigmoid)
def test_softplus(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.softplus(x), Tensor.softplus, atol=1e-6, grad_atol=1e-6)
def test_gelu(self):
- pass
- # fails?
- #helper_test_op([(45,65)], lambda x: 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))), Tensor.gelu)
+ helper_test_op([(45,65)], lambda x: 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))), Tensor.gelu)
def test_elu(self):
helper_test_op([(45,65)], lambda x: torch.nn.functional.elu(x), Tensor.elu)
helper_test_op([(45,65)], lambda x: torch.nn.functional.elu(x, alpha=0.1), lambda x: Tensor.elu(x, alpha=0.1))
@@ -123,10 +120,10 @@ class TestOps(unittest.TestCase):
def test_max(self):
helper_test_op([(45,3)], lambda x: x.max(), Tensor.max)
helper_test_op([(45,3)], lambda x: x.max().mul(0.5), lambda x: Tensor.max(x).mul(0.5))
- #helper_test_op(None, lambda x: x.max().mul(0.5), lambda x: Tensor.max(x).mul(0.5),
- # vals=[
- # [[1.0,1.0,0.0,1.0]],
- # ]) --> broken test
+ helper_test_op(None, lambda x: x.max().mul(0.5), lambda x: Tensor.max(x).mul(0.5),
+ vals=[
+ [[1.0,1.0,0.0,1.0]],
+ ])
helper_test_op([(3,4,5,6)], lambda x: x.max(axis=1)[0], lambda x: Tensor.max(x, axis=1))
def test_mean_axis(self):
helper_test_op([(3,4,5,6)], lambda x: x.mean(axis=(1,2)), lambda x: Tensor.mean(x, axis=(1,2)))
@@ -370,16 +367,14 @@ class TestOps(unittest.TestCase):
ksz = (2,2)
helper_test_op([(1,1,2,3)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
- # TODO: why is this tolerance so high?
- lambda x: Tensor.max_pool2d(x, kernel_size=ksz), grad_atol=1e-4)
+ lambda x: Tensor.max_pool2d(x, kernel_size=ksz))
def test_maxpool2d(self):
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
with self.subTest(kernel_size=ksz):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
- # TODO: why is this tolerance so high?
- lambda x: Tensor.max_pool2d(x, kernel_size=ksz), grad_atol=1e-4)
+ lambda x: Tensor.max_pool2d(x, kernel_size=ksz))
def test_avgpool2d(self):
shape = (32,2,111,28)

enable tests in test_ops.py that are disabled but now work. (#396)

remove custom tolerances that don't appear to be needed.