remove negative from things w/o negative
@@ -6,12 +6,12 @@ import timeit
import functools
from tinygrad.tensor import Tensor, DEFAULT_DEVICE, Device
-def helper_test_op(shps, torch_fxn, tinygrad_fxn, atol=1e-6, rtol=1e-3, grad_atol=1e-6, grad_rtol=1e-3, forward_only=False, vals=None):
+def helper_test_op(shps, torch_fxn, tinygrad_fxn, atol=1e-6, rtol=1e-3, grad_atol=1e-6, grad_rtol=1e-3, forward_only=False, vals=None, a=-0.5, b=20):
torch.manual_seed(0)
if shps is None:
ts = [torch.tensor(x, requires_grad=True) for x in vals]
else:
- ts = [torch.tensor((np.random.random(size=x).astype(np.float32)-0.5)*20, requires_grad=True) for x in shps]
+ ts = [torch.tensor((np.random.random(size=x).astype(np.float32)+a)*b, requires_grad=True) for x in shps]
tst = [Tensor(x.detach().numpy()) for x in ts]
out = torch_fxn(*ts)
@@ -49,9 +49,9 @@ class TestOps(unittest.TestCase):
def test_div(self):
helper_test_op([(45,65), (45,65)], lambda x,y: x/y, Tensor.div)
def test_pow(self):
- helper_test_op([(45,65), (45,65)], lambda x,y: x**y, Tensor.pow)
+ helper_test_op([(45,65), (45,65)], lambda x,y: x**y, Tensor.pow, a=0)
def test_sqrt(self):
- helper_test_op([(45,65)], lambda x: x.sqrt(), Tensor.sqrt)
+ helper_test_op([(45,65)], lambda x: x.sqrt(), Tensor.sqrt, a=0)
def test_relu(self):
helper_test_op([(45,65)], lambda x: x.relu(), Tensor.relu)
def test_leakyrelu(self):
@@ -117,7 +117,7 @@ class TestOps(unittest.TestCase):
(torch.div, Tensor.div), (torch.pow, Tensor.pow)]:
for shapes in [((5,13,24,16), (5,1,24,1)), ((1,3,1,7,1), (2,1,5,1,8))]:
with self.subTest(op=torch_op.__name__, shapes=shapes):
- helper_test_op(shapes, torch_op, tinygrad_op)
+ helper_test_op(shapes, torch_op, tinygrad_op, a=-0.5 if tinygrad_op != Tensor.pow else 0.0)
def test_broadcast_partial(self):
@@ -127,7 +127,7 @@ class TestOps(unittest.TestCase):
((4,1), (4,5)), ((1,4), (5,4))]:
with self.subTest(op=torch_op.__name__, shapes=shapes):
# NOTE: ANE backwards?
- helper_test_op(shapes, torch_op, tinygrad_op, forward_only=DEFAULT_DEVICE!=Device.CPU)
+ helper_test_op(shapes, torch_op, tinygrad_op, a=-0.5 if tinygrad_op != Tensor.pow else 0.0)
def test_slice(self):
helper_test_op([(3,3,3,3)], lambda x: x[1:2], lambda x: x[1:2])
