def test_fuzz(self):
input_size_pairs = (
(7777*77, 555*555),
(777, 555),
(555, 2048*32+1),
(2048*32+1, 555),
(555, 2048*32),
(2048*32, 555),
(33333, 555),
(555, 33333))
appliers = (
MultiTensorApply(2048*32),
MultiTensorApply(333),
MultiTensorApply(33333))
repeat_tensors = (
1,
55)
for sizea, sizeb in input_size_pairs:
for applier in appliers:
for repeat in repeat_tensors:
for x_type in (torch.float32, torch.float16):
for y_type in (torch.float32, torch.float16):
for out_type in (torch.float32, torch.float16):
for inplace in (True, False):
if inplace is True and (y_type is not out_type):
continue
else:
self.axpby(sizea, sizeb, applier, repeat,
x_type, y_type, out_type, inplace=inplace)
def test_fuzz_nhwc(self):
input_size_pairs = (((7, 77, 7, 77), (5, 55, 5, 55)), ((1, 1, 777, 1),
(1, 1, 555, 1)),
((5, 47, 5, 55), (1, 1, 1, 2048 * 32 + 1)),
((1, 1, 1, 2048 * 32 + 1), (55, 47, 5, 55)),
((555, 1, 1, 1), (32, 8, 32, 8)),
((32, 8, 32, 8), (55, 47, 5, 55)),
((1, 1, 33333, 1),
(55, 47, 55, 5)), ((55, 47, 55, 5), (1, 1, 33333,
1)))
appliers = (MultiTensorApply(2048 * 32), MultiTensorApply(333),
MultiTensorApply(33333))
repeat_tensors = (1, 55)
for sizea, sizeb in input_size_pairs:
for applier in appliers:
for repeat in repeat_tensors:
for x_type in (torch.float32, torch.float16):
for y_type in (torch.float32, torch.float16):
for out_type in (torch.float32, torch.float16):
for inplace in (True, False):
if inplace is True and (y_type
is not out_type):
continue
else:
self.axpby(sizea,
sizeb,
applier,
repeat,
x_type,
y_type,
out_type,
inplace=inplace,
nhwc=True)
def test_fuzz(self):
input_size_pairs = (
(7777*77, 555*555),
(777, 555),
(555, 2048*32+1),
(2048*32+1, 555),
(555, 2048*32),
(2048*32, 555),
(33333, 555),
(555, 33333))
appliers = (
MultiTensorApply(2048*32),
MultiTensorApply(333),
MultiTensorApply(33333))
repeat_tensors = (
1,
55)
for sizea, sizeb in input_size_pairs:
for applier in appliers:
for repeat in repeat_tensors:
for in_type in (torch.float32, torch.float16):
for out_type in (torch.float32, torch.float16):
for inplace in (True, False):
if inplace is True and (out_type is not in_type):
continue
else:
self.downscale(sizea, sizeb, applier, repeat, in_type, out_type, inplace=inplace)
self.find_inf(sizea, sizeb, applier, repeat, in_type, out_type,
0, 0, float('nan'), inplace=inplace)
self.find_inf(sizea, sizeb, applier, repeat, in_type, out_type,
2*repeat-1, sizeb-1, float('inf'), inplace=inplace)
self.find_inf(sizea, sizeb, applier, repeat, in_type, out_type,
2*(repeat//2), sizea//2, float('inf'), inplace=inplace)
def test_fuzz(self):
input_size_pairs = ((7777 * 77, 555 * 555), (777, 555),
(555, 2048 * 32 + 1), (2048 * 32 + 1,
555), (555, 2048 * 32),
(2048 * 32, 555), (33333, 555), (555, 33333))
appliers = (MultiTensorApply(2048 * 32), MultiTensorApply(333),
MultiTensorApply(33333))
repeat_tensors = (1, 55)
for sizea, sizeb in input_size_pairs:
for applier in appliers:
for repeat in repeat_tensors:
for in_type in (torch.float32, torch.float16):
for per_tensor in (False, True):
self.l2norm(sizea, sizeb, applier, repeat, in_type,
per_tensor)