def _test_add(self, is_cuda=False, is_double=False):
if is_double:
self.a = self.a.double()
self.b = self.b.double()
self.gt = self.gt.double()
if is_cuda:
print("Testing Halide PyTorch CUDA operator...")
self.a = self.a.cuda()
self.b = self.b.cuda()
self.gt = self.gt.cuda()
else:
print("Testing Halide PyTorch CPU operator...")
output = modules.Add()(self.a, self.b)
if is_double:
print(" Double-precision mode")
else:
print(" Single-precision mode")
diff = (output - self.gt).sum().item()
assert diff == 0.0, "Test failed: sum should be 4, got %f" % diff
# Test the gradient is correct
self.a.requires_grad = True
self.b.requires_grad = True
res = th.autograd.gradcheck(modules.Add(), [self.a, self.b], eps=1e-2)
print(" Test ran successfully: difference is", diff)
def _test_add(self, is_cuda=False, is_double=False):
if is_double:
self.a = self.a.double()
self.b = self.b.double()
self.gt = self.gt.double()
if is_cuda:
print("Testing Halide PyTorch CUDA operator...")
self.a = self.a.cuda()
self.b = self.b.cuda()
self.gt = self.gt.cuda()
else:
print("Testing Halide PyTorch CPU operator...")
output = modules.Add()(self.a, self.b)
if is_double:
print(" Double-precision mode")
else:
print(" Single-precision mode")
diff = (output - self.gt).sum().item()
assert diff == 0.0, "Test failed: sum should be 4, got %f" % diff
# Test the gradient is correct
self.a.requires_grad = True
self.b.requires_grad = True
with warnings.catch_warnings():
# Inputs are float, the gradient checker wants double inputs and
# will issue a warning.
warnings.filterwarnings(
"ignore",
message="At least one of the inputs that requires "
"gradient is not of double precision")
res = th.autograd.gradcheck(modules.Add(), [self.a, self.b],
eps=1e-2)
print(" Test ran successfully: difference is", diff)
def _test_add(self, is_cuda=False, is_double=False):
if is_double:
self.a = self.a.double()
self.b = self.b.double()
self.gt = self.gt.double()
if is_cuda:
print("Testing Halide PyTorch CUDA operator...")
self.a = self.a.cuda()
self.b = self.b.cuda()
self.gt = self.gt.cuda()
else:
print("Testing Halide PyTorch CPU operator...")
for backward_op in ["add_grad", "add_halidegrad"]:
add = modules.Add(backward_op)
output = add(self.a, self.b)
if is_double:
print(" .Double-precision mode, backward_op:", backward_op)
else:
print(" .Single-precision mode, backward_op:", backward_op)
diff = (output - self.gt).sum().item()
assert diff == 0.0, "Test failed: sum should be 4, got %f" % diff
self.a.requires_grad = True
self.b.requires_grad = True
for i in range(100):
output = add(self.a, self.b).sum()
output.backward()
# Inputs are float, the gradient checker wants double inputs and
# will issue a warning.
warnings.filterwarnings("ignore", module=r".*gradcheck*")
# Test the gradient is correct
res = th.autograd.gradcheck(add, [self.a, self.b], eps=1e-2)
print(" Test ran successfully: difference is", diff)