def test_batchnorm2d_autocast():
"""check amp's result is equal to manually converted result"""
amp.enabled = True
tshape = (1, 3, 224, 224)
pshape = (1, 3, 1, 1)
inp = tensor(np.random.randn(*tshape), dtype=np.float32)
weight = tensor(np.ones(pshape, dtype=np.float32))
bias = tensor(np.zeros(pshape, dtype=np.float32))
out = F.batch_norm(inp,
weight=weight,
bias=bias,
training=True,
inplace=False)
amp.enabled = False
expected = F.batch_norm(
inp.astype("float16"),
weight=weight,
bias=bias,
training=True,
inplace=False,
compute_mode="float32",
)
assert out.dtype == np.float16
assert expected.dtype == np.float16
np.testing.assert_allclose(out.numpy(), expected.numpy())
def test_module_api_hooks():
net = MyModule()
pre_hook_num = 0
post_hook_num = 0
hooks = []
def pre_hook(module, inputs):
nonlocal pre_hook_num
pre_hook_num += 1
modified_inputs = tuple(inp + 1 for inp in inputs)
return modified_inputs
def post_hook(module, inputs, outputs):
nonlocal post_hook_num
post_hook_num += 1
outputs += 1
return outputs
net.apply(lambda module: hooks.append(
module.register_forward_pre_hook(pre_hook)))
net.apply(
lambda module: hooks.append(module.register_forward_hook(post_hook)))
shape = (1, 4, 1, 1)
x = tensor(np.zeros(shape, dtype=np.float32))
y = net(x)
assert pre_hook_num == 4
assert post_hook_num == 4
mean1 = Parameter(np.zeros(shape), dtype=np.float32)
bn1 = F.batch_norm(x + 3,
mean1,
Parameter(np.ones(shape), dtype=np.float32),
training=True)
np.testing.assert_allclose(
net.i.bn.running_mean.numpy(),
mean1.numpy(),
)
mean2 = Parameter(np.zeros(shape), dtype=np.float32)
bn2 = F.batch_norm(bn1 + 3,
mean2,
Parameter(np.ones(shape), dtype=np.float32),
training=True)
np.testing.assert_allclose(
net.bn.running_mean.numpy(),
mean2.numpy(),
)
np.testing.assert_allclose((bn2 + 2).numpy(), y.numpy())
assert len(hooks) == 8
for handler in hooks:
handler.remove()
y = net(x)
assert pre_hook_num == 4
assert post_hook_num == 4
def test_regression_1762():
x = F.ones((10, 10, 3, 3))
conv = M.Conv2d(10, 10, kernel_size=3, padding=1)
t_shape = (1, 10, 1, 1)
weight = mge.Parameter(np.ones(t_shape, dtype=np.float32))
bias = mge.Parameter(np.zeros(t_shape, dtype=np.float32))
gm = GradManager()
gm.attach(list(conv.parameters()) + [weight, bias])
with gm:
out1 = conv(x)
out2 = F.batch_norm(
out1,
None,
None,
weight,
bias,
training=True,
)
# Weird error only occur when this action is placed after BN
# Op type is not relevant
loss = out1 + 1
gm.backward(loss)
