def Foo() -> tp.Numpy:
with flow.scope.placement("cpu", device_name):
w = flow.get_variable(
"w",
shape=(10,),
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
ones = flow.constant_like(w, value=1.0, dtype=flow.float)
(ref, value) = flow.experimental.ssp_variable_proxy(
w, buffer_size=buffer_size
)
flow.assign(ref, ref + ones)
return value
def Foo() -> tp.Numpy:
with flow.scope.placement(
"cpu", device_name
), flow.experimental.scope.config(
ssp_num_stages=buffer_size, ssp_stage_id=0
):
w = flow.get_variable(
"w",
shape=(10,),
dtype=flow.float,
initializer=flow.constant_initializer(0),
)
loss = w + flow.constant_like(w, value=0.0, dtype=flow.float)
flow.optimizer.SGD(
flow.optimizer.PiecewiseConstantScheduler([], [-10.0]), momentum=0
).minimize(loss)
return loss
def FuseBnAddReluJob(x: oft.Numpy.Placeholder(
shape, dtype=in_type)) -> oft.Numpy:
addend = flow.constant_like(x, 2)
with flow.scope.placement(device, "0:0-0"):
x = (flow.get_variable(
"x1",
shape=shape,
dtype=in_type,
initializer=flow.random_uniform_initializer(minval=-10,
maxval=10),
trainable=True,
) + x)
loss = flow.nn.relu(_batch_norm(x, last=False) + addend) + 1
flow.optimizer.SGD(flow.optimizer.PiecewiseConstantScheduler(
[], [0.0001]),
momentum=0).minimize(loss)
return loss
def ConstantLikeJob(x: oft.Numpy.Placeholder(x.shape)):
return flow.constant_like(x, value=value, dtype=dtype)