def test_spares_ftrl():
""" test sparse ftrl"""
indices = Tensor(np.array([0, 1]).astype(np.int32))
label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
net = NetWithSparseGatherV2()
net.set_train()
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'Ascend'
train_network = TrainOneStepCell(net, optimizer)
_executor.compile(train_network, indices, label)
def __init__(self, network, sens=1024.0, host_device_mix=False, parameter_server=False, sparse=False):
super(TrainStepWrap, self).__init__()
parallel_mode = context.get_auto_parallel_context("parallel_mode")
is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
self.network = network
self.network.set_train()
self.trainable_params = network.trainable_params()
weights_w = []
weights_d = []
for params in self.trainable_params:
if 'wide' in params.name:
weights_w.append(params)
else:
weights_d.append(params)
self.weights_w = ParameterTuple(weights_w)
self.weights_d = ParameterTuple(weights_d)
if (sparse and is_auto_parallel) or parameter_server:
self.optimizer_d = LazyAdam(
self.weights_d, learning_rate=3.5e-4, eps=1e-8, loss_scale=sens)
self.optimizer_w = FTRL(learning_rate=5e-2, params=self.weights_w,
l1=1e-8, l2=1e-8, initial_accum=1.0, loss_scale=sens)
if host_device_mix or parameter_server:
self.optimizer_w.target = "CPU"
self.optimizer_d.target = "CPU"
else:
self.optimizer_d = Adam(
self.weights_d, learning_rate=3.5e-4, eps=1e-8, loss_scale=sens)
self.optimizer_w = FTRL(learning_rate=5e-2, params=self.weights_w,
l1=1e-8, l2=1e-8, initial_accum=1.0, loss_scale=sens)
self.hyper_map = C.HyperMap()
self.grad_w = C.GradOperation(get_by_list=True,
sens_param=True)
self.grad_d = C.GradOperation(get_by_list=True,
sens_param=True)
self.sens = sens
self.loss_net_w = IthOutputCell(network, output_index=0)
self.loss_net_d = IthOutputCell(network, output_index=1)
self.loss_net_w.set_grad()
self.loss_net_d.set_grad()
self.reducer_flag = False
self.grad_reducer_w = None
self.grad_reducer_d = None
self.reducer_flag = parallel_mode in (ParallelMode.DATA_PARALLEL,
ParallelMode.HYBRID_PARALLEL)
if self.reducer_flag:
mean = context.get_auto_parallel_context("gradients_mean")
degree = context.get_auto_parallel_context("device_num")
self.grad_reducer_w = DistributedGradReducer(self.optimizer_w.parameters, mean, degree)
self.grad_reducer_d = DistributedGradReducer(self.optimizer_d.parameters, mean, degree)
def __init__(self, network, sens=1000.0):
super(TrainStepWarp, self).__init__()
self.network = network
self.network.set_train()
self.trainable_params = network.trainable_params()
weights_w = []
weights_d = []
for params in self.trainable_params:
weights_w.append(params)
weights_d.append(params)
self.weights_w = ParameterTuple(weights_w)
self.weights_d = ParameterTuple(weights_d)
self.optimizer_w = FTRL(learning_rate=1e-2,
params=self.weights_w,
l1=1e-8,
l2=1e-8,
initial_accum=1.0)
self.optimizer_d = Adam(self.weights_d,
learning_rate=3.5e-4,
eps=1e-8,
loss_scale=sens)
self.hyper_map = C.HyperMap()
self.grad_w = C.GradOperation(get_by_list=True, sens_param=True)
self.grad_d = C.GradOperation(get_by_list=True, sens_param=True)
self.sens = sens
self.loss_net_w = IthOutputCell(network, output_index=0)
self.loss_net_d = IthOutputCell(network, output_index=1)
def test_ftrl_net():
indices = Tensor(np.array([0, 0, 1]).astype(np.int32))
label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
net = NetWithSparseGatherV2()
optimizer = FTRL(net.trainable_params(), learning_rate=0.1, weight_decay=0.9, loss_scale=2.0)
optimizer.target = 'Ascend'
train_network = TrainOneStepCell(net, optimizer)
output = train_network(indices, label)
np.allclose(output.asnumpy(), np.array([[[2, 2]], [[2, 2]], [[2, 2]]]))
np.allclose(net.weight1.asnumpy(), np.array([[[0.7884067, 0.7884067]],
[[0.68213105, 0.68213105]],
[[1.0, 1.0]]]))
np.allclose(net.weight2.asnumpy(), np.array([[[0.6821311, 0.6821311]],
[[0.6821311, 0.6821311]],
[[0.6821311, 0.6821311]]]))
def test_ftrl():
epoch = 3
net = NetFtrl()
optimizer = FTRL(filter(lambda x: x.requires_grad,
net.get_parameters()), learning_rate=0.01)
criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
net_with_criterion = WithLossCell(net, criterion)
train_network = TrainOneStepCell(
net_with_criterion, optimizer)
train_network.set_train()
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
losses1 = []
for _ in range(epoch):
data = Tensor(np.arange(0, 16).reshape(
1, 1, 4, 4).astype(np.float32) * 0.01)
label = Tensor(np.array([0]).astype(np.int32))
loss = train_network(data, label)
losses1.append(loss.asnumpy())
assert losses1[0] > losses1[1]
assert losses1[1] > losses1[2]
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
losses2 = []
for _ in range(epoch):
data = Tensor(np.arange(0, 16).reshape(
1, 1, 4, 4).astype(np.float32) * 0.01)
label = Tensor(np.array([0]).astype(np.int32))
loss = train_network(data, label)
losses2.append(loss.asnumpy())
def test_ftrl_target():
""" test_ftrl_target """
net = NetWithSparseGatherV2()
net.set_train()
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
if optimizer.target not in ('CPU', 'Ascend'):
raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(optimizer.target))
def __init__(self, network, config, sens=1000.0):
super(TrainStepWrap, self).__init__()
self.network = network
self.network.set_train()
self.trainable_params = network.trainable_params()
weights_w = []
weights_d = []
for params in self.trainable_params:
if 'wide' in params.name:
weights_w.append(params)
else:
weights_d.append(params)
self.weights_w = ParameterTuple(weights_w)
self.weights_d = ParameterTuple(weights_d)
self.optimizer_w = FTRL(learning_rate=config.ftrl_lr,
params=self.weights_w,
l1=5e-4,
l2=5e-4,
initial_accum=0.1,
loss_scale=sens)
#self.optimizer_d = ProximalAdagrad(self.weights_d, learning_rate=config.adam_lr,loss_scale=sens)
self.optimizer_d = Adam(self.weights_d,
learning_rate=config.adam_lr,
eps=1e-6,
loss_scale=sens)
self.hyper_map = C.HyperMap()
self.grad_w = C.GradOperation('grad_w',
get_by_list=True,
sens_param=True)
self.grad_d = C.GradOperation('grad_d',
get_by_list=True,
sens_param=True)
self.sens = sens
self.loss_net_w = IthOutputCell(network, output_index=0)
self.loss_net_d = IthOutputCell(network, output_index=1)
self.reducer_flag = False
self.grad_reducer_w = None
self.grad_reducer_d = None
parallel_mode = _get_parallel_mode()
if parallel_mode in (ParallelMode.DATA_PARALLEL,
ParallelMode.HYBRID_PARALLEL):
self.reducer_flag = True
if self.reducer_flag:
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer_w = DistributedGradReducer(
self.optimizer_w.parameters, mean, degree)
self.grad_reducer_d = DistributedGradReducer(
self.optimizer_d.parameters, mean, degree)
def test_ftrl():
""" test_ftrl """
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
net = Net()
net.set_train()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = FTRL(net.trainable_params(), weight_decay=0.9, loss_scale=2.0)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
_executor.compile(train_network, inputs, label)