def get_WideDeep_net(config):
WideDeep_net = WideDeepModel(config)
loss_net = NetWithLossClass(WideDeep_net, config)
loss_net = VirtualDatasetCellTriple(loss_net)
train_net = TrainStepWrap(loss_net)
eval_net = PredictWithSigmoid(WideDeep_net)
eval_net = VirtualDatasetCellTriple(eval_net)
return train_net, eval_net
def get_WideDeep_net(config):
"""
Get network of wide&deep model.
"""
WideDeep_net = WideDeepModel(config)
loss_net = NetWithLossClass(WideDeep_net, config)
loss_net = VirtualDatasetCellTriple(loss_net)
train_net = TrainStepWrap(loss_net, host_device_mix=bool(config.host_device_mix))
eval_net = PredictWithSigmoid(WideDeep_net)
eval_net = VirtualDatasetCellTriple(eval_net)
return train_net, eval_net
def get_wide_deep_net(config):
"""
Get network of wide&deep model.
"""
wide_deep_net = WideDeepModel(config)
loss_net = NetWithLossClass(wide_deep_net, config)
if cache_enable:
loss_net = VirtualDatasetCellTriple(loss_net)
train_net = TrainStepWrap(loss_net,
parameter_server=bool(config.parameter_server),
cache_enable=(config.vocab_cache_size > 0))
eval_net = PredictWithSigmoid(wide_deep_net)
if cache_enable:
eval_net = VirtualDatasetCellTriple(eval_net)
return train_net, eval_net
def test_virtualdataset_cell_3_inputs():
class Net(nn.Cell):
def __init__(self, strategy0, strategy1, strategy2, strategy3):
super().__init__()
self.matmul1 = P.MatMul().set_strategy(strategy1)
self.matmul2 = P.MatMul().set_strategy(strategy2)
self.gelu = P.Gelu().set_strategy(strategy3)
def construct(self, x, y, b):
out = self.gelu(self.matmul1(x, y))
out = self.matmul2(out, b)
return out
net = GradWrap(VirtualDatasetCellTriple(NetWithLoss(Net(None, None, None, None))))
context.set_context(save_graphs=True)
context.set_auto_parallel_context(parallel_mode="auto_parallel")
context.set_auto_parallel_context(device_num=8, global_rank=0)
x = Tensor(np.ones([128, 32]), dtype=ms.float32)
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
b = Tensor(np.ones([64, 2048]), dtype=ms.float32)
net.set_auto_parallel()
_executor.compile(net, x, y, b)