[GitHub] [singa] lalitjain99 commented on issue #975: AttributeError: module 'singa.singa_wrap' has no attribute 'Communicator'

[GitBox <gi...@apache.org>]

lalitjain99 commented on issue #975:
URL: https://github.com/apache/singa/issues/975#issuecomment-1201093203

   Hi Team,
   
   I have installed 3.1.0 as per documentation. 
   But when I am applying the distopt api , its crashing.
   
   Below is the code:-
   from singa import singa_wrap as singa
   from singa import device
   from singa import tensor
   from singa import opt
   import numpy as np
   import time
   import argparse
   from PIL import Image
   from singa import layer
   from singa import model
   from singa import tensor
   from singa import opt
   from singa import device
   
   class MLP(model.Model):
   
       def __init__(self, data_size=10, perceptron_size=100, num_classes=10):
           super(MLP, self).__init__()
           self.num_classes = num_classes
           self.dimension = 2
   
           self.relu = layer.ReLU()
           self.linear1 = layer.Linear(perceptron_size)
           self.linear2 = layer.Linear(num_classes)
           self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
   
       def forward(self, inputs):
           y = self.linear1(inputs)
           y = self.relu(y)
           y = self.linear2(y)
           return y
   
       def train_one_batch(self, x, y, dist_option, spars):
           out = self.forward(x)
           loss = self.softmax_cross_entropy(out, y)
   
           if dist_option == 'plain':
               self.optimizer(loss)
           elif dist_option == 'half':
               self.optimizer.backward_and_update_half(loss)
           elif dist_option == 'partialUpdate':
               self.optimizer.backward_and_partial_update(loss)
           elif dist_option == 'sparseTopK':
               self.optimizer.backward_and_sparse_update(loss,
                                                         topK=True,
                                                         spars=spars)
           elif dist_option == 'sparseThreshold':
               self.optimizer.backward_and_sparse_update(loss,
                                                         topK=False,
                                                         spars=spars)
           return out, loss
   
       def set_optimizer(self, optimizer):
           self.optimizer = optimizer
   
   
   def create_model(pretrained=False, **kwargs):
       """Constructs a CNN model.
       Args:
           pretrained (bool): If True, returns a pre-trained model.
       
       Returns:
           The created CNN model.
       """
       model = MLP(**kwargs)
   
       return model
   
   __all__ = ['MLP', 'create_model']
   
   if __name__ == "__main__":
       np.random.seed(0)
   
       # generate the boundary
       f = lambda x: (5 * x + 1)
       bd_x = np.linspace(-1.0, 1, 200)
       bd_y = f(bd_x)
   
       # generate the training data
       x = np.random.uniform(-1, 1, 400)
       y = f(x) + 2 * np.random.randn(len(x))
   
     
       # convert training data to 2d space
       label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)]).astype(np.int32)
       data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32)
   
       dev = device.create_cuda_gpu_on(0)
       sgd = opt.SGD(0.1, 0.9, 1e-5)
       #**sgd = opt.DistOpt(sgd)**
       tx = tensor.Tensor((400, 2), dev, tensor.float32)
       ty = tensor.Tensor((400,), dev, tensor.int32)
       model = MLP(data_size=2, perceptron_size=3, num_classes=2)
   
       # attach model to graph
       model.set_optimizer(sgd)
       model.compile([tx], is_train=True, sequential=True)
       model.train()
   
       for i in range(100):
           tx.copy_from_numpy(data)
           ty.copy_from_numpy(label)
           out, loss = model(tx, ty, 'fp32', spars=None)
   
           if i % 100 == 0:
               print("training loss = ", tensor.to_numpy(loss)[0])
   
   
   
   


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