chrishkchris edited a comment on issue #468: Distributted module URL: https://github.com/apache/incubator-singa/pull/468#issuecomment-527824025 > I have combined all the commits into two commits. Meanwhile, I found that the resnet.py is not compatible with the master branch modified "Add" function with broadcasting. Get the error ( assert(len(self.shape0) <= 2 and len(self.shape1) <= 2),"up till now, the dimensions of tensor a and b should less than 3" > AssertionError: up till now, the dimensions of tensor a and b should less than 3) > Since in resnet we used "out = autograd.add(out, residual)", the input to the add function should have a dimension more than 2, the assert function should return always false and hence assertion error When I disable the assertion `assert(len(self.shape0) <= 2 and len(self.shape1) <= 2)`, the resnet.py can run successfully See the code of Add function ```python class Add(Operation): def __init__(self): super(Add, self).__init__() def forward(self, a, b): #up till now, the dimensions of tensor a and b should less than 3 self.shape0=list(a.shape()) self.shape1=list(b.shape()) assert(len(self.shape0) <= 2 and len(self.shape1) <= 2),"up till now, the dimensions of tensor a and b should less than 3" return singa.__add__(a, b) def backward(self, dy): if(type(dy)==float): assert self.shape0==self.shape1,('should have same shape') return dy,dy db=CTensor(list(dy.shape()), dy.device()) db.CopyData(dy) for i in range(len(self.shape0)-len(self.shape1)): db=singa.Sum(db, 0) return dy, db ``` Can we allow input dimension to ADD more than two? (e.g. change the limit 2 to 4 or disable the assertion). Typically there are four dimensions for conv. feature maps: batch, depth/channel, width, height Or if the input shapes of the two operands are two same, it also passes: ```python assert( (len(self.shape0) <= 2 and len(self.shape1) <= 2) or (len(self.shape0)==len(self.shape1)) )
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