eric-haibin-lin opened a new issue #9980: [Feature Request] broadcast_mul(csr, dense) URL: https://github.com/apache/incubator-mxnet/issues/9980 Let's say we have a MxN CSR matrix, it's quite common to normalize the CSR matrix `A` by a length M vector or a length N vector `B`. However, MXNet doesn't support broadcast_mul(csr, dense) = csr. In scipy, you have to do normalization with the following walk-around with a dot product: ```python A = <some scipy csr metrics> B = np.asarray(A.sum(axis=1)).squeeze() row_scaling = scipy.sparse.spdiags(1/B, 0, dim, dim) normalized_A = row_scaling * A ``` What we can do in MXNet is to directly support broadcast_mul(csr, dense) = csr. Note that an efficient implementation only looks up non-zeros in the csr matrix and find the corresponding element in the dense right-hand-side. This means that if the dense matrix has any `Nan` element, we are ignoring it during the computation (we are already doing this in `sparse.dot`). For a complete implementation, both 1-D and 2-D dense ndarray as rhs should be implemented.
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