[GitHub] yifeim opened a new issue #10786: Feature request: square op for row_sparse and sum op with keepdims

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

yifeim opened a new issue #10786: Feature request: square op for row_sparse and sum op with keepdims
URL: https://github.com/apache/incubator-mxnet/issues/10786
 
 
   ## Description
   Square on row_sparse throws a storage fallback warning message. Sparse sum with keepdims also throws a similar warning message. While I need both functions to write factorization machines, it appears that there is an example that circumvents both obstacles with an internal function:
   
   https://github.com/apache/incubator-mxnet/blob/14206978f461364c53aaf1c787e2f268e2a94b00/example/sparse/factorization_machine/model.py#L38
   
   Still, it would be nice to expose these functionalities separately for general usability.
   
   ## Environment info (Required)
   ----------Python Info----------
   Version      : 3.6.4
   Compiler     : GCC 7.2.0
   Build        : ('default', 'Mar 13 2018 01:15:57')
   Arch         : ('64bit', '')
   ------------Pip Info-----------
   Version      : 9.0.1
   Directory    : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip
   ----------MXNet Info-----------
   Version      : 1.1.0
   Directory    : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet
   Commit Hash   : 07a83a0325a3d782513a04f47d711710972cb144
   ----------System Info----------
   Platform     : Linux-4.4.0-1055-aws-x86_64-with-debian-stretch-sid
   system       : Linux
   node         : ip-172-31-35-190
   release      : 4.4.0-1055-aws
   version      : #64-Ubuntu SMP Thu Apr 5 17:06:36 UTC 2018
   ----------Hardware Info----------
   machine      : x86_64
   processor    : x86_64
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0017 sec, LOAD: 0.5422 sec.
   Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.2314 sec, LOAD: 0.0510 sec.
   Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0396 sec, LOAD: 0.1310 sec.
   Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0176 sec, LOAD: 0.0979 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0023 sec, LOAD: 0.6010 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0096 sec, LOAD: 0.0716 sec.
   
   Package used (Python/R/Scala/Julia):
   I am using python.
   
   ## Error Message:
   ```
   Storage type fallback detected:
   operator = _backward_square
   input storage types = [row_sparse, row_sparse, ]
   output storage types = [row_sparse, ]
   params = {}
   context.dev_mask = gpu
   The operator with default storage type will be dispatched for execution. You're seeing this warning message because the operator above is unable to process the given ndarrays with specified storage types, context and parameter. Temporary dense ndarrays are generated in order to execute the operator. You can set environment variable MXNET_STORAGE_FALLBACK_LOG_VERBOSE to 0 to suppress this warning.
   ```
   
   ## Minimum reproducible example
   This is a similar factorization machines model
   ```
   import numpy as np, scipy.sparse as ss
   import mxnet as mx
   
   import logging, imp
   imp.reload(logging)
   logging.basicConfig(level=logging.INFO)
   
   data = ss.csr_matrix(np.random.rand(100,1000), dtype='float32')
   label = np.random.rand(100,1)>0.5
   data_iter = mx.io.NDArrayIter(
       mx.nd.sparse.csr_matrix(data),
       label,
       batch_size=100,last_batch_handle='discard')
   
   w0 = mx.sym.var('w0_bias', shape=(1,), wd_mult=0.,)
   w  = mx.sym.var('w_weight', shape=(1000, 1), stype='row_sparse', wd_mult=1e-10)
   V  = mx.sym.var('V_weight', shape=(1000, 2), stype='row_sparse', wd_mult=1.)
   X  = mx.sym.var('data', stype='csr')
   fm = mx.sym.broadcast_add(
       mx.sym.sparse.dot(X, w)
       + 0.5 * mx.sym.sparse.dot(X, V).square().sum(axis=1, keepdims=True)
       - 0.5 * mx.sym.sparse.dot(X.square(), V.square()).sum(axis=1, keepdims=True),
       w0,
       name='fm',
   )
   obj = mx.sym.LogisticRegressionOutput(fm, mx.sym.var('softmax_label'))
   
   mod = mx.mod.Module(obj, context=mx.gpu())
   mod.fit(data_iter, num_epoch=10)
   np.fabs(mod.predict(data_iter).asnumpy()-label).mean()
   ```
   
   ## Steps to reproduce
   1. run in environment mxnet_p36
   
   ## What have you tried to solve it?
   1. use the given example in
   
   https://github.com/apache/incubator-mxnet/blob/14206978f461364c53aaf1c787e2f268e2a94b00/example/sparse/factorization_machine/model.py#L38
   

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