ChaiBapchya commented on issue #17444: [Large Tensor] Add LT support for NN optimizers and 1 activation function URL: https://github.com/apache/incubator-mxnet/pull/17444#issuecomment-580917968 @mxnet-label-bot add [pr-awaiting-review] @apeforest > @ChaiBapchya can you paste the tests run log of opperf indicating they run fine w/o giving SIGSEGV ``` >>> import mxnet as mx >>> mx.nd.signum_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), mom=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ 2.2022064 0.7840038 1.0334405 ... 0.18898012 -0.5907004 -1.4777215 ] <NDArray 4294967297 @cpu(0)> >>> mx.nd.signsgd_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ 0.15278001 1.7198559 0.14636855 ... 0.3357248 -0.22160508 1.5340825 ] <NDArray 4294967297 @cpu(0)> >>> mx.nd.sgd_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ 1.6252067 0.22516885 0.00959079 ... -0.688654 0.6969211 0.00631838] <NDArray 4294967297 @cpu(0)> >>> mx.nd.sgd_mom_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), mom=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ 0.9833377 -0.75289315 0.58504266 ... -1.0496317 -0.08228261 -1.7657199 ] <NDArray 4294967297 @cpu(0)> >>> mx.nd.rmspropalex_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), n=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01, g=mx.nd.random_normal(shape=(2**32 + 1)), delta=mx.nd.random_normal(shape=(2**32 + 1))) [2.5003266 nan nan ... nan nan 0.13144751] <NDArray 4294967297 @cpu(0)> >>> mx.nd.mp_sgd_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), weight32=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ 1.1050267 0.6508057 0.13951734 ... -0.73946345 0.55659974 1.9047947 ] <NDArray 4294967297 @cpu(0)> >>> mx.nd.mp_sgd_mom_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), mom=mx.nd.random_normal(shape=(2**32 + 1)), weight32=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ 0.8880665 -1.852293 1.0043188 ... -0.5858472 0.554819 0.26844773] <NDArray 4294967297 @cpu(0)> >>> mx.nd.ftml_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), d=mx.nd.random_normal(shape=(2**32 + 1)), v=mx.nd.random_normal(shape=(2**32 + 1)), z=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01, t=1) [ 0.05790505 -0.819279 nan ... nan nan nan] <NDArray 4294967297 @cpu(0)> >>> mx.nd.adam_update(weight=mx.nd.random_normal(shape=(2**32 + 1)), grad=mx.nd.random_normal(shape=(2**32 + 1)), mean=mx.nd.random_normal(shape=(2**32 + 1)), var=mx.nd.random_normal(shape=(2**32 + 1)), lr=.01) [ nan -1.8923444 nan ... 1.6588118 nan nan] <NDArray 4294967297 @cpu(0)> ``` Previously they all used to give SIGSEGV, now they don't @access2rohit
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