leezu edited a comment on issue #11938: l2_normalization for fp16 got 0.0 when data is very large URL: https://github.com/apache/incubator-mxnet/issues/11938#issuecomment-409411171 @TccccD the reason is that `mx.symbol.norm` uses a numerically stable algorithm to compute the 2-norm (https://github.com/apache/incubator-mxnet/pull/11573), whereas the L2Normalization is prone to under or overflow. The L2Normalization should be fixed to use the same implementation as the norm Op. Below a shorter example of the problem ``` In [15]: a = mx.nd.random.uniform(-5, 5, (512,100000), ctx=mx.gpu(0), dtype='float16') In [16]: mx.nd.L2Normalization(a) Out[16]: [[ 0. -0. 0. ..., 0. -0. 0.] [-0. 0. -0. ..., 0. 0. 0.] [ 0. -0. -0. ..., 0. 0. -0.] ..., [-0. 0. -0. ..., 0. 0. -0.] [ 0. -0. 0. ..., 0. -0. -0.] [-0. -0. 0. ..., -0. -0. -0.]] <NDArray 512x100000 @gpu(0)> In [17]: a / mx.nd.norm(a, axis=1, keepdims=True) Out[17]: [[ 2.19726562e-03 -3.61824036e-03 1.11007690e-03 ..., 3.14950943e-04 -4.92572784e-04 3.10516357e-03] [ -4.07028198e-03 4.61578369e-03 -4.51278687e-03 ..., 2.33650208e-03 5.40542603e-03 3.78608704e-03] [ 5.27572632e-03 -1.81293488e-03 -1.17683411e-03 ..., 1.86920166e-03 4.87518311e-03 -3.04412842e-03] ..., [ -4.39834595e-03 3.74794006e-04 -4.21905518e-03 ..., 1.11007690e-03 3.81278992e-03 -3.80134583e-03] [ 7.90953636e-05 -5.31387329e-03 4.95910645e-03 ..., 3.52859497e-03 -2.10952759e-03 -4.76837158e-04] [ -4.53186035e-03 -3.03459167e-03 2.37083435e-03 ..., -3.93295288e-03 -4.21524048e-03 -5.36727905e-03]] <NDArray 512x100000 @gpu(0)> ```
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