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adding test for softmax operator for inputs with large magnitude #13328

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anirudh2290 merged 6 commits into from
Nov 22, 2018
Merged

adding test for softmax operator for inputs with large magnitude #13328

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d1caa76
adding test for mkldnn softmax operator for large negative inputs
Nov 20, 2018
ed390f8
asserting real output
Nov 20, 2018
d12bbcc
add test for non mkldnn case too
Nov 21, 2018
16e4e3e
adding more corner cases
Nov 21, 2018
09f57d2
replacing assert with assert_almost_equal
Nov 21, 2018
270778f
adding default context
Nov 21, 2018
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15 changes: 15 additions & 0 deletions tests/python/mkl/test_mkldnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -383,6 +383,21 @@ def check_fullyconnected_training(stype):
for stype in stypes:
check_fullyconnected_training(stype)

def test_softmax_with_large_inputs():
def softmax_forward(input_data, true_output):
data = mx.sym.Variable('data')
out1 = data.softmax(axis=1)
exec1 = out1.bind(mx.cpu(), args={'data': input_data})
exec1.forward()[0].wait_to_read()
ndarr = exec1.outputs[0][0][0][0]
nparr = ndarr.asnumpy()
assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5)

softmax_forward(mx.nd.array([[[[-1e30,-1e30]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[1e30,1e30]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[-3.4e38,-3.4e38]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[3.4e38,3.4e38]]]]), np.array([1.0,1.0]))

@with_seed()
def test_non_mkldnn_fcomputeex():
# test special case where MKLDNN formatted NDArray feeds into non-mkldnn fcomputeex operator
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14 changes: 14 additions & 0 deletions tests/python/unittest/test_operator.py
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Expand Up @@ -4449,6 +4449,20 @@ def test_log_softmax():
check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)])
check_numeric_gradient(sym, [data], rtol=0.05, atol=1e-3)

def test_softmax_with_large_inputs():
def softmax_forward(input_data, true_output):
data = mx.sym.Variable('data')
out1 = data.softmax(axis=1)
exec1 = out1.bind(default_context(), args={'data': input_data})
exec1.forward()[0].wait_to_read()
ndarr = exec1.outputs[0][0][0][0]
nparr = ndarr.asnumpy()
assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5)

softmax_forward(mx.nd.array([[[[-1e30,-1e30]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[1e30,1e30]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[-3.4e38,-3.4e38]]]]), np.array([1.0,1.0]))
softmax_forward(mx.nd.array([[[[3.4e38,3.4e38]]]]), np.array([1.0,1.0]))

@with_seed()
def test_pick():
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