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[MXNET-768] Partially enable flaky test for norm operator #12027

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Aug 15, 2018
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[MXNET-768] Partially enable flaky test for norm operator #12027

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32 changes: 17 additions & 15 deletions tests/python/unittest/test_operator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3107,7 +3107,7 @@ def l2norm(input_data, axis=0, keepdims=True):
for order in [1, 2]:
for dtype in [np.float16, np.float32, np.float64]:
in_data = np.random.uniform(-1, 1, in_shape).astype(dtype)
in_data[abs(in_data) < epsilon] = epsilon
in_data[abs(in_data) < epsilon] = 2 * epsilon
for i in range(in_data_dim):
norm_sym = mx.symbol.norm(data=data, ord=order, axis=i, keepdims=True)
npy_out = l1norm(in_data, i) if order is 1 else l2norm(in_data, i)
Expand All @@ -3121,20 +3121,22 @@ def l2norm(input_data, axis=0, keepdims=True):
atol=1e-2 if dtype is np.float16 else 1e-5, ctx=ctx)
# Disable numeric gradient https://github.com/apache/incubator-mxnet/issues/11509
# # check gradient
# check_numeric_gradient(norm_sym, [in_data], numeric_eps=epsilon, rtol=1e-2, atol=1e-3)
# if i < in_data_dim-1:
# norm_sym = mx.symbol.norm(data=data, ord=order, axis=(i, i+1), keepdims=True)
# npy_out = l1norm(in_data, (i, i+1)) if order is 1 else l2norm(in_data, (i, i+1))
# npy_out_backward = np.sign(in_data) if order is 1 else in_data/npy_out
# check_symbolic_forward(norm_sym, [in_data], [npy_out],
# rtol=1e-2 if dtype is np.float16 else 1e-5,
# atol=1e-2 if dtype is np.float16 else 1e-5, ctx=ctx)
# check_symbolic_backward(norm_sym, [in_data], [np.ones(npy_out.shape)],
# [npy_out_backward],
# rtol=1e-2 if dtype is np.float16 else 1e-5,
# atol=1e-2 if dtype is np.float16 else 1e-5, ctx=ctx)
# # check gradient
# check_numeric_gradient(norm_sym, [in_data], numeric_eps=epsilon, rtol=1e-2, atol=1e-3)
# if dtype is not np.float16:
# check_numeric_gradient(norm_sym, [in_data], numeric_eps=epsilon, rtol=1e-1, atol=1e-3)
if i < in_data_dim-1:
norm_sym = mx.symbol.norm(data=data, ord=order, axis=(i, i+1), keepdims=True)
npy_out = l1norm(in_data, (i, i+1)) if order is 1 else l2norm(in_data, (i, i+1))
npy_out_backward = np.sign(in_data) if order is 1 else in_data/npy_out
check_symbolic_forward(norm_sym, [in_data], [npy_out],
rtol=1e-2 if dtype is np.float16 else 1e-5,
atol=1e-2 if dtype is np.float16 else 1e-5, ctx=ctx)
check_symbolic_backward(norm_sym, [in_data], [np.ones(npy_out.shape)],
[npy_out_backward],
rtol=1e-2 if dtype is np.float16 else 1e-5,
atol=1e-2 if dtype is np.float16 else 1e-5, ctx=ctx)
# # check gradient
# if dtype is not np.float16:
# check_numeric_gradient(norm_sym, [in_data], numeric_eps=epsilon, rtol=1e-1, atol=1e-3)
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Please add Github link here to track enabling the numeric_gradient.

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it is there in line 3122



def test_layer_norm():
Expand Down