sandeep-krishnamurthy closed pull request #12027: [MXNET-768] Partially enable
flaky test for norm operator
URL: https://github.com/apache/incubator-mxnet/pull/12027
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diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index 90e85d123d5..53a726e7867 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -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)
@@ -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)
def test_layer_norm():
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