gyshi commented on a change in pull request #15973: Numpy . implement numpy op
exp2 with tvm
URL: https://github.com/apache/incubator-mxnet/pull/15973#discussion_r323523342
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -1744,6 +1747,52 @@ def test_indexing_mode(sampler, set_size, samples_size,
replace, weight=None):
test_indexing_mode(test_choice_weighted, num_classes, num_classes
// 2, replace, weight)
+@with_seed()
+@use_np
+def test_np_exp2():
+ if _features.is_enabled("TVM_OP"):
+ class Testexp2(HybridBlock):
+ def __init__(self):
+ super(Testexp2, self).__init__()
+
+ def hybrid_forward(self, F, x, *args, **kwargs):
+ return F.np.exp2(x)
+
+ shapes = [
+ (),
+ (2,),
+ (2, 1, 2),
+ (2, 0, 2),
+ (1, 2, 3, 4, 5),
+ (6, 6, 6, 6, 6),
+ ]
+ dtypes = ['int8', 'int32', 'int64', 'float32', 'float64']
+
+ for hybridize in [True, False]:
+ for shape in shapes:
+ for dtype in dtypes:
+ test_exp2 = Testexp2()
+ if hybridize:
+ test_exp2.hybridize()
+ x = rand_ndarray(shape=shape, dtype=dtype).as_np_ndarray()
+ x.attach_grad()
+ np_out = _np.exp2(x.asnumpy())
+ with mx.autograd.record():
+ mx_out = test_exp2(x)
+ mx_out.backward()
+ log2 = 0.6931471805599453
Review comment:
thx, i will resolved
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