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taolv pushed a commit to branch v1.7.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.7.x by this push:
new 802e5af [v1.7.x] Backport PRs of numpy features (#18653)
802e5af is described below
commit 802e5af5d0133af88c3c166f6e4fe99508eff42b
Author: Xingjian Shi <xsh...@connect.ust.hk>
AuthorDate: Wed Jul 1 22:41:24 2020 -0700
[v1.7.x] Backport PRs of numpy features (#18653)
* add zero grad for npi_unique (#18080)
* fix np.clip scalar input case (#17788)
* fix true_divide (#18393)
Co-authored-by: Hao Jin <hjjn.a...@gmail.com>
Co-authored-by: Xi Wang <xid...@gmail.com>
---
python/mxnet/numpy/multiarray.py | 5 +++++
src/operator/numpy/np_true_divide-inl.h | 16 ++++++++--------
src/operator/numpy/np_unique_op.cc | 1 +
tests/python/unittest/test_numpy_op.py | 12 +++++++++++-
4 files changed, 25 insertions(+), 9 deletions(-)
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index fceaaf3..9a803d4 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -6174,6 +6174,11 @@ def clip(a, a_min, a_max, out=None):
>>> np.clip(a, 3, 6, out=a)
array([3., 3., 3., 3., 4., 5., 6., 6., 6., 6.], dtype=float32)
"""
+ from numbers import Number
+ if isinstance(a, Number):
+ # In case input is a scalar, the computation would fall back to native
numpy.
+ # The value returned would be a python scalar.
+ return _np.clip(a, a_min, a_max, out=None)
return _mx_nd_np.clip(a, a_min, a_max, out=out)
diff --git a/src/operator/numpy/np_true_divide-inl.h
b/src/operator/numpy/np_true_divide-inl.h
index 0bc60a0..be2ce51 100644
--- a/src/operator/numpy/np_true_divide-inl.h
+++ b/src/operator/numpy/np_true_divide-inl.h
@@ -121,7 +121,7 @@ void TrueDivideElemwiseCompute(const nnvm::NodeAttrs &attrs,
// Case when types of the 2 input tensors are different
if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
// both lhs and rhs are float types, output type is the more precise one
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
} else if (common::is_float(lhs.type_flag_) ||
common::is_float(rhs.type_flag_)) {
// one is float type, the other is integer type, the output type should
be the same as float
CHECK_EQ(out.type_flag_,
@@ -150,14 +150,14 @@ void TrueDivideElemwiseCompute(const nnvm::NodeAttrs
&attrs,
}
} else {
// lhs is integer type, rhs is integer type, output type should be float
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
}
#else
// Windows case: using temp space for casting the type
// Case when types of the 2 input tensors are different
if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) {
// both lhs and rhs are float types, output type is the more precise one
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
} else if (common::is_float(lhs.type_flag_) ||
common::is_float(rhs.type_flag_)) {
// lhs is float type, rhs is integer type, the output type should be the
same as lhs
CHECK_EQ(out.type_flag_,
@@ -187,7 +187,7 @@ void TrueDivideElemwiseCompute(const nnvm::NodeAttrs &attrs,
}
} else {
// lhs is integer type, rhs is integer type, output type should be float
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
}
#endif
}
@@ -241,7 +241,7 @@ void TrueDivideBroadcastCompute(const nnvm::NodeAttrs&
attrs,
} else {
if (common::is_float(lhs.type_flag_) &&
common::is_float(rhs.type_flag_)) {
// lhs and rhs have different float types, the output is the more
precise one
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
} else if (common::is_float(lhs.type_flag_) ||
common::is_float(rhs.type_flag_)) {
// one of lhs and rhs is float, the output is the same type as the
float one
if (common::is_float(lhs.type_flag_)) {
@@ -269,7 +269,7 @@ void TrueDivideBroadcastCompute(const nnvm::NodeAttrs&
attrs,
}
} else {
// lhs and rhs have different integer types, the output is float type
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
}
}
});
@@ -302,7 +302,7 @@ void TrueDivideBroadcastCompute(const nnvm::NodeAttrs&
attrs,
} else {
if (common::is_float(lhs.type_flag_) &&
common::is_float(rhs.type_flag_)) {
// lhs and rhs have different float types, the output is the more
precise one
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
} else if (common::is_float(lhs.type_flag_) ||
common::is_float(rhs.type_flag_)) {
// one of lhs and rhs is float, the output is the same type as the
float one
TBlob temp_tblob;
@@ -333,7 +333,7 @@ void TrueDivideBroadcastCompute(const nnvm::NodeAttrs&
attrs,
}
} else {
// lhs and rhs have different integer types, the output is float type
- LOG(ERROR) << "not implemented yet...";
+ LOG(FATAL) << "not implemented yet...";
}
}
#endif
diff --git a/src/operator/numpy/np_unique_op.cc
b/src/operator/numpy/np_unique_op.cc
index 2f57733..7a299cd 100644
--- a/src/operator/numpy/np_unique_op.cc
+++ b/src/operator/numpy/np_unique_op.cc
@@ -375,6 +375,7 @@ NNVM_REGISTER_OP(_npi_unique)
[](const NodeAttrs& attrs) {
return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
})
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
.add_argument("data", "NDArray-or-Symbol", "The input array")
.add_arguments(NumpyUniqueParam::__FIELDS__());
diff --git a/tests/python/unittest/test_numpy_op.py
b/tests/python/unittest/test_numpy_op.py
index 15e9bd4..0f35dec 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -3644,6 +3644,16 @@ def test_np_clip():
def hybrid_forward(self, F, x):
return x.clip(self._a_min, self._a_max)
+
+ # Test scalar case
+ for _, a_min, a_max, throw_exception in workloads:
+ a = _np.random.uniform() # A scalar
+ if throw_exception:
+ # No need to test the exception case here.
+ continue
+ mx_ret = np.clip(a, a_min, a_max)
+ np_ret = _np.clip(a, a_min, a_max)
+ assert_almost_equal(mx_ret, np_ret, atol=1e-4, rtol=1e-3,
use_broadcast=False)
for shape, a_min, a_max, throw_exception in workloads:
for dtype in dtypes:
@@ -6549,7 +6559,7 @@ def test_np_unique():
((5, 3, 4), True, True, True, 1),
]
for dtype in ['float32', 'float64', 'int8', 'uint8', 'int32', 'int64']:
- for hybridize in [False]:
+ for hybridize in [False, True]:
for config in configs:
test_unique = TestUnique(*config[1:])
if hybridize:
