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new 9c2c5d4 [numpy] Fix mean, prod with input of empty array (#18286)
9c2c5d4 is described below
commit 9c2c5d45a6c838bd8348ce43a6168cf77d5c7125
Author: Yiyan66 <[email protected]>
AuthorDate: Mon May 25 00:14:58 2020 +0800
[numpy] Fix mean, prod with input of empty array (#18286)
* prod
* mean
* nan
* sanity
* change kernel
* include
Co-authored-by: Ubuntu <[email protected]>
---
src/operator/numpy/np_broadcast_reduce_op.h | 39 ++++++++++++++++++++--
.../python/unittest/test_numpy_interoperability.py | 2 ++
2 files changed, 38 insertions(+), 3 deletions(-)
diff --git a/src/operator/numpy/np_broadcast_reduce_op.h
b/src/operator/numpy/np_broadcast_reduce_op.h
index d10e32a..6b59ac0 100644
--- a/src/operator/numpy/np_broadcast_reduce_op.h
+++ b/src/operator/numpy/np_broadcast_reduce_op.h
@@ -275,6 +275,16 @@ inline bool NeedSafeAcc(int itype, int otype) {
return safe_acc_hint && rule;
}
+namespace mxnet_op {
+struct set_to_nan {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(index_t i, DType *out) {
+ out[i] = DType(nanf(""));
+ }
+};
+
+} // namespace mxnet_op
+
void TVMOpReduce(const OpContext& ctx, const TBlob& input,
const dmlc::optional<mxnet::Tuple<int>>& axis,
const TBlob& output, const OpReqType req, const std::string&
reducer_name);
@@ -296,9 +306,32 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
if (outputs[0].shape_.Size() == 0) return;
if (inputs[0].shape_.Size() == 0 && outputs[0].shape_.Size() != 0) {
using namespace mxnet_op;
- MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
- Kernel<set_zero, xpu>::Launch(s, outputs[0].shape_.Size(),
outputs[0].dptr<DType>());
- });
+ if (normalize) {
+ LOG(WARNING) << "WARNING: Mean of empty slice.";
+ if (mxnet::common::is_float(outputs[0].type_flag_)) {
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel<set_to_nan, xpu>::Launch(s, outputs[0].shape_.Size(),
+ outputs[0].dptr<DType>());
+ });
+ } else {
+ LOG(WARNING) << "WARNING: nan is outside the range of"<<
+ "representable values of type 'int'";
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel<set_zero, xpu>::Launch(s, outputs[0].shape_.Size(),
+ outputs[0].dptr<DType>());
+ });
+ }
+ } else if (std::is_same<reducer, mshadow_op::sum>::value) {
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel<set_zero, xpu>::Launch(s, outputs[0].shape_.Size(),
+ outputs[0].dptr<DType>());
+ });
+ } else {
+ MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+ Kernel<set_one, xpu>::Launch(s, outputs[0].shape_.Size(),
+ outputs[0].dptr<DType>());
+ });
+ }
return;
}
CHECK_NE(req[0], kWriteInplace) << "Reduce does not support write in-place";
diff --git a/tests/python/unittest/test_numpy_interoperability.py
b/tests/python/unittest/test_numpy_interoperability.py
index 342372c..0060b73 100644
--- a/tests/python/unittest/test_numpy_interoperability.py
+++ b/tests/python/unittest/test_numpy_interoperability.py
@@ -1105,6 +1105,7 @@ def _add_workload_mean(array_pool):
OpArgMngr.add_workload('mean', array_pool['4x1'])
OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]))
+ OpArgMngr.add_workload('mean', np.array([]).reshape(2,0,0))
OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=0)
OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=1)
@@ -1139,6 +1140,7 @@ def _add_workload_atleast_nd():
def _add_workload_prod(array_pool):
OpArgMngr.add_workload('prod', array_pool['4x1'])
+ OpArgMngr.add_workload('prod', np.array([]).reshape(2,0,0))
def _add_workload_product(array_pool):
