[v1.7.x] Backport PRs of numpy features (apache#18653)

* add zero grad for npi_unique (apache#18080)

* fix np.clip scalar input case (apache#17788)

* fix true_divide (apache#18393)

Co-authored-by: Hao Jin <hjjn.amzn@gmail.com>
Co-authored-by: Xi Wang <xidulu@gmail.com>

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)
 
 

src/operator/numpy/np_true_divide-inl.h

@@ -124,7 +124,7 @@ 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_,
@@ -153,14 +153,14 @@ 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_,
@@ -190,7 +190,7 @@ 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
   }
@@ -244,7 +244,7 @@ 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_)) {
@@ -272,7 +272,7 @@ 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...";
         }
       }
     });
@@ -305,7 +305,7 @@ 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;
@@ -336,7 +336,7 @@ 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

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__());
 

tests/python/unittest/test_numpy_op.py

@@ -3704,6 +3704,16 @@ def __init__(self, a_min=None, a_max=None):
 
         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:
@@ -6605,7 +6615,7 @@ def hybrid_forward(self, F, a):
         ((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: