numpy operator around

* change the name of argument

* add doc in three files and fix some bug

* change the data type in .h and add test function

    cancel optimization when abs(temp) < 0.5
    modify test on cpu and add test on gpu
    do not support float16
    edit testcase on gpu and add 'Do not support float16 on doc'

* edit doc: support scalar

* adjust the format

* add license

* fix format error

* delete gpu test

* move around to np_elemwise_unary_op_basic

python/mxnet/ndarray/numpy/_op.py

@@ -33,7 +33,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'around']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2363,3 +2363,60 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     0.2025
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def around(x, decimals=0, out=None, **kwargs):
+    r"""
+    around(x, decimals=0, out=None)
+
+    Evenly round to the given number of decimals.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    rounded_array : ndarray or scalar
+        An array of the same type as `x`, containing the rounded values.
+        A reference to the result is returned.
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    This function differs from the original numpy.prod in the following aspects:
+
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot support complex-valued number.
+
+    Examples
+    --------
+    >>> np.around([0.37, 1.64])
+    array([ 0.,  2.])
+    >>> np.around([0.37, 1.64], decimals=1)
+    array([ 0.4,  1.6])
+    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+    array([ 0.,  2.,  2.,  4.,  4.])
+    >>> np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
+    array([ 1,  2,  3, 11])
+    >>> np.around([1, 2, 3, 11], decimals=-1)
+    array([ 0,  0,  0, 10])
+    """
+    from ...numpy import ndarray
+    if isinstance(x, numeric_types):
+        return _np.around(x, decimals, **kwargs)
+    elif isinstance(x, ndarray):
+        return _npi.around(x, decimals, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))

python/mxnet/numpy/multiarray.py

@@ -52,7 +52,7 @@
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
-           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'around']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -3808,3 +3808,54 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=None):
     0.2025
     """
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
+
+
+def around(x, decimals=0, out=None, **kwargs):
+    r"""
+    around(x, decimals=0, out=None)
+
+    Evenly round to the given number of decimals.
+
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    rounded_array : ndarray or scalar
+        An array of the same type as `x`, containing the rounded values.
+        A reference to the result is returned.
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    This function differs from the original numpy.prod in the following aspects:
+
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot support complex-valued number.
+
+    Examples
+    --------
+    >>> np.around([0.37, 1.64])
+    array([ 0.,  2.])
+    >>> np.around([0.37, 1.64], decimals=1)
+    array([ 0.4,  1.6])
+    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
+    array([ 0.,  2.,  2.,  4.,  4.])
+    >>> np.around([1, 2, 3, 11], decimals=1) # ndarray of ints is returned
+    array([ 1,  2,  3, 11])
+    >>> np.around([1, 2, 3, 11], decimals=-1)
+    array([ 0,  0,  0, 10])
+    """
+    return _mx_nd_np.around(x, decimals, out=out, **kwargs)

python/mxnet/symbol/numpy/_symbol.py

@@ -35,7 +35,7 @@
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
-           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var']
+           'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'around']
 
 
 def _num_outputs(sym):
@@ -2678,4 +2678,47 @@ def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):  # pylint:
     return _npi.var(a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims, out=out)
 
 
+@set_module('mxnet.symbol.numpy')
+def around(x, decimals=0, out=None, **kwargs):
+    r"""
+    around(x, decimals=0, out=None)
+
+    Evenly round to the given number of decimals.
+    Parameters
+    ----------
+    x : _Symbol or scalar
+        Input data.
+    decimals : int, optional
+        Number of decimal places to round to (default: 0).  If
+        decimals is negative, it specifies the number of positions to
+        the left of the decimal point.
+    out : _Symbol, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    rounded_array : _Symbol or scalar
+        An array of the same type as `x`, containing the rounded values.
+        A reference to the result is returned.
+
+    Notes
+    -----
+    For values exactly halfway between rounded decimal values, NumPy
+    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+    -0.5 and 0.5 round to 0.0, etc.
+
+    This function differs from the original numpy.prod in the following aspects:
+
+        - Cannot cast type automatically. Dtype of `out` must be same as the expected one.
+        - Cannot support complex-valued number.
+    """
+    if isinstance(x, numeric_types):
+        return _np.around(x, decimals, **kwargs)
+    elif isinstance(x, _Symbol):
+        return _npi.around(x, decimals, out=out, **kwargs)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(x))))
+
+
 _set_np_symbol_class(_Symbol)

src/operator/numpy/np_elemwise_unary_op_basic.cc

@@ -362,5 +362,43 @@ computed element-wise.
 )code" ADD_FILELINE)
 .set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{ "_backward_arctanh" });
 
+inline bool AroundOpType(const nnvm::NodeAttrs& attrs,
+                         std::vector<int>* in_attrs,
+                         std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+
+  if (in_attrs->at(0) == mshadow::kFloat16) {
+    std::ostringstream os;
+    os << "Do not support `float16` as input.\n";
+    throw ::mxnet::op::InferTypeError(os.str(), 0);
+  }
+  return out_attrs->at(0) != -1;
+}
+
+DMLC_REGISTER_PARAMETER(AroundParam);
+
+NNVM_REGISTER_OP(_npi_around)
+.set_attr_parser(ParamParser<AroundParam>)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"x"};
+  })
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", AroundOpType)
+.set_attr<FCompute>("FCompute<cpu>", AroundOpForward<cpu>)
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+  [](const NodeAttrs& attrs){
+    return std::vector<std::pair<int, int> >{{0, 0}};
+  })
+.add_argument("x", "NDArray-or-Symbol", "Input ndarray")
+.add_arguments(AroundParam::__FIELDS__())
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes);
+
 }  // namespace op
 }  // namespace mxnet

src/operator/numpy/np_elemwise_unary_op_basic.cu

@@ -106,5 +106,8 @@ MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arccosh, mshadow_op::arccosh);
 
 MXNET_OPERATOR_REGISTER_NUMPY_UNARY_GPU(_npi_arctanh, mshadow_op::arctanh);
 
+NNVM_REGISTER_OP(_npi_around)
+.set_attr<FCompute>("FCompute<gpu>", AroundOpForward<gpu>);
+
 }  // namespace op
 }  // namespace mxnet

src/operator/tensor/elemwise_unary_op.h

@@ -560,6 +560,101 @@ struct ReshapeLikeParam : public dmlc::Parameter<ReshapeLikeParam> {
   }
 };
 
+struct AroundParam : public dmlc::Parameter<AroundParam> {
+  int decimals;
+  DMLC_DECLARE_PARAMETER(AroundParam) {
+    DMLC_DECLARE_FIELD(decimals)
+      .set_default(0)
+      .describe("Number of decimal places to round to.");
+  }
+};
+
+template<int req>
+struct around_forwardint{
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
+                                  const int decimals) {
+    KERNEL_ASSIGN(out_data[i], req, in_data[i]);
+  }
+};
+
+template<int req>
+struct around_forward {
+  template<typename DType>
+  MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data,
+                                  const int decimals) {
+    int d = 0;
+    DType temp = in_data[i];
+    DType roundtemp;
+    while (d != decimals) {
+      if (decimals > 0) {
+        d++;
+        temp *= 10;
+      } else {
+        d--;
+        temp /= 10;
+      }
+    }
+    roundtemp = (DType)round(static_cast<double>(temp));
+    // If temp is x.5 and roundtemp is odd number, decrease or increase roundtemp by 1.
+    // For example, in numpy, around(0.5) should be 0 but in c, round(0.5) is 1.
+    if (roundtemp - temp == 0.5 && (static_cast<int>(roundtemp)) % 2 != 0) {
+      roundtemp -= 1;
+    } else if (temp - roundtemp == 0.5 && (static_cast<int>(roundtemp)) % 2 != 0) {
+      roundtemp += 1;
+    }
+    while (d != 0) {
+      if (roundtemp == 0) {
+        break;
+      }
+      if (decimals > 0) {
+        d--;
+        roundtemp /= 10;
+      } else {
+        d++;
+        roundtemp *= 10;
+      }
+    }
+    KERNEL_ASSIGN(out_data[i], req, roundtemp);
+  }
+};
+
+template<typename xpu>
+void AroundOpForward(const nnvm::NodeAttrs& attrs,
+                     const OpContext& ctx,
+                     const std::vector<TBlob>& inputs,
+                     const std::vector<OpReqType>& req,
+                     const std::vector<TBlob>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob& in_data = inputs[0];
+  const TBlob& out_data = outputs[0];
+  const AroundParam& param = nnvm::get<AroundParam>(attrs.parsed);
+  using namespace mxnet_op;
+  // if the type is uint8, int8, int32 or int64 and decimals is greater than 0
+  // we simply return the number back.
+  if (in_data.type_flag_ >= mshadow::kUint8 && in_data.type_flag_ <= mshadow::kInt64 \
+     && param.decimals > 0) {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<around_forwardint<req_type>, xpu>::Launch(
+          s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(),
+          param.decimals);
+      });
+    });
+  } else {
+    MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, {
+      MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
+        Kernel<around_forward<req_type>, xpu>::Launch(
+          s, out_data.Size(), out_data.dptr<DType>(), in_data.dptr<DType>(),
+          param.decimals);
+      });
+    });
+  }
+}
+
 /*! \brief Unary compute */
 #define MXNET_OPERATOR_REGISTER_UNARY(__name$)                      \
   NNVM_REGISTER_OP(__name$)                                         \

tests/python/gpu/test_operator_gpu.py

@@ -2345,7 +2345,7 @@ def test_arange_like_dtype():
         out = mod.forward(is_train=False)
         for v in out:
             assert v.dtype == t
-    
+
 
 if __name__ == '__main__':
     import nose

tests/python/unittest/test_numpy_op.py

@@ -1647,6 +1647,40 @@ def hybrid_forward(self, F, a):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_around():
+    class TestAround(HybridBlock):
+        def __init__(self, decimals):
+            super(TestAround, self).__init__()
+            # necessary initializations
+            self.decimals = decimals
+
+        def hybrid_forward(self, F, x):
+            return F.np.around(x, self.decimals)
+
+    shapes = [(), (1,), (1, 1), (1, 2, 3), (1, 0), (3, 0, 2)] # test_shapes, remember to include zero-dim shape and zero-size shapes
+    types = ['int32', 'int64', 'float32', 'double']
+    for hybridize in [True, False]:
+        for oneType in types:
+            rtol=1e-3
+            atol=1e-5
+            for shape in shapes:
+                for d in range(-10, 11):
+                    test_around = TestAround(d)
+                    if hybridize:
+                        test_around.hybridize()
+                    x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                    np_out = _np.around(x.asnumpy(), d)
+                    mx_out = test_around(x)
+                    assert mx_out.shape == np_out.shape
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+                    mx_out = np.around(x, d)
+                    np_out = _np.around(x.asnumpy(), d)
+                    assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)    
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()