diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
index 99e35f37844d..7a5cf37403b4 100644
--- a/python/mxnet/ndarray/numpy/_op.py
+++ b/python/mxnet/ndarray/numpy/_op.py
@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -2828,3 +2828,71 @@ def blackman(M, dtype=_np.float32, ctx=None):
     if ctx is None:
         ctx = current_context()
     return _npi.blackman(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.ndarray.numpy')
+def flip(m, axis=None, out=None):
+    r"""
+    flip(m, axis=None, out=None)
+
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : ndarray or scalar
+        Input array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to flip over. The default,
+        axis=None, will flip over all of the axes of the input array.
+        If axis is negative it counts from the last to the first axis.
+
+        If axis is a tuple of ints, flipping is performed on all of the axes
+        specified in the tuple.
+    out : ndarray or scalar, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+
+    Examples
+    --------
+    >>> A = np.arange(8).reshape((2,2,2))
+    >>> A
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.flip(A, 0)
+    array([[[4, 5],
+            [6, 7]],
+           [[0, 1],
+            [2, 3]]])
+    >>> np.flip(A, 1)
+    array([[[2, 3],
+            [0, 1]],
+           [[6, 7],
+            [4, 5]]])
+    >>> np.flip(A)
+    array([[[7, 6],
+            [5, 4]],
+           [[3, 2],
+            [1, 0]]])
+    >>> np.flip(A, (0, 2))
+    array([[[5, 4],
+            [7, 6]],
+           [[1, 0],
+            [3, 2]]])
+    """
+    from ...numpy import ndarray
+    if isinstance(m, numeric_types):
+        return _np.flip(m, axis)
+    elif isinstance(m, ndarray):
+        return _npi.flip(m, axis, out=out)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(m))))
diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
index 801538557c5d..5b387601f59d 100644
--- a/python/mxnet/numpy/multiarray.py
+++ b/python/mxnet/numpy/multiarray.py
@@ -53,7 +53,7 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
-           'copysign', 'ravel', 'hanning', 'hamming', 'blackman']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -4367,3 +4367,65 @@ def blackman(M, dtype=_np.float32, ctx=None):
     >>> plt.show()
     """
     return _mx_nd_np.blackman(M, dtype=dtype, ctx=ctx)
+
+
+@set_module('mxnet.numpy')
+def flip(m, axis=None, out=None):
+    r"""
+    flip(m, axis=None, out=None)
+
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : ndarray or scalar
+        Input array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to flip over. The default,
+        axis=None, will flip over all of the axes of the input array.
+        If axis is negative it counts from the last to the first axis.
+
+        If axis is a tuple of ints, flipping is performed on all of the axes
+        specified in the tuple.
+    out : ndarray or scalar, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+
+    Examples
+    --------
+    >>> A = np.arange(8).reshape((2,2,2))
+    >>> A
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.flip(A, 0)
+    array([[[4, 5],
+            [6, 7]],
+           [[0, 1],
+            [2, 3]]])
+    >>> np.flip(A, 1)
+    array([[[2, 3],
+            [0, 1]],
+           [[6, 7],
+            [4, 5]]])
+    >>> np.flip(A)
+    array([[[7, 6],
+            [5, 4]],
+           [[3, 2],
+            [1, 0]]])
+    >>> np.flip(A, (0, 2))
+    array([[[5, 4],
+            [7, 6]],
+           [[1, 0],
+            [3, 2]]])
+    """
+    return _mx_nd_np.flip(m, axis, out=out)
diff --git a/python/mxnet/symbol/numpy/_symbol.py b/python/mxnet/symbol/numpy/_symbol.py
index f39d201638cb..dc2c84da167d 100644
--- a/python/mxnet/symbol/numpy/_symbol.py
+++ b/python/mxnet/symbol/numpy/_symbol.py
@@ -36,7 +36,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
-           'ravel', 'hanning', 'hamming', 'blackman']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip']
 
 
 def _num_outputs(sym):
@@ -3091,4 +3091,42 @@ def blackman(M, dtype=_np.float32, ctx=None):
     return _npi.blackman(M, dtype=dtype, ctx=ctx)
 
 
+@set_module('mxnet.symbol.numpy')
+def flip(m, axis=None, out=None):
+    r"""
+    flip(m, axis=None, out=None)
+
+    Reverse the order of elements in an array along the given axis.
+
+    The shape of the array is preserved, but the elements are reordered.
+
+    Parameters
+    ----------
+    m : _Symbol or scalar
+        Input array.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which to flip over. The default,
+        axis=None, will flip over all of the axes of the input array.
+        If axis is negative it counts from the last to the first axis.
+
+        If axis is a tuple of ints, flipping is performed on all of the axes
+        specified in the tuple.
+    out : _Symbol or scalar, optional
+        Alternative output array in which to place the result. It must have
+        the same shape and type as the expected output.
+
+    Returns
+    -------
+    out : _Symbol or scalar
+        A view of `m` with the entries of axis reversed.  Since a view is
+        returned, this operation is done in constant time.
+    """
+    if isinstance(m, numeric_types):
+        return _np.flip(m, axis)
+    elif isinstance(m, _Symbol):
+        return _npi.flip(m, axis, out=out)
+    else:
+        raise TypeError('type {} not supported'.format(str(type(m))))
+
+
 _set_np_symbol_class(_Symbol)
diff --git a/src/operator/numpy/np_matrix_op-inl.h b/src/operator/numpy/np_matrix_op-inl.h
index 631196e3cd1a..5e25192d9298 100644
--- a/src/operator/numpy/np_matrix_op-inl.h
+++ b/src/operator/numpy/np_matrix_op-inl.h
@@ -297,6 +297,72 @@ void NumpyRollCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+struct FlipParam : public dmlc::Parameter<FlipParam> {
+  mxnet::Tuple<int> axis;
+  DMLC_DECLARE_PARAMETER(FlipParam) {
+      DMLC_DECLARE_FIELD(axis)
+          .describe("The axis which to flip elements.");
+  }
+};
+
+#define FLIP_MAX_DIM 10
+#define FLIP_MIN_DIM -1
+
+template<typename xpu>
+void NumpyFlipForwardImpl(const OpContext& ctx,
+                          const std::vector<TBlob>& inputs,
+                          const std::vector<TBlob>& outputs,
+                          const std::vector<index_t>& stride_,
+                          const std::vector<index_t>& trailing_,
+                          const index_t& flip_index);
+
+template<typename xpu>
+void NumpyFlipForward(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<TBlob>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<TBlob>& outputs) {
+  const FlipParam& param = nnvm::get<FlipParam>(attrs.parsed);
+  mxnet::Tuple<int> axistemp;
+  CHECK_EQ(inputs[0].type_flag_, outputs[0].type_flag_);
+  CHECK_LT(param.axis.ndim(), FLIP_MAX_DIM);
+  CHECK_GE(param.axis.ndim(), FLIP_MIN_DIM);
+  if (param.axis.ndim() == FLIP_MIN_DIM) {
+    if (inputs[0].shape_.ndim() == 0) {
+      mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+      MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+        mshadow::Copy(outputs[0].FlatTo1D<xpu, DType>(s), inputs[0].FlatTo1D<xpu, DType>(s), s);
+    });
+      return;
+    }
+    std::vector<int> temp;
+    for (int i = 0; i < inputs[0].shape_.ndim(); i++) {
+      temp.push_back(i);
+    }
+    axistemp.assign(temp.begin(), temp.end());
+  } else {
+    axistemp = param.axis;
+  }
+
+  const mxnet::TShape& ishape = inputs[0].shape_;
+  if (ishape.ProdShape(0, ishape.ndim()) == 0) {
+    return;  // zero shape
+  }
+  std::vector<index_t> stride_(axistemp.ndim());
+  std::vector<index_t>  trailing_(axistemp.ndim());
+  index_t flip_index = 0;
+  for (int axis : axistemp) {
+    CHECK_LT(axis, ishape.ndim());
+    stride_[flip_index] = ishape[axis];
+    trailing_[flip_index] = 1;
+    for (int i2 = axis + 1; i2 < ishape.ndim(); ++i2) {
+      trailing_[flip_index] *= ishape[i2];
+    }
+    flip_index++;
+  }
+  NumpyFlipForwardImpl<xpu>(ctx, inputs, outputs, stride_, trailing_, flip_index);
+}
+
 }  // namespace op
 }  // namespace mxnet
 
diff --git a/src/operator/numpy/np_matrix_op.cc b/src/operator/numpy/np_matrix_op.cc
index 45e4c3f2d5b4..96a10561be28 100644
--- a/src/operator/numpy/np_matrix_op.cc
+++ b/src/operator/numpy/np_matrix_op.cc
@@ -565,5 +565,52 @@ NNVM_REGISTER_OP(_np_roll)
 .add_argument("data", "NDArray-or-Symbol", "Input ndarray")
 .add_arguments(NumpyRollParam::__FIELDS__());
 
+template<>
+void NumpyFlipForwardImpl<cpu>(const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<TBlob>& outputs,
+                               const std::vector<index_t>& stride_,
+                               const std::vector<index_t>& trailing_,
+                               const index_t& flip_index) {
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    mxnet_op::Kernel<reverse, cpu>::Launch(s, inputs[0].Size(), flip_index,
+      inputs[0].dptr<DType>(), outputs[0].dptr<DType>(),
+      stride_.data(), trailing_.data());
+  });
+}
+
+DMLC_REGISTER_PARAMETER(FlipParam);
+
+NNVM_REGISTER_OP(_npi_flip)
+.set_num_outputs(1)
+.set_num_inputs(1)
+.set_attr_parser(ParamParser<FlipParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+  return std::vector<std::string> {"data"};
+})
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest> {ResourceRequest::kTempSpace};
+})
+.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCompute>("FCompute<cpu>", NumpyFlipForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_npi_flip"})
+.add_argument("data", "NDArray-or-Symbol", "Input data array")
+.add_arguments(FlipParam::__FIELDS__());
+
+NNVM_REGISTER_OP(_backward_npi_flip)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<FlipParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+  return std::vector<ResourceRequest> {ResourceRequest::kTempSpace};
+})
+.set_attr<FCompute>("FCompute<cpu>", NumpyFlipForward<cpu>);
+
 }  // namespace op
 }  // namespace mxnet
diff --git a/src/operator/numpy/np_matrix_op.cu b/src/operator/numpy/np_matrix_op.cu
index ba49d320575c..caab4108b40e 100644
--- a/src/operator/numpy/np_matrix_op.cu
+++ b/src/operator/numpy/np_matrix_op.cu
@@ -56,5 +56,39 @@ NNVM_REGISTER_OP(_backward_np_vstack)
 NNVM_REGISTER_OP(_np_roll)
 .set_attr<FCompute>("FCompute<gpu>", NumpyRollCompute<gpu>);
 
+template<>
+void NumpyFlipForwardImpl<gpu>(const OpContext& ctx,
+                               const std::vector<TBlob>& inputs,
+                               const std::vector<TBlob>& outputs,
+                               const std::vector<index_t>& stride_,
+                               const std::vector<index_t>& trailing_,
+                               const index_t& flip_index) {
+  mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+  mshadow::Tensor<gpu, 1, uint8_t> workspace =
+    ctx.requested[0].get_space_typed<gpu, 1, uint8_t>(
+      mshadow::Shape1(flip_index * sizeof(index_t) * 2), s);
+
+  auto stride_workspace = workspace.dptr_;
+  auto trailing_workspace = workspace.dptr_ + flip_index * sizeof(index_t);
+
+  cudaMemcpyAsync(stride_workspace, thrust::raw_pointer_cast(stride_.data()),
+                  stride_.size() * sizeof(index_t),
+                  cudaMemcpyHostToDevice, mshadow::Stream<gpu>::GetStream(s));
+  cudaMemcpyAsync(trailing_workspace, thrust::raw_pointer_cast(trailing_.data()),
+                  trailing_.size() * sizeof(index_t),
+                  cudaMemcpyHostToDevice, mshadow::Stream<gpu>::GetStream(s));
+
+  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+    mxnet_op::Kernel<reverse, gpu>::Launch(s, inputs[0].Size(), flip_index,
+      inputs[0].dptr<DType>(), outputs[0].dptr<DType>(),
+      reinterpret_cast<index_t*>(stride_workspace), reinterpret_cast<index_t*>(trailing_workspace));
+  });
+}
+
+NNVM_REGISTER_OP(_npi_flip)
+.set_attr<FCompute>("FCompute<gpu>", NumpyFlipForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_npi_flip)
+.set_attr<FCompute>("FCompute<gpu>", NumpyFlipForward<gpu>);
 }  // namespace op
 }  // namespace mxnet
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index ad877bab5b7d..95140a69ab45 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -30,6 +30,7 @@
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
 import platform
 
+
 @with_seed()
 @use_np
 def test_np_tensordot():
@@ -2307,6 +2308,46 @@ def hybrid_forward(self, F, x, *args, **kwargs):
                     assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_flip():
+    class TestFlip(HybridBlock):
+        def __init__(self, axis):
+            super(TestFlip, self).__init__()
+            self.axis = axis
+
+        def hybrid_forward(self, F, x):
+            return F.np.flip(x, self.axis)
+
+    shapes = [(1, 2, 3), (1, 0), ()]
+    types = ['int32', 'int64', 'float16', 'float32', 'float64']
+    for hybridize in [True, False]:
+        for oneType in types:
+            rtol, atol=1e-3, 1e-5
+            for shape in shapes:
+                axis = random.randint(-1, len(shape) - 1)
+                if axis is -1:
+                    axis = None
+                test_flip = TestFlip(axis)
+                if hybridize:
+                    test_flip.hybridize()
+                x = rand_ndarray(shape, dtype=oneType).as_np_ndarray()
+                x.attach_grad()
+                np_out = _np.flip(x.asnumpy(), axis)
+                with mx.autograd.record():
+                    mx_out = test_flip(x)
+                assert mx_out.shape == np_out.shape
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+                mx_out.backward()
+                np_backward = _np.ones(np_out.shape)
+                assert_almost_equal(x.grad.asnumpy(), np_backward, rtol=rtol, atol=atol)
+
+                # Test imperative once again
+                mx_out = np.flip(x, axis)
+                np_out = _np.flip(x.asnumpy(), axis)
+                assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()