numpy compatible max min

src/operator/numpy/np_broadcast_reduce_op.h

@@ -64,6 +64,24 @@ struct NumpyReduceAxesParam : public dmlc::Parameter<NumpyReduceAxesParam> {
   }
 };
 
+struct NumpyReduceAxesNoDTypeParam : public dmlc::Parameter<NumpyReduceAxesNoDTypeParam> {
+  dmlc::optional<mxnet::Tuple<int>> axis;
+  bool keepdims;
+  dmlc::optional<double> initial;
+  DMLC_DECLARE_PARAMETER(NumpyReduceAxesNoDTypeParam) {
+    DMLC_DECLARE_FIELD(axis)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+      .describe("Axis or axes along which a sum is performed. The default, axis=None, will sum "
+                "all of the elements of the input array. If axis is negative it counts from the "
+                "last to the first axis.");
+    DMLC_DECLARE_FIELD(keepdims).set_default(false)
+      .describe("If this is set to `True`, the reduced axes are left "
+                "in the result as dimension with size one.");
+    DMLC_DECLARE_FIELD(initial).set_default(dmlc::optional<double>())
+      .describe("Starting value for the sum.");
+  }
+};
+
 inline TShape NumpyReduceAxesShapeImpl(const TShape& ishape,
                                        const dmlc::optional<mxnet::Tuple<int>>& axis,
                                        bool keepdims) {
@@ -152,6 +170,39 @@ inline bool NumpyReduceAxesShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(out_attrs->at(0));
 }
 
+inline bool NumpyReduceAxesNoDTypeShape(const nnvm::NodeAttrs& attrs,
+                                        std::vector<TShape> *in_attrs,
+                                        std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  if (!shape_is_known(in_attrs->at(0))) {
+    return false;
+  }
+  const NumpyReduceAxesNoDTypeParam& param = nnvm::get<NumpyReduceAxesNoDTypeParam>(attrs.parsed);
+  // check the case where the reduction axis should not be zero
+  bool is_all_reducded_axes_not_zero = true;
+  const TShape& ishape = (*in_attrs)[0];
+  if (param.axis.has_value()) {
+    const mxnet::Tuple<int>& axes = param.axis.value();
+    for (int i = 0; i < axes.ndim(); ++i) {
+      if (ishape[axes[i]] == 0) {
+        is_all_reducded_axes_not_zero = false;
+        break;
+      }
+    }
+  } else {
+    if (ishape.Size() == 0) {
+      // global reduction should excuted only when input have size more than 0
+      is_all_reducded_axes_not_zero = false;
+    }
+  }
+  CHECK(is_all_reducded_axes_not_zero)
+    << "zero-size array to reduction operation maximum which has no identity";
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0,
+                     NumpyReduceAxesShapeImpl((*in_attrs)[0], param.axis, param.keepdims));
+  return shape_is_known(out_attrs->at(0));
+}
+
 template<bool safe_acc_hint = false>
 inline bool NeedSafeAcc(int itype, int otype) {
   bool rule = (itype != otype) || (itype != mshadow::kFloat32 && itype != mshadow::kFloat64);
@@ -186,6 +237,30 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename reducer, typename OP = op::mshadow_op::identity>
+void NumpyReduceAxesNoDTypeCompute(const nnvm::NodeAttrs& attrs,
+                                   const OpContext& ctx,
+                                   const std::vector<TBlob>& inputs,
+                                   const std::vector<OpReqType>& req,
+                                   const std::vector<TBlob>& outputs) {
+  const NumpyReduceAxesNoDTypeParam& param = nnvm::get<NumpyReduceAxesNoDTypeParam>(attrs.parsed);
+  if (param.initial.has_value()) {
+    LOG(FATAL) << "initial is not supported yet";
+  }
+  if (inputs[0].shape_.Size() == 0U || outputs[0].shape_.Size() == 0U) return;  // zero-size tensor
+  if (param.axis.has_value() && param.axis.value().ndim() == 0) {
+    UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
+  }
+  TShape small;
+  if (param.keepdims) {
+    small = outputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(inputs[0].shape_, param.axis, true);
+  }
+  ReduceAxesComputeImpl<xpu, reducer, false, false, OP>(ctx, inputs, req, outputs, small);
+}
+
+
 template<typename xpu, bool normalize = false>
 inline void NumpyReduceAxesBackwardUseNone(const nnvm::NodeAttrs& attrs,
                                            const OpContext& ctx,
@@ -273,6 +348,24 @@ void NumpyBroadcastToBackward(const nnvm::NodeAttrs& attrs,
   }
 }
 
+template<typename xpu, typename OP>
+void NumpyReduceAxesNoDTypeBackward(const nnvm::NodeAttrs& attrs,
+                                    const OpContext& ctx,
+                                    const std::vector<TBlob>& inputs,
+                                    const std::vector<OpReqType>& req,
+                                    const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  const NumpyReduceAxesNoDTypeParam& param = nnvm::get<NumpyReduceAxesNoDTypeParam>(attrs.parsed);
+  TShape small;
+  if (param.keepdims) {
+    small = inputs[0].shape_;
+  } else {
+    small = NumpyReduceAxesShapeImpl(outputs[0].shape_, param.axis, true);
+  }
+  ReduceAxesBackwardUseInOutImpl<xpu, OP, false>(ctx, small, inputs, req, outputs);
+}
+
 }  // namespace op
 }  // namespace mxnet
 #endif  // MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_H_

src/operator/numpy/np_broadcast_reduce_op_value.cc

@@ -29,6 +29,7 @@ namespace mxnet {
 namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyReduceAxesParam);
+DMLC_REGISTER_PARAMETER(NumpyReduceAxesNoDTypeParam);
 
 inline bool NumpySumType(const nnvm::NodeAttrs& attrs,
                          std::vector<int> *in_attrs,
@@ -74,6 +75,71 @@ NNVM_REGISTER_OP(_backward_np_sum)
 .set_num_inputs(1)
 .set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesBackwardUseNone<cpu>);
 
+inline bool NumpyReduceAxesNoDTypeType(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));
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+NNVM_REGISTER_OP(_np_max)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesNoDTypeShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyReduceAxesNoDTypeType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    return std::vector<std::string>{"a"};
+  })
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyReduceAxesNoDTypeParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeCompute<cpu, mshadow::red::maximum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const NodeAttrs& attrs) {
+    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+  })
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_max"});
+
+NNVM_REGISTER_OP(_backward_np_max)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeBackward<cpu, mshadow_op::eq>);
+
+NNVM_REGISTER_OP(_np_min)
+.describe(R"code()code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", NumpyReduceAxesNoDTypeShape)
+.set_attr<nnvm::FInferType>("FInferType", NumpyReduceAxesNoDTypeType)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+[](const NodeAttrs& attrs) {
+return std::vector<std::string>{"a"};
+})
+.add_argument("a", "NDArray-or-Symbol", "The input")
+.add_arguments(NumpyReduceAxesNoDTypeParam::__FIELDS__())
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeCompute<cpu, mshadow::red::minimum>)
+.set_attr<FResourceRequest>("FResourceRequest",
+[](const NodeAttrs& attrs) {
+return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+})
+.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{"_backward_np_min"});
+
+NNVM_REGISTER_OP(_backward_np_min)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NumpyReduceAxesNoDTypeParam>)
+.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+.set_num_inputs(3)
+.set_attr<FCompute>("FCompute<cpu>", NumpyReduceAxesNoDTypeBackward<cpu, mshadow_op::eq>);
+
 NNVM_REGISTER_OP(_np_prod)
 .set_num_inputs(1)
 .set_num_outputs(1)

src/operator/numpy/np_broadcast_reduce_op_value.cu

@@ -32,6 +32,18 @@ NNVM_REGISTER_OP(_np_sum)
 NNVM_REGISTER_OP(_backward_np_sum)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesBackwardUseNone<gpu>);
 
+NNVM_REGISTER_OP(_np_max)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeCompute<gpu, mshadow::red::maximum>);
+
+NNVM_REGISTER_OP(_backward_np_max)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeBackward<gpu, mshadow_op::eq>);
+
+NNVM_REGISTER_OP(_np_min)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeCompute<gpu, mshadow::red::minimum>);
+
+NNVM_REGISTER_OP(_backward_np_min)
+.set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesNoDTypeBackward<gpu, mshadow_op::eq>);
+
 NNVM_REGISTER_OP(_np_prod)
 .set_attr<FCompute>("FCompute<gpu>", NumpyReduceAxesCompute<gpu, mshadow_op::product, true>);
 

tests/python/unittest/test_numpy_op.py

@@ -277,6 +277,119 @@ def is_int(dtype):
                         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5, use_broadcast=False)
 
 
+@with_seed()
+@use_np
+def test_np_max_min():
+    class TestMax(HybridBlock):
+        def __init__(self, axis=None, keepdims=False):
+            super(TestMax, self).__init__()
+            self._axis = axis
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.max(a, axis=self._axis, keepdims=self._keepdims)
+
+    class TestMin(HybridBlock):
+        def __init__(self, axis=None, keepdims=False):
+            super(TestMin, self).__init__()
+            self._axis = axis
+            self._keepdims = keepdims
+
+        def hybrid_forward(self, F, a, *args, **kwargs):
+            return F.np.min(a, axis=self._axis, keepdims=self._keepdims)
+
+    def is_int(dtype):
+        return 'int' == dtype
+
+    def get_grad(axis, func_name):
+        index = -1 if func_name == 'max' else 0
+        if axis == ():
+            return _np.ones((2,3,4,5))
+        else:
+            temp = _np.zeros((2,3,4,5))
+            if axis == 0:
+                temp[index,:,:,:] = 1
+                return temp
+            elif axis == 1:
+                temp[:,index,:,:] = 1
+                return temp
+            elif axis == 2:
+                temp[:,:,index,:] = 1
+                return temp
+            elif axis == 3:
+                temp[:,:,:,index] = 1
+                return temp
+            elif not axis:
+                temp[index,index,index,index] = 1
+                return temp
+            raise ValueError('axis should be int or None or ()')
+
+    def _test_np_exception(func, shape, dim):
+        x = _np.random.uniform(-1.0, 1.0, shape)
+        x = mx.nd.array(x).as_np_ndarray()
+        if func == 'max':
+            out = mx.np.max(x)
+        else:
+            out = mx.np.min(x)
+        assert out.ndim == dim, 'dimension mismatch, output.ndim={}, dim={}'.format(output.ndim, dim)
+
+    in_data_dim = random.choice([2, 3, 4])
+    shape = rand_shape_nd(in_data_dim, dim=3)
+    for func in ['max', 'min']:
+        for hybridize in [False, True]:
+            for keepdims in [True, False]:
+                for axis in ([i for i in range(in_data_dim)] + [(), None]):
+                    for itype in ['float16', 'float32', 'float64', 'int']:
+                        # test gluon
+                        if func == 'max':
+                            test_gluon = TestMax(axis=axis, keepdims=keepdims)
+                        else:
+                            test_gluon = TestMin(axis=axis, keepdims=keepdims)
+                        if hybridize:
+                            test_gluon.hybridize()
+                        if is_int(itype):
+                            x = mx.nd.arange(120).reshape((2, 3, 4, 5))
+                            x = mx.nd.array(x)
+                        else:
+                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
+                        x = x.as_np_ndarray()
+                        x.attach_grad()
+                        if func == 'max':
+                            expected_ret = _np.amax(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        else:
+                            expected_ret = _np.amin(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        with mx.autograd.record():
+                            y = test_gluon(x)
+                        assert y.shape == expected_ret.shape
+                        assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-3 if itype == 'float16' else 1e-3,
+                                            atol=1e-5 if itype == 'float16' else 1e-5)
+                        y.backward()
+                        # only check the gradient with hardcoded input
+                        if is_int(itype):
+                            assert same(x.grad.asnumpy(), get_grad(axis, func)), \
+                                'x={}\ny={}\nx.grad={}\nnumpy={}'.format(x.asnumpy(), y.asnumpy(), x.grad.asnumpy(), get_grad(axis))
+
+                        # test imperative
+                        if func == 'max':
+                            mx_out = np.max(x, axis=axis, keepdims=keepdims)
+                            np_out = _np.amax(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        else:
+                            mx_out = np.min(x, axis=axis, keepdims=keepdims)
+                            np_out = _np.amin(x.asnumpy(), axis=axis, keepdims=keepdims)
+                        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+    # test zero and zero dim
+    shapes = [(), (0), (2, 0), (0, 2, 1)]
+    exceptions = [False, True, True, True]
+    dims = [0] * len(shapes)
+    for func in ['max', 'min']:
+        for shape, exception, dim in zip(shapes, exceptions, dims):
+            if exception:
+                assertRaises(MXNetError, _test_np_exception, func, shape, dim)
+            else:
+                _test_np_exception(func, shape, dim)
+
+
 @with_seed()
 @use_np
 def test_np_linspace():