* impl - FFI for np_argmax and np_argmin

* impl - FFI for np_indices

benchmark/python/ffi/benchmark_ffi.py

@@ -55,6 +55,9 @@ def prepare_workloads():
     OpArgMngr.add_workload("cumsum", pool['3x2'], axis=0, out=pool['3x2'])
     OpArgMngr.add_workload("add", pool['2x2'], pool['2x2'])
     OpArgMngr.add_workload("random.uniform", low=0, high=1, size=1)
+    OpArgMngr.add_workload("argmax", pool['3x2'], axis=-1)
+    OpArgMngr.add_workload("argmin", pool['3x2'], axis=-1)
+    OpArgMngr.add_workload("indices", dimensions=(1, 2, 3))
 
 
 def benchmark_helper(f, *args, **kwargs):

python/mxnet/ndarray/numpy/_op.py

@@ -4530,7 +4530,7 @@ def argmax(a, axis=None, out=None):
     >>> b
     array([2., 2.])
     """
-    return _npi.argmax(a, axis=axis, keepdims=False, out=out)
+    return _api_internal.argmax(a, axis, False, out)
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4598,7 +4598,7 @@ def argmin(a, axis=None, out=None):
     >>> b
     array([0., 0.])
     """
-    return _npi.argmin(a, axis=axis, keepdims=False, out=out)
+    return _api_internal.argmin(a, axis, False, out)
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4946,8 +4946,10 @@ def indices(dimensions, dtype=_np.int32, ctx=None):
     """
     if isinstance(dimensions, (tuple, list)):
         if ctx is None:
-            ctx = current_context()
-        return _npi.indices(dimensions=dimensions, dtype=dtype, ctx=ctx)
+            ctx = str(current_context())
+        else:
+            ctx = str(ctx)
+        return _api_internal.indices(dimensions, dtype, ctx)
     else:
         raise ValueError("The dimensions must be sequence of ints")
 # pylint: enable=redefined-outer-name

src/api/operator/numpy/np_broadcast_reduce_op_index.cc

@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ * 
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_broadcast_reduce_op_index.cc
+ * \brief Implementation of the API of functions in
+          src/operator/numpy/np_broadcast_reduce_op_index.cc
+ */
+#include <mxnet/api_registry.h>
+#include <mxnet/runtime/packed_func.h>
+#include "../utils.h"
+#include "../../../operator/tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+
+MXNET_REGISTER_API("_npi.argmax")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_argmax");
+  nnvm::NodeAttrs attrs;
+  op::ReduceAxisParam param;
+  // param.axis
+  if (args[1].type_code() == kNull) {
+    param.axis = dmlc::nullopt;
+  } else {
+    param.axis = args[1].operator int();
+  }
+  // param.keepdims
+  param.keepdims = args[2].operator bool();
+
+  attrs.parsed = std::move(param);
+  attrs.op = op;
+  SetAttrDict<op::ReduceAxisParam>(&attrs);
+  // inputs
+  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
+  int num_inputs = 1;
+  // outputs
+  NDArray* out = args[3].operator mxnet::NDArray*();
+  NDArray** outputs = out == nullptr ? nullptr : &out;
+  int num_outputs = out != nullptr;
+  auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
+  if (out) {
+    *ret = PythonArg(3);
+  } else {
+    *ret = reinterpret_cast<mxnet::NDArray*>(ndoutputs[0]);
+  }
+});
+
+MXNET_REGISTER_API("_npi.argmin")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_argmin");
+  nnvm::NodeAttrs attrs;
+  op::ReduceAxisParam param;
+  // param.axis
+  if (args[1].type_code() == kNull) {
+    param.axis = dmlc::nullopt;
+  } else {
+    param.axis = args[1].operator int();
+  }
+  // param.keepdims
+  param.keepdims = args[2].operator bool();
+
+  attrs.parsed = std::move(param);
+  attrs.op = op;
+  SetAttrDict<op::ReduceAxisParam>(&attrs);
+  // inputs
+  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
+  int num_inputs = 1;
+  // outputs
+  NDArray* out = args[3].operator mxnet::NDArray*();
+  NDArray** outputs = out == nullptr ? nullptr : &out;
+  int num_outputs = out != nullptr;
+  auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
+  if (out) {
+    *ret = PythonArg(3);
+  } else {
+    *ret = reinterpret_cast<mxnet::NDArray*>(ndoutputs[0]);
+  }
+});
+
+}  // namespace mxnet

src/api/operator/numpy/np_init_op.cc

@@ -25,6 +25,7 @@
 #include <mxnet/runtime/packed_func.h>
 #include "../utils.h"
 #include "../../../operator/tensor/init_op.h"
+#include "../../../operator/numpy/np_init_op.h"
 
 namespace mxnet {
 
@@ -55,4 +56,35 @@ MXNET_REGISTER_API("_npi.zeros")
   *ret = ndoutputs[0];
 });
 
+MXNET_REGISTER_API("_npi.indices")
+.set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
+  using namespace runtime;
+  const nnvm::Op* op = Op::Get("_npi_indices");
+  nnvm::NodeAttrs attrs;
+  op::IndicesOpParam param;
+  // param.dimensions
+  if (args[0].type_code() == kDLInt) {
+    param.dimensions = TShape(1, args[0].operator int64_t());
+  } else {
+    param.dimensions = TShape(args[0].operator ObjectRef());
+  }
+  // param.dtype
+  if (args[1].type_code() == kNull) {
+    param.dtype = mshadow::kInt32;
+  } else {
+    param.dtype = String2MXNetTypeWithBool(args[1].operator std::string());
+  }
+  attrs.parsed = std::move(param);
+  attrs.op = op;
+  SetAttrDict<op::IndicesOpParam>(&attrs);
+  // param.ctx
+  if (args[2].type_code() != kNull) {
+    attrs.dict["ctx"] = args[2].operator std::string();
+  }
+  int num_inputs = 0;
+  int num_outputs = 0;
+  auto ndoutputs = Invoke(op, &attrs, num_inputs, nullptr, &num_outputs, nullptr);
+  *ret = ndoutputs[0];
+});
+
 }  // namespace mxnet

src/operator/numpy/np_init_op.h

@@ -31,6 +31,7 @@
 #include <string>
 #include "../tensor/init_op.h"
 #include "../tensor/elemwise_unary_op.h"
+#include "../../api/operator/op_utils.h"
 
 
 namespace mxnet {
@@ -79,6 +80,13 @@ struct IndicesOpParam : public dmlc::Parameter<IndicesOpParam> {
     .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
               "Only used for imperative calls.");
   }
+  void SetAttrDict(std::unordered_map<std::string, std::string>* dict) {
+    std::ostringstream dimensions_s, dtype_s;
+    dimensions_s << dimensions;
+    dtype_s << dtype;
+    (*dict)["dimensions"] = dimensions_s.str();
+    (*dict)["dtype"] = String2MXNetTypeWithBool(dtype);
+  }
 };
 
 inline bool NumpyRangeShape(const nnvm::NodeAttrs& attrs,

src/operator/tensor/broadcast_reduce_op.h

@@ -108,6 +108,13 @@ struct ReduceAxisParam : public dmlc::Parameter<ReduceAxisParam> {
       .describe("If this is set to `True`, the reduced axis is left "
                 "in the result as dimension with size one.");
   }
+  void SetAttrDict(std::unordered_map<std::string, std::string>* dict) {
+    std::ostringstream axis_s, keepdims_s;
+    axis_s << axis;
+    keepdims_s << keepdims;
+    (*dict)["axis"] = axis_s.str();
+    (*dict)["keepdims"] = keepdims_s.str();
+  }
 };
 
 enum PickOpMode {kWrap, kClip};