src/api/operator/numpy/np_broadcast_reduce_op_value.cc

/*
 * 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_value.cc
 * \brief Implementation of the API of functions in
 * src/operator/tensor/np_broadcast_reduce_op_value.cc
 */
#include <mxnet/api_registry.h>
#include <mxnet/runtime/packed_func.h>
#include "../utils.h"
#include "../../../operator/numpy/np_broadcast_reduce_op.h"

namespace mxnet {

MXNET_REGISTER_API("_npi.broadcast_to")
    .set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
      using namespace runtime;
      const nnvm::Op* op = Op::Get("_npi_broadcast_to");
      nnvm::NodeAttrs attrs;
      op::BroadcastToParam param = {};
      if (args[1].type_code() == kDLInt) {
        param.shape = TShape(1, args[1].operator int64_t());
      } else {
        param.shape = TShape(args[1].operator ObjectRef());
      }
      attrs.parsed = param;
      attrs.op     = op;
      SetAttrDict<op::BroadcastToParam>(&attrs);

      int num_outputs   = 0;
      NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
      int num_inputs    = 1;
      auto ndoutputs    = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, nullptr);
      *ret              = ndoutputs[0];
    });

MXNET_REGISTER_API("_npi.sum").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  const nnvm::Op* op = Op::Get("_npi_sum");
  op::NumpyReduceAxesParam param;
  nnvm::NodeAttrs attrs;
  attrs.op = op;

  // parse axis
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::nullopt;
  } else {
    if (args[1].type_code() == kDLInt) {
      param.axis = Tuple<int>(1, args[1].operator int64_t());
    } else {
      param.axis = Tuple<int>(args[1].operator ObjectRef());
    }
  }

  // parse dtype
  if (args[2].type_code() == kNull) {
    param.dtype = dmlc::nullopt;
  } else {
    param.dtype = String2MXNetTypeWithBool(args[2].operator std::string());
  }

  // parse keepdims
  if (args[3].type_code() == kNull) {
    param.keepdims = false;
  } else {
    param.keepdims = args[3].operator bool();
  }

  // parse initial
  if (args[4].type_code() == kNull) {
    param.initial = dmlc::nullopt;
  } else {
    param.initial = args[4].operator double();
  }

  attrs.parsed = param;

  SetAttrDict<op::NumpyReduceAxesParam>(&attrs);

  NDArray* inputs[] = {args[0].operator NDArray*()};
  int num_inputs    = 1;

  NDArray* outputs[] = {args[5].operator NDArray*()};
  NDArray** out      = (outputs[0] == nullptr) ? nullptr : outputs;
  int num_outputs    = (outputs[0] != nullptr);
  auto ndoutputs     = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, out);

  if (out) {
    *ret = PythonArg(5);
  } else {
    *ret = reinterpret_cast<mxnet::NDArray*>(ndoutputs[0]);
  }
});

MXNET_REGISTER_API("_npi.mean").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  const nnvm::Op* op = Op::Get("_npi_mean");
  nnvm::NodeAttrs attrs;
  op::NumpyReduceAxesParam param;
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::optional<mxnet::Tuple<int>>();
  } else if (args[1].type_code() == kDLInt) {
    param.axis = Tuple<int>(1, args[1].operator int64_t());
  } else {
    param.axis = mxnet::Tuple<int>(args[1].operator ObjectRef());
  }
  if (args[2].type_code() == kNull) {
    param.dtype = mxnet::common::GetDefaultDtype();
  } else {
    param.dtype = String2MXNetTypeWithBool(args[2].operator std::string());
  }

  if (args[3].type_code() == kNull) {
    param.keepdims = false;
  } else {
    param.keepdims = args[3].operator bool();
  }
  param.initial = dmlc::optional<double>();
  attrs.parsed  = param;
  attrs.op      = op;
  SetAttrDict<op::NumpyReduceAxesParam>(&attrs);
  int num_inputs    = 1;
  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
  NDArray* out      = args[4].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(4);
  } else {
    *ret = ndoutputs[0];
  }
});

MXNET_REGISTER_API("_npi.prod").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  const nnvm::Op* op = Op::Get("_npi_prod");
  nnvm::NodeAttrs attrs;
  op::NumpyReduceAxesParam param;
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::optional<mxnet::Tuple<int>>();
  } else if (args[1].type_code() == kDLInt) {
    param.axis = Tuple<int>(1, args[1].operator int64_t());
  } else {
    param.axis = Tuple<int>(args[1].operator ObjectRef());
  }
  if (args[2].type_code() == kNull) {
    param.dtype = dmlc::optional<int>();
  } else {
    param.dtype = String2MXNetTypeWithBool(args[2].operator std::string());
  }
  if (args[3].type_code() == kNull) {
    param.keepdims = false;
  } else {
    param.keepdims = args[3].operator bool();
  }
  if (args[4].type_code() == kNull) {
    param.initial = dmlc::optional<double>();
  } else {
    param.initial = args[4].operator double();
  }
  attrs.parsed = param;
  attrs.op     = op;
  SetAttrDict<op::NumpyReduceAxesParam>(&attrs);
  int num_inputs    = 1;
  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
  NDArray* out      = args[5].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(5);
  } else {
    *ret = ndoutputs[0];
  }
});

MXNET_REGISTER_API("_npi.max").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  static const nnvm::Op* op = Op::Get("_npi_max");
  nnvm::NodeAttrs attrs;
  op::NumpyReduceAxesNoDTypeParam param;

  NDArray* out      = args[3].operator mxnet::NDArray*();
  NDArray** outputs = out == nullptr ? nullptr : &out;
  int num_outputs   = out != nullptr;
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::nullopt;
  } else if (args[1].type_code() == kDLInt) {
    param.axis = Tuple<int>(1, args[1].operator int64_t());
  } else {
    param.axis = Tuple<int>(args[1].operator ObjectRef());
  }
  param.keepdims    = args[2].operator bool();
  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
  int num_inputs    = 1;
  attrs.parsed      = param;
  attrs.op          = op;
  SetAttrDict<op::NumpyReduceAxesNoDTypeParam>(&attrs);
  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.min").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  static const nnvm::Op* op = Op::Get("_npi_min");
  nnvm::NodeAttrs attrs;
  op::NumpyReduceAxesNoDTypeParam param;

  NDArray* out      = args[3].operator mxnet::NDArray*();
  NDArray** outputs = out == nullptr ? nullptr : &out;
  int num_outputs   = out != nullptr;
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::nullopt;
  } else if (args[1].type_code() == kDLInt) {
    param.axis = Tuple<int>(1, args[1].operator int64_t());
  } else {
    param.axis = Tuple<int>(args[1].operator ObjectRef());
  }
  param.keepdims    = args[2].operator bool();
  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
  int num_inputs    = 1;
  attrs.parsed      = param;
  attrs.op          = op;
  SetAttrDict<op::NumpyReduceAxesNoDTypeParam>(&attrs);
  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.amax").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  static const nnvm::Op* op = Op::Get("_npi_amax");
  nnvm::NodeAttrs attrs;
  op::NumpyReduceAxesNoDTypeParam param;

  NDArray* out      = args[3].operator mxnet::NDArray*();
  NDArray** outputs = out == nullptr ? nullptr : &out;
  int num_outputs   = out != nullptr;
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::nullopt;
  } else if (args[1].type_code() == kDLInt) {
    param.axis = Tuple<int>(1, args[1].operator int64_t());
  } else {
    param.axis = Tuple<int>(args[1].operator ObjectRef());
  }
  param.keepdims    = args[2].operator bool();
  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
  int num_inputs    = 1;
  attrs.parsed      = param;
  attrs.op          = op;
  SetAttrDict<op::NumpyReduceAxesNoDTypeParam>(&attrs);
  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.amin").set_body([](runtime::MXNetArgs args, runtime::MXNetRetValue* ret) {
  using namespace runtime;
  static const nnvm::Op* op = Op::Get("_npi_amin");
  nnvm::NodeAttrs attrs;
  op::NumpyReduceAxesNoDTypeParam param;

  NDArray* out      = args[3].operator mxnet::NDArray*();
  NDArray** outputs = out == nullptr ? nullptr : &out;
  int num_outputs   = out != nullptr;
  if (args[1].type_code() == kNull) {
    param.axis = dmlc::nullopt;
  } else if (args[1].type_code() == kDLInt) {
    param.axis = Tuple<int>(1, args[1].operator int64_t());
  } else {
    param.axis = Tuple<int>(args[1].operator ObjectRef());
  }
  param.keepdims    = args[2].operator bool();
  NDArray* inputs[] = {args[0].operator mxnet::NDArray*()};
  int num_inputs    = 1;
  attrs.parsed      = param;
  attrs.op          = op;
  SetAttrDict<op::NumpyReduceAxesNoDTypeParam>(&attrs);
  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