src/operator/numpy/random/np_power_op.h

/*
 * 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_power_op.h
 * \brief Operator for numpy sampling from power distribution.
 */

#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_POWER_OP_H_
#define MXNET_OPERATOR_NUMPY_RANDOM_NP_POWER_OP_H_

#include <mxnet/operator_util.h>
#include <algorithm>
#include <string>
#include <vector>
#include <cmath>
#include <unordered_map>
#include "../../elemwise_op_common.h"
#include "../../mshadow_op.h"
#include "../../mxnet_op.h"
#include "../../operator_common.h"
#include "../../tensor/elemwise_binary_broadcast_op.h"
#include "./dist_common.h"

namespace mxnet {
namespace op {

struct NumpyPowerParam : public dmlc::Parameter<NumpyPowerParam> {
  dmlc::optional<float> a;
  std::string ctx;
  dmlc::optional<mxnet::Tuple<index_t>> size;
  DMLC_DECLARE_PARAMETER(NumpyPowerParam) {
    DMLC_DECLARE_FIELD(a).set_default(dmlc::optional<float>());
    DMLC_DECLARE_FIELD(size)
        .set_default(dmlc::optional<mxnet::Tuple<index_t>>())
        .describe(
            "Output shape. If the given shape is, "
            "e.g., (m, n, k), then m * n * k samples are drawn. "
            "Default is None, in which case a single value is returned.");
    DMLC_DECLARE_FIELD(ctx).set_default("cpu").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 a_s, size_s;
    a_s << a;
    size_s << size;
    (*dict)["a"]    = a_s.str();
    (*dict)["size"] = size_s.str();
  }
};

template <typename DType>
struct scalar_power_kernel {
  MSHADOW_XINLINE static void Map(index_t i, float a, float* threshold, DType* out) {
    out[i] = powf(1 - threshold[i], DType(1.0 / a));
  }
};

namespace mxnet_op {

template <typename IType>
struct check_legal_a_kernel {
  MSHADOW_XINLINE static void Map(index_t i, IType* a, float* flag) {
    if (a[i] <= 0.0) {
      flag[0] = -1.0;
    }
  }
};

template <int ndim, typename IType, typename OType>
struct power_kernel {
  MSHADOW_XINLINE static void Map(index_t i,
                                  const Shape<ndim>& stride,
                                  const Shape<ndim>& oshape,
                                  IType* aparams,
                                  float* threshold,
                                  OType* out) {
    Shape<ndim> coord = unravel(i, oshape);
    auto idx          = static_cast<index_t>(dot(coord, stride));
    out[i]            = powf(1 - threshold[i], IType(1.0 / aparams[idx]));
  }
};

}  // namespace mxnet_op

template <typename xpu>
void NumpyPowerForward(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 mxnet_op;
  const NumpyPowerParam& param = nnvm::get<NumpyPowerParam>(attrs.parsed);
  Stream<xpu>* s               = ctx.get_stream<xpu>();
  index_t output_len           = outputs[0].Size();
  Random<xpu, float>* prnd     = ctx.requested[0].get_random<xpu, float>(s);
  Tensor<xpu, 1, float> workspace =
      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(output_len + 1), s);
  Tensor<xpu, 1, float> uniform_tensor   = workspace.Slice(0, output_len);
  Tensor<xpu, 1, float> indicator_device = workspace.Slice(output_len, output_len + 1);
  float indicator_host                   = 1.0;
  float* indicator_device_ptr            = indicator_device.dptr_;
  Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr);
  prnd->SampleUniform(&workspace, 0.0, 1.0);
  if (param.a.has_value()) {
    CHECK_GT(param.a.value(), 0.0) << "ValueError: expect a > 0";
    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
      Kernel<scalar_power_kernel<DType>, xpu>::Launch(
          s, outputs[0].Size(), param.a.value(), uniform_tensor.dptr_, outputs[0].dptr<DType>());
    });
  } else {
    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
      Kernel<check_legal_a_kernel<IType>, xpu>::Launch(
          s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr);
    });
    _copy<xpu>(s, &indicator_host, indicator_device_ptr);
    CHECK_GE(indicator_host, 0.0) << "ValueError: expect a > 0";
    mxnet::TShape new_lshape, new_oshape;
    int ndim = FillShape(inputs[0].shape_,
                         inputs[0].shape_,
                         outputs[0].shape_,
                         &new_lshape,
                         &new_lshape,
                         &new_oshape);
    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
      MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
        BROADCAST_NDIM_SWITCH(ndim, NDim, {
          Shape<NDim> oshape = new_oshape.get<NDim>();
          Shape<NDim> stride = calc_stride(new_lshape.get<NDim>());
          Kernel<power_kernel<NDim, IType, OType>, xpu>::Launch(s,
                                                                outputs[0].Size(),
                                                                stride,
                                                                oshape,
                                                                inputs[0].dptr<IType>(),
                                                                uniform_tensor.dptr_,
                                                                outputs[0].dptr<OType>());
        });
      });
    });
  }
}

}  // namespace op
}  // namespace mxnet

#endif  // MXNET_OPERATOR_NUMPY_RANDOM_NP_POWER_OP_H_