broadcast_reduce_norm_value.cc

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 * distributed with this work for additional information
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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
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 *
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 * KIND, either express or implied.  See the License for the
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/*!
 *  Copyright (c) 2016 by Contributors
 * \file broadcast_reduce_norm_value.cc
 * \brief CPU Implementation of broadcast and reduce norm functions based on value.
 */
#include "./broadcast_reduce_op.h"

namespace mxnet {
namespace op {
DMLC_REGISTER_PARAMETER(NormParam);

template<>
void L2NormComputeEx<cpu>(const nnvm::NodeAttrs& attrs,
                          const OpContext& ctx,
                          const std::vector<NDArray>& inputs,
                          const std::vector<OpReqType>& req,
                          const std::vector<NDArray>& outputs) {
  CHECK_EQ(inputs.size(), 1U);
  CHECK_EQ(outputs.size(), 1U);
  CHECK_EQ(req.size(), 1U);
  const NormParam& param = nnvm::get<NormParam>(attrs.parsed);
  mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
  const NDArrayStorageType istype = inputs[0].storage_type();
  const mxnet::TShape axis = param.axis.has_value() ? param.axis.value() : mxnet::TShape(0, -1);
  if ((istype == kRowSparseStorage || istype == kCSRStorage) && axis.ndim() == 0 &&
       param.ord == 2) {
    // l2 norm on the entire array
    L2NormComputeSparseImpl<cpu>(s, inputs[0], req[0], outputs[0].data());
  } else if (istype == kCSRStorage && axis.ndim() == 1 && (axis[0] == 0 || axis[0] == 1) &&
             !param.keepdims && param.ord == 2) {
    // l2 norm on a particular axis
    NDArray output = outputs[0];
    ReduceCsrImpl<cpu, sq_sum, false>(s, ctx, inputs[0], req[0], &output, axis);
    CHECK_EQ(outputs[0].storage_type(), kDefaultStorage);
    SqRootForL2<cpu>(ctx, req[0], outputs[0].data());
  } else {
    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
  }
}

NNVM_REGISTER_OP(norm)
MXNET_ADD_SPARSE_OP_ALIAS(norm)
.describe(R"code(Computes the norm on an NDArray.

This operator computes the norm on an NDArray with the specified axis, depending
on the value of the ord parameter. By default, it computes the L2 norm on the entire
array. Currently only ord=2 supports sparse ndarrays.

Examples::

  x = [[[1, 2],
        [3, 4]],
       [[2, 2],
        [5, 6]]]

  norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
                            [5.3851647 6.3245554]]

  norm(x, ord=1, axis=1) = [[4., 6.],
                            [7., 8.]]

  rsp = x.cast_storage('row_sparse')

  norm(rsp) = [5.47722578]

  csr = x.cast_storage('csr')

  norm(csr) = [5.47722578]

)code" ADD_FILELINE)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser(ParamParser<NormParam>)
.set_attr<mxnet::FInferShape>("FInferShape", NormShape)
.set_attr<nnvm::FInferType>("FInferType", NormType)
.set_attr<FInferStorageType>("FInferStorageType", LpNormStorageType)
.set_attr<nnvm::FGradient>("FGradient", ReduceGrad{ "_backward_norm" })
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
.set_attr<FCompute>("FCompute<cpu>", LpNormCompute<cpu>)
.set_attr<FComputeEx>("FComputeEx<cpu>", L2NormComputeEx<cpu>)
.add_argument("data", "NDArray-or-Symbol", "The input")
.add_arguments(NormParam::__FIELDS__());

NNVM_REGISTER_OP(_backward_norm)
.set_num_outputs(1)
.set_attr_parser(ParamParser<NormParam>)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<FResourceRequest>("FResourceRequest",
  [](const NodeAttrs& attrs) {
    return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
  })
.set_attr<FCompute>("FCompute<cpu>", LpNormGradCompute<cpu>);


}  // namespace op
}  // namespace mxnet