src/operator/contrib/index_array.cc - master - githubmirror/incubator-mxnet

src/operator/contrib/index_array.cc
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[Master] Clang-formatter: only src/ directory (#20571)
13 сен 2021, 16:28
13 сен 2021, 16:28e359bcd
/*
 * 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.
 */
#include <mshadow/tensor.h>
#include "./index_array-inl.h"

namespace mxnet {
namespace op {

void IndexArrayForwardCPU(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;
  CHECK_EQ(inputs.size(), 1U);
  CHECK_EQ(outputs.size(), 1U);
  CHECK_EQ(req.size(), 1U);
  const TBlob& in_data  = inputs[0];
  const TBlob& out_data = outputs[0];

  const IndexArrayParam& param = nnvm::get<IndexArrayParam>(attrs.parsed);

  const TShape inshape = in_data.shape_;
  const int ndim       = inshape.ndim();

  Stream<cpu>* stream = ctx.get_stream<cpu>();

  using namespace mxnet_op;

  if (param.axes.has_value()) {
    const mxnet::Tuple<int>& axes = param.axes.value();
    const int naxes               = axes.ndim();

    std::vector<int64_t> index_products = IndexArrayComputeIndexProducts(inshape);

    Tensor<cpu, 1, int64_t> workspace =
        ctx.requested[0].get_space_typed<cpu, 1, int64_t>(Shape1(2 * naxes), stream);

    IndexArrayBuildSelectedAxesWorkspace(axes, index_products, workspace.dptr_, ndim);

    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
      Kernel<IndexArrayKernel<req_type>, cpu>::Launch(
          stream, in_data.Size(), out_data.dptr<int64_t>(), naxes, workspace.dptr_);
    });
  } else {
    MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, {
      Kernel<IndexArrayDefaultKernel<req_type>, cpu>::Launch(
          stream, in_data.Size(), out_data.dptr<int64_t>(), ndim, inshape.data());
    });
  }
}

DMLC_REGISTER_PARAMETER(IndexArrayParam);

NNVM_REGISTER_OP(_contrib_index_array)
    .describe(R"code(Returns an array of indexes of the input array.

For an input array with shape  :math:`(d_1, d_2, ..., d_n)`, `index_array` returns a
:math:`(d_1, d_2, ..., d_n, n)` array `idx`, where
:math:`idx[i_1, i_2, ..., i_n, :] = [i_1, i_2, ..., i_n]`.

Additionally, when the parameter `axes` is specified, `idx` will be a
:math:`(d_1, d_2, ..., d_n, m)` array where `m` is the length of `axes`, and the following
equality will hold: :math:`idx[i_1, i_2, ..., i_n, j] = i_{axes[j]}`.

Examples::

    x = mx.nd.ones((3, 2))

    mx.nd.contrib.index_array(x) = [[[0 0]
                                     [0 1]]

                                    [[1 0]
                                     [1 1]]

                                    [[2 0]
                                     [2 1]]]

    x = mx.nd.ones((3, 2, 2))

    mx.nd.contrib.index_array(x, axes=(1, 0)) = [[[[0 0]
                                                   [0 0]]

                                                  [[1 0]
                                                   [1 0]]]


                                                 [[[0 1]
                                                   [0 1]]

                                                  [[1 1]
                                                   [1 1]]]


                                                 [[[0 2]
                                                   [0 2]]

                                                  [[1 2]
                                                   [1 2]]]]

)code" ADD_FILELINE)
    .set_num_inputs(1)
    .set_num_outputs(1)
    .set_attr<nnvm::FListInputNames>("FListInputNames",
                                     [](const NodeAttrs& attrs) {
                                       return std::vector<std::string>{"data"};
                                     })
    .set_attr<nnvm::FListOutputNames>("FListOutputNames",
                                      [](const NodeAttrs& attrs) {
                                        return std::vector<std::string>{"output"};
                                      })
    .set_attr_parser(ParamParser<IndexArrayParam>)
    .set_attr<mxnet::FInferShape>("FInferShape",
                                  [](const nnvm::NodeAttrs& attrs,
                                     mxnet::ShapeVector* in_shape,
                                     mxnet::ShapeVector* out_shape) {
                                    const IndexArrayParam& param =
                                        nnvm::get<IndexArrayParam>(attrs.parsed);
                                    CHECK_EQ(in_shape->size(), 1U);
                                    CHECK_EQ(out_shape->size(), 1U);
                                    const mxnet::TShape& inshape =
                                        (*in_shape)[index_array_enum::kIn];
                                    if (!mxnet::ndim_is_known(inshape))
                                      return false;

                                    mxnet::TShape oshape = mxnet::TShape(inshape.ndim() + 1, 0);

                                    for (int i = 0; i < inshape.ndim(); i++) {
                                      oshape[i] = inshape[i];
                                    }
                                    if (param.axes.has_value()) {
                                      oshape[inshape.ndim()] = param.axes.value().ndim();
                                    } else {
                                      oshape[inshape.ndim()] = inshape.ndim();
                                    }

                                    SHAPE_ASSIGN_CHECK(*out_shape, 0, oshape);
                                    return shape_is_known(oshape);
                                  })
    .set_attr<nnvm::FInferType>("FInferType",
                                [](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, mshadow::kInt64);
                                  return out_attrs->at(0) != -1;
                                })
    .set_attr<FCompute>("FCompute<cpu>", IndexArrayForwardCPU)
    .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
    .set_attr<FResourceRequest>("FResourceRequest",
                                [](const NodeAttrs& n) {
                                  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
                                })
    .set_attr<THasDeterministicOutput>("THasDeterministicOutput", true)
    .add_argument("data", "NDArray-or-Symbol", "Input data")
    .add_arguments(IndexArrayParam::__FIELDS__());

}  // namespace op
}  // namespace mxnet