src/operator/contrib/dynamic_shape_ops.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
 * 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 dynamic_shape_ops.cc
 */

#include "./dynamic_shape_ops-inl.h"
#include "../tensor/elemwise_binary_op.h"
#include "../elemwise_op_common.h"

namespace mxnet {
namespace op {

inline bool DynamicReshapeType(const nnvm::NodeAttrs& attrs,
                               std::vector<int>* in_attrs,
                               std::vector<int>* out_attrs) {
  CHECK_EQ(in_attrs->size(), 2U);
  CHECK_EQ(out_attrs->size(), 1U);
  TYPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
  TYPE_ASSIGN_CHECK(*in_attrs, 0, (*out_attrs)[0]);
  return true;
}

bool DynamicReshapeStorageType(const nnvm::NodeAttrs& attrs,
                               const int dev_mask,
                               DispatchMode* dispatch_mode,
                               std::vector<int>* in_attrs,
                               std::vector<int>* out_attrs) {
  CHECK_EQ(in_attrs->size(), 2);
  CHECK_EQ(out_attrs->size(), 1);
  for (size_t i = 0; i < in_attrs->size(); ++i) {
    STORAGE_TYPE_ASSIGN_CHECK(*in_attrs, i, kDefaultStorage);
  }
  for (size_t i = 0; i < out_attrs->size(); ++i) {
    STORAGE_TYPE_ASSIGN_CHECK(*out_attrs, i, kDefaultStorage);
  }
  DISPATCH_MODE_ASSIGN_CHECK(dispatch_mode, 0, DispatchMode::kFComputeEx);
  return true;
}

bool DynamicReshapeBackwardStorageType(const nnvm::NodeAttrs& attrs,
                                       const int dev_mask,
                                       DispatchMode* dispatch_mode,
                                       std::vector<int>* in_attrs,
                                       std::vector<int>* out_attrs) {
  CHECK_EQ(in_attrs->size(), 1);
  CHECK_EQ(out_attrs->size(), 2);
  for (size_t i = 0; i < in_attrs->size(); ++i) {
    STORAGE_TYPE_ASSIGN_CHECK(*in_attrs, i, kDefaultStorage);
  }
  for (size_t i = 0; i < out_attrs->size(); ++i) {
    STORAGE_TYPE_ASSIGN_CHECK(*out_attrs, i, kDefaultStorage);
  }
  DISPATCH_MODE_ASSIGN_CHECK(dispatch_mode, 0, DispatchMode::kFComputeEx);
  return true;
}

NNVM_REGISTER_OP(_contrib_dynamic_reshape)
    .describe(R"code(
Experimental support for reshape operator with dynamic shape.

Accepts 2 inputs - data and shape.
The output returns data in the new shape.

Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below:
- ``0``  copy this dimension from the input to the output shape. Example::

  - input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
  - input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)

- ``-1`` infers the dimension of the output shape by using the remainder of the input dimensions
  keeping the size of the new array same as that of the input array.
  At most one dimension of shape can be -1. Example::

  - input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
  - input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
  - input shape = (2,3,4), shape=(-1,), output shape = (24,)

- ``-2`` copy all/remainder of the input dimensions to the output shape. Example::

  - input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
  - input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
  - input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)

- ``-3`` use the product of two consecutive dimensions of the input shape as the output dimension. Example::

  - input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
  - input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
  - input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
  - input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)

- ``-4`` split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1). Example::

  - input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
  - input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)

Example::

   data = mx.nd.array(np.random.normal(0,1,(2,3,5,5)))
   shape = mx.nd.array((0,-1))
   out = mx.sym.contrib.dynamic_reshape(data = data, shape = shape)
   // out will be of shape (2,75)

)code" ADD_FILELINE)
    .set_num_inputs(2)
    .set_num_outputs(1)
    .set_attr<nnvm::FListInputNames>("FListInputNames",
                                     [](const NodeAttrs& attrs) {
                                       return std::vector<std::string>{"data", "shape"};
                                     })
    .set_attr<nnvm::FInferType>("FInferType", DynamicReshapeType)
    .set_attr<FInferStorageType>("FInferStorageType", DynamicReshapeStorageType)
    .set_attr<FComputeEx>("FComputeEx<cpu>", DynamicReshapeForward<cpu>)
    .set_attr<nnvm::FGradient>("FGradient",
                               ElemwiseGradUseNone{"_backward_contrib_dynamic_reshape"})
    .add_argument("data", "NDArray-or-Symbol", "Data")
    .add_argument("shape", "NDArray-or-Symbol", "Shape");

NNVM_REGISTER_OP(_backward_contrib_dynamic_reshape)
    .set_num_inputs(1)
    .set_num_outputs(2)
    .set_attr<nnvm::TIsBackward>("TIsBackward", true)
    .set_attr<FInferStorageType>("FInferStorageType", DynamicReshapeBackwardStorageType)
    .set_attr<FComputeEx>("FComputeEx<cpu>", DynamicReshapeBackward<cpu>);

}  // namespace op
}  // namespace mxnet