sparse.cc

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 * to you under the Apache License, Version 2.0 (the
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 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
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 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
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/*!
 *  Copyright (c) 2018 by Contributors
 * \file sparse.cc
 * \brief Property def of nn.sparse_dense operator.
 */

#include <tvm/data_layout.h>
#include <tvm/relay/op.h>
#include <tvm/relay/attrs/nn.h>
#include <vector>

#include "../../pass/alter_op_layout.h"

namespace tvm {
namespace relay {

// relay.nn.sparse_dense
TVM_REGISTER_NODE_TYPE(SparseDenseAttrs);

bool SparseDenseRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                    const TypeReporter& reporter) {
  CHECK_EQ(types.size(), 5);
  const auto* data = types[0].as<TensorTypeNode>();
  const auto* weight_data = types[1].as<TensorTypeNode>();
  CHECK(weight_data->shape.size() == 1 || weight_data->shape.size() == 3);
  const auto* weight_indptr = types[3].as<TensorTypeNode>();
  if (data == nullptr) return false;

  if (weight_data->shape.size() == 1) {
    // CSR case.
    Array<IndexExpr> oshape({data->shape[0], weight_indptr->shape[0] - 1});
    reporter->Assign(types[4], TensorTypeNode::make(oshape, data->dtype));
    return true;
  }

  if (weight_data->shape.size() == 3) {
    // BSR case.
    Array<IndexExpr> oshape({
        data->shape[0],
          (weight_indptr->shape[0] - 1) * weight_data->shape[1]});
    reporter->Assign(types[4], TensorTypeNode::make(oshape, data->dtype));
    return true;
  }
  LOG(FATAL) << "Unknown weight ndim for nn.sparse_dense, should be 1 (CSR) or 3 (BSR)";
  return false;
}

// Positional relay function to create dense operator used by frontend FFI.
Expr MakeSparseDense(Expr data, Expr weight_data, Expr weight_indices, Expr weight_indptr) {
  auto attrs = make_node<SparseDenseAttrs>();
  static const Op& op = Op::Get("nn.sparse_dense");
  return CallNode::make(op, {data, weight_data, weight_indices, weight_indptr}, Attrs(attrs), {});
}

TVM_REGISTER_API("relay.op.nn._make.sparse_dense")
    .set_body([](const TVMArgs& args, TVMRetValue* rv) {
      runtime::detail::unpack_call<Expr, 4>(MakeSparseDense, args, rv);
    });

RELAY_REGISTER_OP("nn.sparse_dense")
    .describe(R"code(Applies a sparse linear transformation: :math:`Y = XW^T` with X sparse.

- **data**: `(x1, x2, ..., xn, input_dim)`
- **weight**: `(units, input_dim)`
- **out**: `(x1, x2, ..., xn, units)`.

)code" TVM_ADD_FILELINE)
    .set_attrs_type_key("relay.attrs.SparseDenseAttrs")
    .set_num_inputs(4)
    .add_argument("data", "nD Tensor", "Input data.")
    .add_argument("weight_data", "1D Tensor", "Weight data matrix.")
    .add_argument("weight_indices", "1D Tensor", "Weight indices matrix.")
    .add_argument("weight_indptr", "1D Tensor", "Weight indptr matrix.")
    .set_support_level(1)
    .add_type_rel("SparseDense", SparseDenseRel);

}  // namespace relay
}  // namespace tvm