dense.h

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 * 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
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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
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#ifndef TVM_TOPI_NN_DENSE_H_
#define TVM_TOPI_NN_DENSE_H_
 
#include <tvm/te/operation.h>
#include <tvm/topi/tags.h>
 
#include <string>
 
namespace tvm {
namespace topi {
namespace nn {
 
using namespace tvm::te;
 
inline tvm::te::Tensor dense(const tvm::te::Tensor& data, const tvm::te::Tensor& weight,
                             const tvm::te::Tensor& bias, const PrimType& out_dtype) {
  TVM_FFI_ICHECK_EQ(data->shape.size(), 2) << "dense requires 2-D data";
  TVM_FFI_ICHECK_EQ(weight->shape.size(), 2) << "dense requires 2-D weight";
  if (bias.defined()) {
    TVM_FFI_ICHECK_EQ(bias->shape.size(), 1) << "dense requires 1-D bias";
  }
 
  auto batch = data->shape[0];
  auto in_dim = data->shape[1];
  auto out_dim = weight->shape[0];
 
  auto k = tvm::te::reduce_axis(Range(0, in_dim), "k");
  auto matmul = tvm::te::compute(
      {batch, out_dim},
      [&](PrimVar i, PrimVar j) {
        return tvm::sum(tvm::cast(out_dtype, data(i, k)) * tvm::cast(out_dtype, weight(j, k)), {k});
      },
      "tensor", "dense");
 
  if (bias.defined()) {
    matmul = tvm::te::compute(
        {batch, out_dim},
        [&](PrimVar i, PrimVar j) { return matmul(i, j) + tvm::cast(out_dtype, bias(j)); },
        "tensor", kBroadcast);
  }
 
  return matmul;
}
 
}  // namespace nn
}  // namespace topi
}  // namespace tvm
#endif  // TVM_TOPI_NN_DENSE_H_