dense.h

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 * to you under the Apache License, Version 2.0 (the
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#ifndef TVM_TOPI_ROCM_DENSE_H_
#define TVM_TOPI_ROCM_DENSE_H_

#include <tvm/target/generic_func.h>
#include <tvm/te/operation.h>
#include <tvm/topi/contrib/rocblas.h>
#include <tvm/topi/cuda/dense.h>
#include <tvm/topi/detail/array_utils.h>
#include <tvm/topi/generic/extern.h>
#include <tvm/topi/nn/dense.h>
#include <tvm/topi/tags.h>

namespace tvm {
namespace topi {

using namespace tvm::te;

namespace rocm {
inline tvm::te::Tensor dense_rocm(const Target& target, const tvm::te::Tensor& data,
                                  const tvm::te::Tensor& weight, const tvm::te::Tensor& bias,
                                  const DataType& out_dtype) {
  ICHECK_EQ(data->shape.size(), 2) << "dense requires 2-D data";
  ICHECK_EQ(weight->shape.size(), 2) << "dense requires 2-D weight";
  if (bias.defined()) {
    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];

  if (target->GetLibs().count("rocblas")) {
    ICHECK_EQ(data->dtype, out_dtype) << "Mixed precision not supported.";
    auto mm = topi::contrib::rocblas_matmul(data, weight, false, true);
    if (bias.defined()) {
      mm = tvm::te::compute(
          {batch, out_dim}, [&](Var i, Var j) { return mm(i, j) + bias(j); }, "tensor", kBroadcast);
    }

    return mm;
  } else {
    return topi::nn::dense(data, weight, bias, out_dtype);
  }
}

inline Schedule schedule_dense(const Target& target, const Array<Tensor>& outs) {
  if (target->kind->name == "rocm" && target->GetLibs().count("rocblas")) {
    return topi::generic::schedule_extern(target, outs);
  }

  return topi::cuda::schedule_dense(target, outs);
}

}  // namespace rocm
}  // namespace topi
}  // namespace tvm
#endif  // TVM_TOPI_ROCM_DENSE_H_