api_def_QuantizedMatMulWithBiasAndReluAndRequantize.pbtxt

op {
  graph_op_name: "QuantizedMatMulWithBiasAndReluAndRequantize"
  visibility: HIDDEN
  in_arg {
    name: "a"
    description: <<END
A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`.
END
  }
  in_arg {
    name: "b"
    description: <<END
A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`.
END
  }
  in_arg {
    name: "bias"
    description: <<END
A 1D bias tensor with size matching with inner dimension of `b` (after being
transposed if `transposed_b` is non-zero).
END
  }
  in_arg {
    name: "min_a"
    description: <<END
The float value that the lowest quantized `a` value represents.
END
  }
  in_arg {
    name: "max_a"
    description: <<END
The float value that the highest quantized `a` value represents.
END
  }
  in_arg {
    name: "min_b"
    description: <<END
The float value that the lowest quantized `b` value represents.
END
  }
  in_arg {
    name: "max_b"
    description: <<END
The float value that the highest quantized `b` value represents.
END
  }
  in_arg {
    name: "min_freezed_output"
    description: <<END
The float value that the lowest quantized output value after requantize.
END
  }
  in_arg {
    name: "min_freezed_output"
    description: <<END
The float value that the highest quantized output value after requantize.
END
  }

  out_arg {
    name: "min_out"
    description: <<END
The float value that the lowest quantized output value represents.
END
  }
  out_arg {
    name: "max_out"
    description: <<END
The float value that the highest quantized output value represents.
END
  }
  attr {
    name: "transpose_a"
    description: "If true, `a` is transposed before multiplication."
  }
  attr {
    name: "transpose_b"
    description: "If true, `b` is transposed before multiplication."
  }
  attr {
    name: "input_quant_mode"
    description: <<END
Input data quantization mode. Either MIN_FIRST(default) or SCALED.
END
  }
  summary: <<END
Perform a quantized matrix multiplication of  `a` by the matrix `b` with bias
add and relu and requantize fusion.
END
  description: <<END
The inputs must be two-dimensional matrices and 1D bias vector. And the inner
dimension of `a` (after being transposed if `transpose_a` is non-zero) must
match the outer dimension of `b` (after being transposed if `transposed_b` is
non-zero). Then do broadcast add operation with bias values on the matrix
mulplication result. The bias size must match inner dimension of `b`.  Then do
relu activation to get non-negative result. Then do requantize operation to get
final uint8 result.
END
}