frigate/docker/rocm/migraphx/onnx/parse_qlinearconcat.cpp

/*
 * The MIT License (MIT)
 *
 * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
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 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
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 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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#include <migraphx/onnx/op_parser.hpp>
#include <migraphx/onnx/padding.hpp>
#include <migraphx/onnx/conv.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/onnx/checks.hpp>
#include <migraphx/onnx/broadcast_qdq.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/stringutils.hpp>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace onnx {

struct parse_qlinearconcat : op_parser<parse_qlinearconcat>
{
    std::vector<op_desc> operators() const { return {{"QLinearConcat"}}; }

    // basic type checking for QLinearConcat Operator
    void check_inputs(const std::vector<instruction_ref>& args) const
    {
        auto args_size = args.size();
        // at least 5 input tensors:
        // 1. is Y_scale: tensor(float)
        // 2. is Y_zero_pont: tensor(uint8)/tensor(int8)
        // remaining is a sequence of :
        //     3. Tensor: tensor(uint8)/tensor(int8)
        //     4. Scale: tensor(float),
        //     5. ZeroPoint: tensor(uint8)/tensor(int8) tensors
        // Size can be 5, 8, 11 ...
        if((args_size < 5) or ((args_size - 2) % 3 != 0))
            MIGRAPHX_THROW("QLINEARCONCAT: missing inputs");

        auto y_zp      = args[1];
        auto y_zp_type = y_zp->get_shape().type();
        if(y_zp_type != migraphx::shape::int8_type and y_zp_type != migraphx::shape::uint8_type)
            MIGRAPHX_THROW("QLINEARCONCAT: unsupported output type");

        auto t0_type = args[2]->get_shape().type();
        if(t0_type != migraphx::shape::int8_type and t0_type != migraphx::shape::uint8_type)
            MIGRAPHX_THROW("QLINEARCONCAT: unsupported input type");
        for(auto idx = 2; idx < args.size(); idx += 3)
        {
            if((args[idx]->get_shape().type() != t0_type) or
               (args[idx + 2]->get_shape().type() != t0_type))
            {
                MIGRAPHX_THROW("QLINEARCONCAT: mismatching input types");
            }
        }
    }

    instruction_ref parse(const op_desc& /* opd */,
                          const onnx_parser& parser,
                          const onnx_parser::node_info& info,
                          const std::vector<instruction_ref>& args) const
    {
        check_inputs(args);
        if(not contains(info.attributes, "axis"))
            MIGRAPHX_THROW("QLINEARCONCAT: missing axis attribute");

        auto axis = parser.parse_value(info.attributes.at("axis")).template at<int64_t>();
        std::vector<instruction_ref> tmp;
        for(auto idx = 2; idx < args.size(); idx += 3)
        {
            auto data_tensor = args[idx];
            auto scale       = args[idx + 1];
            auto zero_pt     = args[idx + 2];
            tmp.push_back(bcast_qdq_instr("dequantizelinear", data_tensor, scale, zero_pt, info));
        }
        auto y = info.add_instruction(migraphx::make_op("concat", {{"axis", axis}}), tmp);

        auto y_scale   = args[0];
        auto y_zero_pt = args[1];

        return bcast_qdq_instr("quantizelinear", y, y_scale, y_zero_pt, info);
    }
};

} // namespace onnx
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx