/* * 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 * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * 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. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * THE SOFTWARE. */ #include #include #include #include #include #include #include #include #include namespace migraphx { inline namespace MIGRAPHX_INLINE_NS { namespace onnx { struct parse_qlinearconcat : op_parser { std::vector operators() const { return {{"QLinearConcat"}}; } // basic type checking for QLinearConcat Operator void check_inputs(const std::vector& 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& 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(); std::vector 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