mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-02-19 01:17:06 +03:00
234 lines
9.0 KiB
C++
234 lines
9.0 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*/
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#include <migraphx/onnx/op_parser.hpp>
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#include <migraphx/ranges.hpp>
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#include <migraphx/instruction.hpp>
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#include <migraphx/make_op.hpp>
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#include <migraphx/onnx/checks.hpp>
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namespace migraphx {
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inline namespace MIGRAPHX_INLINE_NS {
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namespace onnx {
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template <typename Derived>
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struct reduce_parser : op_parser<Derived>
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{
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instruction_ref parse_reduce_oper(const std::string& op_name,
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const onnx_parser& parser,
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onnx_parser::node_info info,
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std::vector<instruction_ref> args) const
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{
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auto constant_axes = parse_constant_axes(args, info);
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int noop_with_empty_axes =
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parse_attribute<int>("noop_with_empty_axes", parser, info).value_or(0);
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int keep_dims = parse_attribute<int>("keepdims", parser, info).value_or(1);
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std::vector<int64_t> all_axes(args.front()->get_shape().ndim());
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std::iota(all_axes.begin(), all_axes.end(), 0);
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// Handle axes attribute, constant input axes, and missing both attribute and input cases
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if(constant_axes.has_value())
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{
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if(noop_with_empty_axes != 0 and constant_axes->empty())
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return args[0];
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if(noop_with_empty_axes == 0 and constant_axes->empty())
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constant_axes = all_axes;
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auto reduce =
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info.add_instruction(make_op(op_name, {{"axes", *constant_axes}}), args[0]);
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if(keep_dims == 0)
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return info.add_instruction(make_op("squeeze", {{"axes", *constant_axes}}), reduce);
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return reduce;
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}
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// Handle variable input axes
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if(keep_dims == 0)
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MIGRAPHX_THROW("Keepdims not supported with runtime provided axes");
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// Empty axes attribute indicates to the operator to look for axes in the inputs
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// If the input axes are empty, the default behavior of reduce_op is to be an
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// identity operator
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auto reduce_op = make_op(op_name, {{"axes", {}}});
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if(noop_with_empty_axes != 0)
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return info.add_instruction(reduce_op, args);
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if(args[1]->get_shape().dynamic())
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{
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auto reduce_input_axes = info.add_instruction(reduce_op, args);
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auto all_axes_lit = info.add_literal(
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literal{shape{shape::type_t::int64_type, {all_axes.size()}}, all_axes});
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auto reduce_all_axes = info.add_instruction(reduce_op, args[0], all_axes_lit);
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auto zero = info.add_literal(literal{shape{shape::type_t::int64_type}, {0u}});
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auto axes_size = info.add_instruction(make_op("dimensions_of", {{"end", 1}}), args[1]);
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auto is_axes_empty = info.add_instruction(make_op("equal"), axes_size, zero);
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return info.add_instruction(
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make_op("where"), is_axes_empty, reduce_all_axes, reduce_input_axes);
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}
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else if(args[1]->get_shape().elements() == 0)
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{
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auto all_axes_lit = info.add_literal(
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literal{shape{shape::type_t::int64_type, {all_axes.size()}}, all_axes});
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return info.add_instruction(reduce_op, args[0], all_axes_lit);
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}
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else
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{
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return info.add_instruction(reduce_op, args);
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}
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}
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private:
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template <typename T>
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std::optional<T> parse_attribute(const std::string& attribute_name,
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const onnx_parser& parser,
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onnx_parser::node_info& info) const
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{
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if(not contains(info.attributes, attribute_name))
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return std::nullopt;
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return parser.parse_value(info.attributes[attribute_name]).at<T>();
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}
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std::optional<std::vector<int64_t>> parse_constant_axes(std::vector<instruction_ref>& args,
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onnx_parser::node_info& info) const
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{
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std::vector<int64_t> axes;
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if(args.size() == 2)
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{
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if(not args[1]->can_eval())
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return std::nullopt;
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args[1]->eval().visit([&](auto s) { axes.assign(s.begin(), s.end()); });
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}
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else if(contains(info.attributes, "axes"))
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{
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auto&& attr_axes = info.attributes["axes"].ints();
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axes.assign(attr_axes.begin(), attr_axes.end());
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}
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return axes;
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}
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};
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struct parse_reduce_op : reduce_parser<parse_reduce_op>
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{
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std::vector<op_desc> operators() const
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{
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return {{"ReduceMax", "reduce_max"},
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{"ReduceMean", "reduce_mean"},
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{"ReduceMin", "reduce_min"},
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{"ReduceProd", "reduce_prod"},
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{"ReduceSum", "reduce_sum"}};
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}
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instruction_ref parse(const op_desc& opd,
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const onnx_parser& parser,
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onnx_parser::node_info info,
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std::vector<instruction_ref> args) const
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{
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return parse_reduce_oper(opd.op_name, parser, std::move(info), std::move(args));
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}
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};
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struct parse_reduce_l1 : reduce_parser<parse_reduce_l1>
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{
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std::vector<op_desc> operators() const { return {{"ReduceL1"}}; }
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instruction_ref parse(const op_desc& /*opd*/,
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const onnx_parser& parser,
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onnx_parser::node_info info,
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std::vector<instruction_ref> args) const
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{
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args[0] = info.add_instruction(make_op("abs"), args[0]);
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return parse_reduce_oper("reduce_sum", parser, std::move(info), std::move(args));
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}
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};
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struct parse_reduce_l2 : reduce_parser<parse_reduce_l2>
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{
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std::vector<op_desc> operators() const { return {{"ReduceL2"}}; }
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instruction_ref parse(const op_desc& /*opd*/,
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const onnx_parser& parser,
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const onnx_parser::node_info& info,
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std::vector<instruction_ref> args) const
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{
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args[0] = info.add_instruction(make_op("mul"), args[0], args[0]);
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auto sum_ins = parse_reduce_oper("reduce_sum", parser, info, std::move(args));
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return info.add_instruction(make_op("sqrt"), sum_ins);
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}
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};
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struct parse_reduce_log_sum : reduce_parser<parse_reduce_log_sum>
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{
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std::vector<op_desc> operators() const { return {{"ReduceLogSum"}}; }
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instruction_ref parse(const op_desc& /*opd*/,
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const onnx_parser& parser,
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const onnx_parser::node_info& info,
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std::vector<instruction_ref> args) const
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{
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auto sum_ins = parse_reduce_oper("reduce_sum", parser, info, std::move(args));
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return info.add_instruction(make_op("log"), sum_ins);
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}
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};
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struct parse_reduce_log_sum_exp : reduce_parser<parse_reduce_log_sum_exp>
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{
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std::vector<op_desc> operators() const { return {{"ReduceLogSumExp"}}; }
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instruction_ref parse(const op_desc& /*opd*/,
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const onnx_parser& parser,
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const onnx_parser::node_info& info,
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std::vector<instruction_ref> args) const
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{
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args[0] = info.add_instruction(make_op("exp"), args[0]);
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auto sum_ins = parse_reduce_oper("reduce_sum", parser, info, std::move(args));
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return info.add_instruction(make_op("log"), sum_ins);
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}
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};
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struct parse_reduce_sum_square : reduce_parser<parse_reduce_sum_square>
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{
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std::vector<op_desc> operators() const { return {{"ReduceSumSquare"}}; }
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instruction_ref parse(const op_desc& /*opd*/,
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const onnx_parser& parser,
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onnx_parser::node_info info,
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std::vector<instruction_ref> args) const
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{
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args[0] = info.add_instruction(make_op("mul"), args[0], args[0]);
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return parse_reduce_oper("reduce_sum", parser, std::move(info), std::move(args));
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}
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};
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} // namespace onnx
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} // namespace MIGRAPHX_INLINE_NS
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} // namespace migraphx
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