mirror of
https://github.com/blakeblackshear/frigate.git
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143 lines
5.7 KiB
C++
143 lines
5.7 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/quantize_dequantize_linear.hpp>
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#include <migraphx/ranges.hpp>
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#include <migraphx/make_op.hpp>
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#include <migraphx/tune_axis.hpp>
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#include <migraphx/common.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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std::vector<instruction_ref>
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transform_quantize_dequantize_linear_inputs(const onnx_parser::node_info& info,
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const std::string& onnx_name,
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int block_size,
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int axis,
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std::vector<instruction_ref> args)
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{
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const auto x = args.at(0);
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const auto x_lens = x->get_shape().lens();
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const auto x_rank = x_lens.size();
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instruction_ref y_scale = args.at(1);
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const auto y_scale_lens = y_scale->get_shape().lens();
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const auto y_scale_rank = y_scale_lens.size();
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// Per-tensor (per-layer) granularity
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if(y_scale->get_shape().elements() == 1)
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{
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std::transform(args.begin() + 1, args.end(), args.begin() + 1, [&](auto ins) {
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return info.add_instruction(make_op("multibroadcast", {{"out_lens", x_lens}}), ins);
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});
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}
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// Per-axis granularity
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else if(y_scale_rank == 1)
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{
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axis = tune_axis(x_rank, axis, onnx_name);
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if(x_lens[axis] != y_scale_lens[0])
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{
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MIGRAPHX_THROW(onnx_name +
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": For per axis granularity the length of y_scale (actual: " +
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to_string(y_scale_lens[0]) + ") must be equal to size of x on axis " +
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to_string(axis) + "(actual: " + to_string(x_lens[axis]) + ")");
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}
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std::transform(args.begin() + 1, args.end(), args.begin() + 1, [&](auto ins) {
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return info.add_instruction(
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make_op("broadcast", {{"axis", axis}, {"out_lens", x_lens}}), ins);
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});
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}
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// Blocked granularity
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else
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{
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axis = tune_axis(x_rank, axis, onnx_name);
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if(x_rank != y_scale_rank)
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{
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MIGRAPHX_THROW(onnx_name + ": x(rank: " + to_string(x_rank) +
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") and y_scale(rank: " + to_string(y_scale_rank) +
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") must be of same rank for block granularity");
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}
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for(auto i = 0u; i < x_lens.size(); ++i)
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{
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if(x_lens[i] != y_scale_lens[i] and i != axis)
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{
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MIGRAPHX_THROW(onnx_name + ": x(shape: " + to_string_range(x_lens) +
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") and y_scale(shape: " + to_string_range(y_scale_lens) +
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") shapes may only differ along provided axis(" + to_string(axis) +
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")");
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}
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}
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// Given x shape (D0, ..., Di, ..., Dn), y_scale shape (S0, ... Si, ...Sn) and
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// axis=i, the accepted range is [ceil(Di/Si), ceil(Di/(Si-1))-1]
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float di = x_lens[axis];
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float si = y_scale_lens[axis];
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int block_size_min = std::ceil(di / si);
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int block_size_max = std::ceil(di / (si - 1)) - 1;
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// default block_size if not given is calculated (to support quark generated models):
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if(block_size == 0)
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block_size = block_size_min;
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if(block_size < block_size_min or block_size > block_size_max)
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MIGRAPHX_THROW(onnx_name + ": Block size(actual: " + to_string(block_size) +
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") must be within range [" + to_string(block_size_min) + ", " +
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to_string(block_size_max) + "]");
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std::transform(args.begin() + 1, args.end(), args.begin() + 1, [&](auto ins) {
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if(block_size == 1)
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return ins;
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ins = info.add_instruction(make_op("unsqueeze", {{"axes", {axis + 1}}}), ins);
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auto bc_lens = ins->get_shape().lens();
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bc_lens[axis + 1] = block_size;
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ins = info.add_instruction(make_op("multibroadcast", {{"out_lens", bc_lens}}), ins);
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auto reshape_lens = x_lens;
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reshape_lens[axis] = ins->get_shape().lens()[axis] * block_size;
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ins = info.add_instruction(make_op("reshape", {{"dims", reshape_lens}}), ins);
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// Detect runt block
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if(x_lens[axis] < reshape_lens[axis])
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{
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ins = info.add_instruction(
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make_op("slice", {{"axes", {axis}}, {"starts", {0}}, {"ends", {x_lens[axis]}}}),
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ins);
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}
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return ins;
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});
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}
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return args;
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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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