mirror of
https://github.com/blakeblackshear/frigate.git
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91 lines
3.3 KiB
C++
91 lines
3.3 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2023 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/shape.hpp>
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#include <migraphx/argument.hpp>
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#include <migraphx/dfor.hpp>
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#include <migraphx/gpu/device/multinomial.hpp>
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#include <migraphx/gpu/device/tensor.hpp>
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#include <migraphx/gpu/device/launch.hpp>
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#include <migraphx/gpu/device/types.hpp>
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namespace migraphx {
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inline namespace MIGRAPHX_INLINE_NS {
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namespace gpu {
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namespace device {
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template <class Iterator, class T>
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constexpr Iterator upper_bound(Iterator first, Iterator last, const T& value)
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{
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Iterator it;
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typename std::iterator_traits<Iterator>::difference_type count;
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typename std::iterator_traits<Iterator>::difference_type step;
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count = std::distance(first, last);
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while(count > 0)
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{
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it = first;
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step = count / 2;
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std::advance(it, step);
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if(not(value < *it))
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{
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first = ++it;
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count -= step + 1;
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}
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else
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count = step;
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}
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return first;
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}
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void multinomial(hipStream_t stream,
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const argument& result,
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const argument& arg0,
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const argument& arg1)
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{
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size_t batch_size = arg0.get_shape().lens().front();
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size_t class_size = arg0.get_shape().lens().back();
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size_t sample_size = result.get_shape().lens().back();
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visit_all(arg0, arg1)([&](auto cdf_host, auto dist_host) {
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result.visit([&](auto output_host) {
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hip_visit_views(cdf_host, dist_host, output_host)(
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[&](auto cdf, auto dist, auto output) {
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gs_launch(stream, batch_size * sample_size)([=](auto i) __device__ {
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auto idx = output.get_shape().multi(i);
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auto cdf_begin = cdf.begin() + (idx.front() * class_size);
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auto cdf_end = cdf_begin + class_size;
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auto* sample_iter =
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upper_bound(cdf_begin, cdf_end, dist[i] * *(std::prev(cdf_end)));
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output[i] = std::distance(cdf_begin, sample_iter);
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});
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});
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});
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});
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}
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} // namespace device
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} // namespace gpu
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} // namespace MIGRAPHX_INLINE_NS
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} // namespace migraphx
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