mirror of
https://github.com/blakeblackshear/frigate.git
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81 lines
3.3 KiB
C++
81 lines
3.3 KiB
C++
/*
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* The MIT License (MIT)
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*
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* Copyright (c) 2015-2022 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/gpu/device/logsoftmax.hpp>
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#include <migraphx/gpu/device/reduce.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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void logsoftmax(hipStream_t stream, const argument& result, const argument& arg, int64_t axis)
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{
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auto batch_lens = result.get_shape().lens();
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index_int batch_item_num = batch_lens[axis];
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batch_lens[axis] = 1;
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migraphx::shape batch_shape{result.get_shape().type(), batch_lens};
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hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
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const index_int max_block_size = 256;
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const index_int block_size = compute_block_size(batch_item_num, max_block_size);
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gs_launch(stream,
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batch_shape.elements() * block_size,
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block_size)([=](auto i, auto idx) __device__ {
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auto data_idx = batch.multi(i / block_size);
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using type = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
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type init = lowest();
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auto batch_max = block_reduce<max_block_size>(
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idx, max{}, init, batch_item_num, [&](auto j) __device__ {
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data_idx[axis] = j;
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return input[data_idx];
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});
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auto batch_sum =
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block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
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data_idx[axis] = j;
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auto val = input[data_idx] - batch_max;
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return ::exp(to_hip_type(val));
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});
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auto log_batch_sum = ::log(to_hip_type(batch_sum)) + batch_max;
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idx.local_stride(batch_item_num, [&](auto j) __device__ {
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data_idx[axis] = j;
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output[data_idx] = input[data_idx] - log_batch_sum;
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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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