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/*
* Copyright (c) 2016-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* 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 "arm_compute/runtime/IScheduler.h"
#include "arm_compute/core/CPP/ICPPKernel.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Log.h"
#include "arm_compute/core/Window.h"
#include "src/common/cpuinfo/CpuInfo.h"
#include "src/runtime/SchedulerUtils.h"
namespace arm_compute
{
IScheduler::IScheduler()
{
// Work out the best possible number of execution threads
_num_threads_hint = cpuinfo::num_threads_hint();
}
CPUInfo &IScheduler::cpu_info()
{
return CPUInfo::get();
}
void IScheduler::set_num_threads_with_affinity(unsigned int num_threads, BindFunc func)
{
ARM_COMPUTE_UNUSED(num_threads, func);
ARM_COMPUTE_ERROR("Feature for affinity setting is not implemented");
}
unsigned int IScheduler::num_threads_hint() const
{
return _num_threads_hint;
}
void IScheduler::schedule_common(ICPPKernel *kernel, const Hints &hints, const Window &window, ITensorPack &tensors)
{
ARM_COMPUTE_ERROR_ON_MSG(!kernel, "The child class didn't set the kernel");
#ifndef BARE_METAL
const Window &max_window = window;
if (hints.split_dimension() == IScheduler::split_dimensions_all)
{
/*
* if the split dim is size_t max then this signals we should parallelise over
* all dimensions
*/
const std::size_t m = max_window.num_iterations(Window::DimX);
const std::size_t n = max_window.num_iterations(Window::DimY);
//in c++17 this can be swapped for auto [ m_threads, n_threads ] = split_2d(...
unsigned m_threads, n_threads;
std::tie(m_threads, n_threads) = scheduler_utils::split_2d(this->num_threads(), m, n);
std::vector<IScheduler::Workload> workloads;
for (unsigned int ni = 0; ni != n_threads; ++ni)
{
for (unsigned int mi = 0; mi != m_threads; ++mi)
{
workloads.push_back(
[ni, mi, m_threads, n_threads, &max_window, &kernel](const ThreadInfo &info)
{
//narrow the window to our mi-ni workload
Window win = max_window.split_window(Window::DimX, mi, m_threads)
.split_window(Window::DimY, ni, n_threads);
win.validate();
Window thread_locator;
thread_locator.set(Window::DimX, Window::Dimension(mi, m_threads));
thread_locator.set(Window::DimY, Window::Dimension(ni, n_threads));
thread_locator.validate();
kernel->run_nd(win, info, thread_locator);
});
}
}
run_workloads(workloads);
}
else
{
const unsigned int num_iterations = max_window.num_iterations(hints.split_dimension());
const unsigned int num_threads = std::min(num_iterations, this->num_threads());
if (num_iterations == 0)
{
return;
}
if (!kernel->is_parallelisable() || num_threads == 1)
{
ThreadInfo info;
info.cpu_info = &cpu_info();
if (tensors.empty())
{
kernel->run(max_window, info);
}
else
{
kernel->run_op(tensors, max_window, info);
}
}
else
{
unsigned int num_windows = 0;
switch (hints.strategy())
{
case StrategyHint::STATIC:
num_windows = num_threads;
break;
case StrategyHint::DYNAMIC:
{
const unsigned int granule_threshold =
(hints.threshold() <= 0) ? num_threads : static_cast<unsigned int>(hints.threshold());
// Make sure we don't use some windows which are too small as this might create some contention on the ThreadFeeder
num_windows = num_iterations > granule_threshold ? granule_threshold : num_iterations;
break;
}
default:
ARM_COMPUTE_ERROR("Unknown strategy");
}
// Make sure the smallest window is larger than minimum workload size
num_windows = adjust_num_of_windows(max_window, hints.split_dimension(), num_windows, *kernel, cpu_info());
std::vector<IScheduler::Workload> workloads(num_windows);
for (unsigned int t = 0; t < num_windows; ++t)
{
//Capture 't' by copy, all the other variables by reference:
workloads[t] = [t, &hints, &max_window, &num_windows, &kernel, &tensors](const ThreadInfo &info)
{
Window win = max_window.split_window(hints.split_dimension(), t, num_windows);
win.validate();
if (tensors.empty())
{
kernel->run(win, info);
}
else
{
kernel->run_op(tensors, win, info);
}
};
}
run_workloads(workloads);
}
}
#else /* !BARE_METAL */
ARM_COMPUTE_UNUSED(kernel, hints, window, tensors);
#endif /* !BARE_METAL */
}
void IScheduler::run_tagged_workloads(std::vector<Workload> &workloads, const char *tag)
{
ARM_COMPUTE_UNUSED(tag);
run_workloads(workloads);
}
std::size_t IScheduler::adjust_num_of_windows(const Window &window,
std::size_t split_dimension,
std::size_t init_num_windows,
const ICPPKernel &kernel,
const CPUInfo &cpu_info)
{
// Mitigation of the narrow split issue, which occurs when the split dimension is too small to split (hence "narrow").
if (window.num_iterations(split_dimension) < init_num_windows)
{
auto recommended_split_dim = Window::DimX;
for (std::size_t dims = Window::DimY; dims <= Window::DimW; ++dims)
{
if (window.num_iterations(recommended_split_dim) < window.num_iterations(dims))
{
recommended_split_dim = dims;
}
}
ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE(
"%zu dimension is not a suitable dimension to split the workload. Recommended: %zu recommended_split_dim",
split_dimension, recommended_split_dim);
}
for (auto t = init_num_windows; t > 0; --t) // Trying the highest number of windows ,init_num_windows, first
{
// Try splitting the workload into t, subject to each subworkload size <= mws.
if ((window.num_iterations(split_dimension) / kernel.get_mws(cpu_info, t)) >= t)
{
if (t != init_num_windows)
{
ARM_COMPUTE_LOG_INFO_MSG_CORE(
"The scheduler is using a different thread count than the one assigned by the user.");
}
return t;
}
}
ARM_COMPUTE_LOG_INFO_MSG_CORE(
"The scheduler is using single thread instead of the thread count assigned by the user.");
return 1; // If the workload is so small that it can't be split, we should run a single thread
}
} // namespace arm_compute
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