/* * 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 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(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 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 &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