From c0d1c86b1bb1b4e129c292549845e00dfd8abfee Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 23 Mar 2018 15:13:15 +0000 Subject: COMPMID-734: CLTuner rework Change-Id: I8f20d6ea8a09869d71003e7b08e0d33775282f6c Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125802 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- .../CL/kernels/CLDirectConvolutionLayerKernel.cpp | 73 ----------- src/graph/Graph.cpp | 1 + src/runtime/CL/CLScheduler.cpp | 2 +- src/runtime/CL/CLTuner.cpp | 7 +- .../CL/functions/CLDirectConvolutionLayer.cpp | 5 +- src/runtime/CL/tuners/BifrostTuner.cpp | 143 +++++++++++++++++++++ 6 files changed, 155 insertions(+), 76 deletions(-) create mode 100644 src/runtime/CL/tuners/BifrostTuner.cpp (limited to 'src') diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp index 56ac0c7250..b5526c4fca 100644 --- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp +++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp @@ -315,79 +315,6 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL kernel_name << "_f32_bifrost"; _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_options.options())); - - // Through extensive experimentation with over 30 representative tensor - // shapes, we found a small number of local work size configurations - // that result in nearly optimal execution times. Selecting the right - // lws for a given shape, however, required a complex decision tree, - // until we constructed a simple feature as described below. - // - // We started from the number of multiply-accumulate operations for a - // convolution layer, which is equal to the product of the input - // dimensions 0..2 and the weights dimensions 0..2. Unfortunately, - // this resulted in ties between distinct shapes that required distinct - // lws configurations. Replacing the width of the input with the kernel - // size, however, resulted in nearly optimal predictions. We use underscores - // in variable names to indicate when they are intentionally misleading. - const size_t product_of_weights_dimensions = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2); - const size_t product_of_input_dimensions_ = input->info()->dimension(0) * weights->info()->dimension(1) * input->info()->dimension(2); - const float mega_ops_ = 1e-6 * product_of_weights_dimensions * product_of_input_dimensions_; - - switch(kernel_size) - { - case 1: - { - if(mega_ops_ < 1.f) - { - _lws_hint = cl::NDRange(1, 1, 8); - } - else if(mega_ops_ < 7.f) - { - _lws_hint = cl::NDRange(1, 1, 4); - } - else - { - _lws_hint = cl::NDRange(1, 1, 2); - } - break; - } - case 3: - { - if(mega_ops_ < 1.f) - { - _lws_hint = cl::NDRange(1, 1, 8); - } - else if(mega_ops_ < 13.f) - { - _lws_hint = cl::NDRange(2, 1, 4); - } - else if(mega_ops_ < 50.f) - { - _lws_hint = cl::NDRange(3, 1, 4); - } - else - { - _lws_hint = cl::NDRange(2, 1, 6); - } - break; - } - case 5: - { - if(mega_ops_ < 2.f || mega_ops_ > 80.f) - { - _lws_hint = cl::NDRange(2, 1, 4); - } - else - { - _lws_hint = cl::NDRange(2, 1, 8); - } - break; - } - default: - { - ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost"); - } - } } else { diff --git a/src/graph/Graph.cpp b/src/graph/Graph.cpp index 2fe3a90aef..47bd672114 100644 --- a/src/graph/Graph.cpp +++ b/src/graph/Graph.cpp @@ -30,6 +30,7 @@ #include "arm_compute/graph/Tensor.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/CLTuner.h" #include "arm_compute/runtime/Tensor.h" #include "support/ToolchainSupport.h" diff --git a/src/runtime/CL/CLScheduler.cpp b/src/runtime/CL/CLScheduler.cpp index 65292fe837..2a5d836c33 100644 --- a/src/runtime/CL/CLScheduler.cpp +++ b/src/runtime/CL/CLScheduler.cpp @@ -52,7 +52,7 @@ void CLScheduler::enqueue(ICLKernel &kernel, bool flush) if(_cl_tuner != nullptr) { // Tune the OpenCL kernel - _cl_tuner->tune_kernel(kernel); + _cl_tuner->tune_kernel_dynamic(kernel); } // Run kernel diff --git a/src/runtime/CL/CLTuner.cpp b/src/runtime/CL/CLTuner.cpp index df8e255356..17a62ab46e 100644 --- a/src/runtime/CL/CLTuner.cpp +++ b/src/runtime/CL/CLTuner.cpp @@ -113,7 +113,12 @@ bool CLTuner::tune_new_kernels() const return _tune_new_kernels; } -void CLTuner::tune_kernel(ICLKernel &kernel) +void CLTuner::tune_kernel_static(ICLKernel &kernel) +{ + ARM_COMPUTE_UNUSED(kernel); +} + +void CLTuner::tune_kernel_dynamic(ICLKernel &kernel) { // Get the configuration ID from the kernel const std::string &config_id = kernel.config_id(); diff --git a/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp index d6a335c1ec..c48865a0cc 100644 --- a/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDirectConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -52,6 +52,9 @@ void CLDirectConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weig zero_value = PixelValue(static_cast(input->info()->quantization_info().offset)); } _input_border_handler.configure(input, _direct_conv_kernel.border_size(), BorderMode::CONSTANT, zero_value); + + // Tune kernels + CLScheduler::get().tune_kernel_static(_direct_conv_kernel); } Status CLDirectConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) diff --git a/src/runtime/CL/tuners/BifrostTuner.cpp b/src/runtime/CL/tuners/BifrostTuner.cpp new file mode 100644 index 0000000000..c0ebd24afe --- /dev/null +++ b/src/runtime/CL/tuners/BifrostTuner.cpp @@ -0,0 +1,143 @@ +/* + * Copyright (c) 2018 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/CL/tuners/BifrostTuner.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernels.h" +#include "arm_compute/core/utils/misc/Cast.h" + +namespace arm_compute +{ +namespace tuners +{ +namespace +{ +/** Tunes a @ref CLDirectConvolutionLayerKernel for a bifrost target + * + * @param[in] k Kernels to tune + */ +void tune_direct_convolution_kernel(CLDirectConvolutionLayerKernel &k) +{ + cl::NDRange lws_hint = k.lws_hint(); + + const GPUTarget gpu_target = k.get_target(); + const DataType dt = k._input->info()->data_type(); + const TensorShape weights_shape = k._weights->info()->tensor_shape(); + const TensorShape inputs_shape = k._input->info()->tensor_shape(); + const size_t kernel_size = weights_shape.x(); + const unsigned int stride_x = k._conv_stride_x; + const unsigned int stride_y = k._conv_stride_y; + + if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72) && (kernel_size <= 5) && (stride_x == 1) && (stride_y == 1) && (dt == DataType::F32)) + { + // Through extensive experimentation with over 30 representative tensor + // shapes, we found a small number of local work size configurations + // that result in nearly optimal execution times. Selecting the right + // lws for a given shape, however, required a complex decision tree, + // until we constructed a simple feature as described below. + // + // We started from the number of multiply-accumulate operations for a + // convolution layer, which is equal to the product of the input + // dimensions 0..2 and the weights dimensions 0..2. Unfortunately, + // this resulted in ties between distinct shapes that required distinct + // lws configurations. Replacing the width of the input with the kernel + // size, however, resulted in nearly optimal predictions. We use underscores + // in variable names to indicate when they are intentionally misleading. + const size_t product_of_weights_dimensions = weights_shape[0] * weights_shape[1] * weights_shape[2]; + const size_t product_of_input_dimensions_ = inputs_shape[0] * inputs_shape[1] * inputs_shape[2]; + const float mega_ops_ = 1e-6 * product_of_weights_dimensions * product_of_input_dimensions_; + + switch(kernel_size) + { + case 1: + { + if(mega_ops_ < 1.f) + { + lws_hint = cl::NDRange(1, 1, 8); + } + else if(mega_ops_ < 7.f) + { + lws_hint = cl::NDRange(1, 1, 4); + } + else + { + lws_hint = cl::NDRange(1, 1, 2); + } + break; + } + case 3: + { + if(mega_ops_ < 1.f) + { + lws_hint = cl::NDRange(1, 1, 8); + } + else if(mega_ops_ < 13.f) + { + lws_hint = cl::NDRange(2, 1, 4); + } + else if(mega_ops_ < 50.f) + { + lws_hint = cl::NDRange(3, 1, 4); + } + else + { + lws_hint = cl::NDRange(2, 1, 6); + } + break; + } + case 5: + { + if(mega_ops_ < 2.f || mega_ops_ > 80.f) + { + lws_hint = cl::NDRange(2, 1, 4); + } + else + { + lws_hint = cl::NDRange(2, 1, 8); + } + break; + } + default: + break; + } + k.set_lws_hint(lws_hint); + } +} +} // namespace + +void BifrostTuner::tune_kernel_static(ICLKernel &kernel) +{ + // Continue on tuning if dynamic tuning + if(dynamic_cast(&kernel) != nullptr) + { + tune_direct_convolution_kernel(*utils::cast::polymorphic_downcast(&kernel)); + } +} + +void BifrostTuner::tune_kernel_dynamic(ICLKernel &kernel) +{ + ARM_COMPUTE_UNUSED(kernel); +} +} // namespace tuners +} // namespace arm_compute \ No newline at end of file -- cgit v1.2.1