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-rw-r--r--src/runtime/CL/tuners/BifrostTuner.cpp304
-rw-r--r--src/runtime/CL/tuners/CLTuningParametersList.cpp77
-rw-r--r--src/runtime/CL/tuners/MidgardTuner.cpp82
3 files changed, 43 insertions, 420 deletions
diff --git a/src/runtime/CL/tuners/BifrostTuner.cpp b/src/runtime/CL/tuners/BifrostTuner.cpp
deleted file mode 100644
index 7a06de6d1c..0000000000
--- a/src/runtime/CL/tuners/BifrostTuner.cpp
+++ /dev/null
@@ -1,304 +0,0 @@
-/*
- * Copyright (c) 2018-2021 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 "src/core/CL/CLKernels.h"
-#include "support/Cast.h"
-
-#include "src/core/gpu/cl/kernels/ClPoolingKernel.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);
- }
-}
-
-void tune_col2im_kernel(CLCol2ImKernel &k)
-{
- cl::NDRange lws_hint = k.lws_hint();
- const GPUTarget gpu_target = k.get_target();
-
- // Configure the local work size for Bifrost with a value obtained
- // via exhaustive autotuning over 30 representative tensor shapes.
- if(gpu_target_is_in(gpu_target,
- GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
- GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
- GPUTarget::G52, GPUTarget::G52LIT))
- {
- if((k._convolved_dims.width == 7) || (k._convolved_dims.width == 14))
- {
- lws_hint = cl::NDRange(1, 7, 1);
- }
- else
- {
- lws_hint = cl::NDRange(1, 8, 1);
- }
- }
-
- k.set_lws_hint(lws_hint);
-}
-
-void tune_im2col_kernel(CLIm2ColKernel &k)
-{
- cl::NDRange lws_hint = k.lws_hint();
- const GPUTarget gpu_target = k.get_target();
-
- // Local work size optimized for the 11x11 AlexNet convolution on Bifrost.
- if(gpu_target_is_in(gpu_target,
- GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
- GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
- GPUTarget::G52, GPUTarget::G52LIT)
- && k._kernel_dims.width == 11)
- {
- const bool is_square_kernel = (k._kernel_dims.width == k._kernel_dims.height);
- if(!is_square_kernel && k._kernel_dims.width > 1 && !k._conv_info.has_padding())
- {
- lws_hint = cl::NDRange(1, 1, 1);
- }
- }
- k.set_lws_hint(lws_hint);
-}
-
-void tune_gemm_kernel(CLGEMMMatrixMultiplyKernel &k)
-{
- cl::NDRange lws_hint = k.lws_hint();
- const GPUTarget gpu_target = k.get_target();
-
- // Configure LWS hint
- switch(gpu_target)
- {
- case GPUTarget::G71:
- case GPUTarget::G72:
- case GPUTarget::G51:
- case GPUTarget::G51BIG:
- case GPUTarget::G51LIT:
- case GPUTarget::G52:
- case GPUTarget::G52LIT:
- case GPUTarget::G76:
- if(k._input1->info()->dimension(1) == 24)
- {
- // LWS optimized for the 11x11 AlexNet convolution on Bifrost.
- lws_hint = cl::NDRange(2, 2);
- }
- else if(k._output->info()->dimension(1) == 196)
- {
- lws_hint = cl::NDRange(1, 7);
- }
- else
- {
- lws_hint = cl::NDRange(8, 8);
- }
- break;
- default:
- lws_hint = cl::NullRange;
- }
-
- k.set_lws_hint(lws_hint);
-}
-
-void tune_pooling_kernel(opencl::kernels::ClPoolingKernel &k)
-{
- cl::NDRange lws_hint = k.lws_hint();
- const GPUTarget gpu_target = k.get_target();
-
- // Configure the local work size (hint) from the first two dimensions of the global work size.
- // On Bifrost, this works for up to 35x35xC filters, for which the pooling_layer_3_optimized
- // kernel is launched with gws=(9, 33, C). In any case, the hint will be ignored if it is
- // invalid (e.g. exceeds the maximum workgroup size that the kernel can be launched with).
- if(k._pool_info.data_layout == DataLayout::NCHW)
- {
- if(gpu_target_is_in(gpu_target,
- GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
- GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
- GPUTarget::G52, GPUTarget::G52LIT))
- {
- cl::NDRange gws = ICLKernel::gws_from_window(k.window());
- lws_hint = cl::NDRange(gws[0], gws[1], 1);
- }
- }
-
- k.set_lws_hint(lws_hint);
-}
-
-void tune_scale_kernel(CLScaleKernel &k)
-{
- cl::NDRange lws_hint = k.lws_hint();
- const GPUTarget gpu_target = k.get_target();
- const DataType dt = k.input()->info()->data_type();
- const InterpolationPolicy interpolation = k.get_interpolation_policy();
-
- // Configure the local work size for Bifrost, interpolation (bilinear) and datatype F32.
- // The value are obtained via exhaustive autotuning.
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72) && (dt == DataType::F32) && (interpolation == InterpolationPolicy::BILINEAR))
- {
- auto dim_0 = k.output()->info()->dimension(0);
- if(dim_0 == 480)
- {
- lws_hint = cl::NDRange(2, 1);
- }
- else if(dim_0 == 3120)
- {
- lws_hint = cl::NDRange(2, 8);
- }
- else if(dim_0 == 4160)
- {
- lws_hint = cl::NDRange(4, 8);
- }
- k.set_lws_hint(lws_hint);
- }
-}
-} // namespace
-
-void BifrostTuner::tune_kernel_static(ICLKernel &kernel)
-{
- if(dynamic_cast<CLDirectConvolutionLayerKernel *>(&kernel) != nullptr)
- {
- tune_direct_convolution_kernel(*utils::cast::polymorphic_downcast<CLDirectConvolutionLayerKernel *>(&kernel));
- }
- else if(dynamic_cast<CLCol2ImKernel *>(&kernel) != nullptr)
- {
- tune_col2im_kernel(*utils::cast::polymorphic_downcast<CLCol2ImKernel *>(&kernel));
- }
- else if(dynamic_cast<CLIm2ColKernel *>(&kernel) != nullptr)
- {
- tune_im2col_kernel(*utils::cast::polymorphic_downcast<CLIm2ColKernel *>(&kernel));
- }
- else if(dynamic_cast<CLGEMMMatrixMultiplyKernel *>(&kernel) != nullptr)
- {
- tune_gemm_kernel(*utils::cast::polymorphic_downcast<CLGEMMMatrixMultiplyKernel *>(&kernel));
- }
- else if(dynamic_cast<opencl::kernels::ClPoolingKernel *>(&kernel) != nullptr)
- {
- tune_pooling_kernel(*utils::cast::polymorphic_downcast<opencl::kernels::ClPoolingKernel *>(&kernel));
- }
- else if(dynamic_cast<CLScaleKernel *>(&kernel) != nullptr)
- {
- tune_scale_kernel(*utils::cast::polymorphic_downcast<CLScaleKernel *>(&kernel));
- }
-}
-
-void BifrostTuner::tune_kernel_dynamic(ICLKernel &kernel)
-{
- ARM_COMPUTE_UNUSED(kernel);
-}
-
-void BifrostTuner::tune_kernel_dynamic(ICLKernel &kernel, ITensorPack &tensors)
-{
- ARM_COMPUTE_UNUSED(kernel, tensors);
-}
-} // namespace tuners
-} // namespace arm_compute \ No newline at end of file
diff --git a/src/runtime/CL/tuners/CLTuningParametersList.cpp b/src/runtime/CL/tuners/CLTuningParametersList.cpp
index 6cb2212794..5e3907f1ea 100644
--- a/src/runtime/CL/tuners/CLTuningParametersList.cpp
+++ b/src/runtime/CL/tuners/CLTuningParametersList.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019-2021 Arm Limited.
+ * Copyright (c) 2019-2021, 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -27,20 +27,20 @@ namespace arm_compute
{
namespace cl_tuner
{
-constexpr unsigned int max_lws_supported_x{ 64u };
-constexpr unsigned int max_lws_supported_y{ 32u };
-constexpr unsigned int max_lws_supported_z{ 32u };
+constexpr unsigned int max_lws_supported_x{64u};
+constexpr unsigned int max_lws_supported_y{32u};
+constexpr unsigned int max_lws_supported_z{32u};
-/** Non instantiable base class for Tuning parameters combinations that use Index2Cooard mapping */
+/** Non instantiable base class for Tuning parameters combinations that use Index2Coord mapping */
class CLTuningParametersList : public ICLTuningParametersList
{
protected:
/* Shape of 4-D search space */
- TensorShape search_space_shape{ 0, 0, 0, 0 };
- std::vector<unsigned int> _lws_x{ 0 };
- std::vector<unsigned int> _lws_y{ 0 };
- std::vector<unsigned int> _lws_z{ 0 };
- std::vector<int> _wbsm{ 0 }; /* Modify the batches size of workgroups distributed to compute units.
+ TensorShape search_space_shape{0, 0, 0, 0};
+ std::vector<unsigned int> _lws_x{0};
+ std::vector<unsigned int> _lws_y{0};
+ std::vector<unsigned int> _lws_z{0};
+ std::vector<int> _wbsm{0}; /* Modify the batches size of workgroups distributed to compute units.
The value is in the range [-31,+31].
When 0, the runtime-selected wbs used is unmodified. */
@@ -116,7 +116,8 @@ private:
* @param[in] lws_max Max LWS value allowed to be tested
* @param[in] mod_let_one True if the results of the modulo operation between gws and the lws can be less than one.
*/
- void initialize_lws_values(std::vector<unsigned int> &lws, unsigned int gws, unsigned int lws_max, bool mod_let_one);
+ void
+ initialize_lws_values(std::vector<unsigned int> &lws, unsigned int gws, unsigned int lws_max, bool mod_let_one);
};
/** A minimal subset of LWS values that only have 1,2 and 4/8 */
@@ -162,14 +163,17 @@ CLTuningParams CLTuningParametersListExhaustive::operator[](size_t index)
CLTuningParametersListExhaustive::CLTuningParametersListExhaustive(const cl::NDRange &gws, CLTuningInfo tuning_info)
{
- ARM_COMPUTE_UNUSED(gws);
- search_space_shape[0] = max_lws_supported_x;
- search_space_shape[1] = max_lws_supported_y;
- search_space_shape[2] = max_lws_supported_z;
+ const auto lws_x_max = std::min(static_cast<unsigned int>(gws[0]), max_lws_supported_x);
+ const auto lws_y_max = std::min(static_cast<unsigned int>(gws[1]), max_lws_supported_y);
+ const auto lws_z_max = std::min(static_cast<unsigned int>(gws[2]), max_lws_supported_z);
+
+ search_space_shape[0] = lws_x_max;
+ search_space_shape[1] = lws_y_max;
+ search_space_shape[2] = lws_z_max;
search_space_shape[3] = 1;
- if(tuning_info.tune_wbsm)
+ if (tuning_info.tune_wbsm)
{
- _wbsm = { -3, -2, -1, 0, 1, 2, 3 };
+ _wbsm = {-3, -2, -1, 0, 1, 2, 3};
search_space_shape[3] = _wbsm.size();
}
}
@@ -183,34 +187,39 @@ CLTuningParams CLTuningParametersListNormal::operator[](size_t index)
CLTuningParametersListNormal::CLTuningParametersListNormal(const cl::NDRange &gws, CLTuningInfo tuning_info)
{
- auto lws_x_max = std::min(static_cast<unsigned int>(gws[0]), max_lws_supported_x);
- auto lws_y_max = std::min(static_cast<unsigned int>(gws[1]), max_lws_supported_y);
- auto lws_z_max = std::min(static_cast<unsigned int>(gws[2]), max_lws_supported_z);
+ const auto lws_x_max = std::min(static_cast<unsigned int>(gws[0]), max_lws_supported_x);
+ const auto lws_y_max = std::min(static_cast<unsigned int>(gws[1]), max_lws_supported_y);
+ const auto lws_z_max = std::min(static_cast<unsigned int>(gws[2]), max_lws_supported_z);
// Initialize the tuning parameters values to test
_lws_x = {};
_lws_y = {};
_lws_z = {};
- initialize_lws_values(_lws_x, gws[0], lws_x_max, gws[2] > 16); // Explore lws that are not factors of gws only when gws[2] > 16
- initialize_lws_values(_lws_y, gws[1], lws_y_max, gws[2] > 16); // Explore lws that are not factors of gws only when gws[2] > 16
+ initialize_lws_values(_lws_x, gws[0], lws_x_max,
+ gws[2] > 16); // Explore lws that are not factors of gws only when gws[2] > 16
+ initialize_lws_values(_lws_y, gws[1], lws_y_max,
+ gws[2] > 16); // Explore lws that are not factors of gws only when gws[2] > 16
initialize_lws_values(_lws_z, gws[2], lws_z_max, false);
search_space_shape[0] = _lws_x.size();
search_space_shape[1] = _lws_y.size();
search_space_shape[2] = _lws_z.size();
search_space_shape[3] = 1;
- if(tuning_info.tune_wbsm)
+ if (tuning_info.tune_wbsm)
{
- _wbsm = { -2, -1, 0, 1, 2 };
+ _wbsm = {-2, -1, 0, 1, 2};
search_space_shape[3] = _wbsm.size();
}
}
-void CLTuningParametersListNormal::initialize_lws_values(std::vector<unsigned int> &lws, unsigned int gws, unsigned int lws_max, bool mod_let_one)
+void CLTuningParametersListNormal::initialize_lws_values(std::vector<unsigned int> &lws,
+ unsigned int gws,
+ unsigned int lws_max,
+ bool mod_let_one)
{
lws.push_back(1);
- for(unsigned int i = 2; i <= lws_max; ++i)
+ for (unsigned int i = 2; i <= lws_max; ++i)
{
// Power of two condition
const bool is_power_of_two = (i & (i - 1)) == 0;
@@ -218,7 +227,7 @@ void CLTuningParametersListNormal::initialize_lws_values(std::vector<unsigned in
// Condition for the module accordingly with the mod_let_one flag
const bool mod_cond = mod_let_one ? (gws % i) <= 1 : (gws % i) == 0;
- if(mod_cond || is_power_of_two)
+ if (mod_cond || is_power_of_two)
{
lws.push_back(i);
}
@@ -227,9 +236,9 @@ void CLTuningParametersListNormal::initialize_lws_values(std::vector<unsigned in
CLTuningParametersListRapid::CLTuningParametersListRapid(const cl::NDRange &gws, CLTuningInfo tuning_info)
{
- auto lws_x_max = std::min(static_cast<unsigned int>(gws[0]), 8u); // Limit exploration to 1 - 8
- auto lws_y_max = std::min(static_cast<unsigned int>(gws[1]), 4u); // Limit exploration to 1 - 4
- auto lws_z_max = std::min(static_cast<unsigned int>(gws[2]), 4u); // Limit exploration to 1 - 4
+ const auto lws_x_max = std::min(static_cast<unsigned int>(gws[0]), 8u); // Limit exploration to 1 - 8
+ const auto lws_y_max = std::min(static_cast<unsigned int>(gws[1]), 4u); // Limit exploration to 1 - 4
+ const auto lws_z_max = std::min(static_cast<unsigned int>(gws[2]), 4u); // Limit exploration to 1 - 4
// Initialize the LWS values to test
_lws_x = {};
@@ -243,9 +252,9 @@ CLTuningParametersListRapid::CLTuningParametersListRapid(const cl::NDRange &gws,
search_space_shape[1] = _lws_y.size();
search_space_shape[2] = _lws_z.size();
search_space_shape[3] = 1;
- if(tuning_info.tune_wbsm)
+ if (tuning_info.tune_wbsm)
{
- _wbsm = { -1, 0, 1 };
+ _wbsm = {-1, 0, 1};
search_space_shape[3] = _wbsm.size();
}
}
@@ -254,7 +263,7 @@ void CLTuningParametersListRapid::initialize_lws_values(std::vector<unsigned int
{
lws.push_back(1);
- for(unsigned int i = 2; i <= lws_max; i *= 4)
+ for (unsigned int i = 2; i <= lws_max; i *= 4)
{
lws.push_back(i);
}
@@ -262,7 +271,7 @@ void CLTuningParametersListRapid::initialize_lws_values(std::vector<unsigned int
std::unique_ptr<ICLTuningParametersList> get_tuning_parameters_list(CLTuningInfo tuning_info, const cl::NDRange &gws)
{
- switch(tuning_info.tuner_mode)
+ switch (tuning_info.tuner_mode)
{
case CLTunerMode::EXHAUSTIVE:
return std::make_unique<CLTuningParametersListExhaustive>(gws, tuning_info);
diff --git a/src/runtime/CL/tuners/MidgardTuner.cpp b/src/runtime/CL/tuners/MidgardTuner.cpp
deleted file mode 100644
index 72734f2207..0000000000
--- a/src/runtime/CL/tuners/MidgardTuner.cpp
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Copyright (c) 2018-2020 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/MidgardTuner.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "src/core/CL/CLKernels.h"
-#include "support/Cast.h"
-
-namespace arm_compute
-{
-namespace tuners
-{
-namespace
-{
-void tune_gemm_kernel(CLGEMMMatrixMultiplyKernel &k)
-{
- cl::NDRange lws_hint = k.lws_hint();
- const GPUTarget gpu_target = k.get_target();
-
- switch(gpu_target)
- {
- case GPUTarget::MIDGARD:
- case GPUTarget::T600:
- case GPUTarget::T700:
- case GPUTarget::T800:
- if(k._output->info()->dimension(1) == 196)
- {
- lws_hint = cl::NDRange(1, 7);
- }
- else
- {
- lws_hint = cl::NDRange(8, 8);
- }
- break;
- default:
- lws_hint = cl::NullRange;
- }
-
- k.set_lws_hint(lws_hint);
-}
-} // namespace
-
-void MidgardTuner::tune_kernel_static(ICLKernel &kernel)
-{
- if(dynamic_cast<CLGEMMMatrixMultiplyKernel *>(&kernel) != nullptr)
- {
- tune_gemm_kernel(*utils::cast::polymorphic_downcast<CLGEMMMatrixMultiplyKernel *>(&kernel));
- }
-}
-
-void MidgardTuner::tune_kernel_dynamic(ICLKernel &kernel)
-{
- ARM_COMPUTE_UNUSED(kernel);
-}
-
-void MidgardTuner::tune_kernel_dynamic(ICLKernel &kernel, ITensorPack &tensors)
-{
- ARM_COMPUTE_UNUSED(kernel, tensors);
-}
-} // namespace tuners
-} // namespace arm_compute
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-25 17:25:12 +0000