COMPMID-817: Tuner: Port kernels to new design.
Change-Id: Iaabb1153c2abe0400ec79d51a21347debe92d642
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/134062
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/CL/kernels/CLCol2ImKernel.cpp b/src/core/CL/kernels/CLCol2ImKernel.cpp
index e15da72..4e44420 100644
--- a/src/core/CL/kernels/CLCol2ImKernel.cpp
+++ b/src/core/CL/kernels/CLCol2ImKernel.cpp
@@ -110,21 +110,6 @@
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("col2im", build_opts.options()));
- // Configure the local work size for Bifrost with a value obtained
- // via exhaustive autotuning over 30 representative tensor shapes.
- const GPUTarget gpu_target = get_target();
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
- {
- if((_convolved_dims.first == 7) || (_convolved_dims.first == 14))
- {
- _lws_hint = cl::NDRange(1, 7, 1);
- }
- else
- {
- _lws_hint = cl::NDRange(1, 8, 1);
- }
- }
-
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), output->info(), _convolved_dims);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
index 41ff220..c89b16e 100644
--- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
@@ -90,15 +90,6 @@
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("depthwise_im2col", build_opts.options()));
- // Configure the local work size for Bifrost with a value obtained
- // via exhaustive autotuning for the MobileNets tensor shapes.
- const GPUTarget gpu_target = get_target();
-
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
- {
- _lws_hint = cl::NDRange(1, 2, 1);
- }
-
// Configure kernel window
Window win = calculate_max_window(*output->info(), Steps());
// CLDepthwiseIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
index 7a9760b..fc52f4e 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
@@ -194,51 +194,9 @@
_output = output;
_slide_matrix_b = _input1->info()->num_dimensions() >= _input0->info()->num_dimensions();
- const DataType data_type = input0->info()->data_type();
- const int fp_pos = input0->info()->fixed_point_position();
-
- // Get target architecture
- GPUTarget gpu_target = get_target();
-
- // Configure LWS hint
- switch(gpu_target)
- {
- case GPUTarget::MIDGARD:
- case GPUTarget::T600:
- case GPUTarget::T700:
- case GPUTarget::T800:
- if(output->info()->dimension(1) == 196)
- {
- _lws_hint = cl::NDRange(1, 7);
- }
- else
- {
- _lws_hint = cl::NDRange(8, 8);
- }
- break;
- case GPUTarget::G71:
- case GPUTarget::G72:
- case GPUTarget::G51:
- case GPUTarget::G51BIG:
- case GPUTarget::G51LIT:
- case GPUTarget::TNOX:
- if(input1->info()->dimension(1) == 24)
- {
- // LWS optimized for the 11x11 AlexNet convolution on Bifrost.
- _lws_hint = cl::NDRange(2, 2);
- }
- else if(output->info()->dimension(1) == 196)
- {
- _lws_hint = cl::NDRange(1, 7);
- }
- else
- {
- _lws_hint = cl::NDRange(8, 8);
- }
- break;
- default:
- _lws_hint = cl::NullRange;
- }
+ const DataType data_type = input0->info()->data_type();
+ const int fp_pos = input0->info()->fixed_point_position();
+ const GPUTarget gpu_target = get_target();
ElementsProcessed num_elements_processed{};
diff --git a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
index 1d6f388..d8ecd50 100644
--- a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
@@ -110,14 +110,6 @@
_kernel.setArg<int>(idx++, -_input1->info()->quantization_info().offset);
}
- // Configure the local work size for Bifrost with a value obtained
- // via exhaustive autotuning for the MobileNets tensor shapes.
- const GPUTarget gpu_target = get_target();
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
- {
- _lws_hint = cl::NDRange(1, 1, 1);
- }
-
// Configure kernel window
const unsigned int num_elems_read_per_iteration = 4;
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 378456c..53a4dca 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -61,7 +61,7 @@
} // namespace
CLIm2ColKernel::CLIm2ColKernel()
- : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
+ : _input(nullptr), _output(nullptr), _conv_info(), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr), _kernel_dims()
{
}
@@ -74,6 +74,7 @@
_input = input;
_output = output;
+ _conv_info = conv_info;
_kernel_dims = kernel_dims;
const DataType data_type = input->info()->data_type();
@@ -190,10 +191,9 @@
{
vector_size = kernel_dims.width;
}
- // Local work size and vector size optimized for the 11x11 AlexNet convolution on Bifrost.
+ // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && kernel_dims.width == 11)
{
- _lws_hint = cl::NDRange(1, 1, 1);
vector_size = 8;
}
const size_t width_mod_vector_size = kernel_dims.width % vector_size;
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index 3091df4..b242c55 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -208,8 +208,7 @@
_output = output;
_pool_info = pool_info;
- const GPUTarget gpu_target = get_target();
- const DataType data_type = input->info()->data_type();
+ const DataType data_type = input->info()->data_type();
// Set build options
CLBuildOptions build_opts;
@@ -273,20 +272,11 @@
ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config));
ICLKernel::configure(std::get<1>(win_config));
- // 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(data_layout == DataLayout::NCHW)
{
CLPoolingConfig pooling_config = std::get<2>(win_config);
_num_elems_processed_per_iteration = pooling_config.first;
_border_size = pooling_config.second;
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
- {
- cl::NDRange gws = ICLKernel::gws_from_window(std::get<1>(win_config));
- _lws_hint = cl::NDRange(gws[0], gws[1], 1);
- }
}
else
{
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 676a121..c2b24e3 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -134,6 +134,7 @@
_input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
_im2col_kernel.set_target(gpu_target);
_im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier);
+ CLScheduler::get().tune_kernel_static(_im2col_kernel);
// Weights reshape configuration
const TensorShape shape_weights_reshape(patch_size, weights_z);
@@ -149,6 +150,7 @@
_v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
_v2mm_kernel.set_target(gpu_target);
_v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
+ CLScheduler::get().tune_kernel_static(_v2mm_kernel);
_output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
_vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);
diff --git a/src/runtime/CL/functions/CLFlattenLayer.cpp b/src/runtime/CL/functions/CLFlattenLayer.cpp
index 9f571b2..f5809a2 100644
--- a/src/runtime/CL/functions/CLFlattenLayer.cpp
+++ b/src/runtime/CL/functions/CLFlattenLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -25,6 +25,7 @@
#include "arm_compute/core/CL/kernels/CLIm2ColKernel.h"
#include "arm_compute/core/Size2D.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
#include "support/ToolchainSupport.h"
using namespace arm_compute;
@@ -34,4 +35,5 @@
auto k = arm_compute::support::cpp14::make_unique<CLIm2ColKernel>();
k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
_kernel = std::move(k);
+ CLScheduler::get().tune_kernel_static(*_kernel);
}
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index 44bf283..9248bc5 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -117,6 +117,7 @@
// Configure im2col kernel
_memory_group.manage(&_im2col_output);
_im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+ CLScheduler::get().tune_kernel_static(_im2col_kernel);
// Configure matrix multiply kernel
configure_mm(&_im2col_output, weights, output);
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 7f37520..a0ec66f 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -143,7 +143,9 @@
_transpose_kernel.configure(b, &_tmp_b, mult_transpose1xW_width);
}
+ // Configure and tune matrix multiply kernel
_mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height));
+ CLScheduler::get().tune_kernel_static(_mm_kernel);
if(_is_interleaved_transposed)
{
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 4f87043..27bed44 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -230,10 +230,11 @@
_gemm_output.allocator()->init(info_gemm);
_memory_group.manage(&_gemm_output);
- // Configure im2col
+ // Configure and tune im2col
_im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
+ CLScheduler::get().tune_kernel_static(_im2col_kernel);
- // Configure GEMM
+ // Configure and tune GEMM
configure_mm(&_im2col_output, weights, &_gemm_output);
_im2col_output.allocator()->allocate();
@@ -250,8 +251,9 @@
_gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
}
- // Configure Col2Im
+ // Configure and tune Col2Im
_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
+ CLScheduler::get().tune_kernel_static(_col2im_kernel);
if(_is_quantized)
{
_tmp_output.allocator()->allocate();
diff --git a/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp b/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp
index 986fe00..31d5cd5 100644
--- a/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLLocallyConnectedLayer.cpp
@@ -163,6 +163,8 @@
_weights_reshaped.allocator()->allocate();
_input_im2col_reshaped.allocator()->allocate();
_gemm_output.allocator()->allocate();
+
+ CLScheduler::get().tune_kernel_static(_input_im2col_kernel);
}
void CLLocallyConnectedLayer::run()
diff --git a/src/runtime/CL/functions/CLPoolingLayer.cpp b/src/runtime/CL/functions/CLPoolingLayer.cpp
index 17875a3..cbe1ce3 100644
--- a/src/runtime/CL/functions/CLPoolingLayer.cpp
+++ b/src/runtime/CL/functions/CLPoolingLayer.cpp
@@ -63,6 +63,9 @@
ARM_COMPUTE_ERROR("Data layout not supported");
}
_border_handler.configure(input, _kernel->border_size(), border_mode, pixel_value);
+
+ // Tune kernels
+ CLScheduler::get().tune_kernel_static(*_kernel);
}
Status CLPoolingLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
diff --git a/src/runtime/CL/tuners/BifrostTuner.cpp b/src/runtime/CL/tuners/BifrostTuner.cpp
index c0ebd24..edd074b 100644
--- a/src/runtime/CL/tuners/BifrostTuner.cpp
+++ b/src/runtime/CL/tuners/BifrostTuner.cpp
@@ -124,15 +124,163 @@
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::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
+ {
+ if((k._convolved_dims.first == 7) || (k._convolved_dims.first == 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::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && 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_depthwise_im2col_kernel(CLDepthwiseIm2ColKernel &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 for the MobileNets tensor shapes.
+ if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
+ {
+ lws_hint = cl::NDRange(1, 2, 1);
+ }
+
+ k.set_lws_hint(lws_hint);
+}
+
+void tune_gemv_kernel(CLGEMMMatrixVectorMultiplyKernel &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 for the MobileNets tensor shapes.
+ if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
+ {
+ 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::TNOX:
+ 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(CLPoolingLayerKernel &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._input->info()->data_layout() == DataLayout::NCHW)
+ {
+ if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX))
+ {
+ cl::NDRange gws = ICLKernel::gws_from_window(k.window());
+ lws_hint = cl::NDRange(gws[0], gws[1], 1);
+ }
+ }
+
+ k.set_lws_hint(lws_hint);
+}
} // namespace
void BifrostTuner::tune_kernel_static(ICLKernel &kernel)
{
- // Continue on tuning if dynamic tuning
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<CLDepthwiseIm2ColKernel *>(&kernel) != nullptr)
+ {
+ tune_depthwise_im2col_kernel(*utils::cast::polymorphic_downcast<CLDepthwiseIm2ColKernel *>(&kernel));
+ }
+ else if(dynamic_cast<CLGEMMMatrixVectorMultiplyKernel *>(&kernel) != nullptr)
+ {
+ tune_gemv_kernel(*utils::cast::polymorphic_downcast<CLGEMMMatrixVectorMultiplyKernel *>(&kernel));
+ }
+ else if(dynamic_cast<CLGEMMMatrixMultiplyKernel *>(&kernel) != nullptr)
+ {
+ tune_gemm_kernel(*utils::cast::polymorphic_downcast<CLGEMMMatrixMultiplyKernel *>(&kernel));
+ }
+ else if(dynamic_cast<CLPoolingLayerKernel *>(&kernel) != nullptr)
+ {
+ tune_pooling_kernel(*utils::cast::polymorphic_downcast<CLPoolingLayerKernel *>(&kernel));
+ }
}
void BifrostTuner::tune_kernel_dynamic(ICLKernel &kernel)
diff --git a/src/runtime/CL/tuners/MidgardTuner.cpp b/src/runtime/CL/tuners/MidgardTuner.cpp
new file mode 100644
index 0000000..2c4b1ac
--- /dev/null
+++ b/src/runtime/CL/tuners/MidgardTuner.cpp
@@ -0,0 +1,77 @@
+/*
+ * 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/MIdgardTuner.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
+{
+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);
+}
+} // namespace tuners
+} // namespace arm_compute
\ No newline at end of file