Use heuristics for setting dynamic fusion direct conv2d tile sizes
Resolves: COMPMID-5735
Change-Id: I9958413b69c5052cfa205dd0e9457cc4953aaf35
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/474818
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: bsgcomp <bsgcomp@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8724
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/heuristics/direct_conv/ClDirectConvDefaultConfigValhall.cpp b/src/runtime/heuristics/direct_conv/ClDirectConvDefaultConfigValhall.cpp
new file mode 100644
index 0000000..8f2fd82
--- /dev/null
+++ b/src/runtime/heuristics/direct_conv/ClDirectConvDefaultConfigValhall.cpp
@@ -0,0 +1,400 @@
+/*
+ * Copyright (c) 2022 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 "src/runtime/heuristics/direct_conv/ClDirectConvDefaultConfigValhall.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/GPUTarget.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include <utility>
+
+namespace arm_compute
+{
+namespace cl_direct_conv
+{
+using namespace arm_compute::misc::shape_calculator;
+
+ClDirectConvDefaultConfigValhall::ClDirectConvDefaultConfigValhall(GPUTarget gpu)
+ : IClDirectConvKernelConfig(gpu)
+{
+}
+
+DirectConvComputeKernelInfo ClDirectConvDefaultConfigValhall::configure(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+ using ConfigurationFunctionExecutorPtr = DirectConvComputeKernelInfo (ClDirectConvDefaultConfigValhall::*)(const ITensorInfo * src, const ITensorInfo * wei, const PadStrideInfo & conv_info);
+
+ ClDirectConvConfigArray<ConfigurationFunctionExecutorPtr> configs_G78(&ClDirectConvDefaultConfigValhall::configure_G78_f32,
+ &ClDirectConvDefaultConfigValhall::configure_G78_f16,
+ &ClDirectConvDefaultConfigValhall::configure_G78_u8);
+
+ ClDirectConvConfigArray<ConfigurationFunctionExecutorPtr> configs_G57(&ClDirectConvDefaultConfigValhall::configure_G57_f32,
+ &ClDirectConvDefaultConfigValhall::configure_G57_f16,
+ &ClDirectConvDefaultConfigValhall::configure_G78_u8);
+
+ ConfigurationFunctionExecutorPtr func = nullptr;
+ switch(_target)
+ {
+ case GPUTarget::G57:
+ func = configs_G57.get_function(src->data_type());
+ break;
+ case GPUTarget::G78:
+ default:
+ func = configs_G78.get_function(src->data_type());
+ break;
+ }
+
+ ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not supported for direct convolution");
+ return (this->*func)(src, wei, conv_info);
+}
+
+DirectConvComputeKernelInfo ClDirectConvDefaultConfigValhall::configure_G78_f32(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+ DirectConvComputeKernelInfo desc;
+
+ if(src->data_layout() == DataLayout::NHWC)
+ {
+ // Get the output shape
+ const TensorShape wei_shape = wei->tensor_shape();
+ const TensorShape dst_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info);
+ const bool export_weights_to_cl_image = export_to_cl_image(wei);
+
+ const int32_t ofm = dst_shape[0];
+ const int32_t m = dst_shape[1] * dst_shape[2];
+ const bool is_pointwise = (wei_shape[1] == wei_shape[2]) && wei_shape[1] == 1;
+
+ desc.export_weights_to_cl_image = export_weights_to_cl_image;
+
+ if(dst_shape[0] <= 4)
+ {
+ if(is_pointwise)
+ {
+ if(ofm == 4)
+ {
+ desc.m0 = 1;
+ desc.n0 = 4;
+ desc.k0 = 16;
+ }
+ else
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ desc.k0 = 16;
+ }
+ }
+ else
+ {
+ desc.m0 = 1;
+ desc.n0 = 2;
+ desc.k0 = 16;
+ }
+ }
+ else
+ {
+ if(m < 64)
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ desc.k0 = 16;
+ }
+ else
+ {
+ desc.m0 = 4;
+ desc.n0 = 4;
+ desc.k0 = 4;
+ }
+ }
+ }
+
+ return desc;
+}
+
+DirectConvComputeKernelInfo ClDirectConvDefaultConfigValhall::configure_G78_f16(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+ DirectConvComputeKernelInfo desc;
+
+ if(src->data_layout() == DataLayout::NHWC)
+ {
+ // Get the output shape
+ const TensorShape wei_shape = wei->tensor_shape();
+ const TensorShape dst_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info);
+ const bool export_weights_to_cl_image = export_to_cl_image(wei);
+
+ const int32_t ofm = dst_shape[0];
+ const int32_t m = dst_shape[1] * dst_shape[2];
+ const int32_t k = wei_shape[0];
+ const bool is_pointwise = (wei_shape[1] == wei_shape[2]) && wei_shape[1] == 1;
+
+ desc.export_weights_to_cl_image = export_weights_to_cl_image;
+
+ if(dst_shape[0] <= 4)
+ {
+ // k0 should be as larger as possible. However, we should avoid
+ // having left-over for loops that make the implementation slower.
+ if((k % 16) == 0)
+ {
+ desc.k0 = 16;
+ }
+ else if((k % 8) == 0)
+ {
+ desc.k0 = 8;
+ }
+ else
+ {
+ desc.k0 = 4;
+ }
+
+ if(is_pointwise)
+ {
+ if(ofm == 4)
+ {
+ desc.m0 = 1;
+ desc.n0 = 4;
+ }
+ else
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ }
+ }
+ else
+ {
+ desc.m0 = 1;
+ desc.n0 = dst_shape[0];
+ }
+ }
+ else
+ {
+ if(m < 64)
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ if((k % 16) == 0)
+ {
+ desc.k0 = 16;
+ }
+ else if((k % 8) == 0)
+ {
+ desc.k0 = 8;
+ }
+ else
+ {
+ desc.k0 = 4;
+ }
+ }
+ else
+ {
+ if(ofm >= 16)
+ {
+ if(m / 6 > 24000)
+ {
+ desc.m0 = 6;
+ }
+ else
+ {
+ desc.m0 = 5;
+ }
+ desc.n0 = 8;
+ desc.k0 = 4;
+ }
+ else
+ {
+ desc.m0 = 2;
+ desc.n0 = 8;
+ if((k % 16) == 0)
+ {
+ desc.k0 = 16;
+ }
+ else if((k % 8) == 0)
+ {
+ desc.k0 = 8;
+ }
+ else
+ {
+ desc.k0 = 4;
+ }
+ }
+ }
+ }
+ }
+
+ return desc;
+}
+
+DirectConvComputeKernelInfo ClDirectConvDefaultConfigValhall::configure_G78_u8(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+ DirectConvComputeKernelInfo desc;
+
+ if(src->data_layout() == DataLayout::NHWC)
+ {
+ // Get the output shape
+ TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info);
+
+ desc.n0 = 4;
+
+ if(output_shape[0] > 16)
+ {
+ desc.m0 = 4;
+ }
+
+ desc.k0 = 16;
+
+ desc.export_weights_to_cl_image = false;
+ }
+
+ return desc;
+}
+
+DirectConvComputeKernelInfo ClDirectConvDefaultConfigValhall::configure_G57_f32(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+ DirectConvComputeKernelInfo desc;
+
+ if(src->data_layout() == DataLayout::NHWC)
+ {
+ // Get the output shape
+ const TensorShape wei_shape = wei->tensor_shape();
+ const TensorShape dst_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info);
+ const bool export_weights_to_cl_image = export_to_cl_image(wei);
+
+ const int32_t m = dst_shape[1] * dst_shape[2];
+ const bool is_pointwise = (wei_shape[1] == wei_shape[2]) && wei_shape[1] == 1;
+
+ desc.export_weights_to_cl_image = export_weights_to_cl_image;
+
+ if(dst_shape[0] <= 4)
+ {
+ if(is_pointwise)
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ desc.k0 = 16;
+ }
+ else
+ {
+ desc.m0 = 1;
+ desc.n0 = dst_shape[0];
+ desc.k0 = 16;
+ }
+ }
+ else
+ {
+ if(m < 64)
+ {
+ if(m == 1)
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ desc.k0 = 16;
+ }
+ else
+ {
+ desc.m0 = 4;
+ desc.n0 = 2;
+ desc.k0 = 8;
+ }
+ }
+ else
+ {
+ desc.m0 = 4;
+ desc.n0 = 4;
+ desc.k0 = 4;
+ }
+ }
+ }
+
+ return desc;
+}
+
+DirectConvComputeKernelInfo ClDirectConvDefaultConfigValhall::configure_G57_f16(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+ DirectConvComputeKernelInfo desc;
+
+ if(src->data_layout() == DataLayout::NHWC)
+ {
+ // Get the output shape
+ const TensorShape wei_shape = wei->tensor_shape();
+ const TensorShape dst_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, conv_info);
+ const bool export_weights_to_cl_image = export_to_cl_image(wei);
+
+ const int32_t ofm = dst_shape[0];
+ const int32_t m = dst_shape[1] * dst_shape[2];
+ const bool is_pointwise = (wei_shape[1] == wei_shape[2]) && wei_shape[1] == 1;
+
+ desc.export_weights_to_cl_image = export_weights_to_cl_image;
+
+ if(dst_shape[0] <= 4)
+ {
+ if(is_pointwise)
+ {
+ desc.m0 = 2;
+ desc.n0 = 1;
+ desc.k0 = 16;
+ }
+ else
+ {
+ desc.m0 = 1;
+ desc.n0 = dst_shape[0];
+ desc.k0 = 16;
+ }
+ }
+ else
+ {
+ if(m < 64)
+ {
+ if(m == 1)
+ {
+ desc.m0 = 1;
+ desc.n0 = 1;
+ desc.k0 = 16;
+ }
+ else
+ {
+ desc.m0 = 4;
+ desc.n0 = 2;
+ desc.k0 = 8;
+ }
+ }
+ else
+ {
+ if(ofm > 16)
+ {
+ desc.m0 = 4;
+ desc.n0 = 8;
+ desc.k0 = 8;
+ }
+ else
+ {
+ desc.m0 = 8;
+ desc.n0 = 4;
+ desc.k0 = 4;
+ }
+ }
+ }
+ }
+
+ return desc;
+}
+} // namespace opencl
+} // namespace arm_compute