Update the ClConv2d heuristic

- Update the ClConv2d heuristic to call indirect convolution on Arm® Mali™-G77 Gpus
- Implement the indirect conv2d heuristic for selecting the block size

Resolves COMPMID-5713

Change-Id: If6ad49124561207153685c6abd4f54950a376fbc
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8886
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 161 insertions, 0 deletions

diff --git a/src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp b/src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp
new file mode 100644
index 0000000000..5d3dbf3146
--- /dev/null
+++ b/src/runtime/heuristics/indirect_conv/ClIndirectConvDefaultConfigValhall.cpp
@@ -0,0 +1,161 @@
+/*
+ * 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/indirect_conv/ClIndirectConvDefaultConfigValhall.h"
+
+#include "arm_compute/core/CL/CLHelpers.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"
+
+namespace arm_compute
+{
+namespace cl_indirect_conv
+{
+using namespace arm_compute::misc::shape_calculator;
+
+ClIndirectConvDefaultConfigValhall::ClIndirectConvDefaultConfigValhall(GPUTarget gpu)
+    : IClIndirectConvKernelConfig(gpu)
+{
+}
+
+DirectConvComputeKernelInfo ClIndirectConvDefaultConfigValhall::configure(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+    using ConfigurationFunctionExecutorPtr = DirectConvComputeKernelInfo (ClIndirectConvDefaultConfigValhall::*)(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info);
+
+    ClIndirectConvConfigArray<ConfigurationFunctionExecutorPtr> configs_G77(&ClIndirectConvDefaultConfigValhall::configure_G77_f32,
+                                                                            &ClIndirectConvDefaultConfigValhall::configure_G77_f16);
+
+    // Important note: Indirect convolution should not be used when the kernel size is 1x1 (pointwise). The reason is because the indirect buffer makes
+    // indirect convolution less efficient than direct convolution or gemm. For this reason, the heuristic of indirect convolution has not been tuned
+    // for the pointwise convolution cases.
+
+    ConfigurationFunctionExecutorPtr func = configs_G77.get_function(src->data_type());
+
+    ARM_COMPUTE_ERROR_ON_MSG(func == nullptr, "Data type not supported for indirect convolution");
+    return (this->*func)(src, wei, conv_info);
+}
+
+DirectConvComputeKernelInfo ClIndirectConvDefaultConfigValhall::configure_G77_f32(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+    DirectConvComputeKernelInfo desc;
+
+    if(src->data_layout() == DataLayout::NHWC)
+    {
+        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 stride_x     = conv_info.stride().first;
+        const int32_t stride_y     = conv_info.stride().second;
+        const int32_t ofm          = dst_shape[0];
+        const int32_t m            = (dst_shape[1]/ stride_x) * (dst_shape[2] / stride_y);
+
+        desc.export_weights_to_cl_image = export_weights_to_cl_image;
+
+        if(ofm <= 4)
+        {
+            desc.m0 = 1;
+            desc.n0 = 2;
+            desc.k0 = 16;
+        }
+        else
+        {
+            // The 16000 threshold value has been identified as the right
+            // one for using the biggest block size allowed on F32: 5x4x4
+            if(m < 16000)
+            {
+                desc.m0 = 4;
+                desc.n0 = 4;
+                desc.k0 = 4;
+            }
+            else
+            {
+                desc.m0 = 5;
+                desc.n0 = 4;
+                desc.k0 = 4;
+            }
+        }
+    }
+
+    return desc;
+}
+
+DirectConvComputeKernelInfo ClIndirectConvDefaultConfigValhall::configure_G77_f16(const ITensorInfo *src, const ITensorInfo *wei, const PadStrideInfo &conv_info)
+{
+    DirectConvComputeKernelInfo desc;
+
+    if(src->data_layout() == DataLayout::NHWC)
+    {
+        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];
+
+        desc.export_weights_to_cl_image = export_weights_to_cl_image;
+
+        if(ofm <= 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;
+            }
+
+            desc.m0 = 1;
+            desc.n0 = ofm;
+        }
+        else
+        {
+            // The 16000 threshold value has been identified as the right
+            // one for using the biggest block size allowed on F16: 8x4
+            if(m >= 16000 && k < 4)
+            {
+                desc.m0 = 8;
+                desc.n0 = 4;
+                desc.k0 = 4;    // k0 is clamped to k inside the kernel when k is less than 4
+            }
+            else
+            {
+                desc.m0 = 5;
+                desc.n0 = 4;
+                desc.k0 = 8;
+            }
+        }
+    }
+
+    return desc;
+}
+} // namespace opencl
+} // namespace arm_compute