Optimize CpuGemmConv2d start-up time

When weight has no holes, we can replace CpuWeightsReshapeKernel with:
 - Collapse by reinterpreting weight's 3 spatial dimensions
 - Perform CpuTranspose

For more details see the documentation in
src/cpu/operators/CpuGemmConv2d.cpp

This is one optimization since the CpuTranspose is better performing
than CpuWeightsReshapeKernel

A second optimization is to fuse this transpose with other weight
transformations (e.g. pretranspose_B_array in CpuGemmAssemblyDispatch)

However this second optimization depends on how the underlying gemm
methods (the fall back path: CpuGemmMatrixMultiplyKernel or the assembly
path: CpuGemmAssemblyDispatch) chooses to fuse the transpose.

Therefore, this patch moves the transpose down from CpuGemmConv2d, to
the individual gemm operators where the fusion decision needs to be
made, by passing an extra "transpose_b" flag to CpuGemm

New transpose_b flag in different scopes (they are all the same, but
with different names because pretranspose_b has a different meaning in
GemmAssemblyDispatch):
GEMMInfo::pretranspose_B -> AsmGemmInfo::transpose_b

New auxilliary tensors holding the transposed b result:
- CpuGemm optimized path: CpuGemmAssemblyDispatch::PrePretransposedB
- CpuGemm fallback path:  CpuGemm::PreTransposedRHS

Note that this patch does not yet have the second optimization
(COMPMID-6595), but it prepares for it.

Relates to COMPMID-6595
Resolves COMPMID-6499

Change-Id: I999a2da9da4b2b15369a3cc06d7872c86e0190ea
Signed-off-by: SiCong Li <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10526
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Anitha Raj <Anitha.Raj@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 18 insertions, 0 deletions

diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 7a274906a6..98a5be5484 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -1032,6 +1032,8 @@ TEST_SUITE(GEMMConvolutionLayer)
 template <typename T>
 using NEGEMMConvolutionLayerFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolutionLayer, T>;
 template <typename T>
+using NEGEMMConvolutionLayerPaddedWeightsFixture = ConvolutionValidationPaddedWeightsFixture<Tensor, Accessor, NEConvolutionLayer, T>;
+template <typename T>
 using NEGEMMConvolutionLayerMixedDataLayoutFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolutionLayer, T, true>;
 
 /** Test case for memory injection in @ref cpu::CpuGemmConv2d.
@@ -1184,9 +1186,25 @@ FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEGEMMConvolutionLayerMixedDataLayout
     // Validate output
     validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, float(abs_tolerance_f32));
 }
+/** Padded weights
+ * CpuGemmConv2d uses two different paths for reshaping the weights based on if the weight tensor has holes (a common
+ * way to have "holes" in tensor is via extended paddings)
+ *
+ * We only need to test the padded weight path here on a single floating data type and a single layout, because the fallback path is agnostic of them
+ */
+FIXTURE_DATA_TEST_CASE(RunPaddedWeights, NEGEMMConvolutionLayerPaddedWeightsFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallConvolutionLayerDataset(),
+                                                                                                                    framework::dataset::make("ReshapeWeights", { true }),
+                                                                                                                    framework::dataset::make("DataType", DataType::F32),
+                                                                                                                    framework::dataset::make("DataLayout", { DataLayout::NHWC })
+                                                                                                            ))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, float(abs_tolerance_f32));
+}
 TEST_SUITE_END() // FP32
 TEST_SUITE_END() // Float
 
+// TODO: COMPMID-6596 Extend quantized tests with at least one suite where the weight is padded (the legacy case, see floating point's RunPaddedWeights)
 template <typename T>
 using NEGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEConvolutionLayer, T>;
 template <typename T>