Implement Quantized Matmul Nt/T kernel using MMUL extension
Resolves: COMPMID-6474
Change-Id: Iaff5b512cf77975f2df02dcdf848711b13bf97a6
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10341
Reviewed-by: Mohmun02 <MohammedSuhail.Munshi@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp b/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp
index a361a5a..561c455 100644
--- a/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp
+++ b/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp
@@ -76,15 +76,21 @@
const std::vector<MatMulConfigurationPair> supported_block_sizes =
{
// MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false)
- // Lhs not-transposed, Rhs-not-transposed
- // TODO: Test Cases
+ { MatMulKernelInfo(false, false, 0, 1, 4), false }, // M0 should be > 0
+ { MatMulKernelInfo(false, true, 3, 5, 4), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 6, 4), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 3, 8), false }, // K0 not in 4
+ { MatMulKernelInfo(false, false, 9, 1, 4), true },
+ { MatMulKernelInfo(false, true, 3, 16, 4), true },
+ { MatMulKernelInfo(false, false, 7, 3, 4), true },
+ { MatMulKernelInfo(false, false, 7, 3, 4, true), false }, // export to CLImage is unsupported for quantized types
};
// Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal
// so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here,
// not the shapes themselves.
- const TensorInfo lhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::QASYMM8_SIGNED);
- const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::QASYMM8_SIGNED);
+ const TensorInfo lhs_info = TensorInfo(TensorShape(64U, 64U), 1, DataType::QASYMM8_SIGNED);
+ const TensorInfo rhs_info = TensorInfo(TensorShape(64U, 64U), 1, DataType::QASYMM8_SIGNED);
for(auto &pair : supported_block_sizes)
{
@@ -211,7 +217,7 @@
framework::DatasetMode::ALL,
combine(datasets::SmallMatMulLowpMMULDataset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
m0_values_precommit,
n0_values_precommit,
make("K0", { 4 }),
@@ -229,7 +235,7 @@
framework::DatasetMode::ALL,
combine(datasets::SmallMatMulLowpMMULWithBiasDataset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
m0_values_precommit,
n0_values_precommit,
make("K0", { 4 }),
@@ -247,7 +253,7 @@
framework::DatasetMode::NIGHTLY,
combine(datasets::LargeMatMulLowpMMULDataset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
m0_values_nightly_lhs_nt,
n0_values_nightly_rhs_nt,
make("K0", { 4 }),
@@ -267,7 +273,7 @@
framework::DatasetMode::ALL,
combine(datasets::HighDimensionalMatMulLowpMMULDataset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
make("M0", { 2 }),
make("N0", { 2 }),
make("K0", { 4 }),
@@ -289,7 +295,7 @@
framework::DatasetMode::ALL,
combine(datasets::SmallMatMulLowpMMULDatasetSubset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
m0_values_precommit,
n0_values_precommit,
make("K0", { 4 }),
@@ -307,7 +313,7 @@
framework::DatasetMode::ALL,
combine(datasets::SmallMatMulLowpMMULWithBiasDataset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
m0_values_precommit,
n0_values_precommit,
make("K0", { 4 }),
@@ -325,7 +331,7 @@
framework::DatasetMode::NIGHTLY,
combine(datasets::LargeMatMulLowpMMULDataset(),
make("TransposeA", { false }),
- make("TransposeB", { false }),
+ make("TransposeB", { false, true }),
m0_values_nightly_lhs_nt,
n0_values_nightly_rhs_nt,
make("K0", { 4 }),