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authorGunes Bayir <gunes.bayir@arm.com>2023-04-13 18:22:58 +0100
committerGunes Bayir <gunes.bayir@arm.com>2023-04-17 15:54:44 +0000
commit9d0c4deb760efc2ca07e5e0b8218995201ad8a1f (patch)
tree8f64b754d05768e2f69cfae387137140a6bb22b5 /tests/validation
parent99145f787e9e99b45522f16d861c8527583f2b4e (diff)
downloadComputeLibrary-9d0c4deb760efc2ca07e5e0b8218995201ad8a1f.tar.gz
Add quantized CL MatMul kernels for Lhs NT/T, Rhs NT
Implement OpenCL kernels for batched Matrix Multiplication for the quantized data types QASYMM8 and QASYMM8_SIGNED. Quantized MatMul is supported with the following MatMul attributes: * adj_x = false, adj_y = false * adj_x = true, adj_y = false We consider native format kernels only. In other words, no reshaping of the operand matrices is done. Resolves: COMPMID-5921, COMPMID-5922 Change-Id: I99e0f68054a2bd635c60ec2641acc2e7ff398473 Signed-off-by: Omar Al Khatib <omar.alkhatib@arm.com> Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Signed-off-by: Jakub Sujak <jakub.sujak@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9435 Reviewed-by: SiCong Li <sicong.li@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation')
-rw-r--r--tests/validation/CL/MatMulKernel.cpp286
-rw-r--r--tests/validation/CL/MatMulLowpNativeKernel.cpp337
-rw-r--r--tests/validation/Helpers.cpp102
-rw-r--r--tests/validation/Helpers.h13
-rw-r--r--tests/validation/fixtures/MatMulKernelFixture.h130
5 files changed, 697 insertions, 171 deletions
diff --git a/tests/validation/CL/MatMulKernel.cpp b/tests/validation/CL/MatMulKernel.cpp
index 9c19e42d04..ff872aaa0a 100644
--- a/tests/validation/CL/MatMulKernel.cpp
+++ b/tests/validation/CL/MatMulKernel.cpp
@@ -73,7 +73,7 @@ const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1,
const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 });
template <typename T>
-using CLMatMulKernelFixture = MatMulKernelValidationFixture<T>;
+using CLMatMulKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeKernel>;
TEST_SUITE(CL)
TEST_SUITE(MatMulKernel)
@@ -95,8 +95,8 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
{ MatMulKernelInfo(false, false, 9, 1, 2), true },
{ MatMulKernelInfo(false, false, 3, 16, 3), true },
{ MatMulKernelInfo(false, false, 7, 3, 4), true },
- { MatMulKernelInfo(false, false, 7, 3, 4, true), false }, // N0 not in {4, 8, 16}
- { MatMulKernelInfo(false, false, 7, 1, 4, true), false }, // N0 not in {4, 8, 16}
+ { MatMulKernelInfo(false, false, 7, 3, 4, true), false }, // N0 not in {4, 8, 16}
+ { MatMulKernelInfo(false, false, 7, 1, 4, true), false }, // N0 not in {4, 8, 16}
{ MatMulKernelInfo(false, false, 7, 12, 4, true), false }, // N0 not in {4, 8, 16}
{ MatMulKernelInfo(false, false, 7, 4, 4, true), true },
{ MatMulKernelInfo(false, false, 7, 8, 4, true), true },
@@ -166,7 +166,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported)
{
- ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
}
}
}
@@ -176,9 +176,9 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL)
// We skip this test if the hardware does not support exporting to CL Image
if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device()))
{
- constexpr size_t pixel_size = 4;
- const size_t max_image_w = pixel_size * CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>();
- const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>();
+ constexpr size_t pixel_size = 4;
+ const size_t max_image_w = pixel_size * CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>();
+ const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>();
using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool, bool, bool>;
const std::vector<ShapeConfigurationTuple> shape_configurations =
@@ -186,18 +186,18 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL)
// lhs_shape, rhs_shape, adj_lhs, adj_rhs, expected
// Lhs t/Nt, Rhs Nt
// Transposition of Lhs doesn't add any value to the tests, therefore always assumed false below
- { TensorShape(5U, 1U), TensorShape(3U, 5U), false, false, false }, // N should be multiple of 4
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), false, false, false }, // N should be multiple of 4
{ TensorShape(5U, 1U), TensorShape(14U, 5U), false, false, false }, // N should be multiple of 4
{ TensorShape(5U, 1U), TensorShape(12U, 5U), false, false, true },
{ TensorShape(5U, 1U), TensorShape(8U, 5U), false, false, true },
{ TensorShape(5U, 1U), TensorShape(4U, 5U), false, false, true },
{ TensorShape(max_image_h + 1, 1U), TensorShape(4U, max_image_h + 1), false, false, false }, // Cannot fit into CL Image memory's height
- { TensorShape(5U, 1U), TensorShape(max_image_w + 1, 5U), false, false, false }, // Cannot fit into CL Image memory's width
- { TensorShape(max_image_h, 1U), TensorShape(4U, max_image_h), false, false, true }, // Barely fits into CL Image memory's height
- { TensorShape(5U, 1U), TensorShape(max_image_w, 5U), false, false, true }, // Barely fits into CL Image memory's width
+ { TensorShape(5U, 1U), TensorShape(max_image_w + 1, 5U), false, false, false }, // Cannot fit into CL Image memory's width
+ { TensorShape(max_image_h, 1U), TensorShape(4U, max_image_h), false, false, true }, // Barely fits into CL Image memory's height
+ { TensorShape(5U, 1U), TensorShape(max_image_w, 5U), false, false, true }, // Barely fits into CL Image memory's width
// Lhs Nt/T , Rhs T
- { TensorShape(5U, 1U), TensorShape(5U, 3U), false, true, false }, // K should be multiple of 4
+ { TensorShape(5U, 1U), TensorShape(5U, 3U), false, true, false }, // K should be multiple of 4
{ TensorShape(5U, 1U), TensorShape(5U, 14U), false, true, false }, // K should be multiple of 4
{ TensorShape(4U, 1U), TensorShape(4U, 10U), false, true, true },
{ TensorShape(8U, 1U), TensorShape(8U, 9U), false, true, true },
@@ -216,7 +216,10 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL)
const bool adj_rhs = std::get<3>(tuple);
// We choose M0, N0, K0 equal to 4 so that they're always valid for CLImage in any combination
- const MatMulKernelInfo matmul_kernel_info {adj_lhs, adj_rhs, 4, 4, 4, true /* export_rhs_to_cl_image */};
+ const MatMulKernelInfo matmul_kernel_info
+ {
+ adj_lhs, adj_rhs, 4, 4, 4, true /* export_rhs_to_cl_image */
+ };
TensorInfo output_info;
Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
@@ -330,60 +333,60 @@ TEST_SUITE(Float)
TEST_SUITE(FP32)
TEST_SUITE(Buffer)
FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
- framework::dataset::make("pretransose_A", { false, true })),
- framework::dataset::make("pretransose_B", { false, true })),
- m0_values_precommit),
- n0_values_precommit),
- k0_values_precommit),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
- framework::dataset::make("pretransose_A", { false, true })),
- framework::dataset::make("pretransose_B", { false, true })),
- m0_values_precommit),
- n0_values_precommit),
- k0_values_precommit),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { false })),
- framework::dataset::make("pretransose_B", { false })),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_nt),
n0_values_nightly_rhs_nt),
k0_values_nightly_lhs_nt_rhs_nt),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { false })),
- framework::dataset::make("pretransose_B", { true })),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { true })),
m0_values_nightly_lhs_nt),
n0_values_nightly_rhs_t),
k0_values_nightly_rhs_t),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { true })),
- framework::dataset::make("pretransose_B", { false })),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_t),
n0_values_nightly_rhs_nt),
k0_values_nightly_lhs_t_rhs_nt),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -391,12 +394,12 @@ FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<float>, fram
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { true })),
- framework::dataset::make("pretransose_B", { true })),
- m0_values_nightly_lhs_t),
- n0_values_nightly_rhs_t),
- k0_values_nightly_rhs_t),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { true })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
@@ -405,13 +408,13 @@ FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture
// Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0
// It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels
FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
- framework::dataset::make("pretransose_A", { false, true })),
- framework::dataset::make("pretransose_B", { false, true })),
- framework::dataset::make("M0", { 2 })),
- framework::dataset::make("N0", { 2 })),
- framework::dataset::make("K0", { 2 })),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 2 })),
+ framework::dataset::make("K0", { 2 })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
@@ -419,14 +422,15 @@ FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulKernelFixture<float>, framewo
TEST_SUITE_END() // Buffer
TEST_SUITE(ExportRhsToCLImage)
-FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { false })),
- framework::dataset::make("M0", { 2 })),
- framework::dataset::make("N0", { 4, 8, 16 })),
- framework::dataset::make("K0", { 2, 4 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 4, 8, 16 })),
+ framework::dataset::make("K0", { 2, 4 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -434,14 +438,15 @@ FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<float>, f
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { false })),
- framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
- framework::dataset::make("N0", { 4, 8, 16 })),
- framework::dataset::make("K0", { 1, 2, 3, 4 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
+ framework::dataset::make("N0", { 4, 8, 16 })),
+ framework::dataset::make("K0", { 1, 2, 3, 4 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -449,14 +454,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<float>, f
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { true })),
- framework::dataset::make("M0", { 2 })),
- framework::dataset::make("N0", { 2, 4 })),
- framework::dataset::make("K0", { 4, 8, 16 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { true })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 2, 4 })),
+ framework::dataset::make("K0", { 4, 8, 16 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -464,14 +470,15 @@ FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<float>, fram
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { true })),
- framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
- framework::dataset::make("N0", { 1, 2, 3, 4 })),
- framework::dataset::make("K0", { 4, 8, 16 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { true })),
+ framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
+ framework::dataset::make("N0", { 1, 2, 3, 4 })),
+ framework::dataset::make("K0", { 4, 8, 16 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -485,61 +492,62 @@ TEST_SUITE_END() // FP32
TEST_SUITE(FP16)
TEST_SUITE(Buffer)
FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
- framework::dataset::make("pretransose_A", { false, true })),
- framework::dataset::make("pretransose_B", { false, true })),
- m0_values_precommit),
- n0_values_precommit),
- k0_values_precommit),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
- framework::dataset::make("DataType", DataType::F16)))
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { false })),
- framework::dataset::make("pretransose_B", { false })),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_nt),
n0_values_nightly_rhs_nt),
k0_values_nightly_lhs_nt_rhs_nt),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { false })),
- framework::dataset::make("pretransose_B", { true })),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { true })),
m0_values_nightly_lhs_nt),
n0_values_nightly_rhs_t),
k0_values_nightly_rhs_t),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { true })),
- framework::dataset::make("pretransose_B", { false })),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_t),
n0_values_nightly_rhs_nt),
k0_values_nightly_lhs_t_rhs_nt),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
- framework::dataset::make("pretransose_A", { true })),
- framework::dataset::make("pretransose_B", { true })),
- m0_values_nightly_lhs_t),
- n0_values_nightly_rhs_t),
- k0_values_nightly_rhs_t),
- framework::dataset::make("export_rhs_to_cl_image", { false })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { true })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_t),
+ k0_values_nightly_rhs_t),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
@@ -547,14 +555,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture
TEST_SUITE_END() // Buffer
TEST_SUITE(ExportRhsToCLImage)
-FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { false })),
- framework::dataset::make("M0", { 2 })),
- framework::dataset::make("N0", { 4, 8, 16 })),
- framework::dataset::make("K0", { 2, 4 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 4, 8, 16 })),
+ framework::dataset::make("K0", { 2, 4 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -562,14 +571,15 @@ FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<half>, fr
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { false })),
- framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
- framework::dataset::make("N0", { 4, 8, 16 })),
- framework::dataset::make("K0", { 1, 2, 3, 4 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
+ framework::dataset::make("N0", { 4, 8, 16 })),
+ framework::dataset::make("K0", { 1, 2, 3, 4 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -577,14 +587,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<half>, fr
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { true })),
- framework::dataset::make("M0", { 2 })),
- framework::dataset::make("N0", { 2, 4 })),
- framework::dataset::make("K0", { 4, 8, 16 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { true })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 2, 4 })),
+ framework::dataset::make("K0", { 4, 8, 16 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
if(_device_supports_export_to_cl_image)
@@ -592,14 +603,15 @@ FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<half>, frame
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(),
- framework::dataset::make("pretransose_A", { true, false })),
- framework::dataset::make("pretransose_B", { true })),
- framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
- framework::dataset::make("N0", { 1, 2, 3, 4 })),
- framework::dataset::make("K0", { 4, 8, 16 })),
- framework::dataset::make("export_rhs_to_cl_image", { true })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { true })),
+ framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor
+ framework::dataset::make("N0", { 1, 2, 3, 4 })),
+ framework::dataset::make("K0", { 4, 8, 16 })),
+ framework::dataset::make("ExportRhsToCLImage", { true })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
if(_device_supports_export_to_cl_image)
diff --git a/tests/validation/CL/MatMulLowpNativeKernel.cpp b/tests/validation/CL/MatMulLowpNativeKernel.cpp
new file mode 100644
index 0000000000..5932fa7c21
--- /dev/null
+++ b/tests/validation/CL/MatMulLowpNativeKernel.cpp
@@ -0,0 +1,337 @@
+/*
+ * Copyright (c) 2023 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/CLTensor.h"
+
+#include "src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h"
+
+#include "tests/datasets/LargeMatMulDataset.h"
+#include "tests/datasets/SmallMatMulDataset.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/MatMulKernelFixture.h"
+#include "tests/validation/reference/Permute.h"
+
+#include <tuple>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */
+}
+template <typename T>
+using CLMatMulLowpNativeKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeKernel>;
+
+/** M0 values to test --precommit*/
+const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });
+
+/** N0 values to test --precommit*/
+const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 });
+
+/** K0 values to test --precommit*/
+const auto k0_values_precommit = framework::dataset::make("K0", { 2, 3 });
+
+/** M0 values to test --nightly*/
+const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 });
+const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 });
+
+/** N0 values to test --nightly*/
+const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 });
+// const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 });
+
+/** K0 values to test --nightly*/
+const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 });
+// const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 });
+const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 });
+
+TEST_SUITE(CL)
+TEST_SUITE(MatMulLowpNativeKernel)
+TEST_SUITE(Validate)
+
+TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL)
+{
+ using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>;
+
+ 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
+ { MatMulKernelInfo(false, false, 0, 1, 1), false }, // M0 should be > 0
+ { MatMulKernelInfo(false, false, 3, 5, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 6, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 3, 17), false }, // K0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 3, 7), false }, // K0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 9, 1, 2), true },
+ { MatMulKernelInfo(false, false, 3, 16, 3), true },
+ { MatMulKernelInfo(false, false, 7, 3, 4), true },
+ { MatMulKernelInfo(false, false, 7, 3, 4, true), true }, // 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);
+
+ for(auto &pair : supported_block_sizes)
+ {
+ TensorInfo output_info;
+ Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+
+ ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
+ }
+}
+
+TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
+{
+ // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+ const std::vector<ShapeConfigurationTuple> shape_configurations =
+ {
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
+ { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
+ { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
+ { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+ };
+
+ for(auto &tuple : shape_configurations)
+ {
+ const bool expected = std::get<2>(tuple);
+
+ for(bool adj_lhs :
+ {
+ false, true
+ })
+ {
+ for(bool adj_rhs :
+ {
+ false, true
+ })
+ {
+ TensorShape lhs_shape = std::get<0>(tuple);
+ TensorShape rhs_shape = std::get<1>(tuple);
+
+ if(adj_lhs)
+ {
+ permute(lhs_shape, PermutationVector(1U, 0U));
+ }
+
+ if(adj_rhs)
+ {
+ permute(rhs_shape, PermutationVector(1U, 0U));
+ }
+
+ const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::QASYMM8_SIGNED);
+ const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::QASYMM8_SIGNED);
+ TensorInfo output_info;
+
+ MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
+
+ Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+ }
+ }
+ }
+}
+
+TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
+{
+ using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+ const std::vector<DataTypeConfigurationTuple> data_type_configurations =
+ {
+ { DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types
+ { DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types
+ { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
+ { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, true },
+ { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, true },
+ { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM8, false }, // no mixed data types
+ { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
+ { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
+ { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
+ { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
+ { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
+ { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
+ { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
+ { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
+ };
+
+ // It's enough to test a single shape and block size configuration while checking data types
+ const TensorShape shape = TensorShape(10U, 10U);
+ const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
+ for(auto &tuple : data_type_configurations)
+ {
+ const bool expected = std::get<3>(tuple);
+
+ const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
+ const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
+ TensorInfo output_info(shape, 1, std::get<2>(tuple));
+
+ Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+ }
+}
+
+TEST_SUITE_END() // Validate
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8_SIGNED)
+FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_nt_rhs_nt),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_t_rhs_nt),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+// Running High Dimensional test is enough for qasymm8_signed, because we're stressing the number of dimensions, not data type or M0/N0/K0
+// It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels
+FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 2 })),
+ framework::dataset::make("K0", { 2 })),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+TEST_SUITE_END() // QASYMM8_SIGNED
+
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_nt),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_nt_rhs_nt),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulLowpNativeKernelFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_values_nightly_lhs_t_rhs_nt),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
+TEST_SUITE_END() // QASYMM8
+TEST_SUITE_END() // Quantized
+TEST_SUITE_END() // MatMulLowpNativeKernel
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/Helpers.cpp b/tests/validation/Helpers.cpp
index be194dd266..110325c5a0 100644
--- a/tests/validation/Helpers.cpp
+++ b/tests/validation/Helpers.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2022 Arm Limited.
+ * Copyright (c) 2017-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "tests/validation/Helpers.h"
+#include "tests/framework/Asserts.h"
#include <algorithm>
#include <cmath>
@@ -373,6 +374,105 @@ void add_padding_y(std::initializer_list<ITensor *> tensors, const DataLayout &d
}
}
+QuantizationInfo calculate_mat_mul_dst_q_info(const QuantizationInfo &a_q_info, const QuantizationInfo &b_q_info, int m, int n, int k, DataType data_type)
+{
+ ARM_COMPUTE_UNUSED(m, n);
+ QuantizationInfo c_q_info;
+
+ ARM_COMPUTE_ASSERT(data_type == DataType::QASYMM8 || data_type == DataType::QASYMM8_SIGNED);
+
+ const int32_t t_max = static_cast<int32_t>(data_type == DataType::QASYMM8 ? std::numeric_limits<uint8_t>::max() : std::numeric_limits<int8_t>::max());
+ const int32_t t_min = static_cast<int32_t>(data_type == DataType::QASYMM8 ? std::numeric_limits<uint8_t>::min() : std::numeric_limits<int8_t>::min());
+
+ /** Quantization Setup of matrix multiplication
+ *
+ * We have a matrix multiplication of the form C = A * B
+ * where A is (M X K), B is (K x N) and C is therefore (M x N).
+ *
+ * If we have some distributions statistics of A and B, i.e. mean and variance,
+ * we can estimate the mean and variance of a single value in C matrix and
+ * pick good scale and offset values for the output and have non-saturated tests.
+ *
+ * Each element in the output matrix can be calculated as follows:
+ * C_ij = sum_k(A_ik * B_kj)
+ *
+ * All values are float above.
+ *
+ * Note: All possible A_ik, B_kj random variables are assumed mutually independent.
+ *
+ * Terminology:
+ * E[X]: Mean of the random variable X (sometimes referred as mu_x)
+ * var(X): Variance of the random variable X (someimes referred as sigma^2_x)
+ * std(X): sqrt(var(X)), standard deviation of X
+ *
+ * 1) Calculate the mean:
+ * E[C_ij] = sum_k( E[A_ik] * E[B_kj] ) = K * mean_a * mean_b
+ *
+ * Since elements of A and B are uniformly distributed random variables, we have
+ * mean_a = (max_a + min_a) / 2, mean_b = (max_b + min_b ) / 2
+ * max_a and min_a can be calculated with the scale_a/b and offset_a/b
+ * by replacing data type minimum and maximums in the equations
+ *
+ * 2) Calculate the variance:
+ * var(C_ij) = sum_k( var(A_ik * B_kj) )
+ * = sum_k ( E[A_ik^2 * B_kj^2] - E[A_ik]^2E[B_kj^2] )
+ * = ...
+ * = K * (var_a * var_b + var_a * mean^2_b + var_b * mean^2_a)
+ *
+ * Similarly, due to uniform random variable properties, we have
+ * var_a = (max_a - min_a)^2 / 12
+ * var_b = (max_b - min_b)^2 / 12
+ *
+ *
+ * 3) Now, we have an idea of what would an average C_ij will like and how much deviation
+ * is present around it. The exact distribution of C is not easy to come up with dependent on K.
+ * But, as K increases, due to Central Limit Theorem, it'll look more like a bell shaped figure,
+ * approaching normal distribution.
+ *
+ * This is useful because, in normal distribution, we know that values +- 2 std_deviation around
+ * the mean constitute 95% of the values. Therefore, setting a plausible range for us:
+ * C_range = [C_min, C_max] = [mean_c - 2 * std_c, mean_c + 2 * std_c]
+ *
+ * 4)
+ * If we map this [C_min, C_max] to [0, 255] or [-128, 127] depending on the signedness of the
+ * data type, we can find a suitable scale and offset for the output. On average, it's expected
+ * that 5% of the output values will saturate and 95% will remain in the range.
+ *
+ * The equations to be solved for offset_c and scale_c are:
+ * C_min = scale_c * (type_min - offset_c)
+ * C_max = scale_c * (type_max - offset_c)
+ */
+
+ const int32_t a_offset = a_q_info.uniform().offset;
+ const float a_scale = a_q_info.uniform().scale;
+ const int32_t b_offset = b_q_info.uniform().offset;
+ const float b_scale = b_q_info.uniform().scale;
+
+ // Lhs/A stats
+ const float max_a = (t_max - a_offset) * a_scale;
+ const float min_a = (t_min - a_offset) * a_scale;
+ const float mean_a = (max_a + min_a) / 2;
+ const float var_a = (max_a - min_a) * (max_a - min_a) / 12;
+
+ // Rhs/B stats
+ const float max_b = (t_max - b_offset) * b_scale;
+ const float min_b = (t_min - b_offset) * b_scale;
+ const float mean_b = (max_b + min_b) / 2;
+ const float var_b = (max_b - min_b) * (max_b - min_b) / 12;
+
+ // Output stats
+ const float mean_out = k * mean_a * mean_b;
+ const float var_out = k * (var_a * var_b + var_a * mean_b * mean_b + var_b * mean_a * mean_a);
+ const float std_out = sqrt(var_out);
+
+ // Output quantization setup
+ const float scale_out = 4 * std_out / 255;
+ const int32_t offset_out = static_cast<int32_t>(t_min - (mean_out - 2.f * std_out) / scale_out);
+
+ c_q_info = QuantizationInfo(scale_out, offset_out);
+ return c_q_info;
+}
+
template void get_tile(const SimpleTensor<float> &in, SimpleTensor<float> &roi, const Coordinates &coord);
template void get_tile(const SimpleTensor<half> &in, SimpleTensor<half> &roi, const Coordinates &coord);
template void get_tile(const SimpleTensor<int> &in, SimpleTensor<int> &roi, const Coordinates &coord);
diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h
index 2e48a6b8c6..3449239e45 100644
--- a/tests/validation/Helpers.h
+++ b/tests/validation/Helpers.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2022 Arm Limited.
+ * Copyright (c) 2017-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_TEST_VALIDATION_HELPERS_H
-#define ARM_COMPUTE_TEST_VALIDATION_HELPERS_H
+#ifndef ACL_TESTS_VALIDATION_HELPERS
+#define ACL_TESTS_VALIDATION_HELPERS
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
@@ -250,7 +250,12 @@ void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &d
* @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC
*/
void add_padding_y(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout = DataLayout::NHWC);
+
+/** For MatMulLowp, given the Lhs/Rhs matrix quantization informations and the matrix multiplication dimensions,
+ * calculate a suitable output quantization for obtaining non-saturated outputs with high probability.
+ */
+QuantizationInfo calculate_mat_mul_dst_q_info(const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, int m, int n, int k, DataType data_type);
} // namespace validation
} // namespace test
} // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_VALIDATION_HELPERS_H */
+#endif /* ACL_TESTS_VALIDATION_HELPERS */
diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h
index 10e2a0659a..7d0b1a40a9 100644
--- a/tests/validation/fixtures/MatMulKernelFixture.h
+++ b/tests/validation/fixtures/MatMulKernelFixture.h
@@ -25,11 +25,15 @@
#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE
#include "arm_compute/core/KernelDescriptors.h"
-#include "src/gpu/cl/kernels/ClMatMulNativeKernel.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
#include "tests/CL/CLAccessor.h"
#include "tests/CL/Helper.h"
#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
#include "tests/validation/reference/GEMM.h"
+#include "tests/validation/reference/GEMMLowp.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/ReshapeLayer.h"
@@ -43,14 +47,43 @@ namespace validation
{
using namespace arm_compute::opencl::kernels;
-template <typename T>
+template <typename T, typename KernelType>
class MatMulKernelValidationFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0, bool export_rhs_to_cl_image, DataType data_type)
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
{
// For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
+ QuantizationInfo lhs_q_info;
+ QuantizationInfo rhs_q_info;
+ QuantizationInfo dst_q_info;
+
+ if(is_data_type_quantized(data_type))
+ {
+ const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max());
+ const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min());
+
+ std::mt19937 generator(library->seed());
+ std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f);
+ std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max);
+
+ const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
+ const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
+
+ const int32_t offset_lhs = distribution_t(generator);
+ const int32_t offset_rhs = distribution_t(generator);
+
+ lhs_q_info = QuantizationInfo(scale_lhs, offset_lhs);
+ rhs_q_info = QuantizationInfo(scale_rhs, offset_rhs);
+
+ const int m = shape_a.y();
+ const int n = shape_b.x();
+ const int k = shape_a.x();
+
+ dst_q_info = calculate_mat_mul_dst_q_info(lhs_q_info, rhs_q_info, m, n, k, data_type);
+ }
+
if(pretranspose_a)
{
permute(shape_a, PermutationVector(1U, 0U));
@@ -65,8 +98,8 @@ public:
if(!export_rhs_to_cl_image || _device_supports_export_to_cl_image)
{
- _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type);
- _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type);
+ _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info);
+ _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info);
}
}
@@ -93,23 +126,29 @@ protected:
}
}
+ template <typename U, typename D>
+ void fill_constant(U &&tensor, D value)
+ {
+ library->fill_tensor_value(tensor, value);
+ }
+
CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
- bool export_rhs_to_cl_image, DataType data_type)
+ bool export_rhs_to_cl_image, DataType data_type, const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
{
- // Create tensors
- CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1);
- CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1);
- CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1);
-
- CLSynthetizeOperator<ClMatMulNativeKernel> matMul{};
- MatMulKernelInfo matmul_info;
- matmul_info.adj_lhs = pretranspose_a;
- matmul_info.adj_rhs = pretranspose_b;
- matmul_info.m0 = M0;
- matmul_info.n0 = N0;
- matmul_info.k0 = K0;
+ CLSynthetizeOperator<KernelType> matMul{};
+ MatMulKernelInfo matmul_info;
+ matmul_info.adj_lhs = pretranspose_a;
+ matmul_info.adj_rhs = pretranspose_b;
+ matmul_info.m0 = M0;
+ matmul_info.n0 = N0;
+ matmul_info.k0 = K0;
matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;
+ // Create tensors
+ CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
+ CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
+ CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);
+
matMul.configure(a.info(), b.info(), dst.info(), matmul_info);
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
@@ -138,18 +177,19 @@ protected:
return dst;
}
- SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type)
+ SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type,
+ const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
{
// We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
// This is necessary unless we choose to extend gemm reference for 5D+ tensors
- TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimW);
- TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimW);
- TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimW);
+ TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ);
+ TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimZ);
+ TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimZ);
// Create reference
- SimpleTensor<T> a{ shape_a_collapsed, data_type, 1 };
- SimpleTensor<T> b{ shape_b_collapsed, data_type, 1 };
- SimpleTensor<T> c{ output_shape_collapsed, data_type, 1 };
+ SimpleTensor<T> a{ shape_a_collapsed, data_type, 1, lhs_q_info };
+ SimpleTensor<T> b{ shape_b_collapsed, data_type, 1, rhs_q_info };
+ SimpleTensor<T> c{ output_shape_collapsed, data_type, 1, dst_q_info };
// Fill reference
fill(a, 0);
@@ -185,10 +225,8 @@ protected:
b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
}
- // Setting beta to 0 will effectively disable C for the
- // computation of the reference: alpha * A * B + 0 * C
// Use transposed tensors if boolean enabled else use original tensors
- SimpleTensor<T> result = reference::gemm<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f);
+ SimpleTensor<T> result = gemm_reference<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c);
// We reshape the gemm output back if the tensor is high dimensional
if(output_shape_collapsed != output_shape)
@@ -199,9 +237,43 @@ protected:
return result;
}
+ template <typename U = T>
+ typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
+ {
+ // Setting beta to 0 will effectively disable C for the
+ // computation of the reference: alpha * A * B + 0 * C
+ return reference::gemm<U>(a, b, c, 1.0f, 0.f);
+ }
+
+ template <typename U = T>
+ typename std::enable_if < std::is_same<U, int8_t>::value || std::is_same<U, uint8_t>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
+ {
+ const UniformQuantizationInfo aq = a.quantization_info().uniform();
+ const UniformQuantizationInfo bq = b.quantization_info().uniform();
+ const UniformQuantizationInfo cq = c.quantization_info().uniform();
+
+ const SimpleTensor<int32_t> result = reference::gemmlowp_matrix_multiply_core<int32_t, U, U>(a, b, c.shape(), -aq.offset, -bq.offset);
+
+ std::vector<int32_t> gemmlowp_multipliers{ 1 };
+ std::vector<int32_t> gemmlowp_shifts{ 1 };
+ const int gemmlowp_offset = cq.offset;
+ const float scale = aq.scale * bq.scale / cq.scale;
+
+ quantization::calculate_quantized_multiplier(scale, &gemmlowp_multipliers[0], &gemmlowp_shifts[0]);
+ constexpr int32_t gemmlowp_min_bound = std::numeric_limits<int32_t>::min();
+ constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max();
+
+ SimpleTensor<int> bias{ c.shape(), DataType::S32 };
+ fill_constant(bias, static_cast<int32_t>(0));
+
+ const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias,
+ gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound);
+ return final_result;
+ }
+
CLTensor _target{};
SimpleTensor<T> _reference{};
- bool _device_supports_export_to_cl_image { true };
+ bool _device_supports_export_to_cl_image{ true };
};
} // namespace validation
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-20 16:57:42 +0000