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-rw-r--r--tests/validation/CL/MatMulNativeMMULKernel.cpp154
1 files changed, 93 insertions, 61 deletions
diff --git a/tests/validation/CL/MatMulNativeMMULKernel.cpp b/tests/validation/CL/MatMulNativeMMULKernel.cpp
index b63af75169..70c80985db 100644
--- a/tests/validation/CL/MatMulNativeMMULKernel.cpp
+++ b/tests/validation/CL/MatMulNativeMMULKernel.cpp
@@ -70,6 +70,9 @@ const auto k0_value = framework::dataset::make("K0", { 1 });
template <typename T>
using CLMatMulNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
+template <typename T>
+using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
+
TEST_SUITE(CL)
TEST_SUITE(MatMulNativeMMULKernel)
TEST_SUITE(Validate)
@@ -117,7 +120,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
{ MatMulKernelInfo(true, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
{ MatMulKernelInfo(true, true, 6, 3, 1), false }, // M0 not in {1, 2, 3, 4, 8, 16}
{ MatMulKernelInfo(true, true, 5, 3, 1), false }, // M0 not in {1, 2, 3, 4, 8, 16}
- { MatMulKernelInfo(true, true, 4, 8, 2), false }, // K0 is not 1
+ { MatMulKernelInfo(true, true, 4, 8, 2), false }, // K0 is not 1
{ MatMulKernelInfo(true, true, 4, 8, 1), true },
{ MatMulKernelInfo(true, true, 3, 3, 1), true },
{ MatMulKernelInfo(true, true, 16, 4, 1), true },
@@ -132,7 +135,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
for(auto &pair : supported_block_sizes)
{
TensorInfo output_info;
- Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
}
}
@@ -148,28 +151,30 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
{
// 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>;
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; // lhs, rhs, bias, result
const std::vector<ShapeConfigurationTuple> shape_configurations =
{
- { TensorShape(4U, 1U), TensorShape(3U, 4U), true },
- { TensorShape(12U, 12U), TensorShape(3U, 12U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 4U), false }, // Mismatch in the K dimension
- { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
- { TensorShape(5U, 7U), TensorShape(2U, 5U), false }, // K not a multiple of 4 (MMUL_K0)
- { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 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
+ { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(3U), true },
+ { TensorShape(12U, 12U), TensorShape(3U, 12U), TensorShape(3U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 4U), TensorShape(2U), false }, // Mismatch in the K dimension
+ { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+ { TensorShape(5U, 7U), TensorShape(2U, 5U), TensorShape(2U), false }, // K not a multiple of 4 (MMUL_K0)
+ { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // No batch broadcasting
+ { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // Mismatch in batch dimension
+ { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(1U), false }, // Bias first dimensions != dst first dimension.
+ { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(5U, 6U), false }, // Bias is 2d which is invalid.
};
for(auto &tuple : shape_configurations)
{
- const bool expected = std::get<2>(tuple);
+ const bool expected = std::get<3>(tuple);
- for(bool adj_lhs :
- {
- false, true
- })
+ for(bool adj_lhs :
+ {
+ false, true
+ })
{
for(bool adj_rhs :
{
@@ -178,6 +183,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
{
TensorShape lhs_shape = std::get<0>(tuple);
TensorShape rhs_shape = std::get<1>(tuple);
+ TensorShape bia_shape = std::get<2>(tuple);
if(adj_lhs)
{
@@ -191,11 +197,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
+ const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32);
TensorInfo output_info;
MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
- Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -213,40 +220,44 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
{
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
- using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+ using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>;
const std::vector<DataTypeConfigurationTuple> data_type_configurations =
{
- { DataType::F32, DataType::F32, DataType::F32, true },
- { DataType::F16, DataType::F16, DataType::F16, true },
- { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision
- { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
- { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types
- { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types
- { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
- { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types
- { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types
- { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized 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
+ { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true },
+ { DataType::F16, DataType::F16, DataType::F16, DataType::F16, true },
+ { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true },
+ { DataType::F32, DataType::F32, DataType::F16, DataType::F32, false }, // incorrect bias type
+ { DataType::F16, DataType::F32, DataType::F32, DataType::F32, false }, // no mixed precision
+ { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
+ { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, false }, // no quantized types
+ { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, false }, // no quantized types
+ { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
+ { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false }, // no quantized types
+ { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false }, // no quantized types
+ { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false }, // no quantized types
+ { DataType::S64, DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
+ { DataType::S32, DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
+ { DataType::S16, DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
+ { DataType::S8, DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
+ { DataType::U64, DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
+ { DataType::U32, DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
+ { DataType::U16, DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
+ { DataType::U8, DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
};
- const TensorShape shape = TensorShape(8U, 8U);
+ const TensorShape shape = TensorShape(8U, 8U);
+ const TensorShape bia_shape = TensorShape(8U);
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 bool expected = std::get<4>(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));
+ const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple));
+ TensorInfo output_info(shape, 1, std::get<3>(tuple));
- Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -292,7 +303,23 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<float>, framewo
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ if(_device_supports_mmul)
+ {
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+ }
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_nt),
@@ -308,7 +335,8 @@ FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<floa
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { true })),
m0_values_nightly_lhs_nt),
@@ -323,14 +351,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<flo
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
- framework::dataset::make("TransposeA", { true })),
- framework::dataset::make("TransposeB", { false })),
- m0_values_nightly_lhs_t),
- n0_values_nightly_rhs_nt),
- k0_value),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
// Validate output
@@ -395,7 +424,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<half>, framewor
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_nt),
@@ -410,7 +440,8 @@ FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { true })),
m0_values_nightly_lhs_nt),
@@ -425,14 +456,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<hal
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
- framework::dataset::make("TransposeA", { true })),
- framework::dataset::make("TransposeB", { false })),
- m0_values_nightly_lhs_t),
- n0_values_nightly_rhs_nt),
- k0_value),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
// Validate output
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-23 12:13:36 +0000