COMPMID-399 Add MatrixMultiply to benchmark

Change-Id: I86c3f808c0047c8d97211d21f61c4e79e2d2abb1
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/78617
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

3 files changed, 141 insertions, 8 deletions

diff --git a/tests/benchmark/CL/GEMM.cpp b/tests/benchmark/CL/GEMM.cpp
index b90556df48..a10da09cbe 100644
--- a/tests/benchmark/CL/GEMM.cpp
+++ b/tests/benchmark/CL/GEMM.cpp
@@ -51,6 +51,8 @@ using GEMMFP16GoogLeNet1 = GEMM<GoogLeNetGEMMDataset1, CLTensor, CLAccessor, CLG
 using GEMMFP16GoogLeNet2 = GEMM<GoogLeNetGEMMDataset2, CLTensor, CLAccessor, CLGEMM, DataType::F16>;
 using GEMMFP32GoogLeNet1 = GEMM<GoogLeNetGEMMDataset1, CLTensor, CLAccessor, CLGEMM, DataType::F32>;
 using GEMMFP32GoogLeNet2 = GEMM<GoogLeNetGEMMDataset2, CLTensor, CLAccessor, CLGEMM, DataType::F32>;
+using FP16MatrixMultiply = GEMM<MatrixMultiplyDataset, CLTensor, CLAccessor, CLGEMM, DataType::F16>;
+using FP32MatrixMultiply = GEMM<MatrixMultiplyDataset, CLTensor, CLAccessor, CLGEMM, DataType::F32>;
 } // namespace
 
 BENCHMARK_DEFINE_F(GEMMFP16GoogLeNet1, cl_googlenet)
@@ -490,3 +492,49 @@ BENCHMARK_REGISTER_F(GEMMFP32GoogLeNet2, cl_googlenet)
 BENCHMARK_REGISTER_F(GEMMFP32GoogLeNet2, cl_googlenet)
 ->Threads(1)
 ->Apply(DataSetArg<GoogLeNetGEMMDataset2, 31>);
+
+BENCHMARK_DEFINE_F(FP16MatrixMultiply, cl_matrix_multiply)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        gemm_layer->run();
+        CLScheduler::get().sync();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(FP16MatrixMultiply, cl_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 0>);
+BENCHMARK_REGISTER_F(FP16MatrixMultiply, cl_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 1>);
+BENCHMARK_REGISTER_F(FP16MatrixMultiply, cl_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 2>);
+
+BENCHMARK_DEFINE_F(FP32MatrixMultiply, cl_matrix_multiply)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        gemm_layer->run();
+        CLScheduler::get().sync();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(FP32MatrixMultiply, cl_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 0>);
+BENCHMARK_REGISTER_F(FP32MatrixMultiply, cl_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 1>);
+BENCHMARK_REGISTER_F(FP32MatrixMultiply, cl_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 2>);
diff --git a/tests/benchmark/NEON/GEMM.cpp b/tests/benchmark/NEON/GEMM.cpp
index 9190309f1c..50131b4998 100644
--- a/tests/benchmark/NEON/GEMM.cpp
+++ b/tests/benchmark/NEON/GEMM.cpp
@@ -49,11 +49,14 @@ namespace
 #ifdef ENABLE_FP16
 using GEMMFP16GoogLeNet1 = GEMM<GoogLeNetGEMMDataset1, Tensor, NEAccessor, NEGEMM, DataType::F16>;
 using GEMMFP16GoogLeNet2 = GEMM<GoogLeNetGEMMDataset2, Tensor, NEAccessor, NEGEMM, DataType::F16>;
+using FP16MatrixMultiply = GEMM<MatrixMultiplyDataset, Tensor, NEAccessor, NEGEMM, DataType::F16>;
 #endif /* ENABLE_FP16 */
 using GEMMFP32GoogLeNet1 = GEMM<GoogLeNetGEMMDataset1, Tensor, NEAccessor, NEGEMM, DataType::F32>;
 using GEMMFP32GoogLeNet2 = GEMM<GoogLeNetGEMMDataset2, Tensor, NEAccessor, NEGEMM, DataType::F32>;
 using GEMMQS8GoogLeNet1  = GEMM<GoogLeNetGEMMDataset1, Tensor, NEAccessor, NEGEMM, DataType::QS8>;
 using GEMMQS8GoogLeNet2  = GEMM<GoogLeNetGEMMDataset2, Tensor, NEAccessor, NEGEMM, DataType::QS8>;
+using FP32MatrixMultiply = GEMM<MatrixMultiplyDataset, Tensor, NEAccessor, NEGEMM, DataType::F32>;
+using QS8MatrixMultiply  = GEMM<MatrixMultiplyDataset, Tensor, NEAccessor, NEGEMM, DataType::QS8>;
 } // namespace
 #ifdef ENABLE_FP16
 BENCHMARK_DEFINE_F(GEMMFP16GoogLeNet1, neon_googlenet)
@@ -272,6 +275,28 @@ BENCHMARK_REGISTER_F(GEMMFP16GoogLeNet2, neon_googlenet)
 BENCHMARK_REGISTER_F(GEMMFP16GoogLeNet2, neon_googlenet)
 ->Threads(1)
 ->Apply(DataSetArg<GoogLeNetGEMMDataset2, 31>);
+
+BENCHMARK_DEFINE_F(FP16MatrixMultiply, neon_matrix_multiply)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        gemm_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(FP16MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 0>);
+BENCHMARK_REGISTER_F(FP16MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 1>);
+BENCHMARK_REGISTER_F(FP16MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 2>);
 #endif /* ENABLE_FP16 */
 
 BENCHMARK_DEFINE_F(GEMMFP32GoogLeNet1, neon_googlenet)
@@ -707,3 +732,47 @@ BENCHMARK_REGISTER_F(GEMMQS8GoogLeNet2, neon_googlenet)
 BENCHMARK_REGISTER_F(GEMMQS8GoogLeNet2, neon_googlenet)
 ->Threads(1)
 ->Apply(DataSetArg<GoogLeNetGEMMDataset2, 31>);
+
+BENCHMARK_DEFINE_F(FP32MatrixMultiply, neon_matrix_multiply)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        gemm_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(FP32MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 0>);
+BENCHMARK_REGISTER_F(FP32MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 1>);
+BENCHMARK_REGISTER_F(FP32MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 2>);
+
+BENCHMARK_DEFINE_F(QS8MatrixMultiply, neon_matrix_multiply)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        gemm_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(QS8MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 0>);
+BENCHMARK_REGISTER_F(QS8MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 1>);
+BENCHMARK_REGISTER_F(QS8MatrixMultiply, neon_matrix_multiply)
+->Threads(1)
+->Apply(DataSetArg<MatrixMultiplyDataset, 2>);
diff --git a/tests/dataset/GEMMDataset.h b/tests/dataset/GEMMDataset.h
index f45bc3e838..ddd4a3424f 100644
--- a/tests/dataset/GEMMDataset.h
+++ b/tests/dataset/GEMMDataset.h
@@ -82,10 +82,10 @@ public:
     SmallGEMMDataset()
         : GenericDataset
     {
-        GEMMDataObject{ TensorShape(21u, 13u), TensorShape(33u, 21u), TensorShape(33u, 13u), TensorShape(33u, 13u), 1.0f, 0.0f },
-        GEMMDataObject{ TensorShape(31u, 1u), TensorShape(23u, 31u), TensorShape(23u, 1u), TensorShape(23u, 1u), 1.0f, 0.0f },
-        GEMMDataObject{ TensorShape(38u, 12u), TensorShape(21u, 38u), TensorShape(21u, 12u), TensorShape(21u, 12u), 0.2f, 1.2f },
-        GEMMDataObject{ TensorShape(32u, 1u), TensorShape(17u, 32u), TensorShape(17u, 1u), TensorShape(17u, 1u), 0.4f, 0.7f },
+        GEMMDataObject{ TensorShape(21U, 13U), TensorShape(33U, 21U), TensorShape(33U, 13U), TensorShape(33U, 13U), 1.0f, 0.0f },
+        GEMMDataObject{ TensorShape(31U, 1U), TensorShape(23U, 31U), TensorShape(23U, 1U), TensorShape(23U, 1U), 1.0f, 0.0f },
+        GEMMDataObject{ TensorShape(38U, 12U), TensorShape(21U, 38U), TensorShape(21U, 12U), TensorShape(21U, 12U), 0.2f, 1.2f },
+        GEMMDataObject{ TensorShape(32U, 1U), TensorShape(17U, 32U), TensorShape(17U, 1U), TensorShape(17U, 1U), 0.4f, 0.7f },
     }
     {
     }
@@ -99,10 +99,10 @@ public:
     LargeGEMMDataset()
         : GenericDataset
     {
-        GEMMDataObject{ TensorShape(923u, 429u), TensorShape(871u, 923u), TensorShape(871u, 429u), TensorShape(871u, 429u), 1.0f, 0.0f },
-        GEMMDataObject{ TensorShape(1021u, 1u), TensorShape(783u, 1021u), TensorShape(783u, 1u), TensorShape(783u, 1u), 1.0f, 0.0f },
-        GEMMDataObject{ TensorShape(681u, 1023u), TensorShape(213u, 681u), TensorShape(213u, 1023u), TensorShape(213u, 1023u), 0.2f, 1.2f },
-        GEMMDataObject{ TensorShape(941u, 1u), TensorShape(623u, 941u), TensorShape(623u, 1u), TensorShape(623u, 1u), 0.4f, 0.7f },
+        GEMMDataObject{ TensorShape(923U, 429U), TensorShape(871U, 923U), TensorShape(871U, 429U), TensorShape(871U, 429U), 1.0f, 0.0f },
+        GEMMDataObject{ TensorShape(1021U, 1U), TensorShape(783U, 1021U), TensorShape(783U, 1U), TensorShape(783U, 1U), 1.0f, 0.0f },
+        GEMMDataObject{ TensorShape(681U, 1023U), TensorShape(213U, 681U), TensorShape(213U, 1023U), TensorShape(213U, 1023U), 0.2f, 1.2f },
+        GEMMDataObject{ TensorShape(941U, 1U), TensorShape(623U, 941U), TensorShape(623U, 1U), TensorShape(623U, 1U), 0.4f, 0.7f },
     }
     {
     }
@@ -199,6 +199,22 @@ public:
 
     ~GoogLeNetGEMMDataset2() = default;
 };
+
+class MatrixMultiplyDataset : public GenericDataset<GEMMDataObject, 3>
+{
+public:
+    MatrixMultiplyDataset()
+        : GenericDataset
+    {
+        GEMMDataObject{ TensorShape(1024U, 1U), TensorShape(1000U, 1024U), TensorShape(1000U, 1U), TensorShape(1000U, 1U), 1.0f, 0.0f },
+        GEMMDataObject{ TensorShape(256U, 784U), TensorShape(64U, 256U), TensorShape(64U, 784U), TensorShape(64U, 784U), 1.0f, 0.0f },
+        GEMMDataObject{ TensorShape(1152U, 2704U), TensorShape(256U, 1152U), TensorShape(256U, 2704U), TensorShape(256U, 2704U), 1.0f, 0.0f },
+    }
+    {
+    }
+
+    ~MatrixMultiplyDataset() = default;
+};
 } // namespace test
 } // namespace arm_compute
 #endif //__ARM_COMPUTE_TEST_DATASET_GEMM_DATASET_H__