COMPMID-3451: Add support for F16 in Gemm Tuner

- Comand line option added to specify F32 or F16

Change-Id: Ibd8a5c8d013a6dff601aa773e94946de1ff6e280
Signed-off-by: Eren Kopuz <eren.kopuz@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3322
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 7 insertions, 8 deletions

diff --git a/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp b/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp
index 0d161aab2d..09454990d7 100644
--- a/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp
+++ b/examples/gemm_tuner/cl_gemm_reshaped_rhs_only.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -150,10 +150,9 @@ public:
     bool do_setup(int argc, char **argv) override
     {
         // Default parameters
-        const DataType            data_type = DataType::F32;
-        const float               alpha     = 1.0f;
-        const float               beta      = 0.0f;
-        const ActivationLayerInfo act_info  = ActivationLayerInfo();
+        const float               alpha    = 1.0f;
+        const float               beta     = 0.0f;
+        const ActivationLayerInfo act_info = ActivationLayerInfo();
         CommonGemmExampleParams   params;
         GemmConfigs               configs;
 
@@ -192,9 +191,9 @@ public:
 
         CLScheduler::get().default_init(&tuner);
 
-        lhs.allocator()->init(TensorInfo(TensorShape(params.K, params.M, params.B), 1, data_type));
-        rhs.allocator()->init(TensorInfo(TensorShape(params.N, params.K, params.B), 1, data_type));
-        bias.allocator()->init(TensorInfo(TensorShape(params.N, 1, params.B), 1, data_type));
+        lhs.allocator()->init(TensorInfo(TensorShape(params.K, params.M, params.B), 1, params.data_type));
+        rhs.allocator()->init(TensorInfo(TensorShape(params.N, params.K, params.B), 1, params.data_type));
+        bias.allocator()->init(TensorInfo(TensorShape(params.N, 1, params.B), 1, params.data_type));
 
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0 = configs.m0;