Apply clang-format on repository

Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.

Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/

There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.

Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>

1 files changed, 21 insertions, 9 deletions

diff --git a/src/gpu/cl/operators/ClMatMul.cpp b/src/gpu/cl/operators/ClMatMul.cpp
index 49d14127ca..c14b1f2992 100644
--- a/src/gpu/cl/operators/ClMatMul.cpp
+++ b/src/gpu/cl/operators/ClMatMul.cpp
@@ -47,11 +47,17 @@ ClMatMul::ClMatMul()
 {
 }
 
-Status ClMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulInfo &matmul_info, const ActivationLayerInfo &act_info)
+Status ClMatMul::validate(const ITensorInfo         *lhs,
+                          const ITensorInfo         *rhs,
+                          const ITensorInfo         *dst,
+                          const MatMulInfo          &matmul_info,
+                          const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+                                                         DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(rhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+                                                         DataType::F16, DataType::F32);
 
     const GPUTarget gpu_target = CLScheduler::get().target();
 
@@ -61,11 +67,16 @@ Status ClMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const
 
     const bool is_quantized = is_data_type_quantized_asymmetric(lhs->data_type());
 
-    return is_quantized ? ClMatMulLowpNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info) :
-           ClMatMulNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
+    return is_quantized ? ClMatMulLowpNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info)
+                        : ClMatMulNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
 }
 
-void ClMatMul::configure(const CLCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulInfo &matmul_info, const ActivationLayerInfo &act_info)
+void ClMatMul::configure(const CLCompileContext    &compile_context,
+                         ITensorInfo               *lhs,
+                         ITensorInfo               *rhs,
+                         ITensorInfo               *dst,
+                         const MatMulInfo          &matmul_info,
+                         const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_info);
@@ -81,12 +92,13 @@ void ClMatMul::configure(const CLCompileContext &compile_context, ITensorInfo *l
 
     MatMulKernelInfo kernel_info = t->configure(lhs, rhs, matmul_info);
 
-    if(_is_quantized)
+    if (_is_quantized)
     {
         _matmul_lowp_native_kernel->set_target(gpu_target);
 
         // Configure the low-precision native matrix multiply kernel
-        _matmul_lowp_native_kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
+        _matmul_lowp_native_kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info,
+                                              act_info);
     }
     else
     {
@@ -99,7 +111,7 @@ void ClMatMul::configure(const CLCompileContext &compile_context, ITensorInfo *l
 
 void ClMatMul::run(ITensorPack &tensors)
 {
-    if(_is_quantized)
+    if (_is_quantized)
     {
         CLScheduler::get().enqueue_op(*_matmul_lowp_native_kernel, tensors, true);
     }