Update ACL pin to ec4dee8c68a3d0f6d63db184bfb2f4589429778e

 * Axis for LogSoftMax and SoftMax can be either positive or negative
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: I36b0507ad7600c0a98c3b8be3c0350045ee05b84
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>

5 files changed, 14 insertions, 22 deletions

diff --git a/scripts/get_compute_library.sh b/scripts/get_compute_library.sh
index 8ea0360867..02e2a0ddf0 100755
--- a/scripts/get_compute_library.sh
+++ b/scripts/get_compute_library.sh
@@ -7,10 +7,10 @@
 CMD=$( basename $0 )
 
 # For pinning to a ref use this:
-DEFAULT_CLFRAMEWORKREVISION="branches/arm_compute_20_08" # Release 20.08
+#DEFAULT_CLFRAMEWORKREVISION="branches/arm_compute_20_08" # Release 20.08
 #
 # For pinning to a revision use this:
-#DEFAULT_CLFRAMEWORKREVISION="547b2e7aa07db4dd41f99e492c40710f2548c6ba" #COMPMID-3702: Update documentation
+DEFAULT_CLFRAMEWORKREVISION="ec4dee8c68a3d0f6d63db184bfb2f4589429778e" #COMPMID-3750: Disable asm kernels when shifts are negative.
 
 usage() {
     echo "Usage: $CMD (Use the default clframework SHA)"
diff --git a/src/backends/cl/workloads/ClLogSoftmaxWorkload.cpp b/src/backends/cl/workloads/ClLogSoftmaxWorkload.cpp
index c62c9d24f6..6d53523291 100644
--- a/src/backends/cl/workloads/ClLogSoftmaxWorkload.cpp
+++ b/src/backends/cl/workloads/ClLogSoftmaxWorkload.cpp
@@ -21,8 +21,7 @@ arm_compute::Status ClLogSoftmaxWorkloadValidate(const TensorInfo& input,
     const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
     const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
 
-    int aclAxis_int = ComputeAclAxis(descriptor.m_Axis, input);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, input);
+    int aclAxis = ComputeAclAxis(descriptor.m_Axis, input);
     return arm_compute::CLLogSoftmaxLayer::validate(&aclInputInfo, &aclOutputInfo, descriptor.m_Beta, aclAxis);
 }
 
@@ -36,8 +35,7 @@ ClLogSoftmaxWorkload::ClLogSoftmaxWorkload(const LogSoftmaxQueueDescriptor& desc
     arm_compute::ICLTensor& input  = static_cast<ClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
     arm_compute::ICLTensor& output = static_cast<ClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
 
-    int aclAxis_int = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, info.m_InputTensorInfos[0]);
+    int aclAxis = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
     m_LogSoftmaxLayer.configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
 }
 
diff --git a/src/backends/cl/workloads/ClSoftmaxWorkload.cpp b/src/backends/cl/workloads/ClSoftmaxWorkload.cpp
index 37746c83a6..8bc2a765ed 100644
--- a/src/backends/cl/workloads/ClSoftmaxWorkload.cpp
+++ b/src/backends/cl/workloads/ClSoftmaxWorkload.cpp
@@ -21,8 +21,7 @@ arm_compute::Status ClSoftmaxWorkloadValidate(const TensorInfo& input,
     const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
     const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
 
-    int aclAxis_int = ComputeAclAxis(descriptor.m_Axis, input);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, input);
+    int aclAxis = ComputeAclAxis(descriptor.m_Axis, input);
     return arm_compute::CLSoftmaxLayer::validate(&aclInputInfo, &aclOutputInfo, descriptor.m_Beta, aclAxis);
 }
 
@@ -36,8 +35,7 @@ ClSoftmaxWorkload::ClSoftmaxWorkload(const SoftmaxQueueDescriptor& descriptor, c
     arm_compute::ICLTensor& input  = static_cast<ClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
     arm_compute::ICLTensor& output = static_cast<ClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
 
-    int aclAxis_int = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, info.m_InputTensorInfos[0]);
+    int aclAxis = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
     m_SoftmaxLayer.configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
 }
 
diff --git a/src/backends/neon/workloads/NeonLogSoftmaxWorkload.cpp b/src/backends/neon/workloads/NeonLogSoftmaxWorkload.cpp
index 39ff279728..ba5c9000f4 100644
--- a/src/backends/neon/workloads/NeonLogSoftmaxWorkload.cpp
+++ b/src/backends/neon/workloads/NeonLogSoftmaxWorkload.cpp
@@ -23,12 +23,11 @@ arm_compute::Status NeonLogSoftmaxWorkloadValidate(const TensorInfo& input,
     const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
     const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
 
-    int aclAxis_int = ComputeAclAxis(descriptor.m_Axis, input);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, input);
+    int aclAxis = ComputeAclAxis(descriptor.m_Axis, input);
     return arm_compute::NELogSoftmaxLayer::validate(&aclInputInfo,
                                                     &aclOutputInfo,
                                                     descriptor.m_Beta,
-                                                    static_cast<int>(aclAxis));
+                                                    aclAxis);
 }
 
 NeonLogSoftmaxWorkload::NeonLogSoftmaxWorkload(const LogSoftmaxQueueDescriptor& descriptor,
@@ -42,9 +41,8 @@ NeonLogSoftmaxWorkload::NeonLogSoftmaxWorkload(const LogSoftmaxQueueDescriptor&
     arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
 
     auto layer = std::make_unique<arm_compute::NELogSoftmaxLayer>(memoryManager);
-    int aclAxis_int = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, info.m_InputTensorInfos[0]);
-    layer->configure(&input, &output, m_Data.m_Parameters.m_Beta, static_cast<int>(aclAxis));
+    int aclAxis = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
+    layer->configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
     m_LogSoftmaxLayer.reset(layer.release());
 }
 
diff --git a/src/backends/neon/workloads/NeonSoftmaxWorkload.cpp b/src/backends/neon/workloads/NeonSoftmaxWorkload.cpp
index 7e1501fa3e..505844e24a 100644
--- a/src/backends/neon/workloads/NeonSoftmaxWorkload.cpp
+++ b/src/backends/neon/workloads/NeonSoftmaxWorkload.cpp
@@ -23,12 +23,11 @@ arm_compute::Status NeonSoftmaxWorkloadValidate(const TensorInfo& input,
     const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
     const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
 
-    int aclAxis_int = ComputeAclAxis(descriptor.m_Axis, input);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, input);
+    int aclAxis = ComputeAclAxis(descriptor.m_Axis, input);
     return arm_compute::NESoftmaxLayer::validate(&aclInputInfo,
                                                  &aclOutputInfo,
                                                  descriptor.m_Beta,
-                                                 static_cast<int>(aclAxis));
+                                                 aclAxis);
 }
 
 NeonSoftmaxWorkload::NeonSoftmaxWorkload(const SoftmaxQueueDescriptor& descriptor,
@@ -42,9 +41,8 @@ NeonSoftmaxWorkload::NeonSoftmaxWorkload(const SoftmaxQueueDescriptor& descripto
     arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
 
     auto layer = std::make_unique<arm_compute::NESoftmaxLayer>(memoryManager);
-    int aclAxis_int = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
-    unsigned int aclAxis = ComputePositiveAxis(aclAxis_int, info.m_InputTensorInfos[0]);
-    layer->configure(&input, &output, m_Data.m_Parameters.m_Beta, static_cast<int>(aclAxis));
+    int aclAxis = ComputeAclAxis(m_Data.m_Parameters.m_Axis, info.m_InputTensorInfos[0]);
+    layer->configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
     m_SoftmaxLayer.reset(layer.release());
 }