MLCE-418 Reduce layer does not support multiple axes
* Added backend specific optimization to chain new reduces layers
for each axis to simulate behaviour of a layer with multiple axes.
* Added function to calculate reduced output shape.
* Added unit tests.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I180b0b111b7bcf3d0c283f1db0b82d5f17757682
diff --git a/src/backends/neon/workloads/NeonReduceWorkload.cpp b/src/backends/neon/workloads/NeonReduceWorkload.cpp
index 0e1b46a..6125f36 100644
--- a/src/backends/neon/workloads/NeonReduceWorkload.cpp
+++ b/src/backends/neon/workloads/NeonReduceWorkload.cpp
@@ -21,22 +21,52 @@
const ReduceDescriptor& desc)
{
const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
- const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
- if (!desc.m_vAxis.empty() && desc.m_vAxis.size() > 1)
- {
- return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
- "NeonReduceWorkload: Reduction is supported only on 1 axis.");
- }
arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(aclInputInfo.num_dimensions(),
input.GetNumDimensions(),
desc.m_vAxis);
- return arm_compute::NEReductionOperation::validate(&aclInputInfo,
- &aclOutputInfo,
- static_cast<unsigned int>(coords[0]),
- ConvertReductionOperationToAcl(desc),
- desc.m_KeepDims);
+ // As ACL only support one axis, validate the layer for each axis if more than one is present.
+ if (!desc.m_vAxis.empty() && desc.m_vAxis.size() > 1)
+ {
+ arm_compute::Status status;
+
+ for (unsigned int i = 0; i != desc.m_vAxis.size(); ++i)
+ {
+ TensorInfo inputToModify = input;
+ std::vector<uint32_t> singleAxis(1, desc.m_vAxis[i]);
+
+ // Calculate the output shape using the input shape for a single axis.
+ // Currently the output TensorInfo inferred will be reduced upon multiple axis
+ // which will fail validation as only one axis is supported.
+ const TensorShape& reducedShape = ComputeReductionTensorShape(inputToModify, singleAxis, desc.m_KeepDims);
+ inputToModify.SetShape(reducedShape);
+
+ const arm_compute::TensorInfo aclOutputInfoModified =
+ armcomputetensorutils::BuildArmComputeTensorInfo(inputToModify);
+
+ status = arm_compute::NEReductionOperation::validate(&aclInputInfo,
+ &aclOutputInfoModified,
+ static_cast<unsigned int>(coords[i]),
+ ConvertReductionOperationToAcl(desc),
+ desc.m_KeepDims);
+ if (!status)
+ {
+ break;
+ }
+ }
+ return status;
+ }
+ else
+ {
+ const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+
+ return arm_compute::NEReductionOperation::validate(&aclInputInfo,
+ &aclOutputInfo,
+ static_cast<unsigned int>(coords[0]),
+ ConvertReductionOperationToAcl(desc),
+ desc.m_KeepDims);
+ }
}
NeonReduceWorkload::NeonReduceWorkload(const ReduceQueueDescriptor& descriptor, const WorkloadInfo& info)
@@ -50,6 +80,7 @@
arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(input.info()->num_dimensions(),
info.m_InputTensorInfos[0].GetNumDimensions(),
m_Data.m_Parameters.m_vAxis);
+
m_Layer.configure(&input,
&output,
static_cast<unsigned int>(coords[0]),