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* Added names to Workloads.
* Workloads will be given the name of the Layer that created them.
* Added new profiling macros to CL Neon and Ref that add the
workload name to the event label
* Updated workloads to use new macros.
* Added missing profiling to Rank Workloads.
* Fixed issue where ClConvolution2dWorkload was being reported as
Undefined rather than GpuAcc.
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I0a55eab6c2f455b73943aca8e99a247c3cb2a906
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* ExecutionData holds a void* which can be assigned to data required
for execution in a backend. WorkingMemDescriptors are used in the Ref
backend which hold TensorHandles for inputs and outputs.
* Updated ExecuteAsync functions to take ExecutionData.
* Added CreateExecutionData and UpdateExectutionData to IBackendInternal.
* Streamlined experimental IWorkingMemHandle API by removing map related
function and unused m_workingMemDescriptorMap from WorkingMemHandle.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I54b0aab12872011743a141eb42dae200227769af
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* Find and replace all workloads associated with imported IO
* Only attempt tensorhandle replacement if supported by all workloads
* Add new RefBaseWorkload to enable forced input for ref backend
* Store imported tensorhandles in preImportedTensorhandles instead of outputHandles
* Create pre-imported tensorhandles at network load-time
* Front load import workload validation to load network time
* Only call ReplaceTensorHandle when needed
Change-Id: I3816a71b7f57ae90388bb16462a75d4ef3544fa7
Signed-off-by: Finn Williams <finn.williams@arm.com>
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* Add inter layer memory management to WorkingMemHandle
* Change Const layers to be executed once in loadedNetworkConstruction
and share tensorHandle between all WorkingMemHandles
* Fix various reference workloads pointing to memory in the queueDescriptor
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Change-Id: I69d4b3c5c84d2f5abe4540c3e624ab4f00d88226
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* Added multithreaded StridedSliceEndToEndTest
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Change-Id: I4579db7b5959e0a22256f1bda00238c22e611dec
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* Added support for Signed64 to flatbuffer's schema & updated source tree
* Added support for Signed64 to TFLite Delegate
* Added support for Signed64 to Serializer
* Added support for Signed64 to Deserializer
* Added unit test for ArgMinMax to Deserializer
* Deprecated m_Output_Type from the ArgMinMaxDescriptor: the output type
is solely determined by the DataType of the output Tensor
* Fixed issue where RefArgMinMaxWorkload could output data using
the wrong DataType
* Added Signed64 to RefLayerSupport::IsArgMinMaxSupported as a supported
type
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: Ib622c052a1f8aa3e658262f8bde5a6881a8cbe10
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This patch adds int32 and int64 ArgMax op support.
Current ARMNN already has ArgMax op but not used, and
it doesn't support int64 output type.
So this patch adds a new type, Signed64, and also adds
ArgMinMax computation function for int64 type support.
In default, output tensor type of ArgMax op is int64 in case of
tensorflow lite model so this patch makes a proper function - ArgMax op
for int64 or int32 - to be called according to parsed output_type value.
With this patch, ARMNN supports both types - int64 and int32 - for
ArgMinMax op.
Changelog v1:
- Check if output data type of ArgMinMax op is valid or not.
- Use template function to support int32 and int64 types of ArgMinMax function.
- Keep using Signed32 as default data type of m_Output_Type.
Change-Id: I7a8e7e38dd9e5acc81464571d8b4d51378fc7f14
Signed-off-by: Inki Dae <inki.dae@samsung.com>
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Change-Id: I65209ecec4e3abf808163239748d6e830568c2e3
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
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