IVGCVSW-7628 Update GpuFsa to use ACLs latest fixes

* Updates to the existing GpuFsa backend to incorporate patch:
  https://review.mlplatform.org/c/ml/ComputeLibrary/+/10990
* Update the ACL pin to include the patch with the fixes

Change-Id: I08d111265f4617657ee7f20249aeb111f64ba7a9
Signed-off-by: David Monahan <david.monahan@arm.com>

4 files changed, 66 insertions, 60 deletions

diff --git a/scripts/get_compute_library.sh b/scripts/get_compute_library.sh
index 811988e337..c5e2da1471 100755
--- a/scripts/get_compute_library.sh
+++ b/scripts/get_compute_library.sh
@@ -10,7 +10,7 @@ CMD=$( basename "$0" )
 #DEFAULT_CLFRAMEWORKREVISION="branches/arm_compute_23_11" # Release 23.11
 #
 # For pinning to a revision use this:
-DEFAULT_CLFRAMEWORKREVISION="3a704ae94fc5cbfce1053886f543b31977f1774c" #10989: Update Documentation for 24.01 release
+DEFAULT_CLFRAMEWORKREVISION="fdf56fb9d414a754e7cedfdc1351ab0ce2866a0c" #10990: Make GpuWorkloadContext own all tensor info objects
 
 usage() {
   echo -e "get_compute_library.sh: Clones the Arm Compute Library (ACL) repo from the ML Platform server and checks out
diff --git a/src/backends/gpuFsa/GpuFsaBackend.hpp b/src/backends/gpuFsa/GpuFsaBackend.hpp
index 4c2a5f02e3..f5a866b186 100644
--- a/src/backends/gpuFsa/GpuFsaBackend.hpp
+++ b/src/backends/gpuFsa/GpuFsaBackend.hpp
@@ -23,10 +23,10 @@ namespace armnn
 /**
  * A structure which contains all the elements needed to execute a fused workload in the GpuFsa Backend
  *
- * @param[in, out]  sketch      A unique pointer to the sketch containing the operators which have been fused.
- * @param[in, out]  TensorInfos A shared pointer to a GpuWorkloadContext which contains TensorInfos
- * @param[in, out]  inputIds    A unique pointer to a vector of input Ids used to access workloadContext TensorInfos
- * @param[in, out]  outputIds   A unique pointer to a vector of output Ids used to access workloadContext TensorInfos
+ * @param[in, out]  sketch              A unique pointer to the sketch containing the operators which have been fused.
+ * @param[in, out]  TensorInfos         A shared pointer to a GpuWorkloadContext which creates + stores TensorInfos
+ * @param[in, out]  inputTensorInfos    A unique pointer to a vector of inputTensorInfos used by the sketch
+ * @param[in, out]  outputTensorInfos   A unique pointer to a vector of outputTensorInfos used by the sketch
  *
  */
 struct GpuFsaPreCompiledBlob
@@ -34,8 +34,8 @@ struct GpuFsaPreCompiledBlob
     std::unique_ptr<arm_compute::experimental::dynamic_fusion::GpuWorkloadSketch> sketch = nullptr;
     std::shared_ptr<arm_compute::experimental::dynamic_fusion::GpuWorkloadContext> workloadContext = nullptr;
 
-    std::unique_ptr<std::vector<int32_t>> inputIds = nullptr;
-    std::unique_ptr<std::vector<int32_t>> outputIds = nullptr;
+    std::unique_ptr<std::vector<arm_compute::ITensorInfo*>> inputTensorInfos = nullptr;
+    std::unique_ptr<std::vector<arm_compute::ITensorInfo*>> outputTensorInfos = nullptr;
 };
 
 // add new capabilities here..
diff --git a/src/backends/gpuFsa/layers/GpuFsaConvolution2d.cpp b/src/backends/gpuFsa/layers/GpuFsaConvolution2d.cpp
index c7137d7ac8..7aa643dcb1 100644
--- a/src/backends/gpuFsa/layers/GpuFsaConvolution2d.cpp
+++ b/src/backends/gpuFsa/layers/GpuFsaConvolution2d.cpp
@@ -17,7 +17,6 @@
 
 #include <arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h>
-#include <src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h>
 
@@ -49,8 +48,7 @@ arm_compute::Status GpuFsaConvolution2dValidate(const TensorInfo& input,
 
     // Only create the bias tensor info if enabled, otherwise pass nullptr to validate_op
     arm_compute::TensorInfo aclBiasInfo;
-    arm_compute::TensorInfo biasSketchInfo;
-    arm_compute::TensorInfo* biasSketchInfoPtr = nullptr;
+    arm_compute::ITensorInfo* biasSketchInfoPtr = nullptr;
 
     if (descriptor.m_BiasEnabled)
     {
@@ -61,8 +59,7 @@ arm_compute::Status GpuFsaConvolution2dValidate(const TensorInfo& input,
         aclBiasInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
         aclBiasInfo.set_are_values_constant(biases.value().IsConstant());
 
-        biasSketchInfo    = workloadContext.create_tensor_info(aclBiasInfo);
-        biasSketchInfoPtr = &biasSketchInfo;
+        biasSketchInfoPtr = workloadContext.create_tensor_info(aclBiasInfo);
     }
 
     // Set Conv2d attributes using descriptor
@@ -78,8 +75,8 @@ arm_compute::Status GpuFsaConvolution2dValidate(const TensorInfo& input,
 
     // Validate operator, check status and update reasonIfUnsupported
     arm_compute::Status aclStatus = GpuConv2d::validate_op(sketch,
-                                                           &inputInfo,
-                                                           &weightInfo,
+                                                           inputInfo,
+                                                           weightInfo,
                                                            biasSketchInfoPtr,
                                                            conv2DAttributes);
 
@@ -93,34 +90,32 @@ void GpuFsaConvolution2dCreateOp(GpuFsaPreCompiledBlob* blob,
                                  const Optional<TensorInfo>& biases)
 {
 /*
- * Creating an Op for the GpuFds backend requires us to create and maintain quite a bit of data, which is then stored
+ * Creating an Op for the GpuFsa backend requires us to create and maintain quite a bit of data, which is then stored
  * in a GpuFsaPreCompiledBlob for execution later. Specifically we need:
  * GpuWorkloadContext, this contains the TensorInfos and is unique to the Graph being executed
  * Sketch, this is similar to a subgraph and can contain one or more operations. Multiple ops can be "fused" together
  * using a single sketch.
- * The TensorInfoIds, these are the ids of the TensorInfos used when creating the sketch. They refer to the TensorInfos
- * stored within the GpuWorkloadContext and are used to fetch them later when executing the sketch.
+ * The inputTensorinfos / outputTensorInfos, these are pointers to the TensorInfos used when creating the sketch.
+ * They refer to the TensorInfos stored within the GpuWorkloadContext and are needed when executing the sketch
+ * as the TensorInfos used when creating the Tensors must match those used to create the Sketch. Otherwise the runtime
+ * doesn't know which Tensors to use.
  */
     using namespace arm_compute::experimental::dynamic_fusion;
     GpuWorkloadSketch* sketch = blob->sketch.get();
     GpuWorkloadContext* workloadContext = blob->workloadContext.get();
-    std::vector<int32_t> inputIds = {};
-    std::vector<int32_t> outputIds = {};
+    std::vector<arm_compute::ITensorInfo*> inputTensorInfos = {};
+    std::vector<arm_compute::ITensorInfo*> outputTensorInfos = {};
 
     // Build and create tensor infos using the sketch
     const arm_compute::TensorInfo aclInputInfo   = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
     arm_compute::TensorInfo       aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);
     aclWeightsInfo.set_are_values_constant(weights.IsConstant());
-    auto inputInfo = workloadContext->create_tensor_info(aclInputInfo);
-    aclWeightsInfo.set_are_values_constant(weights.IsConstant());
-    inputIds.emplace_back(inputInfo.id());
 
-    auto weightInfo = workloadContext->create_tensor_info(aclWeightsInfo);
-    inputIds.emplace_back(weightInfo.id());
+    inputTensorInfos.emplace_back(workloadContext->create_tensor_info(aclInputInfo));
+    inputTensorInfos.emplace_back(workloadContext->create_tensor_info(aclWeightsInfo));
 
-    // Only create the bias tensor info if enabled, otherwise pass nullptr to validate_op
+    // Only create the bias tensor info if enabled, otherwise pass nullptr to validate_op / create_op
     arm_compute::TensorInfo aclBiasInfo;
-    arm_compute::TensorInfo biasSketchInfo;
     arm_compute::ITensorInfo* biasSketchInfoPtr = nullptr;
 
     if (descriptor.m_BiasEnabled)
@@ -132,9 +127,8 @@ void GpuFsaConvolution2dCreateOp(GpuFsaPreCompiledBlob* blob,
         aclBiasInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
         aclBiasInfo.set_are_values_constant(biases.value().IsConstant());
 
-        biasSketchInfo    = workloadContext->create_tensor_info(aclBiasInfo);
-        inputIds.emplace_back(biasSketchInfo.id());
-        biasSketchInfoPtr = workloadContext->implementation().get_tensor_info(biasSketchInfo.id());
+        inputTensorInfos.emplace_back(workloadContext->create_tensor_info(aclBiasInfo));
+        biasSketchInfoPtr = inputTensorInfos[2];
     }
 
     // Set Conv2d attributes using descriptor
@@ -149,12 +143,11 @@ void GpuFsaConvolution2dCreateOp(GpuFsaPreCompiledBlob* blob,
     conv2DAttributes.stride(aclStrideInfo);
 
     // Validate operator, check status and update reasonIfUnsupported
-    arm_compute::Status aclStatus =
-        GpuConv2d::validate_op(*sketch,
-                               workloadContext->implementation().get_tensor_info(inputInfo.id()),
-                               workloadContext->implementation().get_tensor_info(weightInfo.id()),
-                               biasSketchInfoPtr,
-                               conv2DAttributes);
+    arm_compute::Status aclStatus = GpuConv2d::validate_op(*sketch,
+                                                           inputTensorInfos[0],
+                                                           inputTensorInfos[1],
+                                                           biasSketchInfoPtr,
+                                                           conv2DAttributes);
 
     const bool supported = (aclStatus.error_code() == arm_compute::ErrorCode::OK);
     if (!supported)
@@ -162,19 +155,20 @@ void GpuFsaConvolution2dCreateOp(GpuFsaPreCompiledBlob* blob,
         throw BackendCapabilityException("\"GpuFsa\" backend failed during Convolution2D operation validation");
     }
 
-    arm_compute::ITensorInfo* convOutInfo =
-        GpuConv2d::create_op(*sketch,
-                             workloadContext->implementation().get_tensor_info(inputInfo.id()),
-                             workloadContext->implementation().get_tensor_info(weightInfo.id()),
-                             biasSketchInfoPtr,
-                             conv2DAttributes);
+    // Create the Op within the Sketch using the TensorInfos we have stored
+    arm_compute::ITensorInfo* convOutInfo = GpuConv2d::create_op(*sketch,
+                                                                 inputTensorInfos[0],
+                                                                 inputTensorInfos[1],
+                                                                 biasSketchInfoPtr,
+                                                                 conv2DAttributes);
 
-    arm_compute::TensorInfo outputDstInfo = workloadContext->create_tensor_info();
-    outputIds.emplace_back(outputDstInfo.id());
+    // Create the Output
+    outputTensorInfos.emplace_back(workloadContext->create_tensor_info());
+    GpuOutput::create_op(*sketch, convOutInfo, outputTensorInfos[0]);
 
-    GpuOutput::create_op(*sketch, convOutInfo, workloadContext->implementation().get_tensor_info(outputDstInfo.id()));
-    blob->inputIds = std::make_unique<std::vector<int32_t>>(inputIds);
-    blob->outputIds = std::make_unique<std::vector<int32_t>>(outputIds);
+    // Store the TensorInfos within the blob as unique_ptrs to be used later
+    blob->inputTensorInfos = std::make_unique<std::vector<arm_compute::ITensorInfo*>>(inputTensorInfos);
+    blob->outputTensorInfos = std::make_unique<std::vector<arm_compute::ITensorInfo*>>(outputTensorInfos);
 }
 
 } // namespace armnn
diff --git a/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp b/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp
index 20386b5d86..1663044a17 100644
--- a/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp
+++ b/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp
@@ -10,6 +10,7 @@
 #include <gpuFsa/GpuFsaTensorHandle.hpp>
 #include <gpuFsa/GpuFsaBackend.hpp>
 #include <aclCommon/ArmComputeTensorUtils.hpp>
+#include <fmt/format.h>
 
 #include <aclCommon/ArmComputeTensorUtils.hpp>
 #include <arm_compute/runtime/CL/CLTensor.h>
@@ -21,7 +22,6 @@
 
 #include <arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h>
-#include <src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h>
 #include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h>
@@ -48,26 +48,38 @@ void GpuFsaPreCompiledWorkload::Execute() const
  * First we need all of the data stored within the PreCompiled blob which was used to setup the workload, namely:
  * The GpuWorkloadContext, this is a context which contains the TensorInfos and is unique to the graph being run
  * The Sketch, this can contain one or many ops and acts as a subgraph within the context
- * The TensorInfoIds, These are the ids of the TensorInfos used during the creation of the Sketch and stored within
- * the context.
+ * The inputTensorInfos / outputTensorInfos, These are vectors containing the TensorInfos used when creating the sketch
+ *
  * It is very important that the Tensors passed into the Runtime being used to execute this sketch are created with
  * the same TensorInfos as used when creating the sketch. We do this by creating new tensors, getting the original
- * TensorInfos from the GpuWorkloadContext via their ids, and then importing the buffers from our own TensorHandles
- * directly into these newly created Tensors. This allows us to link the externally visible Tensors from ArmNN to the
- * Tensors which are needed to execute with the Sketch.
+ * TensorInfos from the vectors of tensorInfos stored in the blob, and then importing the buffers from our own
+ * TensorHandles directly into these newly created Tensors. This allows us to link the externally visible Tensors
+ * from ArmNN to the Tensors which are needed to execute with the Sketch.
  *
  */
     using namespace arm_compute::experimental::dynamic_fusion;
     // Get the runtime and configure it with the precompiled sketch
     ClWorkloadRuntime runtime;
     GpuFsaPreCompiledBlob *preCompiledBlob = static_cast<GpuFsaPreCompiledBlob*>(m_Data.m_PreCompiledObject);
-    auto workloadContext =
-            &(preCompiledBlob->workloadContext->implementation());
     auto sketch = preCompiledBlob->sketch.release();
-    std::vector<int32_t> inputIds = *(preCompiledBlob->inputIds.get());
-    std::vector<int32_t> outputIds = *(preCompiledBlob->outputIds.get());
     auto status = runtime.configure(*sketch);
 
+    // Get the TensorInfos stored within the PreCompiledBlob and check they're the right size
+    auto inputTensorInfos = preCompiledBlob->inputTensorInfos.get();
+    auto outputTensorInfos = preCompiledBlob->outputTensorInfos.get();
+    if (inputTensorInfos->size() != m_Data.m_Inputs.size())
+    {
+        throw InvalidArgumentException(fmt::format("GpuFsaPreCompiledWorkload::Execute: The number of inputTensorInfos"
+                                                   " {} does not match the number of inputs {}.",
+                                                   inputTensorInfos->size(), m_Data.m_Inputs.size()));
+    }
+    if (outputTensorInfos->size() != m_Data.m_Outputs.size())
+    {
+        throw InvalidArgumentException(fmt::format("GpuFsaPreCompiledWorkload::Execute: The number of outputTensorInfos"
+                                                   " {} does not match the number of outputs {}.",
+                                                   outputTensorInfos->size(), m_Data.m_Outputs.size()));
+    }
+
     // (Important) Allocate auxiliary tensor memory if there are any
     for(auto &data : runtime.get_auxiliary_tensors())
     {
@@ -85,8 +97,8 @@ void GpuFsaPreCompiledWorkload::Execute() const
     for (uint32_t inputSlotIdx = 0; inputSlotIdx < m_Data.m_Inputs.size(); ++inputSlotIdx)
     {
         arm_compute::CLTensor* input = new arm_compute::CLTensor{};
-        input->allocator()->init(*(dynamic_cast<arm_compute::TensorInfo*>(
-                workloadContext->get_tensor_info(inputIds[inputSlotIdx]))));
+        // inputTensorInfos is a ptr to a vector of ptrs, so we need to do a double dereference
+        input->allocator()->init(*((*inputTensorInfos)[inputSlotIdx]));
         auto* inputHandle = PolymorphicDowncast<GpuFsaTensorHandle*>(m_Data.m_Inputs[inputSlotIdx]);
         input->allocator()->import_memory(inputHandle->GetTensor().cl_buffer());
         inputsWeightsOutputs.emplace_back(std::move(input));
@@ -95,8 +107,8 @@ void GpuFsaPreCompiledWorkload::Execute() const
     for (uint32_t outputSlotIdx = 0; outputSlotIdx < m_Data.m_Outputs.size(); ++outputSlotIdx)
     {
         arm_compute::CLTensor* output = new arm_compute::CLTensor{};
-        output->allocator()->init(*(dynamic_cast<arm_compute::TensorInfo*>(
-                workloadContext->get_tensor_info(outputIds[outputSlotIdx]))));
+        // outputTensorInfos is a ptr to a vector of ptrs, so we need to do a double dereference
+        output->allocator()->init(*((*outputTensorInfos)[outputSlotIdx]));
         auto* outputHandle = PolymorphicDowncast<GpuFsaTensorHandle*>(m_Data.m_Outputs[outputSlotIdx]);
         output->allocator()->import_memory(outputHandle->GetTensor().cl_buffer());
         inputsWeightsOutputs.emplace_back(std::move(output));