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>
diff --git a/scripts/get_compute_library.sh b/scripts/get_compute_library.sh
index 811988e..c5e2da1 100755
--- a/scripts/get_compute_library.sh
+++ b/scripts/get_compute_library.sh
@@ -10,7 +10,7 @@
#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 4c2a5f0..f5a866b 100644
--- a/src/backends/gpuFsa/GpuFsaBackend.hpp
+++ b/src/backends/gpuFsa/GpuFsaBackend.hpp
@@ -23,10 +23,10 @@
/**
* 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 @@
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 c7137d7..7aa643d 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 @@
// 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 @@
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 @@
// 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 @@
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 @@
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 @@
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 @@
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 20386b5..1663044 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 @@
* 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 @@
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 @@
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));