IVGCVSW-4447 Add Hal 1_3 Support
* Add new 1.3 files HalPolicy, ArmnnDriver, ArmnnDriverImpl
* Add new .rc file for 1.3 service
* Add ArmnnPreparedModel_1_3 and implement new functions
* Update Android.mk with 1.3 driver and service
* Refactor ifdef to include ARMNN_ANDROID_NN_V1_3
* Create Utils getMainModel for new 1.3 Model Main Subgraph
* Use android Utils to convertToV1_X in ArmnnPrepapredModel_1_3
* Refactor HAL 1.2 convert functions into ConversionUtils_1_2.hpp
* Replace ArmnnBurstExecutorWithCache with call to ExecutionBurstServer
Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: I514069e9e1b16bcd1c4abfb5d563d25ac22d02e3
diff --git a/ArmnnPreparedModel_1_2.cpp b/ArmnnPreparedModel_1_2.cpp
index 5031c5f..76ef426 100644
--- a/ArmnnPreparedModel_1_2.cpp
+++ b/ArmnnPreparedModel_1_2.cpp
@@ -2,9 +2,6 @@
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
-// Note: the ArmnnBurstExecutorWithCache in this file is based on Android code
-// under the Apache 2.0 license. See comment below for details.
-//
#define LOG_TAG "ArmnnDriver"
@@ -215,27 +212,6 @@
return Execute(request, measureTiming, cb);
}
-OutputShape ComputeShape(const armnn::TensorInfo& info)
-{
- OutputShape shape;
-
- hidl_vec<uint32_t> dimensions;
-
- armnn::TensorShape tensorShape = info.GetShape();
- const unsigned int numDims = tensorShape.GetNumDimensions();
- dimensions.resize(numDims);
-
- for (unsigned int outputIdx = 0u; outputIdx < numDims; ++outputIdx)
- {
- dimensions[outputIdx] = tensorShape[outputIdx];
- }
-
- shape.dimensions = dimensions;
- shape.isSufficient = true;
-
- return shape;
-}
-
template<typename HalVersion>
Return<V1_0::ErrorStatus> ArmnnPreparedModel_1_2<HalVersion>::PrepareMemoryForInputs(
armnn::InputTensors& inputs,
@@ -348,27 +324,6 @@
return V1_0::ErrorStatus::NONE;
}
-void CommitPools(std::vector<::android::nn::RunTimePoolInfo>& memPools)
-{
- if (memPools.empty())
- {
- return;
- }
- // Commit output buffers.
- // Note that we update *all* pools, even if they aren't actually used as outputs -
- // this is simpler and is what the CpuExecutor does.
- for (auto& pool : memPools)
- {
- // Type android::nn::RunTimePoolInfo has changed between Android P & Q and Android R, where
- // update() has been removed and flush() added.
-#if defined(ARMNN_ANDROID_R) // Use the new Android implementation.
- pool.flush();
-#else
- pool.update();
-#endif
- }
-}
-
template<typename HalVersion>
Return<void> ArmnnPreparedModel_1_2<HalVersion>::executeSynchronously(const V1_0::Request& request,
MeasureTiming measureTiming,
@@ -514,7 +469,7 @@
{
std::vector<std::vector<char>> storage;
armnn::InputTensors inputTensors;
- for (unsigned int i = 0; i < m_Model.inputIndexes.size(); i++)
+ for (unsigned int i = 0; i < getMainModel(m_Model).inputIndexes.size(); i++)
{
const armnn::TensorInfo inputTensorInfo = m_Runtime->GetInputTensorInfo(m_NetworkId, i);
storage.emplace_back(inputTensorInfo.GetNumBytes());
@@ -524,7 +479,7 @@
}
armnn::OutputTensors outputTensors;
- for (unsigned int i = 0; i < m_Model.outputIndexes.size(); i++)
+ for (unsigned int i = 0; i < getMainModel(m_Model).outputIndexes.size(); i++)
{
const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
storage.emplace_back(outputTensorInfo.GetNumBytes());
@@ -600,77 +555,6 @@
return V1_0::ErrorStatus::NONE;
}
-
-/// This class is strongly inspired by the default implementation in Android named DefaultBurstExecutorWithCache.
-/// The original code is licensed under Apache-2.0 and can be found at the following link:
-/// https://android.googlesource.com/platform/frameworks/
-/// ml/+/refs/tags/android-10.0.0_r20/nn/common/ExecutionBurstServer.cpp
-class ArmnnBurstExecutorWithCache : public ExecutionBurstServer::IBurstExecutorWithCache {
-public:
- ArmnnBurstExecutorWithCache(V1_2::IPreparedModel* preparedModel)
- : m_PreparedModel(preparedModel)
- {}
-
- bool isCacheEntryPresent(int32_t slot) const override
- {
- const auto it = m_MemoryCache.find(slot);
- return (it != m_MemoryCache.end()) && it->second.valid();
- }
-
- void addCacheEntry(const hidl_memory& memory, int32_t slot) override
- {
- m_MemoryCache[slot] = memory;
- }
-
- void removeCacheEntry(int32_t slot) override
- {
- m_MemoryCache.erase(slot);
- }
-
- std::tuple<V1_0::ErrorStatus, hidl_vec<OutputShape>, Timing> execute(
- const V1_0::Request& request, const std::vector<int32_t>& slots,
- MeasureTiming measure) override
- {
- ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache::execute");
- hidl_vec<hidl_memory> pools(slots.size());
-
- std::transform(slots.begin(), slots.end(), pools.begin(), [this](int32_t slot)
- {
- return m_MemoryCache[slot];
- });
-
- V1_0::Request fullRequest = request;
- fullRequest.pools = std::move(pools);
-
- // Setup Callback
- V1_0::ErrorStatus returnedStatus = V1_0::ErrorStatus::GENERAL_FAILURE;
- hidl_vec<OutputShape> returnedOutputShapes;
- Timing returnedTiming;
- auto cb = [&returnedStatus, &returnedOutputShapes, &returnedTiming](V1_0::ErrorStatus status,
- const hidl_vec<OutputShape>& outputShapes,
- const Timing& timing)
- {
- returnedStatus = status;
- returnedOutputShapes = outputShapes;
- returnedTiming = timing;
- };
-
- // Execute
- ALOGV("ArmnnPreparedModel_1_2::BurstExecutorWithCache executing");
- const Return<void> ret = m_PreparedModel->executeSynchronously(fullRequest, measure, cb);
-
- if (!ret.isOk() || returnedStatus != V1_0::ErrorStatus::NONE)
- {
- ALOGE("ArmnnPreparedModel_1_2::BurstExecutorWithCache::error executing");
- }
- return std::make_tuple(returnedStatus, std::move(returnedOutputShapes), returnedTiming);
- }
-
-private:
- V1_2::IPreparedModel* const m_PreparedModel;
- std::map<int, hidl_memory> m_MemoryCache;
-};
-
template<typename HalVersion>
Return<void> ArmnnPreparedModel_1_2<HalVersion>::configureExecutionBurst(
const sp<V1_2::IBurstCallback>& callback,
@@ -679,12 +563,10 @@
V1_2::IPreparedModel::configureExecutionBurst_cb cb)
{
ALOGV("ArmnnPreparedModel_1_2::configureExecutionBurst");
- const std::shared_ptr<ArmnnBurstExecutorWithCache> executorWithCache =
- std::make_shared<ArmnnBurstExecutorWithCache>(this);
const sp<V1_2::IBurstContext> burst = ExecutionBurstServer::create(callback,
requestChannel,
resultChannel,
- executorWithCache);
+ this);
if (burst == nullptr)
{
@@ -697,9 +579,7 @@
return Void();
}
-
-
-#ifdef ARMNN_ANDROID_NN_V1_2
+#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
template class ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>;
template bool ArmnnPreparedModel_1_2<hal_1_2::HalPolicy>::ExecuteGraph<CallbackContext_1_2>(
std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,