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-rw-r--r--neuralnetworks/1.0/utils/src/Conversions.cpp6
-rw-r--r--neuralnetworks/1.2/utils/src/Conversions.cpp11
-rw-r--r--neuralnetworks/1.3/utils/src/Conversions.cpp2
-rw-r--r--neuralnetworks/aidl/utils/Android.bp4
-rw-r--r--neuralnetworks/aidl/utils/src/Conversions.cpp197
-rw-r--r--neuralnetworks/aidl/vts/functional/Utils.cpp3
-rw-r--r--neuralnetworks/utils/common/Android.bp2
-rw-r--r--neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h6
-rw-r--r--neuralnetworks/utils/common/src/CommonUtils.cpp121
9 files changed, 299 insertions, 53 deletions
diff --git a/neuralnetworks/1.0/utils/src/Conversions.cpp b/neuralnetworks/1.0/utils/src/Conversions.cpp
index 34862c00cb..7a099cfd49 100644
--- a/neuralnetworks/1.0/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.0/utils/src/Conversions.cpp
@@ -154,7 +154,7 @@ GeneralResult<Model::OperandValues> unvalidatedConvert(const hidl_vec<uint8_t>&
}
GeneralResult<SharedMemory> unvalidatedConvert(const hidl_memory& memory) {
- return createSharedMemoryFromHidlMemory(memory);
+ return hal::utils::createSharedMemoryFromHidlMemory(memory);
}
GeneralResult<Model> unvalidatedConvert(const hal::V1_0::Model& model) {
@@ -347,9 +347,7 @@ nn::GeneralResult<hidl_vec<uint8_t>> unvalidatedConvert(
}
nn::GeneralResult<hidl_memory> unvalidatedConvert(const nn::SharedMemory& memory) {
- CHECK(memory != nullptr);
- return hidl_memory(memory->name, NN_TRY(hal::utils::hidlHandleFromSharedHandle(memory->handle)),
- memory->size);
+ return hal::utils::createHidlMemoryFromSharedMemory(memory);
}
nn::GeneralResult<Model> unvalidatedConvert(const nn::Model& model) {
diff --git a/neuralnetworks/1.2/utils/src/Conversions.cpp b/neuralnetworks/1.2/utils/src/Conversions.cpp
index 50556509d2..7ae483ede2 100644
--- a/neuralnetworks/1.2/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.2/utils/src/Conversions.cpp
@@ -304,7 +304,11 @@ GeneralResult<Extension::OperandTypeInformation> unvalidatedConvert(
}
GeneralResult<SharedHandle> unvalidatedConvert(const hidl_handle& hidlHandle) {
- return hal::utils::sharedHandleFromNativeHandle(hidlHandle.getNativeHandle());
+ if (hidlHandle.getNativeHandle() == nullptr) {
+ return nullptr;
+ }
+ auto handle = NN_TRY(hal::utils::sharedHandleFromNativeHandle(hidlHandle.getNativeHandle()));
+ return std::make_shared<const Handle>(std::move(handle));
}
GeneralResult<DeviceType> convert(const hal::V1_2::DeviceType& deviceType) {
@@ -588,7 +592,10 @@ nn::GeneralResult<Extension::OperandTypeInformation> unvalidatedConvert(
}
nn::GeneralResult<hidl_handle> unvalidatedConvert(const nn::SharedHandle& handle) {
- return hal::utils::hidlHandleFromSharedHandle(handle);
+ if (handle == nullptr) {
+ return {};
+ }
+ return hal::utils::hidlHandleFromSharedHandle(*handle);
}
nn::GeneralResult<DeviceType> convert(const nn::DeviceType& deviceType) {
diff --git a/neuralnetworks/1.3/utils/src/Conversions.cpp b/neuralnetworks/1.3/utils/src/Conversions.cpp
index d96588e34c..6e74a6239d 100644
--- a/neuralnetworks/1.3/utils/src/Conversions.cpp
+++ b/neuralnetworks/1.3/utils/src/Conversions.cpp
@@ -261,7 +261,7 @@ GeneralResult<Request::MemoryPool> unvalidatedConvert(
using Discriminator = hal::V1_3::Request::MemoryPool::hidl_discriminator;
switch (memoryPool.getDiscriminator()) {
case Discriminator::hidlMemory:
- return createSharedMemoryFromHidlMemory(memoryPool.hidlMemory());
+ return hal::utils::createSharedMemoryFromHidlMemory(memoryPool.hidlMemory());
case Discriminator::token:
return static_cast<Request::MemoryDomainToken>(memoryPool.token());
}
diff --git a/neuralnetworks/aidl/utils/Android.bp b/neuralnetworks/aidl/utils/Android.bp
index 56017da52d..147d401201 100644
--- a/neuralnetworks/aidl/utils/Android.bp
+++ b/neuralnetworks/aidl/utils/Android.bp
@@ -21,12 +21,14 @@ cc_library_static {
local_include_dirs: ["include/nnapi/hal/aidl/"],
export_include_dirs: ["include"],
static_libs: [
+ "libarect",
"neuralnetworks_types",
"neuralnetworks_utils_hal_common",
],
shared_libs: [
- "libhidlbase",
"android.hardware.neuralnetworks-V1-ndk_platform",
"libbinder_ndk",
+ "libhidlbase",
+ "libnativewindow",
],
}
diff --git a/neuralnetworks/aidl/utils/src/Conversions.cpp b/neuralnetworks/aidl/utils/src/Conversions.cpp
index 486d01bc51..db3504bb74 100644
--- a/neuralnetworks/aidl/utils/src/Conversions.cpp
+++ b/neuralnetworks/aidl/utils/src/Conversions.cpp
@@ -18,6 +18,8 @@
#include <aidl/android/hardware/common/NativeHandle.h>
#include <android-base/logging.h>
+#include <android/hardware_buffer.h>
+#include <cutils/native_handle.h>
#include <nnapi/OperandTypes.h>
#include <nnapi/OperationTypes.h>
#include <nnapi/Result.h>
@@ -27,6 +29,7 @@
#include <nnapi/Validation.h>
#include <nnapi/hal/CommonUtils.h>
#include <nnapi/hal/HandleError.h>
+#include <vndk/hardware_buffer.h>
#include <algorithm>
#include <chrono>
@@ -127,6 +130,61 @@ GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> validatedConvert(
return canonical;
}
+GeneralResult<Handle> unvalidatedConvertHelper(const NativeHandle& aidlNativeHandle) {
+ std::vector<base::unique_fd> fds;
+ fds.reserve(aidlNativeHandle.fds.size());
+ for (const auto& fd : aidlNativeHandle.fds) {
+ const int dupFd = dup(fd.get());
+ if (dupFd == -1) {
+ // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
+ // here?
+ return NN_ERROR() << "Failed to dup the fd";
+ }
+ fds.emplace_back(dupFd);
+ }
+
+ return Handle{.fds = std::move(fds), .ints = aidlNativeHandle.ints};
+}
+
+struct NativeHandleDeleter {
+ void operator()(native_handle_t* handle) const {
+ if (handle) {
+ native_handle_close(handle);
+ native_handle_delete(handle);
+ }
+ }
+};
+
+using UniqueNativeHandle = std::unique_ptr<native_handle_t, NativeHandleDeleter>;
+
+static nn::GeneralResult<UniqueNativeHandle> nativeHandleFromAidlHandle(
+ const NativeHandle& handle) {
+ std::vector<base::unique_fd> fds;
+ fds.reserve(handle.fds.size());
+ for (const auto& fd : handle.fds) {
+ const int dupFd = dup(fd.get());
+ if (dupFd == -1) {
+ return NN_ERROR() << "Failed to dup the fd";
+ }
+ fds.emplace_back(dupFd);
+ }
+
+ constexpr size_t kIntMax = std::numeric_limits<int>::max();
+ CHECK_LE(handle.fds.size(), kIntMax);
+ CHECK_LE(handle.ints.size(), kIntMax);
+ native_handle_t* nativeHandle = native_handle_create(static_cast<int>(handle.fds.size()),
+ static_cast<int>(handle.ints.size()));
+ if (nativeHandle == nullptr) {
+ return NN_ERROR() << "Failed to create native_handle";
+ }
+ for (size_t i = 0; i < fds.size(); ++i) {
+ nativeHandle->data[i] = fds[i].release();
+ }
+ std::copy(handle.ints.begin(), handle.ints.end(), &nativeHandle->data[nativeHandle->numFds]);
+
+ return UniqueNativeHandle(nativeHandle);
+}
+
} // anonymous namespace
GeneralResult<OperandType> unvalidatedConvert(const aidl_hal::OperandType& operandType) {
@@ -318,10 +376,64 @@ GeneralResult<MeasureTiming> unvalidatedConvert(bool measureTiming) {
return measureTiming ? MeasureTiming::YES : MeasureTiming::NO;
}
+static uint32_t roundUpToMultiple(uint32_t value, uint32_t multiple) {
+ return (value + multiple - 1) / multiple * multiple;
+}
+
GeneralResult<SharedMemory> unvalidatedConvert(const aidl_hal::Memory& memory) {
VERIFY_NON_NEGATIVE(memory.size) << "Memory size must not be negative";
+ if (memory.size > std::numeric_limits<uint32_t>::max()) {
+ return NN_ERROR() << "Memory: size must be <= std::numeric_limits<size_t>::max()";
+ }
+
+ if (memory.name != "hardware_buffer_blob") {
+ return std::make_shared<const Memory>(Memory{
+ .handle = NN_TRY(unvalidatedConvertHelper(memory.handle)),
+ .size = static_cast<uint32_t>(memory.size),
+ .name = memory.name,
+ });
+ }
+
+ const auto size = static_cast<uint32_t>(memory.size);
+ const auto format = AHARDWAREBUFFER_FORMAT_BLOB;
+ const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
+ const uint32_t width = size;
+ const uint32_t height = 1; // height is always 1 for BLOB mode AHardwareBuffer.
+ const uint32_t layers = 1; // layers is always 1 for BLOB mode AHardwareBuffer.
+
+ const UniqueNativeHandle handle = NN_TRY(nativeHandleFromAidlHandle(memory.handle));
+ const native_handle_t* nativeHandle = handle.get();
+
+ // AHardwareBuffer_createFromHandle() might fail because an allocator
+ // expects a specific stride value. In that case, we try to guess it by
+ // aligning the width to small powers of 2.
+ // TODO(b/174120849): Avoid stride assumptions.
+ AHardwareBuffer* hardwareBuffer = nullptr;
+ status_t status = UNKNOWN_ERROR;
+ for (uint32_t alignment : {1, 4, 32, 64, 128, 2, 8, 16}) {
+ const uint32_t stride = roundUpToMultiple(width, alignment);
+ AHardwareBuffer_Desc desc{
+ .width = width,
+ .height = height,
+ .layers = layers,
+ .format = format,
+ .usage = usage,
+ .stride = stride,
+ };
+ status = AHardwareBuffer_createFromHandle(&desc, nativeHandle,
+ AHARDWAREBUFFER_CREATE_FROM_HANDLE_METHOD_CLONE,
+ &hardwareBuffer);
+ if (status == NO_ERROR) {
+ break;
+ }
+ }
+ if (status != NO_ERROR) {
+ return NN_ERROR(ErrorStatus::GENERAL_FAILURE)
+ << "Can't create AHardwareBuffer from handle. Error: " << status;
+ }
+
return std::make_shared<const Memory>(Memory{
- .handle = NN_TRY(unvalidatedConvert(memory.handle)),
+ .handle = HardwareBufferHandle(hardwareBuffer, /*takeOwnership=*/true),
.size = static_cast<uint32_t>(memory.size),
.name = memory.name,
});
@@ -400,22 +512,7 @@ GeneralResult<ExecutionPreference> unvalidatedConvert(
}
GeneralResult<SharedHandle> unvalidatedConvert(const NativeHandle& aidlNativeHandle) {
- std::vector<base::unique_fd> fds;
- fds.reserve(aidlNativeHandle.fds.size());
- for (const auto& fd : aidlNativeHandle.fds) {
- int dupFd = dup(fd.get());
- if (dupFd == -1) {
- // TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
- // here?
- return NN_ERROR() << "Failed to dup the fd";
- }
- fds.emplace_back(dupFd);
- }
-
- return std::make_shared<const Handle>(Handle{
- .fds = std::move(fds),
- .ints = aidlNativeHandle.ints,
- });
+ return std::make_shared<const Handle>(NN_TRY(unvalidatedConvertHelper(aidlNativeHandle)));
}
GeneralResult<ExecutionPreference> convert(
@@ -508,13 +605,11 @@ nn::GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> validatedConvert(
return halObject;
}
-} // namespace
-
-nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::SharedHandle& sharedHandle) {
+nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::Handle& handle) {
common::NativeHandle aidlNativeHandle;
- aidlNativeHandle.fds.reserve(sharedHandle->fds.size());
- for (const auto& fd : sharedHandle->fds) {
- int dupFd = dup(fd.get());
+ aidlNativeHandle.fds.reserve(handle.fds.size());
+ for (const auto& fd : handle.fds) {
+ const int dupFd = dup(fd.get());
if (dupFd == -1) {
// TODO(b/120417090): is ANEURALNETWORKS_UNEXPECTED_NULL the correct error to return
// here?
@@ -522,17 +617,69 @@ nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::SharedHandl
}
aidlNativeHandle.fds.emplace_back(dupFd);
}
- aidlNativeHandle.ints = sharedHandle->ints;
+ aidlNativeHandle.ints = handle.ints;
+ return aidlNativeHandle;
+}
+
+static nn::GeneralResult<common::NativeHandle> aidlHandleFromNativeHandle(
+ const native_handle_t& handle) {
+ common::NativeHandle aidlNativeHandle;
+
+ aidlNativeHandle.fds.reserve(handle.numFds);
+ for (int i = 0; i < handle.numFds; ++i) {
+ const int dupFd = dup(handle.data[i]);
+ if (dupFd == -1) {
+ return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to dup the fd";
+ }
+ aidlNativeHandle.fds.emplace_back(dupFd);
+ }
+
+ aidlNativeHandle.ints = std::vector<int>(&handle.data[handle.numFds],
+ &handle.data[handle.numFds + handle.numInts]);
+
return aidlNativeHandle;
}
+} // namespace
+
+nn::GeneralResult<common::NativeHandle> unvalidatedConvert(const nn::SharedHandle& sharedHandle) {
+ CHECK(sharedHandle != nullptr);
+ return unvalidatedConvert(*sharedHandle);
+}
+
nn::GeneralResult<Memory> unvalidatedConvert(const nn::SharedMemory& memory) {
CHECK(memory != nullptr);
if (memory->size > std::numeric_limits<int64_t>::max()) {
return NN_ERROR() << "Memory size doesn't fit into int64_t.";
}
+ if (const auto* handle = std::get_if<nn::Handle>(&memory->handle)) {
+ return Memory{
+ .handle = NN_TRY(unvalidatedConvert(*handle)),
+ .size = static_cast<int64_t>(memory->size),
+ .name = memory->name,
+ };
+ }
+
+ const auto* ahwb = std::get<nn::HardwareBufferHandle>(memory->handle).get();
+ AHardwareBuffer_Desc bufferDesc;
+ AHardwareBuffer_describe(ahwb, &bufferDesc);
+
+ if (bufferDesc.format == AHARDWAREBUFFER_FORMAT_BLOB) {
+ CHECK_EQ(memory->size, bufferDesc.width);
+ CHECK_EQ(memory->name, "hardware_buffer_blob");
+ } else {
+ CHECK_EQ(memory->size, 0u);
+ CHECK_EQ(memory->name, "hardware_buffer");
+ }
+
+ const native_handle_t* nativeHandle = AHardwareBuffer_getNativeHandle(ahwb);
+ if (nativeHandle == nullptr) {
+ return NN_ERROR() << "unvalidatedConvert failed because AHardwareBuffer_getNativeHandle "
+ "returned nullptr";
+ }
+
return Memory{
- .handle = NN_TRY(unvalidatedConvert(memory->handle)),
+ .handle = NN_TRY(aidlHandleFromNativeHandle(*nativeHandle)),
.size = static_cast<int64_t>(memory->size),
.name = memory->name,
};
diff --git a/neuralnetworks/aidl/vts/functional/Utils.cpp b/neuralnetworks/aidl/vts/functional/Utils.cpp
index 14a496a303..3c7f5f797d 100644
--- a/neuralnetworks/aidl/vts/functional/Utils.cpp
+++ b/neuralnetworks/aidl/vts/functional/Utils.cpp
@@ -135,7 +135,8 @@ void TestBlobAHWB::initialize(uint32_t size) {
ASSERT_EQ(AHardwareBuffer_allocate(&desc, &mAhwb), 0);
ASSERT_NE(mAhwb, nullptr);
- const auto sharedMemory = nn::createSharedMemoryFromAHWB(*mAhwb).value();
+ const auto sharedMemory =
+ nn::createSharedMemoryFromAHWB(mAhwb, /*takeOwnership=*/false).value();
mMapping = nn::map(sharedMemory).value();
mPtr = static_cast<uint8_t*>(std::get<void*>(mMapping.pointer));
CHECK_NE(mPtr, nullptr);
diff --git a/neuralnetworks/utils/common/Android.bp b/neuralnetworks/utils/common/Android.bp
index 6c491ae7ae..50295f1aad 100644
--- a/neuralnetworks/utils/common/Android.bp
+++ b/neuralnetworks/utils/common/Android.bp
@@ -22,10 +22,12 @@ cc_library_static {
export_include_dirs: ["include"],
cflags: ["-Wthread-safety"],
static_libs: [
+ "libarect",
"neuralnetworks_types",
],
shared_libs: [
"libhidlbase",
+ "libnativewindow",
],
}
diff --git a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
index b13785dcd9..547f203d6d 100644
--- a/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
+++ b/neuralnetworks/utils/common/include/nnapi/hal/CommonUtils.h
@@ -74,10 +74,12 @@ nn::GeneralResult<void> unflushDataFromSharedToPointer(
std::vector<uint32_t> countNumberOfConsumers(size_t numberOfOperands,
const std::vector<nn::Operation>& operations);
+nn::GeneralResult<hidl_memory> createHidlMemoryFromSharedMemory(const nn::SharedMemory& memory);
nn::GeneralResult<nn::SharedMemory> createSharedMemoryFromHidlMemory(const hidl_memory& memory);
-nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::SharedHandle& handle);
-nn::GeneralResult<nn::SharedHandle> sharedHandleFromNativeHandle(const native_handle_t* handle);
+nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::Handle& handle);
+nn::GeneralResult<nn::Handle> sharedHandleFromNativeHandle(const native_handle_t* handle);
+
nn::GeneralResult<hidl_vec<hidl_handle>> convertSyncFences(
const std::vector<nn::SyncFence>& fences);
diff --git a/neuralnetworks/utils/common/src/CommonUtils.cpp b/neuralnetworks/utils/common/src/CommonUtils.cpp
index 90215630c9..7a5035f6fc 100644
--- a/neuralnetworks/utils/common/src/CommonUtils.cpp
+++ b/neuralnetworks/utils/common/src/CommonUtils.cpp
@@ -20,11 +20,14 @@
#include <android-base/logging.h>
#include <android-base/unique_fd.h>
+#include <android/hardware_buffer.h>
+#include <hidl/HidlSupport.h>
#include <nnapi/Result.h>
#include <nnapi/SharedMemory.h>
#include <nnapi/TypeUtils.h>
#include <nnapi/Types.h>
#include <nnapi/Validation.h>
+#include <vndk/hardware_buffer.h>
#include <algorithm>
#include <any>
@@ -248,44 +251,128 @@ std::vector<uint32_t> countNumberOfConsumers(size_t numberOfOperands,
return nn::countNumberOfConsumers(numberOfOperands, operations);
}
-nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::SharedHandle& handle) {
- if (handle == nullptr) {
- return {};
+nn::GeneralResult<hidl_memory> createHidlMemoryFromSharedMemory(const nn::SharedMemory& memory) {
+ if (memory == nullptr) {
+ return NN_ERROR() << "Memory must be non-empty";
+ }
+ if (const auto* handle = std::get_if<nn::Handle>(&memory->handle)) {
+ return hidl_memory(memory->name, NN_TRY(hidlHandleFromSharedHandle(*handle)), memory->size);
+ }
+
+ const auto* ahwb = std::get<nn::HardwareBufferHandle>(memory->handle).get();
+ AHardwareBuffer_Desc bufferDesc;
+ AHardwareBuffer_describe(ahwb, &bufferDesc);
+
+ if (bufferDesc.format == AHARDWAREBUFFER_FORMAT_BLOB) {
+ CHECK_EQ(memory->size, bufferDesc.width);
+ CHECK_EQ(memory->name, "hardware_buffer_blob");
+ } else {
+ CHECK_EQ(memory->size, 0u);
+ CHECK_EQ(memory->name, "hardware_buffer");
}
+ const native_handle_t* nativeHandle = AHardwareBuffer_getNativeHandle(ahwb);
+ const hidl_handle hidlHandle(nativeHandle);
+ hidl_handle handle(hidlHandle);
+
+ return hidl_memory(memory->name, std::move(handle), memory->size);
+}
+
+static uint32_t roundUpToMultiple(uint32_t value, uint32_t multiple) {
+ return (value + multiple - 1) / multiple * multiple;
+}
+
+nn::GeneralResult<nn::SharedMemory> createSharedMemoryFromHidlMemory(const hidl_memory& memory) {
+ CHECK_LE(memory.size(), std::numeric_limits<uint32_t>::max());
+
+ if (memory.name() != "hardware_buffer_blob") {
+ return std::make_shared<const nn::Memory>(nn::Memory{
+ .handle = NN_TRY(sharedHandleFromNativeHandle(memory.handle())),
+ .size = static_cast<uint32_t>(memory.size()),
+ .name = memory.name(),
+ });
+ }
+
+ const auto size = memory.size();
+ const auto format = AHARDWAREBUFFER_FORMAT_BLOB;
+ const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
+ const uint32_t width = size;
+ const uint32_t height = 1; // height is always 1 for BLOB mode AHardwareBuffer.
+ const uint32_t layers = 1; // layers is always 1 for BLOB mode AHardwareBuffer.
+
+ // AHardwareBuffer_createFromHandle() might fail because an allocator
+ // expects a specific stride value. In that case, we try to guess it by
+ // aligning the width to small powers of 2.
+ // TODO(b/174120849): Avoid stride assumptions.
+ AHardwareBuffer* hardwareBuffer = nullptr;
+ status_t status = UNKNOWN_ERROR;
+ for (uint32_t alignment : {1, 4, 32, 64, 128, 2, 8, 16}) {
+ const uint32_t stride = roundUpToMultiple(width, alignment);
+ AHardwareBuffer_Desc desc{
+ .width = width,
+ .height = height,
+ .layers = layers,
+ .format = format,
+ .usage = usage,
+ .stride = stride,
+ };
+ status = AHardwareBuffer_createFromHandle(&desc, memory.handle(),
+ AHARDWAREBUFFER_CREATE_FROM_HANDLE_METHOD_CLONE,
+ &hardwareBuffer);
+ if (status == NO_ERROR) {
+ break;
+ }
+ }
+ if (status != NO_ERROR) {
+ return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE)
+ << "Can't create AHardwareBuffer from handle. Error: " << status;
+ }
+
+ return std::make_shared<const nn::Memory>(nn::Memory{
+ .handle = nn::HardwareBufferHandle(hardwareBuffer, /*takeOwnership=*/true),
+ .size = static_cast<uint32_t>(memory.size()),
+ .name = memory.name(),
+ });
+}
+
+nn::GeneralResult<hidl_handle> hidlHandleFromSharedHandle(const nn::Handle& handle) {
std::vector<base::unique_fd> fds;
- fds.reserve(handle->fds.size());
- for (const auto& fd : handle->fds) {
- int dupFd = dup(fd);
+ fds.reserve(handle.fds.size());
+ for (const auto& fd : handle.fds) {
+ const int dupFd = dup(fd);
if (dupFd == -1) {
return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to dup the fd";
}
fds.emplace_back(dupFd);
}
- native_handle_t* nativeHandle = native_handle_create(handle->fds.size(), handle->ints.size());
+ constexpr size_t kIntMax = std::numeric_limits<int>::max();
+ CHECK_LE(handle.fds.size(), kIntMax);
+ CHECK_LE(handle.ints.size(), kIntMax);
+ native_handle_t* nativeHandle = native_handle_create(static_cast<int>(handle.fds.size()),
+ static_cast<int>(handle.ints.size()));
if (nativeHandle == nullptr) {
return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to create native_handle";
}
for (size_t i = 0; i < fds.size(); ++i) {
nativeHandle->data[i] = fds[i].release();
}
- std::copy(handle->ints.begin(), handle->ints.end(), &nativeHandle->data[nativeHandle->numFds]);
+ std::copy(handle.ints.begin(), handle.ints.end(), &nativeHandle->data[nativeHandle->numFds]);
hidl_handle hidlHandle;
hidlHandle.setTo(nativeHandle, /*shouldOwn=*/true);
return hidlHandle;
}
-nn::GeneralResult<nn::SharedHandle> sharedHandleFromNativeHandle(const native_handle_t* handle) {
+nn::GeneralResult<nn::Handle> sharedHandleFromNativeHandle(const native_handle_t* handle) {
if (handle == nullptr) {
- return nullptr;
+ return NN_ERROR() << "sharedHandleFromNativeHandle failed because handle is nullptr";
}
std::vector<base::unique_fd> fds;
fds.reserve(handle->numFds);
for (int i = 0; i < handle->numFds; ++i) {
- int dupFd = dup(handle->data[i]);
+ const int dupFd = dup(handle->data[i]);
if (dupFd == -1) {
return NN_ERROR(nn::ErrorStatus::GENERAL_FAILURE) << "Failed to dup the fd";
}
@@ -295,18 +382,18 @@ nn::GeneralResult<nn::SharedHandle> sharedHandleFromNativeHandle(const native_ha
std::vector<int> ints(&handle->data[handle->numFds],
&handle->data[handle->numFds + handle->numInts]);
- return std::make_shared<const nn::Handle>(nn::Handle{
- .fds = std::move(fds),
- .ints = std::move(ints),
- });
+ return nn::Handle{.fds = std::move(fds), .ints = std::move(ints)};
}
nn::GeneralResult<hidl_vec<hidl_handle>> convertSyncFences(
const std::vector<nn::SyncFence>& syncFences) {
hidl_vec<hidl_handle> handles(syncFences.size());
for (size_t i = 0; i < syncFences.size(); ++i) {
- handles[i] =
- NN_TRY(hal::utils::hidlHandleFromSharedHandle(syncFences[i].getSharedHandle()));
+ const auto& handle = syncFences[i].getSharedHandle();
+ if (handle == nullptr) {
+ return NN_ERROR() << "convertSyncFences failed because sync fence is empty";
+ }
+ handles[i] = NN_TRY(hidlHandleFromSharedHandle(*handle));
}
return handles;
}
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-04 02:07:13 +0000