Merge changes from topics "aosp-nnapi-1.3-operations", "aosp-nnapi-async-cmdqueue"
* changes:
Fix the NNAPI vts tests about validateExecuteFenced
Add VTS tests for NNAPI IPreparedModel::executeFenced
Support sync fence in NNAPI
Add FILL and RANK ops
Add ELU and HARD_SWISH
NNAPI: Regenerate types.hal to sync with types.spec
diff --git a/current.txt b/current.txt
index fd08ec5..b77aafe 100644
--- a/current.txt
+++ b/current.txt
@@ -625,9 +625,10 @@
65c16331e57f6dd68b3971f06f78fe9e3209afb60630c31705aa355f9a52bf0d android.hardware.neuralnetworks@1.3::IBuffer
d1f382d14e1384b907d5bb5780df7f01934650d556fedbed2f15a90773c657d6 android.hardware.neuralnetworks@1.3::IDevice
4167dc3ad35e9cd0d2057d4868c7675ae2c3c9d05bbd614c1f5dccfa5fd68797 android.hardware.neuralnetworks@1.3::IExecutionCallback
-7d23020248194abbee8091cc624f39a5a6d7ccba338b172d5d2d3df0cceffbee android.hardware.neuralnetworks@1.3::IPreparedModel
+29e26e83399b69c7998b787bd30426dd5baa2da350effca76bbee1ba877355c9 android.hardware.neuralnetworks@1.3::IFencedExecutionCallback
+384fd9fd6e4d43ea11d407e52ea81da5242c3c5f4b458b8707d8feb652a13e36 android.hardware.neuralnetworks@1.3::IPreparedModel
0439a1fbbec7f16e5e4c653d85ac685d51bfafbae15b8f8cca530acdd7d6a8ce android.hardware.neuralnetworks@1.3::IPreparedModelCallback
-ee65638f8af3f9f4f222e7208eaa9f1f8e7f8e0a21545846ba67d0e27624efa1 android.hardware.neuralnetworks@1.3::types
+5f1a4e0c29fc686ed476f9f04eed35e4405d21288cb2746b978d6891de5cc37d android.hardware.neuralnetworks@1.3::types
3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi
a64467bae843569f0d465c5be7f0c7a5b987985b55a3ef4794dd5afc68538650 android.hardware.wifi.supplicant@1.3::ISupplicant
44445b8a03d7b9e68b2fbd954672c18a8fce9e32851b0692f4f4ab3407f86ecb android.hardware.wifi.supplicant@1.3::ISupplicantStaIface
diff --git a/neuralnetworks/1.3/Android.bp b/neuralnetworks/1.3/Android.bp
index 56011e2..7b02cc5 100644
--- a/neuralnetworks/1.3/Android.bp
+++ b/neuralnetworks/1.3/Android.bp
@@ -11,6 +11,7 @@
"IBuffer.hal",
"IDevice.hal",
"IExecutionCallback.hal",
+ "IFencedExecutionCallback.hal",
"IPreparedModel.hal",
"IPreparedModelCallback.hal",
],
diff --git a/neuralnetworks/1.3/IFencedExecutionCallback.hal b/neuralnetworks/1.3/IFencedExecutionCallback.hal
new file mode 100644
index 0000000..39076b9
--- /dev/null
+++ b/neuralnetworks/1.3/IFencedExecutionCallback.hal
@@ -0,0 +1,48 @@
+/*
+ * Copyright (C) 2020 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.3;
+
+import @1.2::Timing;
+import ErrorStatus;
+
+/**
+ * IFencedExecutionCallback can be used to query the error status result
+ * and duration information from an IPreparedModel::executeFenced call.
+ */
+interface IFencedExecutionCallback {
+
+ /**
+ * The getExecutionInfo method is used by the clients to query error status
+ * result and duration information. The method must only be called after the actual
+ * evaluation has finished or resulted in an runtime error, as indicated by the status
+ * of the sync fence returned by the IPreparedModel::executeFenced call, otherwise
+ * GENERAL_FAILURE must be returned.
+ *
+ * @return status Error status returned from the asynchronously dispatched execution
+ * must be:
+ * - NONE if the asynchronous execution was successful
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if the asynchronous task resulted in an
+ * unspecified error
+ * @return timing Duration of execution. Unless MeasureTiming::YES was passed when
+ * launching the execution and status is NONE, all times must
+ * be reported as UINT64_MAX. A driver may choose to report
+ * any time as UINT64_MAX, indicating that particular measurement is
+ * not available.
+ */
+ getExecutionInfo() generates (ErrorStatus status, Timing timing);
+};
diff --git a/neuralnetworks/1.3/IPreparedModel.hal b/neuralnetworks/1.3/IPreparedModel.hal
index bce6ee2..f84bcf4 100644
--- a/neuralnetworks/1.3/IPreparedModel.hal
+++ b/neuralnetworks/1.3/IPreparedModel.hal
@@ -24,6 +24,7 @@
import OptionalTimePoint;
import Request;
import IExecutionCallback;
+import IFencedExecutionCallback;
/**
* IPreparedModel describes a model that has been prepared for execution and
@@ -91,7 +92,8 @@
* execution cannot be finished by the deadline, the
* execution must be aborted.
* @param callback A callback object used to return the error status of
- * the execution. The callback object's notify function must
+ * the execution, shape information of model output operands, and
+ * duration of execution. The callback object's notify function must
* be called exactly once, even if the execution was
* unsuccessful.
* @return status Error status of the call, must be:
@@ -187,4 +189,57 @@
OptionalTimePoint deadline)
generates (ErrorStatus status, vec<OutputShape> outputShapes,
Timing timing);
+
+ /**
+ * Launch a fenced asynchronous execution on a prepared model.
+ *
+ * The execution is performed asynchronously with respect to the caller.
+ * executeFenced must fully validate the request, and only accept one that is
+ * guaranteed to be completed, unless a hardware failure or kernel panic happens on the device.
+ * If there is an error during validation, executeFenced must immediately return with
+ * the corresponding ErrorStatus. If the request is valid and there is no error launching,
+ * executeFenced must dispatch an asynchronous task to perform the execution in the
+ * background, and immediately return with ErrorStatus::NONE, a sync_fence that will be
+ * signaled once the execution is completed, and a callback that can be used by the client
+ * to query the duration and runtime error status. If the task has finished
+ * before the call returns, empty handle may be returned for the sync fence. If the
+ * asynchronous task fails to launch, executeFenced must immediately return with
+ * ErrorStatus::GENERAL_FAILURE, and empty handle for the sync fence and nullptr
+ * for callback. The execution must wait for all the sync fences (if any) in wait_for to be
+ * signaled before starting the actual execution.
+ *
+ * If any of sync fences in wait_for changes to error status after the executeFenced
+ * call succeeds, the driver must immediately set the returned sync fence to error status.
+ *
+ * When the asynchronous task has finished its execution, it must
+ * immediately signal the sync_fence created when dispatching. After
+ * the sync_fence is signaled, the task must not modify the content of
+ * any data object referenced by 'request' (described by the
+ * {@link @1.0::DataLocation} of a {@link @1.0::RequestArgument}).
+ *
+ * Any number of calls to the executeFenced, execute* and executeSynchronously*
+ * functions, in any combination, may be made concurrently, even on the same
+ * IPreparedModel object.
+ *
+ * @param request The input and output information on which the prepared
+ * model is to be executed.
+ * @param waitFor A vector of sync fence file descriptors.
+ * Execution must not start until all sync fences have been signaled.
+ * @param measure Specifies whether or not to measure duration of the execution.
+ * The duration runs from the time the driver sees the call
+ * to the executeFenced function to the time sync_fence is triggered.
+ * @return status Error status of the call, must be:
+ * - NONE if task is successfully launched
+ * - DEVICE_UNAVAILABLE if driver is offline or busy
+ * - GENERAL_FAILURE if there is an unspecified error
+ * - INVALID_ARGUMENT if one of the input arguments is invalid, including
+ * fences in error states.
+ * @return syncFence The sync fence that will be triggered when the task is completed.
+ * The sync fence will be set to error if a critical error,
+ * e.g. hardware failure or kernel panic, occurs when doing execution.
+ * @return callback The IFencedExecutionCallback can be used to query information like duration
+ * and error status when the execution is completed.
+ */
+ executeFenced(Request request, vec<handle> waitFor, MeasureTiming measure)
+ generates (ErrorStatus status, handle syncFence, IFencedExecutionCallback callback);
};
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
index b330b50..abc33e7 100644
--- a/neuralnetworks/1.3/types.hal
+++ b/neuralnetworks/1.3/types.hal
@@ -1415,6 +1415,7 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
*
* Supported tensor rank: up to 4
*
@@ -1425,6 +1426,8 @@
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
*
* Outputs:
* * 0: The product, a tensor of the same {@link OperandType} as input0.
@@ -1905,6 +1908,11 @@
* dimensions. The output is the result of dividing the first input tensor
* by the second, optionally modified by an activation function.
*
+ * For inputs of {@link OperandType::TENSOR_INT32}, performs
+ * "floor division" ("//" in Python). For example,
+ * 5 // 2 = 2
+ * -5 // 2 = -3
+ *
* Two dimensions are compatible when:
* 1. they are equal, or
* 2. one of them is 1
@@ -1925,6 +1933,7 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
* * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
*
* Supported tensor rank: up to 4
*
@@ -1935,6 +1944,8 @@
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
@@ -2186,6 +2197,7 @@
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
* * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} (since HAL version 1.3)
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
*
* Supported tensor rank: up to 4
*
@@ -2196,6 +2208,8 @@
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
+ * For a {@link OperandType::TENSOR_INT32} tensor,
+ * the {@link FusedActivationFunc} must be "NONE".
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
@@ -2242,6 +2256,7 @@
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT16}
* * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32} (since HAL version 1.3)
*
* Supported tensor rank: from 1.
*
@@ -4972,6 +4987,106 @@
WHILE = 97,
/**
+ * Computes exponential linear activation on the input tensor element-wise.
+ *
+ * The output is calculated using the following formula:
+ *
+ * ELU(x) = max(0, x) + min(0, alpha * (exp(x) - 1))
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. May be zero-sized.
+ * * 1: A scalar, specifying the alpha parameter.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16},
+ * the alpha value must be of {@link OperandType::FLOAT16}.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT32},
+ * the alpha value must be of {@link OperandType::FLOAT32}.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape and type as input0.
+ */
+ ELU = 98,
+
+ /**
+ * Computes hard-swish activation on the input tensor element-wise.
+ *
+ * Hard swish activation is introduced in
+ * https://arxiv.org/pdf/1905.02244.pdf
+ *
+ * The output is calculated using the following formula:
+ *
+ * h-swish(x) = x * max(0, min(6, (x + 3))) / 6
+
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ *
+ * Inputs:
+ * * 0: A tensor, specifying the input. May be zero-sized.
+ *
+ * Outputs:
+ * * 0: The output tensor of same shape and type as input0.
+ * Scale and zero point of this tensor may be different from the input
+ * tensor's parameters.
+ */
+ HARD_SWISH = 99,
+
+ /**
+ * Creates a tensor filled with a scalar value.
+ *
+ * Supported output tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ *
+ * Inputs:
+ * * 0: A 1-D tensor, specifying the desired output tensor shape.
+ * * 1: A scalar, specifying the value to fill the output tensors with.
+ * For output tensor of {@link OperandType::TENSOR_FLOAT16},
+ * the scalar must be of {@link OperandType::FLOAT16}.
+ * For output tensor of {@link OperandType::TENSOR_FLOAT32},
+ * the scalar must be of {@link OperandType::FLOAT32}.
+ * For output tensor of {@link OperandType::TENSOR_INT32},
+ * the scalar must be of {@link OperandType::INT32}.
+ *
+ * Outputs:
+ * * 0: The output tensor.
+ */
+ FILL = 100,
+
+ /**
+ * Returns the rank of a tensor.
+ *
+ * The rank of a tensor is the number of dimensions in it. Also known as
+ * "order", "degree", "ndims".
+ *
+ * Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16}
+ * * {@link OperandType::TENSOR_FLOAT32}
+ * * {@link OperandType::TENSOR_INT32}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT16_SYMM}
+ * * {@link OperandType::TENSOR_BOOL8}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+ * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+ * * {@link OperandType::TENSOR_QUANT8_SYMM}
+ * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
+ *
+ * Inputs:
+ * * 0: The input tensor.
+ *
+ * Outputs:
+ * * 0: A scalar of {@link OperandType::INT32}, specifying the rank
+ * of the input tensor.
+ */
+ RANK = 101,
+
+ /**
* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
* OEM operation and data types.
*
@@ -4993,7 +5108,7 @@
enum OperationTypeRange : uint32_t {
BASE_MIN = 0,
FUNDAMENTAL_MIN = 0,
- FUNDAMENTAL_MAX = 97,
+ FUNDAMENTAL_MAX = 101,
OEM_MIN = 10000,
OEM_MAX = 10000,
BASE_MAX = 0xFFFF,
diff --git a/neuralnetworks/1.3/vts/functional/Android.bp b/neuralnetworks/1.3/vts/functional/Android.bp
index ce2d3a9..8e7e9b9 100644
--- a/neuralnetworks/1.3/vts/functional/Android.bp
+++ b/neuralnetworks/1.3/vts/functional/Android.bp
@@ -65,6 +65,7 @@
"libhidlmemory",
"libneuralnetworks_generated_test_harness",
"libneuralnetworks_utils",
+ "libsync",
],
whole_static_libs: [
"neuralnetworks_generated_V1_0_example",
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
index a2c0c4e..88837db 100644
--- a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -29,11 +29,13 @@
#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h>
#include <android/hardware/neuralnetworks/1.2/types.h>
#include <android/hardware/neuralnetworks/1.3/IDevice.h>
+#include <android/hardware/neuralnetworks/1.3/IFencedExecutionCallback.h>
#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h>
#include <android/hardware/neuralnetworks/1.3/IPreparedModelCallback.h>
#include <android/hardware/neuralnetworks/1.3/types.h>
#include <android/hidl/allocator/1.0/IAllocator.h>
#include <android/hidl/memory/1.0/IMemory.h>
+#include <android/sync.h>
#include <gtest/gtest.h>
#include <hidlmemory/mapping.h>
@@ -70,7 +72,7 @@
namespace {
-enum class Executor { ASYNC, SYNC, BURST };
+enum class Executor { ASYNC, SYNC, BURST, FENCED };
enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT };
@@ -562,9 +564,48 @@
break;
}
+ case Executor::FENCED: {
+ SCOPED_TRACE("fenced");
+ ErrorStatus result;
+ hidl_handle sync_fence_handle;
+ sp<IFencedExecutionCallback> fenced_callback;
+ Return<void> ret = preparedModel->executeFenced(
+ request, {}, testConfig.measureTiming,
+ [&result, &sync_fence_handle, &fenced_callback](
+ ErrorStatus error, const hidl_handle& handle,
+ const sp<IFencedExecutionCallback>& callback) {
+ result = error;
+ sync_fence_handle = handle;
+ fenced_callback = callback;
+ });
+ ASSERT_TRUE(ret.isOk());
+ if (result != ErrorStatus::NONE) {
+ ASSERT_EQ(sync_fence_handle.getNativeHandle(), nullptr);
+ ASSERT_EQ(fenced_callback, nullptr);
+ executionStatus = ErrorStatus::GENERAL_FAILURE;
+ } else if (sync_fence_handle.getNativeHandle()) {
+ constexpr int kInfiniteTimeout = -1;
+ int sync_fd = sync_fence_handle.getNativeHandle()->data[0];
+ ASSERT_GT(sync_fd, 0);
+ int r = sync_wait(sync_fd, kInfiniteTimeout);
+ ASSERT_GE(r, 0);
+ }
+ if (result == ErrorStatus::NONE) {
+ ASSERT_NE(fenced_callback, nullptr);
+ Return<void> ret = fenced_callback->getExecutionInfo(
+ [&executionStatus, &timing](ErrorStatus error, Timing t) {
+ executionStatus = error;
+ timing = t;
+ });
+ ASSERT_TRUE(ret.isOk());
+ }
+ break;
+ }
}
- if (testConfig.outputType != OutputType::FULLY_SPECIFIED &&
+ // The driver is allowed to reject executeFenced, and if they do, we should skip.
+ if ((testConfig.outputType != OutputType::FULLY_SPECIFIED ||
+ testConfig.executor == Executor::FENCED) &&
executionStatus == ErrorStatus::GENERAL_FAILURE) {
if (skipped != nullptr) {
*skipped = true;
@@ -648,6 +689,11 @@
executorList = {Executor::ASYNC, Executor::SYNC};
memoryType = MemoryType::DEVICE;
} break;
+ case TestKind::FENCED_COMPUTE: {
+ outputTypesList = {OutputType::FULLY_SPECIFIED};
+ measureTimingList = {MeasureTiming::NO, MeasureTiming::YES};
+ executorList = {Executor::FENCED};
+ } break;
case TestKind::QUANTIZATION_COUPLING: {
LOG(FATAL) << "Wrong TestKind for EvaluatePreparedModel";
return;
@@ -671,7 +717,8 @@
const TestModel& coupledModel) {
const std::vector<OutputType> outputTypesList = {OutputType::FULLY_SPECIFIED};
const std::vector<MeasureTiming> measureTimingList = {MeasureTiming::NO, MeasureTiming::YES};
- const std::vector<Executor> executorList = {Executor::ASYNC, Executor::SYNC, Executor::BURST};
+ const std::vector<Executor> executorList = {Executor::ASYNC, Executor::SYNC, Executor::BURST,
+ Executor::FENCED};
for (const OutputType outputType : outputTypesList) {
for (const MeasureTiming measureTiming : measureTimingList) {
@@ -708,7 +755,8 @@
switch (testKind) {
case TestKind::GENERAL:
case TestKind::DYNAMIC_SHAPE:
- case TestKind::MEMORY_DOMAIN: {
+ case TestKind::MEMORY_DOMAIN:
+ case TestKind::FENCED_COMPUTE: {
createPreparedModel(device, model, &preparedModel);
if (preparedModel == nullptr) return;
EvaluatePreparedModel(device, preparedModel, testModel, testKind);
@@ -771,6 +819,9 @@
// Tag for the memory domain tests
class MemoryDomainTest : public GeneratedTest {};
+// Tag for the fenced compute tests
+class FencedComputeTest : public GeneratedTest {};
+
// Tag for the dynamic output shape tests
class QuantizationCouplingTest : public GeneratedTest {};
@@ -786,6 +837,10 @@
Execute(kDevice, kTestModel, /*testKind=*/TestKind::MEMORY_DOMAIN);
}
+TEST_P(FencedComputeTest, Test) {
+ Execute(kDevice, kTestModel, /*testKind=*/TestKind::FENCED_COMPUTE);
+}
+
TEST_P(QuantizationCouplingTest, Test) {
Execute(kDevice, kTestModel, /*testKind=*/TestKind::QUANTIZATION_COUPLING);
}
@@ -793,12 +848,16 @@
INSTANTIATE_GENERATED_TEST(GeneratedTest,
[](const TestModel& testModel) { return !testModel.expectFailure; });
-INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest,
- [](const TestModel& testModel) { return !testModel.expectFailure; });
+INSTANTIATE_GENERATED_TEST(DynamicOutputShapeTest, [](const TestModel& testModel) {
+ return !testModel.expectFailure && !testModel.hasScalarOutputs();
+});
INSTANTIATE_GENERATED_TEST(MemoryDomainTest,
[](const TestModel& testModel) { return !testModel.expectFailure; });
+INSTANTIATE_GENERATED_TEST(FencedComputeTest,
+ [](const TestModel& testModel) { return !testModel.expectFailure; });
+
INSTANTIATE_GENERATED_TEST(QuantizationCouplingTest, [](const TestModel& testModel) {
return testModel.hasQuant8CoupledOperands() && testModel.operations.size() == 1;
});
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
index fe695b4..e597fac 100644
--- a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.h
@@ -65,6 +65,8 @@
DYNAMIC_SHAPE,
// Same as GENERAL but use device memories for inputs and outputs
MEMORY_DOMAIN,
+ // Same as GENERAL but use executeFenced for exeuction
+ FENCED_COMPUTE,
// Tests if quantized model with TENSOR_QUANT8_ASYMM produces the same result
// (OK/SKIPPED/FAILED) as the model with all such tensors converted to
// TENSOR_QUANT8_ASYMM_SIGNED.
diff --git a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
index a211428..1245432 100644
--- a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
@@ -337,6 +337,7 @@
// - TRANSPOSE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - AXIS_ALIGNED_BBOX_TRANSFORM bounding boxes (arg 1) can be of
// TENSOR_QUANT8_ASYMM or TENSOR_QUANT8_ASYMM_SIGNED.
+ // - RANK's input can have any TENSOR_* type.
switch (operation.type) {
case OperationType::LSH_PROJECTION: {
if (operand == operation.inputs[1]) {
@@ -399,6 +400,20 @@
return true;
}
} break;
+ case OperationType::RANK: {
+ if (operand == operation.inputs[0] &&
+ (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
+ type == OperandType::TENSOR_INT32 ||
+ type == OperandType::TENSOR_QUANT8_ASYMM ||
+ type == OperandType::TENSOR_QUANT16_SYMM ||
+ type == OperandType::TENSOR_BOOL8 ||
+ type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
+ type == OperandType::TENSOR_QUANT16_ASYMM ||
+ type == OperandType::TENSOR_QUANT8_SYMM ||
+ type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
+ return true;
+ }
+ } break;
default:
break;
}
diff --git a/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
index be4112a..1ddd09c 100644
--- a/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.3/vts/functional/ValidateRequest.cpp
@@ -16,7 +16,9 @@
#define LOG_TAG "neuralnetworks_hidl_hal_test"
+#include <android/hardware/neuralnetworks/1.3/IFencedExecutionCallback.h>
#include <chrono>
+
#include "1.0/Utils.h"
#include "1.3/Callbacks.h"
#include "ExecutionBurstController.h"
@@ -136,6 +138,22 @@
burst->freeMemory(keys.front());
}
}
+
+ // dispatch
+ {
+ SCOPED_TRACE(message + " [executeFenced]");
+ Return<void> ret = preparedModel->executeFenced(
+ request, {}, MeasureTiming::NO,
+ [](ErrorStatus error, const hidl_handle& handle,
+ const sp<IFencedExecutionCallback>& callback) {
+ if (error != ErrorStatus::DEVICE_UNAVAILABLE) {
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+ }
+ ASSERT_EQ(handle.getNativeHandle(), nullptr);
+ ASSERT_EQ(callback, nullptr);
+ });
+ ASSERT_TRUE(ret.isOk());
+ }
}
///////////////////////// REMOVE INPUT ////////////////////////////////////
diff --git a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
index 93c8f13..c84f5b7 100644
--- a/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
+++ b/neuralnetworks/1.3/vts/functional/VtsHalNeuralnetworks.cpp
@@ -133,6 +133,23 @@
// Forward declaration from ValidateBurst.cpp
void validateBurst(const sp<IPreparedModel>& preparedModel, const V1_0::Request& request);
+// Validate sync_fence handles for dispatch with valid input
+void validateExecuteFenced(const sp<IPreparedModel>& preparedModel, const Request& request) {
+ SCOPED_TRACE("Expecting request to fail [executeFenced]");
+ Return<void> ret_null =
+ preparedModel->executeFenced(request, {hidl_handle(nullptr)}, V1_2::MeasureTiming::NO,
+ [](ErrorStatus error, const hidl_handle& handle,
+ const sp<IFencedExecutionCallback>& callback) {
+ // TODO: fix this once sample driver impl is merged.
+ if (error != ErrorStatus::DEVICE_UNAVAILABLE) {
+ ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, error);
+ }
+ ASSERT_EQ(handle.getNativeHandle(), nullptr);
+ ASSERT_EQ(callback, nullptr);
+ });
+ ASSERT_TRUE(ret_null.isOk());
+}
+
void validateEverything(const sp<IDevice>& device, const Model& model, const Request& request,
std::pair<bool, bool> supportsDeadlines) {
const auto [prepareModelDeadlineSupported, executionDeadlineSupported] = supportsDeadlines;
@@ -144,6 +161,7 @@
if (preparedModel == nullptr) return;
validateRequest(preparedModel, request, executionDeadlineSupported);
+ validateExecuteFenced(preparedModel, request);
// TODO(butlermichael): Check if we need to test burst in V1_3 if the interface remains V1_2.
ASSERT_TRUE(nn::compliantWithV1_0(request));