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Enable onnxruntime_test_all for NNAPI EP (#4476)

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gwang-msft 2020-07-10 13:34:44 -07:00 committed by GitHub
parent 6c7da5e9d3
commit 9b4c54bcef
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7 changed files with 76 additions and 54 deletions
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32
onnxruntime/core/providers/nnapi/nnapi_builtin/builders/op_builder.cc
View file

@ -1453,9 +1453,7 @@ void ConcatOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
inputs.reserve(node_input_size);
if (all_input_have_same_layout) {
// if all the inputs are of same layout, output will be the same layout
if (model_builder.IsOperandNHWC(input0)) {
output_is_nhwc = true;
}
output_is_nhwc = model_builder.IsOperandNHWC(input0);
for (size_t i = 0; i < node_input_size; i++) {
auto input = node.InputDefs()[i]->Name();
@ -1546,24 +1544,30 @@ void SqueezeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const
NodeAttrHelper helper(node);
vector<int32_t> axes = helper.Get("axes", vector<int32_t>());
auto input_dims = shaper[input].size();
const auto& input_shape(shaper[input]);
auto input_dims = input_shape.size();
for (auto& axis : axes) {
if (axis < 0)
axis += input_dims;
}
std::vector<uint32_t> input_indices;
input_indices.push_back(operand_indices.at(input)); // input
if (!axes.empty()) {
const auto axes_name = model_builder.GetUniqueName(node.Name() + input + "_axes");
Shape axes_dimen = {static_cast<uint32_t>(axes.size())};
shaper.AddShape(axes_name, axes_dimen);
const OperandType axes_operand_type(Type::TENSOR_INT32, axes_dimen);
model_builder.AddOperandFromPersistMemoryBuffer(axes_name, axes.data(), axes_operand_type);
input_indices.push_back(operand_indices.at(axes_name)); // axes
if (axes.empty()) { // Squeeze all
for (size_t i = 0; i < input_dims; i++) {
if (input_shape[i] == 1)
axes.push_back(i);
}
}
const auto axes_name = model_builder.GetUniqueName(node.Name() + input + "_axes");
Shape axes_dimen = {static_cast<uint32_t>(axes.size())};
shaper.AddShape(axes_name, axes_dimen);
const OperandType axes_operand_type(Type::TENSOR_INT32, axes_dimen);
model_builder.AddOperandFromPersistMemoryBuffer(axes_name, axes.data(), axes_operand_type);
std::vector<uint32_t> input_indices;
input_indices.push_back(operand_indices.at(input)); // input
input_indices.push_back(operand_indices.at(axes_name)); // axes
const auto& output = node.OutputDefs()[0]->Name();
shaper.Squeeze(input, axes, output);
const OperandType output_operand_type(operand_types.at(input).type, shaper[output]);

13
onnxruntime/core/providers/nnapi/nnapi_builtin/nnapi_execution_provider.cc
View file

@ -43,6 +43,11 @@ NnapiExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph_view
const std::vector<const KernelRegistry*>& /*kernel_registries*/) const {
std::vector<std::unique_ptr<ComputeCapability>> result;
// TODO: Task 812756: NNAPI EP, add support for subgraph (If and Loop operators)
if (graph_view.IsSubgraph()) {
return result;
}
std::unordered_set<std::string> all_node_inputs;
for (const auto& node : graph_view.Nodes()) {
for (auto* input : node.InputDefs()) {
@ -240,10 +245,9 @@ common::Status NnapiExecutionProvider::Compile(const std::vector<onnxruntime::No
auto input_idx = model->GetMappedInputIdx(input_name);
const OrtValue* input_tensor = ort.KernelContext_GetInput(context, input_idx);
const auto tensor_info = ort.GetTensorTypeAndShape(input_tensor);
const auto& tensor_shape = ort.GetTensorShape(tensor_info);
auto* tensor_info = ort.GetTensorTypeAndShape(input_tensor);
std::vector<uint32_t> dimensions;
for (const auto& dim : tensor_shape)
for (const auto& dim : ort.GetTensorShape(tensor_info))
dimensions.push_back(static_cast<uint32_t>(dim));
// it is possible that the input has the detailed size while
@ -299,7 +303,8 @@ common::Status NnapiExecutionProvider::Compile(const std::vector<onnxruntime::No
}
if (model_output_type.GetOperandBlobByteSize() == 0) {
return Status(common::ONNXRUNTIME, common::FAIL, "We do not support dynamic output shape for now");
return Status(common::ONNXRUNTIME, common::FAIL,
"We do not support dynamic output shape or empty output for now");
}
outputs.push_back({output_buffer, std::move(model_output_type)});

47
onnxruntime/test/providers/cpu/activation/activation_op_test.h
View file

@ -10,9 +10,7 @@
#include "core/graph/constants.h"
#include "test/providers/provider_test_utils.h"
namespace onnxruntime {
namespace test {
inline void TestActivationOp(const char* szOp, const std::vector<std::vector<float>>& input_vals_vec,
@ -38,26 +36,34 @@ inline void TestActivationOp(const char* szOp, const std::vector<std::vector<flo
excluded_providers.insert(kTensorrtExecutionProvider);
}
//Disabled because of accuracy issues for MYRIAD FP16 and VAD_M
#if defined(OPENVINO_CONFIG_MYRIAD) || defined(OPENVINO_CONFIG_VAD_M)
int relu = strcmp(szOp, "Relu");
int leaky = strcmp(szOp, "LeakyRelu");
int elu = strcmp(szOp, "Elu");
if (relu == 0 || leaky == 0) {
excluded_providers.insert(kOpenVINOExecutionProvider);
}
if(elu == 0)
excluded_providers.insert(kOpenVINOExecutionProvider);
int relu = strcmp(szOp, "Relu");
int leaky = strcmp(szOp, "LeakyRelu");
int elu = strcmp(szOp, "Elu");
if (relu == 0 || leaky == 0) {
excluded_providers.insert(kOpenVINOExecutionProvider);
}
if (elu == 0)
excluded_providers.insert(kOpenVINOExecutionProvider);
#endif
//Disabled because of accuracy issues for GPU
#if defined(OPENVINO_CONFIG_GPU_FP16) || defined(OPENVINO_CONFIG_GPU_FP32)
int leaky = strcmp(szOp, "LeakyRelu");
if (leaky == 0) {
excluded_providers.insert(kOpenVINOExecutionProvider);
}
int leaky = strcmp(szOp, "LeakyRelu");
if (leaky == 0) {
excluded_providers.insert(kOpenVINOExecutionProvider);
}
#endif
//Disabled because of NNAPI treat float::inf as float::max
#if defined(USE_NNAPI)
int relu = strcmp(szOp, "Relu");
if (relu == 0) {
excluded_providers.insert(kNnapiExecutionProvider);
}
#endif
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_providers);
}
}
@ -87,9 +93,9 @@ class ActivationOpTest : public ::testing::Test {
class ActivationOpNoInfTest : public ::testing::Test {
protected:
std::vector<std::vector<float>> input_values{{-1.0f, 0, 1.0f, // normal input values for activation
FLT_MIN, FLT_MIN / 10, -FLT_MIN / 10, // min, denorm, -denorm
FLT_MAX, -FLT_MAX}}; // max, -max, inf
std::vector<std::vector<float>> input_values{{-1.0f, 0, 1.0f, // normal input values for activation
FLT_MIN, FLT_MIN / 10, -FLT_MIN / 10, // min, denorm, -denorm
FLT_MAX, -FLT_MAX}}; // max, -max, inf
void SetUp() override {
float low = -1.0f, high = 1.0f;
@ -106,5 +112,6 @@ class ActivationOpNoInfTest : public ::testing::Test {
}
}
};
}
}
} // namespace test
} // namespace onnxruntime

9
onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc
View file

@ -13,8 +13,9 @@ namespace test {
TEST(MathOpTest, DimWithZeroHandling) {
auto run = [](OpTester& tester) {
// exclude NGraph and TensorRT as this isn't handled by those EPs
tester.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kNGraphExecutionProvider});
// exclude NGraph, TensorRT and NNAPI as this isn't handled by those EPs
tester.Run(OpTester::ExpectResult::kExpectSuccess, "",
{kTensorrtExecutionProvider, kNGraphExecutionProvider, kNnapiExecutionProvider});
};
// test binary element-wise op broadcasting when there's a dim with value of zero
@ -183,7 +184,7 @@ TEST(MathOpTest, Add_Broadcast_0x0) {
test.AddInput<float>("A", {}, {10.0f});
test.AddInput<float>("B", {}, {2.0f});
test.AddOutput<float>("C", {}, {12.0f});
test.Run();
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kNnapiExecutionProvider}); // NNAPI: Add does not support scalar input
}
TEST(MathOpTest, Add_Broadcast_0x1) {
@ -814,7 +815,7 @@ TEST(MathOpTest, Sum_8_Test1) {
//This test runs fine on CPU Plugin
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider});
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: Expected output shape [{3,3,3}] did not match run output shape [{3,1,1}] for sum
#endif
}

17
onnxruntime/test/providers/cpu/math/softmax_test.cc
View file

@ -12,7 +12,7 @@ static void RunTest(const std::vector<float>& x_vals,
const std::vector<float>& expected_vals,
const std::vector<int64_t>& dimensions,
int64_t axis = 1,
bool is_tensorrt_supported = true,
const std::unordered_set<std::string>& excluded_providers = {},
OpTester::ExpectResult expect_result = OpTester::ExpectResult::kExpectSuccess,
const std::string& error_msg = "",
int opset = 7) {
@ -24,10 +24,6 @@ static void RunTest(const std::vector<float>& x_vals,
test.AddInput<float>("X", dimensions, x_vals);
test.AddOutput<float>("Y", dimensions, expected_vals);
std::unordered_set<std::string> excluded_providers;
if (!is_tensorrt_supported) {
excluded_providers.insert(kTensorrtExecutionProvider);
}
test.Run(expect_result, error_msg, excluded_providers);
}
@ -97,7 +93,7 @@ TEST(SoftmaxOperator, ThreeDimsAxis0) {
0.017545262f, 0.0135920765f, 0.027506188f, 0.010684152f, 0.0049549243f,
0.01401341f, 0.011721271f, 0.027815264f, 0.021463264f, 0.014014485f};
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 0, false); // Axis=0 is not supported by TensorRT
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 0, {kTensorrtExecutionProvider}); // Axis=0 is not supported by TensorRT
}
TEST(SoftmaxOperator, ThreeDimsAxis1) {
@ -123,7 +119,7 @@ TEST(SoftmaxOperator, ThreeDimsAxis1) {
0.050680935f, 0.03926183f, 0.079453886f, 0.030862054f, 0.014312706f,
0.040478885f, 0.033857856f, 0.080346674f, 0.06199841f, 0.040481992f};
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 1, false);
RunTest(x_vals_3dims, expected_vals, three_dimensions, /*axis*/ 1, {kTensorrtExecutionProvider});
}
TEST(SoftmaxOperator, ThreeDimsAxis2) {
@ -187,7 +183,7 @@ TEST(SoftmaxOperator, InvalidAxis) {
RunTest(x_vals,
expected_vals,
dimensions,
/* invalid axis */ -10, false,
/* invalid axis */ -10, {kTensorrtExecutionProvider},
OpTester::ExpectResult::kExpectFailure,
// bug in ONNX error message currently. Message should be
// "[ShapeInferenceError] 'axis' must be in [-2 , 1]. Its actual value is: -10"
@ -201,7 +197,10 @@ TEST(SoftmaxOperator, DimWithZero) {
std::vector<float> expected_vals = {};
std::vector<int64_t> dimensions = {1, 0}; // dim with value of 0 should be handled
RunTest(x_vals, expected_vals, dimensions, 0, false, OpTester::ExpectResult::kExpectSuccess, "", 10);
RunTest(x_vals, expected_vals, dimensions, 0,
{kTensorrtExecutionProvider,
kNnapiExecutionProvider}, // NNAPI softmax does not support empty input
OpTester::ExpectResult::kExpectSuccess, "", 10);
}
} // namespace test

8
onnxruntime/test/providers/cpu/tensor/concat_op_test.cc
View file

@ -62,7 +62,9 @@ TEST(ConcatOpTest, Concat1D_2) {
test.AddInput<float>("input2", {2}, {2.0f, 3.0f});
test.AddInput<float>("input3", {0}, {});
test.AddOutput<float>("concat_result", {3}, {1.0f, 2.0f, 3.0f});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: no support for dynamic shape tensor
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
{kTensorrtExecutionProvider, //TensorRT: no support for dynamic shape tensor
kNnapiExecutionProvider}); // NNAPI: concat does not support 0 size input
}
TEST(ConcatOpTest, Concat2D_1) {
@ -104,7 +106,9 @@ TEST(ConcatOpTest, Concat2D_3) {
test.AddInput<float>("input2", {1, 0}, {});
test.AddInput<float>("input3", {1, 0}, {});
test.AddOutput<float>("concat_result", {1, 0}, {});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: no support for dynamic shape tensor
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
{kTensorrtExecutionProvider, //TensorRT: no support for dynamic shape tensor
kNnapiExecutionProvider}); // NNAPI: concat does not support 0 size input
}
TEST(ConcatOpTest, Concat3D_1) {

4
onnxruntime/test/providers/provider_test_utils.cc
View file

@ -723,6 +723,7 @@ void OpTester::Run(
kDmlExecutionProvider,
kAclExecutionProvider,
kArmNNExecutionProvider,
kNnapiExecutionProvider,
};
bool has_run = false;
@ -807,7 +808,8 @@ void OpTester::Run(
if (provider_type == onnxruntime::kNGraphExecutionProvider ||
provider_type == onnxruntime::kOpenVINOExecutionProvider ||
provider_type == onnxruntime::kTensorrtExecutionProvider ||
provider_type == onnxruntime::kNupharExecutionProvider)
provider_type == onnxruntime::kNupharExecutionProvider ||
provider_type == onnxruntime::kNnapiExecutionProvider)
continue;
auto reg = execution_provider->GetKernelRegistry();
if (!KernelRegistry::HasImplementationOf(*reg, node, execution_provider->Type())) {