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support hyperbolic ops (#223)

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* support hyperbolic fns

This commit adds support for sinh and cosh. Support for hyperbolic inverses is not available in Eigen yet.

* Make constructors explicit

* remove tests from exclude list

* Revert "remove tests from exclude list"

This reverts commit 2112a30b57d5a899991de4847e948e700a44e85d.

* remove test names from excluded list

* remove tanh since its already implemented
This commit is contained in:
ashku-ms 2018-12-19 14:23:09 -08:00 committed by GitHub
parent e63572c1f3
commit 84231ba003
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GPG key ID: 4AEE18F83AFDEB23
5 changed files with 73 additions and 25 deletions
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8
onnxruntime/core/providers/cpu/cpu_execution_provider.cc
View file

@ -196,6 +196,8 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Eye
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, IsNaN);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MLFloat16, IsNaN);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Erf);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Sinh);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Cosh);
void RegisterOnnxOperatorKernels(std::function<void(KernelCreateInfo&&)> fn) {
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Clip)>());
@ -384,6 +386,8 @@ void RegisterOnnxOperatorKernels(std::function<void(KernelCreateInfo&&)> fn) {
fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, IsNaN)>());
fn(BuildKernel<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MLFloat16, IsNaN)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Erf)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Sinh)>());
fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Cosh)>());
}
// Forward declarations of ml op kernels
@ -485,7 +489,7 @@ static void RegisterCPUKernels(std::function<void(KernelCreateInfo&&)> create_fn
std::shared_ptr<KernelRegistry> CPUExecutionProvider::GetKernelRegistry() const {
static std::shared_ptr<KernelRegistry>
kernel_registry = std::make_shared<KernelRegistry>(RegisterCPUKernels);
kernel_registry = std::make_shared<KernelRegistry>(RegisterCPUKernels);
return kernel_registry;
}
@ -493,7 +497,7 @@ std::vector<std::unique_ptr<ComputeCapability>>
CPUExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph,
const std::vector<const KernelRegistry*>& kernel_registries) const {
std::vector<std::unique_ptr<ComputeCapability>>
result = IExecutionProvider::GetCapability(graph, kernel_registries);
result = IExecutionProvider::GetCapability(graph, kernel_registries);
for (auto& rule : fuse_rules_) {
rule(graph, result);

46
onnxruntime/core/providers/cpu/math/element_wise_ops.cc
View file

@ -400,11 +400,10 @@ Status Pow<float>::Compute(OpKernelContext* context) const {
std::function<void(EigenVectorMap<float>, ConstEigenVectorMap<float>, float)> input1scalar =
[](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float input1) { output = Eigen::pow(input0.array(), input1); };
if (Y.Shape().Size() == 1) {
float value = * Y.Data<float>();
float value = *Y.Data<float>();
if (value == 2.0) {
input1scalar = [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float) { output = Eigen::square(input0.array()); };
}
else if (value == 3.0) {
} else if (value == 3.0) {
input1scalar = [](EigenVectorMap<float> output, ConstEigenVectorMap<float> input0, float) { output = Eigen::cube(input0.array()); };
}
}
@ -789,6 +788,46 @@ ONNX_CPU_OPERATOR_KERNEL(
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
Atan<float>);
template <typename T>
class Sinh final : public OpKernel {
public:
explicit Sinh(const OpKernelInfo& info) : OpKernel(info) {
}
Status Compute(OpKernelContext* context) const override {
auto& X = *context->Input<Tensor>(0);
auto& Y = *context->Output(0, X.Shape());
MakeEigenArrayMap<float>(Y) = MakeEigenArrayMap<float>(X).sinh();
return Status::OK();
}
};
ONNX_CPU_OPERATOR_KERNEL(
Sinh,
9,
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
Sinh<float>);
template <typename T>
class Cosh final : public OpKernel {
public:
explicit Cosh(const OpKernelInfo& info) : OpKernel(info) {
}
Status Compute(OpKernelContext* context) const override {
auto& X = *context->Input<Tensor>(0);
auto& Y = *context->Output(0, X.Shape());
MakeEigenArrayMap<float>(Y) = MakeEigenArrayMap<float>(X).cosh();
return Status::OK();
}
};
ONNX_CPU_OPERATOR_KERNEL(
Cosh,
9,
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
Cosh<float>);
template <>
Status PRelu<float>::Compute(OpKernelContext* context) const {
return BroadcastTwo<float, float>(
@ -887,7 +926,6 @@ Status Erf<float>::Compute(OpKernelContext* context) const {
ORT_ENFORCE(X_ptr != nullptr);
auto& X = *X_ptr;
auto& Y = *context->Output(0, X.Shape());
EigenMap<float>(Y) = EigenMap<float>(X).array().erf();
return Status::OK();

2
onnxruntime/test/onnx/main.cc
View file

@ -314,8 +314,6 @@ int real_main(int argc, char* argv[]) {
{"upsample_nearest", "opset 9 not supported yet"},
{"onehot_with_axis", "opset 9 not supported yet"},
{"onehot_without_axis", "opset 9 not supported yet"}, // also has bug in current test re: output type. Spandan to fix.
{"sinh", "opset 9 not supported yet"},
{"cosh", "opset 9 not supported yet"},
{"asinh", "opset 9 not supported yet"},
{"acosh", "opset 9 not supported yet"},
{"atanh", "opset 9 not supported yet"},

40
onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc
View file

@ -163,11 +163,11 @@ TEST(MathOpTest, Sub_int32) {
}
TEST(MathOpTest, Sub_int64) {
OpTester test("Sub");
test.AddInput<int64_t>("A", { 3 }, { 1, 5, 6 });
test.AddInput<int64_t>("B", { 3 }, { 4, 5, 3 });
test.AddOutput<int64_t>("C", { 3 }, { -3, 0, 3 });
test.Run();
OpTester test("Sub");
test.AddInput<int64_t>("A", {3}, {1, 5, 6});
test.AddInput<int64_t>("B", {3}, {4, 5, 3});
test.AddOutput<int64_t>("C", {3}, {-3, 0, 3});
test.Run();
}
TEST(MathOpTest, Sub) {
@ -212,11 +212,11 @@ TEST(MathOpTest, Mul_int32) {
}
TEST(MathOpTest, Mul_int64) {
OpTester test("Mul");
test.AddInput<int64_t>("A", { 3 }, { 3, 6, -3 });
test.AddInput<int64_t>("B", { 3 }, { 4, -3, -2 });
test.AddOutput<int64_t>("C", { 3 }, { 12, -18, 6 });
test.Run();
OpTester test("Mul");
test.AddInput<int64_t>("A", {3}, {3, 6, -3});
test.AddInput<int64_t>("B", {3}, {4, -3, -2});
test.AddOutput<int64_t>("C", {3}, {12, -18, 6});
test.Run();
}
TEST(MathOpTest, Mul) {
@ -246,11 +246,11 @@ TEST(MathOpTest, Div_int32) {
}
TEST(MathOpTest, Div_int64) {
OpTester test("Div");
test.AddInput<int64_t>("A", { 3 }, { 4, 8, 8 });
test.AddInput<int64_t>("B", { 3 }, { 2, 3, 4 });
test.AddOutput<int64_t>("C", { 3 }, { 2, 2, 2 });
test.Run();
OpTester test("Div");
test.AddInput<int64_t>("A", {3}, {4, 8, 8});
test.AddInput<int64_t>("B", {3}, {2, 3, 4});
test.AddOutput<int64_t>("C", {3}, {2, 2, 2});
test.Run();
}
TEST(MathOpTest, Div) {
@ -819,6 +819,16 @@ TEST(MathOpTest, Atan) {
TrigTest<std::atan>(test, {-10.0f, -5.0f, 0.0f, 5.0f, 10.0f});
}
TEST(MathOpTest, Sinh) {
OpTester test("Sinh", 9);
TrigTest<std::sinh>(test, {-1.0f, -0.5f, 0.0f, 0.5f, 1.0f});
}
TEST(MathOpTest, Cosh) {
OpTester test("Cosh", 9);
TrigTest<std::cosh>(test, {-1.0f, -0.5f, 0.0f, 0.5f, 1.0f});
}
TEST(MathOpTest, Expand_8_3x3) {
OpTester test("Expand", 8);
test.AddInput<float>("data_0", {1}, {1.0f});

2
onnxruntime/test/python/onnx_backend_test_series.py
View file

@ -24,7 +24,6 @@ backend_test.exclude(r'(test_acosh_cpu.*'
'|test_atanh_example_cpu.*'
'|test_convtranspose_1d_cpu.*'
'|test_convtranspose_3d_cpu.*'
'|test_cosh_cpu.*'
'|test_cosh_example_cpu.*'
'|test_dynamic_slice_cpu.*'
'|test_dynamic_slice_default_axes_cpu.*'
@ -43,7 +42,6 @@ backend_test.exclude(r'(test_acosh_cpu.*'
'|test_scatter_with_axis_cpu.*'
'|test_scatter_without_axis_cpu.*'
'|test_sign_cpu.*'
'|test_sinh_cpu.*'
'|test_sinh_example_cpu.*'
'|test_AvgPool1d_cpu.*'
'|test_AvgPool1d_stride_cpu.*'