diff --git a/docs/OperatorKernels.md b/docs/OperatorKernels.md index dea71d81f8..ba610515ac 100644 --- a/docs/OperatorKernels.md +++ b/docs/OperatorKernels.md @@ -156,8 +156,10 @@ Do not modify directly.* |||[1, 10]|**B** = tensor(bool)
**V** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)| |ImageScaler|*in* input:**T**
*out* output:**T**|1+|**T** = tensor(float)| |InstanceNormalization|*in* input:**T**
*in* scale:**T**
*in* B:**T**
*out* output:**T**|6+|**T** = tensor(float)| -|IsInf|*in* X:**T1**
*out* Y:**T2**|10+|**T1** = tensor(double), tensor(float)
**T2** = tensor(bool)| -|IsNaN|*in* X:**T1**
*out* Y:**T2**|13+|**T1** = tensor(double), tensor(float), tensor(float16)
**T2** = tensor(bool)| +|IsInf|*in* X:**T1**
*out* Y:**T2**|20+|**T1** = tensor(double), tensor(float), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
**T2** = tensor(bool)| +|||[10, 19]|**T1** = tensor(double), tensor(float)
**T2** = tensor(bool)| +|IsNaN|*in* X:**T1**
*out* Y:**T2**|20+|**T1** = tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz)
**T2** = tensor(bool)| +|||[13, 19]|**T1** = tensor(double), tensor(float), tensor(float16)
**T2** = tensor(bool)| |||[9, 12]|**T1** = tensor(double), tensor(float), tensor(float16)
**T2** = tensor(bool)| |LRN|*in* X:**T**
*out* Y:**T**|13+|**T** = tensor(float)| |||[1, 12]|**T** = tensor(float)| diff --git a/include/onnxruntime/core/framework/float8.h b/include/onnxruntime/core/framework/float8.h index 0fd04f28d4..dd607cbbc6 100644 --- a/include/onnxruntime/core/framework/float8.h +++ b/include/onnxruntime/core/framework/float8.h @@ -208,9 +208,10 @@ struct Float8E4M3FNUZ { val = static_cast((b & 0x80000000) >> 24); // sign if ((b & 0x7fffffff) == 0x7f800000) { // infinity if (saturate) { + // the highest available value val |= 0x7F; } else { - // infinity + // NaN val = 0x80; } } else if ((b & 0x7F800000) == 0x7F800000) { // NaN @@ -362,8 +363,10 @@ struct Float8E5M2 { val = (b & 0x80000000) >> 24; // sign if ((b & 0x7FFFFFFF) == 0x7F800000) { // inf if (saturate) { + // the highest available value val |= 0x7B; } else { + // the infinity val |= 0x7C; } } else if ((b & 0x7F800000) == 0x7F800000) { // NaN diff --git a/onnxruntime/core/providers/cpu/cpu_execution_provider.cc b/onnxruntime/core/providers/cpu/cpu_execution_provider.cc index 3d03abf5b7..a54d999a10 100644 --- a/onnxruntime/core/providers/cpu/cpu_execution_provider.cc +++ b/onnxruntime/core/providers/cpu/cpu_execution_provider.cc @@ -365,7 +365,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, Slice); class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 11, Dropout); class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, NonMaxSuppression); -class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, IsInf); +class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 19, IsInf); class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 15, float, RoiAlign); class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 15, double, RoiAlign); class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ReverseSequence); @@ -682,9 +682,9 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Ga class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 15, ScatterND); class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 15, ScatterElements); class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 13, Identity); -class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, IsNaN); -class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, IsNaN); -class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, MLFloat16, IsNaN); +class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 19, float, IsNaN); +class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 19, double, IsNaN); +class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 19, MLFloat16, IsNaN); class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, bool, NonZero); class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, NonZero); class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int32_t, NonZero); @@ -960,6 +960,16 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, Sh // Opset 20 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, ConstantOfShape); +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, float, IsNaN); +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, double, IsNaN); +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, MLFloat16, IsNaN); +#if !defined(DISABLE_FLOAT8_TYPES) +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, Float8E4M3FN, IsNaN); +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, Float8E4M3FNUZ, IsNaN); +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, Float8E5M2, IsNaN); +class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, Float8E5M2FNUZ, IsNaN); +#endif +class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 20, IsInf); // !!PLEASE READ BELOW!! Following that, add new entries above this comment @@ -1492,7 +1502,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) { Dropout)>, BuildKernelCreateInfo, - BuildKernelCreateInfo, + BuildKernelCreateInfo, BuildKernelCreateInfo, BuildKernelCreateInfo, BuildKernelCreateInfo, BuildKernelCreateInfo, - BuildKernelCreateInfo, - BuildKernelCreateInfo, - BuildKernelCreateInfo, + BuildKernelCreateInfo, + BuildKernelCreateInfo, + BuildKernelCreateInfo, BuildKernelCreateInfo, BuildKernelCreateInfo, + BuildKernelCreateInfo, + BuildKernelCreateInfo, + BuildKernelCreateInfo, +#if !defined(DISABLE_FLOAT8_TYPES) + BuildKernelCreateInfo, + BuildKernelCreateInfo, + BuildKernelCreateInfo, + BuildKernelCreateInfo, +#endif + BuildKernelCreateInfo, }; for (auto& function_table_entry : function_table) { diff --git a/onnxruntime/core/providers/cpu/tensor/isinf.cc b/onnxruntime/core/providers/cpu/tensor/isinf.cc index bc99caa803..1b449f4692 100644 --- a/onnxruntime/core/providers/cpu/tensor/isinf.cc +++ b/onnxruntime/core/providers/cpu/tensor/isinf.cc @@ -14,15 +14,38 @@ namespace onnxruntime { // https://github.com/onnx/onnx/blob/main/docs/Operators.md#IsInf namespace op_kernel_type_control { -ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPES_ALL_OPSETS( - kCpuExecutionProvider, kOnnxDomain, IsInf, Input, 0, - float, double); +using IsInfTypesOpset10 = TypeList; + +ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPE_LIST( + kCpuExecutionProvider, kOnnxDomain, IsInf, 10, Input, 0, + IsInfTypesOpset10); + +using IsInfTypesOpset20 = + TypeList< + float, + double +#if !defined(DISABLE_FLOAT8_TYPES) + , + Float8E4M3FN, Float8E4M3FNUZ, Float8E5M2, Float8E5M2FNUZ +#endif + >; + +ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPE_LIST( + kCpuExecutionProvider, + kOnnxDomain, + IsInf, + 20, + Input, + 0, + IsInfTypesOpset20); } // namespace op_kernel_type_control class IsInf final : public OpKernel { public: - using EnabledDataTypes = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST_ALL_OPSETS(kCpuExecutionProvider, kOnnxDomain, - IsInf, Input, 0); + using EnabledDataTypes10 = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST(kCpuExecutionProvider, kOnnxDomain, + IsInf, 10, Input, 0); + using EnabledDataTypes20 = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST(kCpuExecutionProvider, kOnnxDomain, + IsInf, 20, Input, 0); explicit IsInf(const OpKernelInfo& info); Status Compute(OpKernelContext* context) const override; @@ -30,14 +53,25 @@ class IsInf final : public OpKernel { private: int64_t detect_positive_{1}; int64_t detect_negative_{1}; + int opset_; }; +ONNX_CPU_OPERATOR_VERSIONED_KERNEL( + IsInf, + 10, + 19, + KernelDefBuilder() + .TypeConstraint("T1", + BuildKernelDefConstraintsFromTypeList()) + .TypeConstraint("T2", DataTypeImpl::GetTensorType()), + IsInf); + ONNX_CPU_OPERATOR_KERNEL( IsInf, - 10, + 20, KernelDefBuilder() .TypeConstraint("T1", - BuildKernelDefConstraintsFromTypeList()) + BuildKernelDefConstraintsFromTypeList()) .TypeConstraint("T2", DataTypeImpl::GetTensorType()), IsInf); @@ -46,6 +80,7 @@ IsInf::IsInf(const OpKernelInfo& info) : OpKernel(info) { ORT_ENFORCE(status.IsOK(), "Failed to obtain detect_positive"); status = info.GetAttr("detect_negative", &detect_negative_); ORT_ENFORCE(status.IsOK(), "Failed to obtain detect_negative"); + opset_ = info.node().SinceVersion(); } namespace isinf_internal { @@ -78,6 +113,49 @@ struct ComputeDispatchTarget { } } }; + +#if !defined(DISABLE_FLOAT8_TYPES) +template <> +struct ComputeDispatchTarget { + void operator()(const Tensor&, Tensor& Y, bool, bool) const { + EigenMap(Y).array() = false; + } +}; + +template <> +struct ComputeDispatchTarget { + void operator()(const Tensor&, Tensor& Y, bool, bool) const { + EigenMap(Y).array() = false; + } +}; + +template <> +struct ComputeDispatchTarget { + void operator()(const Tensor& X, Tensor& Y, bool detect_positive, bool detect_negative) const { + auto& dims = X.Shape(); + auto input = ConstEigenVectorMap(static_cast(static_cast(X.Data())), onnxruntime::narrow(dims.Size())); + auto output = EigenMap(Y); + + // S.11111.00 + if (detect_positive && detect_negative) { + output.array() = input.array() == 0b01111100 || input.array() == 0b11111100; + } else if (detect_positive) { + output.array() = input.array() == 0b01111100; + } else if (detect_negative) { + output.array() = input.array() == 0b11111100; + } else { + output.array() = false; + } + } +}; + +template <> +struct ComputeDispatchTarget { + void operator()(const Tensor&, Tensor& Y, bool, bool) const { + EigenMap(Y).array() = false; + } +}; +#endif } // namespace isinf_internal Status IsInf::Compute(OpKernelContext* context) const { @@ -88,8 +166,13 @@ Status IsInf::Compute(OpKernelContext* context) const { using namespace isinf_internal; - utils::MLTypeCallDispatcherFromTypeList dispatcher{X.GetElementType()}; - dispatcher.Invoke(X, Y, detect_positive_ != 0, detect_negative_ != 0); + if (opset_ < 20) { + utils::MLTypeCallDispatcherFromTypeList dispatcher{X.GetElementType()}; + dispatcher.Invoke(X, Y, detect_positive_ != 0, detect_negative_ != 0); + } else { + utils::MLTypeCallDispatcherFromTypeList dispatcher{X.GetElementType()}; + dispatcher.Invoke(X, Y, detect_positive_ != 0, detect_negative_ != 0); + } return Status::OK(); } diff --git a/onnxruntime/core/providers/cpu/tensor/isnan.cc b/onnxruntime/core/providers/cpu/tensor/isnan.cc index 33d0f8eb6c..34495e3822 100644 --- a/onnxruntime/core/providers/cpu/tensor/isnan.cc +++ b/onnxruntime/core/providers/cpu/tensor/isnan.cc @@ -20,10 +20,20 @@ namespace onnxruntime { .TypeConstraint("T2", DataTypeImpl::GetTensorType()), \ IsNaN); +#define ADD_TYPED_ISNAN_OP_13(data_type) \ + ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL( \ + IsNaN, \ + 13, 19, \ + data_type, \ + KernelDefBuilder() \ + .TypeConstraint("T1", DataTypeImpl::GetTensorType()) \ + .TypeConstraint("T2", DataTypeImpl::GetTensorType()), \ + IsNaN); + #define ADD_TYPED_ISNAN_OP(data_type) \ ONNX_CPU_OPERATOR_TYPED_KERNEL( \ IsNaN, \ - 13, \ + 20, \ data_type, \ KernelDefBuilder() \ .TypeConstraint("T1", DataTypeImpl::GetTensorType()) \ @@ -33,10 +43,20 @@ namespace onnxruntime { ADD_TYPED_ISNAN_OP_9(float); ADD_TYPED_ISNAN_OP_9(double); ADD_TYPED_ISNAN_OP_9(MLFloat16); +ADD_TYPED_ISNAN_OP_13(float); +ADD_TYPED_ISNAN_OP_13(double); +ADD_TYPED_ISNAN_OP_13(MLFloat16); ADD_TYPED_ISNAN_OP(float); ADD_TYPED_ISNAN_OP(double); ADD_TYPED_ISNAN_OP(MLFloat16); +#if !defined(DISABLE_FLOAT8_TYPES) +ADD_TYPED_ISNAN_OP(Float8E4M3FN); +ADD_TYPED_ISNAN_OP(Float8E4M3FNUZ); +ADD_TYPED_ISNAN_OP(Float8E5M2); +ADD_TYPED_ISNAN_OP(Float8E5M2FNUZ); +#endif + template Status IsNaN::Compute(OpKernelContext* context) const { const auto* X_ptr = context->Input(0); @@ -70,4 +90,63 @@ Status IsNaN::Compute(OpKernelContext* context) const { return Status::OK(); } + +#if !defined(DISABLE_FLOAT8_TYPES) +template <> +Status IsNaN::Compute(OpKernelContext* context) const { + const auto* X = context->Input(0); + auto& dims = X->Shape(); + auto& Y = *context->Output(0, dims); + + auto input = ConstEigenVectorMap(static_cast(static_cast(X->Data())), onnxruntime::narrow(dims.Size())); + auto output = EigenMap(Y); + + // S.1111.111 + std::transform(input.begin(), input.end(), output.begin(), [](uint8_t c) { return (c & 0x7f) == 0x7f; }); + return Status::OK(); +} + +template <> +Status IsNaN::Compute(OpKernelContext* context) const { + const auto* X = context->Input(0); + auto X_data = X->Data(); + auto& dims = X->Shape(); + auto shape_size = dims.Size(); + auto& Y = *context->Output(0, dims); + + // 1.0000.000 + EigenMap(Y) = + ConstEigenVectorMap(static_cast(static_cast(X_data)), onnxruntime::narrow(shape_size)).array() == 0x80; + + return Status::OK(); +} + +template <> +Status IsNaN::Compute(OpKernelContext* context) const { + const auto* X = context->Input(0); + auto& dims = X->Shape(); + auto& Y = *context->Output(0, dims); + + auto input = ConstEigenVectorMap(static_cast(static_cast(X->Data())), onnxruntime::narrow(dims.Size())); + auto output = EigenMap(Y); + + // S.11111.{01, 10, 11} + std::transform(input.begin(), input.end(), output.begin(), [](uint8_t c) { return ((c & 0x7c) == 0x7c) && ((c & 0x03) != 0x00); }); + return Status::OK(); +} + +template <> +Status IsNaN::Compute(OpKernelContext* context) const { + const auto* X = context->Input(0); + auto X_data = X->Data(); + auto& dims = X->Shape(); + auto shape_size = dims.Size(); + auto& Y = *context->Output(0, dims); + + // 1.0000.000 + EigenMap(Y) = ConstEigenVectorMap(static_cast(static_cast(X_data)), onnxruntime::narrow(shape_size)).array() == 0x80; + + return Status::OK(); +} +#endif } // namespace onnxruntime diff --git a/onnxruntime/test/providers/cpu/tensor/isinf_test.cc b/onnxruntime/test/providers/cpu/tensor/isinf_test.cc index ddb392eb82..2e583c5d25 100644 --- a/onnxruntime/test/providers/cpu/tensor/isinf_test.cc +++ b/onnxruntime/test/providers/cpu/tensor/isinf_test.cc @@ -17,85 +17,137 @@ constexpr double DOUBLE_INF = std::numeric_limits::infinity(); constexpr double DOUBLE_NINF = -std::numeric_limits::infinity(); constexpr double DOUBLE_NAN = std::numeric_limits::quiet_NaN(); -TEST(IsInfTest, test_isinf_float) { - // Defaults for detect_negative = 1 - // detect_positive = 1 - OpTester test("IsInf", 10); - - std::vector input_dim{6}; - std::vector input = {-1.2f, FLOAT_NAN, FLOAT_INF, 2.8f, FLOAT_NINF, FLOAT_INF}; - test.AddInput("X", input_dim, input); - - std::vector output_dim(input_dim); - test.AddOutput("Y", output_dim, {false, false, true, false, true, true}); +template +void run_is_inf_test(int opset, int64_t detect_positive, int64_t detect_negative, const std::initializer_list& input, const std::initializer_list& output) { + OpTester test("IsInf", opset); + test.AddAttribute("detect_positive", detect_positive); + test.AddAttribute("detect_negative", detect_negative); + test.AddInput("X", {onnxruntime::narrow(input.size())}, input); + test.AddOutput("Y", {onnxruntime::narrow(output.size())}, output); test.Run(); } -TEST(IsInfTest, test_isinf_double) { - // Defaults for detect_negative = 1 - // detect_positive = 1 - OpTester test("IsInf", 10); - - std::vector input_dim{6}; - std::vector input = {-1.2, DOUBLE_NAN, DOUBLE_INF, 2.8, DOUBLE_NINF, DOUBLE_INF}; - test.AddInput("X", input_dim, input); - - std::vector output_dim(input_dim); - test.AddOutput("Y", output_dim, {false, false, true, false, true, true}); - test.Run(); +TEST(IsInfTest, test_isinf_float10) { + std::initializer_list input = {-1.2f, FLOAT_NAN, FLOAT_INF, 2.8f, FLOAT_NINF, FLOAT_INF}; + std::initializer_list output = {false, false, true, false, true, true}; + run_is_inf_test(10, 1, 1, input, output); } -TEST(IsInfTest, test_isinf_positive_float) { - OpTester test("IsInf", 10); - test.AddAttribute("detect_negative", 0); - - std::vector input_dim{6}; - std::vector input = {-1.7f, FLOAT_NAN, FLOAT_INF, 3.6f, FLOAT_NINF, FLOAT_INF}; - test.AddInput("X", input_dim, input); - - std::vector output_dim(input_dim); - test.AddOutput("Y", output_dim, {false, false, true, false, false, true}); - test.Run(); +TEST(IsInfTest, test_isinf_float20) { + std::initializer_list input = {-1.2f, FLOAT_NAN, FLOAT_INF, 2.8f, FLOAT_NINF, FLOAT_INF}; + std::initializer_list output = {false, false, true, false, true, true}; + run_is_inf_test(20, 1, 1, input, output); } -TEST(IsInfTest, test_isinf_positive_double) { - OpTester test("IsInf", 10); - test.AddAttribute("detect_negative", 0); - - std::vector input_dim{6}; - std::vector input = {-1.7, DOUBLE_NAN, DOUBLE_INF, 3.6, DOUBLE_NINF, DOUBLE_INF}; - test.AddInput("X", input_dim, input); - - std::vector output_dim(input_dim); - test.AddOutput("Y", output_dim, {false, false, true, false, false, true}); - test.Run(); +TEST(IsInfTest, test_isinf_double10) { + std::initializer_list input = {-1.2, DOUBLE_NAN, DOUBLE_INF, 2.8, DOUBLE_NINF, DOUBLE_INF}; + std::initializer_list output = {false, false, true, false, true, true}; + run_is_inf_test(10, 1, 1, input, output); } -TEST(IsInfTest, test_isinf_negative_float) { - OpTester test("IsInf", 10); - test.AddAttribute("detect_positive", 0); - - std::vector input_dim{6}; - std::vector input = {-1.7f, FLOAT_NAN, FLOAT_INF, 3.6f, FLOAT_NINF, FLOAT_INF}; - test.AddInput("X", input_dim, input); - - std::vector output_dim(input_dim); - test.AddOutput("Y", output_dim, {false, false, false, false, true, false}); - test.Run(); +TEST(IsInfTest, test_isinf_double20) { + std::initializer_list input = {-1.2, DOUBLE_NAN, DOUBLE_INF, 2.8, DOUBLE_NINF, DOUBLE_INF}; + std::initializer_list output = {false, false, true, false, true, true}; + run_is_inf_test(20, 1, 1, input, output); } -TEST(IsInfTest, test_isinf_negative_double) { - OpTester test("IsInf", 10); - test.AddAttribute("detect_positive", 0); - - std::vector input_dim{6}; - std::vector input = {-1.7, DOUBLE_NAN, DOUBLE_INF, 3.6, DOUBLE_NINF, DOUBLE_INF}; - test.AddInput("X", input_dim, input); - - std::vector output_dim(input_dim); - test.AddOutput("Y", output_dim, {false, false, false, false, true, false}); - test.Run(); +TEST(IsInfTest, test_isinf_positive_float10) { + std::initializer_list input = {-1.7f, FLOAT_NAN, FLOAT_INF, 3.6f, FLOAT_NINF, FLOAT_INF}; + std::initializer_list output = {false, false, true, false, false, true}; + run_is_inf_test(10, 1, 0, input, output); } +TEST(IsInfTest, test_isinf_positive_float20) { + std::initializer_list input = {-1.7f, FLOAT_NAN, FLOAT_INF, 3.6f, FLOAT_NINF, FLOAT_INF}; + std::initializer_list output = {false, false, true, false, false, true}; + run_is_inf_test(20, 1, 0, input, output); +} + +TEST(IsInfTest, test_isinf_positive_double10) { + std::initializer_list input = {-1.7, DOUBLE_NAN, DOUBLE_INF, 3.6, DOUBLE_NINF, DOUBLE_INF}; + std::initializer_list output = {false, false, true, false, false, true}; + run_is_inf_test(10, 1, 0, input, output); +} + +TEST(IsInfTest, test_isinf_positive_double20) { + std::initializer_list input = {-1.7, DOUBLE_NAN, DOUBLE_INF, 3.6, DOUBLE_NINF, DOUBLE_INF}; + std::initializer_list output = {false, false, true, false, false, true}; + run_is_inf_test(20, 1, 0, input, output); +} + +TEST(IsInfTest, test_isinf_negative_float10) { + std::initializer_list input = {-1.7f, FLOAT_NAN, FLOAT_INF, 3.6f, FLOAT_NINF, FLOAT_INF}; + std::initializer_list output = {false, false, false, false, true, false}; + run_is_inf_test(10, 0, 1, input, output); +} + +TEST(IsInfTest, test_isinf_negative_float20) { + std::initializer_list input = {-1.7f, FLOAT_NAN, FLOAT_INF, 3.6f, FLOAT_NINF, FLOAT_INF}; + std::initializer_list output = {false, false, false, false, true, false}; + run_is_inf_test(20, 0, 1, input, output); +} + +TEST(IsInfTest, test_isinf_negative_double10) { + std::initializer_list input = {-1.7, DOUBLE_NAN, DOUBLE_INF, 3.6, DOUBLE_NINF, DOUBLE_INF}; + std::initializer_list output = {false, false, false, false, true, false}; + run_is_inf_test(10, 0, 1, input, output); +} + +TEST(IsInfTest, test_isinf_negative_double20) { + std::initializer_list input = {-1.7, DOUBLE_NAN, DOUBLE_INF, 3.6, DOUBLE_NINF, DOUBLE_INF}; + std::initializer_list output = {false, false, false, false, true, false}; + run_is_inf_test(20, 0, 1, input, output); +} + +#if !defined(DISABLE_FLOAT8_TYPES) +TEST(IsInfTest, test_Float8E4M3FN) { + std::initializer_list input = { + Float8E4M3FN(-1.0f), Float8E4M3FN(FLOAT_NAN, false), Float8E4M3FN(1.0f), Float8E4M3FN(FLOAT_NINF, false), Float8E4M3FN(FLOAT_NINF, false), Float8E4M3FN(FLOAT_INF, false)}; + std::initializer_list output = {false, false, false, false, false, false}; + run_is_inf_test(20, 1, 1, input, output); +} + +TEST(IsInfTest, test_Float8E4M3FNUZ) { + std::initializer_list input = { + Float8E4M3FNUZ(-1.0f), Float8E4M3FNUZ(FLOAT_NAN, false), Float8E4M3FNUZ(1.0f), Float8E4M3FNUZ(FLOAT_NINF, false), Float8E4M3FNUZ(FLOAT_NINF, false), Float8E4M3FNUZ(FLOAT_INF, false)}; + std::initializer_list output = {false, false, false, false, false, false}; + run_is_inf_test(20, 1, 1, input, output); +} + +TEST(IsInfTest, test_Float8E5M2_detect_both) { + std::initializer_list input = { + Float8E5M2(-1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(FLOAT_NAN, false), Float8E5M2(FLOAT_INF, false)}; + std::initializer_list output = {false, true, false, true, false, true}; + run_is_inf_test(20, 1, 1, input, output); +} + +TEST(IsInfTest, test_Float8E5M2_detect_positive) { + std::initializer_list input = { + Float8E5M2(-1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(FLOAT_NAN, false), Float8E5M2(FLOAT_INF, false)}; + std::initializer_list output = {false, false, false, false, false, true}; + run_is_inf_test(20, 1, 0, input, output); +} + +TEST(IsInfTest, test_Float8E5M2_detect_negative) { + std::initializer_list input = { + Float8E5M2(-1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(FLOAT_NAN, false), Float8E5M2(FLOAT_INF, false)}; + std::initializer_list output = {false, true, false, true, false, false}; + run_is_inf_test(20, 0, 1, input, output); +} + +TEST(IsInfTest, test_Float8E5M2_none) { + std::initializer_list input = { + Float8E5M2(-1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(1.0f), Float8E5M2(FLOAT_NINF, false), Float8E5M2(FLOAT_NAN, false), Float8E5M2(FLOAT_INF, false)}; + std::initializer_list output = {false, false, false, false, false, false}; + run_is_inf_test(20, 0, 0, input, output); +} + +TEST(IsInfTest, test_Float8E5M2FNUZ) { + std::initializer_list input = { + Float8E5M2FNUZ(-1.0f), Float8E5M2FNUZ(FLOAT_NINF, false), Float8E5M2FNUZ(1.0f), Float8E5M2FNUZ(FLOAT_NINF, false), Float8E5M2FNUZ(FLOAT_NAN, false), Float8E5M2FNUZ(FLOAT_INF, false)}; + std::initializer_list output = {false, false, false, false, false, false}; + run_is_inf_test(20, 1, 1, input, output); +} +#endif } // namespace test } // namespace onnxruntime diff --git a/onnxruntime/test/providers/cpu/tensor/isnan_test.cc b/onnxruntime/test/providers/cpu/tensor/isnan_test.cc index 0dffc452b5..0f1e5c07cd 100644 --- a/onnxruntime/test/providers/cpu/tensor/isnan_test.cc +++ b/onnxruntime/test/providers/cpu/tensor/isnan_test.cc @@ -9,29 +9,84 @@ namespace onnxruntime { namespace test { -TEST(IsNaNOpTest, IsNaNFloat) { - OpTester test("IsNaN", 9, kOnnxDomain); - std::vector dims{2, 2}; - test.AddInput("X", dims, {1.0f, NAN, 2.0f, NAN}); - test.AddOutput("Y", dims, {false, true, false, true}); +template +void run_is_nan_test(int opset, const std::vector& dims, const std::initializer_list& input, const std::initializer_list& output) { + OpTester test("IsNaN", opset, kOnnxDomain); + test.AddInput("X", dims, input); + test.AddOutput("Y", dims, output); test.Run(); } -TEST(IsNaNOpTest, IsNaNFloat16) { - OpTester test("IsNaN", 9, kOnnxDomain); +TEST(IsNaNOpTest, IsNaNFloat9) { std::vector dims{2, 2}; - test.AddInput("X", dims, std::initializer_list({MLFloat16(1.0f), MLFloat16::NaN, MLFloat16(2.0f), MLFloat16::NaN})); - test.AddOutput("Y", dims, {false, true, false, true}); - test.Run(); + std::initializer_list input = {1.0f, NAN, 2.0f, NAN}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(9, dims, input, output); } -TEST(IsNaNOpTest, IsNaNDouble) { - OpTester test("IsNaN", 9, kOnnxDomain); +TEST(IsNaNOpTest, IsNaNFloat20) { std::vector dims{2, 2}; - test.AddInput("X", dims, {1.0, NAN, 2.0, NAN}); - test.AddOutput("Y", dims, {false, true, false, true}); - test.Run(); + std::initializer_list input = {1.0f, NAN, 2.0f, NAN}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); } +TEST(IsNaNOpTest, IsNaNFloat16_9) { + std::vector dims{2, 2}; + std::initializer_list input = {MLFloat16(1.0f), MLFloat16::NaN, MLFloat16(2.0f), MLFloat16::NaN}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(9, dims, input, output); +} + +TEST(IsNaNOpTest, IsNaNFloat16_20) { + std::vector dims{2, 2}; + std::initializer_list input = {MLFloat16(1.0f), MLFloat16::NaN, MLFloat16(2.0f), MLFloat16::NaN}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); +} + +TEST(IsNaNOpTest, IsNaNDouble9) { + std::vector dims{2, 2}; + std::initializer_list input = {1.0, NAN, 2.0, NAN}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(9, dims, input, output); +} + +TEST(IsNaNOpTest, IsNaNDouble20) { + std::vector dims{2, 2}; + std::initializer_list input = {1.0, NAN, 2.0, NAN}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); +} + +#if !defined(DISABLE_FLOAT8_TYPES) +TEST(IsNaNOpTest, IsNaNFloat8E4M3FN) { + std::vector dims{2, 2}; + std::initializer_list input = {Float8E4M3FN(1.0f), Float8E4M3FN(-NAN), Float8E4M3FN(2.0f), Float8E4M3FN(NAN)}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); +} + +TEST(IsNaNOpTest, IsNaN_Float8E4M3FNUZ) { + std::vector dims{2, 2}; + std::initializer_list input = {Float8E4M3FNUZ(1.0f), Float8E4M3FNUZ(-NAN), Float8E4M3FNUZ(2.0f), Float8E4M3FNUZ(-NAN)}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); +} + +TEST(IsNaNOpTest, IsNaNFloat8E5M2) { + std::vector dims{2, 2}; + std::initializer_list input = {Float8E5M2(1.0f), Float8E5M2(-NAN), Float8E5M2(2.0f), Float8E5M2(NAN)}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); +} + +TEST(IsNaNOpTest, IsNaN_Float8E5M2FNUZ) { + std::vector dims{2, 2}; + std::initializer_list input = {Float8E5M2FNUZ(1.0f), Float8E5M2FNUZ(-NAN), Float8E5M2FNUZ(2.0f), Float8E5M2FNUZ(NAN)}; + std::initializer_list output = {false, true, false, true}; + run_is_nan_test(20, dims, input, output); +} +#endif } // namespace test } // namespace onnxruntime diff --git a/onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc b/onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc index b3161a42bb..44db7c0078 100644 --- a/onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc +++ b/onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc @@ -283,12 +283,6 @@ "^test_dft_axis", "^test_dft", "^test_dft_inverse", - "^test_isinf", - "^test_isinf_float16", - "^test_isinf_negative", - "^test_isinf_positive", - "^test_isnan", - "^test_isnan_float16", "^test_reduce_max_bool_inputs", "^test_reduce_min_bool_inputs", "^test_reduce_min_empty_set",