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To support size opset 19 (#15689)

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liqun Fu 2023-08-11 14:48:53 -07:00 committed by GitHub
parent 14a8315f10
commit 6697635b91
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4 changed files with 26 additions and 6 deletions
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3
docs/OperatorKernels.md
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@ -350,7 +350,8 @@ Do not modify directly.*
|SimplifiedLayerNormalization|*in* X:**T**<br> *in* scale:**V**<br> *out* Y:**V**<br> *out* inv_std_var:**U**|1+|**T** = tensor(double), tensor(float)<br/> **U** = tensor(double), tensor(float)<br/> **V** = tensor(double), tensor(float)|
|Sin|*in* input:**T**<br> *out* output:**T**|7+|**T** = tensor(double), tensor(float)|
|Sinh|*in* input:**T**<br> *out* output:**T**|9+|**T** = tensor(float)|
|Size|*in* data:**T**<br> *out* size:**T1**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|Size|*in* data:**T**<br> *out* size:**T1**|19+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||[13, 18]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|||[1, 12]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **T1** = tensor(int64)|
|Slice|*in* data:**T**<br> *in* starts:**Tind**<br> *in* ends:**Tind**<br> *in* axes:**Tind**<br> *in* steps:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|13+|**T** = 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)<br/> **Tind** = tensor(int32), tensor(int64)|
|||[11, 12]|**T** = 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)<br/> **Tind** = tensor(int32), tensor(int64)|

6
onnxruntime/core/providers/cpu/cpu_execution_provider.cc
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@ -581,7 +581,7 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 18, int8_t, QuantizeLinear);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sigmoid);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sign);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Size);
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 18, Size);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Sum);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Sum);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Flatten);
@ -917,6 +917,7 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Op
#endif
// Opset 19
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, Size);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, AveragePool);
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, MLFloat16, AveragePool);
@ -1815,7 +1816,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Gemm)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Gemm)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sign)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Size)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 18, Size)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Sum)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Sum)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Sigmoid)>,
@ -2332,6 +2333,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
#endif
// Opset 19
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, Size)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, AveragePool)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, Cast)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 19, uint8_t,

21
onnxruntime/core/providers/cpu/tensor/size.cc
View file

@ -45,9 +45,28 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int64_t>()),
Size);
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
Size,
13, 18,
KernelDefBuilder().TypeConstraint("T",
std::vector<MLDataType>({DataTypeImpl::GetTensorType<float>(),
DataTypeImpl::GetTensorType<double>(),
DataTypeImpl::GetTensorType<int8_t>(),
DataTypeImpl::GetTensorType<int16_t>(),
DataTypeImpl::GetTensorType<int32_t>(),
DataTypeImpl::GetTensorType<int64_t>(),
DataTypeImpl::GetTensorType<uint8_t>(),
DataTypeImpl::GetTensorType<uint16_t>(),
DataTypeImpl::GetTensorType<uint32_t>(),
DataTypeImpl::GetTensorType<uint64_t>(),
DataTypeImpl::GetTensorType<std::string>(),
DataTypeImpl::GetTensorType<bool>()}))
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int64_t>()),
Size);
ONNX_CPU_OPERATOR_KERNEL(
Size,
13,
19,
KernelDefBuilder().TypeConstraint("T",
std::vector<MLDataType>({DataTypeImpl::GetTensorType<float>(),
DataTypeImpl::GetTensorType<double>(),

2
onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc vendored
View file

@ -196,8 +196,6 @@
"^test_reshape_reordered_last_dims*",
"^test_reshape_zero_and_negative_dim*",
"^test_reshape_zero_dim*",
"^test_size*",
"^test_size_example*",
// TODO: fialures with Windows GPU CI Pipeline that are introduced with ONNX opset 19. Need to be fixed before ORT 1.15 release.
"^test_averagepool_*",
"^test_wrap_pad_cuda",