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Add OpSet 13 Registrations (#5426)
Register Sigmoid for OpSet13 Register OpSet 13 for Sum, Min, Max, Mean. Add Erf OpSet 13 registration. Register Clip for OpSet 13 Add Gemm/MatMul Opset 13 resigstartions Signed-off-by: Dmitri Smirnov <dmitrism@microsoft.com>
This commit is contained in:
Dmitri Smirnov
GitHub
parent
3a9a1a4ef1
commit
9642f1448e
6 changed files with 160 additions and 78 deletions
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@ -40,7 +40,7 @@ REGISTER_UNARY_ELEMENTWISE_KERNEL(HardSigmoid, 6);
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REGISTER_UNARY_ELEMENTWISE_KERNEL(LeakyRelu, 6);
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REGISTER_UNARY_ELEMENTWISE_KERNEL(Relu, 6);
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REGISTER_UNARY_ELEMENTWISE_KERNEL(Selu, 6);
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REGISTER_UNARY_ELEMENTWISE_KERNEL(Sigmoid, 6);
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REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL_ALIAS(Sigmoid, Sigmoid, 6, 12, 13)
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REGISTER_UNARY_ELEMENTWISE_KERNEL(Softplus, 1);
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REGISTER_UNARY_ELEMENTWISE_KERNEL(Softsign, 1);
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REGISTER_VERSIONED_UNARY_ELEMENTWISE_KERNEL_ALIAS(Tanh, Tanh, 6, 12, 13);
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@ -35,7 +35,8 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Har
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, LeakyRelu);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Relu);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Selu);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Sigmoid);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, Sigmoid);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sigmoid);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softplus);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softsign);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, Tanh);
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@ -87,7 +88,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, double, Exp);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, float, Log);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7, float, Sum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, float, Sum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 12, float, Sum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7, float, Min);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 11, Min);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7, float, Max);
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@ -106,7 +107,7 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 10, float, Equal);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 10, double, Equal);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7, float, Mean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, float, Mean);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 12, float, Mean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, float, Sin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, double, Sin);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Cos);
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@ -232,7 +233,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, MLFloat16, IsNaN);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, Sign);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Shrink);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, Erf);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, float, Erf);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, int64_t_int64_t_int64_t, OneHot);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, float_int64_t_int64_t, OneHot);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, int64_t_string_int64_t, OneHot);
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@ -265,10 +266,10 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, uint8_t, Where);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, Flatten);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, Gemm);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, MatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, double, MatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int32_t, MatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t, MatMul);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, float, MatMul);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, double, MatMul);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int32_t, MatMul);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int64_t, MatMul);
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// Opset 10
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, StringNormalizer);
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@ -375,7 +376,7 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Se
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, ConcatFromSequence);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, SplitToSequence);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, ScatterND);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Gemm);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, Gemm);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, GatherElements);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t, BitShift);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint32_t, BitShift);
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@ -408,10 +409,10 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float, ArgMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, int32_t, ArgMin);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Clip);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Clip);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Min);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Max);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Min);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Max);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MaxPool);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Pow);
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@ -430,8 +431,22 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, GatherND);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Einsum);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sign);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Unsqueeze);
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// REVIEW(codemzs): ConstEigenVectorArrayMap.cast<MLFLoat16) does not seem to be supported.
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// However these types work on GPU implementation.
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//class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_MLFloat16, Dropout);
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//class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_float, Dropout);
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//class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_double, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float_float, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float_double, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double_float, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double_double, Dropout);
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// opset 13
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Erf);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Cast);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Clip);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint8_t, DequantizeLinear);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int8_t, DequantizeLinear);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Expand);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Expand);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int8_t, Expand);
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@ -444,25 +459,20 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint64_t, Expand);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, bool, Expand);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, MLFloat16, Expand);
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// REVIEW(codemzs): ConstEigenVectorArrayMap.cast<MLFLoat16) does not seem to be supported.
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// However these types work on GPU implementation.
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//class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_MLFloat16, Dropout);
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//class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_float, Dropout);
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//class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_double, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float_float, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float_double, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double_float, Dropout);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double_double, Dropout);
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// opset 13
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Size);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Cast);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sign);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint8_t, DequantizeLinear);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int8_t, DequantizeLinear);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Gemm);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, MatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, MatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int32_t, MatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int64_t, MatMul);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Min);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Max);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Mean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint8_t, QuantizeLinear);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int8_t, QuantizeLinear);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sign);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Size);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Sum);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Unsqueeze);
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template <>
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KernelCreateInfo BuildKernelCreateInfo<void>() {
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@ -480,7 +490,8 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, LeakyRelu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Relu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Selu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Sigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, Sigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softplus)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, Softsign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 12, Tanh)>,
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@ -554,7 +565,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, float, Log)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7,
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float, Sum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, float, Sum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 12, float, Sum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7,
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float, Min)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 11, Min)>,
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@ -585,7 +596,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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double, Equal)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 7,
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float, Mean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, float, Mean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 8, 12, float, Mean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, float, Sin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, double, Sin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Cos)>,
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@ -790,7 +801,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12,
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Sign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, Shrink)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float, Erf)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, float, Erf)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10,
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int64_t_int64_t_int64_t, OneHot)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10,
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@ -849,13 +860,13 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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Flatten)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10,
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Gemm)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, float,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, float,
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MatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, double,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, double,
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MatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int32_t,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int32_t,
|
||||
MatMul)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, int64_t,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int64_t,
|
||||
MatMul)>,
|
||||
|
||||
// Opset 10
|
||||
|
|
@ -980,7 +991,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
ConcatFromSequence)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, SplitToSequence)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, ScatterND)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Gemm)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, Gemm)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, GatherElements)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t,
|
||||
BitShift)>,
|
||||
|
|
@ -1090,11 +1101,11 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, int32_t,
|
||||
ArgMin)>,
|
||||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Clip)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Clip)>,
|
||||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Min)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Min)>,
|
||||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Max)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, Max)>,
|
||||
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Pow)>,
|
||||
|
||||
|
|
@ -1149,6 +1160,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
Expand)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, MLFloat16,
|
||||
Expand)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Erf)>,
|
||||
// REVIEW(codemzs): ConstEigenVectorArrayMap.cast<MLFLoat16) does not seem to be supported.
|
||||
// However these types work on GPU implementation.
|
||||
//BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_MLFloat16, Dropout)>,
|
||||
|
|
@ -1160,9 +1172,23 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
|
|||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double_double, Dropout)>,
|
||||
|
||||
// opset 13
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Size)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Cast)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Clip)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float,
|
||||
MatMul)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double,
|
||||
MatMul)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int32_t,
|
||||
MatMul)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int64_t,
|
||||
MatMul)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Min)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Max)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Mean)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Gemm)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Sign)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Size)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Sum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint8_t,
|
||||
DequantizeLinear)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int8_t,
|
||||
|
|
|
|||
|
|
@ -14,33 +14,37 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
KernelDefBuilder().MayInplace(0, 0).TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Clip_6<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Clip,
|
||||
11,
|
||||
11,
|
||||
KernelDefBuilder().MayInplace(0, 0).TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Clip);
|
||||
#define REG_KERNEL_VERSIONED_NONTEMPL(OP_TYPE, START_VER, END_VER, KERNEL_CLASS, ...) \
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(OP_TYPE, \
|
||||
START_VER, \
|
||||
END_VER, \
|
||||
KernelDefBuilder() \
|
||||
.MayInplace(0, 0) \
|
||||
.TypeConstraint("T", BuildKernelDefConstraints<__VA_ARGS__>()), \
|
||||
KERNEL_CLASS);
|
||||
|
||||
#define REG_KERNEL_NONTEMPL(OP_TYPE, VERSION, KERNEL_CLASS, ...) \
|
||||
ONNX_CPU_OPERATOR_KERNEL( \
|
||||
OP_TYPE, \
|
||||
VERSION, \
|
||||
KernelDefBuilder() \
|
||||
.MayInplace(0, 0) \
|
||||
ONNX_CPU_OPERATOR_KERNEL( \
|
||||
OP_TYPE, \
|
||||
VERSION, \
|
||||
KernelDefBuilder() \
|
||||
.MayInplace(0, 0) \
|
||||
.TypeConstraint("T", BuildKernelDefConstraints<__VA_ARGS__>()), \
|
||||
KERNEL_CLASS);
|
||||
|
||||
REG_KERNEL_NONTEMPL(Clip, 12, Clip, float, double, int8_t, uint8_t, int64_t, uint64_t);
|
||||
REG_KERNEL_VERSIONED_NONTEMPL(Clip, 11, 11, Clip, float);
|
||||
REG_KERNEL_VERSIONED_NONTEMPL(Clip, 12, 12, Clip, float, double, int8_t, uint8_t, int64_t, uint64_t);
|
||||
REG_KERNEL_NONTEMPL(Clip, 13, Clip, float, double, int8_t, uint8_t, int64_t, uint64_t);
|
||||
|
||||
template<typename T>
|
||||
template <typename T>
|
||||
Status Clip_6<T>::Compute(OpKernelContext* ctx) const {
|
||||
const auto* X = ctx->Input<Tensor>(0);
|
||||
Tensor* Y = ctx->Output(0, X->Shape());
|
||||
EigenVectorMap<T>(Y->template MutableData<T>(), Y->Shape().Size()) =
|
||||
ConstEigenVectorMap<T>(X->template Data<T>(), X->Shape().Size())
|
||||
.cwiseMax(this->min_)
|
||||
.cwiseMin(this->max_);
|
||||
return Status::OK();
|
||||
const auto* X = ctx->Input<Tensor>(0);
|
||||
Tensor* Y = ctx->Output(0, X->Shape());
|
||||
EigenVectorMap<T>(Y->template MutableData<T>(), Y->Shape().Size()) =
|
||||
ConstEigenVectorMap<T>(X->template Data<T>(), X->Shape().Size())
|
||||
.cwiseMax(this->min_)
|
||||
.cwiseMin(this->max_);
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
|
|
|
|||
|
|
@ -134,15 +134,21 @@ REG_ELEMENTWISE_TYPED_KERNEL(Exp, 6, double, Exp);
|
|||
REG_ELEMENTWISE_TYPED_KERNEL(Log, 6, float, Log);
|
||||
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Sum, 6, 7, float, Sum_6);
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(Sum, 8, float, Sum_8);
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Sum, 8, 12, float, Sum_8);
|
||||
// Supposed to add BFloat16 but we are not supporting now, however, separate registration
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(Sum, 13, float, Sum_8);
|
||||
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Max, 6, 7, float, Max_6);
|
||||
REG_ELEMENTWISE_VERSIONED_KERNEL_NONT(Max, 8, 11, Max_8, float, double);
|
||||
REG_ELEMENTWISE_KERNEL_NONT(Max, 12, Max_8, float, double, MLFloat16, int32_t, uint32_t, int64_t, uint64_t);
|
||||
REG_ELEMENTWISE_VERSIONED_KERNEL_NONT(Max, 12, 12, Max_8, float, double, MLFloat16, int32_t, uint32_t, int64_t, uint64_t);
|
||||
// Supposed to add BFloat16 but we are not supporting now, however, separate registration
|
||||
REG_ELEMENTWISE_KERNEL_NONT(Max, 13, Max_8, float, double, MLFloat16, int32_t, uint32_t, int64_t, uint64_t);
|
||||
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Min, 6, 7, float, Min_6);
|
||||
REG_ELEMENTWISE_VERSIONED_KERNEL_NONT(Min, 8, 11, Min_8, float);
|
||||
REG_ELEMENTWISE_KERNEL_NONT(Min, 12, Min_8, float, double, MLFloat16, int32_t, uint32_t, int64_t, uint64_t);
|
||||
REG_ELEMENTWISE_VERSIONED_KERNEL_NONT(Min, 12, 12, Min_8, float, double, MLFloat16, int32_t, uint32_t, int64_t, uint64_t);
|
||||
// Supposed to add BFloat16 but we are not supporting now, however, separate registration
|
||||
REG_ELEMENTWISE_KERNEL_NONT(Min, 13, Min_8, float, double, MLFloat16, int32_t, uint32_t, int64_t, uint64_t);
|
||||
|
||||
REG_ELEMENTWISE_LOGICALOP_VERSIONED_TYPED_KERNEL(Less, 7, 8, float, Less);
|
||||
REG_ELEMENTWISE_LOGICALOP_VERSIONED_TYPED_KERNEL(Less, 7, 8, double, Less);
|
||||
|
|
@ -170,14 +176,18 @@ REG_ELEMENTWISE_LOGICALOP_TYPED_KERNEL(Equal, 11, float, Equal);
|
|||
REG_ELEMENTWISE_LOGICALOP_TYPED_KERNEL(Equal, 11, double, Equal);
|
||||
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Mean, 6, 7, float, Mean_6);
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(Mean, 8, float, Mean_8);
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Mean, 8, 12, float, Mean_8);
|
||||
// Supposed to add BFloat16 but we are not supporting now, however, separate registration
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(Mean, 13, float, Mean_8);
|
||||
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(BitShift, 11, uint8_t, BitShift);
|
||||
//REG_ELEMENTWISE_TYPED_KERNEL(BitShift, 11, uint16_t, BitShift);
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(BitShift, 11, uint32_t, BitShift);
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(BitShift, 11, uint64_t, BitShift);
|
||||
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(Erf, 9, float, Erf);
|
||||
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Erf, 9, 12, float, Erf);
|
||||
// Supposed to add BFloat16 but we are not supporting now, however, separate registration
|
||||
REG_ELEMENTWISE_TYPED_KERNEL(Erf, 13, float, Erf);
|
||||
|
||||
// REG_ELEMENTWISE_LOGICALOP_TYPED_KERNEL(Not, 1, bool, Not);
|
||||
// REG_ELEMENTWISE_LOGICALOP_TYPED_KERNEL(And, 7, bool, And);
|
||||
|
|
|
|||
|
|
@ -15,15 +15,23 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
|||
// opset 9 added support for additional types (int32, uint32, int64, uint64), however we haven't enabled those yet.
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Gemm,
|
||||
9,
|
||||
9,
|
||||
10,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Gemm<float>);
|
||||
|
||||
// opset 11 made bias input 'C' optional
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
|
||||
Gemm,
|
||||
11,
|
||||
12,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Gemm<float>);
|
||||
|
||||
// opset 13 Adds BFloat16 support but we are not supporting it yet
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
Gemm,
|
||||
13,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
Gemm<float>);
|
||||
} // namespace onnxruntime
|
||||
|
|
|
|||
|
|
@ -24,34 +24,68 @@ ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
|||
MatMul<double>);
|
||||
|
||||
// opset 9 supports more types
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
12,
|
||||
float,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
MatMul<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
12,
|
||||
double,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()),
|
||||
MatMul<double>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
12,
|
||||
int32_t,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", BuildKernelDefConstraints<int32_t, uint32_t>()),
|
||||
MatMul<int32_t>);
|
||||
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
12,
|
||||
int64_t,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", BuildKernelDefConstraints<int64_t, uint64_t>()),
|
||||
MatMul<int64_t>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
MatMul,
|
||||
13,
|
||||
float,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
|
||||
MatMul<float>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
13,
|
||||
double,
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()),
|
||||
MatMul<double>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
13,
|
||||
int32_t,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", {DataTypeImpl::GetTensorType<int32_t>(), DataTypeImpl::GetTensorType<uint32_t>()}),
|
||||
.TypeConstraint("T", BuildKernelDefConstraints<int32_t, uint32_t>()),
|
||||
MatMul<int32_t>);
|
||||
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL(
|
||||
MatMul,
|
||||
9,
|
||||
13,
|
||||
int64_t,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T", {DataTypeImpl::GetTensorType<int64_t>(), DataTypeImpl::GetTensorType<uint64_t>()}),
|
||||
.TypeConstraint("T", BuildKernelDefConstraints<int64_t, uint64_t>()),
|
||||
MatMul<int64_t>);
|
||||
|
||||
template <typename T>
|
||||
|
|
|
|||
Loading…
Reference in a new issue