mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-29 20:14:01 +00:00
Opset-11 support (negative axis) for reduce ops (#1929)
This commit is contained in:
shahasad
GitHub
parent
f9bf546e3c
commit
103b92889e
9 changed files with 6401 additions and 5495 deletions
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@ -123,31 +123,31 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 2, Glo
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalAveragePool);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalMaxPool);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, MaxRoiPool);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceL1);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceL1);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceL2);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceL2);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceLogSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceLogSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceLogSumExp);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceLogSumExp);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int64_t, ReduceMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceMean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceMean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int64_t, ReduceMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceProd);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceProd);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, double, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int64_t, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceSumSquare);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, double, ReduceSumSquare);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceSumSquare);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceL1);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceL1);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceL2);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceL2);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceLogSum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceLogSum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceLogSumExp);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceLogSumExp);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceMax);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceMax);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int64_t, ReduceMax);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceMean);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceMean);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceMin);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceMin);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int64_t, ReduceMin);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceProd);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceProd);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceSum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceSum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, double, ReduceSum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int64_t, ReduceSum);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceSumSquare);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceSumSquare);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, double, ReduceSumSquare);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ArgMax);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ArgMax);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ArgMin);
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@ -322,6 +322,32 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ArgMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ArgMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ArgMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceL1);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceL1);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceL2);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceL2);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceLogSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceLogSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceLogSumExp);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceLogSumExp);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, ReduceMax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceMean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceMean);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, ReduceMin);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceProd);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceProd);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, ReduceSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceSumSquare);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, ReduceSumSquare);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceSumSquare);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Hardmax);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, LogSoftmax);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Softmax);
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@ -467,31 +493,56 @@ void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalAveragePool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, GlobalMaxPool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, MaxRoiPool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceL1)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceL1)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceL2)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceL2)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceLogSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceLogSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceLogSumExp)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceLogSumExp)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int64_t, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceMean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceMean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int64_t, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceProd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceProd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, double, ReduceSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int64_t, ReduceSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, float, ReduceSumSquare)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, int32_t, ReduceSumSquare)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, double, ReduceSumSquare)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceL1)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceL1)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceL1)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceL1)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceL2)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceL2)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceL2)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceL2)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceLogSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceLogSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceLogSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceLogSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceLogSumExp)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceLogSumExp)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceLogSumExp)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceLogSumExp)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int64_t, ReduceMax)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceMean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceMean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceMean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceMean)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int64_t, ReduceMin)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceProd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceProd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceProd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceProd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceSum)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, ReduceSum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int64_t, ReduceSum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceSum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceSum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, double, ReduceSum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int64_t, ReduceSum)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, int32_t, ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, double, ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, double, ReduceSumSquare)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ArgMax)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, int32_t, ArgMax)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, ArgMin)>,
|
||||
|
|
|
|||
|
|
@ -47,6 +47,15 @@ namespace onnxruntime {
|
|||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()), \
|
||||
x<double>);
|
||||
|
||||
#define REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_DOUBLE_ONLY(x, startVer, endVer) \
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL( \
|
||||
x, \
|
||||
startVer, \
|
||||
endVer, \
|
||||
double, \
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<double>()), \
|
||||
x<double>);
|
||||
|
||||
#define REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(x, sinceVersion) \
|
||||
ONNX_CPU_OPERATOR_TYPED_KERNEL( \
|
||||
x, \
|
||||
|
|
@ -55,21 +64,56 @@ namespace onnxruntime {
|
|||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<int64_t>()), \
|
||||
x<int64_t>);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceL1, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceL2, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceLogSum, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceLogSumExp, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceMax, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(ReduceMax, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceMean, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceMin, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(ReduceMin, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceProd, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceSum, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(ReduceSum, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_DOUBLE_ONLY(ReduceSum, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceSumSquare, 1);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_DOUBLE_ONLY(ReduceSumSquare, 1);
|
||||
#define REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_INT64_ONLY(x, startVer, endVer) \
|
||||
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL( \
|
||||
x, \
|
||||
startVer, \
|
||||
endVer, \
|
||||
int64_t, \
|
||||
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<int64_t>()), \
|
||||
x<int64_t>);
|
||||
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceL1, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceL1, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceL2, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceL2, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceLogSum, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceLogSum, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceLogSumExp, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceLogSumExp, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceMax, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceMax, 11);
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_INT64_ONLY(ReduceMax, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(ReduceMax, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceMean, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceMean, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceMin, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceMin, 11);
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_INT64_ONLY(ReduceMin, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(ReduceMin, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceProd, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceProd, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceSum, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceSum, 11);
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_INT64_ONLY(ReduceSum, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_INT64_ONLY(ReduceSum, 11);
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_DOUBLE_ONLY(ReduceSum, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_DOUBLE_ONLY(ReduceSum, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ReduceSumSquare, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ReduceSumSquare, 11);
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_DOUBLE_ONLY(ReduceSumSquare, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL_DOUBLE_ONLY(ReduceSumSquare, 11);
|
||||
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ArgMax, 1, 10);
|
||||
REGISTER_UNARY_ELEMENTWISE_KERNEL(ArgMax, 11);
|
||||
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ArgMin, 1, 10);
|
||||
|
|
|
|||
|
|
@ -109,7 +109,6 @@ class FuncReduceV {
|
|||
ProtoHelperNodeContext ctx(node);
|
||||
OpNodeProtoHelper<ProtoHelperNodeContext> info(&ctx);
|
||||
axes_ = info.GetAttrsOrDefault<int64_t>("axes");
|
||||
std::sort(axes_.begin(), axes_.end()); //ReduceV requires sorted axes
|
||||
int64_t keepdims_i = 1;
|
||||
ORT_ENFORCE(info.GetAttr("keepdims", &keepdims_i).IsOK());
|
||||
keep_dims_ = (keepdims_i == 1);
|
||||
|
|
@ -124,6 +123,8 @@ class FuncReduceV {
|
|||
axes.push_back(HandleNegativeAxis(i, gsl::narrow_cast<int64_t>(X->shape.size())));
|
||||
}
|
||||
|
||||
std::sort(axes.begin(), axes.end()); //ReduceV requires sorted axes
|
||||
|
||||
auto p = VectorWidthAndFuseDimForReduce(natural_vector_(X->dtype.bits()), axes, def_);
|
||||
int vector_width = std::get<0>(p);
|
||||
int fuse_dim = std::get<1>(p);
|
||||
|
|
|
|||
|
|
@ -265,8 +265,7 @@ TEST_F(ExecutionFrameTest, MemPatternTest) {
|
|||
EXPECT_EQ(p->GetBlock(4)->offset_, 64);
|
||||
}
|
||||
|
||||
// TODO: Re-enable after registering a kernel for opset 11 'ReduceSum' op
|
||||
TEST(ExecutionFrameTestWithoutSessionState, DISABLED_BadModelInvalidDimParamUsage) {
|
||||
TEST(ExecutionFrameTestWithoutSessionState, BadModelInvalidDimParamUsage) {
|
||||
// load model with 2 Scan ops that both incorrectly use shapes of { 'None', 'None' } for their outputs.
|
||||
// as 'None' is not a special value it's treated as a variable name, leading to a runtime error when we
|
||||
// attempt to re-use the output from the first Scan node for the second. validate we detect this and error out.
|
||||
|
|
|
|||
|
|
@ -436,25 +436,6 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
|
|||
{"sequence_model2", "SequenceConstruct not implemented yet"},
|
||||
{"sequence_model1", "Sequence* not implemented yet"},
|
||||
{"scatter_elements_with_negative_indices", "ScatterElements(11) not implemented yet"},
|
||||
{"reduce_sum_square_negative_axes_keepdims_random", "ReduceSumSquare(11) not implemented yet"},
|
||||
{"reduce_sum_square_negative_axes_keepdims_example", "ReduceSumSquare(11) not implemented yet"},
|
||||
{"reduce_sum_negative_axes_keepdims_random", "ReduceSum(11) not implemented yet"},
|
||||
{"reduce_sum_negative_axes_keepdims_example", "ReduceSum(11) not implemented yet"},
|
||||
{"reduce_prod_negative_axes_keepdims_random", "ReduceProd(11) not implemented yet"},
|
||||
{"reduce_prod_negative_axes_keepdims_example", "ReduceProd(11) not implemented yet"},
|
||||
{"reduce_min_negative_axes_keepdims_random", "ReduceMin(11) not implemented yet"},
|
||||
{"reduce_min_negative_axes_keepdims_example", "ReduceMin(11) not implemented yet"},
|
||||
{"reduce_mean_negative_axes_keepdims_random", "ReduceMean(11) not implemented yet"},
|
||||
{"reduce_mean_negative_axes_keepdims_example", "ReduceMean(11) not implemented yet"},
|
||||
{"reduce_max_negative_axes_keepdims_random", "ReduceMax(11) not implemented yet"},
|
||||
{"reduce_max_negative_axes_keepdims_example", "ReduceMax(11) not implemented yet"},
|
||||
{"reduce_log_sum_negative_axes", "ReduceLogSum(11) not implemented yet"},
|
||||
{"reduce_log_sum_exp_negative_axes_keepdims_random", "ReduceLogSumExp(11) not implemented yet"},
|
||||
{"reduce_log_sum_exp_negative_axes_keepdims_example", "ReduceLogSumExp(11) not implemented yet"},
|
||||
{"reduce_l2_negative_axes_keep_dims_random", "ReduceL2(11) not implemented yet"},
|
||||
{"reduce_l2_negative_axes_keep_dims_example", "ReduceL2(11) not implemented yet"},
|
||||
{"reduce_l1_negative_axes_keep_dims_random", "ReduceL1(11) not implemented yet"},
|
||||
{"reduce_l1_negative_axes_keep_dims_example", "ReduceL1(11) not implemented yet"},
|
||||
{"onehot_without_axis", "OneHot(11) not implemented yet"},
|
||||
{"onehot_with_negative_axis", "OneHot(11) not implemented yet"},
|
||||
{"onehot_with_axis", "OneHot(11) not implemented yet"},
|
||||
|
|
@ -497,6 +478,25 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
|
|||
broken_tests.insert({"flatten_negative_axis4", "not implemented yet for opset 11"});
|
||||
broken_tests.insert({"squeeze_negative_axes", "not implemented yet for opset 11"});
|
||||
broken_tests.insert({"unsqueeze_negative_axes", "not implemented yet for opset 11"});
|
||||
broken_tests.insert({"reduce_sum_square_negative_axes_keepdims_random", "ReduceSumSquare(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_sum_square_negative_axes_keepdims_example", "ReduceSumSquare(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_sum_negative_axes_keepdims_random", "ReduceSum(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_sum_negative_axes_keepdims_example", "ReduceSum(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_prod_negative_axes_keepdims_random", "ReduceProd(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_prod_negative_axes_keepdims_example", "ReduceProd(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_min_negative_axes_keepdims_random", "ReduceMin(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_min_negative_axes_keepdims_example", "ReduceMin(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_mean_negative_axes_keepdims_random", "ReduceMean(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_mean_negative_axes_keepdims_example", "ReduceMean(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_max_negative_axes_keepdims_random", "ReduceMax(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_max_negative_axes_keepdims_example", "ReduceMax(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_log_sum_negative_axes", "ReduceLogSum(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_log_sum_exp_negative_axes_keepdims_random", "ReduceLogSumExp(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_log_sum_exp_negative_axes_keepdims_example", "ReduceLogSumExp(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_l2_negative_axes_keep_dims_random", "ReduceL2(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_l2_negative_axes_keep_dims_example", "ReduceL2(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_l1_negative_axes_keep_dims_random", "ReduceL1(11) not implemented yet"});
|
||||
broken_tests.insert({"reduce_l1_negative_axes_keep_dims_example", "ReduceL1(11) not implemented yet"});
|
||||
#endif
|
||||
|
||||
#ifdef USE_MKLDNN
|
||||
|
|
|
|||
|
|
@ -10,9 +10,9 @@ namespace onnxruntime {
|
|||
namespace test {
|
||||
|
||||
// Disable TensorRT on some of the tests because the limit in its parser: axis >=0 && axis < nbDims
|
||||
|
||||
template <typename OutT>
|
||||
void TestReduceOp(const std::string& op,
|
||||
int opset_version,
|
||||
const std::vector<int64_t>& input_dims,
|
||||
const std::vector<float>& data,
|
||||
const std::vector<int64_t>& axes,
|
||||
|
|
@ -21,7 +21,7 @@ void TestReduceOp(const std::string& op,
|
|||
const std::vector<OutT>& expected_data)
|
||||
|
||||
{
|
||||
OpTester test(op.c_str());
|
||||
OpTester test(op.c_str(), opset_version);
|
||||
bool has_neg_axis = false;
|
||||
|
||||
if (!axes.empty()) {
|
||||
|
|
@ -29,20 +29,23 @@ void TestReduceOp(const std::string& op,
|
|||
test.AddAttribute("axis", axes[0]);
|
||||
if (axes[0] < 0)
|
||||
has_neg_axis = true;
|
||||
}
|
||||
else
|
||||
} else {
|
||||
test.AddAttribute("axes", axes);
|
||||
for (auto& x : axes) {
|
||||
has_neg_axis = (has_neg_axis || (x < 0));
|
||||
}
|
||||
}
|
||||
}
|
||||
test.AddAttribute("keepdims", keepdims);
|
||||
test.AddInput<float>("data", input_dims, data);
|
||||
test.AddOutput<OutT>("reduced", expected_dims, expected_data);
|
||||
|
||||
std::unordered_set<std::string> excluded_eps = {kCudaExecutionProvider, kTensorrtExecutionProvider}; //TensorRT: result differs
|
||||
std::unordered_set<std::string> excluded_eps = {kCudaExecutionProvider, kTensorrtExecutionProvider}; //TensorRT: result differs
|
||||
if (has_neg_axis) {
|
||||
excluded_eps.insert(kNGraphExecutionProvider); // NGraph EP cannot handle negative axis values
|
||||
excluded_eps.insert(kNGraphExecutionProvider); // NGraph EP cannot handle negative axis values
|
||||
}
|
||||
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_eps);
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_eps);
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReductionVariationTest) {
|
||||
|
|
@ -57,11 +60,15 @@ TEST(ReductionOpTest, ReductionVariationTest) {
|
|||
std::vector<int64_t> expected_values;
|
||||
for (auto v : std::get<2>(a.second))
|
||||
expected_values.push_back(static_cast<int64_t>(v));
|
||||
TestReduceOp<int64_t>(a.first, input_dims, input_data, attributes.axes_, attributes.keep_dims_,
|
||||
TestReduceOp<int64_t>(a.first, 7, input_dims, input_data, attributes.axes_, attributes.keep_dims_,
|
||||
expected_dims, expected_values);
|
||||
TestReduceOp<int64_t>(a.first, 11, input_dims, input_data, attributes.axes_, attributes.keep_dims_,
|
||||
expected_dims, expected_values);
|
||||
} else {
|
||||
const std::vector<float> expected_values = std::get<2>(a.second);
|
||||
TestReduceOp<float>(a.first, input_dims, input_data, attributes.axes_, attributes.keep_dims_,
|
||||
TestReduceOp<float>(a.first, 7, input_dims, input_data, attributes.axes_, attributes.keep_dims_,
|
||||
expected_dims, expected_values);
|
||||
TestReduceOp<float>(a.first, 11, input_dims, input_data, attributes.axes_, attributes.keep_dims_,
|
||||
expected_dims, expected_values);
|
||||
}
|
||||
}
|
||||
|
|
@ -479,7 +486,7 @@ TEST(ReductionOpTest, ReduceMax_int32) {
|
|||
9, 10,
|
||||
11, 12});
|
||||
test.AddOutput<int32_t>("reduced", {3, 1, 1}, {4, 8, 12});
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceMax_int64) {
|
||||
|
|
@ -709,14 +716,14 @@ TEST(ReductionOpTest, ReduceSum_double) {
|
|||
test.AddAttribute("axes", std::vector<int64_t>{0, 2});
|
||||
test.AddAttribute("keepdims", (int64_t)1);
|
||||
test.AddInput<double>("data", {3, 2, 2},
|
||||
{1.0, 2.0,
|
||||
3.0, 4.0,
|
||||
{1.0, 2.0,
|
||||
3.0, 4.0,
|
||||
|
||||
5.0, 6.0,
|
||||
7.0, 8.0,
|
||||
5.0, 6.0,
|
||||
7.0, 8.0,
|
||||
|
||||
9.0, 10.0,
|
||||
11.0, 12.0});
|
||||
9.0, 10.0,
|
||||
11.0, 12.0});
|
||||
test.AddOutput<double>("reduced", {1, 2, 1}, {33.0, 45.0});
|
||||
test.Run();
|
||||
}
|
||||
|
|
@ -772,22 +779,21 @@ TEST(ReductionOpTest, ReduceSum_int32) {
|
|||
test.Run();
|
||||
}
|
||||
|
||||
TEST( ReductionOpTest, ReduceSum_int64 )
|
||||
{
|
||||
OpTester test( "ReduceSum" );
|
||||
test.AddAttribute( "axes", std::vector<int64_t>{0, 2} );
|
||||
test.AddAttribute( "keepdims", ( int64_t ) 1 );
|
||||
test.AddInput<int64_t>( "data", { 3, 2, 2 },
|
||||
{ 1, 2,
|
||||
3, 4,
|
||||
TEST(ReductionOpTest, ReduceSum_int64) {
|
||||
OpTester test("ReduceSum");
|
||||
test.AddAttribute("axes", std::vector<int64_t>{0, 2});
|
||||
test.AddAttribute("keepdims", (int64_t)1);
|
||||
test.AddInput<int64_t>("data", {3, 2, 2},
|
||||
{1, 2,
|
||||
3, 4,
|
||||
|
||||
5, 6,
|
||||
7, 8,
|
||||
5, 6,
|
||||
7, 8,
|
||||
|
||||
9, 10,
|
||||
11, 12 } );
|
||||
test.AddOutput<int64_t>( "reduced", { 1, 2, 1 }, { 33, 45 } );
|
||||
test.Run();
|
||||
9, 10,
|
||||
11, 12});
|
||||
test.AddOutput<int64_t>("reduced", {1, 2, 1}, {33, 45});
|
||||
test.Run();
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ReduceSum_default_axes_keepdims) {
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||||
|
|
@ -866,14 +872,14 @@ TEST(ReductionOpTest, ReduceSumSquare_double) {
|
|||
test.AddAttribute("axes", std::vector<int64_t>{0, 2});
|
||||
test.AddAttribute("keepdims", (int64_t)1);
|
||||
test.AddInput<double>("data", {3, 2, 2},
|
||||
{1.0, 2.0,
|
||||
3.0, 4.0,
|
||||
{1.0, 2.0,
|
||||
3.0, 4.0,
|
||||
|
||||
5.0, 6.0,
|
||||
7.0, 8.0,
|
||||
5.0, 6.0,
|
||||
7.0, 8.0,
|
||||
|
||||
9.0, 10.0,
|
||||
11.0, 12.0});
|
||||
9.0, 10.0,
|
||||
11.0, 12.0});
|
||||
test.AddOutput<double>("reduced", {1, 2, 1}, {247.0, 403.});
|
||||
test.Run();
|
||||
}
|
||||
|
|
@ -1063,7 +1069,7 @@ TEST(ReductionOpTest, ArgMax) {
|
|||
{1, 1,
|
||||
1, 1,
|
||||
1, 1});
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ArgMax_do_not_keepdims) {
|
||||
|
|
@ -1083,7 +1089,7 @@ TEST(ReductionOpTest, ArgMax_do_not_keepdims) {
|
|||
{1, 1,
|
||||
1, 1,
|
||||
1, 1});
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ArgMax_do_not_keepdims_2) {
|
||||
|
|
@ -1134,9 +1140,9 @@ TEST(ReductionOpTest, ArgMax_int32_neg_axis) {
|
|||
{1, 1,
|
||||
1, 1,
|
||||
1, 1});
|
||||
|
||||
std::unordered_set<std::string> excluded_eps = {kNGraphExecutionProvider}; // NGraph EP cannot handle negative axis values
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_eps);
|
||||
|
||||
std::unordered_set<std::string> excluded_eps = {kNGraphExecutionProvider}; // NGraph EP cannot handle negative axis values
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_eps);
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ArgMax2D) {
|
||||
|
|
@ -1149,7 +1155,7 @@ TEST(ReductionOpTest, ArgMax2D) {
|
|||
9.0f, 10.0f});
|
||||
test.AddOutput<int64_t>("reduced", {3, 1},
|
||||
{1, 0, 1});
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
|
||||
}
|
||||
|
||||
TEST(ReductionOpTest, ArgMin) {
|
||||
|
|
@ -1236,8 +1242,8 @@ TEST(ReductionOpTest, ArgMin_int32_neg_axis) {
|
|||
{0, 0,
|
||||
0, 0});
|
||||
|
||||
std::unordered_set<std::string> excluded_eps = {kNGraphExecutionProvider}; // NGraph EP cannot handle negative axis values
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_eps);
|
||||
std::unordered_set<std::string> excluded_eps = {kNGraphExecutionProvider}; // NGraph EP cannot handle negative axis values
|
||||
test.Run(OpTester::ExpectResult::kExpectSuccess, "", excluded_eps);
|
||||
}
|
||||
|
||||
} // namespace test
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -78,7 +78,7 @@ def PrintDisableOptimizations():
|
|||
|
||||
def PrintReenableOptimizations():
|
||||
print ("#if defined(_MSC_VER) || defined(__INTEL_COMPILER)")
|
||||
print ("t#pragma optimize (\"\", on)")
|
||||
print ("\t#pragma optimize (\"\", on)")
|
||||
print ("#elif defined(__GNUC__)")
|
||||
print ("#if defined(__clang__)")
|
||||
print ("\t#pragma clang optimize on")
|
||||
|
|
@ -92,7 +92,7 @@ if __name__ == "__main__":
|
|||
input_shape = [2,3,2,2,3]
|
||||
np.random.seed(0)
|
||||
input_data = np.random.uniform(size=input_shape)
|
||||
axes_options = [(2,3), (2, 1, 4), (0, 2, 3), (0,), (2,), (4,), None]
|
||||
axes_options = [(-1,3), (2,3), (2, 1, 4), (0,-2,-3), (0, 2, 3), (0,), (2,), (4,), None]
|
||||
keepdims_options = [0, 1]
|
||||
ops = ["ReduceL1", "ReduceL2", "ReduceLogSum", "ReduceLogSumExp", "ReduceMax", "ReduceMean",
|
||||
"ReduceMin", "ReduceProd", "ReduceSum", "ReduceSumSquare", "ArgMax", "ArgMin"]
|
||||
|
|
|
|||
|
|
@ -145,7 +145,6 @@ def create_backend_test(testname=None):
|
|||
'^test_scatternd_cpu.*',
|
||||
'^test_sequence_*',
|
||||
'^test_scatter_*',
|
||||
'^test_reduce_*',
|
||||
'^test_onehot_*',
|
||||
'^test_constant_pad_cpu.*',
|
||||
'^test_edge_pad_cpu.*',
|
||||
|
|
|
|||
Loading…
Reference in a new issue