mirror of
https://github.com/saymrwulf/onnxruntime.git
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Simplify QLinearConv registration so type reduction works with it. (#10747)
* Simplify QLinearConv registration so type reduction works with it. * Update QLinearMatMul registration to be a standard typed registration
This commit is contained in:
Scott McKay
GitHub
parent
c2c85dd6b1
commit
6072c6b65e
5 changed files with 115 additions and 104 deletions
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@ -72,9 +72,9 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, DynamicQuantizeMatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, DynamicQuantizeLSTM);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, MatMulIntegerToFloat);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, MatMulIntegerToFloat);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t_int8_t, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, NhwcMaxPool);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, NhwcMaxPool);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, QEmbedLayerNormalization);
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@ -120,7 +120,7 @@ KernelCreateInfo BuildKernelCreateInfo<void>() {
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Status RegisterNchwcKernels(KernelRegistry& kernel_registry) {
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static const BuildKernelCreateInfoFn function_table[] = {
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BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSNchwcDomain, 1, float, ReorderInput)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSNchwcDomain, 1, float, ReorderOutput)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSNchwcDomain, 1, float, Conv)>,
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@ -143,35 +143,35 @@ Status RegisterNchwcKernels(KernelRegistry& kernel_registry) {
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Status RegisterQuantizationKernels(KernelRegistry& kernel_registry) {
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static const BuildKernelCreateInfoFn function_table[] = {
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BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MatMulInteger16)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearGlobalAveragePool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearConcat)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearAveragePool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, DequantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, DequantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QuantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QuantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearLeakyRelu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearLeakyRelu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearSigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearSigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearAdd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearAdd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, QAttention)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, DynamicQuantizeMatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, DynamicQuantizeLSTM)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, MatMulIntegerToFloat)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, MatMulIntegerToFloat)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t_int8_t, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, NhwcMaxPool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, NhwcMaxPool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, QEmbedLayerNormalization)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QGemm)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QGemm)>,
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BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, MatMulInteger16)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearGlobalAveragePool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearConcat)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, QLinearAveragePool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, DequantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, DequantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QuantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QuantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearLeakyRelu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearLeakyRelu)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearSigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearSigmoid)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearAdd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearAdd)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, QAttention)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, DynamicQuantizeMatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, DynamicQuantizeLSTM)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, MatMulIntegerToFloat)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, MatMulIntegerToFloat)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, NhwcMaxPool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, NhwcMaxPool)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, QEmbedLayerNormalization)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, int8_t, QGemm)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, uint8_t, QGemm)>,
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};
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for (auto& function_table_entry : function_table) {
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@ -186,7 +186,7 @@ Status RegisterQuantizationKernels(KernelRegistry& kernel_registry) {
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Status RegisterCpuContribKernels(KernelRegistry& kernel_registry) {
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static const BuildKernelCreateInfoFn function_table[] = {
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BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMSDomain, 1, float, SampleOp)>,
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// add more kernels here
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@ -310,13 +310,13 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12, int32_t, DequantizeLinear);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12, uint8_t, QuantizeLinear);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12, int8_t, QuantizeLinear);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, QLinearMatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, uint8_t, QLinearMatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t, QLinearMatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, uint8_t, MatMulInteger);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t_int8_t, QLinearMatMul);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t, MatMulInteger);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ConvInteger);
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class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t_int8_t, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, uint8_t, QLinearConv);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t, QLinearConv);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, Slice);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 11, Dropout);
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class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10, NonMaxSuppression);
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@ -467,9 +467,9 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, Ei
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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, 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_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, float_float, Dropout);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, float_double, Dropout);
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class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, double_float, Dropout);
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@ -669,7 +669,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Softmax);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Softmax);
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//Opset 14
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// Opset 14
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, float, CumSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, double, CumSum);
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class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, int32_t, CumSum);
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@ -763,7 +763,7 @@ KernelCreateInfo BuildKernelCreateInfo<void>() {
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Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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static const BuildKernelCreateInfoFn function_table[] = {
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BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 10,
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Clip)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Elu)>,
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@ -1225,15 +1225,15 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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QuantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 12, int8_t,
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QuantizeLinear)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, QLinearMatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, uint8_t, QLinearMatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t, QLinearMatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, uint8_t,
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MatMulInteger)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t_int8_t, QLinearMatMul)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t,
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MatMulInteger)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ConvInteger)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t_int8_t, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, uint8_t, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, int8_t, QLinearConv)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 10,
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Slice)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, 11,
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@ -1246,7 +1246,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, double,
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RoiAlign)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ReverseSequence)>,
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//opset 11
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// opset 11
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11,
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Clip)>,
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@ -1515,9 +1515,9 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
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BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Erf)>,
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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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//BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_MLFloat16, Dropout)>,
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//BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_float, Dropout)>,
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//BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_double, Dropout)>,
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// BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_MLFloat16, Dropout)>,
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// BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_float, Dropout)>,
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// BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, MLFloat16_double, Dropout)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, float_float, Dropout)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, float_double, Dropout)>,
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BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, 12, double_float, Dropout)>,
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@ -1959,7 +1959,7 @@ KernelCreateInfo BuildKernelCreateInfo<void>() {
|
|||
|
||||
Status RegisterOnnxMLOperatorKernels(KernelRegistry& kernel_registry) {
|
||||
static const BuildKernelCreateInfoFn function_table[] = {
|
||||
BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
|
||||
BuildKernelCreateInfo<void>, // default entry to avoid the list become empty after ops-reducing
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMLDomain, 1, float,
|
||||
ArrayFeatureExtractor)>,
|
||||
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMLDomain, 1, double,
|
||||
|
|
@ -2073,7 +2073,7 @@ KernelRegistryAndStatus GetCpuKernelRegistry() {
|
|||
|
||||
std::shared_ptr<KernelRegistry> CPUExecutionProvider::GetKernelRegistry() const {
|
||||
static KernelRegistryAndStatus k = GetCpuKernelRegistry();
|
||||
//throw if the registry failed to initialize
|
||||
// throw if the registry failed to initialize
|
||||
ORT_THROW_IF_ERROR(k.st);
|
||||
return k.kernel_registry;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@
|
|||
|
||||
//
|
||||
// TODO!! Hack! need to move this to MLAS
|
||||
//
|
||||
//
|
||||
// We use a different job partition with mobile devices
|
||||
// where the model tends to be smaller. When the entire
|
||||
// weight matrix fits in the cache, we process a thin
|
||||
|
|
@ -216,7 +216,7 @@ class QLinearConv : public OpKernel {
|
|||
packed_W_size_ = MlasSymmQgemmPackBSize(group_output_channels,
|
||||
kernel_dim,
|
||||
std::is_same<ActType, int8_t>::value);
|
||||
|
||||
|
||||
if (packed_W_size_ != 0) {
|
||||
size_t packed_W_data_size = SafeInt<size_t>(group_count) * packed_W_size_;
|
||||
auto* packed_W = static_cast<uint8_t*>(alloc->Alloc(packed_W_data_size));
|
||||
|
|
@ -285,42 +285,12 @@ class QLinearConv : public OpKernel {
|
|||
std::vector<int32_t> column_sums_;
|
||||
};
|
||||
|
||||
ONNX_CPU_OPERATOR_KERNEL(
|
||||
// uint8_t kernel supports weight being either uint8_t or int8_t
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX(
|
||||
QLinearConv,
|
||||
kOnnxDomain,
|
||||
10,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>())
|
||||
.TypeConstraint("T2", {DataTypeImpl::GetTensorType<uint8_t>(), DataTypeImpl::GetTensorType<int8_t>()})
|
||||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<uint8_t>())
|
||||
.TypeConstraint("T4", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
QLinearConv<uint8_t>);
|
||||
|
||||
#define REGISTER_QLINEARCONV_TYPED_KERNEL(domain, version, act_type, weight_type) \
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX( \
|
||||
QLinearConv, \
|
||||
domain, \
|
||||
version, \
|
||||
act_type##_##weight_type, \
|
||||
kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<act_type>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<weight_type>()) \
|
||||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<act_type>()) \
|
||||
.TypeConstraint("T4", DataTypeImpl::GetTensorType<int32_t>()), \
|
||||
QLinearConv<act_type>);
|
||||
|
||||
REGISTER_QLINEARCONV_TYPED_KERNEL(kOnnxDomain, 10, int8_t, int8_t);
|
||||
|
||||
#ifndef DISABLE_CONTRIB_OPS
|
||||
|
||||
namespace contrib {
|
||||
|
||||
// Register an alternate version of this kernel that supports the channels_last
|
||||
// attribute in order to consume and produce NHWC tensors.
|
||||
ONNX_OPERATOR_KERNEL_EX(
|
||||
QLinearConv,
|
||||
kMSDomain,
|
||||
1,
|
||||
uint8_t,
|
||||
kCpuExecutionProvider,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>())
|
||||
|
|
@ -329,7 +299,43 @@ ONNX_OPERATOR_KERNEL_EX(
|
|||
.TypeConstraint("T4", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
QLinearConv<uint8_t>);
|
||||
|
||||
REGISTER_QLINEARCONV_TYPED_KERNEL(kMSDomain, 1, int8_t, int8_t);
|
||||
// int8_t kernel only supports weight being int8_t
|
||||
#define REGISTER_QLINEARCONV_INT8_KERNEL(domain, version) \
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX( \
|
||||
QLinearConv, \
|
||||
domain, \
|
||||
version, \
|
||||
int8_t, \
|
||||
kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int8_t>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<int8_t>()) \
|
||||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<int8_t>()) \
|
||||
.TypeConstraint("T4", DataTypeImpl::GetTensorType<int32_t>()), \
|
||||
QLinearConv<int8_t>);
|
||||
|
||||
REGISTER_QLINEARCONV_INT8_KERNEL(kOnnxDomain, 10);
|
||||
|
||||
#ifndef DISABLE_CONTRIB_OPS
|
||||
|
||||
namespace contrib {
|
||||
|
||||
// Register an alternate version of this kernel that supports the channels_last
|
||||
// attribute in order to consume and produce NHWC tensors.
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX(
|
||||
QLinearConv,
|
||||
kMSDomain,
|
||||
1,
|
||||
uint8_t,
|
||||
kCpuExecutionProvider,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>())
|
||||
.TypeConstraint("T2", {DataTypeImpl::GetTensorType<uint8_t>(), DataTypeImpl::GetTensorType<int8_t>()})
|
||||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<uint8_t>())
|
||||
.TypeConstraint("T4", DataTypeImpl::GetTensorType<int32_t>()),
|
||||
QLinearConv<uint8_t>);
|
||||
|
||||
REGISTER_QLINEARCONV_INT8_KERNEL(kMSDomain, 1);
|
||||
|
||||
} // namespace contrib
|
||||
|
||||
|
|
|
|||
|
|
@ -12,10 +12,12 @@
|
|||
#include "core/mlas/inc/mlas.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
ONNX_OPERATOR_KERNEL_EX(
|
||||
// uint8_t kernel supports weight being either uint8_t or int8_t
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX(
|
||||
QLinearMatMul,
|
||||
kOnnxDomain,
|
||||
10,
|
||||
uint8_t,
|
||||
kCpuExecutionProvider,
|
||||
KernelDefBuilder()
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<uint8_t>())
|
||||
|
|
@ -23,20 +25,21 @@ ONNX_OPERATOR_KERNEL_EX(
|
|||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<uint8_t>()),
|
||||
QLinearMatMul);
|
||||
|
||||
#define REGISTER_QLINEARMATMUL_TYPED_KERNEL(act_type, weight_type) \
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX( \
|
||||
QLinearMatMul, \
|
||||
kOnnxDomain, \
|
||||
10, \
|
||||
act_type##_##weight_type, \
|
||||
kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<act_type>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<weight_type>()) \
|
||||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<act_type>()), \
|
||||
// int8_t kernel only supports weight being int8_t
|
||||
#define REGISTER_QLINEARMATMUL_INT8_KERNEL() \
|
||||
ONNX_OPERATOR_TYPED_KERNEL_EX( \
|
||||
QLinearMatMul, \
|
||||
kOnnxDomain, \
|
||||
10, \
|
||||
int8_t, \
|
||||
kCpuExecutionProvider, \
|
||||
KernelDefBuilder() \
|
||||
.TypeConstraint("T1", DataTypeImpl::GetTensorType<int8_t>()) \
|
||||
.TypeConstraint("T2", DataTypeImpl::GetTensorType<int8_t>()) \
|
||||
.TypeConstraint("T3", DataTypeImpl::GetTensorType<int8_t>()), \
|
||||
QLinearMatMul);
|
||||
|
||||
REGISTER_QLINEARMATMUL_TYPED_KERNEL(int8_t, int8_t);
|
||||
REGISTER_QLINEARMATMUL_INT8_KERNEL();
|
||||
|
||||
Status QLinearMatMul::Compute(OpKernelContext* ctx) const {
|
||||
const auto* a = ctx->Input<Tensor>(IN_A);
|
||||
|
|
|
|||
|
|
@ -333,7 +333,7 @@ def _create_operator_type_usage_processors():
|
|||
# ai.onnx: NonMaxSuppression
|
||||
# com.microsoft: FusedConv, FusedGemm, FusedMatMul
|
||||
# - Implementation does not have any significant type specific code:
|
||||
# ai.onnx: Concat, Flatten, Not, QLinearConv, Reshape, Shape, Squeeze, Unsqueeze
|
||||
# ai.onnx: Concat, Flatten, Not, Reshape, Shape, Squeeze, Unsqueeze
|
||||
#
|
||||
default_processor_onnx_ops = ['Abs', 'ArgMax', 'ArgMin', 'AveragePool',
|
||||
'BatchNormalization', 'BitShift',
|
||||
|
|
@ -346,6 +346,7 @@ def _create_operator_type_usage_processors():
|
|||
'MatMul', 'Max', 'MaxPool', 'Mean', 'Min',
|
||||
'NonZero',
|
||||
'Pad',
|
||||
'QLinearConv', 'QLinearMatMul',
|
||||
'Range', 'Reciprocal', 'ReduceL1', 'ReduceL2', 'ReduceLogSum', 'ReduceLogSumExp',
|
||||
'ReduceMax', 'ReduceMean', 'ReduceMin', 'ReduceProd', 'ReduceSum', 'ReduceSumSquare',
|
||||
'Relu', 'Resize', 'ReverseSequence', 'RoiAlign', 'Round',
|
||||
|
|
@ -365,7 +366,8 @@ def _create_operator_type_usage_processors():
|
|||
'Neg', # used in tflite TransposeConv conversion
|
||||
'Sub']
|
||||
|
||||
internal_ops = ['QLinearAdd', 'QLinearMul']
|
||||
# NOTE: QLinearConv has ONNX and internal implementations
|
||||
internal_ops = ['QLinearAdd', 'QLinearMul', 'QLinearConv']
|
||||
|
||||
# TODO - review and add ML ops as needed
|
||||
# ML Op notes.
|
||||
|
|
|
|||
Loading…
Reference in a new issue