QNN HTP Op Support Revision History

Introduced in QNN SDK Version	Runtime	Description
2.39.0	Quant	Logit enabled on all activation type. ScatterElements (Activation type: INT8, INT16) Enabled updateable tensor support DepthWiseConv2d (Activation type: INT8, INT16) Added QNN_DEFINITION_IMPL_GENERATED constraint ElementWiseSqrt enabled on QNN_DATATYPE_SFIXED_POINT_16 Convert (Activation type: INT16) Enabled QNN_DATATYPE_UFIXED_POINT_16, QNN_DATATYPE_UFIXED_POINT_8, QNN_DATATYPE_SFIXED_POINT_8 for out[0] Dequantize enabled QNN_DATATYPE_FLOAT_16 for out[0] Quantize (Activation type: INT16, INT8) Enabled on QNN_DATATYPE_FLOAT_16 to QNN_DATATYPE_UFIXED_POINT_16 Enabled on QNN_DATATYPE_FLOAT_16 to QNN_DATATYPE_SFIXED_POINT_16 Enabled on QNN_DATATYPE_FLOAT_16 to QNN_DATATYPE_UFIXED_POINT_8 Enabled on QNN_DATATYPE_FLOAT_16 to QNN_DATATYPE_SFIXED_POINT_8 ElementWiseSelect, ElementWiseBinary, ReduceMin, MatMul (Activation type: INT16) Enabled updateable tensor support Stft enabled on QNN_DATATYPE_FLOAT_16, QNN_DATATYPE_FLOAT_32
2.38.0	Quant	Adjust constraint message from support/not support to accept/reject. UnPack enabled on QNN_DATATYPE_SFIXED_POINT_16 ElementWiseBinary (Activation type: INT8) Removed operation constraints in HTP GatherElements, Cast, Pad added rank 5d support on all activation type Cast enabled on QNN_DATATYPE_SFIXED_POINT_16 to QNN_DATATYPE_UFIXED_POINT_16 Quantize, enabled on QNN_DATATYPE_FLOAT_32 ElementWiseAbs enabled on QNN_DATATYPE_INT_32 ElementWiseUnary enabled on QNN_DATATYPE_INT_32 ElementWiseMaximum enabled on QNN_DATATYPE_INT_32 ElementWiseMinimum enabled on QNN_DATATYPE_INT_32 RandomUniformLike enabled on QNN_DATATYPE_UINT_32 for in[0], QNN_DATATYPE_FLOAT_32 for in[1]
2.37.0	Quant	Gather (Activation type: INT8) Added math invariant tags for out[0] Tile (Activation type: INT8, INT16) Added math invariant tags for out[0] Cast (Activation type: INT8) Added support from UINT8 to FP16 ChannelShuffle (Activation type: INT8, INT16) Added math invariant tags for out[0] StridedSlice enabled on QNN_DATATYPE_UINT_8 TopK (Activation type: INT8) Added math invariant tags for out[0] Nv12ToRgb enabled on QNN_DATATYPE_UFIXED_POINT_8 Split enabled on QNN_DATATYPE_SFIXED_POINT_16 IsNan enabled on FP16
2.36.0	Quant	Pad (Activation type: INT16) Added updateable quantization support for in[0], out[0] Gather (Activation type: FP16) Added updateable quantization support for in[1] Gather (Activation type: INT8) Added updateable quantization support for out[0] RmsNorm enabled on QNN_DATATYPE_SFIXED_POINT_16 Gru enabled on QNN_DATATYPE_FLOAT_16, QNN_DATATYPE_FLOAT_32 Buffer enabled on FP16
2.35.0	Quant	Enable enabled on QNN_DATATYPE_UINT_8 Tile (Activation type: INT8, INT16) Added quantization constraint for in[0], out[0] ElementWiseAsin enabled on QNN_DATATYPE_SFIXED_POINT_16 Conv2d (Activation type: IN16) Changed constraint for in[1] LayerNorm (Activation type: FP16) Added rank constraint for in[1], in[2] IsInf enabled on QNN_DATATYPE_FLOAT_16, QNN_DATATYPE_FLOAT_32
2.34.0	Quant	BatchToSpace, SpaceToBatch (Activation type: INT8, INT16) Added math invariant constraint for out[0] LayerNorm (Activation type: INT8, INT16) Adjusted rank constraint for in[2] Tile (Activation type: INT8, INT16) Added 5D support for in[0], out[0] Conv2d (Activation type: INT16) Updated Quant constraint for in[1] ElementWiseNeuron (Activation type: FP16) Added 5D Sigmoid support for in[0], out[0] Sigmoid (Activation type: FP16) Added 5D support for in[0], out[0]
2.33.0	Quant	ElementWiseRsqrt, ElementWiseUnary, ElementWiseNeuron, Gelu (Activation type: INT16) Added QNN_DATATYPE_SFIXED_POINT_16 support for in[0], out[0] ElementWiseGreater, ElementWiseGreaterEqual, ElementWiseLess, ElementWiseLessEqual, ElementWiseNotEqual, ElementWiseBinary (Activation type: FP16) Added 5D support for in[0], in[1], out[0] Concat enabled on QNN_DATATYPE_BOOL_8
2.32.0	Quant	Reshape (Activation type: INT8, INT16) Removed in[1] constraint Added Dynamic_Shape type constraint for in[0], out[0] ElementWiseSubtract enabled on QNN_DATATYPE_SFIXED_POINT_16 ReduceSum enabled on QNN_DATATYPE_SFIXED_POINT_16 ScatterElements (Activation type: INT8) Added support for max reduction Gather (Activation type: INT8) Added udateable quantization support for in[0], in[1] Enabled on QNN_DATATYPE_SFIXED_POINT_16 Softmax enabled on QNN_DATATYPE_SFIXED_POINT_16 MatMul enabled on QNN_DATATYPE_SFIXED_POINT_16 StridedSlice enabled on QNN_DATATYPE_SFIXED_POINT_16 FullyConnected enabled on QNN_DATATYPE_SFIXED_POINT_16 Conv2d (Activation type: INT16) Added supplement constraints for in[1], in[2] Split enabled on QNN_DATATYPE_BOOL_8
2.31.0	Quant	Tanh (Activation type: INT16) Removed 1/32768.0 scale and 0 offset constraint for out[0] Conv2d (Activation type: INT16) Added 16-bit per-channel quantization for in[1] FullyConnected (Activation type: INT16) Added 16-bit per-channel quantization for in[1] MatMul (Activation type: INT16) Added 16-bit per-channel quantization for in[1]
2.30.0	Quant	Conv2d (Activation type: INT8) Added updateable tensor support for in[2] Conv2d (Activation type: INT16) Added updateable quantization support for in[0], in[1], in[2], out[0] RmsNorm (Activation type: INT16) Added dynamic tensor support on width and channel for in[0], out[0] DepthWiseConv2d (Activation type: INT16) Added updateable quantization support for in[0], in[1]
2.29.0	Quant	Dynamic tensor ops have been enabled in OpValidator TopK (Activation type: All) Added support to largest parameter that largest can be equal to 0 ElementWiseNeuron (Activation type: INT8, INT16) Added Softplus support with rank 4d BatchNorm, LayerNorm (Activation type: INT8) Added QNN_DATATYPE_SFIXED_POINT_8 support for BatchNorm and LayerNorm Convert (Activation type: INT16) Added updateable quantization support for in[0] and out[0] when in[0] and out[0] both are QNN_DATATYPE_UFIXED_POINT_16 datatype RmsNorm (Activation type: INT16) Added updateable quantization support for in[0], in[1] and in[2] when in[0] is QNN_DATATYPE_UFIXED_POINT_16, in[1] is QNN_DATATYPE_UFIXED_POINT_8 and in[2] is QNN_DATATYPE_SFIXED_POINT_32 ElementWiseBinary Equal (Activation type: FP16) Added rank 5d support for in[0], in[1] and out[0] for ElementWiseBinary Equal ReduceSum (Activation type: FP16, FP32) Added rank 5d support for in[0] and out [0]
2.28.0	Quant	Axes (Activation type: All) Added support to normalization on final dimension or last 3 diminsions of 4D inputs Prelu (Activation type: All) Added rank 5d support for in[0], in[1] and out[0] NonZero (Activation type: INT16, FP16) Added NonZero op support Tile (Activation type: FP16, FP32) Added rank 5d support for in[0] and out [0] RmsNorm (Activation type: INT16, INT8) Added updateable quantization support for in[0], in[2] and out[0] Conv2d (Activation type:INT8) Added updateable quantization support for in[0], in[1] and out[0] ElementwiseNeuron Relu (Activation type: INT8, INT16) Added QNN_DATATYPE_SFIXED_POINT_16 support for in[0] and out[0] ExtractGlimpse (Activation type: INT16) Added ExtractGlimpse op support Convert (Activation type: INT16) Added updateable quantization support for in[0] and out[0] ElementWiseBinary (Activation type: FP16) Added rank 5d support for in[0], in[1] and out[0] for Add, Sub and Pow ElementWiseUnary Sqrt (Activation type: FP16) Added rank 5d support for in[0] and out[0] for Sqrt ReduceMax, ReduceMin, ReduceMean (Activation type: FP16) Added rank 5d support for in[0] ElementWiseNeuron SoftPlus (Activation type: FP16) Added QNN_DATATYPE_FLOAT_16, QNN_DATATYPE_FLOAT_32 datatype support for in[0] and out[0] ReduceMin (Activation type: INT32) Added ReduceMin op support with 5d rank and QNN_DATATYPE_INT_32 datatype for in [0] and out[0]
2.27.0	Quant	ElementWiseNeuron, Sigmoid (Activation type: All) Added constraint that dynamic dimensions is not supported currently Pack (Activation type: All) Added rank 4d support for in[0] and 5d support for out[0] CumulativeSum (Activation type: INT8) Added rank 5d support for in[0] and out [0] HardSigmoid (Activation type: INT8, INT16) Added rank 5d support for in[0] and out [0] Sigmoid, Softmax (Activation type: INT16) Added dynamic dimensions support on width and channel for in[0] and out[0] ReduceMean (Activation type: INT16) Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] and out[0] ReduceSum (Activation type: FP16, FP32) Added rank 5d support for in[0] and out [0]
2.26.0	Quant	RmsNorm Added QNN_DATATYPE_UFIXED_POINT_8 datatype support for in[0], in[1], in[2] and out[0] UnPack Added rank 5d support for in[0] Conv2d, TransposeConv2d, Convert Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] and out[0] MaMul Added rank 5d support for in[0], in[1] and out[0] ElementWiseSelect Added rank 5d support for in[0], in[1], in[2] and out[0] Pad Enabled EDGE scheme (QNN_OP_PAD_SCHEME_EDGE) to scheme parameter for FP16 and FP32 Transpose Added constraint that dynamic dimensions are not supported for in[0] and out[0] Added QNN_DATATYPE_BOOL_8 datatype support for in[0] to Transpose 5d Tile Added QNN_DATATYPE_BOOL_8 datatype support for in[0] and out[0] Softmax Added constraint that dynamic dimensions are not supported for in[0] and out[0]
2.25.0	Quant	Gather Added constraint that input/output quant info must match for quantized models GatherElements Added QNN_DATATYPE_INT_32 datatype support for in[1] ElementWiseMultiply, ElementWiseAdd, ElementWiseSub, ElementWisePow Added rank 5d support for in[0], in[1] and out[0] Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for ElementWiseAdd, ElementWiseMultiply ElementwiseRsqrt Added rank 5d support for in[0] and out[0] to both float and quant Rsqrt TopK Added “largest” parameter and only support default value (true) currently Relu Added support to updateable quantization for in[0] Conv2d, Matmul, FullyConnected Added support to block quant weight tensors on V79 devices with the constraint that the block axis should be the second last axis of the weights Prelu Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0], in[1] and out[0] ResizeBilinear Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] and out[0] PoolMax2d Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] and out[0] ExtractPatches Added QNN_DATATYPE_FLOAT_16 datatype support for in[0] and out[0]
2.24.0	Quant	HTP Backend Op Definition Supplement has been updated to enhance clarity on each supported datatype combination. Please refer to HTP Backend Op Definition Supplement for details. Reshape Added constraint that HTP currently does not support dynamic input/output
2.23.0	Quant	Cast Added QNN_DATATYPE_INT_64 datatype support for in[0] and out[0] Transpose Added QNN_DATATYPE_BOOL_8 datatype support for in[0] Relu Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] and out[0] Gelu Added QNN_DATATYPE_SFIXED_POINT_8 datatype support for in[0] and out[0] ElementwiseUnary Added rank 5d support to Abs op for in[0] and out[0] Added QNN_DATATYPE_SFIXED_POINT_8 datatype support to Abs op for in[0] and out[0]
2.22.0	Quant	ElementwiseUnary Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for in[0] and out[0] in RSQRT op Conv3d, TransposeConv3d Added QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET support with only channel axis and the weights are expected to be signed and symmetrically quantized Reshape Added constraint that 0D tensors are not supported for in[0] and out[0] ElementWiseAdd, ElementWiseAnd, ElementWiseDivide, ElementWiseNotEqual, ElementWiseMaximum, ElementWiseMinimum, ElementWiseMultiply, ElementWisePower, ElementWiseSquaredDifference, ElementWiseSubtract, ElementWiseEqual, ElementWiseGreater, ElementWiseGreaterEqual, ElementWiseLess, ElementWiseLessEqual, ElementWiseBinary, ElementWiseFloorDiv Added constraint that 0D tensors are not supported for inputs and outputs ElementWiseAbs Added QNN_DATATYPE_SFIXED_POINT_8 datatype support for in[0] and out[0] TopK Removed the constraint that for UFIXED_POINT_16 inputs, only k less than or equal to 64 is supported Transpose Added constraint for in[0] of Tranpose 4D: QNN_DATATYPE_UFIXED_POINT_8, QNN_DATATYPE_SFIXED_POINT_8, QNN_DATATYPE_UFIXED_POINT_16, QNN_DATATYPE_SFIXED_POINT_16, QNN_DATATYPE_INT_32, QNN_DATATYPE_UINT_32 are supported Added constraint for in[0] of Transpose 5D: QNN_DATATYPE_UFIXED_POINT_8, QNN_DATATYPE_SFIXED_POINT_8, QNN_DATATYPE_UFIXED_POINT_16, QNN_DATATYPE_FLOAT_16, QNN_DATATYPE_FLOAT_32 are supported
2.21.0	Quant	Dequantize Added QNN_DATATYPE_FLOAT_16 datatype support for out[0] ElementWiseBinary Added QNN_DATATYPE_BOOL_8 support for all ElementWiseBinary:comparison ops for in[0] Added QNN_DATATYPE_BOOL_8 support for all ElementWiseBinary:comparison ops for out[0]
2.20.0	Quant	ElementWiseXor, CreateSparse, GetSparseIndices, GetSparseValues, SparseToDense, ElementWiseNeuron Added support ElementWiseSin, ElementWiseCos Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for in[0], out[0] Conv3d Added default handling to ignore reuse_sparse_indicies parameter as HTP doesn’t support sparsity Resize Fixed constraint on nearest_mode parameter to match the behaviour in HTP core Convert Added QNN_DATATYPE_UINT_8 datatype support for in[0] Added QNN_DATATYPE_BOOL_8 datatype support for out[0]
2.19.0	Quant	Gather Added constraint to comunicate that HTP does not support negative indices GatherElements Added QNN_DATATYPE_INT_32 support for in[0], out[0] BatchNorm, LayerNorm Added constraint to support 16 bit data types for v73 or beyond architecture only Convert Added max supported rank to 5d for in[0] and out[0]
2.18.0	Quant	Conv2d, DepthWiseConv2d, TransposeConv2d, FullyConnected, MatMul Added constraint to support 16 bit data types for v73 or beyond architecture only GridSample Added max supported rank to 5d for in[0], in[1] and out[0] Lstm Added rest input for in[24] Added input rank constraint of 2 for in[0] Added description of 2d input not applicable for time_major parameter
2.17.0	Quant	Quantize, Dequantize Added max supported rank to 5d for in[0] and out[0] EltwiseMul Added overflow detect for input scales
2.16.0	Quant	Matmul Corrected supported rank constraints back to 4D Resize Added constraint to match nearest_mode only supporting default value ElementwiseOr support added ElementWiseBinary Enabled OR Added QNN_DATATYPE_BOOL_8 support for in[0], in[1], out[0]
2.15.0	Quant	Split, ReduceMax, ReduceMean, ReduceMin, Convert, ElementWiseAbs Added 5D constraints for inputs and outputs ElementWiseBinary support added Convert Added QNN_DATATYPE_SFIXED_POINT_16 support for out[0] Tile Added QNN_DATATYPE_FLOAT_32, QNN_DATATYPE_INT_32 support for input and output Batchnorm Added QNN_DATATYPE_SFIXED_POINT_16 support for in[0] and in[1] Added QNN_DATATYPE_UFIXED_POINT_16 support for in[1] Conv2d, DepthWiseConv2d, TransposeConv2 Added QNN_DATATYPE_UFIXED_POINT_16 support for in[1] ScatterNd Added QNN_DATATYPE_BOOL_8 support for in[0], in[2] and out[2]
2.14.0	Quant	SpaceToDepth Added operations, enabled QNN_DATATYPE_UINT_32 mode ReduceSum Added 5D constraints for inputs and outputs LayerNorm Added QNN_DATATYPE_SFIXED_POINT_16 support for in[1] and in[2] ElementWiseSquaredDifference Added QNN_DATATYPE_UFIXED_POINT_16, QNN_DATATYPE_SFIXED_POINT_16 support for in[0], in[1] and out[0] FullyConnected, MatMul Added QNN_DATATYPE_UFIXED_POINT_16 support for in[1] ElementWiseRsqrt Added QNN_DATATYPE_UFIXED_POINT_16 support for in[0] and out[0]
2.13.0	Quant	Convert Added QNN_DATATYPE_BOOL_8 support for inputs GroupNorm support added ElementWiseRsqrt Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for in[0] and out[0]
2.12.0	Quant	ElementwiseUnary support added Conv2d, DepthWiseConv2d, FullyConnected, MatMul, TransposeConv2d Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] Constraint added for in[1]: QNN_DATATYPE_SFIXED_POINT_16 Weight must have QNN_DATATYPE_UFIXED_POINT_16 Activation and must be symmetric quantized RoiAlign Added default support for new params: aligned and allow_invalid_roi
2.11.0	Quant	ElementWiseAsin, ExtractPatches, RoiAlign Added operations Resize Removed transformation_mode parameter constraint to support Asymmetric Resize Mode NonMaxSuppression Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for in[0] and in[1] DetectionOutput, MultiClassNms Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for all inputs and out[0]
2.10.0	Quant	ElementWiseSin, ElementWiseCos, NonMaxSuppression Added operations Conv2d, DepthWiseConv2d, FullyConnected, MatMul, TransposeConv2d Added QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[1] TopK Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for in[0] and out[0] Transpose Added QNN_DATATYPE_BOOL_8 datatype support for in[0] and out[0] Fully-connected, MatMul Fixed Axis quantization validation Conv2d, DepthWiseConv2d, FullyConnected, MatMul, TransposeConv2d Removed static tensor check on INT8 axis quantized weights for in[1] Conv2d, DepthWiseConv2d, TransposeConv2d Allowed Non-Zero Bias Encoding with Per-Channel quantization parameters for in[2]
2.9.0	Quant	L2Norm, LogSoftmax Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for in[0] and out[0] ElementWiseEqual, ElementWiseGreater, ElementWiseGreaterEqual, ElementWiseLess ElementWiseLessEqual, ElementWiseNotEqual Added QNN_DATATYPE_UFIXED_POINT_16 and QNN_DATATYPE_SFIXED_POINT_16 datatype support for in[0] and in[1] Conv2d, DepthWiseConv2d, FullyConnected, MatMul, TransposeConv2d Added static tensor constraint for in[1] MatMul Added channel axis constraint for quantization parameters for in[1] ElementWiseAdd, ElementWiseDivide, ElementWiseMaximum, ElementWiseMinimum, ElementWiseMultiply, ElementWiseSquaredDifference, ElementWiseSubtract, ElementWiseEqual, ElementWiseGreater, ElementWiseGreaterEqual, ElementWiseLess, ElementWiseLessEqual, ElementWiseNotEqual, ElementWiseSelect, Gather, GatherNd, MatMul, ScatterNd Fixed rank constraint on incorrect input for in[1] ElementWiseSelect, ScatterNd Fixed rank constraint on incorrect input for in[2] ElementWisePower, ExpandDims Added 5D rank constraint for in[1] OneHot Added max rank constraint of 2 for in[0] and 3 for out[0] ReluMinMax Added max rank constraint of 5 for in[0] and out[0]
2.8.0	Quant	Cast, ElementWiseLog Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for inputs and outputs ElementWiseGreaterEqual, ElementWiseLessEqual, ElementWiseNotEqual Added QNN_DATATYPE_INT_32 datatype support for inputs ReduceSum, TopK Added QNN_DATATYPE_INT_32 datatype support for inputs and outputs ElementWiseGreater, ElementWiseGreaterEqual, ElementWiseLess, ElementWiseLessEqual, ElementWiseNotEqual, ElementWisePower, ElementWiseSelect, GatherNd, Softmax Added 5D constraints for inputs and outputs ElementWiseSelect Added QNN_DATATYPE_UFIXED_POINT_8, QNN_DATATYPE_SFIXED_POINT_8, QNN_DATATYPE_SFIXED_POINT_16, QNN_DATATYPE_UFIXED_POINT_16, QNN_DATATYPE_INT_32 datatype support for in[1]
2.7.0	Quant	Argmax Added QNN_DATATYPE_UINT_32 datatype support for out[0] Transpose Added QNN_DATATYPE_UINT_32 datatype support for in[0] and out[0] DepthWiseConv2d Added support for dilation parameter with additional stride and kernel size ElementWiseAdd, ElementWiseDivide, ElementWiseExp, ElementWiseMaximum, ElementWiseMinimum, ElementWiseMultiply, ElementWiseSquaredDifference, ElementWiseSubtract, MatMul, Pad, Relu, Sigmoid, Transpose Fixed 5D constraints for out[0] ElementWiseExp, ElementWiseFloor, ReduceMax, ReduceMin, ScatterNd, LayerNorm Added QNN_DATATYPE_UFIXED_POINT_16 datatype support for all inputs and outputs ElementWiseEqual, ElementWiseLess, ElementWiseSelect Added QNN_DATATYPE_INT_32 datatype support for all inputs and outputs ElementWiseGreater Added QNN_DATATYPE_INT_32 datatype support for in[0] and in[1] Reshape Added QNN_DATATYPE_BOOL_8 datatype support for in[0] and out[0] ScatterNd Added 5D constraints for in[0], in[2] and out[0], and 6D constraint for in[1] Softmax Added QNN_DATATYPE_FLOAT_32 datatype support for out[0] InstanceNorm Added constraint to support in[0] with rank of less than 4D Gather, GatherNd Added support for in[1] GatherNd Added constraint for out[0] for input and output quantization check FullyConnected, MatMul Added constraints to support per-channel tensors
2.6.0	Quant	Cast Added QNN_DATATYPE_BOOL_8 datatype support for out[0]
2.4.0	Quant	Resize support added Gather Constraint added for in[0] and out[0]: “Max Supported rank is 5”
2.3.0	Quant	Cast, ExpandDims, Squeeze Added QNN_DATATYPE_UINT_8 support for in[0] and out[0] GatherNd Added QNN_DATATYPE_INT_32 support for in[0] and out[0] MatMul Added QNN_DATATYPE_UFIXED_POINT_8 and QNN_DATATYPE_SFIXED_POINT_32 support for in[2] Pad, Relu constraint added for in[0] and out[0]: “Max Supported rank is 5”
2.2.0	Quant	Gelu Added QNN_DATATYPE_UFIXED_POINT_16 support for in[0] and out[0] Reshape Added QNN_DATATYPE_UINT_8 support for in[0] and out[0]
2.1.0	Quant	Concat, ElementWiseAdd, ElementWiseDivide, ElementWiseMaximum, ElementWiseMinimum, ElementWiseMultiply, ElementWiseSubtract, ElementWiseSquaredDifference, MatMul, Sigmoid, StridedSlice support for rank 5 added Gather constraint added for out[0]: “Input quantization must be equal to output quantization”