TensorRT 8.2 supports operators up to Opset 13. Latest information of ONNX operators can be found here
TensorRT supports the following ONNX data types: DOUBLE, FLOAT32, FLOAT16, INT8, and BOOL
Note: There is limited support for INT32, INT64, and DOUBLE types. TensorRT will attempt to cast down INT64 to INT32 and DOUBLE down to FLOAT, clamping values to
+-INT_MAXor+-FLT_MAXif necessary.
See below for the support matrix of ONNX operators in ONNX-TensorRT.
| Operator | Supported | Supported Types | Restrictions |
|---|---|---|---|
| Abs | Y | FP32, FP16, INT32 | |
| Acos | Y | FP32, FP16 | |
| Acosh | Y | FP32, FP16 | |
| Add | Y | FP32, FP16, INT32 | |
| And | Y | BOOL | |
| ArgMax | Y | FP32, FP16 | |
| ArgMin | Y | FP32, FP16 | |
| Asin | Y | FP32, FP16 | |
| Asinh | Y | FP32, FP16 | |
| Atan | Y | FP32, FP16 | |
| Atanh | Y | FP32, FP16 | |
| AveragePool | Y | FP32, FP16, INT8, INT32 | 2D or 3D Pooling only |
| BatchNormalization | Y | FP32, FP16 | |
| BitShift | N | ||
| Cast | Y | FP32, FP16, INT32, INT8, BOOL | |
| Ceil | Y | FP32, FP16 | |
| Celu | Y | FP32, FP16 | |
| Clip | Y | FP32, FP16, INT8 | |
| Compress | N | ||
| Concat | Y | FP32, FP16, INT32, INT8, BOOL | |
| ConcatFromSequence | N | ||
| Constant | Y | FP32, FP16, INT32, INT8, BOOL | |
| ConstantOfShape | Y | FP32 | |
| Conv | Y | FP32, FP16, INT8 | 2D or 3D convolutions only. Weights W must be an initailizer |
| ConvInteger | N | ||
| ConvTranspose | Y | FP32, FP16, INT8 | 2D or 3D deconvolutions only. Weights W must be an initializer |
| Cos | Y | FP32, FP16 | |
| Cosh | Y | FP32, FP16 | |
| CumSum | Y | FP32, FP16 | axis must be an initializer |
| DepthToSpace | Y | FP32, FP16, INT32 | |
| DequantizeLinear | Y | INT8 | x_zero_point must be zero |
| Det | N | ||
| Div | Y | FP32, FP16, INT32 | |
| Dropout | Y | FP32, FP16 | |
| DynamicQuantizeLinear | N | ||
| Einsum | Y | FP32, FP16 | Ellipsis and diagonal operations are not supported. Broadcasting between inputs is not supported |
| Elu | Y | FP32, FP16, INT8 | |
| Equal | Y | FP32, FP16, INT32 | |
| Erf | Y | FP32, FP16 | |
| Exp | Y | FP32, FP16 | |
| Expand | Y | FP32, FP16, INT32, BOOL | |
| EyeLike | Y | FP32, FP16, INT32, BOOL | |
| Flatten | Y | FP32, FP16, INT32, BOOL | |
| Floor | Y | FP32, FP16 | |
| Gather | Y | FP32, FP16, INT8, INT32 | |
| GatherElements | Y | FP32, FP16, INT8, INT32 | |
| GatherND | Y | FP32, FP16, INT8, INT32 | |
| Gemm | Y | FP32, FP16, INT8 | |
| GlobalAveragePool | Y | FP32, FP16, INT8 | |
| GlobalLpPool | Y | FP32, FP16, INT8 | |
| GlobalMaxPool | Y | FP32, FP16, INT8 | |
| Greater | Y | FP32, FP16, INT32 | |
| GreaterOrEqual | Y | FP32, FP16, INT32 | |
| GRU | Y | FP32, FP16 | For bidirectional GRUs, activation functions must be the same for both the forward and reverse pass |
| HardSigmoid | Y | FP32, FP16, INT8 | |
| Hardmax | N | ||
| Identity | Y | FP32, FP16, INT32, INT8, BOOL | |
| If | Y | FP32, FP16, INT32, BOOL | Output tensors of the two conditional branches must have broadcastable shapes, and must have different names |
| ImageScaler | Y | FP32, FP16 | |
| InstanceNormalization | Y | FP32, FP16 | Scales scale and biases B must be initializers. Input rank must be >=3 & <=5 |
| IsInf | N | ||
| IsNaN | Y | FP32, FP16, INT32 | |
| LeakyRelu | Y | FP32, FP16, INT8 | |
| Less | Y | FP32, FP16, INT32 | |
| LessOrEqual | Y | FP32, FP16, INT32 | |
| Log | Y | FP32, FP16 | |
| LogSoftmax | Y | FP32, FP16 | |
| Loop | Y | FP32, FP16, INT32, BOOL | |
| LRN | Y | FP32, FP16 | |
| LSTM | Y | FP32, FP16 | For bidirectional LSTMs, activation functions must be the same for both the forward and reverse pass |
| LpNormalization | Y | FP32, FP16 | |
| LpPool | Y | FP32, FP16, INT8 | |
| MatMul | Y | FP32, FP16 | |
| MatMulInteger | N | ||
| Max | Y | FP32, FP16, INT32 | |
| MaxPool | Y | FP32, FP16, INT8 | 2D or 3D pooling only. Indices output tensor unsupported |
| MaxRoiPool | N | ||
| MaxUnpool | N | ||
| Mean | Y | FP32, FP16, INT32 | |
| MeanVarianceNormalization | N | ||
| Min | Y | FP32, FP16, INT32 | |
| Mod | N | ||
| Mul | Y | FP32, FP16, INT32 | |
| Multinomial | N | ||
| Neg | Y | FP32, FP16, INT32 | |
| NegativeLogLikelihoodLoss | N | ||
| NonMaxSuppression | Y [EXPERIMENTAL] | FP32, FP16 | Inputs max_output_boxes_per_class, iou_threshold, and score_threshold must be initializers. Output has fixed shape and is padded to [max_output_boxes_per_class, 3]. |
| NonZero | N | ||
| Not | Y | BOOL | |
| OneHot | N | ||
| Or | Y | BOOL | |
| Pad | Y | FP32, FP16, INT8, INT32 | |
| ParametricSoftplus | Y | FP32, FP16, INT8 | |
| Pow | Y | FP32, FP16 | |
| PRelu | Y | FP32, FP16, INT8 | |
| QLinearConv | N | ||
| QLinearMatMul | N | ||
| QuantizeLinear | Y | FP32, FP16 | y_zero_point must be 0 |
| RandomNormal | N | ||
| RandomNormalLike | N | ||
| RandomUniform | Y | FP32, FP16 | seed value is ignored by TensorRT |
| RandomUniformLike | Y | FP32, FP16 | seed value is ignored by TensorRT |
| Range | Y | FP32, FP16, INT32 | Floating point inputs are only supported if start, limit, and delta inputs are initializers |
| Reciprocal | N | ||
| ReduceL1 | Y | FP32, FP16 | |
| ReduceL2 | Y | FP32, FP16 | |
| ReduceLogSum | Y | FP32, FP16 | |
| ReduceLogSumExp | Y | FP32, FP16 | |
| ReduceMax | Y | FP32, FP16 | |
| ReduceMean | Y | FP32, FP16 | |
| ReduceMin | Y | FP32, FP16 | |
| ReduceProd | Y | FP32, FP16 | |
| ReduceSum | Y | FP32, FP16 | |
| ReduceSumSquare | Y | FP32, FP16 | |
| Relu | Y | FP32, FP16, INT8 | |
| Reshape | Y | FP32, FP16, INT32, INT8, BOOL | |
| Resize | Y | FP32, FP16 | Supported resize transformation modes: half_pixel, pytorch_half_pixel, tf_half_pixel_for_nn, asymmetric, and align_corners.Supported resize modes: nearest, linear.Supported nearest modes: floor, ceil, round_prefer_floor, round_prefer_ceil |
| ReverseSequence | Y | FP32, FP16 | Dynamic input shapes are unsupported |
| RNN | Y | FP32, FP16 | For bidirectional RNNs, activation functions must be the same for both the forward and reverse pass |
| RoiAlign | N | ||
| Round | Y | FP32, FP16, INT8 | |
| ScaledTanh | Y | FP32, FP16, INT8 | |
| Scan | Y | FP32, FP16 | |
| Scatter | Y | FP32, FP16, INT8, INT32 | |
| ScatterElements | Y | FP32, FP16, INT8, INT32 | |
| ScatterND | Y | FP32, FP16, INT8, INT32 | |
| Selu | Y | FP32, FP16, INT8 | |
| SequenceAt | N | ||
| SequenceConstruct | N | ||
| SequenceEmpty | N | ||
| SequenceErase | N | ||
| SequenceInsert | N | ||
| SequenceLength | N | ||
| Shape | Y | FP32, FP16, INT32, INT8, BOOL | |
| Shrink | N | ||
| Sigmoid | Y | FP32, FP16, INT8 | |
| Sign | Y | FP32, FP16, INT8, INT32 | |
| Sin | Y | FP32, FP16 | |
| Sinh | Y | FP32, FP16 | |
| Size | Y | FP32, FP16, INT32, INT8, BOOL | |
| Slice | Y | FP32, FP16, INT32, INT8, BOOL | axes must be an initializer |
| Softmax | Y | FP32, FP16 | |
| SoftmaxCrossEntropyLoss | N | ||
| Softplus | Y | FP32, FP16, INT8 | |
| Softsign | Y | FP32, FP16, INT8 | |
| SpaceToDepth | Y | FP32, FP16, INT32 | |
| Split | Y | FP32, FP16, INT32, BOOL | |
| SplitToSequence | N | ||
| Sqrt | Y | FP32, FP16 | |
| Squeeze | Y | FP32, FP16, INT32, INT8, BOOL | axes must be an initializer |
| StringNormalizer | N | ||
| Sub | Y | FP32, FP16, INT32 | |
| Sum | Y | FP32, FP16, INT32 | |
| Tan | Y | FP32, FP16 | |
| Tanh | Y | FP32, FP16, INT8 | |
| TfIdfVectorizer | N | ||
| ThresholdedRelu | Y | FP32, FP16, INT8 | |
| Tile | Y | FP32, FP16, INT32, BOOL | |
| TopK | Y | FP32, FP16 | K input must be an initializer |
| Transpose | Y | FP32, FP16, INT32, INT8, BOOL | |
| Unique | N | ||
| Unsqueeze | Y | FP32, FP16, INT32, INT8, BOOL | axes must be a constant tensor |
| Upsample | Y | FP32, FP16 | |
| Where | Y | FP32, FP16, INT32, BOOL | |
| Xor | N |