Onnx-mlir currently supports ONNX operations targeting up to opset 20. Limitations are listed when applicable. This documentation highlights the minimum and maximum opset versions that are fully supported by onnx-mlir and not the version changes.
- Operations are defined by the ONNX Standard.
- Supported Opsets indicates the lowest and highest opset a model may have for onnx-mlir to support compiling a model with the operator.
- A * indicates onnx-mlir is compatible with the latest version of that operator available as of opset 20.
NNPA has hardware limitations in dimension index size and tensor size, which are described in NNPALimit.hpp. They are large enough for normal use cases, but if your model exceeds the limitations, CPU is used instead of NNPA.
| Op | Supported Opsets (inclusive) | Limitations | Notes |
|---|---|---|---|
| Add | 6 - * | - Shape of input tensors must be the same since broadcasting is not supported. - Input tensors must have static dimensions. |
|
| AveragePool | 6 - * | - auto_pad must be NOTSET, VALID, and SAME_UPPER. If NOTSET is used, pads must be set so that the padding valid type or same upper.- ceil_mode must be default value(0) - Input and output tensors must be 4D tensors (N x C x H x W). - kernel_shape must be static.- count_include_pad must be default value(0).- ceil_mode must be default value(0). |
|
| BatchNormalization | 6 - * | Input and output tensor must be 4D(N x C x H x W). | |
| Conv | 6 - * | - auto_pad must be NOTSET, VALID, and SAME_UPPER. If NOTSET is used, pads must be set so that the padding valid type or same upper.- Dimension in Height and weight must be static. - group must be default value(1).- dilations must be default value(1).- Input and output tensors must have 4D (N x C x H x W). - kernel_shape must be static. |
|
| ConvTranspose | 6 - * | - 1D and 3D not supported because Conv1D and Conv3D not supported in zDNN. non-default dilations not supported because dilated convolution not supported in zDNN. |
|
| Div | 6 - * | - Shape of input tensors must be the same since broadcasting is not supported. - Input tensors must have static dimensions. |
|
| Exp | 6 - * | Input tensor must have 4 dimensions. | |
| GRU | 7 - * | - direction and hidden_size in W must have static dimensions.- R must have static dimensions.- If B and initial_h are given, they must have static dimensions.- sequence_lens is not supported for bidirectional GRU.- activations must be ["Sigmoid", "Tanh", "Tanh"].- clip is not supported.- linear_before_reset must be 1.- layout is not supported. |
|
| Gemm | 6 - * | - alpha and beta must be default value(1).- Rank of C must be 1 or 2. If the rank is 1, the dimension of C must be the same with the seconde dimension of B. |
|
| GlobalAveragePool | 6 - * | - Input shape must be 4D tensor(NCHW). - Dimensions in H and W must be static. |
|
| LSTM | 7 - * | - direction and hidden_size in W must have static dimensions.- R must have static dimensions.- B and initial_h have static dimensions if given. B's direction dim must be 1 or 2.- P(peepholes), activation_alpha, and activation_beta are not supported.- activations must be ["Sigmoid", "Tanh", "Tanh"].- clip is not supported.- input_forget must be default value(0).- layout is not supported. |
|
| LeakyRelu | 6 - * | The operations immediately before and after the LeakyRelu operation must be executed on the NNPA. Otherwise, LeakyRelu is executed on the CPU. This limitation is set to avoid performance degradation. | |
| Log | 6 - * | Input tensor must have 4 dimensions. | |
| LogSoftmax | 6 - * | ||
| MatMul | 6 - * | Ranks of input tensors must be (Rank of A, Rank of B) = (M, N), where M >= 2 and N >= 2. | |
| Max | 6 - * | - Shape of input tensors must be the same since broadcasting is not supported. - Input tensors must have static dimensions. |
|
| MaxPool | 6 - * | - auto_pad must be NOTSET, VALID, and SAME_UPPER. If NOTSET is used, pads must be set so that the padding valid type or same upper.- ceil_mode must be default value(0) - Input and output tensors must be 4D tensors(N x C x H x W). - kernel_shape must be static.- ceil_mode must be default value(0).- dilations must be default value(1). |
|
| Min | 6 - * | - Shape of input tensors must be the same since broadcasting is not supported. - Input tensors must have static dimensions. |
|
| Mul | 6 - * | - Shape of input tensors should be the same since broadcasting is not supported. - Input tensors must have static dimensions. |
|
| Pow | 7 - * | - Exponent should be a scalar integer and less or equal to 64. | |
| ReduceMean | 6 - * | - keepdims must be 1.- Input tensor must be 4D tensors and axis must be [2, 3]. |
|
| Relu | 6 - * | Input tensor must be less than or equal to 4 dimensions. | |
| Sigmoid | 6 - * | Input tensor must be less than or equal to 4 dimensions. | |
| Softmax | 6 - * | - axis must be the last dimension, i.e. rank - 1 or -1. |
|
| Softplus | 6 - * | The operations immediately before and after the Softplus operation must be executed on the NNPA. Otherwise, Softplus is executed on the CPU. This limitation is set to avoid performance degradation. | |
| Sub | 6 - * | - Shape of input tensors should be the same since broadcasting is not supported. - Input tensors must have static dimensions. |
|
| Sum | 6 - * | - All inputs must have the same static shape (Broadcasting not supported.) - Single input not supported. |
|
| Tanh | 6 - * | Input tensor must be less than or equal to 4 dimensions. |