Supported Models
The Vollo compiler supports PyTorch models that use the following operations:
| Operation | Support Notes |
|---|---|
| Pointwise arithmetic ops | +, -, *, / |
| Inequality | >, <, >=, <= |
max and min | |
| Clamp ops | clamp, relu |
| Matrix multiplication | Linear; matmul / @ where one side is a constant |
| Convolution | Via vollo_torch.nn.PaddedConv1d |
| LSTM | Via vollo_torch.nn.LSTM |
| Indexing / slicing | Partial square bracket [] support; index_select |
sum | keepdim = True required when summing over data dim |
where | If the where condition is an inequality comparison |
| Concatenation | cat, concat |
LayerNorm | |
RMSNorm | via vollo_torch.nn.RMSNorm for torch versions < 2.4 |
| Batch Normalization | BatchNorm1d, BatchNorm2d, BatchNorm3d |
transpose | See section below |
squeeze, unsqueeze | |
sqrt | torch.sqrt, torch.rsqrt |
Note that for operations like Dropout and BatchNorm1d (which change behaviour at inference time) to be handled correctly, the model should be in eval mode.
Tensor Memory Format
Vollo supports operations on tensors in data- or channels- last memory format, i.e. the innermost dimension of the tensors should be the data or channels dimension rather than the batch or sequence dimension if there is one. This is because the Vollo accelerator's compute units operate on contiguous vectors (1D tensors) and has limited support for rearranging tensor data, particularly transposing them.
There are some notable exceptions that do not require channels-last tensors:
- Layers that operate on sequences:
Conv1d,LSTM. Vollo supports the same (batch, channels, sequence) memory format that PyTorch uses for these layers, but requires applying the streaming transform to models that contain them. - General matrix multiplication (as opposed to the more restrictive
Linear):matmul,@.
TorchScript
The Vollo compiler supports standard PyTorch modules (torch.nn.Module); it
does not support TorchScript modules (torch.jit.ScriptModule).