| # MLX is not shipped for this model |
|
|
| The MLX build was measured and rejected. The exporter in `export-scripts` is |
| kept so the decision can be re-tested against a future ExecuTorch/MLX release, |
| but no `.pte` or `config.json` is published here. |
|
|
| ## Why: it is slower, and its output is not finite |
|
|
| Measured on device (iPhone 16, ExecuTorch 1.4.1, 3 warmup runs + 15 timed runs, |
| median, inputs allocated from each model's own schema and filled with 0.5). All |
| four styles share one architecture and differ only in weights, so all four were |
| run rather than measuring one and generalising: |
|
|
| | style | Core ML fp16 | MLX fp16 | MLX penalty | MLX output | |
| | --- | --- | --- | --- | --- | |
| | candy | **193.4 ms** | 564.5 ms | 2.92x slower | non-finite | |
| | mosaic | **194.7 ms** | 546.4 ms | 2.81x slower | non-finite | |
| | rain princess | **193.3 ms** | 566.7 ms | 2.93x slower | non-finite | |
| | udnie | **196.9 ms** | 574.7 ms | 2.92x slower | non-finite | |
|
|
| The latency alone would be reason enough, but the output settles it. Every MLX |
| build emitted NaN or Inf, while the Core ML build fed the identical input on the |
| same device in the same run returned finite output in all four cases. This |
| artifact does not merely run slowly, it produces garbage. |
|
|
| Size is not an argument either. MLX fp16 is 3.48 MB against Core ML fp16 at |
| 3.79 MB, a 0.3 MB difference on a model of this size. |
|
|
| ## Root cause: op coverage |
|
|
| The ExecuTorch MLX backend has no handler for three ops this architecture uses |
| everywhere, so the graph shatters into **50 subgraphs**: |
|
|
| | unsupported op | count | where it comes from | |
| | --- | --- | --- | |
| | `aten.flip` | 64 | the reflection pads | |
| | `aten._native_batch_norm_legit.no_stats` | 15 | InstanceNorm | |
| | `aten.upsample_bilinear2d` | 2 | the two upsampling stages | |
|
|
| Note the specific batch-norm overload. The MLX backend does register |
| `_native_batch_norm_legit_no_training`, but not the `no_stats` variant that |
| InstanceNorm lowers to, and a style transfer network is InstanceNorm in every |
| block. |
|
|
| This is the same class of failure as the SDXS text-to-image model, which |
| fragments into 28 subgraphs for want of `native_group_norm`, filed upstream as |
| https://github.com/pytorch/executorch/issues/22017. |
|
|
| ## Why style transfer is not a special case |
|
|
| MLX runs on the Metal GPU; Core ML lowers to the ANE, which is purpose-built for |
| this kind of network. MLX was measured behind Core ML on every convolutional |
| vision model tested. Its real advantage is transformer workloads, where |
| weight-only quantization shrinks the artifact substantially: CLIP vision is 97% |
| `nn.Linear` and goes 351 MB to 98 MB. A style transfer network is pure |
| convolution and lands on the wrong side of that line. |
|
|
