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Co-authored-by: Aditi Gautam <adgautam@nvidia.com>
Add optional `attn_residual_norm` and `mlp_residual_norm` modules to `TransformerBlock`, and apply its dropout to the attention output before the residual addition. Build the Kumo-Anomaly `ResidualBlock` on a new `KumoAnomalyTransformerBlock` instead of `torch.nn.TransformerEncoderLayer`.
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## aw471/kumo-anomaly-model #1106 +/- ##
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Coverage 82.91% 82.91%
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Files 172 172
Lines 11107 11133 +26
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+ Hits 9209 9231 +22
- Misses 1898 1902 +4 ☔ View full report in Codecov by Harness. |
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# Summary > Loads the released Kumo-Anomaly weights in `KumoAnomaly()`, filling in the checkpoint TODO from #1220. # Description of the change > `_load_from_pretrained` loads `final_model.pth` from `nvidia/Kumo-Anomaly` at the pinned revision, and `remap_ckpt` maps the checkpoint's state dict onto the SDM modules for a strict load. Inside each time and feature layer, the attention, feedforward and residual-norm weights are renamed to `attn.qkv_lin`, `attn.out_lin`, `mlp.0`/`mlp.3` and `attn_residual_norm`/`mlp_residual_norm`. The three kernel-size-1 `Conv1d` projection weights are squeezed for the `Linear` layers. > > The model is built on the target device rather than on `meta`. The step embedding's sinusoidal lookup is a non-persistent buffer that the checkpoint does not contain, so `load_state_dict(..., assign=True)` would leave it on `meta`. > > The loader and key mapping come from Aditi Gautam's `ckpt.py` in #1167. This PR is stacked on #1106. # Validation > CI does not download the weights, so these checks were run manually on CPU: > > - All 260 checkpoint tensors load strictly. > - Compared with #1167's pretrained `KumoAnomaly` (`num_samples=3`, `num_steps=5`, seeded generator), scores for series of shape `[250, 5]` and `[130, 30]` differ by at most 7.2e-7 and 1.5e-7, with identical top-10 rows. Swapping `norm1` and `norm2` in the remap changes scores by up to 0.52, so this comparison catches same-shape remap mistakes. > - On a 400-step series with a level shift at steps 250-259, the default call ranks exactly those steps highest. --------- Co-authored-by: Aditi Gautam <adgautam@nvidia.com> Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>
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Summary
Description of the change
Validation