[aw471]

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 Add KumoAnomaly example #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.

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[codecov]

Codecov Report

❌ Patch coverage is 23.52941% with 13 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.86%. Comparing base (74a87e7) to head (01e54e7).
⚠️ Report is 1 commits behind head on main.
✅ All tests successful. No failed tests found.

Files with missing lines	Patch %	Lines
sdm/models/kumo/timeseries/anomaly/ckpt.py	15.38%	11 Missing ⚠️
sdm/models/kumo/timeseries/anomaly/model.py	50.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1209      +/-   ##
==========================================
- Coverage   82.95%   82.86%   -0.09%     
==========================================
  Files         172      173       +1     
  Lines       11121    11136      +15     
==========================================
+ Hits         9225     9228       +3     
- Misses       1896     1908      +12     

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[coderabbitai]

📝 Summary

Summary by CodeRabbit

• Bug Fixes
  • Pretrained anomaly detection models now load their checkpoint weights correctly and initialize on the requested device.
• New Features
  • Transformer blocks now support configurable normalization on attention and MLP residual paths, along with dropout on attention outputs during training.
• Documentation
  • Clarified that the anomaly model’s recipe argument describes custom preprocessing.

The PR extends TransformerBlock with residual normalization and attention-output dropout. The Kumo anomaly model now uses a Kumo-specific transformer block and loads remapped pretrained checkpoint weights.

Changes: Anomaly Transformer

Layer / File(s)	Summary
TransformerBlock residual behavior sdm/nn/attention.py, test/nn/test_attention.py, test/nn/test_compile.py	TransformerBlock supports attention and MLP residual normalizers and applies dropout to attention output during training. Tests cover residual paths, output buffers, aliasing, dropout, and compiled execution.
Kumo anomaly transformer block sdm/models/kumo/timeseries/anomaly/block.py, test/models/kumo/timeseries/anomaly/test_block.py	KumoAnomalyTransformerBlock configures GELU, dropout, residual layer norms, and attention initialization. ResidualBlock uses it for time and feature layers. The reference test builds a PyTorch encoder layer from the Kumo block's weights.
Pretrained checkpoint loading sdm/models/kumo/timeseries/anomaly/ckpt.py, sdm/models/kumo/timeseries/anomaly/model.py	remap_ckpt translates checkpoint keys and squeezes selected projection weights. KumoAnomaly constructs its model on the requested device and loads the remapped checkpoint model state. The recipe description now specifies custom preprocessing.

Priority: ⚪ Pending latest changes

Estimated code review effort: 3 (Moderate) | ~25 minutes

Merge Risk: 🟠 High · up to ca1b5

The default KumoAnomaly() call fails with a NameError because remap_ckpt is never imported, so the pretrained checkpoint loading this change adds does not work. The Lint check also fails. Adding the import should fix both before merge.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 18.75% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 16 functions across 7 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly identifies the primary change: loading the pretrained KumoAnomaly checkpoint.
Description check		The description directly explains checkpoint loading, key remapping, device handling, and validation results, all of which match the changeset.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 1

---

• 🪄 Fix comments: Fix CodeRabbit comments on this PR

🤖 Prompt to fix review comments

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Inline comments:
Review comments at @sdm/models/kumo/timeseries/anomaly/model.py:
- Line 85: Add the missing import for remap_ckpt in the module import block of
the anomaly model so _load_from_pretrained can resolve the symbol when loading
the checkpoint; follow the relative or absolute import style used by neighboring
imports.

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• Source mode: Auto
• Repository: NVIDIA/structured-data-models
• Branch: aw471/kumo-anomaly-4-targetless-icl
• File: .coderabbit.yaml
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📥 Commits

Reviewing files that changed from the base of the PR and between 74a87e7 and ca1b565.

📒 Files selected for processing: 7

• sdm/models/kumo/timeseries/anomaly/block.py
• sdm/models/kumo/timeseries/anomaly/ckpt.py
• sdm/models/kumo/timeseries/anomaly/model.py
• sdm/nn/attention.py
• test/models/kumo/timeseries/anomaly/test_block.py
• test/nn/test_attention.py
• test/nn/test_compile.py

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