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Codecov Report

❌ Patch coverage is 52.38095% with 10 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.79%. Comparing base (73636a8) to head (54f5a0f).
✅ All tests successful. No failed tests found.

Files with missing lines	Patch %	Lines
sdm/models/kumo/tabular/icl.py	55.55%	4 Missing ⚠️
sdm/models/kumo/tabular/cell_embedding.py	50.00%	3 Missing ⚠️
sdm/models/kumo/tabular/row_embedding.py	40.00%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1297      +/-   ##
==========================================
- Coverage   82.85%   82.79%   -0.07%     
==========================================
  Files         177      177              
  Lines       11499    11516      +17     
==========================================
+ Hits         9528     9535       +7     
- Misses       1971     1981      +10     

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📝 Summary

Summary by CodeRabbit

• New Features
  • Tabular models now support batches with different training-context sizes for each example. Context boundaries are applied consistently when processing embeddings and attention, allowing examples with variable amounts of training data to be handled together.
  • Existing behavior is retained when no per-example context size is provided.

The tabular model now accepts per-example training sizes. It uses them to limit cell and target embeddings, pass context lengths to transformer calls, and combine context and query outputs.

Changes: Per-batch training context

Layer / File(s)	Summary
Training-size embedding and propagation sdm/models/kumo/tabular/model.py, sdm/models/kumo/tabular/row_embedding.py, sdm/models/kumo/tabular/cell_embedding.py	forward_train passes train_size through RowEmbedding. Cell means use rows below each size, and target embeddings are zeroed at and beyond each size.
Training-size context in ICL layers sdm/models/kumo/tabular/icl.py	ICLBlock masks labels outside each training context, passes sizes to transformer calls, and combines context outputs at masked positions with query outputs elsewhere.

Priority: Normal

Estimated code review effort: 3 (Moderate) | ~20 minutes

Sequence Diagram(s)

sequenceDiagram
  participant KumoTabular
  participant RowEmbedding
  participant CellEmbedding
  participant ICLBlock
  participant TransformerLayers
  KumoTabular->>RowEmbedding: forward_train passes train_size
  RowEmbedding->>CellEmbedding: compute cell embeddings with train_size
  CellEmbedding-->>RowEmbedding: return cell embeddings
  KumoTabular->>ICLBlock: forward with train_size
  ICLBlock->>TransformerLayers: pass train_size as seqused_key_value
  TransformerLayers-->>ICLBlock: return context and query outputs

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Possibly related PRs

• NVIDIA/structured-data-models#219: Adds padded-context masking to TabICLv2 using seqused_train and seqused_cols.

Merge Risk: Moderate · up to 54f5a

Common padding values can break training or produce invalid gradients. Mask padded targets before embedding them prior to merge.

Pre-merge checks: 3 · 1 · 1

Failed checks: 1 warning · 1 inconclusive

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…	Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check		No pull request description was provided, so the description cannot be evaluated for relevance to the changeset.	Add a concise description that explains how the Kumo Tabular code supports padded contexts and variable training sizes.

Passed checks: 3 passed

Check name	Status	Explanation
Title check		The title clearly identifies the main change: support for padded contexts in Kumo Tabular.
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.

Full details: Docstring Coverage

Explanation

Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 3 functions across 4 files.

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

Finishing Touches: 💡 1

📝 Generate docstrings: 💡

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Actionable comments posted: 1

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• 🪄 Fix comments: Fix CodeRabbit comments on this PR

Prompt to fix review comments

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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @sdm/models/kumo/tabular/row_embedding.py:
- Around line 164-167: Update the target-processing paths in RowEmbedding and
ICLBlock to replace padded y values with valid, finite placeholders before
classification embedding or regression projection; retain the existing output
mask so padded targets remain excluded.

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• Source mode: Auto
• Repository: NVIDIA/structured-data-models
• Branch: kumo-tabular-padded-context
• File: .coderabbit.yaml
• Commit: 54f5a0f
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• Run ID: a6f35fd5-85b9-48ed-897e-acd2c0c146ec

Commits

Reviewing files that changed from the base of the PR and between 73636a8 and 54f5a0f.

Files selected for processing: 4

• sdm/models/kumo/tabular/cell_embedding.py
• sdm/models/kumo/tabular/icl.py
• sdm/models/kumo/tabular/model.py
• sdm/models/kumo/tabular/row_embedding.py

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