fix(models): pass mask and is_causal through gradient checkpointing in MaskedCausalVisionTransformer (#2049)

[harmehak0173]

Fixes #2049

Summary

When gradient checkpointing is enabled (model.set_grad_checkpointing(True)), MaskedCausalVisionTransformer.forward_features() previously used _manipulate.checkpoint_seq(self.blocks, x), which only forwarded tensor x and dropped the mask and is_causal arguments. Consequently, attention masking was ignored when checkpointing was enabled.

Changes

1. lightly/models/modules/masked_causal_vision_transformer.py:
   • Updated MaskedCausalBlock.forward to forward is_causal to self.attn.
   • Updated MaskedCausalVisionTransformer.forward_features to checkpoint each block with torch.utils.checkpoint.checkpoint(block, x, mask, is_causal, use_reentrant=False), preserving the mask and is_causal arguments during both forward and backward passes.
   • Removed unused _manipulate import.
2. tests/models/modules/test_masked_causal_vision_transformer.py:
   • Added unit tests verifying output parity between checkpointed and non-checkpointed forward passes with masked and unmasked inputs.
   • Added unit tests verifying gradient parity (parameter gradients and input gradients) during backward pass with gradient checkpointing enabled.
   • Added tests for is_causal=True and is_causal=False.

• Preserved mask and is_causal during gradient-checkpointed execution.
• Added output and gradient parity tests for causal and non-causal modes.
• Removed the unused _manipulate import.

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• lightly/models/modules/masked_causal_vision_transformer.py
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• tests/models/modules/test_masked_causal_vision_transformer.py
• lightly/models/modules/masked_causal_vision_transformer.py

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📝 Walkthrough

Walkthrough

MaskedCausalVisionTransformer now preserves mask and is_causal during gradient checkpointing. Masked attention supports bidirectional mode when is_causal=False. Tests compare outputs and gradients across execution modes.

Changes

Masked causal checkpointing

Layer / File(s)	Summary
Propagate causal attention settings lightly/models/modules/masked_causal_vision_transformer.py	MaskedCausalAttention applies the causal mask only when is_causal=True and a mask is provided. MaskedCausalBlock.forward and forward_features forward is_causal.
Checkpoint execution and validation lightly/models/modules/masked_causal_vision_transformer.py, tests/models/modules/test_masked_causal_vision_transformer.py	Per-block non-reentrant checkpointing forwards mask and is_causal. Tests compare masked and unmasked outputs, input gradients, parameter gradients, and both causal modes.

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

Merge Risk: ⚪ Minimal · up to 1a013

This localized change preserves attention masking and causal behavior during gradient checkpointing and adds forward and backward parity tests; no actionable merge-blocking risk remains beyond normal checks.

Sequence Diagram(s)

sequenceDiagram
  participant MaskedCausalVisionTransformer
  participant PyTorchCheckpoint
  participant MaskedCausalBlock
  participant MaskedCausalAttention
  MaskedCausalVisionTransformer->>PyTorchCheckpoint: checkpoint block with mask and is_causal
  PyTorchCheckpoint->>MaskedCausalBlock: execute forward
  MaskedCausalBlock->>MaskedCausalAttention: pass mask and is_causal
  MaskedCausalAttention->>MaskedCausalAttention: apply causal mask only when is_causal is true

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🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 40.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 10 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly describes the primary change: preserving mask and is_causal arguments through gradient checkpointing in MaskedCausalVisionTransformer.
Linked Issues check	✅ Passed	The changes address issue #2049 by forwarding mask through checkpointed blocks and replacing checkpoint_seq with per-block checkpointing that preserves x, mask, and is_causal. The added tests verify m…
Out of Scope Changes check	✅ Passed	The changes remain within scope. The is_causal behavior, related tests, and removal of the unused import support the stated pull request objectives and do not introduce unrelated functionality.

Full details: Linked Issues check

Explanation

The changes address issue #2049 by forwarding mask through checkpointed blocks and replacing checkpoint_seq with per-block checkpointing that preserves x, mask, and is_causal. The added tests verify masked output parity and gradient parity between checkpointed and ordinary execution.

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Actionable comments posted: 1

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Inline comments:
In `@lightly/models/modules/masked_causal_vision_transformer.py`:
- Line 154: Update MaskedCausalAttention.forward and its _get_attention_mask
usage so the causal mask is applied only when is_causal is true, while false
enables bidirectional attention for masked inputs. Add a test covering the same
masked input in both modes and verifying their attention behavior differs as
expected.

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📒 Files selected for processing (2)

• lightly/models/modules/masked_causal_vision_transformer.py
• tests/models/modules/test_masked_causal_vision_transformer.py

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