fix: train API LoRA on MPS in bfloat16

[mchosc]

Summary

The API LoRA trainer (acestep.training.trainer.LoRATrainer, used by the training start route) ran Apple Silicon in fp16 (16-mixed). On the XL decoder every LoRA gradient was non-finite. With one sample and gradient accumulation of 4, the only optimizer step is the remainder path. That path zeroed the accumulation count and then divided by it: ZeroDivisionError: float division by zero.

MPS now uses bfloat16 / bf16-mixed, which is what CUDA and XPU already select. CPU stays fp32. The finite-gradient check runs only when Fabric is not in 16-mixed, because that mode scales the loss and the check would see scaled infs. bf16-mixed does not scale. A non-finite remainder is discarded and does not divide. A successful remainder always counts toward the epoch loss, including steps that are not on the log interval. An epoch that applies no updates stops with “every step produced non-finite gradients” instead of logging a zero loss.

Scope

• acestep/training/trainer.py
• acestep/training/trainer_precision_test.py

training_v2 is a separate trainer and is unchanged. Its automatic MPS precision is still fp16.

Risk and Compatibility

• Target: MPS LoRA training through the API trainer.
• CUDA and XPU now share the MPS branch in the selector, and the dtype and precision string they return are the same as before.
• CPU is unchanged.
• LoKr uses the same dtype selector, so MPS LoKr also moves from fp16 to bf16. The LoKr remainder path already skipped non-finite steps and was not edited.
• Decoder weights stay fp32. The forward autocasts to the compute dtype.

Regression Checks

• python -m unittest acestep.training.trainer_precision_test
• Manual, Apple Silicon, XL DiT, one 240-second latent, rank 64, accumulation 4, one epoch: finite loss (about 1.06 raw). A later 10-epoch run completed and wrote checkpoints.
• CUDA and XPU were not re-run. Their selector results are covered by the unit test and are unchanged.

Reviewer Notes

The “no finite updates” stop sits at the end of an epoch, so the first epoch that applies no update ends training.

Summary by CodeRabbit

• Training
  • MPS training now uses bfloat16 mixed precision, matching CUDA and XPU, while keeping decoder weights in full precision.
• Bug Fixes
  • Improved gradient validation and update tracking in LoRA and LoKr training, including partial gradient-accumulation batches.
  • LoRA training now stops with an error if an epoch completes without a successful optimizer update.
• Tests
  • Added coverage for precision selection across MPS, CUDA, XPU, and CPU.

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📥 Commits

Reviewing files that changed from the base of the PR and between ccb72ed and 7564cda.

📒 Files selected for processing (2)

• acestep/training/trainer.py
• acestep/training/trainer_precision_test.py

🚧 Files skipped from review as they are similar to previous changes (2)

• acestep/training/trainer_precision_test.py
• acestep/training/trainer.py

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📝 Walkthrough

Walkthrough

The trainer now selects bfloat16 for MPS and uses bf16-mixed Fabric precision. LoRA and LoKr apply manual non-finite-gradient checks for precisions other than 16-mixed. LoRA also records remainder-batch updates and returns an error when an epoch has no successful updates.

Changes

Training precision and optimizer updates

Layer / File(s)	Summary
Device precision selection acestep/training/trainer.py, acestep/training/trainer_precision_test.py	MPS now uses torch.bfloat16 and Fabric precision bf16-mixed. Tests cover precision selection for MPS, CUDA, XPU, and CPU. Comments describe bfloat16 autocasting and float32 decoder weights.
Gradient checks and accumulation updates acestep/training/trainer.py	LoRA and LoKr perform manual non-finite-gradient checks for precisions other than 16-mixed. LoRA checks leftover accumulation when enabled, records successful remainder updates, and returns an error when an epoch has no successful updates.

Priority: ➖ Normal

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

Change: Bug fix

Merge Risk: ⚪ Minimal · up to 7564c

The MPS bf16 change and remainder-step handling are consistent with the supported environment. No merge-blocking issue remains; normal checks should complete before merging.

Security Architecture Review

Security architecture risk: 🔵 Low · up to ccb72

The reviewed changes improve handling of invalid training updates without establishing a new privilege or access boundary. Risk is bounded to existing training execution and saved results. Deployment-wide concurrency, isolation, and compatibility guarantees were not fully established.

Retained concerns
No architecture-level concerns identified.

Security review details

Security Blast Radius

• inferred — The demonstrated exposure remains the existing service-owned decoder and configured training-output storage. Request-controlled training parameters and tensor paths already reach this trainer; the reviewed changes alter numerical execution and update outcomes rather than adding a new caller or credential-bearing authority.

Trust Boundaries and Controls

• observed — The existing API start route invokes a verify_api_key dependency, rejects starts when shared state reports active training, creates a fresh trainer and run_id, and checks run identity while consuming progress. These code-level guards do not establish effective authentication policy or cross-process and cross-tenant isolation.
• observed — Although the new manual-check condition mentions 16-mixed, the reviewed selector never returns that mode. Fabric receives the selector result directly, so the hypothetical GradScaler-skipped-update accounting problem is not a reachable PR concern in this construction path.

Resilience and Maintainability Implications

• observed — The trainer resets is_training in its existing finally block. The API worker also clears active state, restores decoder evaluation mode, and invokes component restoration in finally. These recovery paths remain present for the new zero-update termination; transactional rollback of model changes or checkpoint writes is not established.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the primary change: fixing API LoRA training on MPS by using bfloat16.
Docstring Coverage	✅ Passed	Docstring coverage is 87.50% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 8 functions across 2 files.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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_{A rabbit checks the gradients with care
Bfloat16 hops through the training air
A final batch gets its update counted
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Actionable comments posted: 2

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• 🪄 Fix CodeRabbit comments on this PR

🤖 Prompt to fix review comments

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Inline comments:
Review comments at @acestep/training/trainer_precision_test.py:
- Line 13: Add concise purpose docstrings to the three test methods in the
trainer precision test module, including test_mps_uses_bfloat16 and the methods
at the other cited locations.

Review comments at @acestep/training/trainer.py:
- Line 93: Update the shared precision selection near device_type and LoKr’s
manual gradient-check condition so selecting bf16 on MPS does not disable LoKr
validation. Apply the LoRA-specific `precision != "16-mixed"` condition to LoKr
before changing its precision, or restrict this precision change to LoRA;
preserve LoKr checks for MPS `bf16-mixed`.

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ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Advanced

Run ID: c3602147-298d-4fd0-ad1a-16b3147b5f8b

📥 Commits

Reviewing files that changed from the base of the PR and between ca1e85f and ccb72ed.

📒 Files selected for processing (2)

• acestep/training/trainer.py
• acestep/training/trainer_precision_test.py

Included review availability: This review used your included allowance. Your plan provides up to 10 included reviews per hour; 8 remain after this review.