Debug a transformer training pipeline
Overview
This question evaluates a candidate's ability to diagnose and debug Transformer training pipelines, covering competencies in data preprocessing, tokenization and masking semantics, loss configuration, mixed-precision stability, optimizer dynamics, distributed training, and reproducibility.
Debugging Plan: PyTorch Transformer Text Model with Mask Errors, Metric Plateau, AMP Crashes, and Nondeterminism
Context
You are training a Transformer-based text model in PyTorch for a sequence task (e.g., causal language modeling, sequence classification, or token classification). The model shows four symptoms:
- Occasional CUDA shape/index errors around attention masks.
- Validation metrics plateau near chance while training loss decreases.
- Intermittent crashes when using mixed precision (AMP) and gradient accumulation.
- Nondeterministic results across runs.
Assume a standard training stack: PyTorch, Hugging Face–style tokenization, DataLoader(s), CrossEntropy loss variants (ignore_index, label smoothing, class weights), AdamW + scheduler, optional DDP, AMP, and gradient clipping.
Task
Propose a systematic, end-to-end debugging plan to localize and resolve all four issues. For each area below, specify concrete checks/experiments, describe the failure signal(s), outline a minimal reproducible example or unit test, and state how you would implement and verify the fix:
- Data preprocessing: padding, truncation, label alignment
- Tokenization and attention/causal masks
- Positional encodings
- Loss computation: ignore_index, label smoothing, class weights
- Optimizer/scheduler/zero_grad/gradient clipping
- AMP/GradScaler settings and gradient accumulation
- Seed control and deterministic kernels
- DDP and Sampler configuration
Explain how each suspected bug would manifest, how you’d isolate it, and how to confirm the fix.
Solution
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