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Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at OpenAI.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at OpenAI during technical interviews.

Diagnose Transformer training and inference bugs

Quick Overview

This question evaluates competency in debugging Transformer-based models, including tokenizer and padding correctness, attention mask and positional embedding validation, gradient and optimizer behavior, numerical stability and NaN sources, multi-GPU and checkpointing issues, data integrity, and performance profiling within the Machine Learning domain. It is commonly asked to assess an engineer's ability to diagnose complex, system-level training and inference failures and tests both conceptual understanding of model internals and practical application skills in instrumentation, reproducibility, and large-scale debugging.

Debugging a Transformer That Intermittently Throws Shape/Type Errors and Fails to Converge

You are given a Transformer-based sequence model that:

Intermittently raises shape or dtype mismatch errors during training.
Fails to converge after several thousand steps (loss stalls or diverges).

Describe your end-to-end debugging approach. Include concrete checks, metrics, and code-level instrumentation you would add.

Cover These Areas

Reproducibility and failure capture
- Seed control, logging, anomaly detection, and making the error reproducible.
Tokenization, padding, and special tokens
- Validate tokenizer round-trips, PAD/BOS/EOS/UNK handling, truncation rules, and padding policy.
Attention masks
- Verify causal vs. bidirectional masks, mask shapes/dtypes/values, and mask application order.
Positional embeddings
- Check absolute vs. rotary/sinusoidal positions, max context length, and KV-cache position handling.
Assertions and unit tests
- Add assertions for tensor shapes/dtypes/sequence lengths; create unit tests for collate functions and model forward.
Language modeling targets
- Verify label shifting for decoder-only LM, ignore_index for PAD, and BOS/EOS conventions.
Gradients and optimization
- Detect exploding/vanishing gradients, gradient norms, clipping, optimizer hyperparameters, LR schedule, and mixed precision.
Numerical stability and NaNs
- Diagnose NaN sources, softmax masking in FP16, loss scaling, epsilon settings, and safe reductions.
Data bugs
- Spot dataset corruption, inconsistent tokenizers, unexpected truncation, and distribution shifts.
Checkpoint/resume and randomness

Validate checkpoint integrity, optimizer/scheduler/scaler state, sampler state, and RNG across workers/ranks.

Multi-GPU/DP/ZeRO/AMP issues

Synchronization bugs, gradient scaling, unused parameters, grad clipping across shards, and per-rank logging.

Throughput and memory profiling

Tokens/sec, dataloader stalls, operator-level hotspots, and OOM sources.

Minimal reproducible example (MRE)

Build a tiny, deterministic setup with targeted logging to localize the fault.

Provide concrete code snippets, assertions, metrics, and logs you would implement.

Quick Overview

Debugging a Transformer That Intermittently Throws Shape/Type Errors and Fails to Converge

You are given a Transformer-based sequence model that:

Intermittently raises shape or dtype mismatch errors during training.

Fails to converge after several thousand steps (loss stalls or diverges).

Describe your end-to-end debugging approach. Include concrete checks, metrics, and code-level instrumentation you would add.

Cover These Areas

Reproducibility and failure capture

Seed control, logging, anomaly detection, and making the error reproducible.

Tokenization, padding, and special tokens

Validate tokenizer round-trips, PAD/BOS/EOS/UNK handling, truncation rules, and padding policy.

Attention masks

Verify causal vs. bidirectional masks, mask shapes/dtypes/values, and mask application order.

Positional embeddings

Check absolute vs. rotary/sinusoidal positions, max context length, and KV-cache position handling.

Assertions and unit tests

Add assertions for tensor shapes/dtypes/sequence lengths; create unit tests for collate functions and model forward.

Language modeling targets

Verify label shifting for decoder-only LM, ignore_index for PAD, and BOS/EOS conventions.

Gradients and optimization

Detect exploding/vanishing gradients, gradient norms, clipping, optimizer hyperparameters, LR schedule, and mixed precision.

Numerical stability and NaNs

Diagnose NaN sources, softmax masking in FP16, loss scaling, epsilon settings, and safe reductions.

Data bugs

Spot dataset corruption, inconsistent tokenizers, unexpected truncation, and distribution shifts.

Checkpoint/resume and randomness

Validate checkpoint integrity, optimizer/scheduler/scaler state, sampler state, and RNG across workers/ranks.

Multi-GPU/DP/ZeRO/AMP issues

Synchronization bugs, gradient scaling, unused parameters, grad clipping across shards, and per-rank logging.

Throughput and memory profiling

Tokens/sec, dataloader stalls, operator-level hotspots, and OOM sources.

Minimal reproducible example (MRE)

Build a tiny, deterministic setup with targeted logging to localize the fault.

Provide concrete code snippets, assertions, metrics, and logs you would implement.

Diagnose Transformer training and inference bugs

Quick Overview

Debugging a Transformer That Intermittently Throws Shape/Type Errors and Fails to Converge

Cover These Areas

Solution

Comments (0)

Diagnose Transformer training and inference bugs

Quick Overview

Debugging a Transformer That Intermittently Throws Shape/Type Errors and Fails to Converge

Cover These Areas

Solution

Comments (0)