This is where I spent most of my prep and it showed.
Start by defining LoRA and its core mechanism, then systematically compare each variant (QLoRA, DoRA, LoRA+, AdaLoRA) in terms of their key innovations, trade-offs, and use cases. Emphasize practical considerations like memory, compute, and performance to show you can choose the right method for a given scenario.
Pro tip: Relate each variant to real-world constraints such as GPU memory, training time, and model quality, and mention that the choice often depends on the specific hardware and task requirements—demonstrating a pragmatic, engineering-focused mindset.
Briefly describe LoRA: it freezes the pre-trained model weights and injects trainable low-rank matrices into each layer, reducing trainable parameters and memory footprint.
Explain QLoRA: it quantizes the base model to 4-bit and applies LoRA, enabling fine-tuning of large models on a single GPU with minimal accuracy loss.
Cover DoRA: decomposes weight updates into magnitude and direction, improving learning capacity; and LoRA+: uses different learning rates for the low-rank matrices to speed up convergence.
Discuss AdaLoRA: dynamically allocates rank across layers based on importance, optimizing parameter budget for better performance.
Summarize trade-offs: QLoRA for memory-constrained scenarios, DoRA for higher quality, LoRA+ for faster training, AdaLoRA for adaptive resource allocation. Provide guidance on when to pick each.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew the high-level story but stumbled a bit on why DPO sidesteps the reward model entirely and what that costs you in terms of flexibility.
Start by defining each method (RLHF, DPO, PPO) and explaining their roles in aligning LLMs, then compare their trade-offs in terms of complexity, stability, and data efficiency. Emphasize practical considerations for deploying these methods at scale, especially in a production environment like Amazon.
Pro tip: Highlight that DPO simplifies the RLHF pipeline by eliminating the need for a separate reward model and PPO, but note that PPO can still be more effective when you have a high-quality reward model and need fine-grained control. This shows you understand both theory and practical deployment trade-offs.
Explain that RLHF (Reinforcement Learning from Human Feedback) involves training a reward model on human preferences and then using RL (typically PPO) to fine-tune the LLM to maximize that reward.
Describe PPO (Proximal Policy Optimization) as an RL algorithm that updates policies conservatively using a clipped surrogate objective, ensuring stable training. Mention its use in RLHF to optimize the LLM policy against the reward model.
Explain that DPO (Direct Preference Optimization) directly optimizes the LLM on human preference data without training a separate reward model or using RL, simplifying the alignment process.
Discuss trade-offs: RLHF/PPO can be more powerful but complex and unstable; DPO is simpler, more stable, and computationally efficient, but may lack the fine-grained control of RL-based methods.
Tie the discussion to practical deployment: data requirements, compute cost, ease of implementation, and alignment quality, especially in a large-scale setting like Amazon.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the KV cache and explaining its role in autoregressive decoding, then quantify the memory implications and introduce paged attention as a solution. Use a concrete example (e.g., transformer decoding) to illustrate the trade-offs between speed and memory.
Pro tip: Emphasize that KV cache memory grows linearly with sequence length and batch size, and that paged attention (like vLLM) reduces fragmentation by managing cache in fixed-size blocks, enabling higher throughput.
Explain that the KV cache stores key and value tensors from previous tokens during autoregressive decoding, avoiding recomputation.
Describe how caching keys/values allows each new token to attend to all previous tokens without recomputing their representations, reducing per-token latency.
Quantify memory usage: 2 * layers * heads * head_dim * seq_len * batch_size * precision bytes, and note it grows linearly with sequence length and batch size, often becoming the bottleneck.
Explain that paged attention divides the KV cache into fixed-size blocks (pages) stored non-contiguously, reducing fragmentation and enabling efficient memory sharing and eviction.
Conclude that KV cache speeds up decoding at the cost of memory, and paged attention mitigates memory issues, improving throughput for large models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The part I fumbled was articulating why ALiBi generalizes to longer sequences better than sinusoidal without just saying 'it adds a bias to attention scores.' Tried to explain the extrapolation behavior and got a bit tangled.
Start by categorizing positional encoding schemes into absolute (sinusoidal, learned) and relative (RoPE, ALiBi), then compare them along dimensions like extrapolation, computational cost, and compatibility with attention mechanisms. Conclude with practical recommendations for when to use each, tying back to real-world constraints like sequence length and training efficiency.
Pro tip: Emphasize that the choice often depends on whether you need length extrapolation and whether your architecture can afford the overhead—Amazon values pragmatic trade-off analysis over theoretical purity.
Explain why positional encoding is needed in Transformers and classify schemes into absolute (sinusoidal, learned) and relative (RoPE, ALiBi).
Contrast sinusoidal (fixed, no parameters, poor extrapolation) and learned (flexible, but limited to training length and adds parameters).
Describe RoPE (rotates queries/keys, good extrapolation, used in LLaMA) and ALiBi (linear bias, no parameters, strong extrapolation, used in BLOOM).
Discuss dimensions: extrapolation to longer sequences, computational overhead, memory, ease of implementation, and impact on model quality.
Suggest when to use each: e.g., sinusoidal for simplicity, learned for fixed-length tasks, RoPE for long-context with rotation, ALiBi for efficient extrapolation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem: standard attention is memory-bound due to large intermediate matrices and limited GPU SRAM. Then explain how FlashAttention's IO-aware design, tiling, and recomputation reduce memory traffic and improve speed. Conclude with trade-offs and practical implications for ML systems at Amazon.
Pro tip: Emphasize that FlashAttention is not an approximation—it computes exact attention while being faster and more memory-efficient, which is crucial for production systems. Mention that it enables longer context lengths and larger batch sizes, directly impacting model quality and cost.
Explain that standard attention is memory-bound: it materializes large N×N attention matrices, causing excessive reads/writes to HBM. Contrast with compute-bound operations.
Highlight that FlashAttention minimizes HBM accesses by keeping intermediate results in on-chip SRAM and fusing operations. This reduces memory traffic, which is the key to speedups.
Detail how the attention matrix is computed in blocks (tiles) that fit in SRAM. Each tile is processed independently, and softmax is computed incrementally to avoid storing the full matrix.
Describe how FlashAttention recomputes attention scores during the backward pass instead of storing them, trading extra compute for reduced memory usage. This is feasible because compute is cheaper than memory access.
Conclude with speed and memory improvements, exactness, and suitability for long sequences. Mention potential trade-offs like increased compute or implementation complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.