I started with self-attention because I knew it well, explained the query-key-value thing, then talked about multi-head attention letting the model attend to multiple representation subspaces at once.
Start with a high-level overview of the Transformer architecture, then dive into self-attention, and finally compare with RNNs/LSTMs. Emphasize the parallelization and long-range dependency advantages, tying them to practical benefits like training efficiency and scalability.
Pro tip: Relate the architecture to real-world impact, such as how self-attention enables models like BERT and GPT to capture context better, and mention trade-offs like quadratic complexity. This shows you understand both theory and practical implications.
Briefly describe the encoder-decoder structure, highlighting components like multi-head attention, feed-forward networks, residual connections, and layer normalization.
Walk through how queries, keys, and values are computed and used to produce weighted sums, enabling each token to attend to all others. Mention scaling and softmax.
Contrast the sequential processing of RNNs/LSTMs with the parallelizable, global context of Transformers, highlighting advantages in handling long-range dependencies and training speed.
Connect the architecture to benefits like scalability, transfer learning, and state-of-the-art results in NLP, and acknowledge limitations like memory usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on how to frame the core motivation.
Start by defining the core problem MoE solves: scaling model capacity without proportional compute cost. Then explain routing as a learned gating mechanism that selects top-k experts per token. Finally, discuss trade-offs like load balancing, communication overhead, and training instability, tying them to real-world deployment constraints.
Pro tip: Emphasize that MoE is not just about efficiency—it's about conditional computation, and the routing mechanism is where most engineering challenges (and failures) occur. Mention that Amazon's scale makes communication overhead and expert parallelism critical considerations.
Explain that dense LLMs scale compute with parameters, making training and inference expensive. MoE decouples model capacity from compute by activating only a subset of parameters per token.
Describe how a gating network (e.g., softmax over experts) computes affinity scores for each token, then selects top-k experts. The token is processed by those experts and outputs are combined, often weighted by gate probabilities.
Mention increased model capacity without proportional FLOPs, better specialization of experts, and potential for faster inference if routing is efficient.
Cover load balancing (expert collapse), communication overhead in distributed settings, training instability, and increased memory requirements for storing all experts.
Connect to Amazon-scale deployment: expert parallelism, all-to-all communication costs, and the need for robust routing to avoid bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Data parallelism vs tensor parallelism vs pipeline parallelism, I knew the rough shapes of all three but tensor parallelism always makes me nervous because the partitioning logic gets subtle fast.
Start by categorizing the main forms of parallelism (data, tensor, pipeline, and hybrid) and briefly explain when each is used. Then define all-reduce as a collective operation that aggregates values across devices, and illustrate its role in synchronizing gradients during data-parallel training of a Transformer. Finally, give a concrete example such as all-reduce in the backward pass to average gradients across GPUs.
Pro tip: Emphasize that all-reduce is not just for gradients—it's also used in tensor parallelism to combine partial results, and mention that efficient implementations (e.g., ring all-reduce) are critical for scaling. This shows depth beyond textbook definitions.
List and briefly describe data, tensor, pipeline, and hybrid parallelism, noting their trade-offs (e.g., communication overhead, memory savings).
Define all-reduce as an operation where all processes contribute data and receive the reduced result (e.g., sum) across devices.
Describe how all-reduce is used in data parallelism to average gradients across replicas before the optimizer step.
Walk through a specific scenario: in a data-parallel Transformer training with multiple GPUs, after backpropagation, all-reduce sums gradients across GPUs and divides by world size.
Mention communication bottlenecks, overlap of communication with computation, and algorithms like ring all-reduce for efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.