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Example of Model Parallelism with a Transformer Decoder
Layer-wise model parallelism can be applied to a Transformer decoder composed of stacked blocks. To distribute the computational load, each block is assigned to a different worker. During a single run of the model, the forward pass () processes the input sequentially from the lowest layer (Worker 1) up to the highest layer (Worker ). Subsequently, the backward pass () propagates error gradients in the reverse order, moving from Worker back down to Worker 1. This creates a sequential execution flow across the workers, which can be visualized as follows:
| Worker | () | () | ||||||
| ... | ... | ... | ||||||
| Worker 2 | () | () | ||||||
| Worker 1 | () | () |
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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Process Flow in Layer-wise Model Parallelism
Example of Model Parallelism with a Transformer Decoder
Worker Idle Time in Layer-wise Model Parallelism
An engineer is tasked with training a very large neural network composed of 24 sequential layers. The model is too large to fit into the memory of a single processing device. To solve this, the engineer decides to distribute the model across 4 identical devices by partitioning it based on its layers. Which of the following strategies correctly applies this layer-based distribution method?
Analyzing Efficiency in a Distributed Model
Consider a large neural network with 12 sequential layers that needs to be distributed across 3 processing devices because it is too large for a single device. An engineer proposes the following distribution: Device 1 runs layers 1, 4, 7, 10; Device 2 runs layers 2, 5, 8, 11; and Device 3 runs layers 3, 6, 9, 12. This proposed method represents a correct implementation of a layer-based partitioning strategy where groups of consecutive layers are assigned to different devices.
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Symbolic Representation of Layer-wise Parallelism
A large neural network decoder, consisting of 12 sequential processing blocks, is distributed across 12 separate workers, with each worker assigned exactly one block. For a single input, the computation proceeds sequentially through the workers from 1 to 12 during the forward pass, and then in reverse from 12 to 1 during the backward pass. What is the primary factor limiting the overall computational efficiency of this specific arrangement?
A 3-block neural network decoder is distributed across 3 workers using layer-wise parallelism, with each worker responsible for one block (Worker 1 has Block 1, Worker 2 has Block 2, and Worker 3 has Block 3). For a single training iteration, arrange the following computational events in the correct chronological order.
GPU Utilization in a Distributed System