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Process Flow in Layer-wise Model Parallelism
In layer-wise model parallelism, workers operate sequentially according to the order of the layers in the model's architecture. The forward pass processes input by moving from lower-level to upper-level layers across the workers. Conversely, the backward pass propagates error gradients in the reverse direction, from the upper-level layers back down to the lower-level ones.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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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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An 8-layer neural network is distributed across 4 workers, with each worker holding 2 consecutive layers (Worker 1 has layers 1-2, Worker 2 has layers 3-4, etc.). During the forward pass for a single data batch, what is the state of Worker 1 and Worker 4 at the exact moment Worker 3 is actively computing its layers (layers 5-6)?
A 4-layer neural network is distributed across two workers using layer-wise model parallelism (Worker 1 holds layers 1-2, Worker 2 holds layers 3-4). Arrange the following events in the correct chronological order for a single training step, which includes one forward and one backward pass.
Backward Pass Latency in Sequential Model Parallelism