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In layer-wise parallelism, the computation for a given block, denoted as $$l$$, is represented by $$\mathrm{B}_l$$. The forward pass is symbolized by an upward arrow ($$\uparrow$$), and the backward pass by a downward arrow ($$\downarrow$$). For instance, the forward and backward computations for Block 1 would be shown as $$\mathrm{B}_1$$ ($$\uparrow$$) and $$\mathrm{B}_1$$ ($$\downarrow$$), while for Block 2 they would be $$\mathrm{B}_2$$ ($$\uparrow$$) and $$\mathrm{B}_2$$ ($$\downarrow$$).

Symbolic Representation of Layer-wise Parallelism

A computational process involving two sequential blocks, Block 1 (B₁) and Block 2 (B₂), is represented by the following sequence of operations: B₁ ↑, B₂ ↑, B₂ ↓, B₁ ↓. What does this sequence describe?

A neural network model is divided into three computational blocks (Block 1, Block 2, and Block 3) for layer-wise parallelism. Arrange the symbolic representations of the computations for a single, complete forward and backward pass in the correct chronological order.

A machine learning engineer is implementing a 3-block model using layer-wise parallelism. They have documented the sequence of computations for a single training step. Analyze the provided sequence and identify any logical errors. Explain your reasoning based on the flow of data and gradients.

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