Implement a single reverse-denoising step: predict all masked tokens, keep the most confid

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Diffusion & Non-Autoregressive Language Models2 / 4

Chapter 222 / 4

Diffusion & Non-Autoregressive Language Models

Implement a single reverse-denoising step: predict all masked tokens, keep the most confid

Medium

Implement a single reverse-denoising step: predict all masked tokens, keep the most confident, re-mask the rest.

Target interface

reverse_step(model, x_t, mask_id, k)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.

solution.pyPython

Local draft

import torch
import torch.nn.functional as F

@torch.no_grad()
def reverse_step(model, x_t, mask_id, k):
    raise NotImplementedError

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Chapter 222 / 4

Diffusion & Non-Autoregressive Language Models

Implement a single reverse-denoising step: predict all masked tokens, keep the most confid

Medium

Implement a single reverse-denoising step: predict all masked tokens, keep the most confident, re-mask the rest.

Target interface

reverse_step(model, x_t, mask_id, k)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.