File size: 1,790 Bytes
e0eb79a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | """Forward masking process q(z_t | x_0).
The craftax twin is src/diffusion/forward.py:forward_process.
Each token is independently replaced with mask_token with probability
sigma_t = 1 - alpha_t. PAD positions are never masked.
"""
from __future__ import annotations
from collections.abc import Callable
import torch
from torch import Tensor
def q_sample(
x0: Tensor,
t: Tensor,
mask_token: int,
pad_token: int,
schedule_fn: Callable[[Tensor], Tensor],
) -> Tensor:
"""Sample z_t from the forward masking process.
Args:
x0: Clean action sequences. Shape ``[B, L]``, dtype int64.
t: Per-sample diffusion time in [0, 1]. Shape ``[B]``.
mask_token: Integer ID of the MASK token.
pad_token: Integer ID of the PAD token.
schedule_fn: Noise schedule returning alpha(t).
Returns:
Noisy sequence z_t. Shape ``[B, L]``, dtype int64.
PAD positions are preserved unchanged.
"""
if x0.ndim != 2 or t.ndim != 1 or x0.shape[0] != t.shape[0]:
raise ValueError(
f"q_sample expects x0 [B, L] and t [B]; got {tuple(x0.shape)}, {tuple(t.shape)}"
)
alpha_t = schedule_fn(t) # [B]
sigma_t = 1.0 - alpha_t # mask probability per sample
sigma_t = sigma_t.unsqueeze(-1) # [B, 1]
# Independent Bernoulli masking per position
mask_draws = torch.rand_like(x0, dtype=torch.float32) # [B, L]
do_mask = mask_draws < sigma_t # [B, L]
zt = torch.where(do_mask, mask_token, x0)
# (A) benchmark-forced: PAD exists only in this repo (variable-length
# oracle trajectories); craftax windows are fixed-length
# Restore PAD positions — never mask padding
pad_mask = x0 == pad_token # [B, L]
return torch.where(pad_mask, pad_token, zt)
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