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Zero
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import numpy as np
import torch
def rope_precompute(x, grid_sizes, freqs, start=None):
b, s, n, c = x.size(0), x.size(1), x.size(2), x.size(3) // 2
# split freqs
if type(freqs) is list:
trainable_freqs = freqs[1]
freqs = freqs[0]
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
# loop over samples
output = torch.view_as_complex(x.detach().reshape(b, s, n, -1,
2).to(torch.float64))
seq_bucket = [0]
if not type(grid_sizes) is list:
grid_sizes = [grid_sizes]
for g in grid_sizes:
if not type(g) is list:
g = [torch.zeros_like(g), g]
batch_size = g[0].shape[0]
for i in range(batch_size):
if start is None:
f_o, h_o, w_o = g[0][i]
else:
f_o, h_o, w_o = start[i]
f, h, w = g[1][i]
t_f, t_h, t_w = g[2][i]
seq_f, seq_h, seq_w = f - f_o, h - h_o, w - w_o
seq_len = int(seq_f * seq_h * seq_w)
if seq_len > 0:
if t_f > 0:
factor_f, factor_h, factor_w = (t_f / seq_f).item(), (
t_h / seq_h).item(), (t_w / seq_w).item()
# Generate a list of seq_f integers starting from f_o and ending at math.ceil(factor_f * seq_f.item() + f_o.item())
if f_o >= 0:
f_sam = np.linspace(f_o.item(), (t_f + f_o).item() - 1,
seq_f).astype(int).tolist()
else:
f_sam = np.linspace(-f_o.item(),
(-t_f - f_o).item() + 1,
seq_f).astype(int).tolist()
h_sam = np.linspace(h_o.item(), (t_h + h_o).item() - 1,
seq_h).astype(int).tolist()
w_sam = np.linspace(w_o.item(), (t_w + w_o).item() - 1,
seq_w).astype(int).tolist()
assert f_o * f >= 0 and h_o * h >= 0 and w_o * w >= 0
freqs_0 = freqs[0][f_sam] if f_o >= 0 else freqs[0][
f_sam].conj()
freqs_0 = freqs_0.view(seq_f, 1, 1, -1)
freqs_i = torch.cat([
freqs_0.expand(seq_f, seq_h, seq_w, -1),
freqs[1][h_sam].view(1, seq_h, 1, -1).expand(
seq_f, seq_h, seq_w, -1),
freqs[2][w_sam].view(1, 1, seq_w, -1).expand(
seq_f, seq_h, seq_w, -1),
],
dim=-1).reshape(seq_len, 1, -1)
elif t_f < 0:
freqs_i = trainable_freqs.unsqueeze(1)
# apply rotary embedding
output[i, seq_bucket[-1]:seq_bucket[-1] + seq_len] = freqs_i
seq_bucket.append(seq_bucket[-1] + seq_len)
return output
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