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| import numpy as np
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| import torch
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|
|
|
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| def rope_precompute(x, grid_sizes, freqs, start=None):
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| b, s, n, c = x.size(0), x.size(1), x.size(2), x.size(3) // 2
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|
|
|
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| if type(freqs) is list:
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| trainable_freqs = freqs[1]
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| freqs = freqs[0]
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| freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
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|
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|
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| output = torch.view_as_complex(x.detach().reshape(b, s, n, -1,
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| 2).to(torch.float64))
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| seq_bucket = [0]
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| if not type(grid_sizes) is list:
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| grid_sizes = [grid_sizes]
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| for g in grid_sizes:
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| if not type(g) is list:
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| g = [torch.zeros_like(g), g]
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| batch_size = g[0].shape[0]
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| for i in range(batch_size):
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| if start is None:
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| f_o, h_o, w_o = g[0][i]
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| else:
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| f_o, h_o, w_o = start[i]
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|
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| f, h, w = g[1][i]
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| t_f, t_h, t_w = g[2][i]
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| seq_f, seq_h, seq_w = f - f_o, h - h_o, w - w_o
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| seq_len = int(seq_f * seq_h * seq_w)
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| if seq_len > 0:
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| if t_f > 0:
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| factor_f, factor_h, factor_w = (t_f / seq_f).item(), (
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| t_h / seq_h).item(), (t_w / seq_w).item()
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|
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| if f_o >= 0:
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| f_sam = np.linspace(f_o.item(), (t_f + f_o).item() - 1,
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| seq_f).astype(int).tolist()
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| else:
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| f_sam = np.linspace(-f_o.item(),
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| (-t_f - f_o).item() + 1,
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| seq_f).astype(int).tolist()
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| h_sam = np.linspace(h_o.item(), (t_h + h_o).item() - 1,
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| seq_h).astype(int).tolist()
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| w_sam = np.linspace(w_o.item(), (t_w + w_o).item() - 1,
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| seq_w).astype(int).tolist()
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|
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| assert f_o * f >= 0 and h_o * h >= 0 and w_o * w >= 0
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| freqs_0 = freqs[0][f_sam] if f_o >= 0 else freqs[0][
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| f_sam].conj()
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| freqs_0 = freqs_0.view(seq_f, 1, 1, -1)
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|
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| freqs_i = torch.cat([
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| freqs_0.expand(seq_f, seq_h, seq_w, -1),
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| freqs[1][h_sam].view(1, seq_h, 1, -1).expand(
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| seq_f, seq_h, seq_w, -1),
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| freqs[2][w_sam].view(1, 1, seq_w, -1).expand(
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| seq_f, seq_h, seq_w, -1),
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| ],
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| dim=-1).reshape(seq_len, 1, -1)
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| elif t_f < 0:
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| freqs_i = trainable_freqs.unsqueeze(1)
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|
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| output[i, seq_bucket[-1]:seq_bucket[-1] + seq_len] = freqs_i
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| seq_bucket.append(seq_bucket[-1] + seq_len)
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| return output
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|
|
