Make HyperTile deterministic
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2db86b4676
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@ -2,9 +2,10 @@
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import math
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from einops import rearrange
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import random
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# Use torch rng for consistency across generations
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from torch import randint
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def random_divisor(value: int, min_value: int, /, max_options: int = 1, counter = 0) -> int:
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def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
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min_value = min(min_value, value)
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# All big divisors of value (inclusive)
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@ -12,8 +13,7 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1, counter
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ns = [value // i for i in divisors[:max_options]] # has at least 1 element
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random.seed(counter)
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idx = random.randint(0, len(ns) - 1)
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idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()
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return ns[idx]
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@ -42,7 +42,6 @@ class HyperTile:
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latent_tile_size = max(32, tile_size) // 8
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self.temp = None
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self.counter = 1
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def hypertile_in(q, k, v, extra_options):
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if q.shape[-1] in apply_to:
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@ -53,10 +52,8 @@ class HyperTile:
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h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
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factor = 2**((q.shape[-1] // model_channels) - 1) if scale_depth else 1
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nh = random_divisor(h, latent_tile_size * factor, swap_size, self.counter)
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self.counter += 1
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nw = random_divisor(w, latent_tile_size * factor, swap_size, self.counter)
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self.counter += 1
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nh = random_divisor(h, latent_tile_size * factor, swap_size)
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nw = random_divisor(w, latent_tile_size * factor, swap_size)
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if nh * nw > 1:
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q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
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