mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-21 07:30:02 +08:00
103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
"""RNG imitiating torch cuda randn on CPU. You are welcome.
|
||
|
||
Usage:
|
||
|
||
```
|
||
g = Generator(seed=0)
|
||
print(g.randn(shape=(3, 4)))
|
||
```
|
||
|
||
Expected output:
|
||
```
|
||
[[-0.92466259 -0.42534415 -2.6438457 0.14518388]
|
||
[-0.12086647 -0.57972564 -0.62285122 -0.32838709]
|
||
[-1.07454231 -0.36314407 -1.67105067 2.26550497]]
|
||
```
|
||
"""
|
||
|
||
import numpy as np
|
||
|
||
philox_m = [0xD2511F53, 0xCD9E8D57]
|
||
philox_w = [0x9E3779B9, 0xBB67AE85]
|
||
|
||
two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
|
||
two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
|
||
|
||
|
||
def uint32(x):
|
||
"""Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
|
||
return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
|
||
|
||
|
||
def philox4_round(counter, key):
|
||
"""A single round of the Philox 4x32 random number generator."""
|
||
|
||
v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
|
||
v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
|
||
|
||
counter[0] = v2[1] ^ counter[1] ^ key[0]
|
||
counter[1] = v2[0]
|
||
counter[2] = v1[1] ^ counter[3] ^ key[1]
|
||
counter[3] = v1[0]
|
||
|
||
|
||
def philox4_32(counter, key, rounds=10):
|
||
"""Generates 32-bit random numbers using the Philox 4x32 random number generator.
|
||
|
||
Parameters:
|
||
counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
|
||
key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
|
||
rounds (int): The number of rounds to perform.
|
||
|
||
Returns:
|
||
numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
|
||
"""
|
||
|
||
for _ in range(rounds - 1):
|
||
philox4_round(counter, key)
|
||
|
||
key[0] = key[0] + philox_w[0]
|
||
key[1] = key[1] + philox_w[1]
|
||
|
||
philox4_round(counter, key)
|
||
return counter
|
||
|
||
|
||
def box_muller(x, y):
|
||
"""Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
|
||
u = x * two_pow32_inv + two_pow32_inv / 2
|
||
v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
|
||
|
||
s = np.sqrt(-2.0 * np.log(u))
|
||
|
||
r1 = s * np.sin(v)
|
||
return r1.astype(np.float32)
|
||
|
||
|
||
class Generator:
|
||
"""RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
|
||
|
||
def __init__(self, seed):
|
||
self.seed = seed
|
||
self.offset = 0
|
||
|
||
def randn(self, shape):
|
||
"""Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
|
||
|
||
n = 1
|
||
for x in shape:
|
||
n *= x
|
||
|
||
counter = np.zeros((4, n), dtype=np.uint32)
|
||
counter[0] = self.offset
|
||
counter[2] = np.arange(n, dtype=np.uint32) # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
|
||
self.offset += 1
|
||
|
||
key = np.empty(n, dtype=np.uint64)
|
||
key.fill(self.seed)
|
||
key = uint32(key)
|
||
|
||
g = philox4_32(counter, key)
|
||
|
||
return box_muller(g[0], g[1]).reshape(shape) # discard g[2] and g[3]
|