File size: 3,221 Bytes
dbd41fe | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import torch
import math
class KetOptimizer(torch.optim.Optimizer):
def __init__(self, params, lr=1e-3, dt=0.01, A=-1.0, B=1.0, C=1.0, rank_ratio=20):
"""
The Ket Optimizer: A zero-overhead, hyper-compressed State Space Optimizer.
Uses BlackMamba / GaLore Subspace Projection to kill the Adam RAM bottleneck by 95%.
rank_ratio: Shrinks the state space to 1/rank_ratio of the original size.
(e.g., 20 means it uses exactly 5% of the original RAM).
"""
defaults = dict(lr=lr, dt=dt, A=A, B=B, C=C, rank_ratio=rank_ratio)
super().__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
lr = group['lr']
dt = group['dt']
# Discretize continuous A via Exact Exponential for the SSM
A_bar = math.exp(group['A'] * dt)
B_bar = group['B'] * dt
C_val = group['C']
rank_ratio = group['rank_ratio']
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
state = self.state[p]
# State initialization: This only happens once on the very first step
if len(state) == 0:
state['step'] = 0
if len(p.shape) == 2:
M, N = p.shape
# Instead of a fixed random matrix, we track the row and column momentums!
state['h_row'] = torch.zeros(M, 1, device=p.device, dtype=p.dtype)
state['h_col'] = torch.zeros(1, N, device=p.device, dtype=p.dtype)
state['is_projected'] = True
else:
state['h'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['is_projected'] = False
state['step'] += 1
if state['is_projected']:
h_row = state['h_row']
h_col = state['h_col']
# Track momentum of the row averages and column averages separately
G_row = grad.mean(dim=1, keepdim=True)
G_col = grad.mean(dim=0, keepdim=True)
h_row.mul_(A_bar).add_(G_row, alpha=B_bar)
h_col.mul_(A_bar).add_(G_col, alpha=B_bar)
# Reconstruct full matrix gradient via broadcasting!
y = (h_row + h_col) * C_val
else:
h = state['h']
h.mul_(A_bar).add_(grad, alpha=B_bar)
y = h * C_val
# 4. Final weight update (Hyperspeed!)
p.add_(y, alpha=-lr)
return loss
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