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'''
Adapted from
https://github.com/openai/sparse_autoencoder/blob/main/sparse_autoencoder/train.py
'''
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from typing import Callable, Iterable, Iterator
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed import ReduceOp
from SAE.dataset_iterator import ActivationsDataloader
from SAE.sae import SparseAutoencoder, unit_norm_decoder_, unit_norm_decoder_grad_adjustment_
from SAE.sae_utils import SAETrainingConfig, Config
from types import SimpleNamespace
from typing import Optional, List
import json
import tqdm
def weighted_average(points: torch.Tensor, weights: torch.Tensor):
weights = weights / weights.sum()
return (points * weights.view(-1, 1)).sum(dim=0)
@torch.no_grad()
def geometric_median_objective(
median: torch.Tensor, points: torch.Tensor, weights: torch.Tensor
) -> torch.Tensor:
norms = torch.linalg.norm(points - median.view(1, -1), dim=1) # type: ignore
return (norms * weights).sum()
def compute_geometric_median(
points: torch.Tensor,
weights: Optional[torch.Tensor] = None,
eps: float = 1e-6,
maxiter: int = 100,
ftol: float = 1e-20,
do_log: bool = False,
):
"""
:param points: ``torch.Tensor`` of shape ``(n, d)``
:param weights: Optional ``torch.Tensor`` of shape :math:``(n,)``.
:param eps: Smallest allowed value of denominator, to avoid divide by zero.
Equivalently, this is a smoothing parameter. Default 1e-6.
:param maxiter: Maximum number of Weiszfeld iterations. Default 100
:param ftol: If objective value does not improve by at least this `ftol` fraction, terminate the algorithm. Default 1e-20.
:param do_log: If true will return a log of function values encountered through the course of the algorithm
:return: SimpleNamespace object with fields
- `median`: estimate of the geometric median, which is a ``torch.Tensor`` object of shape :math:``(d,)``
- `termination`: string explaining how the algorithm terminated.
- `logs`: function values encountered through the course of the algorithm in a list (None if do_log is false).
"""
with torch.no_grad():
if weights is None:
weights = torch.ones((points.shape[0],), device=points.device)
# initialize median estimate at mean
new_weights = weights
median = weighted_average(points, weights)
objective_value = geometric_median_objective(median, points, weights)
if do_log:
logs = [objective_value]
else:
logs = None
# Weiszfeld iterations
early_termination = False
pbar = tqdm.tqdm(range(maxiter))
for _ in pbar:
prev_obj_value = objective_value
norms = torch.linalg.norm(points - median.view(1, -1), dim=1) # type: ignore
new_weights = weights / torch.clamp(norms, min=eps)
median = weighted_average(points, new_weights)
objective_value = geometric_median_objective(median, points, weights)
if logs is not None:
logs.append(objective_value)
if abs(prev_obj_value - objective_value) <= ftol * objective_value:
early_termination = True
break
pbar.set_description(f"Objective value: {objective_value:.4f}")
median = weighted_average(points, new_weights) # allow autodiff to track it
return SimpleNamespace(
median=median,
new_weights=new_weights,
termination=(
"function value converged within tolerance"
if early_termination
else "maximum iterations reached"
),
logs=logs,
)
def maybe_transpose(x):
return x.T if not x.is_contiguous() and x.T.is_contiguous() else x
import wandb
RANK = 0
class Logger:
def __init__(self, sae_name, **kws):
self.vals = {}
self.enabled = (RANK == 0) and not kws.pop("dummy", False)
self.sae_name = sae_name
def logkv(self, k, v):
if self.enabled:
self.vals[f'{self.sae_name}/{k}'] = v.detach() if isinstance(v, torch.Tensor) else v
return v
def dumpkvs(self, step):
if self.enabled:
wandb.log(self.vals, step=step)
self.vals = {}
class FeaturesStats:
def __init__(self, dim, logger):
self.dim = dim
self.logger = logger
self.reinit()
def reinit(self):
self.n_activated = torch.zeros(self.dim, dtype=torch.long, device="cuda")
self.n = 0
def update(self, inds):
self.n += inds.shape[0]
inds = inds.flatten().detach()
self.n_activated.scatter_add_(0, inds, torch.ones_like(inds))
def log(self):
self.logger.logkv('activated', (self.n_activated / self.n + 1e-9).log10().cpu().numpy())
def training_loop_(
aes,
train_acts_iter,
loss_fn,
log_interval,
save_interval,
loggers,
sae_cfgs,
):
sae_packs = []
for ae, cfg, logger in zip(aes, sae_cfgs, loggers):
pbar = tqdm.tqdm(unit=" steps", desc="Training Loss: ")
fstats = FeaturesStats(ae.n_dirs, logger)
opt = torch.optim.Adam(ae.parameters(), lr=cfg.lr, eps=cfg.eps, fused=True)
sae_packs.append((ae, cfg, logger, pbar, fstats, opt))
for i, flat_acts_train_batch in enumerate(train_acts_iter):
flat_acts_train_batch = flat_acts_train_batch.cuda()
for ae, cfg, logger, pbar, fstats, opt in sae_packs:
recons, info = ae(flat_acts_train_batch)
loss = loss_fn(ae, cfg, flat_acts_train_batch, recons, info, logger)
fstats.update(info['inds'])
bs = flat_acts_train_batch.shape[0]
logger.logkv('not-activated 1e4', (ae.stats_last_nonzero > 1e4 / bs).mean(dtype=float).item())
logger.logkv('not-activated 1e6', (ae.stats_last_nonzero > 1e6 / bs).mean(dtype=float).item())
logger.logkv('not-activated 1e7', (ae.stats_last_nonzero > 1e7 / bs).mean(dtype=float).item())
logger.logkv('explained variance', explained_variance(recons, flat_acts_train_batch))
logger.logkv('l2_div', (torch.linalg.norm(recons, dim=1) / torch.linalg.norm(flat_acts_train_batch, dim=1)).mean())
if (i + 1) % log_interval == 0:
fstats.log()
fstats.reinit()
if (i + 1) % save_interval == 0:
ae.save_to_disk(f"{cfg.save_path}/{i + 1}")
loss.backward()
unit_norm_decoder_(ae)
unit_norm_decoder_grad_adjustment_(ae)
opt.step()
opt.zero_grad()
logger.dumpkvs(i)
pbar.set_description(f"Training Loss {loss.item():.4f}")
pbar.update(1)
for ae, cfg, logger, pbar, fstats, opt in sae_packs:
pbar.close()
ae.save_to_disk(f"{cfg.save_path}/final")
def init_from_data_(ae, stats_acts_sample):
ae.pre_bias.data = (
compute_geometric_median(stats_acts_sample[:32768].float().cpu()).median.cuda().float()
)
def mse(recons, x):
# return ((recons - x) ** 2).sum(dim=-1).mean()
return ((recons - x) ** 2).mean()
def normalized_mse(recon: torch.Tensor, xs: torch.Tensor) -> torch.Tensor:
# only used for auxk
xs_mu = xs.mean(dim=0)
loss = mse(recon, xs) / mse(
xs_mu[None, :].broadcast_to(xs.shape), xs
)
return loss
def explained_variance(recons, x):
# Compute the variance of the difference
diff = x - recons
diff_var = torch.var(diff, dim=0, unbiased=False)
# Compute the variance of the original tensor
x_var = torch.var(x, dim=0, unbiased=False)
# Avoid division by zero
explained_var = 1 - diff_var / (x_var + 1e-8)
return explained_var.mean()
def main():
cfg = Config(json.load(open('SAE/config.json')))
dataloader = ActivationsDataloader(cfg.paths_to_latents, cfg.block_name, cfg.bs)
acts_iter = dataloader.iterate()
stats_acts_sample = torch.cat([
next(acts_iter).cpu() for _ in range(10)
], dim=0)
aes = [
SparseAutoencoder(
n_dirs_local=sae.n_dirs,
d_model=sae.d_model,
k=sae.k,
auxk=sae.auxk,
dead_steps_threshold=sae.dead_toks_threshold // cfg.bs,
).cuda()
for sae in cfg.saes
]
for ae in aes:
init_from_data_(ae, stats_acts_sample)
mse_scale = (
1 / ((stats_acts_sample.float().mean(dim=0) - stats_acts_sample.float()) ** 2).mean()
)
mse_scale = mse_scale.item()
del stats_acts_sample
wandb.init(
project=cfg.wandb_project,
name=cfg.wandb_name,
)
loggers = [Logger(
sae_name=cfg_sae.sae_name,
dummy=False,
) for cfg_sae in cfg.saes]
training_loop_(
aes,
acts_iter,
lambda ae, cfg_sae, flat_acts_train_batch, recons, info, logger: (
# MSE
logger.logkv("train_recons", mse_scale * mse(recons, flat_acts_train_batch))
# AuxK
+ logger.logkv(
"train_maxk_recons",
cfg_sae.auxk_coef
* normalized_mse(
ae.decode_sparse(
info["auxk_inds"],
info["auxk_vals"],
),
flat_acts_train_batch - recons.detach() + ae.pre_bias.detach(),
).nan_to_num(0),
)
),
sae_cfgs = cfg.saes,
loggers=loggers,
log_interval=cfg.log_interval,
save_interval=cfg.save_interval,
)
if __name__ == "__main__":
main() |