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import os
import json
import torch
from torch import nn
class SparseAutoencoder(nn.Module):
def __init__(
self,
n_dirs_local: int,
d_model: int,
k: int,
auxk: int, #| None,
dead_steps_threshold: int,
auxk_coef: float
):
super().__init__()
self.n_dirs_local = n_dirs_local
self.d_model = d_model
self.k = k
self.auxk = auxk
self.dead_steps_threshold = dead_steps_threshold
self.auxk_coef = auxk_coef
self.encoder = nn.Linear(d_model, n_dirs_local, bias=False)
self.decoder = nn.Linear(n_dirs_local, d_model, bias=False)
self.pre_bias = nn.Parameter(torch.zeros(d_model))
self.latent_bias = nn.Parameter(torch.zeros(n_dirs_local))
self.stats_last_nostats_last_nonzeronzero: torch.Tensor
self.register_buffer("stats_last_nonzero", torch.zeros(n_dirs_local, dtype=torch.long))
def auxk_mask_fn(x):
dead_mask = self.stats_last_nonzero > dead_steps_threshold
x.data *= dead_mask # inplace to save memory
return x
self.auxk_mask_fn = auxk_mask_fn
## initialization
# "tied" init
self.decoder.weight.data = self.encoder.weight.data.T.clone()
# store decoder in column major layout for kernel
self.decoder.weight.data = self.decoder.weight.data.T.contiguous().T
self.mse_scale = 1
unit_norm_decoder_(self)
def save_to_disk(self, path: str):
PATH_TO_CFG = 'config.json'
PATH_TO_WEIGHTS = 'state_dict.pth'
cfg = {
"n_dirs_local": self.n_dirs_local,
"d_model": self.d_model,
"k": self.k,
"auxk": self.auxk,
"dead_steps_threshold": self.dead_steps_threshold,
"auxk_coef": self.auxk_coef
}
os.makedirs(path, exist_ok=True)
with open(os.path.join(path, PATH_TO_CFG), 'w') as f:
json.dump(cfg, f)
torch.save({
"state_dict": self.state_dict(),
}, os.path.join(path, PATH_TO_WEIGHTS))
@classmethod
def load_from_disk(cls, path: str):
PATH_TO_CFG = 'config.json'
PATH_TO_WEIGHTS = 'state_dict.pth'
with open(os.path.join(path, PATH_TO_CFG), 'r') as f:
cfg = json.load(f)
ae = cls(
n_dirs_local=cfg["n_dirs_local"],
d_model=cfg["d_model"],
k=cfg["k"],
auxk=cfg["auxk"],
dead_steps_threshold=cfg["dead_steps_threshold"],
auxk_coef = cfg["auxk_coef"] if "auxk_coef" in cfg else 1/32
)
state_dict = torch.load(os.path.join(path, PATH_TO_WEIGHTS), map_location=torch.device('cpu'))["state_dict"]
ae.load_state_dict(state_dict)
return ae
@property
def n_dirs(self):
return self.n_dirs_local
def encode(self, x):
x = x - self.pre_bias
latents_pre_act = self.encoder(x) + self.latent_bias
vals, inds = torch.topk(
latents_pre_act,
k=self.k,
dim=-1
)
latents = torch.zeros_like(latents_pre_act)
latents.scatter_(-1, inds, torch.relu(vals))
return latents
def encode_with_k(self, x, k):
x = x - self.pre_bias
latents_pre_act = self.encoder(x) + self.latent_bias
vals, inds = torch.topk(
latents_pre_act,
k=k,
dim=-1
)
latents = torch.zeros_like(latents_pre_act)
latents.scatter_(-1, inds, torch.relu(vals))
return latents
def encode_without_topk(self, x):
x = x - self.pre_bias
latents_pre_act = torch.relu(self.encoder(x) + self.latent_bias)
return latents_pre_act
def forward(self, x):
x = x - self.pre_bias
latents_pre_act = self.encoder(x) + self.latent_bias
l0 = (latents_pre_act > 0).float().sum(-1).mean()
vals, inds = torch.topk(
latents_pre_act,
k=self.k,
dim=-1
)
with torch.no_grad(): # Disable gradients for statistics
## set num nonzero stat ##
tmp = torch.zeros_like(self.stats_last_nonzero)
tmp.scatter_add_(
0,
inds.reshape(-1),
(vals > 1e-3).to(tmp.dtype).reshape(-1),
)
self.stats_last_nonzero *= 1 - tmp.clamp(max=1)
self.stats_last_nonzero += 1
del tmp
## auxk
if self.auxk is not None: # for auxk
auxk_vals, auxk_inds = torch.topk(
self.auxk_mask_fn(latents_pre_act),
k=self.auxk,
dim=-1
)
else:
auxk_inds = None
auxk_vals = None
## end auxk
vals = torch.relu(vals)
if auxk_vals is not None:
auxk_vals = torch.relu(auxk_vals)
rows, cols = latents_pre_act.size()
row_indices = torch.arange(rows).unsqueeze(1).expand(-1, self.k).reshape(-1)
vals = vals.reshape(-1)
inds = inds.reshape(-1)
indices = torch.stack([row_indices.to(inds.device), inds])
sparse_tensor = torch.sparse_coo_tensor(indices, vals, torch.Size([rows, cols]))
recons = torch.sparse.mm(sparse_tensor, self.decoder.weight.T) + self.pre_bias
mse_loss = self.mse_scale * self.mse(recons, x)
## Calculate AuxK loss if applicable
if auxk_vals is not None:
auxk_recons = self.decode_sparse(auxk_inds, auxk_vals)
auxk_loss =self.auxk_coef * self.normalized_mse(auxk_recons, x - recons.detach() + self.pre_bias.detach()).nan_to_num(0)
else:
auxk_loss = 0.0
total_loss = mse_loss + auxk_loss
return recons, total_loss, {
"inds": inds,
"vals": vals,
"auxk_inds": auxk_inds,
"auxk_vals": auxk_vals,
"l0": l0,
"train_recons": mse_loss,
"train_maxk_recons": auxk_loss
}
def decode_sparse(self, inds, vals):
rows, cols = inds.shape[0], self.n_dirs
row_indices = torch.arange(rows).unsqueeze(1).expand(-1, inds.shape[1]).reshape(-1)
vals = vals.reshape(-1)
inds = inds.reshape(-1)
indices = torch.stack([row_indices.to(inds.device), inds])
sparse_tensor = torch.sparse_coo_tensor(indices, vals, torch.Size([rows, cols]))
recons = torch.sparse.mm(sparse_tensor, self.decoder.weight.T) + self.pre_bias
return recons
@property
def device(self):
return next(self.parameters()).device
def mse(self, recons, x):
# return ((recons - x) ** 2).sum(dim=-1).mean()
return ((recons - x) ** 2).mean()
def normalized_mse(self, recon: torch.Tensor, xs: torch.Tensor) -> torch.Tensor:
# only used for auxk
xs_mu = xs.mean(dim=0)
loss = self.mse(recon, xs) / self.mse(
xs_mu[None, :].broadcast_to(xs.shape), xs
)
return loss
def unit_norm_decoder_(autoencoder: SparseAutoencoder) -> None:
autoencoder.decoder.weight.data /= autoencoder.decoder.weight.data.norm(dim=0)
def unit_norm_decoder_grad_adjustment_(autoencoder) -> None:
assert autoencoder.decoder.weight.grad is not None
autoencoder.decoder.weight.grad +=\
torch.einsum("bn,bn->n", autoencoder.decoder.weight.data, autoencoder.decoder.weight.grad) *\
autoencoder.decoder.weight.data * -1 |