Create topk_sae.py

topk_sae.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import numpy as np
+from torch.utils.data import DataLoader, TensorDataset
+from tqdm import tqdm
+import wandb
+import os
+import glob
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class FastAutoencoder(nn.Module):
+ def __init__(self, n_dirs: int, d_model: int, k: int, auxk: int, multik: int, dead_steps_threshold: int = 266):
+ super().__init__()
+ self.n_dirs = n_dirs
+ self.d_model = d_model
+ self.k = k
+ self.auxk = auxk
+ self.multik = multik
+ self.dead_steps_threshold = dead_steps_threshold
+
+ self.encoder = nn.Linear(d_model, n_dirs, bias=False)
+ self.decoder = nn.Linear(n_dirs, d_model, bias=False)
+
+ self.pre_bias = nn.Parameter(torch.zeros(d_model))
+ self.latent_bias = nn.Parameter(torch.zeros(n_dirs))
+
+ self.stats_last_nonzero = torch.zeros(n_dirs, dtype=torch.long, device=device)
+
+ def forward(self, x):
+ x = x - self.pre_bias
+ latents_pre_act = self.encoder(x) + self.latent_bias
+
+ # Main top-k selection
+ topk_values, topk_indices = torch.topk(latents_pre_act, k=self.k, dim=-1)
+ topk_values = F.relu(topk_values)
+ multik_values, multik_indices = torch.topk(latents_pre_act, k=4*self.k, dim=-1)
+ multik_values = F.relu(multik_values)
+
+ latents = torch.zeros_like(latents_pre_act)
+ latents.scatter_(-1, topk_indices, topk_values)
+ multik_latents = torch.zeros_like(latents_pre_act)
+ multik_latents.scatter_(-1, multik_indices, multik_values)
+
+ # Update stats_last_nonzero
+ self.stats_last_nonzero += 1
+ self.stats_last_nonzero.scatter_(0, topk_indices.unique(), 0)
+
+ recons = self.decoder(latents) + self.pre_bias
+ multik_recons = self.decoder(multik_latents) + self.pre_bias
+
+ # AuxK
+ if self.auxk is not None:
+ # Create dead latents mask
+ dead_mask = (self.stats_last_nonzero > self.dead_steps_threshold).float()
+ 
+ # Apply mask to latents_pre_act
+ dead_latents_pre_act = latents_pre_act * dead_mask
+ 
+ # Select top-k_aux from dead latents
+ auxk_values, auxk_indices = torch.topk(dead_latents_pre_act, k=self.auxk, dim=-1)
+ auxk_values = F.relu(auxk_values)
+
+ else:
+ auxk_values, auxk_indices = None, None
+
+ return recons, {
+ "topk_indices": topk_indices,
+ "topk_values": topk_values,
+ "multik_indices": multik_indices,
+ "multik_values": multik_values,
+ "multik_recons": multik_recons,
+ "auxk_indices": auxk_indices,
+ "auxk_values": auxk_values,
+ "latents_pre_act": latents_pre_act,
+ "latents_post_act": latents,
+ }
+
+ def decode_sparse(self, indices, values):
+ latents = torch.zeros(self.n_dirs, device=indices.device)
+ latents.scatter_(-1, indices, values)
+ return self.decoder(latents) + self.pre_bias
+
+ # def decode_sparse(self, indices, values):
+ # latents = torch.zeros(1, self.n_dirs, device=indices.device, dtype=torch.float32)
+ # latents.scatter_(-1, indices.unsqueeze(0), values.unsqueeze(0))
+ # return self.decoder(latents.squeeze(0)) + self.pre_bias
+ 
+ def print_tensor_info(self, tensor, name):
+ print(f"{name} - Shape: {tensor.shape}, Dtype: {tensor.dtype}, Device: {tensor.device}")
+
+ def decode_clamp(self, latents, clamp):
+ topk_values, topk_indices = torch.topk(latents, k = 64, dim=-1)
+ topk_values = F.relu(topk_values)
+ latents = torch.zeros_like(latents)
+ latents.scatter_(-1, topk_indices, topk_values)
+ # multiply latents by clamp, which is 1D but has has the same size as each latent vector
+ latents = latents * clamp
+ 
+ return self.decoder(latents) + self.pre_bias
+ 
+ def decode_at_k(self, latents, k):
+ topk_values, topk_indices = torch.topk(latents, k=k, dim=-1)
+ topk_values = F.relu(topk_values)
+ latents = torch.zeros_like(latents)
+ latents.scatter_(-1, topk_indices, topk_values)
+ 
+ return self.decoder(latents) + self.pre_bias
+
+def unit_norm_decoder_(autoencoder: FastAutoencoder) -> None:
+ with torch.no_grad():
+ autoencoder.decoder.weight.div_(autoencoder.decoder.weight.norm(dim=0, keepdim=True))
+
+def unit_norm_decoder_grad_adjustment_(autoencoder: FastAutoencoder) -> None:
+ if autoencoder.decoder.weight.grad is not None:
+ with torch.no_grad():
+ proj = torch.sum(autoencoder.decoder.weight * autoencoder.decoder.weight.grad, dim=0, keepdim=True)
+ autoencoder.decoder.weight.grad.sub_(proj * autoencoder.decoder.weight)
+
+def mse(output, target):
+ return F.mse_loss(output, target)
+
+def normalized_mse(recon, xs):
+ return mse(recon, xs) / mse(xs.mean(dim=0, keepdim=True).expand_as(xs), xs)
+
+def loss_fn(ae, x, recons, info, auxk_coef, multik_coef):
+ recons_loss = normalized_mse(recons, x)
+ recons_loss += multik_coef * normalized_mse(info["multik_recons"], x)
+ 
+ if ae.auxk is not None:
+ e = x - recons.detach() # reconstruction error
+ auxk_latents = torch.zeros_like(info["latents_pre_act"])
+ auxk_latents.scatter_(-1, info["auxk_indices"], info["auxk_values"])
+ e_hat = ae.decoder(auxk_latents) # reconstruction of error using dead latents
+ auxk_loss = normalized_mse(e_hat, e)
+ total_loss = recons_loss + auxk_coef * auxk_loss
+ else:
+ auxk_loss = torch.tensor(0.0, device=device)
+ total_loss = recons_loss
+ 
+ return total_loss, recons_loss, auxk_loss
+
+def init_from_data_(ae, data_sample):
+ # set pre_bias to median of data
+ ae.pre_bias.data = torch.median(data_sample, dim=0).values
+ nn.init.xavier_uniform_(ae.decoder.weight)
+
+ # decoder is unit norm
+ unit_norm_decoder_(ae)
+
+ # encoder as transpose of decoder
+ ae.encoder.weight.data = ae.decoder.weight.t().clone()
+
+ nn.init.zeros_(ae.latent_bias)
+
+def train(ae, train_loader, optimizer, epochs, k, auxk_coef, multik_coef, clip_grad=None, save_dir="../models", model_name=""):
+ os.makedirs(save_dir, exist_ok=True)
+ step = 0
+ num_batches = len(train_loader)
+ for epoch in range(epochs):
+ ae.train()
+ total_loss = 0
+ for batch in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}"):
+ optimizer.zero_grad()
+ x = batch[0].to(device)
+ recons, info = ae(x)
+ loss, recons_loss, auxk_loss = loss_fn(ae, x, recons, info, auxk_coef, multik_coef)
+ loss.backward()
+ step += 1
+
+ # calculate proportion of dead latents (not fired in last num_batches = 1 epoch)
+ dead_latents_prop = (ae.stats_last_nonzero > num_batches).float().mean().item()
+
+ wandb.log({
+ "total_loss": loss.item(),
+ "reconstruction_loss": recons_loss.item(),
+ "auxiliary_loss": auxk_loss.item(),
+ "dead_latents_proportion": dead_latents_prop,
+ "l0_norm": k,
+ "step": step
+ })
+ 
+ unit_norm_decoder_grad_adjustment_(ae)
+ 
+ if clip_grad is not None:
+ torch.nn.utils.clip_grad_norm_(ae.parameters(), clip_grad)
+ 
+ optimizer.step()
+ unit_norm_decoder_(ae)
+ 
+ total_loss += loss.item()
+ 
+ avg_loss = total_loss / len(train_loader)
+ print(f"Epoch {epoch+1}, Average Loss: {avg_loss:.4f}")
+
+ # Delete previous model saves for this configuration
+ for old_model in glob.glob(os.path.join(save_dir, f"{model_name}_epoch_*.pth")):
+ os.remove(old_model)
+
+ # Save new model
+ save_path = os.path.join(save_dir, f"{model_name}_epoch_{epoch+1}.pth")
+ torch.save(ae.state_dict(), save_path)
+ print(f"Model saved to {save_path}")
+
+def main():
+ d_model = 1536
+ n_dirs = 3072 #9216
+ k = 64 #64
+ auxk = k*2 #256
+ multik = 128
+ batch_size = 1024
+ lr = 1e-4
+ auxk_coef = 1/32
+ clip_grad = 1.0
+ multik_coef = 0 # turn it off
+
+ csLG = False
+
+ # Create model name
+ model_name = f"{k}_{n_dirs}_{auxk}_auxk" if not csLG else f"{k}_{n_dirs}_{auxk}_auxk_csLG"
+ epochs = 50 if not csLG else 137
+
+ wandb.init(project="saerch", name=model_name, config={
+ "n_dirs": n_dirs,
+ "d_model": d_model,
+ "k": k,
+ "auxk": auxk,
+ "batch_size": batch_size,
+ "lr": lr,
+ "epochs": epochs,
+ "auxk_coef": auxk_coef,
+ "multik_coef": multik_coef,
+ "clip_grad": clip_grad,
+ "device": device.type
+ })
+
+ if not csLG:
+ data = np.load("../data/vector_store_astroPH/abstract_embeddings.npy")
+ print("Doing astro.ph...")
+ else:
+ data = np.load("../data/vector_store_csLG/abstract_embeddings.npy")
+ print("Doing csLG...")
+ data_tensor = torch.from_numpy(data).float()
+ # Print shape
+ print(f"Data shape: {data_tensor.shape}")
+ dataset = TensorDataset(data_tensor)
+ train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+ ae = FastAutoencoder(n_dirs, d_model, k, auxk, multik).to(device)
+ init_from_data_(ae, data_tensor[:10000].to(device))
+
+ optimizer = optim.Adam(ae.parameters(), lr=lr)
+
+ train(ae, train_loader, optimizer, epochs, k, auxk_coef, multik_coef, clip_grad=clip_grad, model_name=model_name)
+
+ wandb.finish()
+
+if __name__ == "__main__":
+ main()