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00dd625 | 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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | """Task-Feature Matrix computation for AlignX Stage 1.
T_a = W1 @ compress(Δθ_a) + W2 @ F_a
Δθ_a: task vector (compressed LoRA delta to latent_dim)
F_a: alignment feature vector (mean hidden state, size hidden_dim)
T_a: k-dim latent alignment representation
"""
import torch
import torch.nn as nn
from typing import Dict, Optional
def compress_task_vector(
delta_state_dict: Dict[str, torch.Tensor],
target_dim: int = 4096,
) -> torch.Tensor:
"""Flatten and mean-pool all parameter deltas to a fixed-size vector.
LoRA deltas are small (~4M params for rank-8) so we can flatten them
and chunk-average down to `target_dim` to make W1 tractable.
"""
all_params = []
for v in delta_state_dict.values():
if v.dtype in (torch.float32, torch.float16, torch.bfloat16):
all_params.append(v.detach().cpu().float().flatten())
if not all_params:
return torch.zeros(target_dim)
flat = torch.cat(all_params)
total = flat.shape[0]
if total >= target_dim:
chunk = total // target_dim
remainder = total - chunk * target_dim
trimmed = flat[:chunk * target_dim]
compressed = trimmed.view(target_dim, chunk).mean(dim=1)
else:
# Pad with zeros
compressed = torch.zeros(target_dim)
compressed[:total] = flat
return compressed.float()
def extract_alignment_features(
model,
dataset,
layer_idx: int = -1,
device: str = "cuda",
max_samples: int = 200,
batch_size: int = 4,
) -> torch.Tensor:
"""Extract mean hidden-state vector F_a at transformer layer `layer_idx`.
Runs a forward pass over `dataset` samples, collecting the hidden state
from the chosen layer, then returns the mean across all samples and tokens.
"""
import torch
from torch.utils.data import DataLoader
model.eval()
# Only call .to(device) if the model isn't already dispatched across multiple devices
# (dispatch_model sets model.hf_device_map; calling .to() on a dispatched model
# would un-dispatch it and may OOM a single GPU).
if not getattr(model, "hf_device_map", None):
model.to(device)
hidden_dim = model.config.hidden_size
feature_accum = torch.zeros(hidden_dim, device=device)
count = 0
# Identify the target layer
layers = _get_transformer_layers(model)
target_layer = layers[layer_idx]
captured = []
def _hook(module, input, output):
# output can be tuple (hidden_state, ...) or just tensor
hs = output[0] if isinstance(output, tuple) else output
# hs: (batch, seq_len, hidden_dim)
captured.append(hs.detach().mean(dim=(0, 1)).cpu()) # (hidden_dim,)
handle = target_layer.register_forward_hook(_hook)
# When the model is dispatched across GPUs (hf_device_map set), inputs must go
# to cuda:0 (where the embedding layer lives), not to the `device` argument.
input_device = "cuda:0" if getattr(model, "hf_device_map", None) else device
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
with torch.no_grad():
for i, batch in enumerate(loader):
if count >= max_samples:
break
input_ids = batch["input_ids"].to(input_device)
attention_mask = batch["attention_mask"].to(input_device)
try:
model(input_ids=input_ids, attention_mask=attention_mask)
except Exception:
pass
count += input_ids.shape[0]
handle.remove()
if captured:
F_a = torch.stack(captured).mean(0) # (hidden_dim,)
else:
F_a = torch.zeros(hidden_dim)
print(f"[FeatureExtraction] Extracted F_a of shape {F_a.shape} from {count} samples")
return F_a.float()
def _get_transformer_layers(model):
"""Return the list of transformer layers for common model families."""
# Handles LLaMA, Mistral, Gemma, DeepSeek
if hasattr(model, "model") and hasattr(model.model, "layers"):
return model.model.layers
if hasattr(model, "transformer") and hasattr(model.transformer, "h"):
return model.transformer.h
raise ValueError(f"Cannot locate transformer layers for {type(model)}")
class TaskFeatureMatrix(nn.Module):
"""T_a = W1 @ Δθ_a + W2 @ F_a
Maps the compressed task vector and alignment feature vector
into a shared k-dimensional latent alignment space.
"""
def __init__(self, k: int = 256, compress_dim: int = 4096, hidden_dim: int = 4096):
super().__init__()
self.k = k
self.compress_dim = compress_dim
self.hidden_dim = hidden_dim
# W1: (k, compress_dim) maps compressed delta-theta to latent space
self.W1 = nn.Linear(compress_dim, k, bias=False)
# W2: (k, hidden_dim) maps feature vector to latent space
self.W2 = nn.Linear(hidden_dim, k, bias=False)
nn.init.xavier_uniform_(self.W1.weight)
nn.init.xavier_uniform_(self.W2.weight)
def forward(
self,
delta_theta_compressed: torch.Tensor,
F_a: torch.Tensor,
) -> torch.Tensor:
"""
delta_theta_compressed: (compress_dim,) — compressed task vector
F_a: (hidden_dim,) — alignment feature vector
Returns T_a: (k,)
"""
T_a = self.W1(delta_theta_compressed) + self.W2(F_a)
return T_a
def compute_and_cache(
self,
delta_theta_compressed: torch.Tensor,
F_a: torch.Tensor,
) -> torch.Tensor:
with torch.no_grad():
return self.forward(delta_theta_compressed, F_a)
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