File size: 8,322 Bytes
7acde1f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
import time
import numpy as np
import torch
from PIL import Image
import glob
import sys
import argparse
import datetime
import json
from pathlib import Path
class PRSLogger(object):
def __init__(self, model, device, spatial: bool = True):
self.current_layer = 0
self.device = device
self.attentions = []
self.mlps = []
self.ks = []
self.qs = []
self.vs = []
self.attn_mats = []
self.spatial = spatial
self.post_ln_std = None
self.post_ln_mean = None
self.model = model
@torch.no_grad()
def compute_attentions_spatial(self, ret):
assert (
len(ret.shape) == 5
), "Verify that you use method=`head` and not method=`head_no_spatial`" # [b, n, m, h, d]
assert (
self.spatial
), "Verify that you use method=`head` and not method=`head_no_spatial`"
bias_term = self.model.visual.transformer.resblocks[
self.current_layer
].attn.out_proj.bias
self.current_layer += 1
return_value = ret[:, 0] # This is only for the cls token
self.attentions.append(
return_value
+ bias_term[np.newaxis, np.newaxis, np.newaxis]
/ (return_value.shape[1] * return_value.shape[2])
) # [b, n, h, d]
return ret
@torch.no_grad()
def compute_attentions_non_spatial(self, ret):
assert (
len(ret.shape) == 4
), "Verify that you use method=`head_no_spatial` and not method=`head`" # [b, n, h, d]
assert (
not self.spatial
), "Verify that you use method=`head_no_spatial` and not method=`head`"
bias_term = self.model.visual.transformer.resblocks[
self.current_layer
].attn.out_proj.bias
self.current_layer += 1
# return_value = ret[:, 0] # This is only for the cls token
return_value = ret
self.attentions.append(
return_value + bias_term / (return_value.shape[-2])
) # [b, h, d]
return ret
@torch.no_grad()
def compute_k(self, ret):
self.ks.append(ret) # [b, n, h, d]
return ret
@torch.no_grad()
def compute_q(self, ret):
self.qs.append(ret)
return ret
@torch.no_grad()
def compute_v(self, ret):
self.vs.append(ret)
return ret
@torch.no_grad()
def compute_attn_mat(self, ret):
self.attn_mats.append(ret)
return ret
@torch.no_grad()
def compute_mlps(self, ret):
# self.mlps.append(ret[:, 0]) # [b, d]
self.mlps.append(ret) # [b, d]
return ret
@torch.no_grad()
def log_post_ln_mean(self, ret):
self.post_ln_mean = ret # [b, 1]
return ret
@torch.no_grad()
def log_post_ln_std(self, ret):
self.post_ln_std = ret # [b, 1]
return ret
def _normalize_mlps(self):
len_intermediates = self.attentions.shape[1] + self.mlps.shape[1]
# This is just the normalization layer:
mean_centered = (
self.mlps
- self.post_ln_mean[:, :, np.newaxis].to(self.device) / len_intermediates
)
weighted_mean_centered = (
self.model.visual.ln_post.weight.detach().to(self.device) * mean_centered
)
weighted_mean_by_std = weighted_mean_centered / self.post_ln_std[
:, :, np.newaxis
].to(self.device)
bias_term = (
self.model.visual.ln_post.bias.detach().to(self.device) / len_intermediates
)
post_ln = weighted_mean_by_std + bias_term
return post_ln @ self.model.visual.proj.detach().to(self.device)
def _normalize_attentions_spatial(self):
len_intermediates = self.attentions.shape[1] + self.mlps.shape[1] # 2*l + 1
normalization_term = (
self.attentions.shape[2] * self.attentions.shape[3]
) # n * h
# This is just the normalization layer:
mean_centered = self.attentions - self.post_ln_mean[
:, :, np.newaxis, np.newaxis, np.newaxis
].to(self.device) / (len_intermediates * normalization_term)
weighted_mean_centered = (
self.model.visual.ln_post.weight.detach().to(self.device) * mean_centered
)
weighted_mean_by_std = weighted_mean_centered / self.post_ln_std[
:, :, np.newaxis, np.newaxis, np.newaxis
].to(self.device)
bias_term = self.model.visual.ln_post.bias.detach().to(self.device) / (
len_intermediates * normalization_term
)
post_ln = weighted_mean_by_std + bias_term
return post_ln @ self.model.visual.proj.detach().to(self.device)
def _normalize_attentions_non_spatial(self):
len_intermediates = self.attentions.shape[1] + self.mlps.shape[1] # 2*l + 1
normalization_term = self.attentions.shape[2] # h
# This is just the normalization layer:
mean_centered = self.attentions - self.post_ln_mean[
:, :, np.newaxis, np.newaxis
].to(self.device) / (len_intermediates * normalization_term)
weighted_mean_centered = (
self.model.visual.ln_post.weight.detach().to(self.device) * mean_centered
)
weighted_mean_by_std = weighted_mean_centered / self.post_ln_std[
:, :, np.newaxis, np.newaxis
].to(self.device)
bias_term = self.model.visual.ln_post.bias.detach().to(self.device) / (
len_intermediates * normalization_term
)
post_ln = weighted_mean_by_std + bias_term
return post_ln @ self.model.visual.proj.detach().to(self.device)
@torch.no_grad()
def finalize(self, representation):
"""We calculate the post-ln scaling, project it and normalize by the last norm."""
self.attentions = torch.stack(self.attentions, axis=1).to(
self.device
) # [b, l, n, h, d]
self.mlps = torch.stack(self.mlps, axis=1).to(self.device) # [b, l + 1, d]
if self.spatial:
projected_attentions = self._normalize_attentions_spatial()
else:
projected_attentions = self._normalize_attentions_non_spatial()
projected_mlps = self._normalize_mlps()
norm = representation.norm(dim=-1).detach()
if self.spatial:
return (
projected_attentions
/ norm[:, np.newaxis, np.newaxis, np.newaxis, np.newaxis],
projected_mlps / norm[:, np.newaxis, np.newaxis],
)
return (
projected_attentions / norm[:, np.newaxis, np.newaxis, np.newaxis],
projected_mlps / norm[:, np.newaxis, np.newaxis],
)
def reinit(self):
self.current_layer = 0
self.attentions = []
self.mlps = []
self.ks = []
self.qs = []
self.vs = []
self.attn_mats = []
self.post_ln_mean = None
self.post_ln_std = None
torch.cuda.empty_cache()
def hook_prs_logger(model, device, spatial: bool = True):
"""Hooks a projected residual stream logger to the model."""
prs = PRSLogger(model, device, spatial=spatial)
if spatial:
model.hook_manager.register(
"visual.transformer.resblocks.*.attn.out.post",
prs.compute_attentions_spatial,
)
else:
model.hook_manager.register(
"visual.transformer.resblocks.*.attn.out.post",
prs.compute_attentions_non_spatial,
)
model.hook_manager.register(
"visual.transformer.resblocks.*.mlp.c_proj.post", prs.compute_mlps
)
# model.hook_manager.register("visual.transformer.resblocks.*.attn.in_k.post", prs.compute_k)
# model.hook_manager.register("visual.transformer.resblocks.*.attn.in_q.post", prs.compute_q)
# model.hook_manager.register("visual.transformer.resblocks.*.attn.in_v.post", prs.compute_v)
model.hook_manager.register(
"visual.transformer.resblocks.*.attn.attention.pre_mask", prs.compute_attn_mat
)
model.hook_manager.register("visual.ln_pre_post", prs.compute_mlps)
model.hook_manager.register("visual.ln_post.mean", prs.log_post_ln_mean)
model.hook_manager.register("visual.ln_post.sqrt_var", prs.log_post_ln_std)
return prs
|