Test2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "949d02a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/disk1/miniconda3/envs/baodq_hal/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "\u001b[32m2026-03-06 19:42:49.736\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msae.autoencoder.decoder\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m141\u001b[0m - \u001b[1mTriton found\u001b[0m\n",
      "\u001b[32m2026-03-06 19:42:49.737\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msae.autoencoder.decoder\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m143\u001b[0m - \u001b[1mTriton enabled, using Triton decoder\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚨 `inputs` is part of LlavaModel.forward's signature, but not documented. Make sure to add it to the docstring of the function in /mnt/disk4/baodq/hallucination/model/llava/modeling_llava.py.\n",
      "🚨 `inputs` is part of LlavaForConditionalGeneration.forward's signature, but not documented. Make sure to add it to the docstring of the function in /mnt/disk4/baodq/hallucination/model/llava/modeling_llava.py.\n"
     ]
    }
   ],
   "source": [
    "from model.blip.hooked_blip import HookedSAEBlipConditionalGeneration\n",
    "from model.llava.hooked_llava import HookedSAELlavaConditionalGeneration\n",
    "import torch\n",
    "from sae.SAE_Tools import *\n",
    "from sae.SAE_Trainer import DataConfig\n",
    "from sae.SAE_Blip_Explaining_Utils import *\n",
    "\n",
    "from sae.Load_Data import load_lvlm_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a045d6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 3/3 [00:07<00:00,  2.57s/it]\n",
      "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "dtype = t.bfloat16\n",
    "\n",
    "# blip model\n",
    "# tok_name=\"Salesforce/blip-image-captioning-base\"\n",
    "# model = HookedSAEBlipConditionalGeneration.from_pretrained(tok_name)\n",
    "# processor = BlipProcessor.from_pretrained(tok_name)\n",
    "\n",
    "\n",
    "# llava model\n",
    "tok_name=\"llava-hf/llava-1.5-7b-hf\"\n",
    "model = HookedSAELlavaConditionalGeneration.from_pretrained(tok_name)\n",
    "processor = LlavaProcessor.from_pretrained(tok_name)\n",
    "\n",
    "\n",
    "\n",
    "model = model.to(device, dtype=dtype)\n",
    "\n",
    "\n",
    "# change hf_dataset and path to change dataset\n",
    "\n",
    "num_workers=4\n",
    "hf_dataset=\"yerevann/coco-karpathy\" # yerevann/coco-karpathy\n",
    "local_train_path=\"COCO-Dataset/train\"\n",
    "local_val_path=\"COCO-Dataset/val\"\n",
    "\n",
    "batch_size=1\n",
    "max_length=512\n",
    "filter_seq_length=None # 30 for blip\n",
    "\n",
    "data_config = DataConfig(\n",
    "    batch_size=batch_size,\n",
    "    hf_dataset=hf_dataset,\n",
    "    local_train_path=local_train_path,\n",
    "    local_val_path=local_val_path,\n",
    "    num_workers=num_workers,\n",
    "    max_length=max_length, # the processor of blip only allow max tokens (fixed)\n",
    "    processor=tok_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddda91c3",
   "metadata": {},
   "source": [
    "TEXT SAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e0e09bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_list = [\n",
    "    'cc3m_checkpoints/topk_32.0_32_text_decoder.bert.encoder.layer.0.attention.self.hook_resid_pre_0.001_256_0.03125_42.ckpt', # text\n",
    "    'cc3m_checkpoints/topk_32.0_32_text_decoder.bert.encoder.layer.11.attention.self.hook_resid_pre_0.001_256_0.03125_42.ckpt', # both\n",
    "]\n",
    "\n",
    "saes = [\n",
    "    load_sae_model(\n",
    "        file_path=sae_path,\n",
    "        model_type=\"blip\",\n",
    "        hook_type=\"text\",\n",
    "        device=device,\n",
    "    ).to(dtype) for sae_path in path_list\n",
    "]\n",
    "\n",
    "train_loader, val_loader = load_lvlm_data(data_config)\n",
    "\n",
    "cache_dict, data_toks = cache_sae_lvlm(\n",
    "    saes,\n",
    "    model,\n",
    "    val_loader,\n",
    "    device,\n",
    "    filter_seq_length=filter_seq_length,\n",
    "    stop_at_batch=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63318051",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, val in cache_dict.items():\n",
    "    print(key, val[0].shape)\n",
    "    \n",
    "sae = saes[0]\n",
    "values, indices = cache_dict[sae.cfg.hook_name]\n",
    "print(indices[values > 0].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2722c70d",
   "metadata": {},
   "outputs": [],
   "source": [
    "FEAT_IDX = 9  # The feature you want to analyze\n",
    "N_INTERVALS = 5 # How many splits (e.g., High, Med-High, Med-Low, Low)\n",
    "K_EXAMPLES = 5  # Examples per interval\n",
    "\n",
    "# 2. Fetch the data\n",
    "data = fetch_feature_activation_intervals_blip(\n",
    "    processor=processor,\n",
    "    feat_idx=FEAT_IDX,\n",
    "    values=values, \n",
    "    indices=indices,\n",
    "    toks=data_toks,\n",
    "    n_intervals=N_INTERVALS,\n",
    "    k_per_interval=K_EXAMPLES,\n",
    "    buffer=10 # Context window size\n",
    ")\n",
    "\n",
    "# 3. Generate HTML\n",
    "html_code = generate_interactive_html(data)\n",
    "from IPython.display import HTML\n",
    "display(HTML(html_code))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe4cb8d",
   "metadata": {},
   "source": [
    "VISION SAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1506f9be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading COCO nocap dataset...\n",
      "Using Llava processor\n",
      "Total crops per image: 1\n"
     ]
    }
   ],
   "source": [
    "path_list = [\n",
    "    # 'cc3m_checkpoints/batchtopk_8.0_8_vision_model.encoder.layers.11.hook_resid_post_0.001_128_0.03125_42.ckpt', # image\n",
    "    # \"cc3m_checkpoints/topk_32.0_32_vision_model.encoder.layers.11.hook_resid_post_0.001_256_0.03125_42.ckpt\"\n",
    "    \"cc3m_checkpoints/batchtopk_8.0_8_model.vision_tower.vision_model.encoder.layers.23.hook_resid_post_0.0001_256_0.03125_42.ckpt\",\n",
    "    \"cc3m_checkpoints/batchtopk_8.0_8_model.vision_tower.vision_model.encoder.layers.22.hook_resid_post_0.0001_256_0.03125_42.ckpt\"\n",
    "]\n",
    "\n",
    "saes = [\n",
    "    load_sae_model(\n",
    "        file_path=sae_path,\n",
    "        model_type=\"llava\",\n",
    "        hook_type=\"vision\",\n",
    "        device=device,\n",
    "    ).to(dtype) for sae_path in path_list\n",
    "]\n",
    "\n",
    "nocap_train, nocap_val = load_lvlm_data_nocap(config=data_config)\n",
    "\n",
    "\n",
    "processed_ds = DebatchNoCapDataset(nocap_val, processor=data_config.processor)\n",
    "\n",
    "multi_crop_dataset = MultiScaleCropDataset(\n",
    "    original_dataset=processed_ds,\n",
    "    img_size=model.config.vision_config.image_size,\n",
    "    crop_ratios=[1],\n",
    "    stride_ratio=0.5,\n",
    "    resize_to=model.config.vision_config.image_size,\n",
    ")\n",
    "\n",
    "dataloader = DataLoader(multi_crop_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fdcfcf49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Literal\n",
    "def new_cache_vision_sae_lvlm(\n",
    "    saes: List[Any],\n",
    "    model,\n",
    "    dataloader: DataLoader,\n",
    "    device: str,\n",
    "    mode: str | Literal[\"acts_post\", \"acts_pre\"] = \"acts_post\",\n",
    "    filter_seq_length: int | None = None, # filter too long seq\n",
    "    return_toks: bool = True,\n",
    "    stop_at_batch: int | None = None,\n",
    "):      \n",
    "    act_dict = {sae.cfg.hook_name: [] for sae in saes}\n",
    "    def caching_hook(act: Tensor, hook: HookPoint):\n",
    "        # print(act.shape)\n",
    "        act_dict[\".\".join(hook.name.split(\".\")[:-1])].append((act.squeeze(1).detach().cpu(), data_ids[-1]))\n",
    "        \n",
    "    @contextmanager\n",
    "    def _hook_vision_sae():\n",
    "        pass_through_vision_sae_cache = [t.tensor(0)] # placeholder\n",
    "        def hook_fn(act: Tensor, hook: HookPoint, sae_name: str):\n",
    "            if sae_name + \".hook_sae_input\" == hook.name:\n",
    "                pass_through_vision_sae_cache[0] = act\n",
    "                return act.mean(dim=1, keepdim=True)\n",
    "                \n",
    "            elif sae_name + \".hook_sae_output\" == hook.name:\n",
    "                return pass_through_vision_sae_cache[0]\n",
    "            \n",
    "            elif sae_name + \".hook_sae_error\" == hook.name:\n",
    "                act = t.zeros_like(act).mean(dim=1, keepdim=True)\n",
    "                return act\n",
    "            \n",
    "        try:\n",
    "            for vision_sae in saes:\n",
    "                vision_sae.add_hook(\n",
    "                    lambda name: True,\n",
    "                    partial(hook_fn, sae_name=vision_sae.cfg.hook_name),\n",
    "                    dir=\"fwd\",\n",
    "                )\n",
    "            yield\n",
    "        finally:\n",
    "            for vision_sae in saes:\n",
    "                vision_sae.reset_hooks()\n",
    "        \n",
    "    data_toks = []\n",
    "    data_ids = []\n",
    "    with _hook_vision_sae():\n",
    "        with model.saes(saes, use_error_term=True):\n",
    "            with model.hooks(fwd_hooks=[(lambda name: mode in name, caching_hook)]):\n",
    "                for batch_idx_iter, batch in enumerate(tqdm(dataloader, desc=\"Caching SAE acts\")):\n",
    "                    if stop_at_batch is not None:\n",
    "                        if batch_idx_iter > stop_at_batch:\n",
    "                            break\n",
    "                    inputs = {\n",
    "                        \"pixel_values\": batch[\"pixel_values\"].to(device),\n",
    "                        \"input_ids\": batch[\"input_ids\"].to(device),\n",
    "                        \"attention_mask\": batch[\"attention_mask\"].to(device),\n",
    "                    }\n",
    "                    if filter_seq_length is not None and batch[\"input_ids\"].shape[1] > filter_seq_length:\n",
    "                        continue\n",
    "                    if return_toks:\n",
    "                        data_toks.append(batch[\"input_ids\"].cpu())\n",
    "                    data_ids.append(batch[\"imgid\"].item())\n",
    "                    model(inputs)\n",
    "    \n",
    "    if return_toks:\n",
    "        data_toks = pad_and_concat(data_toks, dim=0, padding_value=0) # [PAD] token  \n",
    "    \n",
    "    for key, act_list in act_dict.items():\n",
    "        acts_list = [x[0] for x in act_list]\n",
    "        img_ids = [x[1] for x in act_list]\n",
    "        acts = pad_and_concat(acts_list, dim=0, padding_value=0) # (b, d_sae), pad 0 activation value in batch\n",
    "        # print(acts.shape)\n",
    "        max_k = int((acts > 1e-3).sum(dim=-1).max().item())\n",
    "        values, indices = acts.topk(k=max_k, dim=-1)\n",
    "\n",
    "        img_ids = t.tensor(img_ids)\n",
    "        act_dict[key] = (values, indices, img_ids)\n",
    "    \n",
    "    return act_dict, data_toks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43ad73e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Caching SAE acts:   0%|          | 0/5000 [00:00<?, ?it/s]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "Caching SAE acts: 100%|██████████| 5000/5000 [05:51<00:00, 14.21it/s]\n",
      "Pad and concatenate tensors: 100%|██████████| 5000/5000 [00:00<00:00, 194888.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num unique features:  696\n",
      "Num images 5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cache_dict, _ = new_cache_vision_sae_lvlm(\n",
    "    saes,\n",
    "    model,\n",
    "    dataloader,\n",
    "    device,\n",
    "    mode=\"acts_post\",\n",
    "    filter_seq_length=filter_seq_length,\n",
    "    stop_at_batch=None,\n",
    "    return_toks=False,\n",
    ")\n",
    "sae = saes[0]\n",
    "# values, indices  = cache_dict[sae.cfg.hook_name]\n",
    "values, indices, img_ids = cache_dict[sae.cfg.hook_name]\n",
    "print(\"Num unique features: \", len(t.unique(indices)))\n",
    "print(\"Num images\", len(indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38bf25fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "img_idx = 20\n",
    "\n",
    "x = multi_crop_dataset[img_idx][\"pixel_values\"].detach().cpu()   # (3,H,W)\n",
    "\n",
    "mean = torch.tensor([0.485, 0.456, 0.406]).view(3,1,1)\n",
    "std  = torch.tensor([0.229, 0.224, 0.225]).view(3,1,1)\n",
    "\n",
    "img = (x * std + mean).clamp(0, 1)                        # back to [0,1]\n",
    "img = img.permute(1, 2, 0).numpy()\n",
    "\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2481716",
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_fetch_topk_activating_crops_lvlm(\n",
    "    feat_idx: int,\n",
    "    values: Tensor,\n",
    "    indices: Tensor,\n",
    "    img_ids: Tensor,\n",
    "    dataset: Dataset,\n",
    "    top_k: int,\n",
    "    output_dir: str,\n",
    "    verbose: bool = True,\n",
    "):  \n",
    "    mask = (values > 1e-3) * (indices == feat_idx)\n",
    "    if verbose:\n",
    "        print(\"Feature ID:\", feat_idx, \"Density:\", mask.sum().item() / mask.numel(), \"Max act:\", (values * mask).max().item())\n",
    "    if mask.sum() == 0:\n",
    "        if verbose:\n",
    "            print(\"No activation!\")\n",
    "        return\n",
    "    filtered_values = values * mask.to(values.dtype) # (b, activated_dim)\n",
    "    \n",
    "    sumed_filtered_values = filtered_values.sum(dim=1) # (b)\n",
    "    top_vals, top_indices = sumed_filtered_values.topk(k=top_k) # (topk)\n",
    "    top_acts = filtered_values[top_indices, :] # (topk, activated_dim)\n",
    "    top_img_ids = img_ids[top_indices]\n",
    "    print(\"img_ids:\", top_img_ids)\n",
    "    \n",
    "    visualize_crops(\n",
    "        top_vals = top_vals,\n",
    "        top_indices = top_indices,\n",
    "        top_acts = top_acts,\n",
    "        feat_idx = feat_idx,\n",
    "        multi_crop_dataset = dataset,\n",
    "        output_dir = output_dir,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c16e9a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ID: 1903 Density: 0.1245 Max act: 10.125\n",
      "img_ids: tensor([133928, 472960, 124018, 566049, 196681, 204324,  95692, 539226, 155051,\n",
      "        199977, 535997, 563295, 573008, 110359, 304924])\n",
      "Feature act: 7.90625\n",
      "Feature ID: 3696 Density: 0.114925 Max act: 7.78125\n",
      "img_ids: tensor([189267, 338325, 263073, 117014, 537864, 259421, 574343, 545407, 509867,\n",
      "        408535, 296848, 146659,  99242,  40361, 566518])\n",
      "Feature act: 6.625\n",
      "Feature ID: 4647 Density: 0.1036 Max act: 7.03125\n",
      "img_ids: tensor([545407, 189267, 263073, 259421, 509867, 475271, 117014, 251395, 472078,\n",
      "        338325, 556157, 208135,  17484,  99242, 237735])\n",
      "Feature act: 6.40625\n",
      "Feature ID: 3674 Density: 0.0892 Max act: 6.21875\n",
      "img_ids: tensor([545407, 208135, 259421, 263073,  64157, 392650, 524186,   5477,  81505,\n",
      "        326308, 297220, 176828, 462931, 117014,  84870])\n",
      "Feature act: 5.375\n",
      "Feature ID: 1366 Density: 0.02925 Max act: 6.71875\n",
      "img_ids: tensor([ 64157, 208135, 323925, 297220, 545407,   5477,  71815, 117014, 326308,\n",
      "        522198, 524186, 279806, 263073, 462931, 304545])\n",
      "Feature act: 4.5625\n"
     ]
    }
   ],
   "source": [
    "# Visualize top_k_features for image_idx\n",
    "top_k_features = 5\n",
    "for each in range(top_k_features):\n",
    "    new_fetch_topk_activating_crops_lvlm(\n",
    "        feat_idx=indices[img_idx][each].item(),\n",
    "        values=values,\n",
    "        indices=indices,\n",
    "        dataset=multi_crop_dataset,\n",
    "        top_k=15, # top activating crops\n",
    "        output_dir=\"./vision_sae_vis\",\n",
    "        img_ids=img_ids,\n",
    "    )\n",
    "    print(\"Feature act:\", values[img_idx][each].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d31b04f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sae_feature = 3674\n",
    "raw_image = Image.open(f\"/mnt/disk4/baodq/hallucination/vision_sae_vis/feature_{sae_feature}_top_crops/top_grid.jpg\")\n",
    "\n",
    "# 3. Format prompt correctly\n",
    "prompt = \"USER: <image>\\nASSISTANT:\"\n",
    "\n",
    "# 4. Process and Generate\n",
    "inputs = processor(text=prompt, images=raw_image, return_tensors=\"pt\")\n",
    "inputs = {k: v.to(device) for k, v in inputs.items()}\n",
    "\n",
    "\n",
    "\n",
    "_, cache = model.run_with_cache_with_saes(\n",
    "    inputs,\n",
    "    saes=saes,\n",
    "    names_filter=lambda name: (\"vision_model\" in name) and (\"hook_mlp_out\" in name)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "11ee35a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([])\n"
     ]
    }
   ],
   "source": [
    "print(cache.keys())\n",
    "# print(cache[\"model.vision_tower.vision_model.encoder.layers.22.hook_resid_post.hook_sae_output\"].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a27b43d8",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cache' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m act \u001b[38;5;241m=\u001b[39m \u001b[43mcache\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.vision_tower.vision_model.encoder.layers.22.hook_resid_post.hook_sae_acts_post\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      2\u001b[0m sae_feature \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3674\u001b[39m\n\u001b[1;32m      3\u001b[0m mean \u001b[38;5;241m=\u001b[39m cache[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.vision_tower.vision_model.encoder.layers.22.hook_resid_post.hook_sae_acts_post\u001b[39m\u001b[38;5;124m'\u001b[39m][:, :, sae_feature]\u001b[38;5;241m.\u001b[39mmean(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cache' is not defined"
     ]
    }
   ],
   "source": [
    "act = cache['model.vision_tower.vision_model.encoder.layers.22.hook_resid_post.hook_sae_acts_post']\n",
    "sae_feature = 3674\n",
    "mean = cache['model.vision_tower.vision_model.encoder.layers.22.hook_resid_post.hook_sae_acts_post'][:, :, sae_feature].mean(dim=1, keepdim=False)\n",
    "feature_act = act[:, 1:, sae_feature]\n",
    "\n",
    "mask = ((feature_act < mean).float()).reshape(24, 24).unsqueeze(0).unsqueeze(0)\n",
    "\n",
    "\n",
    "upsampled_mask = F.interpolate(\n",
    "    mask,\n",
    "    size=raw_image.size[::-1],  # (H, W)\n",
    "    mode=\"bilinear\",\n",
    "    align_corners=False\n",
    ")[0,0]\n",
    "\n",
    "import torchvision.transforms as T\n",
    "\n",
    "img_tensor = T.ToTensor()(raw_image).to(device)\n",
    "masked_img = img_tensor * upsampled_mask\n",
    "plt.imshow(masked_img.permute(1,2,0).cpu())\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31a025da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete code for visualization features\n",
    "\n",
    "from model.blip.hooked_blip import HookedSAEBlipConditionalGeneration\n",
    "import torch\n",
    "from sae.SAE_Tools import *\n",
    "from sae.SAE_Trainer import DataConfig\n",
    "from sae.SAE_Blip_Explaining_Utils import *\n",
    "from sae.Load_Data import load_lvlm_data\n",
    "\n",
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "model = HookedSAEBlipConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
    "model.to(device)\n",
    "\n",
    "\n",
    "# change hf_dataset and path to change dataset\n",
    "\n",
    "num_workers=0\n",
    "hf_dataset=\"yerevann/coco-karpathy\" # yerevann/coco-karpathy\n",
    "local_train_path=\"COCO-Dataset/train\"\n",
    "local_val_path=\"COCO-Dataset/val\"\n",
    "tok_name=\"Salesforce/blip-image-captioning-base\"\n",
    "batch_size=32\n",
    "max_length=512\n",
    "filter_seq_length=30\n",
    "\n",
    "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
    "\n",
    "data_config = DataConfig(\n",
    "    batch_size=batch_size,\n",
    "    hf_dataset=hf_dataset,\n",
    "    local_train_path=local_train_path,\n",
    "    local_val_path=local_val_path,\n",
    "    num_workers=num_workers,\n",
    "    max_length=max_length, # the processor of blip only allow max tokens (fixed)\n",
    "    processor=tok_name,\n",
    ")\n",
    "\n",
    "path_list = [\n",
    "    'cc3m_checkpoints/topk_32.0_32_vision_model.encoder.layers.0.hook_resid_post_0.001_256_0.03125_42.ckpt', # image\n",
    "]\n",
    "\n",
    "saes = [\n",
    "    load_sae_model(\n",
    "        file_path=sae_path,\n",
    "        model=model, # type: ignore\n",
    "        device=device,\n",
    "    ) for sae_path in path_list\n",
    "]\n",
    "\n",
    "nocap_train, nocap_val = load_lvlm_data_nocap(config=data_config)\n",
    "\n",
    "processed_ds = DebatchNoCapDataset(nocap_val, processor=data_config.processor)\n",
    "\n",
    "multi_crop_dataset = MultiScaleCropDataset(\n",
    "    original_dataset=processed_ds,\n",
    "    img_size=model.config.vision_config.image_size,\n",
    "    crop_ratios=[1],\n",
    "    stride_ratio=0.5,\n",
    "    resize_to=model.config.vision_config.image_size,\n",
    ")\n",
    "\n",
    "dataloader = DataLoader(multi_crop_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "\n",
    "cache_dict, _ = cache_vision_sae_lvlm(\n",
    "    saes,\n",
    "    model,\n",
    "    dataloader,\n",
    "    device,\n",
    "    filter_seq_length=filter_seq_length,\n",
    "    stop_at_batch=0,\n",
    "    return_toks=False,\n",
    ")\n",
    "\n",
    "for key, val in cache_dict.items():\n",
    "    print(key, val[0].shape)\n",
    "    \n",
    "sae = saes[0]\n",
    "values, indices = cache_dict[sae.cfg.hook_name]\n",
    "\n",
    "\n",
    "fetch_topk_activating_crops_lvlm(\n",
    "    feat_idx=114,\n",
    "    values=values,\n",
    "    indices=indices,\n",
    "    dataset=multi_crop_dataset,\n",
    "    top_k=15,\n",
    "    output_dir=\"./vision_sae_vis\",\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9387a847",
   "metadata": {},
   "source": [
    "# Steering Vision Encoder (layer 24) -> not use in model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "70b01cd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USER:  \n",
      "Describe this image in detail.\n",
      "ASSISTANT: The image features a red and white airplane with a red wing, flying through a cloudy sky. The airplane is positioned in the middle of the scene, with its wings spread out. The sky is filled with clouds, creating a dramatic backdrop for the airplane. The scene captures the essence of air travel and the beauty of the sky.\n"
     ]
    }
   ],
   "source": [
    "image = Image.open(\"/mnt/disk4/baodq/hallucination/output.png\")\n",
    "prompt = \"USER: <image>\\nDescribe this image in detail.\\nASSISTANT:\"\n",
    "\n",
    "inputs = processor(text=prompt, images=image, return_tensors=\"pt\")\n",
    "inputs = {k: v.to(device) for k, v in inputs.items()}\n",
    "\n",
    "feature_id = 88\n",
    "\n",
    "def ablate_feature_hook(acts, hook):\n",
    "    # acts shape: (batch, seq_len, d_sae)\n",
    "    acts[..., feature_id] = 0\n",
    "    return acts\n",
    "\n",
    "with model.saes(saes):\n",
    "    with model.hooks(\n",
    "        fwd_hooks=[\n",
    "            (\n",
    "                \"model.vision_tower.vision_model.encoder.layers.23.hook_resid_post.hook_sae_acts_post\",\n",
    "                ablate_feature_hook\n",
    "            )\n",
    "        ]\n",
    "    ):\n",
    "        output = model.generate(\n",
    "            **inputs,\n",
    "            max_new_tokens=300,\n",
    "            do_sample=False,\n",
    "        )\n",
    "        \n",
    "generated_text = processor.decode(output[0], skip_special_tokens=True)\n",
    "print(generated_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6b440894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USER:  \n",
      "Describe this image in detail.\n",
      "ASSISTANT: The image features a woman standing in front of a large body of water, possibly the ocean. She is wearing a white shirt and appears to be enjoying her time by the water. The woman is the main focus of the scene, with the vast body of water in the background.\n"
     ]
    }
   ],
   "source": [
    "image = Image.open(\"/mnt/disk4/baodq/hallucination/output.png\")\n",
    "prompt = \"USER: <image>\\nDescribe this image in detail.\\nASSISTANT:\"\n",
    "\n",
    "inputs = processor(text=prompt, images=image, return_tensors=\"pt\")\n",
    "inputs = {k: v.to(device) for k, v in inputs.items()}\n",
    "\n",
    "feature_id = 3674\n",
    "\n",
    "def ablate_feature_hook(acts, hook):\n",
    "    # acts shape: (batch, seq_len, d_sae)\n",
    "    acts[..., feature_id] = 300\n",
    "    return acts\n",
    "\n",
    "with model.saes(saes):\n",
    "    with model.hooks(\n",
    "        fwd_hooks=[\n",
    "            (\n",
    "                \"model.vision_tower.vision_model.encoder.layers.22.hook_resid_post.hook_sae_acts_post\",\n",
    "                ablate_feature_hook\n",
    "            )\n",
    "        ]\n",
    "    ):\n",
    "        output = model.generate(\n",
    "            **inputs,\n",
    "            max_new_tokens=300,\n",
    "            do_sample=False,\n",
    "        )\n",
    "        \n",
    "generated_text = processor.decode(output[0], skip_special_tokens=True)\n",
    "print(generated_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a35c7478",
   "metadata": {},
   "source": [
    "# Steering Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c812669",
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.llava.hooked_llava import HookedSAELlavaConditionalGeneration\n",
    "from transformers import LlavaProcessor\n",
    "from sae_lens import SAE\n",
    "import torch\n",
    "\n",
    "model = HookedSAELlavaConditionalGeneration.from_pretrained(\"gpt2-small\", device=\"cuda\")\n",
    "sae, _, _ = SAE.from_pretrained(\n",
    "    release=\"gpt2-small-res-jb\",\n",
    "    sae_id=\"blocks.8.hook_resid_pre\",\n",
    "    device=\"cuda\"\n",
    ")\n",
    "\n",
    "# 1. Feature attribution for a specific prediction\n",
    "prompt = \"USER: <image>\\nDescribe this image in detail.\\nASSISTANT:\"\n",
    "processor = LlavaProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\")\n",
    "image = np.zeros((224, 224, 3))  \n",
    "inputs = processor(images=image, text=prompt, return_tensors=\"pt\")\n",
    "\n",
    "_, cache = model.run_with_cache(inputs, \n",
    "                                names_filter=lambda name: \"resid\" in name and \"language\" in name))\n",
    "\n",
    "hook_name = \"model.language_model.layers.25.hook_resid_post\"\n",
    "activations = cache[hook_name]\n",
    "features = sae.encode(activations)\n",
    "\n",
    "# Target token\n",
    "target_token = processor.tokenizer.encode(\" Paris\", add_special_tokens=False)\n",
    "\n",
    "# Compute feature contributions to target logit\n",
    "\n",
    "# # contribution = feature_activation * sae_decoder_weight * unembedding\n",
    "# W_dec = sae.W_dec  # [d_sae, d_model]\n",
    "# W_U = model.W_U    # [d_model, d_vocab]\n",
    "# # Feature direction projected to vocabulary\n",
    "# feature_to_logit = W_dec @ W_U  # [d_sae, d_vocab]\n",
    "# # Contribution of each feature to \"Paris\" at final position\n",
    "# feature_acts = features[0, -1]  # [d_sae]\n",
    "# contributions = feature_acts * feature_to_logit[:, target_token]\n",
    "\n",
    "\n",
    "\n",
    "# norm=model.language_model.norm\n",
    "# lm_head=model.lm_head # [d_model, d_vocab]\n",
    "# W_dec = sae.W_dec  # [d_sae, d_model]\n",
    "\n",
    "# with torch.no_grad():\n",
    "    # baseline_logits = lm_head(norm(residual_stream)) # [b, seq, d_vocab]\n",
    "    # modified = residual_stream + feature_activation * W_dec[feature_idx] # [b, seq, d_model]\n",
    "    # modified_logits = lm_head(norm(modified)) # [b, seq, d_vocab]\n",
    "    # contribution = modified_logits[:, -1, target_token] - baseline_logits[:, -1, target_token] # [b] - contribution score for feature to target token\n",
    "\n",
    "\n",
    "\n",
    "# Top contributing features\n",
    "top_features = contributions.topk(10)\n",
    "print(\"Top features contributing to 'Paris':\")\n",
    "for idx, val in zip(top_features.indices, top_features.values):\n",
    "    print(f\"  Feature {idx.item()}: {val.item():.3f}\")\n",
    "\n",
    "# 2. Feature steering\n",
    "def steer_with_feature(feature_idx, strength=5.0):\n",
    "    \"\"\"Add a feature direction to the residual stream.\"\"\"\n",
    "    feature_direction = sae.W_dec[feature_idx]  # [d_model]\n",
    "\n",
    "    def hook(activation, hook_obj):\n",
    "        activation[:, -1, :] += strength * feature_direction\n",
    "        return activation\n",
    "\n",
    "    output = model.generate(\n",
    "        **inputs,\n",
    "        max_new_tokens=10,\n",
    "        fwd_hooks=[(hook_name, hook)]\n",
    "    )\n",
    "    return processor.tokenizer.decode(output[0], add_special_tokens=False)\n",
    "\n",
    "# Try steering with top feature\n",
    "top_feature_idx = top_features.indices[0].item()\n",
    "print(f\"\\nSteering with feature {top_feature_idx}:\")\n",
    "print(steer_with_feature(top_feature_idx, strength=10.0))"
   ]
  }
 ],
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