static-train_sparse_sae_connector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on only MLP part\n",
    "# Make the hidden layer sparse w/ L1\n",
    "# No need to normalize decoder weights since we're making those weights sparse anyways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/logan/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"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from torch.utils.data import DataLoader\n",
    "from datasets import Dataset, load_dataset\n",
    "from tqdm.auto import tqdm\n",
    "from functools import partial\n",
    "from einops import rearrange\n",
    "from huggingface_hub import hf_hub_download\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification\n",
    "from baukit import TraceDict\n",
    "\n",
    "# Download the model\n",
    "device = \"cuda:0\"\n",
    "model_name=\"EleutherAI/Pythia-70M-deduped\"\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name).to(device)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "# set seed\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autoencoders import *\n",
    "# ae_model_id = [\"jbrinkma/Pythia-70M-chess_sp51_r4_gpt_neox.layers.1\", \"jbrinkma/Pythia-70M-chess_sp51_r4_gpt_neox.layers.2.mlp\"]\n",
    "model_id = \"jbrinkma/Pythia-70M-deduped-SAEs\"\n",
    "autoencoders = []\n",
    "# layers = model.config.num_hidden_layers\n",
    "layers = 6\n",
    "cache_names = [(f\"gpt_neox.layers.{i}\", f\"gpt_neox.layers.{i+1}.mlp\") for i in range(layers-1)]\n",
    "# cache_names = [(f\"gpt_neox.layers.{i}\", f\"gpt_neox.layers.{i+1}.attention\") for i in range(layers-1)]\n",
    "# cache_names = [(f\"gpt_neox.layers.{i}\", f\"gpt_neox.layers.{i+1}.mlp\") for i in range(1,2)]\n",
    "num_layers = len(cache_names)\n",
    "cache_names = [item for sublist in cache_names for item in sublist]\n",
    "filenames = [(f\"Pythia-70M-deduped-{i}.pt\", f\"Pythia-70M-deduped-mlp-{i+1}.pt\") for i in range(layers-1)]\n",
    "# filenames = [(f\"Pythia-70M-deduped-{i}.pt\", f\"Pythia-70M-deduped-attention-{i+1}.pt\") for i in range(layers-1)]\n",
    "# filenames = [(f\"Pythia-70M-deduped-{i}.pt\", f\"Pythia-70M-deduped-mlp-{i+1}.pt\") for i in range(1,2)]\n",
    "filenames = [item for sublist in filenames for item in sublist]\n",
    "for filen in filenames:\n",
    "    ae_download_location = hf_hub_download(repo_id=model_id, filename=filen)\n",
    "    autoencoder = torch.load(ae_download_location)\n",
    "    autoencoder.to_device(device)\n",
    "    # Freeze autoencoder weights\n",
    "    autoencoder.encoder.requires_grad_(False)\n",
    "    autoencoder.encoder_bias.requires_grad_(False)\n",
    "    autoencoders.append(autoencoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset openwebtext-10k (/root/.cache/huggingface/datasets/stas___openwebtext-10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b)\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/stas___openwebtext-10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-89f7d956ed9de0f6_*_of_00008.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tokens: 11226112\n"
     ]
    }
   ],
   "source": [
    "from activation_dataset import setup_token_data\n",
    "from utils import dotdict\n",
    "\n",
    "# Download the dataset\n",
    "# This formats it, so every datapoint is max_length tokens long\n",
    "# The batch size is for loading activations from the LLM, not for inference on the autoencoder\n",
    "cfg = dotdict()\n",
    "cfg.dataset_name=\"stas/openwebtext-10k\"\n",
    "cfg.max_length = 256\n",
    "cfg.model_batch_size = 4\n",
    "dataset = setup_token_data(cfg, tokenizer, model)\n",
    "num_tokens = cfg.max_length*cfg.model_batch_size*len(dataset)\n",
    "print(f\"Number of tokens: {num_tokens}\")\n",
    "num_features, d_model = autoencoder.encoder.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim import Adam\n",
    "from torch import nn  \n",
    "class sae(nn.Module):\n",
    "    def __init__(self, input_size, output_size=None):\n",
    "        super().__init__()\n",
    "        if(output_size is None):\n",
    "            output_size = input_size\n",
    "        self.linear = nn.Linear(input_size, input_size, bias=False)\n",
    "        self.linear2 = nn.Linear(input_size, output_size, bias=False)\n",
    "        self.relu = nn.ReLU()\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)\n",
    "        x_hidden = self.relu(x)\n",
    "        x = self.linear2(x_hidden)\n",
    "        return x, x_hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the features indices\n",
    "# Note: linear & MLP features are a subet of alive features, not total features\n",
    "import pickle\n",
    "with open(\"linear_features.pkl\", \"rb\") as f:\n",
    "    linear_features = pickle.load(f)\n",
    "with open(\"mlp_features.pkl\", \"rb\") as f:\n",
    "    mlp_features = pickle.load(f)\n",
    "with open(\"alive_features.pkl\", \"rb\") as f:\n",
    "    alive_features_ind = pickle.load(f)\n",
    "normalization_per_layer = torch.load(\"normalization_per_layer.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train_sparse_model(model, dataset, device, sparse_weights, autoencoders, cache_names, optimizers, l1_alphas, feature_ind, max_batches = 500):\n",
    "    mse = nn.MSELoss()\n",
    "    losses = [[[] for _ in range(len(l1_alphas))] for _ in range(len(sparse_weights))]\n",
    "    losses_mse = [[[] for _ in range(len(l1_alphas))] for _ in range(len(sparse_weights))]\n",
    "    losses_l1 = [[[] for _ in range(len(l1_alphas))] for _ in range(len(sparse_weights))]\n",
    "    losses_l1_hidden = [[[] for _ in range(len(l1_alphas))] for _ in range(len(sparse_weights))]\n",
    "    \n",
    "    # with dataset.formatted_as(\"pt\"):\n",
    "    #     dl = DataLoader(dataset[\"input_ids\"], batch_size=batch_size)\n",
    "    for i, batch in enumerate(tqdm(dataset)):\n",
    "        batch = batch[\"input_ids\"].to(device)\n",
    "        with torch.no_grad():\n",
    "            with TraceDict(model, cache_names) as ret:\n",
    "                _ = model(batch)\n",
    "        \n",
    "        for cache_name_ind in range(len(cache_names) // 2):\n",
    "            with torch.no_grad():\n",
    "                input_cache_name = cache_names[cache_name_ind * 2]\n",
    "                output_cache_name = cache_names[cache_name_ind * 2 + 1]\n",
    "                input_autoencoder = autoencoders[cache_name_ind * 2]\n",
    "                output_autoencoder = autoencoders[cache_name_ind * 2 + 1]\n",
    "                input_activations = ret[input_cache_name].output\n",
    "                output_activations = ret[output_cache_name].output\n",
    "\n",
    "                if isinstance(input_activations, tuple):\n",
    "                    input_activations = input_activations[0]\n",
    "                if isinstance(output_activations, tuple):\n",
    "                    output_activations = output_activations[0]\n",
    "\n",
    "                input_internal_activations = rearrange(input_activations, \"b s n -> (b s) n\")\n",
    "                output_internal_activations = rearrange(output_activations, \"b s n -> (b s) n\")\n",
    "\n",
    "                input_internal_activations = input_autoencoder.encode(input_internal_activations)\n",
    "                output_internal_activations = output_autoencoder.encode(output_internal_activations)\n",
    "\n",
    "            for l1_alpha_ind, l1_alpha in enumerate(l1_alphas):\n",
    "                sparse_weight = sparse_weights[cache_name_ind][l1_alpha_ind].to(device)\n",
    "                x_hat, hidden_act = sparse_weight(input_internal_activations)\n",
    "\n",
    "                # Find ind of alive features & selected features (linear or MLP features)\n",
    "                alive_features = alive_features_ind[cache_name_ind].nonzero(as_tuple=True)[0]\n",
    "                alive_and_selected_features = alive_features[feature_ind[cache_name_ind]].to(device)\n",
    "                indexed_x_hat = x_hat\n",
    "                indexed_output_internal_activations = output_internal_activations.index_select(dim=1, index=alive_and_selected_features)\n",
    "                \n",
    "                # Divide MSE_loss by normalization factor\n",
    "                mse_loss = mse(indexed_x_hat, indexed_output_internal_activations) / normalization_per_layer[cache_name_ind]\n",
    "                l1_loss = torch.norm(sparse_weight.linear.weight, dim=1, p=1).mean()\n",
    "                # check if sparse_weight.linear2 exists\n",
    "                if hasattr(sparse_weight, \"linear2\"):\n",
    "                    l1_loss += torch.norm(sparse_weight.linear2.weight, dim=1, p=1).mean()\n",
    "                    l1_loss /= 2\n",
    "                hidden_loss = torch.norm(hidden_act, 1, dim=-1).mean()\n",
    "                unweighted_loss = mse_loss + l1_loss + hidden_loss\n",
    "                loss = mse_loss + l1_alpha * l1_loss + l1_alpha * hidden_loss\n",
    "                \n",
    "\n",
    "                loss.backward()\n",
    "                optimizers[cache_name_ind][l1_alpha_ind].step()\n",
    "                optimizers[cache_name_ind][l1_alpha_ind].zero_grad()\n",
    "                losses[cache_name_ind][l1_alpha_ind].append(unweighted_loss.item())\n",
    "                losses_mse[cache_name_ind][l1_alpha_ind].append(mse_loss.item())\n",
    "                losses_l1[cache_name_ind][l1_alpha_ind].append(l1_loss.item())\n",
    "                losses_l1_hidden[cache_name_ind][l1_alpha_ind].append(hidden_loss.item())\n",
    "\n",
    "                sparse_weight = sparse_weight.to(\"cpu\")\n",
    "                if i % 100 == 0:\n",
    "                    sparsity = (hidden_act != 0).float().mean(dim=0).sum().cpu().item()\n",
    "                    print(f\"Layers: {cache_name_ind} | l1 alpha MSE Loss: {mse_loss.item():.4f} | L1 Loss: {l1_loss.item():.2f} | Hidden Loss: {hidden_loss.item():.2f}  L0: {sparsity:.0f}\")\n",
    "\n",
    "                del x_hat, indexed_x_hat, indexed_output_internal_activations, loss, l1_loss, mse_loss, unweighted_loss, alive_and_selected_features\n",
    "                torch.cuda.empty_cache()\n",
    "        if i > max_batches:\n",
    "            # Stop training after 1000 batches\n",
    "            break\n",
    "    return losses, losses_mse, losses_l1, losses_l1_hidden\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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      "Layers: 0 | l1 alpha MSE Loss: 0.0120 | L1 Loss: 27.72 | Hidden Loss: 56.12  L0: 1536\n",
      "Layers: 1 | l1 alpha MSE Loss: 0.5441 | L1 Loss: 27.73 | Hidden Loss: 65.02  L0: 1530\n",
      "Layers: 2 | l1 alpha MSE Loss: 0.1212 | L1 Loss: 27.69 | Hidden Loss: 89.69  L0: 1533\n"
     ]
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      "Layers: 3 | l1 alpha MSE Loss: 0.1912 | L1 Loss: 27.70 | Hidden Loss: 111.81  L0: 1548\n",
      "Layers: 4 | l1 alpha MSE Loss: 1.6684 | L1 Loss: 27.73 | Hidden Loss: 113.56  L0: 1532\n"
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      "Layers: 0 | l1 alpha MSE Loss: 0.0067 | L1 Loss: 1.37 | Hidden Loss: 1.08  L0: 34\n",
      "Layers: 1 | l1 alpha MSE Loss: 0.0590 | L1 Loss: 20.76 | Hidden Loss: 25.37  L0: 349\n",
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      "Layers: 4 | l1 alpha MSE Loss: 0.0567 | L1 Loss: 21.55 | Hidden Loss: 644.89  L0: 1306\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layers: 4 | l1 alpha MSE Loss: 0.0514 | L1 Loss: 9.87 | Hidden Loss: 38.25  L0: 34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 3001/10963 [14:54<39:32,  3.36it/s]\n"
     ]
    }
   ],
   "source": [
    "# Let's just think about this for 1 layer\n",
    "# If we're doing 10-different l1 alpha terms, then we have 10 different sparse weights\n",
    "mlp_weights_no_bias = []\n",
    "# l1_alphas = [1e-5, 4e-5, 7e-5, 1e-4, 4e-4, 7e-4, 1e-3, 4e-3, 7e-3, 1e-2]\n",
    "l1_alphas = [4e-4]\n",
    "\n",
    "mlp_optimizers_no_bias = []\n",
    "l1_alpha = len(l1_alphas)\n",
    "for layer in range(num_layers): # This is per layer\n",
    "    num_linear_features = linear_features[layer].sum()\n",
    "    num_mlp_features = mlp_features[layer].sum()\n",
    "    mlp_weights_no_bias.append([])\n",
    "    mlp_optimizers_no_bias.append([])\n",
    "    for l1_alpha_ind in range(l1_alpha):\n",
    "        mlp_weights_no_bias[-1].append(sae(num_features, output_size=num_mlp_features))\n",
    "        mlp_optimizers_no_bias[-1].append(Adam(mlp_weights_no_bias[-1][-1].parameters(), lr=1e-3))\n",
    "max_batches = 3000\n",
    "mlp_losses_no_bias, mlp_losses_no_bias_mse, mlp_losses_no_bias_l1, mlp_losses_no_bias_l1_hidden = train_sparse_model(model, dataset, device, mlp_weights_no_bias, autoencoders, cache_names, mlp_optimizers_no_bias, l1_alphas, mlp_features, max_batches=max_batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x3000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot both losses, just the last value for both MSE and L1\n",
    "import matplotlib.pyplot as plt\n",
    "# mlp_losses_no_bias, mlp_losses_no_bias_mse, mlp_losses_no_bias_l1, mlp_losses_no_bias_l1_hidden\n",
    "num_layers = len(mlp_losses_no_bias)\n",
    "num_columns = 2  # Two columns: one for each type of loss\n",
    "num_rows = num_layers  # One row for each layer\n",
    "\n",
    "l1_alpha_num = len(l1_alphas)\n",
    "mlp_mse_losses_last = np.zeros((num_layers, l1_alpha_num))\n",
    "mlp_l1_losses_last = np.zeros((num_layers, l1_alpha_num))\n",
    "mlp_l1_hidden_losses_last = np.zeros((num_layers, l1_alpha_num))\n",
    "for layer in range(num_layers):\n",
    "    for l1_alpha_ind, l1_alpha in enumerate(l1_alphas):\n",
    "        mlp_mse_losses_last[layer, l1_alpha_ind] = mlp_losses_no_bias_mse[layer][l1_alpha_ind][-1]\n",
    "        mlp_l1_losses_last[layer, l1_alpha_ind] = mlp_losses_no_bias_l1[layer][l1_alpha_ind][-1]\n",
    "        mlp_l1_hidden_losses_last[layer, l1_alpha_ind] = mlp_losses_no_bias_l1_hidden[layer][l1_alpha_ind][-1]\n",
    "\n",
    "plt.figure(figsize=(12, 6 * num_rows))  # Adjust the size as needed\n",
    "\n",
    "for layer in range(num_layers):\n",
    "    # Subplot for MSE Loss\n",
    "    plt.subplot(num_rows, num_columns, layer+1)\n",
    "    plt.title(f\"MSE vs L1 : Layer {layer+1}\")\n",
    "    # Grabe the last value of MSE and L1\n",
    "    mlp_l1_loss = mlp_l1_losses_last[layer] \n",
    "    mlp_mse_loss = mlp_mse_losses_last[layer] \n",
    "    mlp_l1_hidden_loss = mlp_l1_hidden_losses_last[layer]\n",
    "    # Plot\n",
    "    plt.plot(mlp_l1_loss, mlp_mse_loss, 'o', label=\"MLP_no_bias\")\n",
    "    plt.xlabel(\"L1 Loss\")\n",
    "    plt.ylabel(\"MSE Loss\")\n",
    "    # put both on logplot\n",
    "    plt.yscale('log')\n",
    "    plt.xscale('log')\n",
    "    plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save these mlp weights\n",
    "dir = \"sparse_weights\"\n",
    "for layer in range(num_layers):\n",
    "    for l1_alpha_ind, l1_alpha in enumerate(l1_alphas):\n",
    "        torch.save(mlp_weights_no_bias[layer][l1_alpha_ind], f\"{dir}/Pythia-70M-deduped-mlp-{layer+1}-l1_alpha-{l1_alpha}.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l1_alpha: 1.0E-05 | MSE: 1.8E-03 | L1: 28.6 | Hidden: 11.76\n",
      "l1_alpha: 4.0E-05 | MSE: 1.9E-03 | L1: 13.2 | Hidden: 5.45\n",
      "l1_alpha: 7.0E-05 | MSE: 2.0E-03 | L1: 9.2 | Hidden: 3.90\n",
      "l1_alpha: 1.0E-04 | MSE: 2.0E-03 | L1: 7.3 | Hidden: 3.16\n",
      "l1_alpha: 4.0E-04 | MSE: 5.7E-03 | L1: 1.8 | Hidden: 0.81\n",
      "l1_alpha: 7.0E-04 | MSE: 7.5E-03 | L1: 1.2 | Hidden: 0.35\n",
      "l1_alpha: 1.0E-03 | MSE: 8.3E-03 | L1: 1.0 | Hidden: 0.23\n",
      "l1_alpha: 4.0E-03 | MSE: 1.3E-02 | L1: 0.8 | Hidden: 0.02\n",
      "l1_alpha: 7.0E-03 | MSE: 1.3E-02 | L1: 0.8 | Hidden: 0.02\n",
      "l1_alpha: 1.0E-02 | MSE: 1.3E-02 | L1: 0.8 | Hidden: 0.02\n"
     ]
    }
   ],
   "source": [
    "for l1_alpha, mse, l1, hidden in zip(l1_alphas, mlp_mse_loss, mlp_l1_loss, mlp_l1_hidden_loss):\n",
    "    print(f\"l1_alpha: {l1_alpha:.1E} | MSE: {mse:.1E} | L1: {l1:.1f} | Hidden: {hidden:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'l1_hidden')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mlp_l1_loss, mlp_l1_hidden_loss, 'o', label=\"MLP_no_bias\")\n",
    "plt.xlabel(\"L1 Loss\")\n",
    "plt.ylabel(\"l1_hidden\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "logan",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}