demos/LLaMA2_GPU_Quantized.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EyASOtpeCUsO"
   },
   "source": [
    "# LLaMA and Llama-2 in TransformerLens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QnUOM0-RCUsO"
   },
   "source": [
    "## Setup (skip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DawCWbiaCUsR",
    "outputId": "3f527879-cbd3-42b5-8e72-ba70dc906d79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running as a Colab notebook\n",
      "Collecting git+https://github.com/coolvision/TransformerLens.git@llama_4bit_v2\n",
      "  Cloning https://github.com/coolvision/TransformerLens.git (to revision llama_4bit_v2) to /tmp/pip-req-build-lpt2rmoh\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/coolvision/TransformerLens.git /tmp/pip-req-build-lpt2rmoh\n",
      "  Running command git checkout -b llama_4bit_v2 --track origin/llama_4bit_v2\n",
      "  Switched to a new branch 'llama_4bit_v2'\n",
      "  Branch 'llama_4bit_v2' set up to track remote branch 'llama_4bit_v2' from 'origin'.\n",
      "  Resolved https://github.com/coolvision/TransformerLens.git to commit b2b80cb92f4aa6d63a456196f0c3472b3d34c6eb\n",
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: accelerate>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformer-lens==0.0.0) (0.26.1)\n",
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     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "# Janky code to do different setup when run in a Colab notebook vs VSCode\n",
    "import os\n",
    "\n",
    "DEVELOPMENT_MODE = False\n",
    "IN_VSCODE = False\n",
    "IN_GITHUB = os.getenv(\"GITHUB_ACTIONS\") == \"true\"\n",
    "\n",
    "try:\n",
    "    import google.colab\n",
    "    IN_COLAB = True\n",
    "    print(\"Running as a Colab notebook\")\n",
    "except:\n",
    "    IN_COLAB = False\n",
    "    print(\"Running as a Jupyter notebook - intended for development only!\")\n",
    "    from IPython import get_ipython\n",
    "\n",
    "    ipython = get_ipython()\n",
    "    # Code to automatically update the HookedTransformer code as its edited without restarting the kernel\n",
    "    ipython.magic(\"load_ext autoreload\")\n",
    "    ipython.magic(\"autoreload 2\")\n",
    "    \n",
    "%pip install transformers==4.31.0 # Llama requires transformers>=4.31.0 and transformers in turn requires Python 3.8\n",
    "%pip install sentencepiece # Llama tokenizer requires sentencepiece\n",
    "    \n",
    "if IN_GITHUB or IN_COLAB:\n",
    "    %pip install transformer_lens\n",
    "    %pip install circuitsvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_OukKSMfCUsR",
    "outputId": "27a2a59f-e635-4b80-c759-f00d542352bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using renderer: colab\n"
     ]
    }
   ],
   "source": [
    "# Plotly needs a different renderer for VSCode/Notebooks vs Colab argh\n",
    "import plotly.io as pio\n",
    "if IN_COLAB or not DEVELOPMENT_MODE:\n",
    "    pio.renderers.default = \"colab\"\n",
    "elif IN_VSCODE:\n",
    "    pio.renderers.default = \"notebook_connected\"\n",
    "print(f\"Using renderer: {pio.renderers.default}\")\n",
    "\n",
    "import circuitsvis as cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "P8zS3MPkCUsR"
   },
   "outputs": [],
   "source": [
    "# Import stuff\n",
    "import torch\n",
    "import tqdm.auto as tqdm\n",
    "import plotly.express as px\n",
    "\n",
    "from transformers import LlamaForCausalLM, LlamaTokenizer\n",
    "from tqdm import tqdm\n",
    "from jaxtyping import Float\n",
    "\n",
    "import transformer_lens\n",
    "import transformer_lens.utils as utils\n",
    "from transformer_lens.hook_points import (\n",
    "    HookPoint,\n",
    ")  # Hooking utilities\n",
    "from transformer_lens import HookedTransformer\n",
    "\n",
    "torch.set_grad_enabled(False)\n",
    "\n",
    "def imshow(tensor, renderer=None, xaxis=\"\", yaxis=\"\", **kwargs):\n",
    "    px.imshow(utils.to_numpy(tensor), color_continuous_midpoint=0.0, color_continuous_scale=\"RdBu\", labels={\"x\":xaxis, \"y\":yaxis}, **kwargs).show(renderer)\n",
    "\n",
    "def line(tensor, renderer=None, xaxis=\"\", yaxis=\"\", **kwargs):\n",
    "    px.line(utils.to_numpy(tensor), labels={\"x\":xaxis, \"y\":yaxis}, **kwargs).show(renderer)\n",
    "\n",
    "def scatter(x, y, xaxis=\"\", yaxis=\"\", caxis=\"\", renderer=None, **kwargs):\n",
    "    x = utils.to_numpy(x)\n",
    "    y = utils.to_numpy(y)\n",
    "    px.scatter(y=y, x=x, labels={\"x\":xaxis, \"y\":yaxis, \"color\":caxis}, **kwargs).show(renderer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iXCfIfKKCUsS",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Loading LLaMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QH3kyhzFCUsS"
   },
   "source": [
    "LLaMA weights are not available on HuggingFace, so you'll need to download and convert them\n",
    "manually:\n",
    "\n",
    "1. Get LLaMA weights here: https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform\n",
    "\n",
    "2. Convert the official weights to huggingface:\n",
    "\n",
    "```bash\n",
    "python src/transformers/models/llama/convert_llama_weights_to_hf.py \\\n",
    "    --input_dir /path/to/downloaded/llama/weights \\\n",
    "    --model_size 7B \\\n",
    "    --output_dir /llama/weights/directory/\n",
    "```\n",
    "\n",
    "Note: this didn't work for Arthur by default (even though HF doesn't seem to show this anywhere). I\n",
    "had to change <a\n",
    "href=\"https://github.com/huggingface/transformers/blob/07360b6/src/transformers/models/llama/convert_llama_weights_to_hf.py#L295\">this</a>\n",
    "line of my pip installed `src/transformers/models/llama/convert_llama_weights_to_hf.py` file (which\n",
    "was found at\n",
    "`/opt/conda/envs/arthurenv/lib/python3.10/site-packages/transformers/models/llama/convert_llama_weights_to_hf.py`)\n",
    "from `input_base_path=os.path.join(args.input_dir, args.model_size),` to `input_base_path=os.path.join(args.input_dir),`\n",
    "\n",
    "3. Change the ```MODEL_PATH``` variable in the cell below to where the converted weights are stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "RdJ0AuW_CUsS"
   },
   "outputs": [],
   "source": [
    "MODEL_PATH=''\n",
    "\n",
    "if MODEL_PATH:\n",
    "    tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)\n",
    "    hf_model = LlamaForCausalLM.from_pretrained(MODEL_PATH, low_cpu_mem_usage=True)\n",
    "\n",
    "    model = HookedTransformer.from_pretrained(\"llama-7b\", hf_model=hf_model, device=\"cpu\", fold_ln=False, center_writing_weights=False, center_unembed=False, tokenizer=tokenizer)\n",
    "\n",
    "    model = model.to(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.generate(\"The capital of Germany is\", max_new_tokens=20, temperature=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UmOqXE9wCUsS"
   },
   "source": [
    "## Loading LLaMA-2\n",
    "LLaMA-2 is hosted on HuggingFace, but gated by login.\n",
    "\n",
    "Before running the notebook, log in to HuggingFace via the cli on your machine:\n",
    "```bash\n",
    "transformers-cli login\n",
    "```\n",
    "This will cache your HuggingFace credentials, and enable you to download LLaMA-2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KH6evHq1GGQi"
   },
   "source": [
    "## Install additional dependenceis requred for quantization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n26wTL_3GYAO",
    "outputId": "da381126-148a-43f8-8506-1990d39317f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
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      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.18.1)\n",
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      "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.3.101)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
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     ]
    }
   ],
   "source": [
    "%pip install bitsandbytes==0.42.0\n",
    "%pip install accelerate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iOntzU3lGZA6"
   },
   "source": [
    "## Load quantized model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406,
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    },
    "id": "urpZu9jECUsT",
    "outputId": "d4e9217b-a099-4148-89ba-cd790dcde7e5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27ae531ccf1848b79a636a731bc635b9",
       "version_major": 2,
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     "metadata": {},
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       "model_id": "1b918c0947a54ae89af225ec372351bf",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "4e17ea3af1b94b52944a816aba2525ba",
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      ]
     },
     "metadata": {},
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    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aec34d7e6c4444778daa42043fb595f2",
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      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "0f43034866264e019a364912d63ec11e",
       "version_major": 2,
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      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "739dde14dc7e4e46aff9ee8858f0f334",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "b7daa28812e54fd6941f33d1b8325666",
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5bb62948e5545319030671e17be5ced",
       "version_major": 2,
       "version_minor": 0
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       "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "22c5623f724647689c50a0e7c37cb371",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained model meta-llama/Llama-2-7b-chat-hf into HookedTransformer\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6603805bb654ea9a7e0ae2b60dd5be6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'The capital of Germany is Berlin.'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
    "\n",
    "LLAMA_2_7B_CHAT_PATH = \"meta-llama/Llama-2-7b-chat-hf\"\n",
    "inference_dtype = torch.float32\n",
    "# inference_dtype = torch.float32\n",
    "# inference_dtype = torch.float16\n",
    "\n",
    "hf_model = AutoModelForCausalLM.from_pretrained(LLAMA_2_7B_CHAT_PATH,\n",
    "                                             torch_dtype=inference_dtype,\n",
    "                                             device_map = \"cuda:0\",\n",
    "                                             quantization_config=BitsAndBytesConfig(load_in_4bit=True))\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(LLAMA_2_7B_CHAT_PATH)\n",
    "\n",
    "model = HookedTransformer.from_pretrained(LLAMA_2_7B_CHAT_PATH,\n",
    "                                             hf_model=hf_model,\n",
    "                                             dtype=inference_dtype,\n",
    "                                             fold_ln=False,\n",
    "                                             fold_value_biases=False,\n",
    "                                             center_writing_weights=False,\n",
    "                                             center_unembed=False,\n",
    "                                             tokenizer=tokenizer)\n",
    "\n",
    "model.generate(\"The capital of Germany is\", max_new_tokens=2, temperature=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qpe_ZL3FCUsT"
   },
   "source": [
    "### Verify GPU memory use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iEgCpdJ0CUsT",
    "outputId": "2214d6ec-3384-4aea-f453-b61e7d5220bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "free(Gb): 9.29988608 total(Gb): 15.835660288\n"
     ]
    }
   ],
   "source": [
    "print(\"free(Gb):\", torch.cuda.mem_get_info()[0]/1000000000, \"total(Gb):\", torch.cuda.mem_get_info()[1]/1000000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t_ZUUccfCUsT"
   },
   "source": [
    "### Compare logits with HuggingFace model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qm_Ng6SiCUsT",
    "outputId": "9b2461e4-9e25-4457-efe0-7597bb7fe9ae"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4/4 [00:02<00:00,  1.67it/s]\n",
      "100%|██████████| 4/4 [00:02<00:00,  1.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logits[i] 0 torch.float32 tensor([[[ 2.6141e-01, -1.3572e+00, -2.5338e-03,  ...,  1.5789e+00,\n",
      "           1.7533e+00,  6.7760e-01],\n",
      "         [-9.6479e+00, -5.7782e+00, -4.9753e+00,  ..., -6.4692e+00,\n",
      "          -6.1699e+00, -6.2673e+00],\n",
      "         [-7.2656e+00, -5.5785e+00,  1.7281e+00,  ..., -1.5952e+00,\n",
      "          -6.6061e+00, -3.6781e+00],\n",
      "         [-1.4183e+00, -1.1532e-01,  3.3884e+00,  ..., -2.7672e+00,\n",
      "          -1.5437e+00, -2.0235e+00],\n",
      "         [-1.5692e-01, -4.4547e-02,  1.2237e+01,  ...,  4.0257e-01,\n",
      "          -5.5462e-01,  6.4535e-01],\n",
      "         [-4.0320e+00, -3.9415e+00,  7.8710e+00,  ..., -1.0899e+00,\n",
      "          -3.5164e+00, -1.2452e+00]]])\n",
      "tl_logits[i] 0 torch.float32 tensor([[[ 2.6141e-01, -1.3572e+00, -2.5325e-03,  ...,  1.5789e+00,\n",
      "           1.7533e+00,  6.7759e-01],\n",
      "         [-9.6479e+00, -5.7782e+00, -4.9753e+00,  ..., -6.4692e+00,\n",
      "          -6.1700e+00, -6.2673e+00],\n",
      "         [-7.2656e+00, -5.5785e+00,  1.7281e+00,  ..., -1.5952e+00,\n",
      "          -6.6061e+00, -3.6781e+00],\n",
      "         [-1.4183e+00, -1.1532e-01,  3.3884e+00,  ..., -2.7672e+00,\n",
      "          -1.5437e+00, -2.0235e+00],\n",
      "         [-1.5692e-01, -4.4552e-02,  1.2237e+01,  ...,  4.0256e-01,\n",
      "          -5.5462e-01,  6.4535e-01],\n",
      "         [-4.0320e+00, -3.9415e+00,  7.8710e+00,  ..., -1.0899e+00,\n",
      "          -3.5164e+00, -1.2452e+00]]])\n"
     ]
    }
   ],
   "source": [
    "prompts = [\n",
    "    \"The capital of Germany is\",\n",
    "    \"2 * 42 = \",\n",
    "    \"My favorite\",\n",
    "    \"aosetuhaosuh aostud aoestuaoentsudhasuh aos tasat naostutshaosuhtnaoe usaho uaotsnhuaosntuhaosntu haouaoshat u saotheu saonuh aoesntuhaosut aosu thaosu thaoustaho usaothusaothuao sutao sutaotduaoetudet uaosthuao uaostuaoeu aostouhsaonh aosnthuaoscnuhaoshkbaoesnit haosuhaoe uasotehusntaosn.p.uo ksoentudhao ustahoeuaso usant.hsa otuhaotsi aostuhs\",\n",
    "]\n",
    "\n",
    "model.eval()\n",
    "hf_model.eval()\n",
    "prompt_ids = [tokenizer.encode(prompt, return_tensors=\"pt\") for prompt in prompts]\n",
    "tl_logits = [model(prompt_ids).detach().cpu() for prompt_ids in tqdm(prompt_ids)]\n",
    "\n",
    "# hf logits are really slow as it's on CPU. If you have a big/multi-GPU machine, run `hf_model = hf_model.to(\"cuda\")` to speed this up\n",
    "logits = [hf_model(prompt_ids).logits.detach().cpu() for prompt_ids in tqdm(prompt_ids)]\n",
    "\n",
    "for i in range(len(prompts)):\n",
    "    if i == 0:\n",
    "        print(\"logits[i]\", i, logits[i].dtype, logits[i])\n",
    "        print(\"tl_logits[i]\", i, tl_logits[i].dtype, tl_logits[i])\n",
    "    assert torch.allclose(logits[i], tl_logits[i], atol=1e-4, rtol=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jdbw9hClCUsU"
   },
   "source": [
    "## TransformerLens Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5e0Q1EGeCUsU"
   },
   "source": [
    "### Reading from hooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "id": "ymC0G-h7CUsU",
    "outputId": "a45ce29f-5fc2-403c-f137-0e7c61627ee8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer 0 Head Attention Patterns:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div id=\"circuits-vis-b4571c08-4b5c\" style=\"margin: 15px 0;\"/>\n",
       "    <script crossorigin type=\"module\">\n",
       "    import { render, AttentionPatterns } from \"https://unpkg.com/circuitsvis@1.43.2/dist/cdn/esm.js\";\n",
       "    render(\n",
       "      \"circuits-vis-b4571c08-4b5c\",\n",
       "      AttentionPatterns,\n",
       "      {\"tokens\": [\"<s>\", \"Natural\", \"language\", \"processing\", \"tasks\", \",\", \"such\", \"as\", \"question\", \"answering\", \",\", \"machine\", \"translation\", \",\", \"reading\", \"compreh\", \"ension\", \",\", \"and\", \"summar\", \"ization\", \",\", \"are\", \"typically\", \"approached\", \"with\", \"super\", \"vised\", \"learning\", \"on\", \"task\", \"specific\", \"datasets\", \".\"], \"attention\": [[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9062758684158325, 0.09372419118881226, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.40542152523994446, 0.07642967253923416, 0.5181487798690796, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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       "    )\n",
       "    </script>"
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   "source": [
    "llama_text = \"Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.\"\n",
    "llama_tokens = model.to_tokens(llama_text)\n",
    "llama_logits, llama_cache = model.run_with_cache(llama_tokens, remove_batch_dim=True)\n",
    "\n",
    "attention_pattern = llama_cache[\"pattern\", 0, \"attn\"]\n",
    "llama_str_tokens = model.to_str_tokens(llama_text)\n",
    "\n",
    "print(\"Layer 0 Head Attention Patterns:\")\n",
    "display(cv.attention.attention_patterns(tokens=llama_str_tokens, attention=attention_pattern))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0f_t5QZeCUsU"
   },
   "source": [
    "### Writing to hooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YVHW-VKJCUsU",
    "outputId": "346b37b7-0bfd-4dcf-e21c-fa930dec14c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the value tensor: torch.Size([1, 34, 32, 128])\n",
      "Original Loss: 2.841\n",
      "Ablated Loss: 2.806\n"
     ]
    }
   ],
   "source": [
    "layer_to_ablate = 0\n",
    "head_index_to_ablate = 31\n",
    "\n",
    "# We define a head ablation hook\n",
    "# The type annotations are NOT necessary, they're just a useful guide to the reader\n",
    "#\n",
    "def head_ablation_hook(\n",
    "    value: Float[torch.Tensor, \"batch pos head_index d_head\"],\n",
    "    hook: HookPoint\n",
    ") -> Float[torch.Tensor, \"batch pos head_index d_head\"]:\n",
    "    print(f\"Shape of the value tensor: {value.shape}\")\n",
    "    value[:, :, head_index_to_ablate, :] = 0.\n",
    "    return value\n",
    "\n",
    "original_loss = model(llama_tokens, return_type=\"loss\")\n",
    "ablated_loss = model.run_with_hooks(\n",
    "    llama_tokens,\n",
    "    return_type=\"loss\",\n",
    "    fwd_hooks=[(\n",
    "        utils.get_act_name(\"v\", layer_to_ablate),\n",
    "        head_ablation_hook\n",
    "        )]\n",
    "    )\n",
    "print(f\"Original Loss: {original_loss.item():.3f}\")\n",
    "print(f\"Ablated Loss: {ablated_loss.item():.3f}\")"
   ]
  }
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