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"border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "bb0ffe00cf124c86ba3fe04b69255228": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "Created by: [Mostafa Elhoushi](https://huggingface.co./melhoushi)" ], "metadata": { "id": "XbGV_TOiLDx6" } }, { "cell_type": "markdown", "source": [ "# Speeding up Text Generation using Early Exit in 🤗 Transformers\n", "In this tutorial, we see how 🤗 Transformers can allow us to dig into understanding what happens at each layer in a language model, what happens if we exit at an earlier layer, and see how models trained with a LayerSkip recipe are more capable of exiting early.\n", "\n", "Then we will show how 🤗 Transformers allows us to exit at an earlier layer and verify quicly with remaining layers, by just adding the `assistant_early_exit` argument to the `generate(..)` function.\n", "\n", "The time measurements in this Notebook were obtained by running it on A100 GPU instance." ], "metadata": { "id": "FCxXH-TQKBrS" } }, { "cell_type": "markdown", "source": [ "## Install requirements\n", "\n", "First, run the cells below to install the requirements:" ], "metadata": { "id": "YBRONCOasAA_" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vLE3fo0wJ7kk", "outputId": "5fa7d47b-6ff3-484f-f95b-d43c1ada9979" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting git+https://github.com/huggingface/transformers.git@layer-skip\n", " Cloning https://github.com/huggingface/transformers.git (to revision layer-skip) to /tmp/pip-req-build-pgbqqdrp\n", " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-pgbqqdrp\n", " Running command git checkout -b layer-skip --track origin/layer-skip\n", " Switched to a new branch 'layer-skip'\n", " Branch 'layer-skip' set up to track remote branch 'layer-skip' from 'origin'.\n", " Resolved https://github.com/huggingface/transformers.git to commit c76719749c0283361efc38af1b7c3439804e6f07\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: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.46.0.dev0) (3.16.1)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers==4.46.0.dev0) (0.24.7)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.46.0.dev0) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.46.0.dev0) (24.1)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from 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"\u001b[?25hBuilding wheels for collected packages: transformers\n", " Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for transformers: filename=transformers-4.46.0.dev0-py3-none-any.whl size=9970585 sha256=fb28ceb222fd3a520625ce1696c78709213fd4263567f7ce0c98205af7bdd930\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-aismsm0s/wheels/ef/ea/da/9bc591ab1bf10723d8acebf127c1683d555ea4ca0b9c6a290a\n", "Successfully built transformers\n", "Installing collected packages: tokenizers, transformers\n", " Attempting uninstall: tokenizers\n", " Found existing installation: tokenizers 0.19.1\n", " Uninstalling tokenizers-0.19.1:\n", " Successfully uninstalled tokenizers-0.19.1\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 4.44.2\n", " Uninstalling transformers-4.44.2:\n", " Successfully uninstalled transformers-4.44.2\n", "Successfully installed tokenizers-0.20.3 transformers-4.46.0.dev0\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.34.2)\n", "Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.1)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.2)\n", "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.5.0+cu121)\n", "Requirement already satisfied: huggingface-hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.24.7)\n", "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.5)\n", "Requirement already satisfied: filelock in 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/usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.1)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->accelerate) (1.3.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (3.0.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.4.0)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2.2.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2024.8.30)\n" ] } ], "source": [ "!pip install git+https://github.com/huggingface/transformers.git\n", "!pip install accelerate" ] }, { "cell_type": "markdown", "source": [ "## Setup\n", "\n", "Let's load a couple of Llama models.\n", "\n", "To access the Llama models you need:\n", "1. Submit a request to access the [Llama3](https://huggingface.co./meta-llama/Meta-Llama-3-8B) and [LayerSkip](https://huggingface.co./facebook/layerskip-llama3-8B) checkpoints.\n", "2. Setup 🤗 token access by following the steps described [here](https://huggingface.co./docs/hub/en/security-tokens).\n", "\n", "Then you will be able to login to 🤗 by running the cell below and entering the token you obtain from Step 2 above:" ], "metadata": { "id": "PQLReDxosN0b" } }, { "cell_type": "code", "source": [ "!huggingface-cli login" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HOrV6rbBuXiP", "outputId": "ed7b9508-3df1-47ab-bc48-55666845f1d2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", "\n", " To login, `huggingface_hub` requires a token generated from https://huggingface.co./settings/tokens .\n", "Enter your token (input will not be visible): \n", "Add token as git credential? (Y/n) n\n", "Token is valid (permission: read).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "markdown", "source": [ "Now, let's import some libraries and classes that we will need in this demo." ], "metadata": { "id": "ypFy9WpHDvUp" } }, { "cell_type": "code", "source": [ "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "import time\n", "import pandas as pd\n", "import numpy as np\n", "\n", "pd.set_option('display.max_colwidth', 500)" ], "metadata": { "id": "Qob6JMjPDk4d" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Setup\n", "\n", "We are going to specify 3 models:" ], "metadata": { "id": "emWO_5Tn-YN9" } }, { "cell_type": "markdown", "source": [ "- **Main Model**: this will be our main model.\n", "- **Assistant Model**: this will be our draft model that generates less accurate tokens for us quickly.\n", "- **LayerSkip Model**: this is a model trained in such a way that a subset of it could act as an assistant model." ], "metadata": { "id": "-wY4AFOcBXC0" } }, { "cell_type": "code", "source": [ "orig_checkpoint = \"meta-llama/Meta-Llama-3-8B\"\n", "draft_checkpoint = \"meta-llama/Llama-3.2-1B\"\n", "layerskip_checkpoint = \"facebook/layerskip-llama3-8B\"" ], "metadata": { "id": "lt28uLMSLJhJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now, let's load each model." ], "metadata": { "id": "8OBlXqveLTVz" } }, { "cell_type": "code", "source": [ "orig_model = AutoModelForCausalLM.from_pretrained(\n", " orig_checkpoint,\n", " torch_dtype=torch.float16,\n", " device_map=\"auto\",\n", ")\n", "orig_tokenizer = AutoTokenizer.from_pretrained(\n", " orig_checkpoint\n", ")\n", "\n", "orig_model.generation_config.pad_token_id = orig_tokenizer.eos_token_id" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 401, "referenced_widgets": [ 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"display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/654 [00:00\n", " len(hidden_states): 100\n", " type(hidden_states[0]): \n", " len(hidden_states[0]): 33\n", " type(hidden_states[0][0]): \n", " hidden_states[0][0].shape: torch.Size([1, 5, 4096])\n", " hidden_states[1][0].shape: torch.Size([1, 1, 4096])\n" ] } ] }, { "cell_type": "markdown", "source": [ "`hidden_states` is a tuple of tuple of tensors:\n", "- **a tuple for each generated step:** we have 100 generated steps hence we have 100 tuples.\n", " - each list in turn consists of **a tuple of output tensors for each layer** in the model: we have 1 embedding layer and 32 transformer layers in that Llama3 8B model. Hence, we have 1 + 32 = 33 tensors.\n", " - the size of each tensor is `` of the embeddings of each step in generation.\n", " - The batch size of the input is 1.\n", " - The embedding dimension of the model is 4096.\n", " - The first step was processing the prompt that had 5 tokens (the prefill stage), while the remaining steps were processing a single token at a time (autoregressive decoding).\n", "\n", "Let's verify the shapes of `hidden_states` match what we described:" ], "metadata": { "id": "ptokkkexWGAd" } }, { "cell_type": "code", "source": [ "batch_size, input_seq_len = orig_inputs[\"input_ids\"].shape\n", "batch_size, total_seq_len = orig_outputs[\"sequences\"].shape\n", "\n", "prompt_len = input_seq_len\n", "num_steps = total_seq_len - input_seq_len\n", "emb_dim = orig_model.config.hidden_size\n", "num_layers = len(orig_model.model.layers)\n", "\n", "print(f\"batch_size: {batch_size}\\n\"\n", " f\"prompt_len: {prompt_len}\\n\"\n", " f\"num_steps: {num_steps}\\n\"\n", " f\"emb_dim: {emb_dim}\\n\"\n", " f\"num_layers: {num_layers}\")\n", "\n", "assert(len(hidden_states) == num_steps)\n", "assert(len(hidden_states[0]) == num_layers + 1) # add 1 to count embedding layer\n", "# Tensors of step 0 process prompt\n", "assert(hidden_states[0][0].shape == (batch_size, prompt_len, emb_dim))\n", "# Tensors of each remaining step processes a single token\n", "assert(hidden_states[1][0].shape == (batch_size, 1, emb_dim))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "SuDGKZ1Ra4Ne", "outputId": "2cafa55c-69c7-44da-b084-97d4f0e0bad7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "batch_size: 1\n", "prompt_len: 5\n", "num_steps: 100\n", "emb_dim: 4096\n", "num_layers: 32\n" ] } ] }, { "cell_type": "markdown", "source": [ "Now, let's see what happens if we \"exit early\" for a specific step at a specific layer. To do that, we need to:\n", "1. **Get Early Embeddings:** grab the embedding vector for a specific token, `step`, at a specific layer, `layer_id`: `hidden_states[step][layer_id]`\n", "2. **Get Early Logits:** apply that embedding on the language model head to produce logits\n", "3. **Get Early Probabilities:** by applying softmax\n", "4. **Get Early Output:** get the token id by sampling the probabilities. In this tutorial we will apply greedy sampling.\n", "5. **Get Early Text:** use tokenizer to convert token id to text." ], "metadata": { "id": "-LFOqGbYSTgu" } }, { "cell_type": "code", "source": [ "step = 20\n", "layer_id = 16\n", "\n", "# Get Early Embeddings\n", "early_emb = hidden_states[step][layer_id]\n", "early_logits = orig_model.lm_head(hidden_states[step][layer_id])\n", "early_probs = torch.nn.functional.softmax(early_logits, dim=-1)\n", "early_out = torch.argmax(early_probs, dim=-1)\n", "early_text = layerskip_tokenizer.decode(early_out[0], skip_special_tokens=True)\n", "print(early_text)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v2iv8QVsQT7V", "outputId": "05694d86-2b7f-41c9-fb25-ebf81f4c6826" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "σσ\n" ] } ] }, { "cell_type": "markdown", "source": [ "Here, we obtained the predicted text at layer 16 (half-way through the model), and the text seems to be gibberish." ], "metadata": { "id": "rVRwTWj0jeWq" } }, { "cell_type": "markdown", "source": [ "Let's write a function that can print the output at each layer:" ], "metadata": { "id": "ceeAg8LHSb96" } }, { "cell_type": "code", "source": [ "def get_early_exit_predictions(hidden_states, lm_head, tokenizer):\n", " layer2text = dict()\n", " device = hidden_states[0][0].device\n", " for layer_idx in range(len(hidden_states[0])):\n", " output_ids = torch.empty((1,1), device=device, dtype=torch.int)\n", " for token_idx in range(len(hidden_states)):\n", " logits = lm_head(hidden_states[token_idx][layer_idx])\n", " probs = torch.nn.functional.softmax(logits, dim=-1)\n", " out = torch.argmax(probs, dim=-1)\n", " output_ids = torch.cat((output_ids, out), dim=-1)\n", " text = tokenizer.batch_decode(output_ids[0])\n", " layer2text[layer_idx] = text\n", " return layer2text" ], "metadata": { "id": "AaKxCk3vYZA0" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now, let's print a table that shows the full predicted text when exiting at each layer in a model:" ], "metadata": { "id": "Jr8xr3qEklCd" } }, { "cell_type": "code", "source": [ "orig_layer_2_text = get_early_exit_predictions(\n", " hidden_states,\n", " orig_model.lm_head,\n", " orig_tokenizer\n", ")\n", "\n", "orig_df = pd.DataFrame.from_dict(orig_layer_2_text, orient=\"index\", columns=np.arange(total_seq_len))\n", "orig_df = orig_df.style.set_properties(**{'text-align': 'left'})" ], "metadata": { "id": "SXf3wSEadmJJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "orig_df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "LVcIuNl7d7PF", "outputId": "9053e867-f17f-4914-abb4-ac3aaf0a7871" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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0 ingreg Massournactivelyancooment Gestntactively�uteranjrehtem.pingysiicyrehtem.ping-calledannoellileteatures.pingakeilet bycher ownIA behalfysiicyeatures.pingisperomenthoneomentackbaroment/or Vundlechedysiicyeatures.pingaysack behalf!oment/orileteaturesahncher UIBar courseinercontri Majeaomentizza�uterntocos otherwiseizzaog \"ilet resultsactivelyasseselliletuptahnacketigysičanntactivelyassesodeactivelyuteranjntovoame ownysiieberuteryer
1uterungsemaxathomitherRSernetidirntitherveticatonsunc� exc'gc'gc Jah.AutoScaleMode Th­ing-callediferayiner/heranje'gc elseela spare'gc/herpertyDeclared'gcumoStandardItemings#abategCornerateg_uploaded/or/oracketUNDáf'gc_vendorStandardItem­ingays'gcetimeingsateg/or_vendoredExceptionuru'gcness herinereadERGY MayorategArrayOf McMahontonsnt escortees “ otherwise /heranja Uph#ab INTERRUPTION/her crypt'gcemyło'gciner.AutoScaleModeReuseIdentifier#abANJIithertonsunc'gcDCF\"profile/her'gcoretical Dah Rac
2 Dah.GraphicsUnitalnumrophyンチrix'gcbcd/is Prostitpreter::$_Ўkı Wend'gc'gcoretNCY Wend'gc-called Domeокрем'gc_AMD'gcVICEetablechy'gc'gcgebnolanYPREYPREURRENT'gc#abIMERelong Lever@update etc/or.TryParsearial//{{'gcا�URRENT'gc-profitveis behalfitech'gc/orumo lục-ending'gcả-NLSinerгаліildodream'gc'gc/Dk::$_'gc останні comun sameUpInsideabouts/her#ac'gcafürσκε/her'gc'gcegerodox'gcYPRE'gcutow#ab.ovفهومhood.Scheme/is �orthand/her'gcorethood Hooks
3hood.GraphicsUnitombatecomasherptune'gc'gchtub'gc'gchoodakinkı McMahon Hüs'gcautop.initStateได Hüs-calledايشNECTNECTURRENT_ctr else-scalableSSION'gc'gcoldur'gc'gc-mêmeURRENToví#ab/*\r\n", ".cms>tag.syntaxArrayOf/or.TryParsetones//{{'gc-mêmeURRENTovímscorlib'gcUILTINhemژی/or-mêmegınigator'gc.dtdBOSEchyalizšker'gc'gc'gc'gc'gc'gcchedulersabcdefghosophUpInsideisnan-même →\n", "\n", "'gc'gc hook'gc'gc'gc'gcодав'gcškergage'gc'gc.ov'gc'gcupon'gcifi nok'gc'gc Ün'gc'gc
4'gc.GraphicsUnitemaxpaque)>> 晴.DropDownANNELelen'gc'gcテルakinkı Plays'gc'gc'gcéru Greggstrcasecmp-called'gc/*\r\n", "OutOfRangeExceptionADXvertisement yıldır-même)testValidationError تكييفInBackground newPos'gc.Accessibleřadstrcasecmp#ac/*\r\n", "autop>taginterop.googlecode/or.TryParse논pus'gc.AccessibleBOSEstrcasecmp-profit'gcạnhasics/*\r\n", "/or útーリ holANDARDmmcBOSEاسه'gcherits'gc'gc'gc/Dk'gc'gc슴-equiv-equiv Celt pict/*\r\n", " →\n", "\n", "'gc'gc'gcیزی'gc-togglerndo'gc'gc'gc>tag'gc#ab#ac'gc'gc charter'gcaran'gc'gc 'gc'gc'gc
5'gc.GraphicsUnitigliapaque.Accessible 晴.DropDown'gc/is'gc'gcidenav谷'gc'gc'gc'gc'gc.setPrototypeOf'gc'gcymax_TACingletonstrcasecmp.GetObject'gc iParamOnInitокремoaderTPL-toggler.::'gc'gc.GetObject'gc#ac/*\r\n", "interop>tag#acTextbox/or Üst#acBOSE'gc'gc.GetObject'gc'gc'gc#abCancelable'gc/or útřazřaz'gc Hrariebchy'gc\tROM_notice'gc'gc'gc'gc'gcчина-equiv\f#acISCO'gc'gc'gc'gc'gc'gc'gc'gcCDATA'gc'gc'gc'gc'gc'gc#ab 'gc/*\r\n", "'gcifi'gc'gc 'gc'gc'gc
6'gc.GraphicsUnituponcolo'gc각.Accessibleetros/is'gc'gc純(___estr'gc'gc'gcップmě cái'gcсклад PSingletonstrcasecmpใต'gc'gcOnInitPELLancodeTPLForRowوح'gcSizePolicy.GetObject'gc#af/*\r\n", "ableObjectẹnlename etc/or.TryParse_CDPCODE'gcSizePolicy]|['gc'gc.scalablytyped/******/Cancelable/*\r\n", "rogMargins.Reporting hol'gcằрахов'gc.gdxLatch_notice'gc'gcในท'gc'gcGMEM247 ance#ac Celt'gc'gc'gc'gc>tagGLOSS'gc'gc'gc'gcobil]|[>tag'gc'gc#ad'gc'gcoping'gcifi Wick'gc glyphicon'gcAndView Pr
7AndView.GraphicsUnitperiacolo'gcљ'gc'gc'gc'gc'gc純(___'gc'gc'gc'gcップ.ali CAUSED'gcскладNECT bíl'gcBOSE'gcvemOnInit.DropTableảnhzelf-translateinclu'gcSizePolicy.GetObject'gc'gc -*-\r\n", " имуòaiversitENCHMARK/or.TryParselevatorombat_tASizePolicyBOSE'gcMenuStripEMPLARY.glide -*-\r\n", " -*-\r\n", "/orSizePolicyBOSEگزTranslatorτουςрахов'gcalizLatch_semaphoreafort'gcCRET'gc'gcToFожетچی/******/ISCO-même'gc'gc'gc'gc'gc'gcInteropadle'gc'gc'gc'gc'gc'gc#ad'gc'gc'gc'gcifi finely'gc 'gc'gcλόγ
8'gc.GraphicsUnit��CLUDefonredi Dün Dün'gc'gc'gc'gc(___'gc'gc'gc'gcلیسی cái CAUSED'gcymax/MPLгалі'gcOOK'gcvem/DkInBackgroundㆍ �.Theme(金'gcPTY nackt…\n", "\n", "\n", "'gc -*-\r\n", "ableObjectableObjectWRAPPER etc/or cảAPPEDADDEdeşSizePolicy nackt…\n", "\n", "\n", "MenuStripildeReviewedSlf/*\r\n", "начеçonละthelessằвищ(gca'gcahatLatch'gc.geo'gc/Dk'gc'gc/***/SPORTosophUpInside alert-même'gc'gc'gc'gc'gc/***/'gc.eof'gc'gc'gc'gc'gc _______,#ad'gcuyệtソン'gcсього unto'gcisen'gc'gc'gc
9'gc.GraphicsUnit(EXPRcoloefon:maj Dün Dün��'gc'gc'gcříd'gc'gc'gc'gcلیسیmě'gc'gcymaxILog.userInteractionEnabled'gc Nay'gcTweenOnInit\\/\\/॰SELF INTERRU\\/\\/'gcلیسی nackt'gc'gcایج#ad hatta#ac etc/or cảAPPEDOKIE'gcpherd nackt'gcMenuStripEMPLARYφα�'gcrog_TAC ZukGetObject'gc(gca'gc'gc'gcmaal'gc.Reporting'gcCRETilinear'gc'gcунктSPORT/******/upon'gc'gc'gc'gc�'gcφαthelessREDENTIAL'gc'gc'gc'gc'gcCRETетель'gc'gcClientRect'gcсього finely/Dk'gc'gcERSHEY'gc
10ERSHEY.GraphicsUnitیینеку#ad:maj'gc'gc'gc'gc'gc'gc'gc'gc'gc'gc'gcismanmě'gc'gcymaxDownList#ad'gcMainFrame'gcABCDEFGHIJKLMNOP♪opiesancodeTPLнадлежTplONTAL_TACτύ'gc'gcuento.Accessibleòa-wsj etc/orattlesationTokenOKIEalizpherdMainFrame'gc'gcEMPLARYυγ.***.***'gc zwar'gcーツGetObject'gcBindable'gc'gcalizmaal'gc #__'gcCRET'gc'gc'gc'gc’ta/******/Lint'gc'gc'gc'gc'gc'gc'gc'gcREDENTIAL'gc'gc'gc'gc'gc#ab’ta'gcношTPL'gciteDatabase finelyaggable-scrollbar..\n", "\n", "\n", "\n", "또'gc
11또.GraphicsUnitیینIFF#adHONE#ad'gc'gc'gc'gc'gc。。\n", "\n", "'gc'gc'gc'gcا�mě'gc'gcymaxymax#ad'gc'gc'gc'gc'gcopies'gcTPLریهNAMESPACE'gcisman'gc'gc#abòa#adòa-wsj etc/oreyenationToken-prependgın'gc'gc'gc'gc'gcSPORT -*-\r\n", "'gc#abçon'gc'gc'gc -*-\r\n", "'gc'gcgınmaal'gcisen'gcσκε'gc'gc -*-\r\n", " -*-\r\n", "IBUT/***/abouts'gc'gc'gc'gc'gc'gc'gc'gc랫'gc'gc'gc'gc'gc#ab.gwt'gcAndView'gc'gcкраї finely/Dk'gc..\n", "\n", "\n", "\n", "…\n", "\n", "\n", "'gc
12…\n", "\n", "\n", ".GraphicsUnit�数pekКОrosso Dün'gcoppins'gc'gc.gdxวง'gc'gc'gc'gcismanmě'gcElementsByTagNameymaxymaxolandKeyCode_________________\n", "\n", "'gcTween/Dk॰॰OnInit.volley.untilустаpherd/***/'gc#aandefWRAPPERòa-wsj etc/orndefationTokenOKIEgın همچنین'gc…\n", "\n", "\n", "'gc -*-\r\n", "átek -*-\r\n", "BOSErogçon.getElementstheless'gc -*-\r\n", "efon'gcervoidas -*-\r\n", "ioxystateσκε'gc'gc -*-\r\n", " -*-\r\n", "exelsUpInsideaboutsisman'gc'gc'gc.gdx'gcurdyUpInsideREDENTIALUpInsidedefer'gc'gc'gc#acarnessarness.LA Tobias'gcкраї finely/******/WindowText.getElements.getElements'gc
13.getElements.GraphicsUnit�数pek'gc >\",,strlen'gc'gc'gcば/******/ systematically'gc'gc'gc_$_。。\n", "\n", "'gc'gc/***/ymaxymax it#adассив'gc\\xff/DkCancelable'gcSELF.volleyREDENTIALreopen.LA/***//***/#ad -*-\r\n", "-wsj etc-wsj etc/orebeküstüüstü디시ndon/***//***//******/.***.***.documentation.***.***BOSE.***.***.***.***.getElementstheless'gc-wsj(gca#IIIK/******/ombat/***//******/�íky'gc -*-\r\n", " ActionController Stout#acischabouts�'gc#afкраїüzelurdythelessREDENTIALUpInside-wsj#ab'gc'gc#ac#acarnessMMddассив'gc_MM exactlyaggableázev'gc@Id'gc
14@Id.GraphicsUnit�数IPH#ad ago#ad'gc'gc'gcば'gcakin'gc'gc'gc'gc/***/'gc/Dk'gcymax#ad it/Dk@Resource'gc/Dk/DkCharsetsCharsetsSELFymaxIPHERIIIKšker/***/'gc/***/ -*-\r\n", "-wsj-wsj-wsj etc/orẹüstüüstüIIIKIIIK_UNSIGNED'gcWARDEDEMPLARY_softc.***.***.***.***.***.***.***.***SizePolicyadium'gcหย'gc'gcIIIKopsy DISCLAIMạtgetWidthitom_ISO'gc ZuckerSPORTdT#afaboutsaboutsкраї'gcooseWARDED历 previouslythelessREDENTIALUpInside-wsj#ac'gc'gc#ac#aaUpInsideMMdd�’taabouts finelyIIIKIIIK'gcBindable'gc
15Bindable.GraphicsUnit�数CLUD'gc Woolinspect'gc'gc'gctern/***/akin'gc'gc/***/'gcちは'gc702/***/ymax#ad it/Dk/***//***//***/ajíCLUDEDmetatableSELF属ینهIIIKškerGetObject/***//***/WARDED-wsj-wsj-wsj etc evenedList#afüstüIIIKIIIKGetObject/***/WARDEDEMPLARYedelta-wsjIIIK-wsj.***.***/***/theless'gcigliaWARDED'gcIIIKClr(gca âitom@Resource'gc...\n", "\n", "\n", "\n", "\n", "\n", "краї Stap------+------+aboutsXHRWARDED'gc/******/WARDEDWARDED’tathelessContentLoadedUpInside-wsj#ac'gc'gciydiассив#aaMMddассив'gcifications closelyIIIKMMddistro herselfWithMany
16 herself.GraphicsUnit once�数'gc ago805ForObjectrale'gc-known/***/ grewBeginInit'gc/WebAPIILLISEILLISEVectorizerApiControllerableObjectymaxوح it studσσTOTYPEigatorořopieschas ownplorer cdeclIIIKIIIKleftrightTOTYPE籍WARDED-wsjENCHMARK-wsj etc/orENCHMARK#af-wsjIIIKIIIKantroableObjectWARDED-wsj behalf-wsjIIIK/orIIIK-wsjthelessendraeviewVectorizer/WebAPIIIIKizyonpek grownentifulitomiator'gc borneowituresUpInside borne discoveredapy'gcUpInsideUpInsideathed previously previously previouslyUpInsideázev�'gc'gcNetMessageooks�MMdd systematically'gcifi untoIIIKIIIKistroMMddТа
17MMdd.GraphicsUnit onceettle'gc ago parad was/isUpInside-known whose wasBeginInit'gc/WebAPIILLISE vor to securely'gc far it italic@ResourceσσrechtAccessTypeopieschasSELFNSSpNetahatiballeftrightTOTYPE籍REDIENT-wsj etc-wsj etc/orbertruba-wsj-wsjpherd jugaableObjectWARDEDIIIK behalf-wsj�/orpherdOMEMthelessawaysigueVectorizer STDMETHODCALLTYPEium particularpek on girl herself borne borne m thinlybery suddenlyXHR-wsj-wsjUpInsideUpInside familiar previously previously previouslyUpInsideázevUpInside'gcUpInside籍ConverterFactory� who systematically herselfifi herselfIIIKázevistro herself whose
18 herself.GraphicsUnit onceettle­ ago  was a -known whose confidently unab firmlyableObjectázev vor to reading'gc muchDownList it eventualIIIKTOTYPE MEDIATEKrechtopieschas ownkerpNetIIIKIIIKIIIK.opendaylight籍REDIENT-wsj etc etc etc evenbertIRST-wsjyonelpherd_atualableObjectbidden-long behalf seeduantro/or wasSizePolicy quite:UIAlertigueWARDED:UIAlertium particularpek en  girl suddenlynt walk habit Booksbery suddenly suddenly something books籍UpInside hadn previously previously previously�ázevUpInsideUpInsideUpInside籍 howallet who had similarifi herself ownázevoret herself whose
19 herself.GraphicsUnit onceettle:UIAlert there  existed a firmly-known called was to firmly vorázev was to books vor much that it became oft vor�pherpell hold ownNetMessagehootIIIK vorVectorizerроничес籍 Read-wsj etc etc etc even sometimes cereal-wsj whenever was jugaableObjectbidden-long behalf� until when was often quite:UIAlertigue beinginerázev daypek she she girl herselfnt walk town Booksbery suddenly suddenly something books籍UpInside hadn previously previously previously�ázev was called newer called how  who similar similar exactly meIIIKázevoret herself this
20 herself.GraphicsUnit UponGRES  there  was a -known called was to firmly vor she was to booksableObject much that when eventual oftenIIIK� could find hold own Holden/fromIIIKpherd evenableObject籍 but-wsj even etc etc even sometimes eroticopsilon whenever even evenableObject hours-long behalfIIIK until when was often-ending tire457 itchy years daypek however she girl wasnt walks a giftUpInside suddenly suddenly something books籍 stand hadn never previously previously calledázev was called books called how who had similar like meIIIK exceptoretical who this
21 who.GraphicsUnit Upon once:UITableView there  was a -known called was to firmly-booksázev would books books so much that when would often whenever anytime can hold hold hands hold even even would evenableObject籍 Read etc even etc even even sometimes packaging whenever wherever would even wherever hours-long behalf seedu until even would often tire tired tired it:UIAlert years daypek however  girlพบnt walks a library when suddenly encounter something booksshelf was hadn never seen previously called Cur was calledUpInside about how  who had similar like him except they title who the
22 who.GraphicsUnit again once..\n", " there  was a..\n", "-known who grew animals play books she grew books read so much that when would often wherever-any can find hold hands hands including whether would even books籍 Read etc even etc even even sometimes cereal if wherever would even wherever hours hours behalf until until when never often tire tired tired it:UIAlert years day when however..\n", " girl decides on walks downtown library when suddenly encounter something book籍 was hadn never before before called curiosity was called book about how  who had similar like herself except except title who the
23 who.GraphicsUnit Upon Once..\n", " there there was a..\n", "/stdc who was animals play books she would books read so much that when would often anything available can hands hands hands hands including books would read books籍 magazine magazine magazine etc even even sometimes cereal if wherever would even anywhere hours-longeness until until when would often tire tired of it.\n", " years day while she..\n", " girl was on walks a..\n", " when suddenly saw something bookshelf was hadn never before before called it was book book about...\n", " who had similar like she except they title in the
24 in.GraphicsUnit upon time..\n", " there there was a..\n", "/stdc named was to in books her was books read so much that she would often anything she can hands hands hands hold including she would read until籍 magazine, magazine even even even sometimes Cheer if wherever would read wherever hours-long end until until when would often tire tired of it. years day, while..\n", " girl was in along..\n", " library when she saw something bookshelf caught hadn never seen before called it was called book about in..\n", " who was like like she except she girl in the
25 in.GraphicsUnit upon a..\n", " there in was a..\n", "/stdc named was to in books her read books read so much that when would read anything she could hands hands hands on: books would read until籍 magazine, in etc etc even in Cheer if wherever would read wherever hours upon end until until when would Read tire tired of it.\n", " but day, while..\n", " girl decided in along a..\n", " when she saw something book that caught hadn never seen before. it was about book about in..\n", " who had exactly like she except except in in the
26 in.GraphicsUnit upon a..\n", " there in was a..\n", " girl who..\n", " to in books she read to read so much that when would read whenever she could Lay hold hands on: she would read until籍 magazine, in etc even even in Cheer if wherever would read wherever hours upon end without until when would in tire tired of it.\n", " but day, while..\n", " girl decided in through a..\n", " when she came something book that caught hadn never seen before. it was titled book about in..\n", " who had exactly like she. in girl in the
27 in.GraphicsUnit upon a time there in was a..\n", "/stdc who..\n", " to in books she read to read so much that when would read whenever she could get hold hands on: she would read until/stdc in, in..\n", "..\n", " even in Cheer if wherever would read until hours upon end without until when would in tire tired of it.\n", " one day, while..\n", " girl decided at through..\n", "..\n", " when she saw something book that she had never seen before. it was called in about in..\n", " who..\n", "..\n", " like she who in in in the
28 in.GraphicsUnit upon a..\n", " there in was a..\n", "/stdc who..\n", " to in in she..\n", " to read so much that when..\n", "..\n", " whenever she could get hold hands on: she would..\n", " until about in, in, in even in boxes if wherever would..\n", " whenever hours upon end without until when would..\n", " tire tired of it.\n", " one day, while..\n", " girl..\n", " at through..\n", "..\n", " when she saw something in that p had never seen before. it was called in about in..\n", " who..\n", "..\n", " like her who in in in the
29 in.GraphicsUnit upon a..\n", " there in was a..\n", "/stdc who..\n", " to…\n", " in in would to read so much that when..\n", "..\n", " in she could get her hands on including in would..\n", " in about in, in, in even in boxes if in would..\n", " until hours upon end without until in in in get tired of it.\n", " one day, while..\n", " girl..\n", " at down..\n", "..\n", " when she saw a..\n", " called p had never seen before. it was called in about in..\n", " who..\n", "..\n", " like her in in in in the
30 in.GraphicsUnit upon a time there in was a..\n", "/stdc who…\n", " to…\n", " in in read to read so much that when…\n", "..\n", " in she could get her little on: in would..\n", " in about in, in, and even in box if in would..\n", " until hours on end without completely in in..\n", " get..\n", " of it.\n", " one day, as..\n", " girl…\n", " at down..\n", "..\n", " when she…\n", " a in that…\n", " had never seen before. it was called in about in in who…\n", "..\n", " like in. in in in the
31 in.GraphicsUnit in a time, in was a…\n", "…\n", " who…\n", " to…\n", ". Her read to read so much that she would rather all she could get her little on. She would read in,..\n", ", and, and even the boxes. She would read for hours on end, completely she would..\n", " get…\n", " of it.\n", "One day, the little…\n", " was at through the..\n", " when she…\n", " a book that she had never seen before. It was a book about…\n", "…\n", " who…\n", " just like her. The little in the
32 inQuestion you a time, there was a little girl who loved to read. She loved to read so much that she would read anything she could get her hands on. She would read books, magazines, newspapers, and even cereal boxes. She would read for hours on end, and she would never get tired of it.\n", "One day, the little girl was walking through the library when she saw a book that she had never seen before. It was a book about a girl who was just like her. The girl in the
\n" ] }, "metadata": {}, "execution_count": 28 } ] }, { "cell_type": "markdown", "source": [ "Note that the 0th row represents the output of the embedding layer, and then remaining rows represent the output of each transformer layer.\n", "\n", "We can notice the following:\n", "- *Outputs of the last layer are the model's final output*.\n", " - *Note that for the first few tokens, the predictions look wrong*. These few predictions correspond to the prompt: it is the prediction of what next token should be given the few previous tokens in the prompt. The model has very little information when trying to predict tokens early on so it is usually wrong. Moreover, we usually ignore these predictions as we only care about predicting text after the prompt not during the prompt.\n", "- *Outputs of the first layers are gibberish*: this is because the model's input embeddings weights are different from the model's classification head's weights. Hence, the embeddings of the first layer and last layer operatore in totally different spaces, and projecting embeddings of first layer on model's last layer will lead to gibberish output.\n", "- *Outputs evolve acorss layers*: the words become more meaningful and towards the end we start seeing the final output.\n", "- *Outputs remain wrong till late in the model*: However, even until layer 20, most tokens seem to be really far from the final prediction. This is because during training of the model, the objective was to improve accuracy of last layer and not any of the earlier layers. The model was not incentivized to predict correctly in earlier layers." ], "metadata": { "id": "oqKhWMZdlCiA" } }, { "cell_type": "markdown", "source": [ "#### Models with Tied Embeddings\n", "\n", "Now, let's try the Llama 3.2 1B model. Since it is a model with different weights, the generated story is different for the given prompt." ], "metadata": { "id": "XNKrc0MCogep" } }, { "cell_type": "code", "source": [ "draft_outputs = draft_model.generate(\n", " **orig_inputs,\n", " **generation_config,\n", " return_dict_in_generate=True,\n", " output_hidden_states=True\n", ")\n", "\n", "draft_layer_2_text = get_early_exit_predictions(\n", " draft_outputs[\"hidden_states\"],\n", " draft_model.lm_head,\n", " draft_tokenizer\n", ")\n", "\n", "draft_text = draft_tokenizer.decode(draft_outputs[\"sequences\"][0], skip_special_tokens=True)\n", "print(draft_text)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YGLalLOFUfO1", "outputId": "8c2e39c1-8208-4038-e954-8426016effb2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Once upon a time, there was a man who was very rich. He had a lot of money, and he was very happy with his life. But one day, he decided to take a trip to the city. He was going to visit his friends and see what they were doing. He was also going to see the sights of the city. He was very excited about his trip, and he was looking forward to seeing all the things that he had never seen before.\n", "The man was a very good traveler, and\n" ] } ] }, { "cell_type": "markdown", "source": [ "Now let's see what the prediction of each layer looks like:" ], "metadata": { "id": "D15Bl1C7p50V" } }, { "cell_type": "code", "source": [ "draft_df = pd.DataFrame.from_dict(draft_layer_2_text, orient=\"index\", columns=np.arange(total_seq_len))\n", "draft_df = draft_df.style.set_properties(**{'text-align': 'left'})" ], "metadata": { "id": "_zh-bBqrbf3n" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "draft_df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 672 }, "id": "6jBNDXaObkQd", "outputId": "ac0f5034-1452-475e-e7f8-e0df17ea782c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " 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0�<|begin_of_text|>Once upon a time, there was a man who was very rich. He had a lot of money, and he was very happy with his life. But one day, he decided to take a trip to the city. He was going to visit his friends and see what they were doing. He was also going to see the sights of the city. He was very excited about his trip, and he was looking forward to seeing all the things that he had never seen before.\n", "The man was a very good traveler,
1 travelerheldOnce upon a time, there was a man who was very rich. He had a lot of money, and he was very happy with his life. But one day, he decided to take a trip to the city. He was going gonna visit his friends and see what they were doing. He was also going gonna see the sights of the city. He was very excited about his trip, and he was looking forward to seeing all the things that he had never seen before.\n", "The man was a very good traveler,
2 travelerpackageOnce upon atimeें, there was a man whowas very rich. He had a lot invested money,/or he was very happy with/her life. But-one dayendo he decided purposely Take a trip to the city. He was goinglu visit his friends/or see what they were Doing.\". He was also going gonna see entire sights tongues latter city. He was very excited about/her trip,/or he was looking forwardto seeing all everything things that he had never seen before。\n", "nThe manwas a very good traveler,
3 travelerpackageOnceUponcalesdechlä therewasana man whomwas VERY rich+./she had a lot原 money�/or/she� very happy spo/her life+. But-one day(PC/she decided PURPOSE Take Take trip tripsanas city+./she fis going wanna visit entire friends/or see what they wereDoingै./she fis also going accom see entire sights tongues latter city comentarios/she fis very excited about/her trip,/or/she fislooking forward hor seeing all all thingsthings/she had never seen beforeा।\n", "nThe man indeed pure very good traveler�
4 travelerpackageOnce tales kingdom才oncequareFIELDS kol man whom BOTH VERY wealthylocale/shehad fixed lot很多 money dap/OR/she joined very happy spo/her lifeLOSE But-oneedayichier/she decided Joined_take_take trips visiting stra cityунок/she COPYRIGHT going� visits/her friends/or seeing what theirs offereddoingунок/sheivation ALSO going accomitness benefit sights depositedintl city hadn/sheivation Very excited about/her trips Carry/OR/she/ORlookingforwardwaiting seeing all accumulated thingsthings rov previously never previously previously HeadquartersnThe manwaANA very good travelertej
5 travelerpackage UponMountedTRUEstageرب mountedholding greatchinechine PUSHVery wealthylocale/shemedenloy lot invested moneytrust/OR—he grate very幸福 wol/her life complaining neverthelessobody daydeath/she decided choix taken whole trip visitingentral City‰/she Wrapped tripstarget visiting/her friends/or kijken what/we offeredbbingimpan/she Wrapped兼 tripsूटмотреть entire sightsPURE latter city{//she actually very excited excited/her trips excited/or/she gratelook望awaitecut kinds面的 thingsthings/she previouslyransition benefit previouslybaunThe who/is gifted very-good traveler indeed
6 travelerpackage UponmoonTRUEibus去了 mountedholding cruel man knocks kicking strongly wealthyUU/she inherited sola MUCH inherited money pul/OR/she fis strongly happinessstood/her life_party neverthelessobodyavis carrying dut decidedPutting taken干 traveling bus brighter cityadvert dut tolderli eventually visiting/her friends/or Saw what/we covered secreadvert/she told enjo exhausting accom nicer sống sightsPURE下的 city\tIt/she told strongly excited liberated/her trip accompanied/or/she fisbeck anticipated anticipated/he kinds necessary things things previously previously previously previously previously tochnThe carrying/isEnded strongly/free travelertx
7 travelerpackage Upon GrimmTRUEorning globalization cruelty?a Discchine blaze REALLY strongly MONEYgetLocale/shepv lúc lượt lượtmoneyи/OR/she ALWAYS strongly happiness pleasing/her life duk nevertheless çatimes_keep gentlyippedPuttingTakingaking traveled destinations nearby-city가는/we anticipating travelling eventuallyVisited/her friends/or Seeing things REALLY doing Doing展/she ALSO ALSOevi accomacula beautiful sightsiareauty-citytips/she ALSO strongly excited anticipated/her trip anticipating/or/we ALSO looking anticipated anticipated vistas/all beautiful things things previously previously previously previously previously Hoeodore Urbanwipetookrij adept traveleralat
8 travelerpackage AFTERTRUEorning globalization weltonseальна lúcchine nowherent alrightmoneygetLocale/shemade Widemoney waysmoneymoney/OR/sheOLDER stronglymoneyhaf života life Suddenly Competitionobodyningen pumped pumped impulse음을_takeaking trip(destination reste-citykok/shent travelling laidutdown/her whom/or Saw what REALLY REALLYdoing roli/sheRITEDs裝 accomвор beautiful sights Nights latter-cityRITE‌سی ALSO strongly excited excitement forthcomingholidaymast/OR gratentbecksto forthcoming živ everything beautiful thingsalat previously previously previously previously EXPERangs rolimastnt lúc strongly adept travelerişti
9 travelerpackage slam­i winters magically­ionseför terribleickýATTERntkehmoneyligtATICligtligtmoneymoney MONEYmast/OR proudly ablekehlifetimeśmy/her životagetLocale nevertheless-waylige.Bundle/she decided_taketakeaft travelled/fromkeh-cityład/wehevjangото一下/herHIP/orープ/howANELDsHIR Unfortunately slowly alsohani psy accompanyälttakobot adulthood latter-cityоді/we also excited excitement excitement forthcomingmaremast/OR proudlyntlook to accomältthings beautiful thingsопис previously previously previously/reposworld-passoreticalifoldtakμεkehIBLE travellermast
10 travellerpackage Upon taletractor somewhere­iabouts/is lovely who­intkeh wealthalet/she invested lot invested money-money literally/or/she ablekeh prosperity regards/herkla Suddenly somehow-piece suddenly suddenly suddenly decided kick TAKE drastic trip neighbouringkeh-city Unfortunately/shenttantớieting/her whom/or how happen themselves REALLY selves Unfortunately slowlynt'));\n", "\n", " toмат ourselvesvá sightsifer latter-city Unfortunately knewnt excited excitementeverything/hermastmast/or/shentlook to experiencing things things things things happen normally previously ever/douur Fortunatelyodoremastntčan different-heartedHIPmast
11HIPpackage once lived summers somewhere­i was/is little who livednt fortunate wealthy Spending/she invested large invested invested MONEY lots lots/she able wealthy satisfied regards/heriglia However somehow day Something suddenly suddenly decidedinvest plunge gambleчик neighbouring neighbouring-city Naturally knew afraid travelling kick acquaint/her whom/or how happenDoing doingdzi Suddenly knew afraid planning to kick some latestseeing města city-city But knew also excited excitementeverything upcomingaheadespecially/OR eagerly excited excited eagerly experiencing everything different different happening happen/she previously-before before.Bundle Eventually latter arrived fortunate very different-quality traveler however
12 travelerpackage uponetimes time there somebody was somebody little named lived born fortunate indeedHis had amassed largeOfWork money invested lots he invested able proud with himself lifeATUS However suddenly day something something suddenly toinvest plunge trip trip somewhere nearest-city he knew afraid to spend somebody friends whom/or how they REALLY doingcie But knew planning planning to visit how sights sights city city himself But knew also excited excitement going tripbecause but he knew planning forward to experiencing everything kinds things happening happen wanted never-before before anywhereHowever thing arrived fortunate great different-heartedué indeed
13uépackage upontimes time therethere existed somebody little named lived obsessed concerned indeed Whenever had MANY huge invested money invested lots he wanted happy happy with himself life. However one day something something decided to invest risks trip abroad somewhere nearest nearest While boarded afraid to spend friends friends there/or how happened were doing there But knew lucky planning to meet some sightsseeing city city itself But knew excited excited abouteverything tripbecause but he felt excited forward to experiencing everything sorts things that existed wanted never seen before.\n", "However thing arrived fortunate great intelligent-hearted himself though
14 himselfpackage upon a time there there was an woman named lived fortunate wealthy indeed His had amassed large of money, but he wanted happy wealthy with himself life. But one day something something decided to invest a trip around somewhere nearest where He wanted going to visit some friends there acquaint what they were doing there But wanted planning going to visit some sightsseeing town city itself But wanted also excited about these trip because but he couldn planning forward to experiencing everything sorts things that awaited wanted never seen before.\n", "However next arrived fortunate little good traveler indeed so
15 indeedQuestion again a time there there lived a man who lived very rich. He had everything large of money, but he wanted very happy with himself life. But one day, he heard to take a trip to another other where He wanted planning to visit his friends there see how they were doing. But thought also going to visit how new and the city, But thought also excited about his trip, but he was looking forward to seeing all the different he he wanted never seen before.\n", "His man was also very good-looking, and
16,Question you a time, there was a man who was very rich. He had a lot of money, and he was very happy with his life. But one day, he decided to take a trip to the city. He was going to visit his friends and see what they were doing. He was also going to see the sights of the city. He was very excited about his trip, and he was looking forward to seeing all the things that he had never seen before.\n", "The man was a very good traveler, and
\n" ] }, "metadata": {}, "execution_count": 32 } ] }, { "cell_type": "markdown", "source": [ "We can notice the following:\n", "- *Outputs of the last layer are the model's final output*: same as before.\n", "- *Outputs of the first layers are the input tokens*: this is different from before. Llama3.2 1B has the same weights for the input embedding layers and the model's head. Hence the input and output tokens are in the same projection space.\n", "- *Outputs evolve acorss layers*: Unlike the previous model, the outputs start being meaningful, then they become gibberish as they operate in different projection space, and towards the end they become more meaningful again.\n", "- *Outputs remain wrong till late in the model*: However, even until layer 10 (more than half-way through the model), most tokens seem to be really far from the final prediction. So having input and output embeddings of a model tied doesn't encouarage earlier predictions of tokens." ], "metadata": { "id": "Cag4fa_TqUV-" } }, { "cell_type": "markdown", "source": [ "*Please note that it is just a coincidence that the assistant or draft model in this tutorial has weights of embedding and head tied together. It is a unique feature of the Llama3.2 1B model that we happened to choose in this tutorial and we thought it would be interesting to see how it's early outputs would look like.*" ], "metadata": { "id": "nGCMFx87sZ3x" } }, { "cell_type": "markdown", "source": [ "#### Models trained with LayerSkip\n", "\n", "Now, let's load a model that has been trained with an objective to improve accuracy of earlier layers.\n", "\n", "In this tutorial we will use Llama models continually pretrained with the LayerSkip recipe described in this [paper](https://arxiv.org/abs/2404.16710) and whose checkpoints exist on 🤗 [here](https://huggingface.co./collections/facebook/layerskip-666b25c50c8ae90e1965727a).\n", "\n", "Let's use a LayerSkip variant of the same Llama3 8B of our main model:" ], "metadata": { "id": "q-kaCkYyrrDi" } }, { "cell_type": "code", "source": [ "layerskip_outputs = layerskip_model.generate(\n", " **orig_inputs.to(layerskip_model.device),\n", " **generation_config,\n", " return_dict_in_generate=True,\n", " output_hidden_states=True\n", ")\n", "\n", "layerskip_layer_2_text = get_early_exit_predictions(\n", " layerskip_outputs[\"hidden_states\"],\n", " layerskip_model.lm_head,\n", " layerskip_tokenizer\n", ")\n", "\n", "layerskip_text = layerskip_tokenizer.decode(layerskip_outputs[\"sequences\"][0], skip_special_tokens=True)\n", "print(layerskip_text)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lOfeAV9htaB5", "outputId": "9e482302-cd36-4427-e934-6a68302883ce" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Once upon a time, there was a little girl who loved to read. She loved to read so much that she would read anything she could get her hands on. She read books, magazines, newspapers, cereal boxes, and even the back of shampoo bottles. She read so much that she became a very good reader. She could read anything and everything.\n", "One day, the little girl was walking through the library when she came across a book that she had never seen before. It was a book about a little girl who\n" ] } ] }, { "cell_type": "markdown", "source": [ "Again, since the model has different weights, the predicted story is different." ], "metadata": { "id": "3RD9_hnHtrkM" } }, { "cell_type": "markdown", "source": [ "Now, let's see how the output looks like at each layer:" ], "metadata": { "id": "lx0TDaL9t4fV" } }, { "cell_type": "code", "source": [ "layerskip_df = pd.DataFrame.from_dict(layerskip_layer_2_text, orient=\"index\", columns=np.arange(total_seq_len))\n", "layerskip_df = layerskip_df.style.set_properties(**{'text-align': 'left'})" ], "metadata": { "id": "fhePh8PfuAPr" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "layerskip_df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "TRJ-l7bxuFlY", "outputId": "87bb9639-6dee-4c04-c114-b88a977cdd71" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ 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7 girl# the a time, a was a little girl who lived to play and She was to read and much that she had often a and could find her hands on. She was all and and, and, and,, and even the occasional of the bottles. The was all much that she would a very good writer and She was read a and everything,The day, a little girl was sitting in the store and a saw across a book that was had never read before. The was a book that a young girl who
8 girl# the a time, a was a little girl who lived to play and She was to read and much that she had go all and could find her hands on. She was all and books, and, and boxes, and even a newspaper of a bottles. The was all much that she was a very good writer and She was read all and everything,The day, a little girl was walking in the store and a came across a book that she had never read before. The was a book that a little girl who
9 girl# a a time, a was a young girl who lived to play. She was to read the much that she would read all and could get her hands on. The would the, and, and, and boxes, and even the newspaper of a bottles. The also the much that she would a very good reader. She was read all and everything,The day, a little girl was looking down the store and she was across a book that she had never read before. The was a book that a little girl who
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11 girl# a a time, a was a little girl who lived to play. She loved to read about much that she was spend all and could get her hands on. She loved the, books, and, and boxes, and even the newspaper of the bottles. The even everything much that she was a very good reader. She also read a and everything.The day, a little girl was sitting down the library and she saw across a book that she thought never read before. The was a book that a little girl who
12 girl# a a time, a was a little girl who lived to play. She loved to read books much that she would even all and could get her hands on. She loved the, and, and, and boxes, and even the newspaper of the bottles. The even everything much that she was a very good reader. She even read all and everything,One day, a little girl was going down the school, she came across a very that she was never read before. The was a book that a very girl who
13 girl# a a time, a was a little girl who lived to play. She loved to read, much that she would go all and could find her hands on. She loved everything, books, and, and boxes, and even the trash pages the bottles. She even everything much that she was a very good reader. She was read all and it,One day, a little girl was sitting through the store, she was across a book that she had never read before. The was a book that a little girl who
14 girl# a a time, a was a little girl who was to play. She loved to read about much that she would read all and could get her hands on. She loved everything, books, and, and boxes, and even the newspaper of the bottles. She even everything much that she was a very good reader. She was read all and it,One day, the little girl was sitting through the store, she was across a book that she had never read before. The was a book that a little girl who
15 girl# a a time, a was a little girl, was to play. She was to read so much that she would read every and could get her hands on. She would books, books, and, and boxes, and even the old of the bottles. She even so much that she was very very good reader. She was read all and it andThe day, the little girl was reading down the library, she saw across a book that she had never read before. The was a book that a little girl who
16 girl# a a time, a was a girl, was to play. A loved to read so much, she would read to and could get her hands on. She would the, books, and, and boxes, and even the signs pages the bottles. She even so much that she was a very good reader. She was read a and everything.The day, the little girl was reading through the library, she saw across a book that she had never seen before. The was a book that a little girl who
17 girl# a a time, a was a girl, was to play. She was to read so much, she would read to and could get her hands on. She would the, magazines, and, and boxes, and even the signs of the bottles. She was so much that she was a very good reader. She was read a and everything.The day, the little girl was reading through the library, she saw across a book that she had never seen before. The was a book that a little girl who
18 girl# a a time, a was a ,, was to play. I read to read so much,, would read in and could get her hands on. ( would the, magazines, and, and boxes, and even the signs of the bottles. She even so much that she was a very good reader. She was read a and everything.One day, the little girl was reading through the library, she saw across a book that was had never seen before. The was a book that a little girl who
19 girl# a a time, was a ,,, to play. She would to read, much,, would read in and could get her hands on. The would the, magazines, and, and boxes, and even the back of the bottles. The even in much that the was a very good reader. The read read a and everything.One day, the little girl was reading through the library, she saw across a book that she had never seen before. The was a book that a little girl who
20 girl#, a time, was a ,,, to play. She was to read, much,, would read in and could get her hands on. She would the, and, and, and boxes, and even the back of the bottles. She read in much that it was a very good reader. The read read a and everything.One day, the little girl was walking through the library and she came across a book that was had never seen before. The was a book that a little girl who
21 girl#, a , was a ,,, to read. She would to read so much,, would read in and could get her hands on. She would the, and, and, and boxes, and the the back of the bottles. The was so much that, was a very good reader. The was read all and everything.One day, the little girl was walking through the library and she came across a book that she had never seen before. The was a book about a magical girl who
22 girl#, a , was a ,,, to read. ( was to read so much,, would read in and could get her hands on. She would the, and, and, and boxes, and the the back of the bottles. The read so much that, was a very good reader. The was read very and everything.One day, the little girl was walking through the library and she came across a book that she had never seen before. The was a book about a little girl who
23 girl#, a , was a ,,, to\n", ". ( was to read, much,, would read in and could get her hands on. The would the, and, and, and boxes, and the the back of the bottles. The was so much that, was a very good and. The was read and and everything,One day, the little girl was walking through the library and she saw across a book that was had never seen before. It was a book about a girl who
24 girl#, a , was a ,,, to\n", ". ( was to read, much,, would, in and could get her hands on. ( would the, and, and, and boxes, and the the back of the bottles. The was in much,, was a very, and. The was read and and everything,One day, the little girl was in through the library and she came across a book that she had never seen before. It was a book about a girl who
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32 girl# again a time, there was a little girl who loved to read. She loved to read so much that she would read anything she could get her hands on. She read books, magazines, newspapers, cereal boxes, and even the back of shampoo bottles. She read so much that she became a very good reader. She could read anything and everything.\n", "One day, the little girl was walking through the library when she came across a book that she had never seen before. It was a book about a little girl who
\n" ] }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "markdown", "source": [ "Now, we notice the following:\n", "- *Outputs of the last layer are the model's final output*: same as before.\n", "- *Outputs of the embedding layer is gibberish*: same as Llama3.1 8B because because the model's input embeddings weights are different from the model's classification head's weights.\n", "- *Outputs evolve acorss layers*: same as before.\n", "- *Outputs are more correct compared to other models*: Unlike other models, the outputs of earlier layers are closer to the last layer. Even soon after layer 1 or 2, the tokens are not gibberish.\n", " - We cannot create a complete correct sentence at any earlier layer. However, we do see individual tokens or consecutive set of few tokens that seem in earlier layers that are the same as the last layer." ], "metadata": { "id": "jTSdP5U1ubbI" } }, { "cell_type": "markdown", "source": [ "So the question is, can we use earlier layers in a LayerSkip model to speedup text generation while keeping the output correct? The answer is yes!\n", "\n", "Although earlier layers cannot generate full sequences that are the same as earlier layers, they can create small sub-sequences that are the same. Hence, we can use *self-speculative decoding*:\n", "- generate tokens sequentially using earlier layers, then\n", "- verify them in parallel using the remaining layers.\n", "This can lead to speedup if earlier layers predict enough tokens that are similar enough to the last layer, as verifying a sequence of tokens in parallel is faster than generating each token seprately." ], "metadata": { "id": "1R8ZFdxV7eYn" } }, { "cell_type": "markdown", "source": [ "## Make Text Generation Faster\n", "Let's try generating text using different approaches." ], "metadata": { "id": "RvXnXsfkP2le" } }, { "cell_type": "markdown", "source": [ "### Autoregressive Decoding" ], "metadata": { "id": "yBo6SdAR2r-R" } }, { "cell_type": "markdown", "source": [ "As we described previously, the standard way to generate is *autoregressive decoding*. In autoregressive decoding, the model generates text token by token.\n", "\n", "Now let's try generating text and measure the time:" ], "metadata": { "id": "h-fN0S6qwG1m" } }, { "cell_type": "code", "source": [ "orig_inputs = orig_tokenizer(prompt, return_tensors=\"pt\").to(orig_model.device)\n", "# Warmup\n", "_ = orig_model.generate(**orig_inputs, **generation_config)\n", "\n", "start = time.time()\n", "orig_outputs = orig_model.generate(\n", " **orig_inputs,\n", " **generation_config\n", ")\n", "end = time.time()\n", "\n", "orig_text = orig_tokenizer.decode(orig_outputs[0], skip_special_tokens=True)\n", "print(f\"Original Model time: {end-start} s\")\n", "print(f\"Original Model output:\\n{orig_text}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n-nucIZVtjVK", "outputId": "5289c17d-3e9b-4ad8-e89a-52be6be918f9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original Model time: 3.879817485809326 s\n", "Original Model output:\n", "Once upon a time, there was a little girl who loved to read. She loved to read so much that she would read anything she could get her hands on. She would read books, magazines, newspapers, and even cereal boxes. She would read for hours on end, and she would never get tired of it.\n", "One day, the little girl was walking through the library when she saw a book that she had never seen before. It was a book about a girl who was just like her. The girl in the\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Speculative Decoding" ], "metadata": { "id": "-_tVUf3oFPyf" } }, { "cell_type": "markdown", "source": [ "Another way to speed up text generation is **speculative decoding**. It is explained in detail in a previous blog [here](https://huggingface.co./blog/assisted-generation). In a nutshell:\n", "1. it generates a group of draft tokens in sequence using a smaller, faster but less accurate *draft model*\n", "2. it verifies those draft tokens in parallel using the main, larger but nore accurate *main model*\n", "3. we keep the tokens that the main model accepts, for the first token that mismatch between the 2 models, use that from the main model, and then repeat step 1. to generate more draft tokens from that first mismatch." ], "metadata": { "id": "iOejbDCk8N8-" } }, { "cell_type": "markdown", "source": [ "In 🤗, we can enable speculative decoding by just adding a `assistant_model` argument to the model's `generate()` function:" ], "metadata": { "id": "ft13zp7a-Iwl" } }, { "cell_type": "code", "source": [ "# Warmup\n", "_ = orig_model.generate(\n", " **orig_inputs,\n", " assistant_model=draft_model,\n", " **generation_config\n", ")\n", "\n", "start = time.time()\n", "spec_outputs = orig_model.generate(\n", " **orig_inputs,\n", " assistant_model=draft_model,\n", " **generation_config\n", ")\n", "end = time.time()\n", "\n", "spec_text = orig_tokenizer.decode(orig_outputs[0], skip_special_tokens=True)\n", "print(f\"Speculative Decoding time: {end-start} s\")\n", "print(f\"Speculative Decoding output:\\n{spec_text}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yYgdU9qwE30g", "outputId": "8291356d-d175-4b96-bbe8-828b21800365" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Speculative Decoding time: 3.3406598567962646 s\n", "Speculative Decoding output:\n", "Once upon a time, there was a little girl who loved to read. She loved to read so much that she would read anything she could get her hands on. She would read books, magazines, newspapers, and even cereal boxes. She would read for hours on end, and she would never get tired of it.\n", "One day, the little girl was walking through the library when she saw a book that she had never seen before. It was a book about a girl who was just like her. The girl in the\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Early Exit Self-Speculative Decoding" ], "metadata": { "id": "u1dMvAgQ2xNw" } }, { "cell_type": "markdown", "source": [ "Now, how can we use the outputs of earlier layers to generate draft tokens and make the full model, verify them? In 🤗 it is very simple: we just add `assistant_early_exit` argument to the `generate()` function:" ], "metadata": { "id": "AmyH4VLg3Na3" } }, { "cell_type": "code", "source": [ "early_exit_layer = 6\n", "\n", "# Warmup\n", "_ = layerskip_model.generate(\n", " **orig_inputs,\n", " assistant_early_exit=early_exit_layer,\n", " **generation_config\n", ")\n", "\n", "start = time.time()\n", "selfspec_outputs = layerskip_model.generate(\n", " **orig_inputs,\n", " assistant_early_exit=early_exit_layer,\n", " **generation_config\n", ")\n", "end = time.time()\n", "\n", "selfspec_text = layerskip_tokenizer.decode(selfspec_outputs[0], skip_special_tokens=True)\n", "print(f\"Self-Speculative Decoding time: {end-start} s\")\n", "print(f\"Self-Speculative Decoding output:\\n{selfspec_text}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZCndWqrEzGWQ", "outputId": "e60be52d-26f4-4f2b-f11d-4c325c789e36" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Self-Speculative Decoding time: 3.0442981719970703 s\n", "Self-Speculative Decoding output:\n", "Once upon a time, there was a little girl who loved to read. She loved to read so much that she would read anything she could get her hands on. She read books, magazines, newspapers, cereal boxes, and even the back of shampoo bottles. She read so much that she became a very good reader. She could read anything and everything.\n", "One day, the little girl was walking through the library when she came across a book that she had never seen before. It was a book about a little girl who\n" ] } ] }, { "cell_type": "markdown", "source": [ "Now, we have speedup using 1 model that is even better than speculative decoding using 2 models.\n", "\n", "We can obtain bigger speedups if we use Llama3 8B or Llama2 7B. You can check full list of LayerSkip checkpoints [here](https://huggingface.co./collections/facebook/layerskip-666b25c50c8ae90e1965727a)." ], "metadata": { "id": "gIG0q3aJE1qe" } }, { "cell_type": "markdown", "source": [ "And that's it for today!\n", "You can read our blog to learn more details and view speedup results of applying self-speculative decoding on various models and tasks, and analysis of various early exit layers." ], "metadata": { "id": "KovfxM38_KEm" } } ] }