diff --git a/week10_efficiency/practice.ipynb b/week10_efficiency/hw_quantization.ipynb similarity index 100% rename from week10_efficiency/practice.ipynb rename to week10_efficiency/hw_quantization.ipynb diff --git a/week10_efficiency/hw_speculative.ipynb b/week10_efficiency/hw_speculative.ipynb new file mode 100644 index 00000000..dd72eba0 --- /dev/null +++ b/week10_efficiency/hw_speculative.ipynb @@ -0,0 +1,5318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "qclzljFDqKk3" + }, + "source": [ + "# Speculative Decoding\n", + "\n", + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T6u2Z05JsvlL", + "outputId": "5ce1f325-9798-4c4b-d4e7-d70cc0ca697d" + }, + "outputs": [], + "source": [ + "!pip install triton\n", + "!pip install datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ahhcFqfBqKk4" + }, + "source": [ + "### Greedy Generation Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wAoayJ-GqKk5" + }, + "source": [ + "**Load the model**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 356, + "referenced_widgets": [ + "64690f2e263748e19411bd6a1c9b9d92", + "78b8ce55753c484db8bc48e6b458991b", + "aed464eed5f04f6fad4fd9b824783903", + "25aa8d43c1fb4b7a8c4e0d39889a2daf", + "4a4a48f06d94451789da3bb399877b08", + "0094622aafa441d7a8f0f0e9b52bcc15", + "5b56139990a042a6a03a8a195b3d03e2", + "1da773e10a014b62a248ff9d7acc3465", + "43f1b008363c41d9afbac6e5944d7f93", + "848caa68a8ed491c9b22e012a2a03a06", + "3ddc44ced5554dad8061effb69cff405", + "993e78a44d8c4879944fade1d464d49f", + "430f9d58fb5f447c8f468e3463cd07b5", + "e07bd449871d47a5a464570696c078d2", + "216a1d19e9f24196ab22510d7e1cf697", + "f49f0aec365843c5a5bbe868b130a005", + "9769afe9bbd04fd3aac102ab4bc5906a", + "84ada2276be64faf85b331ebc4fccff9", + "01d95ab09ec3482d814dbaf4eebb4681", + "2a5f2c914c064df9bbababbb6b9e9289", + "1d8cf059bc504f7f8becba5c5270b9ba", + "06eaa2c4c0e2494dad9cd5c2e3aa276f", + "a995db64b1ab479ea927562fe6c2ac9f", + "08a19441ba8141868203a71605306777", + "ff9bec1f2e8d4b6a88303768ebd76660", + "0cce051fea2c4ea68ce109972efd20f2", + "d308d06e5b294a58bbb1756e26dc0b8d", + "8cdb80881f8b4755b9bb81589d94b5e3", + "0f95a3993628424aaaf87c9bc40e0724", + "feea279b0dfe45e7b1ea8db2ddc1643c", + "7bb699f8c27c482fa4f51d2adda818b9", + "67ae59fcdccd4154b59db3d5332f6b74", + "a53103b2c5c344a4b23420a5d00f6561", + "e90a527832e743e3a3a7c0b37995ce84", + "3e61a4ca6fa1402b88a3f665b60f84fd", + "22cf5a0639d74741b556be00171754d0", + "88fba02aa7724a20a0ee17387f39910b", + "1049ac0bee4a4f79a8f4ab99fc65d007", + "c95ce1cd07ce4ffe99a4690830f11e10", + "6953549564844df3ab0cb13b917590a7", + "b14782d1c39849e7ac9abc85bab8c4fa", + "7b20579077a04f8e9c9d36e72fc35458", + "c7dd1e3b333c442fb663b3dbadf99b56", + "01b1d6c50b5a497c907454ce74551d19", + "4af92d25298e472f9764880c6d9827b5", + "e97ac2e44bc64ff6a7322f462fe0c8d2", + "95120af02fa2437dbcb04e7d12ed50f0", + "44fa205246e346b596310597ce0da209", + "43fd430855ff4fe8abc55cb700956dd9", + "d28470ebfa2e4425a95fd86eb25eac70", + "4179a9a219de44fe8dbdb84f1f20bc01", + "7061b8854db74a2e9a7c250f94114615", + "4f9a82cd804143a28ad138bf70e46fd7", + "17e1afafab104285b20282896c4e5cd7", + "678c7d1cbaaf4b4d912f2ff1d94cd686", + "6f6cd31097964784b91068ece2f12b17", + "85a3f25240d74162932d6d1910c240ba", + "bcfb5adaf77e4447885c17cd36e4499b", + "bf7ec77181dd4ee780f9d6226835ac34", + "92d6229a62434cc8bad0ccba547c122f", + "64464d06c5c04074b7b9fd75caf28718", + "a76d08dcdc5c44dabf22d0097859dddc", + "b52584f73eee4c688c1a8de317ec069b", + "cc8ed7a2fc8b4113aa9d8c6032e501bf", + "1041041043924a709975715f2c5188c2", + "81b111b24f604b2ebe8f8f393973874f" + ] + }, + "id": "UX4Fg1aUqKk5", + "outputId": "8148de74-0466-4b92-9552-93c6ae0b9c59" + }, + "outputs": [], + "source": [ + "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n", + "%env CUDA_VISIBLE_DEVICES=0 # Change it if you're on a multy-GPU machine\n", + "\n", + "import torch\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "LLAMA_REPO = \"unsloth/Llama-3.2-3B\"\n", + "model = AutoModelForCausalLM.from_pretrained(LLAMA_REPO, torch_dtype=torch.float16, device_map=\"cuda\")\n", + "model.generation_config.pad_token_id = 128001\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(LLAMA_REPO)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tABKQ69wqKk6" + }, + "source": [ + "**Compile model forward for more accurate benchmarks**\n", + "\n", + "`transformers` is not a very efficient inference engine because of high python overhead and almost no kernel optimization.\n", + "\n", + "However, with `PyTorch` `v2.0.0`, [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) was introduced. This feature allows for capturing, isolating and optimizing CUDA runtime in `PyTorch`. Using this feature, we can effectively eliminate almost all python overhead and optimize the kernels.\n", + "\n", + "Starting with [`transformers` `v4.44.0`](https://github.com/huggingface/transformers/releases/tag/v4.44.0), this feature is integrated with `transformers` text generation utils end-to-end. However, for simplicity, we'll apply it to the forward pass of the model specifically." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "-QLsL7m0qKk6" + }, + "outputs": [], + "source": [ + "model.forward = torch.compile(\n", + " model.forward, # the function call to compile\n", + " fullgraph=True, # Compile all the CUDA kernels into a single entity\n", + " mode=\"reduce-overhead\", # Optimize for speed\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1x5ojJbrqKk7" + }, + "source": [ + "**Benchmark forward passes with different seq_len**\n", + "\n", + "Run the following cell 2+ times. The first time is slow because that's when the compilation is taking place.\n", + "\n", + "The following runs are much faster.\n", + "\n", + "(EXTRA: run this cell without compiling to measure scompilation speedup)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6y_geHUkqKk7", + "outputId": "120bc5df-4cef-4aee-8dee-227e8720c8a4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 tokens: 31.35 passes/s\n", + "2 tokens: 33.34 passes/s\n", + "4 tokens: 32.99 passes/s\n", + "8 tokens: 32.37 passes/s\n", + "16 tokens: 31.21 passes/s\n", + "32 tokens: 25.95 passes/s\n", + "64 tokens: 23.99 passes/s\n", + "128 tokens: 18.13 passes/s\n", + "256 tokens: 9.83 passes/s\n", + "512 tokens: 5.19 passes/s\n", + "1024 tokens: 2.47 passes/s\n" + ] + } + ], + "source": [ + "from time import perf_counter\n", + "\n", + "NUM_REPEATS = 100\n", + "\n", + "throughpus = {}\n", + "\n", + "for seq_len in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:\n", + " input_ids = torch.randint(0, tokenizer.vocab_size, (1, seq_len)).to(\"cuda\")\n", + "\n", + " with torch.no_grad():\n", + " start = perf_counter()\n", + " for _ in range(NUM_REPEATS):\n", + " model(\n", + " input_ids,\n", + " use_cache=False,\n", + " )\n", + " torch.cuda.synchronize()\n", + " end = perf_counter()\n", + " throughpus[seq_len] = NUM_REPEATS * seq_len / (end - start)\n", + " print(f\"{seq_len} tokens: {NUM_REPEATS / (end - start):.2f} passes/s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U-qz7ahOqKk8" + }, + "source": [ + "As we can see, the forward pass speed almost doesn't depend on the number of tokens passed through the model up to around **16** tokens at a time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "PwAeHLbAqKk9", + "outputId": "3ad17dbf-5097-4fd1-a56c-07d29d668dd2" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(throughpus.keys(), throughpus.values())\n", + "plt.xscale(\"log\")\n", + "plt.yscale(\"log\")\n", + "\n", + "plt.xlabel(\"Tokens in pass\")\n", + "plt.ylabel(\"Troughput, tokens per second\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Y_TbvSatqKk9" + }, + "outputs": [], + "source": [ + "del model\n", + "model = AutoModelForCausalLM.from_pretrained(LLAMA_REPO, torch_dtype=torch.float16, device_map=\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vFIT6AmUqKk9" + }, + "source": [ + "## Speculative Decoding\n", + "\n", + "As a baseline, we'll generate hypotheses using a very simple bigram model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rd7JF2jQqKk-" + }, + "source": [ + "### Load the Data\n", + "\n", + "We'll use the [wikitext2](https://paperswithcode.com/dataset/wikitext-2) dataset as a sample of natural language." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 259, + "referenced_widgets": [ + "cf7cfa71e4df4702befc7cb043972b25", + "fb49d8d521ae4ae6a311956ce7993c7a", + "cffd840b843f42ceaca50681b86599a1", + "6c5dee16658c49b2a467e03cb0a84980", + "98dba8f11ec24a72aeafc989d2daffa0", + "1db74f492a17412caf880abbfc4268b2", + "3857479cd1da4bf6b8c2f6808fe2e052", + "b03a6ebcbfcd4537b90ab40fa0b32a34", + "f040d8a7150d4f4499a805cf66056075", + "e65dfd55764e4150897b549b0ae9f5ca", + "163fbad10c614dc5a580af4fa6f822f2", + "d44bd48f62304b3fb4fe3329bac990b0", + "26dfa3c95146498aa79dbc1258daa1d1", + "96f74a8692804ebaabcdf3531c44e65a", + "873f8834954c4cadb3c39bf0fd3bfdbb", + "7d434bc70ea54fcf97e4cdddc84c952b", + "1a1b5cf4a430464f9e325e03801431e3", + "7b2cba07ddaa4166bdc8c00c040bb5ff", + "d0922b073cb841b28b4f5b0a42dc715e", + "505fecc40db0468aadcd14c68977d2dc", + "086cf6ba02bc404fb0a9d91b15d950ed", + "3adec396264743089d0f66889c5aa5a6", + "232c3a0ccc564762a42adbfd1e83c244", + "b428ee5678ef41d1848b7a3fc8b36bf6", + "5231747de3f54ec8b4083eeba6384f04", + "988400ac04c447d09c9bf7f9c549001f", + "757f7a56116b46aeb7922873e95288ac", + "d546166a77b34a7cb79238810f2d802f", + "e3528852e1a4406a9d3bb83084680817", + "418e6efeb0974bd498f6a864dbcf0f3f", + "82eb0b8152d346048050a774b215db3f", + "2e41f8dc807646618ae4e6fb61ef4a53", + "67c81f42320e476788ead8ac5bffe2f6", + "801e0e8ab16a4eac87fb628e901e8788", + "e62468bd4a92404ca49932be701d34e1", + "513f0e571b094658acdb36107fb14e35", + "55cd2f22d4b9442796cd81a77b4dbab8", + "b25d224aa69c45a68aad9812c0132942", + "5a81079a9ae34d7f94cf9cc7f2fd01cb", + "4739f40f0cab4502b7c3ff4d7dd38709", + "01df2518859e499f953122fd6609bd0a", + "4aceb8618fc241f2b861c558e7c557ff", + "e2d2c7e6b548412a88c33817628d0519", + "d7d0f21334c6472fbc10a178f2f92dfb", + "7902738f77a0439bb9fe7fefd715a44a", + "f5ed60f25fa74b7eb80f78062892f5cf", + "5c96101dd0e04f998c339df6725b9406", + "1c84e13392b74a72bf46ca75de23209f", + "44a3f3c61b7843a39fed8b9c622d5e1c", + "ba0158f6016a4939ab68a4179a91e39d", + "2d4f756243cb4514bd4bc6116bb5a5d5", + "95320ea021ab4629bf59be5da4a225d2", + "e23251139baa441ba2cc4e28abda1139", + "6e9a9671db104d7883014624720db4d4", + "936cfdfe857f41bfb7c0bc4c3323a01e", + "57fea4db911f477d9ea42daef13e1a57", + "f162fdc6e2324f73816d9972f51f844d", + "6596c7235652435b8945029e01dca78c", + "ef98c5f1ab0f4d32bc290f328045a705", + "fa7fc8ebe61e4b36bcc37762e32f3718", + "343045b1cf8a487bbc2d39874e517267", + "8d68d0ec6bc8474ab42e0d67eccca70f", + "42ce6c06d2494ada95c4b04196054ff8", + "d358f3983444409f9cbdfa0f832436fa", + "1b3e8206c6fa43dab4a8bfa8f5dd6705", + "9638453ddb3b49be919511aee81054e8", + "52cb0ff90fa243988ac7ffa553d0888d", + "8f6074e1ae7a418d8cddcef454db0631", + "df56484b68d84f18afa7b2d9d6bb6a0f", + "86caccb92d9d4da990f31ddaa090df76", + "3d13e82b432a4ba0a989c40d0da975b2", + "b80c7420cc894b769c748f343526fc44", + "33b12ff051164b65bcc6edbc283e2b9b", + "ab1b8a177fdc45a199144e5330dd6790", + "3783eddb84e84cf9a2ec4676552179a4", + "5afcdd40b3774e8ea4bd810a34a1bcc7", + "00cb672b77d04fa888bd35dfb83ac31e" + ] + }, + "id": "rk8KcWC4qKk-", + "outputId": "4ffcc321-6e0f-494a-b437-3b5ebf88b50f" + }, + "outputs": [], + "source": [ + "import random\n", + "\n", + "from datasets import load_dataset\n", + "\n", + "SEED = 0\n", + "\n", + "def get_wikitext2(seed, seqlen, nsamples=64):\n", + " traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')\n", + " testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')\n", + "\n", + " tokenizer = AutoTokenizer.from_pretrained(LLAMA_REPO, use_fast=False)\n", + "\n", + " train_input_ids = tokenizer(\"\\n\\n\".join(traindata['text']), return_tensors='pt').input_ids\n", + " random.seed(seed)\n", + " train_batch = []\n", + " for _ in range(nsamples):\n", + " i = random.randint(0, train_input_ids.shape[1] - seqlen - 1)\n", + " j = i + seqlen\n", + " inp = train_input_ids[:, i:j]\n", + " tar = inp.clone()\n", + " tar[:, :-1] = -100\n", + " train_batch.append(inp[0])\n", + "\n", + " test_input_ids = tokenizer(\"\\n\\n\".join(testdata['text']), return_tensors='pt').input_ids\n", + " test_input_ids = test_input_ids[:, :(test_input_ids.shape[1] // seqlen) * seqlen]\n", + " test_input_ids = test_input_ids.reshape(test_input_ids.shape[1] // seqlen, seqlen)\n", + "\n", + " return torch.stack(train_batch), test_input_ids\n", + "\n", + "train_batch, test_input_ids = get_wikitext2(SEED, 8192)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2K-tFOxyqKk-" + }, + "source": [ + "**Task (0.5pt points):** Build a bigram model\n", + "\n", + "Using sequences from `train_batch`, build a bigram model for predicting `n` tokens into the future." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "Anchr3_TqKk-" + }, + "outputs": [], + "source": [ + "from tqdm.auto import tqdm, trange\n", + "\n", + "from typing import Mapping\n", + "\n", + "\n", + "import numpy as np\n", + "from collections import defaultdict\n", + "\n", + "def build_next_token_array(train_data, vocab_size: int=128256, default_next_token: int=220) -> Mapping[int, int]:\n", + " \"\"\"\n", + " Builds an array mapping each token in the vocabulary to its most likely next token based on training data.\n", + "\n", + " Args:\n", + " train_data (np.ndarray): Array of training data tokens.\n", + " vocab_size (int): The size of the vocabulary.\n", + " default_next_token (int): Default token to use if no next token can be determined.\n", + "\n", + " Returns:\n", + " Mapping[int, int]: Array where each index represents a token and the value at that index is the most likely next token.\n", + " \"\"\"\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " # Step 1: Prepare the Data\n", + "\n", + " # Step 2: Create Pairs and Count Occurrences\n", + "\n", + " # Step 3: Build Mapping from Current Token to Next Token Counts\n", + "\n", + " # Step 4: Determine the Most Likely Next Token\n", + "\n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + " return next_tokens_array\n", + "\n", + "def speculate_bigram(input_ids: torch.Tensor, position: int, n: int, next_tokens_array: Mapping[int, int]) -> int:\n", + " \"\"\"\n", + " Generates a speculative sequence by predicting next tokens in a sequence using a bigram model.\n", + "\n", + " Args:\n", + " input_ids (torch.Tensor): Tensor of input token IDs.\n", + " position (int): Position in the sequence to begin speculation.\n", + " n (int): Number of tokens to generate.\n", + " next_tokens_array (Mapping[int, int]): Mapping of tokens to their most likely next token.\n", + "\n", + " Returns:\n", + " int: Number of tokens generated.\n", + " \"\"\"\n", + " for i in range(n):\n", + " hypo_next_token = next_tokens_array[input_ids[0, position - 1].item()]\n", + " input_ids[0, position] = hypo_next_token\n", + " position += 1\n", + " return n\n", + "\n", + "\n", + "NEXT_TOKEN_WIKI2 = build_next_token_array(train_batch.flatten())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8ZP5SyfXqKk_" + }, + "source": [ + "**Task (1.5 points)** Implement greedy sequential speculative decoding:\n", + "\n", + "You're given a prototype of the function that generate a token sequence greedily by speculating `n` tokens into the fulure and verifying those tokens.\n", + "\n", + "Your task is to:\n", + " * Correctly fill a hypothesis inplace (using `speculate_fn`)\n", + " * Pass the hypothesis through the model (with correct `past_key_values`)\n", + " * Find where the hypothesis diverges.\n", + " * Update the current position in the generation, as well as the number of forward pass calls." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MjF97B3gqKk_", + "outputId": "1988ae5a-29eb-40a8-9ae1-b54ca577d0f7" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The 'max_batch_size' argument of StaticCache is deprecated and will be removed in v4.46. Use the more precisely named 'batch_size' argument instead.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HIT: ' is'->' a'\n", + "HIT: ' region'->' of the'\n", + "HIT: ' United'->' States'\n", + "HIT: ' home'->' to'\n", + "HIT: ' number'->' of'\n", + "HIT: ' some'->' of the'\n", + "HIT: ' in'->' the'\n", + "HIT: ' is'->' a'\n", + "HIT: ' destination'->' for'\n", + "HIT: ' interested'->' in'\n", + "HIT: ' home'->' to the'\n", + "Tokens per forward pass: 1.1627906976744187\n", + "\n", + "<|begin_of_text|>The Pacific Northwest is a region of the United States that is known for its natural beauty, diverse landscapes, and rich cultural heritage. The region is home to a number of unique and interesting places that are worth visiting. In this article, we will explore some of the best places to visit in the Pacific Northwest.\n", + "1. Mount Rainier National Park: Located in Washington state, Mount Rainier National Park is a must-visit destination for anyone interested in natural beauty. The park is home to the tallest mountain in\n" + ] + } + ], + "source": [ + "from transformers import StaticCache\n", + "\n", + "\n", + "def truncate_past_key_values(past_key_values, position):\n", + " \"\"\"\n", + " Truncates the past key and value caches at a specific position. `transformers`-specific stuff. Might change with their next update.\n", + "\n", + " Args:\n", + " past_key_values (object): Object containing key and value caches for each layer.\n", + " position (int): Position from which to truncate the caches.\n", + " \"\"\"\n", + " for layer_idx in range(len(past_key_values.key_cache)):\n", + " past_key_values.key_cache[layer_idx][:,:,position - 1:] = 0.0\n", + " past_key_values.value_cache[layer_idx][:,:,position - 1:] = 0.0\n", + "\n", + "\n", + "def generate_speculative(model, tokenizer, num_tokens_to_generate: int, speculate_fn: callable, max_speculated_len: int=128, initial_prompt: str=\"The Pacific\", verbose: bool=False):\n", + " \"\"\"\n", + " Generates text using speculative decoding, a technique that combines conventional forward passes with speculative predictions to reduce computation by hypothesizing multiple tokens at each step.\n", + "\n", + " Args:\n", + " model (transformers.PreTrainedModel): The language model used to generate text.\n", + " tokenizer (transformers.PreTrainedTokenizer): Tokenizer corresponding to the model for encoding and decoding text.\n", + " num_tokens_to_generate (int): The number of tokens to generate in total.\n", + " speculate_fn (callable): A function that generates speculative tokens based on the input IDs and current position.\n", + " This function takes `input_ids` and `position` as arguments and returns the number of tokens speculated.\n", + " max_speculated_len (int, optional): The maximum length of speculative tokens allowed in a single step. Defaults to 128.\n", + " initial_prompt (str, optional): The starting prompt for text generation. Defaults to \"The Pacific\".\n", + " verbose (bool, optional): If True, prints debugging information about successful speculative predictions. Defaults to False.\n", + "\n", + " Returns:\n", + " torch.Tensor: The generated sequence of input IDs up to the generated position.\n", + " int: The number of forward passes required for generation, which can be used to assess efficiency.\n", + "\n", + " Example:\n", + " >>> output, forward_passes = generate_speculative(\n", + " ... model,\n", + " ... tokenizer,\n", + " ... num_tokens_to_generate=100,\n", + " ... speculate_fn=lambda input_ids, position: fill_hypothesis(input_ids, position, n=2, next_tokens_array=next_tokens_array),\n", + " ... verbose=True\n", + " ... )\n", + " >>> print(\"Generated Text:\", tokenizer.decode(output))\n", + " >>> print(\"Tokens per forward pass:\", 100 / forward_passes)\n", + "\n", + " Notes:\n", + " - Speculative decoding reduces the number of model passes by hypothesizing tokens using the `speculate_fn` function. The actual model output is used to verify these hypotheses, allowing efficient token generation.\n", + " - If verbose mode is enabled, it will display speculative matches as they occur.\n", + " \"\"\"\n", + "\n", + " # Encode the initial prompt and get input IDs, then move them to the GPU\n", + " prompt_ids = tokenizer(initial_prompt, return_tensors=\"pt\", truncation=True).input_ids.to(\"cuda\")\n", + " position = prompt_ids.shape[1] # Initial position for the prompt\n", + "\n", + " # Calculate the maximum cache length to accommodate generated tokens and speculative tokens\n", + " max_cache_len = num_tokens_to_generate + position + max_speculated_len\n", + " # Initialize the cache for past key values with model configuration, setting cache size and device\n", + " past_key_values = StaticCache(config=model.config, max_batch_size=1, max_cache_len=max_cache_len, device=\"cuda\", dtype=torch.float16)\n", + " # Allocate space for input IDs and cache positions on the GPU\n", + " input_ids = torch.zeros((1, max_cache_len), device=\"cuda\", dtype=torch.long)\n", + " # Fill in the initial prompt\n", + " input_ids[0, :position] = prompt_ids[0, :position]\n", + "\n", + " with torch.no_grad(): # Disable gradients for inference\n", + " # Pre-fill cache with the prompt to start the model's internal state\n", + " past_key_values = model(input_ids[:, :position], past_key_values=past_key_values).past_key_values\n", + "\n", + " forward_passes = 0 # Track number of forward passes\n", + " tokens_generated = 0 # Track number of tokens generated\n", + " while tokens_generated < num_tokens_to_generate:\n", + " # Adjust cache by removing outdated values to avoid memory overflow\n", + " truncate_past_key_values(past_key_values, position)\n", + "\n", + " # Speculate the next few tokens based on current position\n", + " num_tokens_speculated = speculate_fn(input_ids, position) # Number of tokens hypothesized\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " output = model(\n", + " ...\n", + " )\n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + " past_key_values = output.past_key_values # Update the cache with new predictions\n", + "\n", + " # Extract predicted tokens and the speculative tokens for comparison\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " real_tokens = \n", + " hypothesis_tokens = \n", + "\n", + " match_len = \n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + "\n", + " # Optionally, print successful speculative predictions\n", + " if verbose and match_len > 0:\n", + " print(\n", + " f\"HIT: '{tokenizer.decode(input_ids...)}'->'{tokenizer.decode(real_tokens...)}'\"\n", + " )\n", + "\n", + " # Copy matched tokens to the input IDs array\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " input_ids... = \n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + "\n", + " # Update the position, tokens generated count, and forward passes\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " position += \n", + " forward_passes += \n", + " tokens_generated += \n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + " return input_ids[0, :position], forward_passes\n", + "\n", + "NUM_TOKENS_TO_GENERATE = 100\n", + "\n", + "# Call the function with parameters and lambda function for speculative decoding\n", + "output, forward_passes = generate_speculative(\n", + " model,\n", + " tokenizer,\n", + " NUM_TOKENS_TO_GENERATE,\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " speculate_fn=,\n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + " verbose=True)\n", + "\n", + "# Display metrics and the decoded output\n", + "print(f\"Tokens per forward pass: {NUM_TOKENS_TO_GENERATE / forward_passes}\\n\")\n", + "print(tokenizer.decode(output))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vSbqLx7lqKlA", + "outputId": "98cfd690-e41b-4ac1-dce3-fe384b43981f" + }, + "outputs": [], + "source": [ + "output0, n0 = generate_speculative(model, tokenizer, NUM_TOKENS_TO_GENERATE, speculate_fn=lambda input_ids, position: speculate_bigram(input_ids, position, 0, NEXT_TOKEN_WIKI2), verbose=False)\n", + "output1, n1 = generate_speculative(model, tokenizer, NUM_TOKENS_TO_GENERATE, speculate_fn=lambda input_ids, position: speculate_bigram(input_ids, position, 1, NEXT_TOKEN_WIKI2), verbose=False)\n", + "output2, n2 = generate_speculative(model, tokenizer, NUM_TOKENS_TO_GENERATE, speculate_fn=lambda input_ids, position: speculate_bigram(input_ids, position, 2, NEXT_TOKEN_WIKI2), verbose=False)\n", + "output4, n4 = generate_speculative(model, tokenizer, NUM_TOKENS_TO_GENERATE, speculate_fn=lambda input_ids, position: speculate_bigram(input_ids, position, 4, NEXT_TOKEN_WIKI2), verbose=False)\n", + "\n", + "try:\n", + " assert n0 == NUM_TOKENS_TO_GENERATE, \"Model that doesnt' speculate does exatcly one forward pass per token\"\n", + " assert n4 <= n2 and n2 <= n1 and n1 < n0, \"It's very unlikely that the performance decreases with stronger speculation\"\n", + " assert (output1 == output0).all(), \"The outputs don't match\"\n", + " print(\"All tests passed\")\n", + "except AssertionError as e:\n", + " print(\"Error occured. Generated texts:\\n\")\n", + " print(*tokenizer.batch_decode([output0, output1, output2, output4]), sep=\"\\n\", end=\"\\n\\n\")\n", + " raise e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b0TPKWJaqKlA" + }, + "source": [ + "As we have shown, we can increase the hypotheses length up to `16` at almost no inference cost.\n", + "\n", + "Let's see how much it actually helps:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vIplTHc7qKlA", + "outputId": "fba7223f-f8c0-4af4-dbdc-d629e211363e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Speculate length 0: 1.00 tokens per forward pass\n", + "Speculate length 1: 1.14 tokens per forward pass\n", + "Speculate length 2: 1.16 tokens per forward pass\n", + "Speculate length 3: 1.16 tokens per forward pass\n", + "Speculate length 4: 1.16 tokens per forward pass\n", + "Speculate length 5: 1.16 tokens per forward pass\n", + "Speculate length 6: 1.16 tokens per forward pass\n" + ] + } + ], + "source": [ + "for speculate_length in [0, 1, 2, 3, 4, 5, 6]:\n", + " _, num_passes = generate_speculative(model, tokenizer, NUM_TOKENS_TO_GENERATE, speculate_fn=lambda input_ids, position: speculate_bigram(input_ids, position, speculate_length, NEXT_TOKEN_WIKI2), verbose=False)\n", + " print(f\"Speculate length {speculate_length}: {NUM_TOKENS_TO_GENERATE / num_passes:.2f} tokens per forward pass\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T48EpfDmqKlA" + }, + "source": [ + "It appears the bigram model doesn't improve above `2`. That is because it's very unlikely that a bigram model outputs a long meaningful sequence.\n", + "\n", + "We need a stronger model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7eupNMG2qKlA" + }, + "source": [ + "SEMINAR ENDS HERE HOPEFULLY" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RS4KLXTwqKlA" + }, + "source": [ + "## Stronger Model\n", + "\n", + "**Task (2.0+ points)** Use a stronger model for speculative decoding:\n", + "\n", + "The `generate_speculative` function can take any callable inplace of `speculate_fn`. You have many options as what can be used here:\n", + " * **Larger N-gram models**: use larger n and more data to fit it.\n", + " * **A tiny Llama model**: the problem with using an LLM as a speculative generator is that it has to share tokenizer with the verifier model. That is, your tiny llama has to have the same tokenizer.\n", + " * Make sure to properly store and reuse `past_key_values` for it.\n", + " * Verify that the model forward pass is really negligible in terms of hypothesis generation time (time verify >> time speculate)\n", + " * **An LSTM**: Train it from scratch. You might initialize the embedding layer with the llama embeddings for easier convergence.\n", + " * **word2vec/CBOW**: Train it on the `train_batch` or anything else you want. Make sure that it only uses past tokens and that the context is longer than what you want to speculate.\n", + " * **Wide Speculation**: Your hypothesis doesn't have to be linear. You can actually speculate a tree of possible next tokens and verify it with a single pass. That requires some nontrivial `attention_mask` manipulations, however. Read more [here](https://arxiv.org/abs/2305.09781) and [here](https://huggingface.co/blog/poedator/4d-masks).\n", + "\n", + "*The amount of points to be received here depends on how complex your solution is and what token acceptance rate per forward pass of the verifier it achieves. Achieving accepting 2+ tokens per forward pass on average guarantees at leas 2 points.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4HHjWNT9qKlB" + }, + "outputs": [], + "source": [ + "# A LOT OF YOUR CODE HERE\n", + "\n", + "super_duper_speculator =" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U5MZONgCqKlB" + }, + "outputs": [], + "source": [ + "NUM_TOKENS_TO_GENERATE = 100\n", + "\n", + "# Call the function with parameters and lambda function for speculative decoding\n", + "output, forward_passes = generate_speculative(\n", + " model,\n", + " tokenizer,\n", + " NUM_TOKENS_TO_GENERATE,\n", + " # YOUR CODE HERE>>>>>>>>>\n", + " speculate_fn=super_duper_speculator,\n", + " # <<<<<<<<<<<<<<<<<<<<<<<\n", + " verbose=True)\n", + "\n", + "# Display metrics and the decoded output\n", + "print(f\"Tokens per forward pass: {NUM_TOKENS_TO_GENERATE / forward_passes}\\n\")\n", + "print(tokenizer.decode(output))\n", + "\n", + "if NUM_TOKENS_TO_GENERATE / forward_passes >= 2.0:\n", + " print(\"Great Success!\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0094622aafa441d7a8f0f0e9b52bcc15": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + 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