diff --git a/README.md b/README.md index 6223031..60ffc62 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ A multi-part seminar series on Large Language Models (LLMs). ![Session 1](images/home_page/Large%20Language%20Models.png) -

Large Language Models Full Topic List

+

Click here for Large Language Models Full Topic List

## ✨ [Emergence, Fundamentals and Landscape of LLMs](session_1) @@ -34,14 +34,12 @@ Explore diverse applications of Large Language Models (LLMs) and the frameworks Coming soon... -## ✨ Training and Evaluating LLMs On Custom Datasets +## ✨ [Training and Evaluating LLMs On Custom Datasets](session_4) Delve into the intricacies of training and evaluating Large Language Models (LLMs) on your custom datasets. Gain insights into optimizing performance, fine-tuning, and assessing model effectiveness tailored to your specific data. ![Session 4](images/home_page/Session%204.png) -Coming soon... - ## ✨ Optimizing LLMs For Inference and Deployment Techniques Learn techniques to optimize Large Language Models (LLMs) for efficient inference. Explore strategies for seamless deployment, ensuring optimal performance in real-world applications. @@ -50,7 +48,7 @@ Learn techniques to optimize Large Language Models (LLMs) for efficient inferenc Coming soon... -## ✨ Open Challanges With LLMs +## ✨ Open Challenges With LLMs Delve into the dichotomy of small vs large LLMs, navigating production challenges, addressing research hurdles, and understanding the perils associated with the utilization of pretrained LLMs. Explore the evolving landscape of challenges within the realm of Large Language Models. diff --git a/images/session_1/part_3_landscape_of_llms/Large Language Models-challanges.png b/images/session_1/part_3_landscape_of_llms/Large Language Models-challenges.png similarity index 100% rename from images/session_1/part_3_landscape_of_llms/Large Language Models-challanges.png rename to images/session_1/part_3_landscape_of_llms/Large Language Models-challenges.png diff --git a/images/site/infocusp_logo_blue.png b/images/site/infocusp_logo_blue.png new file mode 100644 index 0000000..121623f Binary files /dev/null and b/images/site/infocusp_logo_blue.png differ diff --git a/images/site/infocusp_logo_blue.svg b/images/site/infocusp_logo_blue.svg deleted file mode 100644 index 39cc1bc..0000000 --- a/images/site/infocusp_logo_blue.svg +++ /dev/null @@ -1,5 +0,0 @@ - - - --> - - diff --git a/mkdocs.yaml b/mkdocs.yaml index 886ddaf..290715d 100644 --- a/mkdocs.yaml +++ b/mkdocs.yaml @@ -5,7 +5,7 @@ docs_dir: . site_dir: ../site theme: name: material - # logo: assets/icons8-code-64.png + logo: images/site/infocusp_logo_blue.png palette: primary: white features: @@ -22,6 +22,7 @@ theme: markdown_extensions: - toc: permalink: true + toc_depth: 3 - pymdownx.highlight: anchor_linenums: true line_spans: __span @@ -41,6 +42,8 @@ extra_css: extra: generator: false social: + - icon: fontawesome/solid/globe + link: https://infocusp.com - icon: fontawesome/brands/linkedin link: https://in.linkedin.com/company/infocusp - icon: fontawesome/brands/github @@ -51,4 +54,6 @@ extra: link: https://twitter.com/_infocusp plugins: - search - - same-dir \ No newline at end of file + - same-dir + - mkdocs-jupyter: + ignore_h1_titles: True \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 8be8bd5..df62450 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ mkdocs>=1.2.2 mkdocs-material>=7.1.11 mkdocs-static-i18n>=0.18 -mkdocs-same-dir>=0.1.3 \ No newline at end of file +mkdocs-same-dir>=0.1.3 +mkdocs-jupyter>=0.16.1 \ No newline at end of file diff --git a/session_1/README.md b/session_1/README.md index eeb5e1c..484b5ba 100644 --- a/session_1/README.md +++ b/session_1/README.md @@ -6,6 +6,7 @@ Covers important building blocks of what we call an LLM today, where they came from, etc. and then we'll dive into the deep universe that has sprung to life around these LLMs. This session is aimed to help: + * People who are new to LLMs * People who have just started working on them * People who are working on different use cases surrounding LLMs and need a roadmap. diff --git a/session_1/part_1_emergence_of_llms/README.md b/session_1/part_1_emergence_of_llms/README.md index f79c38a..6dbdf48 100644 --- a/session_1/part_1_emergence_of_llms/README.md +++ b/session_1/part_1_emergence_of_llms/README.md @@ -1,5 +1,8 @@ # The Emergence of LLMs +**Author:** [Hetul Patel](https://in.linkedin.com/in/hetul-v-patel) | **Published on**: 6 Dec, 2023 +
+ ```mermaid timeline diff --git a/session_1/part_3_landscape_of_llms/README.md b/session_1/part_3_landscape_of_llms/README.md index 23b2e28..92ea5db 100644 --- a/session_1/part_3_landscape_of_llms/README.md +++ b/session_1/part_3_landscape_of_llms/README.md @@ -1,5 +1,8 @@ # Landscape of LLMs +**Author:** [Hetul Patel](https://in.linkedin.com/in/hetul-v-patel) | **Published on**: 6 Dec, 2023 +
+ ![LLM Landscape](./../../images/session_1/part_3_landscape_of_llms/Large%20Language%20Models-Main.png) @@ -465,9 +468,9 @@ -## Challanges with LLMs +## Challenges with LLMs -![Challanges with LLMs](./../../images/session_1/part_3_landscape_of_llms/Large%20Language%20Models-challanges.png) +![Challenges with LLMs](./../../images/session_1/part_3_landscape_of_llms/Large%20Language%20Models-challenges.png)
diff --git a/session_4/README.md b/session_4/README.md new file mode 100644 index 0000000..6421874 --- /dev/null +++ b/session_4/README.md @@ -0,0 +1,34 @@ +# Session 4 - Training and Evaluating LLMs On Custom Datasets + +

Session 4

+ +This session aims to equip you with the knowledge to train Large Language Models (LLMs) by exploring techniques like unsupervised pretraining and supervised fine-tuning with various preference optimization methods. It will also cover efficient fine-tuning techniques, retrieval-based approaches, and language agent fine-tuning. Additionally, the session will discuss LLM training frameworks and delve into evaluation methods for LLMs, including evaluation-driven development and using LLMs for evaluation itself. + +This session is aimed to help: + +* People who are already familiar basics of LLMs and Transformers +* People who already knows how to use pre-trained LLMs prompt engineering and RAG +* People who want train or finetune their own LLMs on custom data. +* People who want to lear how to evaluate LLMs + +## Outline + +### Part 1: Training Foundational LLMs + +Coming soon... + +### Part 2: [Finetuning LMs To Human Preferences](part_2_finetuning_lms_to_human_preferences/RLHF.ipynb) + +#### Details + +* Date: 14 March, 2024 +* Speaker: [Abhor Gupta](https://in.linkedin.com/in/abhor-gupta-565386145) +* Location: [Infocusp Innovations LLP](https://www.infocusp.com/) + +#### Material + +* Recording: TODO + +### Part 3: LLM Training Frameworks + +Coming soon... diff --git a/session_4/part_2_finetuning_lms_to_human_preferences/RLHF.ipynb b/session_4/part_2_finetuning_lms_to_human_preferences/RLHF.ipynb new file mode 100644 index 0000000..ce5dd1e --- /dev/null +++ b/session_4/part_2_finetuning_lms_to_human_preferences/RLHF.ipynb @@ -0,0 +1,1769 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "321adccc", + "metadata": { + "id": "321adccc" + }, + "source": [ + "# RLHF - An Independent Illustration\n", + "\n", + "**Author:** [Abhor Gupta](https://in.linkedin.com/in/abhor-gupta-565386145) | **Published on**: 14 March, 2024 (😊 π day)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b5085966", + "metadata": { + "id": "b5085966" + }, + "source": [ + "Reinforcement learning from human feedback (RLHF) is a transformative technique that enables us to fine-tune large language models (LLMs) or transformer-based models for improved alignment with our intended goals. This approach goes beyond the standard techniques that train LLMs on massive volumes of text data. RLHF uses human feedback to teach LLMs how to better adhere to our preferences and values." + ] + }, + { + "cell_type": "markdown", + "id": "119f232e", + "metadata": { + "id": "119f232e" + }, + "source": [ + "There are several very well written blogs on the topic - [here](https://medium.com/towards-generative-ai/reward-model-training-2209d1befb5f), [here](https://medium.com/@madhur.prashant7/rlhf-reward-model-ppo-on-llms-dfc92ec3885f) and [here](https://huggingface.co/blog/rlhf). I am especially fond of the one written by Chip Huyen [here](https://huyenchip.com/2023/05/02/rlhf.html). **The intention behind writing this is to understand RLHF using a simple and _mostly_ self-contained implementation to solve a demonstrative problem.** Let it be sufficiently trivial that we may open up our model and visually observe some effects of RLHF using different techniques." + ] + }, + { + "cell_type": "markdown", + "id": "01c86337", + "metadata": { + "id": "01c86337" + }, + "source": [ + "## Overview" + ] + }, + { + "cell_type": "markdown", + "id": "c9b868df", + "metadata": { + "id": "c9b868df" + }, + "source": [ + "Let us first go over some basics." + ] + }, + { + "cell_type": "markdown", + "id": "514c9112", + "metadata": { + "id": "514c9112" + }, + "source": [ + "### Training LLMs" + ] + }, + { + "cell_type": "markdown", + "id": "ab3275a8", + "metadata": { + "id": "ab3275a8" + }, + "source": [ + "Can't help but love this _beautiful_ depiction of RLHF among the broader spectrum of an LLM's training, by twitter.com/anthrupad. \n", + "\n", + "![](https://huyenchip.com/assets/pics/rlhf/2-shoggoth.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "5fffa9f4", + "metadata": { + "id": "5fffa9f4" + }, + "source": [ + "The image above shows the different methods of training an LLM, their \"size\" in terms of the space of outputs they represent and their \"quality\" in terms of their social acceptance to humans:" + ] + }, + { + "cell_type": "markdown", + "id": "e728ca93", + "metadata": { + "id": "e728ca93" + }, + "source": [ + "1. **Unsupervised Learning**: _Train an LLM on a massive corpus of raw text; this teaches the LLM a language - the fundamentals of its structure, grammer and relationship between words._ In terms of its objective, the LLM is trained to predict the next word in a context. \n", + "But! Though an LLM may know a language, it doesn't necessarily know how to converse. In its space of outputs, it is aware of what it _can_ do, but not necessarily what it _should_ do. It is like the Shoggoth, massive but ugly. \n", + "2. **Supervised Fine-tuning**: _Tailor the LLM to specific tasks like translation, question-answering, or summarization._ Here, the LLM is trained on a set of input-output pairs demonstrating the task. \n", + "Following the example from the point above, this is akin to teaching the LLM how to converse. Its output space here is refined to answer in specific ways, perhaps with domain specific know-how, or in accordance to a particular task. This is like the deformed human face, you'll accept it but it's not necessarily very pleasing.\n", + "3. **RLHF**: _Refine the LLM's output to better align with human values, preferences, and safety guidelines._ The training here involves giving feedback signals on the LLM's output to guide it to some desired behavior. \n", + "Following the same context from (1) and (2), after it has learnt language and knows how to converse, it learns to adhere to the social norms. Within its output space, this is the refinement that narrows down the conversation ability of the LLM to answer in a way that pleases its general reader - ethical speech, truthful statements, intelligent use of vocabulary etc. It is the smiley face that you want to talk to. :)" + ] + }, + { + "cell_type": "markdown", + "id": "f50c1d47", + "metadata": { + "id": "f50c1d47" + }, + "source": [ + "For the scope of this notebook, we will only be exploring RLHF. Supervised training will be a part - though it is more a requirement for the sake of thoroughness, than an intented guide on the topic. Therefore, I'll be using a very simple supervised pretraining that is closer to the description of supervised fine-tuning above, than unsupervised learning." + ] + }, + { + "cell_type": "markdown", + "id": "4d8b0884", + "metadata": { + "id": "4d8b0884" + }, + "source": [ + "### RLHF components" + ] + }, + { + "cell_type": "markdown", + "id": "de807a3d", + "metadata": { + "id": "de807a3d" + }, + "source": [ + "A complete RLHF pipeline requires the following components:" + ] + }, + { + "cell_type": "markdown", + "id": "f5a40a2d", + "metadata": { + "id": "f5a40a2d" + }, + "source": [ + "1. **A pre-trained base model**: We begin with a pre-trained LLM. This is a powerful language model that has already learned the intricacies of language structure from vast amounts of text data. This may be followed by supervised fine-tuning to attune the LLM to a specific task like question-answering or summarization.\n", + "2. **Training a reward model from human feedback**: We then create a \"reward model\" specifically designed to capture human preferences. This involves gathering human feedback on various LLM outputs, where people rate the responses based on their desired qualities like helpfulness, safety, and adherence to instructions. By analyzing this feedback, the reward model learns to assign scores to future LLM responses, essentially mimicking human judgment.\n", + "3. **Fine tuning using Reinforcement Learning**: Finally, we put the LLM and the reward model to work together. When presented with a prompt, the LLM generates multiple potential responses. Each response is then analyzed by the reward model, which assigns a score based on its learned understanding of human preferences. Finally, a reinforcement learning algorithm like PPO uses these scores to fine-tune the LLM's internal parameters. Responses that received higher scores become more likely to be generated in the future, while those with lower scores are gradually downplayed. This iterative process progressively aligns the LLM's outputs with human expectations and values." + ] + }, + { + "cell_type": "markdown", + "id": "549a5801", + "metadata": { + "id": "549a5801" + }, + "source": [ + "This pipeline effectively utilizes human feedback to bridge the gap between raw LLM capabilities and human-desired outcomes. It allows us to shape these powerful language models into not just masters of language, but also responsible and valuable tools aligned with our needs." + ] + }, + { + "cell_type": "markdown", + "id": "9709c5d5", + "metadata": { + "id": "9709c5d5" + }, + "source": [ + "### Applications of RLHF" + ] + }, + { + "cell_type": "markdown", + "id": "d0881a0b", + "metadata": { + "id": "d0881a0b" + }, + "source": [ + "The most prevelant example of RLHF being applied in AI is for text generation to align chatbots with human preferences ([InstructGPT](https://openai.com/research/instruction-following), [ChatGPT](https://openai.com/blog/chatgpt), [Gemini](https://blog.google/technology/ai/google-gemini-ai/) are famous examples). Similarly, RLHF has seen application in image generation as well ([ImageReward](https://arxiv.org/abs/2304.05977), [DPOK](https://arxiv.org/abs/2305.16381)). Though limited, some research groups have also explored its application in games ([DeepMind](https://deepmind.google/discover/blog/learning-through-human-feedback/) and [OpenAI](https://openai.com/research/learning-from-human-preferences)).\n", + "\n", + "Even though currently the applications of RLHF in AI are limited, the scope for RLHF is much wider.\n", + "\n", + "Do you use e-commerce websites like Amazon? Do you use Uber for requesting cabs? Do you use Google Maps for deciding which restaurant, bar or hospital to visit? You must have seen ratings for products, or people, or services, or food. You likely would have given some yourself. These are all examples of human feedback. And when these affect the product or service to comform to user satisfaction, it is also a form of RLHF.\n", + "\n", + "Take, for instance, cooking robots are a thing now ([Moley](https://www.moley.com/), [Nymble](https://www.eatwithnymble.com/)). For the food that is cooked by the robots based on some recipe, the recipe can be adjusted for duration of cooking, quantity of spices etc for user preference based on their feedback. Self-driving cars are also real now ([Waymo](https://waymo.com/), [Tesla](https://www.tesla.com/support/autopilot)). Based on customer's feedback, the ride be adjusted to be faster/slower, less jerky, smoother maneuverability." + ] + }, + { + "cell_type": "markdown", + "id": "bd574943", + "metadata": { + "id": "bd574943" + }, + "source": [ + "In the next section, we will establish a small toy problem to solve using a tiny LLM. Then we will dive into each of the RLHF concepts in detail along with some code to establish an implementational understanding as well some nice visualizations to complement our findings." + ] + }, + { + "cell_type": "markdown", + "id": "581953f1", + "metadata": { + "id": "581953f1" + }, + "source": [ + "## Problem Statement" + ] + }, + { + "cell_type": "markdown", + "id": "0f11a388", + "metadata": { + "id": "0f11a388" + }, + "source": [ + "To keep the scale of things simple, let us work with a \"language\" of numbers. Our vocabulary consist of digits 0-9 as well as a special digit 10 that separates our input and output.\n", + "\n", + "Typically for large LLMs, the language training is followed by a task specialisation training like question-answering before moving on to RLHF. To keep things simple, we avoid differentiating between language training and task specialisation and do a supervised training once to get our base model." + ] + }, + { + "cell_type": "markdown", + "id": "fee373ac", + "metadata": { + "id": "fee373ac" + }, + "source": [ + "### Language (supervised learning)" + ] + }, + { + "cell_type": "markdown", + "id": "a4b32e14", + "metadata": { + "id": "a4b32e14" + }, + "source": [ + "The structure of the language is that for the current output to be generated, one of the last four digits (in the whole sequence of input+output) is chosen and its increment modulo 10 is outputted.\n", + "\n", + "Given $a_1, a_2, ..., a_{n-1}, a_n$, $\\forall n > 4$,\n", + "$$a'_{n+1} \\sim \\{a_{n-3}, a_{n-2}, a_{n-1}, a_{n}\\}$$\n", + "$$a_{n+1} = (a'_{n+1} + 1)\\ \\%\\ 10$$" + ] + }, + { + "cell_type": "markdown", + "id": "75fc02dd", + "metadata": { + "id": "75fc02dd" + }, + "source": [ + "For example, \n", + "Input: 4, 5, 9, 1\n", + "\n", + "Generation: \n", + "4, 5, 9, 1, 10, **6** \n", + "4, 5, 9, 1, 10, 6, **2** \n", + "4, 5, 9, 1, 10, 6, 2, **7** \n", + "4, 5, 9, 1, 10, 6, 2, 7, **8**\n", + "\n", + "The underlined digits are the sampling set for the output. The digit marked in red is the digit sampled. Finally, the digit bolded at the end of the sequence is generated as the increment of the sampled digit." + ] + }, + { + "cell_type": "markdown", + "id": "cf00c517", + "metadata": { + "id": "cf00c517" + }, + "source": [ + "### Alignment (RLHF)" + ] + }, + { + "cell_type": "markdown", + "id": "605ee56b", + "metadata": { + "id": "605ee56b" + }, + "source": [ + "Now we need a \"preference\" to incorporate. Mathematically, we wish to adjust the probability of some generation rule learned during the previous step.\n", + "\n", + "For this toy example, let's change only the generation of the first output token to be the increment of the last input token.\n", + "\n", + "That is, given $a_1, a_2, ..., a_{n-1}, a_n$, $\\forall n > 4$,\n", + "$$\n", + " a_{n+1}=\n", + "\\begin{cases}\n", + " a_n + 1,& \\text{if } {a_{n+1}} = y_1\\\\\n", + " (a'_{n+1} + 1)\\ \\%\\ 10, & \\text{otherwise}\n", + "\\end{cases}\n", + "$$\n", + "\n", + "Where $y_1$ is the first output token." + ] + }, + { + "cell_type": "markdown", + "id": "3d1e59b8", + "metadata": { + "id": "3d1e59b8" + }, + "source": [ + "Considering the same example as above, \n", + "Input: 4, 5, 9, 1\n", + "\n", + "Generation: \n", + "4, 5, 9, 1, 10, **2** (notice the change in underline) \n", + "4, 5, 9, 1, 10, 2, **2** \n", + "4, 5, 9, 1, 10, 2, 2, **3\n", + "** \n", + "4, 5, 9, 1, 10, 2, 2, 3, **4**\n", + "\n", + "For the first generation step, the model will only sample from the last digit and continue the original rule thereafter. Notice that except for the first generation step, the rest of the outputs are sampled from the same positions as the earlier example, yet the entire sequence has changed. Thus, though we wish to preserve the rule for the rest of the outputs, the actual outputs are not independent of the first output. Therefore, this small change in the output of a single token can have a cascading effect and lead to very different generations.\n", + "\n", + "This is in line with the spirit of RLHF, where say, if we want to reduce the toxicity, reducing the probability of toxic words will have a cascading effect and we do not need to affect the probability of several thousands of unrelated words." + ] + }, + { + "cell_type": "markdown", + "id": "f4178080", + "metadata": { + "id": "f4178080" + }, + "source": [ + "## Code and Commentary" + ] + }, + { + "cell_type": "markdown", + "id": "151d002f", + "metadata": { + "id": "151d002f" + }, + "source": [ + "Before RLHF: _Enough talking, show me the code!_ \n", + "After RLHF: _You've explained what we're doing here well enough. We would like to move on to the implementational details._\n", + "\n", + "😉" + ] + }, + { + "cell_type": "markdown", + "id": "beca59c0", + "metadata": { + "id": "beca59c0" + }, + "source": [ + "### Supervised pre-training" + ] + }, + { + "cell_type": "markdown", + "id": "a2ba0080", + "metadata": { + "id": "a2ba0080" + }, + "source": [ + "First we learn the language using supervised learning. I'm using Karpathy's [minGPT](https://github.com/karpathy/minGPT/tree/master) for the LLM and supervised training." + ] + }, + { + "cell_type": "markdown", + "id": "338dbd44", + "metadata": { + "id": "338dbd44" + }, + "source": [ + "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/rlhf/pretraining.png)\n", + "\n", + "Source: [HuggingFace - RLHF blog](https://huggingface.co/blog/rlhf)" + ] + }, + { + "cell_type": "markdown", + "id": "08b9ae9e", + "metadata": { + "id": "08b9ae9e" + }, + "source": [ + "_Imports --_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "991285e6", + "metadata": { + "id": "991285e6" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "from torch.utils.data.dataloader import DataLoader\n", + "from mingpt.utils import set_seed\n", + "import numpy as np\n", + "set_seed(3407)\n", + "\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'" + ] + }, + { + "cell_type": "markdown", + "id": "d6bde156", + "metadata": { + "id": "d6bde156" + }, + "source": [ + "_Hyperparams for size of vocab and length of input --_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "125ad91b", + "metadata": { + "id": "125ad91b" + }, + "outputs": [], + "source": [ + "VOCAB_SIZE = 10\n", + "INPUT_SIZE = 4" + ] + }, + { + "cell_type": "markdown", + "id": "af08cdc7", + "metadata": { + "id": "af08cdc7" + }, + "source": [ + "_Class for generating training pairs for supervised language learning --_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bab82cc", + "metadata": { + "id": "2bab82cc" + }, + "outputs": [], + "source": [ + "class SupervisedDataset(Dataset):\n", + " \"\"\"\n", + " Problem: Look at last 4 digits and sample one of them to output its increment.\n", + "\n", + " Input: 3 8 1 4\n", + " Possible ouputs: 2 2 3 5 || 5 9 6 0 || 2 9 5 3 etc\n", + "\n", + " Which will feed into the transformer concatenated as:\n", + " input: 3 8 1 4 S 2 2 3\n", + " output: I I I I 2 2 3 5\n", + " where I is \"ignore\", and S is the separation token\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " self.EOS = VOCAB_SIZE\n", + "\n", + " def __len__(self):\n", + " return 10000 # ...\n", + "\n", + " def get_vocab_size(self):\n", + " return VOCAB_SIZE+1 # normal vocab + serparation token\n", + "\n", + " def __getitem__(self, idx):\n", + " inputs = torch.randint(VOCAB_SIZE, size=(INPUT_SIZE,), dtype=torch.long)\n", + " ouptputs = []\n", + "\n", + " # Create input output pairs\n", + " inp = np.random.randint(VOCAB_SIZE, size=(INPUT_SIZE,)).tolist()\n", + " sol = []\n", + " for i in range(INPUT_SIZE):\n", + " sol.append((np.random.choice(inp[i:] + sol) + 1)%10)\n", + "\n", + " # concatenate the problem specification and the solution\n", + " cat = torch.Tensor(inp + [self.EOS] + sol).long()\n", + "\n", + " # the inputs to the transformer will be the offset sequence\n", + " x = cat[:-1].clone()\n", + " y = cat[1:].clone()\n", + "\n", + " # we only want to predict at output locations, mask out the loss at the input locations\n", + " y[:INPUT_SIZE] = -1\n", + " return x, y" + ] + }, + { + "cell_type": "markdown", + "id": "a7f95daa", + "metadata": { + "id": "a7f95daa" + }, + "source": [ + "_Looking at one sample --_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca079295", + "metadata": { + "id": "ca079295", + "outputId": "2a004e27-fddb-4423-bf6c-bddabcfc91a2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([ 4, 3, 7, 6, 10, 8, 4, 8]),\n", + " tensor([-1, -1, -1, -1, 8, 4, 8, 5]))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Supervised dataset\n", + "st_dataset = SupervisedDataset()\n", + "st_dataset[0]" + ] + }, + { + "cell_type": "markdown", + "id": "0b27620d", + "metadata": { + "id": "0b27620d" + }, + "source": [ + "_Create a GPT instance --_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6e0d707", + "metadata": { + "id": "a6e0d707" + }, + "outputs": [], + "source": [ + "from mingpt.model import GPT\n", + "\n", + "def get_model(block_size, vocab_size, output_size=None):\n", + " '''\n", + " block_size = length of input\n", + " vocab_size = digits allowed\n", + " output_size = length of output\n", + " '''\n", + " if output_size is None:\n", + " output_size = vocab_size\n", + " model_config = GPT.get_default_config()\n", + " model_config.model_type = 'gpt-nano'\n", + " model_config.vocab_size = vocab_size\n", + " model_config.block_size = block_size\n", + " model_config.output_size = output_size\n", + " model = GPT(model_config)\n", + " return model\n", + "\n", + "st_model = get_model(INPUT_SIZE*2, st_dataset.get_vocab_size())" + ] + }, + { + "cell_type": "markdown", + "id": "aa9a1014", + "metadata": { + "id": "aa9a1014" + }, + "source": [ + "Set up training --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8e8906b", + "metadata": { + "id": "d8e8906b" + }, + "outputs": [], + "source": [ + "# create a Trainer object\n", + "from mingpt.trainer import Trainer\n", + "\n", + "train_config = Trainer.get_default_config()\n", + "train_config.learning_rate = 5e-4 # the model we're using is so small that we can go a bit faster\n", + "train_config.max_iters = 5000\n", + "train_config.num_workers = 0\n", + "trainer = Trainer(train_config, st_model, st_dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "33ace95c", + "metadata": { + "id": "33ace95c" + }, + "source": [ + "Training --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11318c89", + "metadata": { + "id": "11318c89" + }, + "outputs": [], + "source": [ + "def batch_end_callback(trainer):\n", + " if trainer.iter_num % 100 == 0:\n", + " print(f\"iter_dt {trainer.iter_dt * 1000:.2f}ms; iter {trainer.iter_num}: train loss {trainer.loss.item():.5f}\")\n", + "trainer.set_callback('on_batch_end', batch_end_callback)\n", + "\n", + "trainer.run()" + ] + }, + { + "cell_type": "markdown", + "id": "d5effd22", + "metadata": { + "id": "d5effd22" + }, + "source": [ + "Loss stabilizes around 1.25. It cannot go lower because we don't have fixed outputs. We are trying to have a probability distribution such that multiple outputs have equal probability of being sampled." + ] + }, + { + "cell_type": "markdown", + "id": "10fb240f", + "metadata": { + "id": "10fb240f" + }, + "source": [ + "Now let us give the model a random input and see what the model has learned to generate as the next token --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb947447", + "metadata": { + "id": "fb947447", + "outputId": "e525cf85-41c7-4f56-9f56-47a5e3bcc7c4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input: tensor([[ 0, 2, 8, 6, 10]])\n", + "Possible outputs: tensor([1, 3, 7, 9])\n" + ] + } + ], + "source": [ + "x, _ = st_dataset[0]\n", + "x = x[:INPUT_SIZE+1].reshape(1, -1)\n", + "print(\"Input:\", x)\n", + "print(\"Possible outputs:\", torch.arange(11)[torch.nn.Softmax(dim=-1)(st_model(x)[0])[0, -1] > 0.1])" + ] + }, + { + "cell_type": "markdown", + "id": "beb3aaa1", + "metadata": { + "id": "beb3aaa1" + }, + "source": [ + "Works like a charm!" + ] + }, + { + "cell_type": "markdown", + "id": "b76344b3", + "metadata": { + "id": "b76344b3" + }, + "source": [ + "Let's save the model too. We'll need to load it later before we start the RL training --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53b8edb4", + "metadata": { + "id": "53b8edb4" + }, + "outputs": [], + "source": [ + "# Save model weights\n", + "torch.save(st_model.state_dict(), \"models/minimal_RLHF_basic_supervised.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "0626b1f5", + "metadata": { + "id": "0626b1f5" + }, + "source": [ + "### Training a reward model" + ] + }, + { + "cell_type": "markdown", + "id": "ba50428c", + "metadata": { + "id": "ba50428c" + }, + "source": [ + "Now we will train a reward model.\n", + "\n", + "The data required to train the reward model is collected as preferences in the format: \n", + "\\, \\, \\ \n", + "\n", + "The accepted and rejected responses are simply two difference generations by the supervised training model with human labels marking their preference among the two." + ] + }, + { + "cell_type": "markdown", + "id": "48414a18", + "metadata": { + "id": "48414a18" + }, + "source": [ + "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/rlhf/reward-model.png)\n", + "\n", + "Source: [HuggingFace - RLHF blog](https://huggingface.co/blog/rlhf)" + ] + }, + { + "cell_type": "markdown", + "id": "a6d93d11", + "metadata": { + "id": "a6d93d11" + }, + "source": [ + "I don't have the money to hire humans to do this labeling and neither the time myself to do it. :) \n", + "So here's a dataset class that'll generate the required data for us--" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f9ea8dc", + "metadata": { + "id": "9f9ea8dc" + }, + "outputs": [], + "source": [ + "class PreferenceDataset(Dataset):\n", + " \"\"\"\n", + " Same as MyDataset, except this has output as where x and y are input output from MyDataset. y' is the output that is sampled\n", + " from preferred distribution - and is preferred over y.\n", + " \"\"\"\n", + " def __init__(self, dataset):\n", + " self.dataset = dataset\n", + "\n", + " def __len__(self):\n", + " return len(self.dataset)\n", + "\n", + " def get_vocab_size(self):\n", + " return self.dataset.get_vocab_size()\n", + "\n", + " def __getitem__(self, idx):\n", + " x, y = self.dataset[idx]\n", + "\n", + " _x = x[:INPUT_SIZE+1]\n", + " _y_reject = torch.concat([y[-INPUT_SIZE:], torch.Tensor([11]).long()])\n", + " _y_accept = _y_reject.clone()\n", + "\n", + " # Replace first element with increment of last digit in input\n", + " _y_accept[0] = (_x[INPUT_SIZE-1] + 1) % 10\n", + " if _y_accept[0] == _y_reject[0]:\n", + " _y_reject[0] = (_y_accept[0] - np.random.randint(1, 10)) % 10\n", + "\n", + " return _x, _y_accept, _y_reject\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "8994ee3f", + "metadata": { + "id": "8994ee3f" + }, + "source": [ + "Let's look at one datapoint in this dataset --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5b1f23a", + "metadata": { + "id": "b5b1f23a", + "outputId": "04361ab2-3c42-4e47-945f-65834791ced0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([ 8, 0, 3, 4, 10]),\n", + " tensor([ 5, 4, 5, 5, 11]),\n", + " tensor([ 1, 4, 5, 5, 11]))" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pf_dataset = PreferenceDataset(st_dataset)\n", + "pf_dataset[0]" + ] + }, + { + "cell_type": "markdown", + "id": "a1bdf280", + "metadata": { + "id": "a1bdf280" + }, + "source": [ + "The first tensor is the \\, the second is the \\ and the last is the \\. Notice the \\ and \\ only differ in their first digits. Unlike a usual RLHF pipeline where the pretrained model would be used to generate the outputs to be ranked for preference, here we artifically generate the data to look like this for our convenience." + ] + }, + { + "cell_type": "markdown", + "id": "5338f3c2", + "metadata": { + "id": "5338f3c2" + }, + "source": [ + "Finally, it is time to train the reward model. For this we use the following loss function:\n", + "\n", + "$$loss = -log(\\sigma(R_{acc} - R_{rej}))$$\n", + "\n", + "Where $\\sigma$ is the sigmoid function, $R_{acc}$ and $R_{rej}$ are the rewards obtained by passing the \\ and \\ through the reward model. The intuition behind the loss function is to increase the difference between the rewards of the two types of responses. This becomes clear by looking at the training plot below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9997e06a", + "metadata": { + "id": "9997e06a" + }, + "outputs": [], + "source": [ + "import tqdm\n", + "\n", + "# Hyperparams\n", + "epochs = 40\n", + "batch_size = 64\n", + "rm_lr = 1e-4\n", + "acc_list = []\n", + "rej_list = []\n", + "\n", + "# Dataloader\n", + "train_loader = DataLoader(pf_dataset, shuffle=False, batch_size=batch_size)\n", + "\n", + "# Optimizer\n", + "reward_model = get_model(block_size=INPUT_SIZE*2+2, vocab_size=pf_dataset.get_vocab_size()+1, output_size=1)\n", + "rm_opt = torch.optim.Adam(reward_model.parameters(), lr=rm_lr)\n", + "\n", + "# Training\n", + "reward_model.train()\n", + "for ep_i in tqdm.tqdm(range(epochs)):\n", + " for b_i, batch in enumerate(train_loader):\n", + " inp, acc, rej = batch\n", + "\n", + " # Get rewards\n", + " r_acc = reward_model(torch.concat([inp, acc], dim=-1))[0][:, -1, 0]\n", + " r_rej = reward_model(torch.concat([inp, rej], dim=-1))[0][:, -1, 0]\n", + "\n", + " # Loss and backprop\n", + " loss = -torch.log(torch.nn.Sigmoid()(r_acc-r_rej)).mean()\n", + " rm_opt.zero_grad()\n", + " loss.backward()\n", + " rm_opt.step()\n", + "\n", + " # Save for plotting\n", + " acc_list.append(r_acc.mean().detach().item())\n", + " rej_list.append(r_rej.mean().detach().item())\n", + "\n", + "# print(ep_i, np.mean(acc_list[-20:]), np.mean(rej_list[-20:]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a37b9c0f", + "metadata": { + "scrolled": false, + "id": "a37b9c0f", + "outputId": "48243807-3047-44b4-c4d5-74bb6c9d317c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Reward (moving average)')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "def moving_average(a, n=10):\n", + " ret = np.cumsum(a, dtype=float)\n", + " ret[n:] = ret[n:] - ret[:-n]\n", + " return ret[n - 1:] / n\n", + "\n", + "plt.plot(moving_average(acc_list, 20), label=\"R_acc\")\n", + "plt.plot(moving_average(rej_list, 20), label=\"R_rej\")\n", + "plt.legend()\n", + "plt.title(f\"Reward model learning\")\n", + "plt.xlabel(\"Epochs\")\n", + "plt.ylabel(\"Reward (moving average)\")" + ] + }, + { + "cell_type": "markdown", + "id": "de9a4613", + "metadata": { + "id": "de9a4613" + }, + "source": [ + "Let's take a look at the kind of rewards the model generates for a fixed sequence and different values at the first position of the output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b93643d8", + "metadata": { + "scrolled": true, + "id": "b93643d8", + "outputId": "2819c24f-f2aa-41dc-f3ba-2faf977efee7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 || -2.0374112129211426\n", + "1 || 4.983941078186035\n", + "2 || -0.8424915075302124\n", + "3 || -6.84158182144165\n", + "4 || -6.4326276779174805\n", + "5 || -6.820979118347168\n", + "6 || -6.832927703857422\n", + "7 || -6.83734130859375\n", + "8 || -6.613164901733398\n", + "9 || -4.2613844871521\n", + "10 || -6.424010753631592\n" + ] + } + ], + "source": [ + "i = 0\n", + "for i in range(11):\n", + " print(i, \"||\", reward_model(torch.Tensor([[ 6, 1, 7, 0, 10, i, 5, 5, 6, 11]]).long())[0][0, -1, 0].item())" + ] + }, + { + "cell_type": "markdown", + "id": "7c723d96", + "metadata": { + "id": "7c723d96" + }, + "source": [ + "Given the input sequence [6, 1, 7, 0], the first output token should be last_token+1 = 0 + 1 = 1. All other generations are \"wrong\", so the reward model gives positive reward for token 1 and negative rewards for others." + ] + }, + { + "cell_type": "markdown", + "id": "ff7bfac9", + "metadata": { + "id": "ff7bfac9" + }, + "source": [ + "### RL fine-tuning" + ] + }, + { + "cell_type": "markdown", + "id": "667455cf", + "metadata": { + "id": "667455cf" + }, + "source": [ + "Finally, we have arrived at our final training that will use our reward model to update the supervised learning model using reinforecement learning." + ] + }, + { + "cell_type": "markdown", + "id": "ef6e4095", + "metadata": { + "id": "ef6e4095" + }, + "source": [ + "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/rlhf/rlhf.png)\n", + "\n", + "Source: [HuggingFace - RLHF blog](https://huggingface.co/blog/rlhf)" + ] + }, + { + "cell_type": "markdown", + "id": "cb10079e", + "metadata": { + "id": "cb10079e" + }, + "source": [ + "Following is a function for calculating logprob given a model and outputs. This'll help us calculate loss for RL and KL Divergence. More details on these a few code blocks below --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3c8d54d", + "metadata": { + "id": "e3c8d54d" + }, + "outputs": [], + "source": [ + "from torch.distributions import Categorical\n", + "\n", + "def get_logprob(agent, outputs):\n", + " '''\n", + " Get the logprobs for outputs acc. to agent's policy.\n", + "\n", + " Args:\n", + " agent: Actor network (or reference)\n", + " outputs: output ids\n", + " Shape = (sequence, tokens)\n", + "\n", + " returns\n", + " logprob of outputs acc to agent's policy\n", + " Shape = (sequence, tokens)\n", + " '''\n", + " logits = agent(outputs[:, :-1])[0][:, -INPUT_SIZE:, :]\n", + " logprob = Categorical(logits=logits).log_prob(outputs[:, -INPUT_SIZE:])\n", + " return logprob" + ] + }, + { + "cell_type": "markdown", + "id": "207bfe14", + "metadata": { + "id": "207bfe14" + }, + "source": [ + "Hyperparameters --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa705304", + "metadata": { + "id": "fa705304" + }, + "outputs": [], + "source": [ + "# Hyperparams\n", + "epochs = 100\n", + "actor_lr = 1e-5\n", + "critic_lr = 1e-4\n", + "train_actor_iter = 4 # Train the networks this many times per epoch\n", + "train_critic_iter = 4\n", + "clip_ratio = 0.2 # PPO Clip\n", + "gamma = 0.99 # Discount factor\n", + "kl_beta = 1 # KL coeff for reward\n", + "save = False\n", + "\n", + "# For plotting\n", + "rew_list = []\n", + "kl_list = []" + ] + }, + { + "cell_type": "markdown", + "id": "add83ee2", + "metadata": { + "id": "add83ee2" + }, + "source": [ + "Here we set up our models and optimizers. We typically need 3 models for RLHF training:" + ] + }, + { + "cell_type": "markdown", + "id": "60f32e29", + "metadata": { + "id": "60f32e29" + }, + "source": [ + "1. Actor: This is the LLM that we will fine-tune using reinforcement learning(RL). It is initialised as a copy of the pretrained model.\n", + "2. Reference: To prevent the actor's output distribution (or \"policy\" in RL terms) from diverging too much from the pretrained model's distribution, we need to apply some constraint on the distance/difference of the two distributions. For this, we keep this reference model which is a frozen copy of the pretrained model to calculate KL divergence during our RL training.\n", + "3. Critic: The critic network is also a copy of the base LLM but with the last layer replaced with a single output. This is used to estimate the value function, which is a component required to calculate the actor's loss.\n", + "\n", + "In our simple problem statement, the rewards are given at the end of the sequence. Therefore, we don't need to estimate the value function and hence, don't train a critic network. For more information, see this [answer](https://stats.stackexchange.com/questions/380123/reinforcement-learning-what-is-the-logic-behind-actor-critic-methods-why-use) on StackExchange." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1920d08f", + "metadata": { + "id": "1920d08f" + }, + "outputs": [], + "source": [ + "# Actor\n", + "actor = get_model(block_size=INPUT_SIZE*2, vocab_size=st_dataset.get_vocab_size())\n", + "actor.load_state_dict(torch.load(\"models/minimal_RLHF_basic_supervised.pt\")) # Load ST model from disk\n", + "# Reference\n", + "reference = get_model(block_size=INPUT_SIZE*2, vocab_size=st_dataset.get_vocab_size())\n", + "reference.load_state_dict(torch.load(\"models/minimal_RLHF_basic_supervised.pt\")) # Clone of actor\n", + "\n", + "# Optimizers\n", + "actor_opt = torch.optim.AdamW(actor.parameters(), lr=actor_lr)\n", + "\n", + "# Set models to train/eval\n", + "reference.eval()\n", + "reward_model.eval()\n", + "actor.train()" + ] + }, + { + "cell_type": "markdown", + "id": "11b4372a", + "metadata": { + "id": "11b4372a" + }, + "source": [ + "At last, we come to our main RL training. We use PPO, a famous RL algorithm for fine-tuning our model along with a KL divergence penalty." + ] + }, + { + "cell_type": "markdown", + "id": "eaff1744", + "metadata": { + "id": "eaff1744" + }, + "source": [ + "**PPO:** \n", + "The main idea behind PPO is to induce stability in the training process by preventing large updates.\n", + "\n", + "Let's look at the PPO loss:\n", + "\n", + "$$L = \\text{min}\\biggl( \\frac{\\pi_{k+1} (a|s)}{\\pi_{k} (a|s)} R, \\text{ clip}\\Bigl(\\frac{\\pi_{k+1} (a|s)}{\\pi_{k} (a|s)}, 1-\\epsilon, 1+\\epsilon\\Bigr) R\\biggr)$$\n", + "\n", + "Where $\\pi_k$ represents the policy at $k$'th training step, R is reward and $\\epsilon$ is a hyperparameter for clipping the policy update. I have partly modified the loss to prevent too many new ideas at once for beginners. This version is sufficient for our current case. To learn more about the PPO loss, look at [SpinningUp](https://spinningup.openai.com/en/latest/algorithms/ppo.html) and [Eric's article](https://medium.com/analytics-vidhya/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8).\n", + "\n", + "The PPO loss looks complicated but is fairly straightforward. To understand what the PPO loss does, consider the two cases: \n", + "1. R is positive \n", + "2. R is negative \n", + "\n", + "**Case 1**: R is positive. \n", + "Then the loss reduces to\n", + "$$L = \\text{min}\\biggl( \\frac{\\pi_{k+1} (a|s)}{\\pi_{k} (a|s)}, 1+\\epsilon \\biggr)R$$\n", + "So if the policy at the next training step is _increasing_ too far from the previous step, we clip it to $1+\\epsilon$.\n", + "\n", + "**Case 2**: R is negative.\n", + "Then the loss reduces to\n", + "$$L = \\text{max}\\biggl( \\frac{\\pi_{k+1} (a|s)}{\\pi_{k} (a|s)}, 1-\\epsilon \\biggr)|R|$$\n", + "So if the policy at the next training step is _decreasing_ too far from the previous step, we clip it to $1-\\epsilon$." + ] + }, + { + "cell_type": "markdown", + "id": "9f77c733", + "metadata": { + "id": "9f77c733" + }, + "source": [ + "**KL Divergence:** \n", + "[KL divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) is a measure of difference between two distributions. We use KL divergence penalty to ensure our actor's policy (probability distribution over next tokens) does not stray too far from the reference model's policy. For two distributions P and Q defined on the same sample space, $X$, the KL divergence is given by:\n", + "\n", + "$$D_{KL}(P||Q) = \\sum_{x \\in X} P(x) log\\biggl(\\frac{P(x)}{Q(x)}\\biggr)$$" + ] + }, + { + "cell_type": "markdown", + "id": "597c8ce5", + "metadata": { + "id": "597c8ce5" + }, + "source": [ + "The final reward used for PPO is then a linear combination of the scalar output from the reward model $R_{RM}$ and the value of KL divergence $R_{KL}$ with a hyperparameter $\\beta$.\n", + "\n", + "$$R = R_{RM} - \\beta R_{KL}$$" + ] + }, + { + "cell_type": "markdown", + "id": "28f6b4b4", + "metadata": { + "id": "28f6b4b4" + }, + "source": [ + "Both of PPO and KL divergence are crucial components to the RLHF training due to the inherent fragile nature of RLHF. Mainly, the issue lies with the reward model and the fact that it _cannot_ completely capture human preferences. The data used to train the reward model is generated using the base LLM's policy. Therefore, if the actor diverges too far from the base policy and the reward model is asked to give feedback for samples that do not come from the training distribution, we cannot predict the behaviour of the reward model. In fact, this exact issue often leads to adversarial training (see [Deepak's article](https://medium.com/@prdeepak.babu/reward-hacking-in-large-language-models-llms-c57abbc0cde7) on Reward Hacking in LLMs). This issue is avoided by taking small steps in PPO and using a KL penalty to prevent moving too far from base policy." + ] + }, + { + "cell_type": "markdown", + "id": "4b9594c0", + "metadata": { + "id": "4b9594c0" + }, + "source": [ + "Now we have the RL code --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8f6c590", + "metadata": { + "id": "a8f6c590" + }, + "outputs": [], + "source": [ + "# Dataloader - we use the same as reward model for now since we only need the inputs and it's in the correct format for what we want in RLHF training\n", + "# Can't use the supervised training's dataloader directly since the input has some part of output concatenated in that\n", + "ft_dataloader = train_loader\n", + "\n", + "# Train\n", + "for ep in range(epochs):\n", + " for b_i, batch in enumerate(ft_dataloader):\n", + " # Get some inputs from supervised dataset (only inputs - we don't care about the ground truths anymore)\n", + " inp, _, __ = batch\n", + "\n", + " # Generate output sequence\n", + " out = actor.generate(inp, max_new_tokens=INPUT_SIZE, do_sample=True) # Not sampling since good and bad in our problem is fairly deterministic, otherwise prefer to sample.\n", + " start_logprob = get_logprob(actor, out).detach()\n", + " start_logprob = start_logprob.sum(-1)\n", + "\n", + " # Reward\n", + " rew_out = torch.concat([out, torch.Tensor([[11]]*out.shape[0])], dim=-1).long() # Add [CLS] = 11\n", + " rew = reward_model(rew_out)[0][:, -1, 0]\n", + " rew_list.append(rew.mean().item())\n", + "\n", + " # Actor train loop\n", + " for _iter_actor in range(train_actor_iter):\n", + " # Get logprobs\n", + " cur_logprob = get_logprob(actor, out)\n", + " ref_logprob = get_logprob(reference, out)\n", + " cur_logprob = cur_logprob.sum(dim=-1) # Summing because we don't have rewards for each timestep\n", + " ref_logprob = ref_logprob.sum(dim=-1)\n", + "\n", + " # KL and reward update\n", + " kl_div = (cur_logprob - ref_logprob).detach()\n", + " rew = rew - kl_beta * kl_div\n", + "\n", + " # PPO loss\n", + " ratio = torch.exp(cur_logprob - start_logprob)\n", + " clip_rat = torch.clamp(ratio, 1-clip_ratio, 1+clip_ratio)\n", + " actor_loss = -(torch.min(ratio * rew, clip_rat * rew)).mean()\n", + "\n", + " # Update actor\n", + " actor_opt.zero_grad()\n", + " actor_loss.backward(retain_graph=True)\n", + " actor_opt.step()\n", + "\n", + " # Save kl div for plotting\n", + " kl_list.append(kl_div.mean().item())\n", + "\n", + " # Eval\n", + " if ep % 1 == 0 and b_i % 50 == 0:\n", + " print(f\"Epoch={ep} -- batch={b_i} || \" + \\\n", + " f\"Reward={round(rew_list[-1], 2)} || \" + \\\n", + " f\"KLD={round(kl_list[-1], 2)} || \" + \\\n", + " f\"actor loss={round(actor_loss.item(), 2)}\")\n", + " print(out[0])\n", + " print(\"#\"*100)" + ] + }, + { + "cell_type": "markdown", + "id": "2441630c", + "metadata": { + "id": "2441630c" + }, + "source": [ + "Output is ommitted for brevity. Here is an image of some of the outputs: \n", + "\n", + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "a9091014", + "metadata": { + "id": "a9091014" + }, + "source": [ + "Save model --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dcc1b12", + "metadata": { + "id": "9dcc1b12" + }, + "outputs": [], + "source": [ + "import datetime, os, json\n", + "\n", + "save = True\n", + "\n", + "# RUN TO SAVE MODEL\n", + "folder = f\"models/08_min_rlhf_basic_{datetime.datetime.now().__str__()}\"\n", + "os.makedirs(folder, exist_ok=True)\n", + "\n", + "torch.save(reward_model, f\"{folder}/reward_nodel.pt\")\n", + "torch.save(reference, f\"{folder}/reference.pt\")\n", + "torch.save(actor, f\"{folder}/actor.pt\")\n", + "\n", + "with open(f\"{folder}/config.json\", 'w') as f:\n", + " json.dump({\n", + " \"epochs\": epochs,\n", + " \"actor_lr\": actor_lr,\n", + " \"critic_lr\": critic_lr,\n", + " \"train_actor_iter\": train_actor_iter,\n", + " \"train_critic_iter\": train_critic_iter,\n", + " \"clip_ratio\": clip_ratio,\n", + " \"gamma\": gamma,\n", + " \"kl_beta\": kl_beta,\n", + " }, f)" + ] + }, + { + "cell_type": "markdown", + "id": "d7706531", + "metadata": { + "id": "d7706531" + }, + "source": [ + "Plot rewards --" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d187c707", + "metadata": { + "id": "d187c707", + "outputId": "2d82ebe4-5550-41da-caae-896ec7dc7313" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "def moving_average(a, n=10):\n", + " ret = np.cumsum(a, dtype=float)\n", + " ret[n:] = ret[n:] - ret[:-n]\n", + " return ret[n - 1:] / n\n", + "\n", + "plt.plot(moving_average(rew_list, 20), label=\"reward\")\n", + "plt.plot(moving_average(kl_list, 20), label=\"kl\")\n", + "plt.legend()\n", + "plt.title(f\"Reward plot || KL beta = {kl_beta}\")\n", + "plt.xlabel(\"Train steps\")\n", + "plt.ylabel(\"Reward (moving average)\")\n", + "if save:\n", + " plt.savefig(f\"{folder}/plot.png\")\n", + "else:\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1465c909", + "metadata": { + "id": "1465c909" + }, + "source": [ + "With our hyperparameters, we have maximized reward while maintaining a non-divergent KL. Looking at the outputs above, the model seems to behave the way we want it to.\n", + "\n", + "Hurray!" + ] + }, + { + "cell_type": "markdown", + "id": "6a6226b8", + "metadata": { + "id": "6a6226b8" + }, + "source": [ + "## Explainability / Interpretability" + ] + }, + { + "cell_type": "markdown", + "id": "d70c9c26", + "metadata": { + "id": "d70c9c26" + }, + "source": [ + "First I run some visualizations on the base LLM to set the stage." + ] + }, + { + "cell_type": "markdown", + "id": "4877fde5", + "metadata": { + "id": "4877fde5" + }, + "source": [ + "### Base model's visualizations" + ] + }, + { + "cell_type": "markdown", + "id": "9da71d36", + "metadata": { + "id": "9da71d36" + }, + "source": [ + "Let's look at some probability plots over the output tokens for different input lengths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66501500", + "metadata": { + "id": "66501500" + }, + "outputs": [], + "source": [ + "x, y = torch.Tensor([[5, 7, 1, 5, 10]]).long(), torch.Tensor([[6, 2, 7, 3]]).long()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "244f1fda", + "metadata": { + "id": "244f1fda", + "outputId": "ddb7e787-a418-4d94-d712-5e69ead40cb1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 4, sharey=True, figsize=(15, 3))\n", + "for i in range(4):\n", + " _x = torch.concat([x, y[:, :i]], dim=1)\n", + " axes[i].bar(np.arange(11), torch.softmax((reference(_x)[0][0, -1].detach()), dim=0).tolist())\n", + " axes[i].title.set_text(f\"Input {_x[0].tolist()}\")\n", + " axes[i].set_ylim([0, 1])\n", + "axes[0].set_ylabel(\"Probability\")" + ] + }, + { + "cell_type": "markdown", + "id": "9ef20486", + "metadata": { + "id": "9ef20486" + }, + "source": [ + "The supervised model learns an almost equal probability over the increment of last 4 tokens. This was our intended behaviour.\n", + "\n", + "The exact probabilities vary but are mostly within a some threshold of each other. For the first plot though, since the input contains two 5's, the probability of 6 is much higher than others." + ] + }, + { + "cell_type": "markdown", + "id": "6952efbe", + "metadata": { + "id": "6952efbe" + }, + "source": [ + "Now, let's look at what the attention heads focus on. I'm using [BertViz](https://github.com/jessevig/bertviz) for visualizing attention weights." + ] + }, + { + "cell_type": "markdown", + "id": "166f77b7", + "metadata": { + "id": "166f77b7" + }, + "source": [ + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "cd112409", + "metadata": { + "id": "cd112409" + }, + "source": [ + "Here also, the model successfully learns to look at the last 4 input tokens while ignoring the separation token." + ] + }, + { + "cell_type": "markdown", + "id": "7d3b08ab", + "metadata": { + "id": "7d3b08ab" + }, + "source": [ + "### Fine tuned model with beta = 1" + ] + }, + { + "cell_type": "markdown", + "id": "07d8f884", + "metadata": { + "id": "07d8f884" + }, + "source": [ + "We look at the same visualizations for the fine tuned model with $\\beta = 1$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81e9ae26", + "metadata": { + "id": "81e9ae26", + "outputId": "505461a1-8767-4fbf-949e-f29188c34daf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 4, sharey=True, figsize=(15, 3))\n", + "for i in range(4):\n", + " _x = torch.concat([x, y[:, :i]], dim=1)\n", + " axes[i].bar(np.arange(11), torch.softmax((actor(_x)[0][0, -1].detach()), dim=0).tolist())\n", + " axes[i].title.set_text(f\"Input {_x[0].tolist()}\")\n", + " axes[i].set_ylim([0, 1])\n", + "axes[0].set_ylabel(\"Probability\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b37b059", + "metadata": { + "id": "5b37b059" + }, + "source": [ + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "69f41ade", + "metadata": { + "id": "69f41ade" + }, + "source": [ + "The model learnt to change the distribution over the first output token only. For the first plot and attention figure, the model learns to focus on a single token and output its increment. As for the rest of the tokens, it retains a similar behavior as the base model." + ] + }, + { + "cell_type": "markdown", + "id": "dd7024d2", + "metadata": { + "id": "dd7024d2" + }, + "source": [ + "### Fine tuned model with beta 0" + ] + }, + { + "cell_type": "markdown", + "id": "bb27d860", + "metadata": { + "id": "bb27d860" + }, + "source": [ + "As as additional exercise, we also look at how the model behaves when no KL divergence penalty is applied.\n", + "\n", + "I've run the training separately and compiled the results here." + ] + }, + { + "cell_type": "markdown", + "id": "554d9eab", + "metadata": { + "id": "554d9eab" + }, + "source": [ + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "c77b1643", + "metadata": { + "id": "c77b1643" + }, + "source": [ + "The reward hits the max but the KL is diverging, which means that the policy we are learning is moving further away from the base distribution as the training goes on. This is a result of not applying a penalty to KL divergence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54050769", + "metadata": { + "scrolled": false, + "id": "54050769", + "outputId": "d062c57e-1b1e-4341-95f2-a124debfec1c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 4, sharey=True, figsize=(15, 3))\n", + "kl0_actor = torch.load(\"models/kl_beta=0/actor.pt\")\n", + "for i in range(4):\n", + " _x = torch.concat([x, y[:, :i]], dim=1)\n", + " axes[i].bar(np.arange(11), torch.softmax((kl0_actor(_x)[0][0, -1].detach()), dim=0).tolist())\n", + " axes[i].title.set_text(f\"Input {_x[0].tolist()}\")\n", + " axes[i].set_ylim([0, 1])\n", + "axes[0].set_ylabel(\"Probability\")" + ] + }, + { + "cell_type": "markdown", + "id": "cd3a185d", + "metadata": { + "id": "cd3a185d" + }, + "source": [ + "The first output is correct but the rest are all messed up. Let's look at the attention heads." + ] + }, + { + "cell_type": "markdown", + "id": "d9d6a87a", + "metadata": { + "id": "d9d6a87a" + }, + "source": [ + "![image.png](data:image/png;base64,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)![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "b2abf30f", + "metadata": { + "id": "b2abf30f" + }, + "source": [ + "The model seems to have learnt to not look at tokens before the last input token. We can see this in the output probability plots too, that the tokens before 5 do not have high probability.\n", + "That is only for the input though. The effect of sampling from the output on the probability distribution is harder to interpret since we do not have a way to visualize how the weights affect the probability during the forward pass." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" + }, + "colab": { + "provenance": [], + "toc_visible": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/stylesheets/extra.css b/stylesheets/extra.css index 858b297..21c5ba1 100644 --- a/stylesheets/extra.css +++ b/stylesheets/extra.css @@ -1,3 +1,7 @@ .md-grid { max-width: 1520px; - } \ No newline at end of file + } + +.md-header { + margin-top: 10px; +} \ No newline at end of file