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Let’s construct GPT: from scratch, in code, spelled out.

September 14, 2023

We construct a Generatively Pretrained Transformer (GPT), following the paper “Consideration is All You Want” and OpenAI’s GPT-2 / GPT-3. We discuss connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, assist us write a GPT (meta :D!) . I like to recommend folks watch the sooner makemore movies to get snug with the autoregressive language modeling framework and fundamentals of tensors and PyTorch nn, which we take without any consideration on this video.

Supplementary hyperlinks:– Consideration is All You Want paper: https://arxiv.org/abs/1706.03762– OpenAI GPT-3 paper: https://arxiv.org/abs/2005.14165– OpenAI ChatGPT weblog put up: https://openai.com/blog/chatgpt/– The GPU I am coaching the mannequin on is from Lambda GPU Cloud, I feel one of the best and best strategy to spin up an on-demand GPU occasion within the cloud that you would be able to ssh to: https://lambdalabs.com . If you happen to favor to work in notebooks, I feel the best path at the moment is Google Colab.

Prompt workouts:– EX1: The n-dimensional tensor mastery problem: Mix the `Head` and `MultiHeadAttention` into one class that processes all of the heads in parallel, treating the heads as one other batch dimension (reply is in nanoGPT).– EX2: Prepare the GPT by yourself dataset of alternative! What different information may very well be enjoyable to blabber on about? (A enjoyable superior suggestion should you like: practice a GPT to do addition of two numbers, i.e. a+b=c. You might discover it useful to foretell the digits of c in reverse order, as the everyday addition algorithm (that you just’re hoping it learns) would proceed proper to left too. You might need to modify the info loader to easily serve random issues and skip the technology of practice.bin, val.bin. You might need to masks out the loss on the enter positions of a+b that simply specify the issue utilizing y=-1 within the targets (see CrossEntropyLoss ignore_index). Does your Transformer be taught so as to add? After you have this, swole doge mission: construct a calculator clone in GPT, for all of +-*/. Not a simple downside. You might want Chain of Thought traces.)– EX3: Discover a dataset that could be very massive, so massive that you would be able to’t see a niche between practice and val loss. Pretrain the transformer on this information, then initialize with that mannequin and finetune it on tiny shakespeare with a smaller variety of steps and decrease studying fee. Are you able to receive a decrease validation loss by way of pretraining?– EX4: Learn some transformer papers and implement one further function or change that individuals appear to make use of. Does it enhance the efficiency of your GPT?

Chapters:00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespearebaseline language modeling, code setup00:07:52 studying and exploring the info00:09:28 tokenization, practice/val break up00:14:27 information loader: batches of chunks of information00:22:11 easiest baseline: bigram language mannequin, loss, technology00:34:53 coaching the bigram mannequin00:38:00 port our code to a scriptConstructing the “self-attention”00:42:13 model 1: averaging previous context with for loops, the weakest type of aggregation00:47:11 the trick in self-attention: matrix multiply as weighted aggregation00:51:54 model 2: utilizing matrix multiply00:54:42 model 3: including softmax00:58:26 minor code cleanup01:00:18 positional encoding01:02:00 THE CRUX OF THE VIDEO: model 4: self-attention01:11:38 observe 1: consideration as communication01:12:46 observe 2: consideration has no notion of house, operates over units01:13:40 observe 3: there is no such thing as a communication throughout batch dimension01:14:14 observe 4: encoder blocks vs. decoder blocks01:15:39 observe 5: consideration vs. self-attention vs. cross-attention01:16:56 observe 6: “scaled” self-attention. why divide by sqrt(head_size)Constructing the Transformer01:19:11 inserting a single self-attention block to our community01:21:59 multi-headed self-attention01:24:25 feedforward layers of transformer block01:26:48 residual connections01:32:51 layernorm (and its relationship to our earlier batchnorm)01:37:49 scaling up the mannequin! creating a number of variables. including dropoutNotes on Transformer01:42:39 encoder vs. decoder vs. each (?) Transformers01:46:22 tremendous fast walkthrough of nanoGPT, batched multi-headed self-attention01:48:53 again to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF01:54:32 conclusions

Corrections:00:57:00 Oops “tokens from the _future_ can not talk”, not “previous”. Sorry! 01:20:05 Oops I needs to be utilizing the head_size for the normalization, not C