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A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

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Quick Overview

minGPT is a minimal PyTorch implementation of the GPT (Generative Pre-trained Transformer) language model. Created by Andrej Karpathy, it serves as an educational tool to understand the core concepts of GPT models while maintaining simplicity and readability.

Pros

  • Lightweight and easy to understand implementation of GPT
  • Well-documented code with clear explanations
  • Suitable for educational purposes and experimentation
  • Implements core GPT functionality without unnecessary complexity

Cons

  • Not optimized for large-scale production use
  • Limited features compared to full-scale GPT implementations
  • May not include the latest advancements in transformer architectures
  • Requires some background knowledge in deep learning and transformers

Code Examples

  1. Creating a GPT model:
from mingpt.model import GPT

model = GPT(vocab_size=10000, n_layer=12, n_head=12, n_embd=768)
  1. Training the model:
from mingpt.trainer import Trainer

trainer = Trainer(model, train_dataset, test_dataset, 
                  learning_rate=3e-4, max_iters=1000)
trainer.train()
  1. Generating text:
context = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long)
generated = model.generate(context, max_new_tokens=20)
print(generated)

Getting Started

To get started with minGPT:

  1. Clone the repository:

    git clone https://github.com/karpathy/minGPT.git
cd minGPT

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the example script:

    python play_char.py

This will train a character-level language model on the tiny shakespeare dataset and generate some text.

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transformers

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🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Pros of transformers

  • Comprehensive library with support for numerous architectures and tasks
  • Extensive documentation and community support
  • Regular updates and maintenance

Cons of transformers

  • Steeper learning curve due to its complexity
  • Larger codebase, which may be overwhelming for beginners
  • Higher computational requirements for some models

Code comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)

transformers:

class GPT2Model(GPT2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
        self.wpe = nn.Embedding(config.n_positions, config.n_embd)
        self.drop = nn.Dropout(config.embd_pdrop)

Summary

transformers offers a more comprehensive and well-maintained library with extensive support, while minGPT provides a simpler, more educational implementation. The choice between them depends on the project's requirements and the user's familiarity with transformer architectures.

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gpt-2

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Code for the paper "Language Models are Unsupervised Multitask Learners"

Pros of GPT-2

  • More comprehensive and production-ready implementation
  • Includes pre-trained models and tokenizers
  • Supports advanced features like model parallelism and distributed training

Cons of GPT-2

  • More complex codebase, harder to understand for beginners
  • Requires more computational resources to run and train
  • Less flexible for experimentation and customization

Code Comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)
        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd)
        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

GPT-2:

class GPT2Model(GPT2PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
        self.wpe = nn.Embedding(config.n_positions, config.n_embd)
        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([GPT2Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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gpt-neo

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An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.

Pros of gpt-neo

  • Larger scale and more advanced model, capable of generating higher quality text
  • Supports distributed training across multiple GPUs for faster processing
  • Implements more sophisticated techniques like sparse attention

Cons of gpt-neo

  • More complex codebase, harder to understand and modify
  • Requires more computational resources to train and run
  • Less suitable for educational purposes or quick experimentation

Code Comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)
        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd)

gpt-neo:

class GPTNeoModel(GPTNeoPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.embed_dim = config.hidden_size
        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([GPTNeoBlock(config) for _ in range(config.num_layers)])
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DeepSpeed

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DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Pros of DeepSpeed

  • Highly optimized for large-scale distributed training
  • Supports a wide range of model architectures and training scenarios
  • Offers advanced features like ZeRO optimizer and pipeline parallelism

Cons of DeepSpeed

  • Steeper learning curve due to its complexity and extensive features
  • Requires more setup and configuration compared to simpler implementations
  • May be overkill for smaller projects or individual researchers

Code Comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)
        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])

DeepSpeed:

model = GPT2LMHeadModel.from_pretrained("gpt2")
model_engine, optimizer, _, _ = deepspeed.initialize(
    args=args,
    model=model,
    model_parameters=model.parameters(),
    config=ds_config
)

The code snippets highlight the simplicity of minGPT's implementation versus the more complex but powerful DeepSpeed setup, which offers advanced optimization and distributed training capabilities.

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bert

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TensorFlow code and pre-trained models for BERT

Pros of BERT

  • More comprehensive and production-ready implementation
  • Extensive pre-training and fine-tuning capabilities
  • Wider range of supported tasks and applications

Cons of BERT

  • More complex codebase, potentially harder to understand for beginners
  • Requires more computational resources for training and inference
  • Less focused on educational purposes compared to minGPT

Code Comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)
        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd)

BERT:

class BertModel(BertPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = BertEmbeddings(config)
        self.encoder = BertEncoder(config)
        self.pooler = BertPooler(config) if add_pooling_layer else None
        self.init_weights()
facebookresearch logo

fairseq

30,331

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Pros of fairseq

  • Comprehensive toolkit with support for various NLP tasks and architectures
  • Highly optimized for performance and scalability
  • Extensive documentation and active community support

Cons of fairseq

  • Steeper learning curve due to its complexity
  • Heavier resource requirements for setup and usage
  • May be overkill for simple projects or experimentation

Code Comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)
        self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd)
        self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

fairseq:

class TransformerModel(FairseqEncoderDecoderModel):
    def __init__(self, args, encoder, decoder):
        super().__init__(encoder, decoder)
        self.args = args
        self.supports_align_args = True

    @classmethod
    def build_model(cls, args, task):
        base_architecture(args)
        encoder = cls.build_encoder(args, task.source_dictionary)
        decoder = cls.build_decoder(args, task.target_dictionary)
        return cls(args, encoder, decoder)

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README

minGPT

mingpt

A PyTorch re-implementation of GPT, both training and inference. minGPT tries to be small, clean, interpretable and educational, as most of the currently available GPT model implementations can a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code (see mingpt/model.py). All that's going on is that a sequence of indices feeds into a Transformer, and a probability distribution over the next index in the sequence comes out. The majority of the complexity is just being clever with batching (both across examples and over sequence length) for efficiency.

note (Jan 2023): though I may continue to accept and change some details, minGPT is in a semi-archived state. For more recent developments see my rewrite nanoGPT. Basically, minGPT became referenced across a wide variety of places (notebooks, blogs, courses, books, etc.) which made me less willing to make the bigger changes I wanted to make to move the code forward. I also wanted to change the direction a bit, from a sole focus on education to something that is still simple and hackable but has teeth (reproduces medium-sized industry benchmarks, accepts some tradeoffs to gain runtime efficiency, etc).

The minGPT library is three files: mingpt/model.py contains the actual Transformer model definition, mingpt/bpe.py contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, mingpt/trainer.py is (GPT-independent) PyTorch boilerplate code that trains the model. Then there are a number of demos and projects that use the library in the projects folder:

  • projects/adder trains a GPT from scratch to add numbers (inspired by the addition section in the GPT-3 paper)
  • projects/chargpt trains a GPT to be a character-level language model on some input text file
  • demo.ipynb shows a minimal usage of the GPT and Trainer in a notebook format on a simple sorting example
  • generate.ipynb shows how one can load a pretrained GPT2 and generate text given some prompt

Library Installation

If you want to import mingpt into your project:

git clone https://github.com/karpathy/minGPT.git
cd minGPT
pip install -e .

Usage

Here's how you'd instantiate a GPT-2 (124M param version):

from mingpt.model import GPT
model_config = GPT.get_default_config()
model_config.model_type = 'gpt2'
model_config.vocab_size = 50257 # openai's model vocabulary
model_config.block_size = 1024  # openai's model block_size (i.e. input context length)
model = GPT(model_config)

And here's how you'd train it:

# your subclass of torch.utils.data.Dataset that emits example
# torch LongTensor of lengths up to 1024, with integers from [0,50257)
train_dataset = YourDataset()

from mingpt.trainer import Trainer
train_config = Trainer.get_default_config()
train_config.learning_rate = 5e-4 # many possible options, see the file
train_config.max_iters = 1000
train_config.batch_size = 32
trainer = Trainer(train_config, model, train_dataset)
trainer.run()

See demo.ipynb for a more concrete example.

Unit tests

Coverage is not super amazing just yet but:

python -m unittest discover tests

todos

  • add gpt-2 finetuning demo on arbitrary given text file
  • add dialog agent demo
  • better docs of outcomes for existing projects (adder, chargpt)
  • add mixed precision and related training scaling goodies
  • distributed training support
  • reproduce some benchmarks in projects/, e.g. text8 or other language modeling
  • proper logging instead of print statement amateur hour haha
  • i probably should have a requirements.txt file...
  • it should be possible to load in many other model weights other than just gpt2-*

References

Code:

  • openai/gpt-2 has the model definition in TensorFlow, but not the training code
  • openai/image-gpt has some more modern gpt-3 like modification in its code, good reference as well
  • huggingface/transformers has a language-modeling example. It is full-featured but as a result also somewhat challenging to trace. E.g. some large functions have as much as 90% unused code behind various branching statements that is unused in the default setting of simple language modeling

Papers + some implementation notes:

Improving Language Understanding by Generative Pre-Training (GPT-1)

  • Our model largely follows the original transformer work
  • We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and 12 attention heads). For the position-wise feed-forward networks, we used 3072 dimensional inner states.
  • Adam max learning rate of 2.5e-4. (later GPT-3 for this model size uses 6e-4)
  • LR decay: increased linearly from zero over the first 2000 updates and annealed to 0 using a cosine schedule
  • We train for 100 epochs on minibatches of 64 randomly sampled, contiguous sequences of 512 tokens.
  • Since layernorm is used extensively throughout the model, a simple weight initialization of N(0, 0.02) was sufficient
  • bytepair encoding (BPE) vocabulary with 40,000 merges
  • residual, embedding, and attention dropouts with a rate of 0.1 for regularization.
  • modified version of L2 regularization proposed in (37), with w = 0.01 on all non bias or gain weights
  • For the activation function, we used the Gaussian Error Linear Unit (GELU).
  • We used learned position embeddings instead of the sinusoidal version proposed in the original work
  • For finetuning: We add dropout to the classifier with a rate of 0.1. learning rate of 6.25e-5 and a batchsize of 32. 3 epochs. We use a linear learning rate decay schedule with warmup over 0.2% of training. λ was set to 0.5.
  • GPT-1 model is 12 layers and d_model 768, ~117M params

Language Models are Unsupervised Multitask Learners (GPT-2)

  • LayerNorm was moved to the input of each sub-block, similar to a pre-activation residual network
  • an additional layer normalization was added after the final self-attention block.
  • modified initialization which accounts for the accumulation on the residual path with model depth is used. We scale the weights of residual layers at initialization by a factor of 1/√N where N is the number of residual layers. (weird because in their released code i can only find a simple use of the old 0.02... in their release of image-gpt I found it used for c_proj, and even then only for attn, not for mlp. huh. https://github.com/openai/image-gpt/blob/master/src/model.py)
  • the vocabulary is expanded to 50,257
  • increase the context size from 512 to 1024 tokens
  • larger batchsize of 512 is used
  • GPT-2 used 48 layers and d_model 1600 (vs. original 12 layers and d_model 768). ~1.542B params

Language Models are Few-Shot Learners (GPT-3)

  • GPT-3: 96 layers, 96 heads, with d_model of 12,288 (175B parameters).
  • GPT-1-like: 12 layers, 12 heads, d_model 768 (125M)
  • We use the same model and architecture as GPT-2, including the modified initialization, pre-normalization, and reversible tokenization described therein
  • we use alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer
  • we always have the feedforward layer four times the size of the bottleneck layer, dff = 4 ∗ dmodel
  • all models use a context window of nctx = 2048 tokens.
  • Adam with β1 = 0.9, β2 = 0.95, and eps = 10−8
  • All models use weight decay of 0.1 to provide a small amount of regularization. (NOTE: GPT-1 used 0.01 I believe, see above)
  • clip the global norm of the gradient at 1.0
  • Linear LR warmup over the first 375 million tokens. Then use cosine decay for learning rate down to 10% of its value, over 260 billion tokens.
  • gradually increase the batch size linearly from a small value (32k tokens) to the full value over the first 4-12 billion tokens of training, depending on the model size.
  • full 2048-sized time context window is always used, with a special END OF DOCUMENT token delimiter

Generative Pretraining from Pixels (Image GPT)

  • When working with images, we pick the identity permutation πi = i for 1 ≤ i ≤ n, also known as raster order.
  • we create our own 9-bit color palette by clustering (R, G, B) pixel values using k-means with k = 512.
  • Our largest model, iGPT-XL, contains L = 60 layers and uses an embedding size of d = 3072 for a total of 6.8B parameters.
  • Our next largest model, iGPT-L, is essentially identical to GPT-2 with L = 48 layers, but contains a slightly smaller embedding size of d = 1536 (vs 1600) for a total of 1.4B parameters.
  • We use the same model code as GPT-2, except that we initialize weights in the layerdependent fashion as in Sparse Transformer (Child et al., 2019) and zero-initialize all projections producing logits.
  • We also train iGPT-M, a 455M parameter model with L = 36 and d = 1024
  • iGPT-S, a 76M parameter model with L = 24 and d = 512 (okay, and how many heads? looks like the Github code claims 8)
  • When pre-training iGPT-XL, we use a batch size of 64 and train for 2M iterations, and for all other models we use a batch size of 128 and train for 1M iterations.
  • Adam with β1 = 0.9 and β2 = 0.95
  • The learning rate is warmed up for one epoch, and then decays to 0
  • We did not use weight decay because applying a small weight decay of 0.01 did not change representation quality.
  • iGPT-S lr 0.003
  • No dropout is used.

License

MIT