Minimal text diffusion

A minimal implementation of diffusion models of text: learns a diffusion model of a given text corpus, allowing to generate text samples from the learned model.


Diffusion in action: a DDPM model gradually denoising random text `hotnutggy pi greentedsty rawyaented` to `the white eggplant is dried` and `mac clement star fe honey spin theapple purpleip` to `the brown radicchio is sour`

This repo has been refactored by taking a large amount of code from https://github.com/XiangLi1999/Diffusion-LM (which includes some code from: https://github.com/openai/glide-text2im), thanks to the authors for their work!

The main idea was to retain just enough code to allow training a simple diffusion model and generating samples, remove image-related terms, and make it easier to use.

I've included an extremely simple corpus (data/simple-{train,test}.txt) I used for quick iterations and testing.

Minimal text diffusion

Getting started

Setup

Install the requirements: pip install -r requirements.txt
Some of the dependencies might be easier to install via conda:

conda install mpi4py
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

Preparing dataset

We will use data/simple.txt as a running example. To begin, we need to create a tokenizer over the dataset. I found that word-level tokenization works best, but the implementation in src/utils/custom_tokenizer includes options to create BPE tokenizer.

python src/utils/custom_tokenizer.py train-word-level data/simple/simple.txt

Training

To train a model, run scripts/train.sh. By default, this will train a model on the simple corpus. However, you can change this to any text file using --train_data argument. Note that you may have to increase the sequence length (--seq_len) if your corpus is longer than the simple corpus. The other default arguments are set to match the best setting I found for the simple corpus (see discussion below).
Once training finishes, the model will be saved in ckpts/simple. You can then use this model to generate samples.
The checkpoint can also be downloaded from here.

Inference

To generate samples, run:

bash scripts/text_sample.sh ckpts/simple/ema_0.9999_025000.pt 2000 10

Here:
- ckpts/simple/ema_0.9999_025000.pt is the path to the checkpoint
- 2000 is the number of diffusion steps.
- 10 is the number of samples to generate.
By default, this will generate 10 samples from the model trained on the simple corpus. Changing SEED in scripts/text_sample.sh will generate different samples. You can also change the number of samples generated by changing the NUM_SAMPLES argument.
During inference (denoising), the intermediate sentences will be printed to the console.
The generated samples will be saved in ckpt/simple/.
Complete set of outputs are available here.

Training from scratch on the greetings dataset

I've added another trainign from scratch tutorial here: greetings.

Experiments with using pre-trained models and embeddings

Update 10/24: The most fluent/realistic outputs are obtained using i) word-level tokenization, ii) initializing a model from scratch, and iii) fine-tuning the embeddings. This is the default in run_train.sh now. Please see docs/old_experiments.md for details on the experiments I ran before this update.

Controllable Generation

The diffusion model can be combined with a classifier to perform classifier-guided diffusion. Please see details in docs/controllable.md.

Gory details

Below are my rough notes on how the code works. [TODO] Clean this up and add more details.

Training

Input text is embedded. This is the mean of x_start_mean. Some noise is added to x_start_mean to get x_start.
Using random t, a noisy version of the input is created from q(x_t | x_0). This is simply x_t = x_0 * sqrt(1 - \beta_t) + \epsilon_t * sqrt(\beta_t). The function used for this is q_sample. Any operation that involves going ahead in the diffusion process is carried out by functions that start with q_.
x_t is fed to the transformer model. Then, the transformer model is trained to generate an approximation of x_start given x_t and t (the timestep). Specifically, the embedded text is passed through a BERT encoder and downsampled. The size of the output embeddings and input embeddings is the same for this reason. Maybe this is the trick mentioned in the paper where they want to tie each weight with the x_start term, but I'm not sure how it's different from DDIM.
The loss has several terms:

Difference between the actual x_start and the output of the transformer model. This is the MSE loss.
Mean of the xT should be close to zero. This is the tT_loss term. It is obtained by calling q_mean_variance for the t=T. q_mean_variance is like q_sample, but it returns the mean and variance of the distribution x_t | x0 instead of a sample.
Decoder NLL loss. This is the decoder_nll term. It is obtained by calling token_discrete_loss. token_discrete_loss calls get_logits, which in turns uses the embeddings to convert to logits. The logits are then used to calculate the NLL loss. Essentially this is how the embeddings are trained.

    def get_logits(self, hidden_repr):
        return self.lm_head(hidden_repr)

One thing to note is that:

    print(model.lm_head.weight == model.word_embedding.weight)
    print(model.lm_head.weight.shape, model.word_embedding.weight.shape)

They are identical! Intuitively, the model is trained to predict the embedded input. Thus, having a linear layer with the weights from word_embedding is like doing a nearest neighbor search. While initializing, the weights are assigned to lm_head from word_embedding under torch.no_grad(), so that the gradients are not computed for lm_head.

Evolving input

Note that the embeddings are trained. Although initial embeddings are passed in training losses, they are not used. Instead, the get_embeds method is used to get the embeddings. This is because the embeddings are trained to predict the input text. Thus, the embeddings are not the same as the input embeddings.

Sampling

p_mean_variance: returns the distribution p(x_{t-1} | x_t) (the mean and variance). In addition, returns a prediction for the initial x_0.
q_posterior_mean_variance: returns the distribution q(x_{t-1} | x_t, x_0).
Additionally, recall that our model is trained to predict x_start given x_t and t.

Putting these together, we can sample from the model. The sampling is done in the following way:

Starting with noise xT, a noisy x_start is first generated using the model.
The xT and x_start are used to generate x_{T-1} using q_posterior_mean_variance (x_{T-1} ~ q(x_{T-1} | x_T, x_start)).

The process is repeated until x_0 is generated.

TODO

Add more details to the inner workings section.
Add classifier-guided sampling.
Add more experiments.

Opportunities for further minimization

logger.py can be completely deleted.
args.py and factory_methods.py can be combined.

Acknowledgements

Thanks to the team behind Diffusion-LM Improves Controllable Text Generation for releasing their code, which I used as a starting point.
Thanks to the authors of several open-source implementations of DDPM/DDIM, helpful blogs, and videos. Some of the ones I bookmarked are:

Title	Url
Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications	https://www.youtube.com/watch?v=cS6JQpEY9cs
Composable Text Control Operations in Latent Space with Ordinary Differential Equations	http://arxiv.org/abs/2208.00638
Diffusion-LM Improves Controllable Text Generation	http://arxiv.org/abs/2205.14217
Step-unrolled Denoising Autoencoders for Text Generation	http://arxiv.org/abs/2112.06749
Latent Diffusion Energy-Based Model for Interpretable Text Modeling	http://arxiv.org/abs/2206.05895
Parti - Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (Paper Explained)	https://www.youtube.com/watch?v=qS-iYnp00uc
Deep Unsupervised Learning using Nonequilibrium Thermodynamics	http://arxiv.org/abs/1503.03585
lucidrains/denoising-diffusion-pytorch	https://github.com/lucidrains/denoising-diffusion-pytorch
Guidance: a cheat code for diffusion models	https://benanne.github.io/2022/05/26/guidance.html
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise	http://arxiv.org/abs/2208.09392
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning	http://arxiv.org/abs/2208.04202
Diffusion Maps for Textual Network Embedding	https://proceedings.neurips.cc/paper/2018/hash/211a7a84d3d5ce4d80347da11e0c85ed-Abstract.html
Diffusion-LM Improves Controllable Text Generation	https://github.com/XiangLi1999/Diffusion-LM
Denoising Diffusion Probabilistic Models	http://arxiv.org/abs/2006.11239
Variational Diffusion Models	http://arxiv.org/abs/2107.00630
Elucidating the Design Space of Diffusion-Based Generative Models	http://arxiv.org/abs/2206.00364
Diffusion Models Beat GANs on Image Synthesis	http://arxiv.org/abs/2105.05233
guided-diffusion	https://github.com/openai/guided-diffusion
Minimal implementation of diffusion models ⚛	https://github.com/VSehwag/minimal-diffusion
minDiffusion	https://github.com/cloneofsimo/minDiffusion
What are Diffusion Models?	https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
High-Resolution Image Synthesis with Latent Diffusion Models	http://arxiv.org/abs/2112.10752
Generative Modeling by Estimating Gradients of the Data Distribution \| Yang Song	https://yang-song.net/blog/2021/score/
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models	http://arxiv.org/abs/2112.10741
Blended Diffusion for Text-driven Editing of Natural Images	http://arxiv.org/abs/2111.14818
Generative Modeling by Estimating Gradients of the Data Distribution	http://arxiv.org/abs/1907.05600
Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modeling	http://arxiv.org/abs/2106.01357
Score-based Generative Modeling in Latent Space	http://arxiv.org/abs/2106.05931
A Connection Between Score Matching and Denoising Autoencoders	https://direct.mit.edu/neco/article/23/7/1661-1674/7677
Maximum Likelihood Training of Score-Based Diffusion Models	http://arxiv.org/abs/2101.09258

License

MIT License

heychhavi/minimal-text-diffusion_old

Minimal text diffusion

Table of Contents

Getting started

Setup

Preparing dataset

Training

Inference

Training from scratch on the greetings dataset

Experiments with using pre-trained models and embeddings

Controllable Generation

Gory details

Training

Evolving input

Sampling

TODO

Opportunities for further minimization

Acknowledgements

License