open replication of the prior about dalle2-pytorch HOT 36 CLOSED

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https://huggingface.co/rom1504/dalle2-diffusion-prior/resolve/main/1651432174.5708027_saved_model.pth here's a first checkpoint by @krish240574
thanks to him for building the training code and running that first training!

it's time for the evaluation to start.

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btw I think CoCa should be trained dropping out the image features so you can generate captions with superconditioning (it should work well there for the same reason it works well when generating images, the typical image/caption pair in most training sets doesn't match all that well).

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@rom1504 update: Romain reported that a research group out there have replicated the prior using the code in this repository for their CLIP generations - in other words, the code in this repository works, and we have confirmation that the prior is effective, per paper

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ohhh you're right, well that makes thing much easier
I'll remove that from the plan

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I was able to over-fit a small subset of LAION-2B locally (using the new CLIP-less DiffusionPrior class). I'll be working to make that data loader next so we can do the first "real" training run.

referencing:

• use https://github.com/rom1504/embedding-reader to build a data loader
  • use laion2B-en 2B image/text embeddings https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/

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Cool!
The corresponding text embeddings are now in https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/

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You can use 2 instances of embedding reader and a zip to get both embeddings batch

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https://huggingface.co/rom1504/dalle2-diffusion-prior/resolve/main/1651432174.5708027_saved_model.pth here's a first checkpoint by @krish240574 thanks to him for building the training code and running that first training!

it's time for the evaluation to start.

Here is a run happening, 100 million data points -
https://wandb.ai/laion/diffusion-prior/runs/3o0ic6ou?workspace=user-krish240574

Hyperparameters -
learning_rate=0.001,
max_grad_norm=0.5,
weight_decay=0.01
batch_size=10 ** 4
Train-val-test = 0.7 - 0.2 - 0.1
Refer to https://github.com/lucidrains/DALLE2-pytorch/blob/main/train_diffusion_prior.py for training details.

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@lucidrains awesome, thanks for doing that!

Since we're starting to pull out meaningful results from our mini-prior, what would you recommend in terms of network hp params next?

I think I saw somewhere there was a discussion about moving to something like...?

# change to 12 layers, 128 dim, 16 heads
prior_network = DiffusionPriorNetwork(
    dim = 768,
    depth = 12,
    dim_head = 128,
    heads = 16
).cuda()
# i'd also like to try 1000 steps and compare results (open to thoughts on this)
diffusion_prior = DiffusionPrior(
    net = prior_network,
    clip = clip,
    timesteps = 1000,
    cond_drop_prob = 0.1
).cuda()

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@nousr no problem! :)

yea we can aim for maybe the same size as GPT2 small? https://huggingface.co/docs/transformers/model_doc/gpt2

so it would translate to

prior_network = DiffusionPriorNetwork(
    dim = 768,
    depth = 12,
    dim_head = 64,
    heads = 12
).cuda()

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Classifier-free guidance works fine with predicting x_0, I trained mine that way. :) As for training with the text encodings, I am feeding in the hidden states at the end of the frozen CLIP text encoder to my prior (along with the corresponding padding mask for attention) instead of trying to learn language from scratch. This works pretty well, a lot better than trying to feed in only the CLIP text embedding!

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i was also puzzled why they didn't take the output token of the image embedding

I tried this and it was worse/didn't learn as well. I suspect it might be easier to predict a clean x_0 without the residual bias from the noisy input (because it would have to generate exact anti-noise in the ffns and add it to that token's residual stream), whereas if you have a separate output token you just have to copy information into it via attention.

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I think so too because it can't just throw away as much information about the contents of the individual tokens bc it needs to do the next token prediction task too.

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btw I think CoCa should be trained dropping out the image features so you can generate captions with superconditioning (it should work well there for the same reason it works well when generating images, the typical image/caption pair in most training sets doesn't match all that well).

yes! agreed on the superconditioning!

last thought i've had that is worth sharing is perhaps there can be one more level of indirection. If one were to train CLIP with a multi-stage efficient ViT, we can do text -> text embed + text encoding -> image embed + image encoding (~49-64 tokens) -> image. in the ideal world, we have a 540B parameter PaLM + CoCa (with FILIP fine interactions between the text tokens and image tokens) + cascading DDPM generator of about 3B parameters conditioned on the image tokens from the hierarchical ViT. One can dream 😆

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@lucidrains have you started working on config-file support for the prior?

clarification: (I was thinking about tackling that to simplify the training script, but didn't wanna duplicate work!)

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@nousr hey yea i did, only partly. but the general scaffold is there that it shouldn't take too much code to convert it to be config-based (by looking at the decoder training script and what was done in dalle2_pytorch/train_configs.py as example)

this week i'm back to attending a bunch of meetings, so will be generally unproductive. feel free to jump in with a PR!

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I believe we got everything done here.
One last thing that would be helpful in my opinion would be a prior.md with these information:

• What is the prior and what can it do
• How to prepare a dataset for it
• How to run a training
• how to use a pretrained model
• What are good metrics
• how to plug into the decoder

I think that would close this topic.
I believe most of the information is already present in readme.md but having it in its dedicated file would help as the readme is getting very large now.

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@rom1504 ah looks like a great plan :) so i think the PCA portion was only for the autoregressive prior method, and not the diffusion prior (they needed to quantize so they can use the straightforward cross entropy loss). but correct me if i'm wrong i'm happy to build that into the framework

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I'll keep this issue updated as we make progress

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@rom1504
The text embedding you mentioned is from which network? Is it a clip?

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Yes, image and text embeddings link above are ViT-L/14 clip.
Check the laion5B blogpost for details

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@krish240574 so just a word of caution, that learning rate of 1e-3 is quite high for transformers. a more conservative value would be Karpathy's favorite, 3e-4 (however, I don't know how transformers behave within a DDPM framework, so I could be wrong)

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@krish240574 is running that new run with the newly added metrics https://wandb.ai/laion/diffusion-prior/runs/aul0rhv5?workspace=user-rom1504

seems cosine similarity is going up.

@lucidrains do you have any opinion on what would be the best way to know if the prior is doing its job? (except for plugging it into a generator and training of course)

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Seems like the prior is doing a great job here. Please share the training details and checkpoint and dataset if possible. Thanks.

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Dataset is
parser.add_argument("--image-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
parser.add_argument("--text-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")

IE laion2B-en clip ViT-l/14 text/image embeddings

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Here is a sample run, 600 million data points - https://wandb.ai/laion/diffusion-prior/runs/ar65uq6n?workspace=user-krish240574, hyperparameters as in the script(default values) train_diffision_prior.py

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Just wanted to make a note that Katherine recommended we use EMA while training the prior as well @lucidrains

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Just wanted to make a note that Katherine recommended we use EMA while training the prior as well @lucidrains

ohh got it, i can take care of that tomorrow morning 👍

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Just wanted to make a note that Katherine recommended we use EMA while training the prior as well @lucidrains
@nousr

740d644 ok all done! @crowsonkb thank you for the advice yet again 🙏

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in light of #71 should definitely be retrained at the latest version! i've also turned off classifier free guidance, since i'm uncertain if it works well with the predict x0 objective. final thought is that we should be training with the text encodings + corresponding text mask if possible. i think the paper did have this (although if Katherine was able to get good results without, let us aim for that first)

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Actually do you think I need learned queries if I am feeding in a sequence of text encoder hidden states? I don't have them and don't know how much they help. There are always at least two text encoder hidden states from the SOT and EOT tokens (for the null condition), more for actual prompts.

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Classifier-free guidance works fine with predicting x_0, I trained mine that way. :) As for training with the text encodings, I am feeding in the hidden states at the end of the frozen CLIP text encoder to my prior (along with the corresponding padding mask for attention) instead of trying to learn language from scratch. This works pretty well, a lot better than trying to feed in only the CLIP text embedding!

thanks for sharing! that's great to know! was wondering if this would work well given the l2norm constraint - i'll revert the commit next week so others can use it

and yes, i have things setup exactly like you did (the text encodings being the output of the final layer of the CLIP text transformer), so it should be ready to go, provided Laion can save and dataload the text encodings efficiently in addition to the text embeddings

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Actually do you think I need learned queries if I am feeding in a sequence of text encoder hidden states? I don't have them and don't know how much they help. There are always at least two text encoder hidden states from the SOT and EOT tokens (for the null condition), more for actual prompts.

so the literature is scant on this, but https://arxiv.org/abs/2006.11527 does suggest adding learned queries (up to 16) can be beneficial. i was also puzzled why they didn't take the output token of the image embedding, and one possibility is that they projected the "noised | predicted" image embedding into multiple tokens for attention (which i'll build into the repository as a setting eventually). the more realistic answer is no matter which token you choose, as long as the position information is there, and as long as the transformer is big enough, it won't matter that much 😆

tldr: it doesn't hurt to add a few memory (learned query) tokens. with a big enough transformer, probably matters little

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this is valuable information! thanks!

at the end of the day, I think the team still struggled with the generator not treating the text as a bag of words, and the bottleneck is the clip text encoder. I suspect the new Coca, which is an LM and Clip trained end to end, would help

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pretty much done now

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