fix classifier free guidance for diffusion prior, thanks to @jaykim9870 for spotting the issue

Fixed issues with clip and deepspeed fp16

.github/FUNDING.yml

@@ -1 +1 @@
-github: [lucidrains]
+github: [nousr, Veldrovive, lucidrains]

.github/workflows/ci.yml

@@ -0,0 +1,33 @@
+name: Continuous integration
+
+on:
+  push:
+    branches:
+    - main
+  pull_request:
+    branches:
+    - main
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.8]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install
+      run: |
+        python3 -m venv .env
+        source .env/bin/activate
+        make install
+    - name: Tests
+      run: |
+        source .env/bin/activate
+        make test
+

.gitignore

@@ -1,3 +1,12 @@
+# default experiment tracker data
+.tracker-data/
+
+# Configuration Files
+configs/*
+!configs/*.example
+!configs/*_defaults.py
+!configs/README.md
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -127,3 +136,5 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+.tracker_data
+*.pth

Makefile

@@ -0,0 +1,6 @@
+install:
+	pip install -U pip
+	pip install -e .
+
+test:
+	CUDA_VISIBLE_DEVICES= python train_decoder.py --config_file configs/train_decoder_config.test.json

README.md

@@ -10,9 +10,48 @@ The main novelty seems to be an extra layer of indirection with the prior networ
 
 This model is SOTA for text-to-image for now.
 
-Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in helping out with the replication
+Please join <a href="https://discord.gg/xBPBXfcFHd"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a> if you are interested in helping out with the replication with the <a href="https://laion.ai/">LAION</a> community | <a href="https://www.youtube.com/watch?v=AIOE1l1W0Tw">Yannic Interview</a>
 
-There was enough interest for a <a href="https://github.com/lucidrains/dalle2-jax">Jax version</a>. I will also eventually extend this to <a href="https://github.com/lucidrains/dalle2-video">text to video</a>, once the repository is in a good place.
+As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lucidrains/imagen-pytorch">here</a>. Jax versions as well as text-to-video project will be shifted towards the Imagen architecture, as it is way simpler.
+
+## Status
+
+- A research group has used the code in this repository to train a functional diffusion prior for their CLIP generations. Will share their work once they release their preprint. This, and <a href="https://github.com/crowsonkb">Katherine's</a> own experiments, validate OpenAI's finding that the extra prior increases variety of generations.
+
+- Decoder is now verified working for unconditional generation on my experimental setup for Oxford flowers. 2 researchers have also confirmed Decoder is working for them.
+
+<img src="./samples/oxford.png" width="450px" />
+
+*ongoing at 21k steps*
+
+- <a href="https://twitter.com/Buntworthy/status/1529475416775434240?t=0GEge3Kr9I36cjcUVCQUTg">Justin Pinkney</a> successfully trained the diffusion prior in the repository for his CLIP to Stylegan2 text-to-image application
+
+- <a href="https://github.com/rom1504">Romain</a> has scaled up training to 800 GPUs with the available scripts without any issues
+
+## Pre-Trained Models
+
+- LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>.
+- Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧
+- Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/3d5rytsa?workspace=">Another test run with sparse attention</a>
+- DALL-E 2 🚧 - <a href="https://github.com/LAION-AI/dalle2-laion">DALL-E 2 Laion repository</a>
+
+## Appreciation
+
+This library would not have gotten to this working state without the help of
+
+- <a href="https://github.com/nousr">Zion</a> for the distributed training code for the diffusion prior
+- <a href="https://github.com/Veldrovive">Aidan</a> for the distributed training code for the decoder as well as the dataloaders
+- <a href="https://github.com/krish240574">Kumar</a> for working on the initial diffusion training script
+- <a href="https://github.com/rom1504">Romain</a> for the pull request reviews and project management
+- <a href="https://github.com/Ciaohe">He Cao</a> and <a href="https://github.com/xiankgx">xiankgx</a> for the Q&A and for identifying of critical bugs
+- <a href="https://github.com/marunine">Marunine</a> for identifying issues with resizing of the low resolution conditioner, when training the upsampler, in addition to various other bug fixes
+- <a href="https://github.com/malumadev">MalumaDev</a> for proposing the use of pixel shuffle upsampler for fixing checkboard artifacts
+- <a href="https://github.com/crowsonkb">Katherine</a> for her advice
+- <a href="https://stability.ai/">Stability AI</a> for the generous sponsorship
+- <a href="https://huggingface.co">🤗 Huggingface</a> and in particular <a href="https://github.com/sgugger">Sylvain</a> for the <a href="https://github.com/huggingface/accelerate">Accelerate</a> library
+- <a href="https://github.com/arogozhnikov">Alex</a> for <a href="https://github.com/arogozhnikov/einops">einops</a>, indispensable tool for tensor manipulation
+
+... and many others. Thank you! 🙏
 
 ## Install
 
@@ -318,7 +357,8 @@ prior_network = DiffusionPriorNetwork(
 diffusion_prior = DiffusionPrior(
     net = prior_network,
     clip = clip,
-    timesteps = 100,
+    timesteps = 1000,
+    sample_timesteps = 64,
     cond_drop_prob = 0.2
 ).cuda()
 
@@ -332,9 +372,11 @@ loss.backward()
 unet1 = Unet(
     dim = 128,
     image_embed_dim = 512,
+    text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    cond_on_text_encodings = True    # set to True for any unets that need to be conditioned on text encodings
 ).cuda()
 
 unet2 = Unet(
@@ -351,12 +393,11 @@ decoder = Decoder(
     clip = clip,
     timesteps = 100,
     image_cond_drop_prob = 0.1,
-    text_cond_drop_prob = 0.5,
-    condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 for unet_number in (1, 2):
-    loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
+    loss = decoder(images, text = text, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
     loss.backward()
 
 # do above for many steps
@@ -382,7 +423,7 @@ For the layperson, no worries, training will all be automated into a CLI tool, a
 
 ## Training on Preprocessed CLIP Embeddings
 
-It is likely, when scaling up, that you would first preprocess your images and text into corresponding embeddings before training the prior network. You can do so easily by simply passing in `image_embed`, `text_embed`, and optionally `text_encodings` and `text_mask`
+It is likely, when scaling up, that you would first preprocess your images and text into corresponding embeddings before training the prior network. You can do so easily by simply passing in `image_embed`, `text_embed`, and optionally `text_encodings`
 
 Working example below
 
@@ -508,7 +549,7 @@ To use a pretrained OpenAI CLIP, simply import `OpenAIClipAdapter` and pass it i
 import torch
 from dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder, OpenAIClipAdapter
 
-# openai pretrained clip - defaults to ViT/B-32
+# openai pretrained clip - defaults to ViT-B/32
 
 clip = OpenAIClipAdapter()
 
@@ -545,7 +586,9 @@ unet1 = Unet(
     image_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    text_embed_dim = 512,
+    cond_on_text_encodings = True  # set to True for any unets that need to be conditioned on text encodings (ex. first unet in cascade)
 ).cuda()
 
 unet2 = Unet(
@@ -560,14 +603,14 @@ decoder = Decoder(
     unet = (unet1, unet2),
     image_sizes = (128, 256),
     clip = clip,
-    timesteps = 100,
+    timesteps = 1000,
+    sample_timesteps = (250, 27),
     image_cond_drop_prob = 0.1,
-    text_cond_drop_prob = 0.5,
-    condition_on_text_encodings = False  # set this to True if you wish to condition on text during training and sampling
+    text_cond_drop_prob = 0.5
 ).cuda()
 
 for unet_number in (1, 2):
-    loss = decoder(images, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
+    loss = decoder(images, text = text, unet_number = unet_number) # this can optionally be decoder(images, text) if you wish to condition on the text encodings as well, though it was hinted in the paper it didn't do much
     loss.backward()
 
 # do above for many steps
@@ -585,8 +628,96 @@ images = dalle2(
 # save your image (in this example, of size 256x256)
 ```
 
+Alternatively, you can also use <a href="https://github.com/mlfoundations/open_clip">Open Clip</a>
+
+```bash
+$ pip install open-clip-torch
+```
+
+```python
+from dalle2_pytorch import OpenClipAdapter
+
+clip = OpenClipAdapter()
+```
+
 Now you'll just have to worry about training the Prior and the Decoder!
 
+## Inpainting
+
+Inpainting is also built into the `Decoder`. You simply have to pass in the `inpaint_image` and `inpaint_mask` (boolean tensor where `True` indicates which regions of the inpaint image to keep)
+
+This repository uses the formulation put forth by <a href="https://arxiv.org/abs/2201.09865">Lugmayr et al. in Repaint</a>
+
+```python
+import torch
+from dalle2_pytorch import Unet, Decoder, CLIP
+
+# trained clip from step 1
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 6,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 6,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+).cuda()
+
+# 2 unets for the decoder (a la cascading DDPM)
+
+unet = Unet(
+    dim = 16,
+    image_embed_dim = 512,
+    cond_dim = 128,
+    channels = 3,
+    dim_mults = (1, 1, 1, 1)
+).cuda()
+
+
+# decoder, which contains the unet(s) and clip
+
+decoder = Decoder(
+    clip = clip,
+    unet = (unet,),               # insert both unets in order of low resolution to highest resolution (you can have as many stages as you want here)
+    image_sizes = (256,),         # resolutions, 256 for first unet, 512 for second. these must be unique and in ascending order (matches with the unets passed in)
+    timesteps = 1000,
+    image_cond_drop_prob = 0.1,
+    text_cond_drop_prob = 0.5
+).cuda()
+
+# mock images (get a lot of this)
+
+images = torch.randn(4, 3, 256, 256).cuda()
+
+# feed images into decoder, specifying which unet you want to train
+# each unet can be trained separately, which is one of the benefits of the cascading DDPM scheme
+
+loss = decoder(images, unet_number = 1)
+loss.backward()
+
+# do the above for many steps for both unets
+
+mock_image_embed = torch.randn(1, 512).cuda()
+
+# then to do inpainting
+
+inpaint_image = torch.randn(1, 3, 256, 256).cuda()      # (batch, channels, height, width)
+inpaint_mask = torch.ones(1, 256, 256).bool().cuda()    # (batch, height, width)
+
+inpainted_images = decoder.sample(
+    image_embed = mock_image_embed,
+    inpaint_image = inpaint_image,    # just pass in the inpaint image
+    inpaint_mask = inpaint_mask       # and the mask
+)
+
+inpainted_images.shape # (1, 3, 256, 256)
+```
+
 ## Experimental
 
 ### DALL-E2 with Latent Diffusion
@@ -706,7 +837,7 @@ mock_image_embed = torch.randn(1, 512).cuda()
 images = decoder.sample(mock_image_embed) # (1, 3, 1024, 1024)
 ```
 
-## Training wrapper (wip)
+## Training wrapper
 
 ### Decoder Training
 
@@ -732,8 +863,8 @@ clip = CLIP(
 
 # mock data
 
-text = torch.randint(0, 49408, (4, 256)).cuda()
-images = torch.randn(4, 3, 256, 256).cuda()
+text = torch.randint(0, 49408, (32, 256)).cuda()
+images = torch.randn(32, 3, 256, 256).cuda()
 
 # decoder (with unet)
 
@@ -743,25 +874,23 @@ unet1 = Unet(
     text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
-    dim_mults=(1, 2, 4, 8)
+    dim_mults=(1, 2, 4, 8),
+    cond_on_text_encodings = True,
 ).cuda()
 
 unet2 = Unet(
     dim = 16,
     image_embed_dim = 512,
-    text_embed_dim = 512,
     cond_dim = 128,
     channels = 3,
     dim_mults = (1, 2, 4, 8, 16),
-    cond_on_text_encodings = True
 ).cuda()
 
 decoder = Decoder(
     unet = (unet1, unet2),
     image_sizes = (128, 256),
     clip = clip,
-    timesteps = 1000,
-    condition_on_text_encodings = True
+    timesteps = 1000
 ).cuda()
 
 decoder_trainer = DecoderTrainer(
@@ -774,31 +903,187 @@ decoder_trainer = DecoderTrainer(
 )
 
 for unet_number in (1, 2):
-    loss = decoder_trainer(images, text = text, unet_number = unet_number)  # use the decoder_trainer forward
-    loss.backward()
+    loss = decoder_trainer(
+        images,
+        text = text,
+        unet_number = unet_number, # which unet to train on
+        max_batch_size = 4         # gradient accumulation - this sets the maximum batch size in which to do forward and backwards pass - for this example 32 / 4 == 8 times
+    )
 
     decoder_trainer.update(unet_number) # update the specific unet as well as its exponential moving average
 
 # after much training
 # you can sample from the exponentially moving averaged unets as so
 
-mock_image_embed = torch.randn(4, 512).cuda()
-images = decoder_trainer.sample(mock_image_embed, text = text) # (4, 3, 256, 256)
+mock_image_embed = torch.randn(32, 512).cuda()
+images = decoder_trainer.sample(image_embed = mock_image_embed, text = text) # (4, 3, 256, 256)
 ```
 
-## CLI (wip)
+### Diffusion Prior Training
 
-```bash
-$ dream 'sharing a sunset at the summit of mount everest with my dog'
+Similarly, one can use the `DiffusionPriorTrainer` to automatically instantiate and keep track of an exponential moving averaged prior.
+
+```python
+import torch
+from dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, DiffusionPriorTrainer, Unet, Decoder, CLIP
+
+clip = CLIP(
+    dim_text = 512,
+    dim_image = 512,
+    dim_latent = 512,
+    num_text_tokens = 49408,
+    text_enc_depth = 6,
+    text_seq_len = 256,
+    text_heads = 8,
+    visual_enc_depth = 6,
+    visual_image_size = 256,
+    visual_patch_size = 32,
+    visual_heads = 8
+).cuda()
+
+# mock data
+
+text = torch.randint(0, 49408, (512, 256)).cuda()
+images = torch.randn(512, 3, 256, 256).cuda()
+
+# prior networks (with transformer)
+
+prior_network = DiffusionPriorNetwork(
+    dim = 512,
+    depth = 6,
+    dim_head = 64,
+    heads = 8
+).cuda()
+
+diffusion_prior = DiffusionPrior(
+    net = prior_network,
+    clip = clip,
+    timesteps = 100,
+    cond_drop_prob = 0.2
+).cuda()
+
+diffusion_prior_trainer = DiffusionPriorTrainer(
+    diffusion_prior,
+    lr = 3e-4,
+    wd = 1e-2,
+    ema_beta = 0.99,
+    ema_update_after_step = 1000,
+    ema_update_every = 10,
+)
+
+loss = diffusion_prior_trainer(text, images, max_batch_size = 4)
+diffusion_prior_trainer.update()  # this will update the optimizer as well as the exponential moving averaged diffusion prior
+
+# after much of the above three lines in a loop
+# you can sample from the exponential moving average of the diffusion prior identically to how you do so for DiffusionPrior
+
+image_embeds = diffusion_prior_trainer.sample(text, max_batch_size = 4) # (512, 512) - exponential moving averaged image embeddings
 ```
 
-Once built, images will be saved to the same directory the command is invoked
+## Bonus
 
-<a href="https://github.com/lucidrains/big-sleep">template</a>
+### Unconditional Training
 
-## Training CLI (wip)
+The repository also contains the means to train unconditional DDPM model, or even cascading DDPMs. You simply have to set `unconditional = True` in the `Decoder`
 
-<a href="https://github.com/lucidrains/stylegan2-pytorch">template</a>
+ex.
+
+```python
+import torch
+from dalle2_pytorch import Unet, Decoder, DecoderTrainer
+
+# unet for the cascading ddpm
+
+unet1 = Unet(
+    dim = 128,
+    dim_mults=(1, 2, 4, 8)
+).cuda()
+
+unet2 = Unet(
+    dim = 32,
+    dim_mults = (1, 2, 4, 8, 16)
+).cuda()
+
+# decoder, which contains the unets
+
+decoder = Decoder(
+    unet = (unet1, unet2),
+    image_sizes = (256, 512),  # first unet up to 256px, then second to 512px
+    timesteps = 1000,
+    unconditional = True
+).cuda()
+
+# decoder trainer
+
+decoder_trainer = DecoderTrainer(decoder)
+
+# images (get a lot of this)
+
+images = torch.randn(1, 3, 512, 512).cuda()
+
+# feed images into decoder
+
+for i in (1, 2):
+    loss = decoder_trainer(images, unet_number = i)
+    decoder_trainer.update(unet_number = i)
+
+# do the above for many many many many images
+# then it will learn to generate images
+
+images = decoder_trainer.sample(batch_size = 36, max_batch_size = 4) # (36, 3, 512, 512)
+```
+
+## Dataloaders
+
+### Decoder Dataloaders
+
+In order to make loading data simple and efficient, we include some general dataloaders that can be used to train portions of the network.
+
+#### Decoder: Image Embedding Dataset
+
+When training the decoder (and up samplers if training together) in isolation, you will need to load images and corresponding image embeddings. This dataset can read two similar types of datasets. First, it can read a [webdataset](https://github.com/webdataset/webdataset) that contains `.jpg` and `.npy` files in the `.tar`s that contain the images and associated image embeddings respectively. Alternatively, you can also specify a source for the embeddings outside of the webdataset. In this case, the path to the embeddings should contain `.npy` files with the same shard numbers as the webdataset and there should be a correspondence between the filename of the `.jpg` and the index of the embedding in the `.npy`. So, for example, `0001.tar` from the webdataset with image `00010509.jpg` (the first 4 digits are the shard number and the last 4 are the index) in it should be paralleled by a `img_emb_0001.npy` which contains a NumPy array with the embedding at index 509.
+
+Generating a dataset of this type: 
+1. Use [img2dataset](https://github.com/rom1504/img2dataset) to generate a webdataset.
+2. Use [clip-retrieval](https://github.com/rom1504/clip-retrieval) to convert the images to embeddings.
+3. Use [embedding-dataset-reordering](https://github.com/Veldrovive/embedding-dataset-reordering) to reorder the embeddings into the expected format.
+
+Usage:
+
+```python
+from dalle2_pytorch.dataloaders import ImageEmbeddingDataset, create_image_embedding_dataloader
+
+# Create a dataloader directly.
+dataloader = create_image_embedding_dataloader(
+    tar_url="/path/or/url/to/webdataset/{0000..9999}.tar", # Uses bracket expanding notation. This specifies to read all tars from 0000.tar to 9999.tar
+    embeddings_url="path/or/url/to/embeddings/folder",     # Included if .npy files are not in webdataset. Left out or set to None otherwise
+    num_workers=4,
+    batch_size=32,
+    shard_width=4,                                         # If a file in the webdataset shard 3 is named 0003039.jpg, we know the shard width is 4 and the last three digits are the index
+    shuffle_num=200,                                       # Does a shuffle of the data with a buffer size of 200
+    shuffle_shards=True,                                   # Shuffle the order the shards are read in
+    resample_shards=False,                                 # Sample shards with replacement. If true, an epoch will be infinite unless stopped manually
+)
+for img, emb in dataloader:
+    print(img.shape)  # torch.Size([32, 3, 256, 256])
+    print(emb.shape)  # torch.Size([32, 512])
+    # Train decoder only as shown above
+
+# Or create a dataset without a loader so you can configure it manually
+dataset = ImageEmbeddingDataset(
+    urls="/path/or/url/to/webdataset/{0000..9999}.tar",
+    embedding_folder_url="path/or/url/to/embeddings/folder",
+    shard_width=4,
+    shuffle_shards=True,
+    resample=False
+)
+```
+
+### Scripts
+
+#### `train_diffusion_prior.py`
+
+For detailed information on training the diffusion prior, please refer to the [dedicated readme](prior.md)
 
 ## Todo
 
@@ -821,21 +1106,27 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
 - [x] bring in tools to train vqgan-vae
 - [x] add convnext backbone for vqgan-vae (in addition to vit [vit-vqgan] + resnet)
-- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo)
-- [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
-- [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
-- [ ] pull logic for training diffusion prior into a class DiffusionPriorTrainer, for eventual script based + CLI based training
-- [ ] train on a toy task, offer in colab
-- [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
-- [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
-- [ ] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
-- [ ] figure out if possible to augment with external memory, as described in https://arxiv.org/abs/2204.11824
-- [ ] test out grid attention in cascading ddpm locally, decide whether to keep or remove
-- [ ] use an experimental tracker agnostic setup, as done <a href="https://github.com/lucidrains/tf-bind-transformer#simple-trainer-class-for-fine-tuning">here</a>
-- [ ] make sure for the latter unets in the cascade, one can train on crops for learning super resolution (constrain the unet to be only convolutions in that case, or allow conv-like attention with rel pos bias)
+- [x] make sure DDPMs can be run with traditional resnet blocks (but leave convnext as an option for experimentation)
+- [x] make sure for the latter unets in the cascade, one can train on crops for learning super resolution (constrain the unet to be only convolutions in that case, or allow conv-like attention with rel pos bias)
+- [x] offer setting in diffusion prior to split time and image embeddings into multiple tokens, configurable, for more surface area during attention
+- [x] make sure resnet hyperparameters can be configurable across unet depth (groups and expansion factor)
+- [x] pull logic for training diffusion prior into a class DiffusionPriorTrainer, for eventual script based + CLI based training
+- [x] make sure the cascading ddpm in the repository can be trained unconditionally, offer a one-line CLI tool for training on a folder of images
+- [x] bring in cross-scale embedding from iclr paper https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/crossformer.py#L14
+- [x] cross embed layers for downsampling, as an option
+- [x] use an experimental tracker agnostic setup, as done <a href="https://github.com/lucidrains/tf-bind-transformer#simple-trainer-class-for-fine-tuning">here</a>
+- [x] use pydantic for config drive training
+- [x] for both diffusion prior and decoder, all exponential moving averaged models needs to be saved and restored as well (as well as the step number)
+- [x] offer save / load methods on the trainer classes to automatically take care of state dicts for scalers / optimizers / saving versions and checking for breaking changes
+- [x] allow for creation of diffusion prior model off pydantic config classes - consider the same for tracker configs
+- [x] bring in skip-layer excitations (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training (doesnt work well)
+- [x] test out grid attention in cascading ddpm locally, decide whether to keep or remove https://arxiv.org/abs/2204.01697 (keeping, seems to be fine)
+- [x] allow for unet to be able to condition non-cross attention style as well
+- [x] speed up inference, read up on papers (ddim)
+- [x] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
+- [x] add the final combination of upsample feature maps, used in unet squared, seems to have an effect in local experiments
+- [ ] consider elucidated dalle2 https://arxiv.org/abs/2206.00364
 - [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
-- [ ] make sure FILIP works with DALL-E2 from x-clip https://arxiv.org/abs/2111.07783
-- [ ] make sure DDPMs can be run with traditional resnet blocks (but leave convnext as an option for experimentation)
 
 ## Citations
 
@@ -865,14 +1156,6 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
-```bibtex
-@inproceedings{Liu2022ACF,
-    title   = {A ConvNet for the 2020s},
-    author  = {Zhuang Liu and Hanzi Mao and Chaozheng Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
-    year    = {2022}
-}
-```
-
 ```bibtex
 @article{shen2019efficient,
     author  = {Zhuoran Shen and Mingyuan Zhang and Haiyu Zhao and Shuai Yi and Hongsheng Li},
@@ -883,14 +1166,6 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
-```bibtex
-@inproceedings{Tu2022MaxViTMV,
-    title   = {MaxViT: Multi-Axis Vision Transformer},
-    author  = {Zhe-Wei Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
-    year    = {2022}
-}
-```
-
 ```bibtex
 @article{Yu2021VectorquantizedIM,
     title   = {Vector-quantized Image Modeling with Improved VQGAN},
@@ -911,4 +1186,103 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
+```bibtex
+@article{Yu2022CoCaCC,
+    title   = {CoCa: Contrastive Captioners are Image-Text Foundation Models},
+    author  = {Jiahui Yu and Zirui Wang and Vijay Vasudevan and Legg Yeung and Mojtaba Seyedhosseini and Yonghui Wu},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2205.01917}
+}
+```
+
+```bibtex
+@misc{wang2021crossformer,
+    title   = {CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention},
+    author  = {Wenxiao Wang and Lu Yao and Long Chen and Binbin Lin and Deng Cai and Xiaofei He and Wei Liu},
+    year    = {2021},
+    eprint  = {2108.00154},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
+```bibtex
+@article{ho2021cascaded,
+    title   = {Cascaded Diffusion Models for High Fidelity Image Generation},
+    author  = {Ho, Jonathan and Saharia, Chitwan and Chan, William and Fleet, David J and Norouzi, Mohammad and Salimans, Tim},
+    journal = {arXiv preprint arXiv:2106.15282},
+    year    = {2021}
+}
+```
+
+```bibtex
+@misc{Saharia2022,
+    title   = {Imagen: unprecedented photorealism × deep level of language understanding},
+    author  = {Chitwan Saharia*, William Chan*, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi*},
+    year    = {2022}
+}
+```
+
+```bibtex
+@article{Choi2022PerceptionPT,
+    title   = {Perception Prioritized Training of Diffusion Models},
+    author  = {Jooyoung Choi and Jungbeom Lee and Chaehun Shin and Sungwon Kim and Hyunwoo J. Kim and Sung-Hoon Yoon},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2204.00227}
+}
+```
+
+```bibtex
+@article{Saharia2021PaletteID,
+    title   = {Palette: Image-to-Image Diffusion Models},
+    author  = {Chitwan Saharia and William Chan and Huiwen Chang and Chris A. Lee and Jonathan Ho and Tim Salimans and David J. Fleet and Mohammad Norouzi},
+    journal = {ArXiv},
+    year    = {2021},
+    volume  = {abs/2111.05826}
+}
+```
+
+```bibtex
+@article{Lugmayr2022RePaintIU,
+    title   = {RePaint: Inpainting using Denoising Diffusion Probabilistic Models},
+    author  = {Andreas Lugmayr and Martin Danelljan and Andr{\'e}s Romero and Fisher Yu and Radu Timofte and Luc Van Gool},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2201.09865}
+}
+```
+
+```bibtex
+@misc{chen2022analog,
+    title   = {Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning},
+    author  = {Ting Chen and Ruixiang Zhang and Geoffrey Hinton},
+    year    = {2022},
+    eprint  = {2208.04202},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
+```bibtex
+@article{Qiao2019WeightS,
+    title   = {Weight Standardization},
+    author  = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Loddon Yuille},
+    journal = {ArXiv},
+    year    = {2019},
+    volume  = {abs/1903.10520}
+}
+```
+
+```bibtex
+@inproceedings{rogozhnikov2022einops,
+    title   = {Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation},
+    author  = {Alex Rogozhnikov},
+    booktitle = {International Conference on Learning Representations},
+    year    = {2022},
+    url     = {https://openreview.net/forum?id=oapKSVM2bcj}
+}
+```
+
 *Creating noise from data is easy; creating data from noise is generative modeling.* - <a href="https://arxiv.org/abs/2011.13456">Yang Song's paper</a>

configs/README.md

@@ -0,0 +1,185 @@
+## DALLE2 Training Configurations
+
+For more complex configuration, we provide the option of using a configuration file instead of command line arguments.
+
+### Decoder Trainer
+
+The decoder trainer has 7 main configuration options. A full example of their use can be found in the [example decoder configuration](train_decoder_config.example.json).
+
+**<ins>Unet</ins>:**
+
+This is a single unet config, which belongs as an array nested under the decoder config as a list of `unets`
+
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `dim`  | Yes      | N/A     | The starting channels of the unet. |
+| `image_embed_dim` | Yes | N/A | The dimension of the image embeddings. |
+| `dim_mults` | No | `(1, 2, 4, 8)` | The growth factors of the channels. |
+
+Any parameter from the `Unet` constructor can also be given here.
+
+**<ins>Decoder</ins>:**
+
+Defines the configuration options for the decoder model. The unets defined above will automatically be inserted.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `unets` | Yes | N/A | A list of unets, using the configuration above |
+| `image_sizes` | Yes | N/A | The resolution of the image after each upsampling step. The length of this array should be the number of unets defined. |
+| `image_size` | Yes | N/A | Not used. Can be any number. |
+| `timesteps` | No | `1000` | The number of diffusion timesteps used for generation. |
+| `loss_type` | No | `l2` | The loss function. Options are `l1`, `huber`, or `l2`. |
+| `beta_schedule` | No | `cosine` | The noising schedule. Options are `cosine`, `linear`, `quadratic`, `jsd`, or `sigmoid`. |
+| `learned_variance` | No | `True` | Whether to learn the variance. |
+| `clip` | No | `None` | The clip model to use if embeddings are being generated on the fly. Takes keys `make` and `model` with defaults `openai` and `ViT-L/14`. |
+
+Any parameter from the `Decoder` constructor can also be given here.
+
+**<ins>Data</ins>:**
+
+Settings for creation of the dataloaders.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `webdataset_base_url` | Yes | N/A | The url of a shard in the webdataset with the shard replaced with `{}`[^1]. |
+| `img_embeddings_url` | No | `None` | The url of the folder containing image embeddings shards. Not required if embeddings are in webdataset or clip is being used. |
+| `text_embeddings_url` | No | `None` | The url of the folder containing text embeddings shards. Not required if embeddings are in webdataset or clip is being used. |
+| `num_workers` | No | `4` | The number of workers used in the dataloader. |
+| `batch_size` | No | `64` | The batch size. |
+| `start_shard` | No | `0` | Defines the start of the shard range the dataset will recall. |
+| `end_shard` | No | `9999999` | Defines the end of the shard range the dataset will recall. |
+| `shard_width` | No | `6` | Defines the width of one webdataset shard number[^2]. |
+| `index_width` | No | `4` | Defines the width of the index of a file inside a shard[^3]. |
+| `splits` | No | `{ "train": 0.75, "val": 0.15, "test": 0.1 }` | Defines the proportion of shards that will be allocated to the training, validation, and testing datasets. |
+| `shuffle_train` | No | `True` | Whether to shuffle the shards of the training dataset. |
+| `resample_train` | No | `False` | If true, shards will be randomly sampled with replacement from the datasets making the epoch length infinite if a limit is not set. Cannot be enabled if `shuffle_train` is enabled. |
+| `preprocessing` | No | `{ "ToTensor": True }` | Defines preprocessing applied to images from the datasets. |
+
+[^1]: If your shard files have the paths `protocol://path/to/shard/00104.tar`, then the base url would be `protocol://path/to/shard/{}.tar`. If you are using a protocol like `s3`, you need to pipe the tars. For example `pipe:s3cmd get s3://bucket/path/{}.tar -`.
+
+[^2]: This refers to the string length of the shard number for your webdataset shards. For instance, if your webdataset shard has the filename `00104.tar`, your shard length is 5.
+
+[^3]: Inside the webdataset `tar`, you have files named something like `001045945.jpg`. 5 of these characters refer to the shard, and 4 refer to the index of the file in the webdataset (shard is `001041` and index is `5945`). The `index_width` in this case is 4.
+
+**<ins>Train</ins>:**
+
+Settings for controlling the training hyperparameters.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `epochs` | No | `20` | The number of epochs in the training run. |
+| `lr` | No | `1e-4` | The learning rate. |
+| `wd` | No | `0.01` | The weight decay. |
+| `max_grad_norm`| No | `0.5` | The grad norm clipping. |
+| `save_every_n_samples` | No | `100000` | Samples will be generated and a checkpoint will be saved every `save_every_n_samples` samples. |
+| `cond_scale` | No | `1.0` | Conditioning scale to use for sampling. Can also be an array of values, one for each unet. |
+| `device` | No | `cuda:0` | The device to train on. |
+| `epoch_samples` | No | `None` | Limits the number of samples iterated through in each epoch. This must be set if resampling. None means no limit. |
+| `validation_samples` | No | `None` | The number of samples to use for validation. None mean the entire validation set. |
+| `use_ema` | No | `True` | Whether to use exponential moving average models for sampling. |
+| `ema_beta` | No | `0.99` | The ema coefficient. |
+| `unet_training_mask` | No | `None` | A boolean array of the same length as the number of unets. If false, the unet is frozen. A value of `None` trains all unets. |
+
+**<ins>Evaluate</ins>:**
+
+Defines which evaluation metrics will be used to test the model.
+Each metric can be enabled by setting its configuration. The configuration keys for each metric are defined by the torchmetrics constructors which will be linked.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `n_evaluation_samples` | No | `1000` | The number of samples to generate to test the model. |
+| `FID` | No | `None` | Setting to an object enables the [Frechet Inception Distance](https://torchmetrics.readthedocs.io/en/stable/image/frechet_inception_distance.html) metric. 
+| `IS` | No | `None` | Setting to an object enables the [Inception Score](https://torchmetrics.readthedocs.io/en/stable/image/inception_score.html) metric.
+| `KID` | No | `None` | Setting to an object enables the [Kernel Inception Distance](https://torchmetrics.readthedocs.io/en/stable/image/kernel_inception_distance.html) metric. |
+| `LPIPS` | No | `None` | Setting to an object enables the [Learned Perceptual Image Patch Similarity](https://torchmetrics.readthedocs.io/en/stable/image/learned_perceptual_image_patch_similarity.html) metric. |
+
+**<ins>Tracker</ins>:**
+
+Selects how the experiment will be tracked.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `data_path` | No | `./.tracker-data` | The path to the folder where temporary tracker data will be saved. |
+| `overwrite_data_path` | No | `False` | If true, the data path will be overwritten. Otherwise, you need to delete it yourself. |
+| `log` | Yes | N/A | Logging configuration. |
+| `load` | No | `None` | Checkpoint loading configuration. |
+| `save` | Yes | N/A | Checkpoint/Model saving configuration. |
+Tracking is split up into three sections:
+* Log: Where to save run metadata and image output. Options are `console` or `wandb`.
+* Load: Where to load a checkpoint from. Options are `local`, `url`, or `wandb`.
+* Save: Where to save a checkpoint to. Options are `local`, `huggingface`, or `wandb`.
+
+**Logging:**
+
+All loggers have the following keys:
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `log_type` | Yes | N/A | The type of logger class to use. |
+| `resume` | No | `False` | For loggers that have the option to resume an old run, resume it using maually input parameters. |
+| `auto_resume` | No | `False` | If true, the logger will attempt to resume an old run using parameters from that previous run. |
+
+If using `console` there is no further configuration than setting `log_type` to `console`.
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `log_type` | Yes | N/A | Must be `console`. |
+
+If using `wandb`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `log_type` | Yes | N/A | Must be `wandb`. |
+| `wandb_entity` | Yes | N/A | The wandb entity to log to. |
+| `wandb_project` | Yes | N/A | The wandb project save the run to. |
+| `wandb_run_name` | No | `None` | The wandb run name. |
+| `wandb_run_id` | No | `None` | The wandb run id. Used if resuming an old run. |
+
+**Loading:**
+
+All loaders have the following keys:
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `load_from` | Yes | N/A | The type of loader class to use. |
+| `only_auto_resume` | No | `False` | If true, the loader will only load the model if the run is being auto resumed. |
+
+If using `local`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `load_from` | Yes | N/A | Must be `local`. |
+| `file_path` | Yes | N/A | The path to the checkpoint file. |
+
+If using `url`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `load_from` | Yes | N/A | Must be `url`. |
+| `url` | Yes | N/A | The url of the checkpoint file. |
+
+If using `wandb`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `load_from` | Yes | N/A | Must be `wandb`. |
+| `wandb_run_path` | No | `None` | The wandb run path. If `None`, uses the run that is being resumed. |
+| `wandb_file_path` | Yes | N/A | The path to the checkpoint file in the W&B file system. |
+
+**Saving:**
+Unlike `log` and `load`, `save` may be an array of options so that you can save to different locations in a run.
+
+All save locations have these configuration options
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `save_to` | Yes | N/A | Must be `local`, `huggingface`, or `wandb`. |
+| `save_latest_to` | No | `None` | Sets the relative path to save the latest model to. |
+| `save_best_to` | No | `None` | Sets the relative path to save the best model to every time the model has a lower validation loss than all previous models. |
+| `save_meta_to` | No | `None` | The path to save metadata files in. This includes the config files used to start the training. |
+| `save_type` | No | `checkpoint` | The type of save. `checkpoint` saves a checkpoint, `model` saves a model without any fluff (Saves with ema if ema is enabled). |
+
+If using `local`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `save_to` | Yes | N/A | Must be `local`. |
+
+If using `huggingface`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `save_to` | Yes | N/A | Must be `huggingface`. |
+| `huggingface_repo` | Yes | N/A | The huggingface repository to save to. |
+| `token_path` | No | `None` | If logging in with the huggingface cli is not possible, point to a token file instead. |
+
+If using `wandb`
+| Option | Required | Default | Description |
+| ------ | -------- | ------- | ----------- |
+| `save_to` | Yes | N/A | Must be `wandb`. |
+| `wandb_run_path` | No | `None` | The wandb run path. If `None`, uses the current run. You will almost always want this to be `None`. |

configs/train_decoder_config.example.json

@@ -0,0 +1,109 @@
+{
+    "decoder": {
+        "unets": [
+            {
+                "dim": 128,
+                "image_embed_dim": 768,
+                "cond_dim": 64,
+                "channels": 3,
+                "dim_mults": [1, 2, 4, 8],
+                "attn_dim_head": 32,
+                "attn_heads": 16
+            }
+        ],
+        "image_sizes": [64],
+        "channels": 3,
+        "timesteps": 1000,
+        "loss_type": "l2",
+        "beta_schedule": ["cosine"],
+        "learned_variance": true
+    },
+    "data": {
+        "webdataset_base_url": "pipe:s3cmd get s3://bucket/path/{}.tar -",
+        "img_embeddings_url": "s3://bucket/img_embeddings/path/",
+        "num_workers": 4,
+        "batch_size": 64,
+        "start_shard": 0,
+        "end_shard": 9999999,
+        "shard_width": 6,
+        "index_width": 4,
+        "splits": {
+            "train": 0.75,
+            "val": 0.15,
+            "test": 0.1
+        },
+        "shuffle_train": true,
+        "resample_train": false,
+        "preprocessing": {
+            "RandomResizedCrop": {
+                "size": [128, 128],
+                "scale": [0.75, 1.0],
+                "ratio": [1.0, 1.0]
+            },
+            "ToTensor": true
+        }
+    },
+    "train": {
+        "epochs": 20,
+        "lr": 1e-4,
+        "wd": 0.01,
+        "max_grad_norm": 0.5,
+        "save_every_n_samples": 100000,
+        "n_sample_images": 6,
+        "device": "cuda:0",
+        "epoch_samples": null,
+        "validation_samples": null,
+        "use_ema": true,
+        "ema_beta": 0.99,
+        "amp": false,
+        "unet_training_mask": [true]
+    },
+    "evaluate": {
+        "n_evaluation_samples": 1000,
+        "FID": {
+            "feature": 64
+        },
+        "IS": {
+            "feature": 64,
+            "splits": 10
+        },
+        "KID": {
+            "feature": 64,
+            "subset_size": 10
+        },
+        "LPIPS": {
+            "net_type": "vgg",
+            "reduction": "mean"
+        }
+    },
+    "tracker": {
+        "overwrite_data_path": true,
+
+        "log": {
+            "log_type": "wandb",
+
+            "wandb_entity": "your_wandb",
+            "wandb_project": "your_project",
+
+            "verbose": true
+        },
+
+        "load": {
+            "load_from": null
+        },
+
+        "save": [{
+            "save_to": "wandb",
+            "save_latest_to": "latest.pth"
+        }, {
+            "save_to": "huggingface",
+            "huggingface_repo": "Veldrovive/test_model",
+
+            "save_latest_to": "path/to/model_dir/latest.pth",
+            "save_best_to": "path/to/model_dir/best.pth",
+            "save_meta_to": "path/to/directory/for/assorted/files",
+
+            "save_type": "model"
+        }]
+    }
+}

configs/train_decoder_config.test.json

@@ -0,0 +1,100 @@
+{
+    "decoder": {
+        "unets": [
+            {
+                "dim": 16,
+                "image_embed_dim": 768,
+                "cond_dim": 16,
+                "channels": 3,
+                "dim_mults": [1, 2, 4, 8],
+                "attn_dim_head": 16,
+                "attn_heads": 4,
+		"self_attn": [false, true, true, true]
+            }
+        ],
+        "clip": {
+            "make": "openai",
+            "model": "ViT-L/14"
+        },
+
+	"timesteps": 10,
+        "image_sizes": [64],
+        "channels": 3,
+        "loss_type": "l2",
+        "beta_schedule": ["cosine"],
+        "learned_variance": true
+    },
+    "data": {
+        "webdataset_base_url": "test_data/{}.tar",
+        "num_workers": 4,
+        "batch_size": 4,
+        "start_shard": 0,
+        "end_shard": 9,
+        "shard_width": 1,
+        "index_width": 1,
+        "splits": {
+            "train": 0.75,
+            "val": 0.15,
+            "test": 0.1
+        },
+        "shuffle_train": false,
+        "resample_train": true,
+        "preprocessing": {
+            "RandomResizedCrop": {
+                "size": [224, 224],
+                "scale": [0.75, 1.0],
+                "ratio": [1.0, 1.0]
+            },
+            "ToTensor": true
+        }
+    },
+    "train": {
+        "epochs": 1,
+        "lr": 1e-16,
+        "wd": 0.01,
+        "max_grad_norm": 0.5,
+        "save_every_n_samples": 100,
+        "n_sample_images": 1,
+        "device": "cpu",
+        "epoch_samples": 50,
+        "validation_samples": 5,
+        "use_ema": true,
+        "ema_beta": 0.99,
+        "amp": false,
+        "unet_training_mask": [true]
+    },
+    "evaluate": {
+        "n_evaluation_samples": 2,
+        "FID": {
+            "feature": 64
+        },
+        "IS": {
+            "feature": 64,
+            "splits": 10
+        },
+        "KID": {
+            "feature": 64,
+            "subset_size": 2
+        },
+        "LPIPS": {
+            "net_type": "vgg",
+            "reduction": "mean"
+        }
+    },
+    "tracker": {
+        "overwrite_data_path": true,
+
+	"log": {
+            "log_type": "console"
+	},
+
+        "load": {
+            "load_from": null
+        },
+
+       "save": [{
+            "save_to": "local",
+            "save_latest_to": "latest.pth"
+        }]
+    }
+}

configs/train_prior_config.example.json

@@ -0,0 +1,81 @@
+{
+    "prior": {
+        "clip": {
+            "make": "openai",
+            "model": "ViT-L/14"
+        },
+        "net": {
+            "dim": 768,
+            "depth": 12,
+            "num_timesteps": 1000,
+            "max_text_len": 77,
+            "num_time_embeds": 1,
+            "num_image_embeds": 1,
+            "num_text_embeds": 1,
+            "dim_head": 64,
+            "heads": 12,
+            "ff_mult": 4,
+            "norm_out": true,
+            "attn_dropout": 0.05,
+            "ff_dropout": 0.05,
+            "final_proj": true,
+            "normformer": true,
+            "rotary_emb": true
+        },
+        "image_embed_dim": 768,
+        "image_size": 224,
+        "image_channels": 3,
+        "timesteps": 1000,
+        "sample_timesteps": 64,
+        "cond_drop_prob": 0.1,
+        "loss_type": "l2",
+        "predict_x_start": true,
+        "beta_schedule": "cosine",
+        "condition_on_text_encodings": true
+    },
+    "data": {
+        "batch_size": 128,
+        "num_data_points": 100000,
+        "eval_every_seconds": 1600,
+        "image_url": "<path to your images>",
+        "meta_url": "<path to your metadata>",
+        "splits": {
+            "train": 0.8,
+            "val":  0.1,
+            "test": 0.1
+        }
+    },
+    "train": {
+        "epochs": 5,
+        "lr": 1.1e-4,
+        "wd": 6.02e-2,
+        "max_grad_norm": 0.5,
+        "use_ema": true,
+        "ema_beta": 0.9999,
+        "ema_update_after_step": 50,
+        "warmup_steps": 50,
+        "amp": false,
+        "save_every_seconds": 3600,
+        "eval_timesteps": [64, 1000],
+        "random_seed": 84513
+    },
+    "tracker": {
+        "data_path": ".prior",
+        "overwrite_data_path": true,
+        "log": {
+            "log_type": "wandb",
+            "wandb_entity": "<your wandb username>",
+            "wandb_project": "prior_debugging",
+            "wandb_resume": false,
+            "verbose": true
+        },
+        "save": [
+            {
+                "save_to": "local",
+                "save_type": "checkpoint",
+                "save_latest_to": ".prior/latest_checkpoint.pth",
+                "save_best_to": ".prior/best_checkpoint.pth"
+            }
+        ]
+    }
+}

dalle2_pytorch/__init__.py

@@ -1,6 +1,7 @@
+from dalle2_pytorch.version import __version__
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
 from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter
-from dalle2_pytorch.train import DecoderTrainer
+from dalle2_pytorch.trainer import DecoderTrainer, DiffusionPriorTrainer
 
 from dalle2_pytorch.vqgan_vae import VQGanVAE
 from x_clip import CLIP

dalle2_pytorch/dataloaders/README.md

@@ -0,0 +1,75 @@
+## Dataloaders
+In order to make loading data simple and efficient, we include some general dataloaders that can be used to train portions of the network.
+
+### Decoder: Image Embedding Dataset
+When training the decoder (and up samplers if training together) in isolation, you will need to load images and corresponding image embeddings. This dataset can read two similar types of datasets. First, it can read a [webdataset](https://github.com/webdataset/webdataset) that contains `.jpg` and `.npy` files in the `.tar`s that contain the images and associated image embeddings respectively. Alternatively, you can also specify a source for the embeddings outside of the webdataset. In this case, the path to the embeddings should contain `.npy` files with the same shard numbers as the webdataset and there should be a correspondence between the filename of the `.jpg` and the index of the embedding in the `.npy`. So, for example, `0001.tar` from the webdataset with image `00010509.jpg` (the first 4 digits are the shard number and the last 4 are the index) in it should be paralleled by a `img_emb_0001.npy` which contains a NumPy array with the embedding at index 509.
+
+Generating a dataset of this type:
+1. Use [img2dataset](https://github.com/rom1504/img2dataset) to generate a webdataset.
+2. Use [clip-retrieval](https://github.com/rom1504/clip-retrieval) to convert the images to embeddings.
+3. Use [embedding-dataset-reordering](https://github.com/Veldrovive/embedding-dataset-reordering) to reorder the embeddings into the expected format.
+
+Usage:
+```python
+from dalle2_pytorch.dataloaders import ImageEmbeddingDataset, create_image_embedding_dataloader
+
+# Create a dataloader directly.
+dataloader = create_image_embedding_dataloader(
+    tar_url="/path/or/url/to/webdataset/{0000..9999}.tar", # Uses bracket expanding notation. This specifies to read all tars from 0000.tar to 9999.tar
+    embeddings_url="path/or/url/to/embeddings/folder",     # Included if .npy files are not in webdataset. Left out or set to None otherwise
+    num_workers=4,
+    batch_size=32,
+    shard_width=4,                                         # If a file in the webdataset shard 3 is named 0003039.jpg, we know the shard width is 4 and the last three digits are the index
+    shuffle_num=200,                                       # Does a shuffle of the data with a buffer size of 200
+    shuffle_shards=True,                                   # Shuffle the order the shards are read in
+    resample_shards=False,                                 # Sample shards with replacement. If true, an epoch will be infinite unless stopped manually
+)
+for img, emb in dataloader:
+    print(img.shape)  # torch.Size([32, 3, 256, 256])
+    print(emb.shape)  # torch.Size([32, 512])
+    # Train decoder only as shown above
+
+# Or create a dataset without a loader so you can configure it manually
+dataset = ImageEmbeddingDataset(
+    urls="/path/or/url/to/webdataset/{0000..9999}.tar",
+    embedding_folder_url="path/or/url/to/embeddings/folder",
+    shard_width=4,
+    shuffle_shards=True,
+    resample=False
+)
+```
+
+### Diffusion Prior: Prior Embedding Dataset
+When training the prior it is much more efficient to work with pre-computed embeddings. The `PriorEmbeddingDataset` class enables you to leverage the same script (with minimal modification) for both embedding-only and text-conditioned prior training. This saves you from having to worry about a lot of the boilerplate code.
+
+To utilize the `PriorEmbeddingDataset`, all you need to do is make a single call to `get_reader()` which will create `EmbeddingReader` object(s) for you. Afterwards, you can utilize `make_splits()` to cleanly create DataLoader objects from for your training run.
+
+If you are training in a distributed manner, `make_splits()` accepts `rank` and `world_size` arguments to properly distribute to each process. The defaults for these values are `rank=0` and `world_size=1`, so single-process training can safely ignore these parameters.
+
+Usage:
+```python
+from dalle2_pytorch.dataloaders import get_reader, make_splits
+
+# grab embeddings from some specified location
+IMG_URL = "data/img_emb/"
+META_URL = "data/meta/"
+
+reader = get_reader(text_conditioned=True, img_url=IMG_URL, meta_url=META_URL)
+
+# some config for training
+TRAIN_ARGS = {
+    "world_size": 3,
+    "text_conditioned": True,
+    "start": 0,
+    "num_data_points": 10000,
+    "batch_size": 2,
+    "train_split": 0.5,
+    "eval_split": 0.25,
+    "image_reader": reader,
+}
+
+# specifying a rank will handle allocation internally
+rank0_train, rank0_eval, rank0_test = make_splits(rank=0, **TRAIN_ARGS)
+rank1_train, rank1_eval, rank1_test = make_splits(rank=1, **TRAIN_ARGS)
+rank2_train, rank2_eval, rank2_test = make_splits(rank=2, **TRAIN_ARGS)
+```

dalle2_pytorch/dataloaders/__init__.py

@@ -0,0 +1,2 @@
+from dalle2_pytorch.dataloaders.decoder_loader import ImageEmbeddingDataset, create_image_embedding_dataloader
+from dalle2_pytorch.dataloaders.prior_loader import make_splits, get_reader, PriorEmbeddingDataset

dalle2_pytorch/dataloaders/decoder_loader.py

@@ -0,0 +1,266 @@
+import os
+import webdataset as wds
+import torch
+from torch.utils.data import DataLoader
+import numpy as np
+import fsspec
+import shutil
+
+def get_shard(filename):
+    """
+    Filenames with shards in them have a consistent structure that we can take advantage of
+    Standard structure: path/to/file/prefix_string_00001.ext
+    """
+    try:
+        return filename.split("_")[-1].split(".")[0]
+    except ValueError:
+        raise RuntimeError(f"Could not find shard for filename {filename}")
+
+def get_example_file(fs, path, file_format):
+    """
+    Given a file system and a file extension, return the example file
+    """
+    return fs.glob(os.path.join(path, f"*.{file_format}"))[0]
+
+def embedding_inserter(samples, embeddings_url, index_width, sample_key='npy', handler=wds.handlers.reraise_exception):
+    """Given a datum of {"__key__": str, "__url__": str, ...} adds the cooresponding embedding and yields"""
+    previous_tar_url = None
+    current_embeddings = None
+    # Get a reference to an abstract file system where the embeddings are stored
+    embeddings_fs, embeddings_path = fsspec.core.url_to_fs(embeddings_url)
+    example_embedding_file = get_example_file(embeddings_fs, embeddings_path, "npy")
+    example_embedding_shard = get_shard(example_embedding_file)
+    emb_shard_width = len(example_embedding_shard)
+    # Easier to get the basename without the shard once than search through for the correct file every time
+    embedding_file_basename = '_'.join(example_embedding_file.split("_")[:-1]) + "_"
+
+    def load_corresponding_embeds(tar_url):
+      """Finds and reads the npy files that contains embeddings for the given webdataset tar"""
+      shard = int(tar_url.split("/")[-1].split(".")[0])
+      embedding_url = embedding_file_basename + str(shard).zfill(emb_shard_width) + '.npy'
+      with embeddings_fs.open(embedding_url) as f:
+        data = np.load(f)
+      return torch.from_numpy(data)
+
+    for sample in samples:
+        try:
+            tar_url = sample["__url__"]
+            key = sample["__key__"]
+            if tar_url != previous_tar_url:
+                # If the tar changed, we need to download new embeddings
+                # This means if we shuffle before inserting it will load many more files than we expect and be very inefficient.
+                previous_tar_url = tar_url
+                current_embeddings = load_corresponding_embeds(tar_url)
+                
+            embedding_index = int(key[-index_width:])
+            embedding = current_embeddings[embedding_index]
+            # We need to check if this sample is nonzero. If it is, this embedding is not valid and we should continue to the next loop
+            if torch.count_nonzero(embedding) == 0:
+                raise RuntimeError(f"Webdataset had a sample, but no embedding was found. ImgShard: {key[:-index_width]} - Index: {key[-index_width:]}")
+            sample[sample_key] = embedding
+            yield sample
+        except Exception as exn:  # From wds implementation
+            if handler(exn):
+                continue
+            else:
+                break
+insert_embedding = wds.filters.pipelinefilter(embedding_inserter)
+
+def unassociated_shard_skipper(tarfiles, embeddings_url, handler=wds.handlers.reraise_exception):
+    """Finds if the is a corresponding embedding for the tarfile at { url: [URL] }"""
+    embeddings_fs, embeddings_path = fsspec.core.url_to_fs(embeddings_url)
+    embedding_files = embeddings_fs.ls(embeddings_path)
+    get_embedding_shard = lambda embedding_file: int(embedding_file.split("_")[-1].split(".")[0])
+    embedding_shards = set([get_embedding_shard(filename) for filename in embedding_files])  # Sets have O(1) check for member
+
+    get_tar_shard = lambda tar_file: int(tar_file.split("/")[-1].split(".")[0])
+    for tarfile in tarfiles:
+        try:
+            webdataset_shard = get_tar_shard(tarfile["url"])
+            # If this shard has an associated embeddings file, we pass it through. Otherwise we iterate until we do have one
+            if webdataset_shard in embedding_shards:
+                yield tarfile
+        except Exception as exn:  # From wds implementation
+            if handler(exn):
+                continue
+            else:
+                break
+skip_unassociated_shards = wds.filters.pipelinefilter(unassociated_shard_skipper)
+
+def join_embeddings(samples, handler=wds.handlers.reraise_exception):
+    """
+    Takes the img_emb and text_emb keys and turns them into one key "emb": { "text": text_emb, "img": img_emb }
+    either or both of text_emb and img_emb may not be in the sample so we only add the ones that exist
+    """
+    for sample in samples:
+        try:
+            sample['emb'] = {}
+            if 'text_emb' in sample:
+                sample['emb']['text'] = sample['text_emb']
+            if 'img_emb' in sample:
+                sample['emb']['img'] = sample['img_emb']
+            yield sample
+        except Exception as exn:  # From wds implementation
+            if handler(exn):
+                continue
+            else:
+                break
+
+def verify_keys(samples, required_keys, handler=wds.handlers.reraise_exception):
+    """
+    Requires that both the image and embedding are present in the sample
+    This is important to do as a user may forget they do not have embeddings in their webdataset and neglect to add them using the embedding_folder_url parameter.
+    """
+    for sample in samples:
+        try:
+            for key in required_keys:
+                assert key in sample, f"Sample {sample['__key__']} missing {key}. Has keys {sample.keys()}"
+            yield sample
+        except Exception as exn:  # From wds implementation
+            if handler(exn):
+                continue
+            else:
+                break
+key_verifier = wds.filters.pipelinefilter(verify_keys)
+
+class ImageEmbeddingDataset(wds.DataPipeline, wds.compat.FluidInterface):
+    """
+    A fluid interface wrapper for DataPipline that returns image embedding pairs
+    Reads embeddings as npy files from the webdataset if they exist. If embedding_folder_url is set, they will be inserted in from the alternate source.
+    """
+
+    def __init__(
+            self,
+            urls,
+            img_embedding_folder_url=None,
+            text_embedding_folder_url=None,
+            index_width=None,
+            img_preproc=None,
+            extra_keys=[],
+            handler=wds.handlers.reraise_exception,
+            resample=False,
+            shuffle_shards=True
+    ):
+        """
+        Modeled directly off of the WebDataset constructor
+
+        :param urls: A url pointing to the tar files of the webdataset formatted as /path/to/webdataset/{0000..9999}.tar
+        :param embedding_folder_url: Required if webdataset does not contain embeddings. A url pointing to the npy files of the embeddings. Should have the same number of shards as the webdataset.
+            Webdataset image keys should align with the index of the embedding. This means missing image indices must have a corresponding embedding of all zeros.
+        :param index_width: The number of digits in the index. This is used to align the embedding index with the image index.
+            For example, if a file in the webdataset shard 3 is named 0003039.jpg, we know the shard is 4 digits and the last 3 digits are the index_width.
+        :param img_preproc: This function is run on the img before it is batched and returned. Useful for data augmentation or converting to torch tensor.
+        :param handler: A webdataset handler.
+        :param resample: If true, resample webdataset shards with replacement. You need to set your own epoch size if this is true since it will resample infinitely.
+        :param shuffle_shards: If true, shuffle the shards before resampling. This cannot be true if resample is true.
+
+
+        """
+        super().__init__()
+        keys = ["jpg", "emb"] + extra_keys
+        # if img_embedding_folder_url is not None:
+        #     keys.append("img_emb")
+        # if text_embedding_folder_url is not None:
+        #     keys.append("text_emb")
+        # keys.extend(extra_keys)
+        self.key_map = {key: i for i, key in enumerate(keys)}
+        self.resampling = resample
+        self.img_preproc = img_preproc
+        # If s3, check if s3fs is installed and s3cmd is installed and check if the data is piped instead of straight up
+        if (isinstance(urls, str) and "s3:" in urls) or (isinstance(urls, list) and any(["s3:" in url for url in urls])):
+            # Then this has an s3 link for the webdataset and we need extra packages
+            if shutil.which("s3cmd") is None:
+                raise RuntimeError("s3cmd is required for s3 webdataset")
+        if (img_embedding_folder_url is not None and "s3:" in img_embedding_folder_url) or (text_embedding_folder_url is not None and "s3:" in text_embedding_folder_url):
+            # Then the embeddings are being loaded from s3 and fsspec requires s3fs
+            try:
+                import s3fs
+            except ImportError:
+                raise RuntimeError("s3fs is required to load embeddings from s3")
+        # Add the shardList and randomize or resample if requested
+        if resample:
+            assert not shuffle_shards, "Cannot both resample and shuffle"
+            self.append(wds.ResampledShards(urls))
+        else:
+            self.append(wds.SimpleShardList(urls))
+            if shuffle_shards:
+                self.append(wds.filters.shuffle(1000))
+        
+        if img_embedding_folder_url is not None:
+            # There may be webdataset shards that do not have a embedding shard associated with it. If we do not skip these, they would cause issues.
+            self.append(skip_unassociated_shards(embeddings_url=img_embedding_folder_url, handler=handler))
+        if text_embedding_folder_url is not None:
+            self.append(skip_unassociated_shards(embeddings_url=text_embedding_folder_url, handler=handler))
+
+        self.append(wds.tarfile_to_samples(handler=handler))
+        self.append(wds.decode("pilrgb", handler=handler))
+        if img_embedding_folder_url is not None:
+            # Then we are loading image embeddings for a remote source
+            assert index_width is not None, "Reading embeddings separately requires index width length to be given"
+            self.append(insert_embedding(embeddings_url=img_embedding_folder_url, index_width=index_width, sample_key='img_emb', handler=handler))
+        if text_embedding_folder_url is not None:
+            # Then we are loading image embeddings for a remote source
+            assert index_width is not None, "Reading embeddings separately requires index width length to be given"
+            self.append(insert_embedding(embeddings_url=text_embedding_folder_url, index_width=index_width, sample_key='text_emb', handler=handler))
+        self.append(join_embeddings)
+        self.append(key_verifier(required_keys=keys, handler=handler))
+        # Apply preprocessing
+        self.append(wds.map(self.preproc))
+        self.append(wds.to_tuple(*keys))
+
+    def preproc(self, sample):
+        """Applies the preprocessing for images"""
+        if self.img_preproc is not None:
+            sample["jpg"] = self.img_preproc(sample["jpg"])
+        return sample
+
+def create_image_embedding_dataloader(
+    tar_url,
+    num_workers,
+    batch_size,
+    img_embeddings_url=None,
+    text_embeddings_url=None,
+    index_width=None,
+    shuffle_num = None,
+    shuffle_shards = True,
+    resample_shards = False, 
+    img_preproc=None,
+    extra_keys=[],
+    handler=wds.handlers.reraise_exception#warn_and_continue
+):
+    """
+    Convenience function to create an image embedding dataseta and dataloader in one line
+
+    :param tar_url: A url pointing to the tar files of the webdataset formatted as /path/to/webdataset/{0000..9999}.tar
+    :param num_workers: The number of workers to use for the dataloader
+    :param batch_size: The batch size to use for the dataloader
+    :param embeddings_url: Required if webdataset does not contain embeddings. A url pointing to the npy files of the embeddings. Should have the same number of shards as the webdataset.
+        Webdataset image keys should align with the index of the embedding. This means missing image indices must have a corresponding embedding of all zeros.
+    :param index_width: The number of digits in the index. This is used to align the embedding index with the image index.
+            For example, if a file in the webdataset shard 3 is named 0003039.jpg, we know the shard is 4 digits and the last 3 digits are the index_width.
+    :param shuffle_num: If not None, shuffle the dataset with this size buffer after sampling.
+    :param shuffle_shards: If true, shuffle the shards before sampling. This cannot be true if resample is true.
+    :param resample_shards: If true, resample webdataset shards with replacement. You need to set your own epoch size if this is true since it will resample infinitely.
+    :param handler: A webdataset handler.
+    """
+    ds = ImageEmbeddingDataset(
+        tar_url,
+        img_embedding_folder_url=img_embeddings_url,
+        text_embedding_folder_url=text_embeddings_url,
+        index_width=index_width,
+        shuffle_shards=shuffle_shards,
+        resample=resample_shards,
+        extra_keys=extra_keys,
+        img_preproc=img_preproc,
+        handler=handler
+    )
+    if shuffle_num is not None and shuffle_num > 0:
+        ds.shuffle(1000)
+    return DataLoader(
+        ds,
+        num_workers=num_workers,
+        batch_size=batch_size,
+        prefetch_factor=2,  # This might be good to have high so the next npy file is prefetched
+        pin_memory=True,
+        shuffle=False
+    )

dalle2_pytorch/dataloaders/prior_loader.py

@@ -0,0 +1,282 @@
+from math import ceil
+from clip import tokenize
+from embedding_reader import EmbeddingReader
+from torch import from_numpy
+from torch.utils.data import IterableDataset, DataLoader
+
+
+class PriorEmbeddingDataset(IterableDataset):
+    """
+    PriorEmbeddingDataset is a wrapper of EmbeddingReader.
+
+    It enables one to simplify the logic necessary to yield samples from
+    the different EmbeddingReader configurations available.
+    """
+
+    def __init__(
+        self,
+        text_conditioned: bool,
+        batch_size: int,
+        start: int,
+        stop: int,
+        image_reader,
+        text_reader: EmbeddingReader = None,
+    ) -> None:
+        super(PriorEmbeddingDataset).__init__()
+
+        self.text_conditioned = text_conditioned
+
+        if not self.text_conditioned:
+            self.text_reader = text_reader
+
+        self.image_reader = image_reader
+        self.start = start
+        self.stop = stop
+        self.batch_size = batch_size
+
+    def __len__(self):
+        return self.stop - self.start
+
+    def __iter__(self):
+        # D.R.Y loader args
+        loader_args = dict(
+            batch_size=self.batch_size,
+            start=self.start,
+            end=self.stop,
+            show_progress=False,
+        )
+
+        # if the data requested is text conditioned, only load images
+        if self.text_conditioned:
+            self.loader = self.image_reader(**loader_args)
+        # otherwise, include text embeddings and bypass metadata
+        else:
+            self.loader = zip(
+                self.image_reader(**loader_args), self.text_reader(**loader_args)
+            )
+
+        # return the data loader in its formatted state
+        return self
+
+    def __next__(self):
+        try:
+            return self.get_sample()
+        except StopIteration:
+            raise StopIteration
+
+    def __str__(self):
+        return f"<PriorEmbeddingDataset: start: {self.start}, stop: {self.stop}, len: {self.__len__()}>"
+
+    def set_start(self, start):
+        """
+        Adjust the starting point within the reader, useful for resuming an epoch
+        """
+        self.start = start
+
+    def get_start(self):
+        return self.start
+
+    def get_sample(self):
+        """
+        pre-proocess data from either reader into a common format
+        """
+        if self.text_conditioned:
+            image_embedding, caption = next(self.loader)
+
+            image_embedding = from_numpy(image_embedding)
+            tokenized_caption = tokenize(caption["caption"].to_list(), truncate=True)
+
+            return image_embedding, tokenized_caption
+
+        else:
+            (image_embedding, _), (text_embedding, _) = next(self.loader)
+
+            image_embedding = from_numpy(image_embedding)
+            text_embedding = from_numpy(text_embedding)
+
+            return image_embedding, text_embedding
+
+
+# helper functions
+
+
+def distribute_to_rank(start, stop, rank, world_size):
+    """
+    Distribute data to each rank given the world size.
+
+    Return:
+        - New start and stop points for this rank.
+    """
+    num_samples = int(stop - start)
+
+    per_rank = int(ceil((num_samples) / float(world_size)))
+
+    assert (
+        per_rank > 0
+    ), f"Number of samples per rank must be larger than 0, (found: {per_rank})"
+
+    rank_start = start + rank * per_rank
+
+    rank_stop = min(rank_start + per_rank, stop)
+
+    new_length = rank_stop - rank_start
+
+    assert (
+        new_length > 0
+    ), "Calculated start and stop points result in a length of zero for this rank."
+
+    return rank_start, rank_stop
+
+
+def get_reader(
+    text_conditioned: bool, img_url: str, meta_url: str = None, txt_url: str = None
+):
+    """
+    Create an EmbeddingReader object from the specified URLs
+
+    get_reader() will always expect a url to image embeddings.
+
+    If text-conditioned, it will also expect a meta_url for the captions.
+    Otherwise, it will need txt_url for the matching text embeddings.
+
+    Returns an image_reader object if text-conditioned.
+    Otherwise it returns both an image_reader and a text_reader
+    """
+
+    assert img_url is not None, "Must supply a image url"
+
+    if text_conditioned:
+        assert meta_url is not None, "Must supply meta url if text-conditioned"
+
+        image_reader = EmbeddingReader(
+            embeddings_folder=img_url,
+            file_format="parquet_npy",
+            # will assume the caption column exists and is the only one requested
+            meta_columns=["caption"],
+            metadata_folder=meta_url,
+        )
+
+        return image_reader
+
+    # otherwise we will require text embeddings as well and return two readers
+    assert (
+        txt_url is not None
+    ), "Must supply text embedding url if not text-conditioning"
+
+    image_reader = EmbeddingReader(img_url, file_format="npy")
+    text_reader = EmbeddingReader(txt_url, file_format="npy")
+
+    return image_reader, text_reader
+
+
+def make_splits(
+    text_conditioned: bool,
+    batch_size: int,
+    num_data_points: int,
+    train_split: float,
+    eval_split: float,
+    image_reader: EmbeddingReader,
+    text_reader: EmbeddingReader = None,
+    start=0,
+    rank=0,
+    world_size=1,
+):
+    """
+    Split an embedding reader object as needed.
+
+    NOTE: make_splits() will infer the test set size from your train and eval.
+
+    Input:
+        - text_conditioned: whether to prepare text-conditioned training data
+        - batch_size: the batch size for a single gpu
+        - num_data_points: the total number of data points you wish to train on
+        - train_split: the percentage of data you wish to train on
+        - eval_split: the percentage of data you wish to validate on
+        - image_reader: the image_reader you wish to split
+        - text_reader: the text_reader you want to split (if !text_conditioned)
+        - start: the starting point within your dataset
+        - rank: the rank of your worker
+        - world_size: the total world size of your distributed training run
+
+    Returns:
+        - PyTorch Dataloaders that yield tuples of (img, txt) data.
+    """
+
+    assert start < image_reader.count, "start position cannot exceed reader count."
+
+    # verify that the num_data_points does not exceed the max points
+    if num_data_points > (image_reader.count - start):
+        print(
+            "Specified count is larger than what's available...defaulting to reader's count."
+        )
+        num_data_points = image_reader.count
+
+    # compute split points
+    train_set_size = int(train_split * num_data_points)
+    eval_set_size = int(eval_split * num_data_points)
+    eval_start = train_set_size
+    eval_stop = int(eval_start + eval_set_size)
+
+    assert (
+        train_split + eval_split
+    ) < 1.0, "Specified train and eval split is too large to infer a test split."
+
+    # distribute to rank
+    rank_train_start, rank_train_stop = distribute_to_rank(
+        start, train_set_size, rank, world_size
+    )
+    rank_eval_start, rank_eval_stop = distribute_to_rank(
+        train_set_size, eval_stop, rank, world_size
+    )
+    rank_test_start, rank_test_stop = distribute_to_rank(
+        eval_stop, num_data_points, rank, world_size
+    )
+
+    # wrap up splits into a dict
+    train_split_args = dict(
+        start=rank_train_start, stop=rank_train_stop, batch_size=batch_size
+    )
+    eval_split_args = dict(
+        start=rank_eval_start, stop=rank_eval_stop, batch_size=batch_size
+    )
+    test_split_args = dict(
+        start=rank_test_start, stop=rank_test_stop, batch_size=batch_size
+    )
+
+    if text_conditioned:
+        # add the text-conditioned args to a unified dict
+        reader_args = dict(
+            text_conditioned=text_conditioned,
+            image_reader=image_reader,
+        )
+
+        train_split_args = dict(**reader_args, **train_split_args)
+        eval_split_args = dict(**reader_args, **eval_split_args)
+        test_split_args = dict(**reader_args, **test_split_args)
+
+        train = PriorEmbeddingDataset(**train_split_args)
+        val = PriorEmbeddingDataset(**eval_split_args)
+        test = PriorEmbeddingDataset(**test_split_args)
+
+    else:
+        # add the non-conditioned args to a unified dict
+        reader_args = dict(
+            text_conditioned=text_conditioned,
+            image_reader=image_reader,
+            text_reader=text_reader,
+        )
+
+        train_split_args = dict(**reader_args, **train_split_args)
+        eval_split_args = dict(**reader_args, **eval_split_args)
+        test_split_args = dict(**reader_args, **test_split_args)
+
+        train = PriorEmbeddingDataset(**train_split_args)
+        val = PriorEmbeddingDataset(**eval_split_args)
+        test = PriorEmbeddingDataset(**test_split_args)
+
+    # true batch size is specifed in the PriorEmbeddingDataset
+    train_loader = DataLoader(train, batch_size=None)
+    eval_loader = DataLoader(val, batch_size=None)
+    test_loader = DataLoader(test, batch_size=None)
+
+    return train_loader, eval_loader, test_loader

dalle2_pytorch/dataloaders/simple_image_only_dataloader.py

@@ -0,0 +1,59 @@
+from pathlib import Path
+
+import torch
+from torch.utils import data
+from torchvision import transforms, utils
+
+from PIL import Image
+
+# helpers functions
+
+def cycle(dl):
+    while True:
+        for data in dl:
+            yield data
+
+# dataset and dataloader
+
+class Dataset(data.Dataset):
+    def __init__(
+        self,
+        folder,
+        image_size,
+        exts = ['jpg', 'jpeg', 'png']
+    ):
+        super().__init__()
+        self.folder = folder
+        self.image_size = image_size
+        self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
+
+        self.transform = transforms.Compose([
+            transforms.Resize(image_size),
+            transforms.RandomHorizontalFlip(),
+            transforms.CenterCrop(image_size),
+            transforms.ToTensor()
+        ])
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, index):
+        path = self.paths[index]
+        img = Image.open(path)
+        return self.transform(img)
+
+def get_images_dataloader(
+    folder,
+    *,
+    batch_size,
+    image_size,
+    shuffle = True,
+    cycle_dl = True,
+    pin_memory = True
+):
+    ds = Dataset(folder, image_size)
+    dl = data.DataLoader(ds, batch_size = batch_size, shuffle = shuffle, pin_memory = pin_memory)
+
+    if cycle_dl:
+        dl = cycle(dl)
+    return dl

dalle2_pytorch/optimizer.py

@@ -1,29 +1,34 @@
 from torch.optim import AdamW, Adam
 
 def separate_weight_decayable_params(params):
-    no_wd_params = set([param for param in params if param.ndim < 2])
-    wd_params = set(params) - no_wd_params
+    wd_params, no_wd_params = [], []
+    for param in params:
+        param_list = no_wd_params if param.ndim < 2 else wd_params
+        param_list.append(param)
     return wd_params, no_wd_params
 
 def get_optimizer(
     params,
-    lr = 3e-4,
+    lr = 1e-4,
     wd = 1e-2,
-    betas = (0.9, 0.999),
-    filter_by_requires_grad = False
+    betas = (0.9, 0.99),
+    eps = 1e-8,
+    filter_by_requires_grad = False,
+    group_wd_params = True,
+    **kwargs
 ):
     if filter_by_requires_grad:
         params = list(filter(lambda t: t.requires_grad, params))
 
     if wd == 0:
-        return Adam(params, lr = lr, betas = betas)
+        return Adam(params, lr = lr, betas = betas, eps = eps)
 
-    params = set(params)
-    wd_params, no_wd_params = separate_weight_decayable_params(params)
+    if group_wd_params:
+        wd_params, no_wd_params = separate_weight_decayable_params(params)
 
-    param_groups = [
-        {'params': list(wd_params)},
-        {'params': list(no_wd_params), 'weight_decay': 0},
-    ]
+        params = [
+            {'params': wd_params},
+            {'params': no_wd_params, 'weight_decay': 0},
+        ]
 
-    return AdamW(param_groups, lr = lr, weight_decay = wd, betas = betas)
+    return AdamW(params, lr = lr, weight_decay = wd, betas = betas, eps = eps)

dalle2_pytorch/tokenizer.py

@@ -2,7 +2,6 @@
 # to give users a quick easy start to training DALL-E without doing BPE
 
 import torch
-import youtokentome as yttm
 
 import html
 import os
@@ -11,6 +10,8 @@ import regex as re
 from functools import lru_cache
 from pathlib import Path
 
+from dalle2_pytorch.utils import import_or_print_error
+
 # OpenAI simple tokenizer
 
 @lru_cache()
@@ -156,7 +157,9 @@ class YttmTokenizer:
         bpe_path = Path(bpe_path)
         assert bpe_path.exists(), f'BPE json path {str(bpe_path)} does not exist'
 
-        tokenizer = yttm.BPE(model = str(bpe_path))
+        self.yttm = import_or_print_error('youtokentome', 'you need to install youtokentome by `pip install youtokentome`')
+
+        tokenizer = self.yttm.BPE(model = str(bpe_path))
         self.tokenizer = tokenizer
         self.vocab_size = tokenizer.vocab_size()
 
@@ -167,7 +170,7 @@ class YttmTokenizer:
         return self.tokenizer.decode(tokens, ignore_ids = pad_tokens.union({0}))
 
     def encode(self, texts):
-        encoded = self.tokenizer.encode(texts, output_type = yttm.OutputType.ID)
+        encoded = self.tokenizer.encode(texts, output_type = self.yttm.OutputType.ID)
         return list(map(torch.tensor, encoded))
 
     def tokenize(self, texts, context_length = 256, truncate_text = False):

dalle2_pytorch/trackers.py

@@ -0,0 +1,601 @@
+import urllib.request
+import os
+import json
+from pathlib import Path
+import shutil
+from itertools import zip_longest
+from typing import Any, Optional, List, Union
+from pydantic import BaseModel
+
+import torch
+from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
+from dalle2_pytorch.utils import import_or_print_error
+from dalle2_pytorch.trainer import DecoderTrainer, DiffusionPriorTrainer
+from dalle2_pytorch.version import __version__
+from packaging import version
+
+# constants
+
+DEFAULT_DATA_PATH = './.tracker-data'
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+class BaseLogger:
+    """
+    An abstract class representing an object that can log data.
+    Parameters:
+        data_path (str): A file path for storing temporary data.
+        verbose (bool): Whether of not to always print logs to the console.
+    """
+    def __init__(self, data_path: str, resume: bool = False, auto_resume: bool = False, verbose: bool = False, **kwargs):
+        self.data_path = Path(data_path)
+        self.resume = resume
+        self.auto_resume = auto_resume
+        self.verbose = verbose
+
+    def init(self, full_config: BaseModel, extra_config: dict, **kwargs) -> None:
+        """
+        Initializes the logger.
+        Errors if the logger is invalid.
+        full_config is the config file dict while extra_config is anything else from the script that is not defined the config file.
+        """
+        raise NotImplementedError
+
+    def log(self, log, **kwargs) -> None:
+        raise NotImplementedError
+
+    def log_images(self, images, captions=[], image_section="images", **kwargs) -> None:
+        raise NotImplementedError
+
+    def log_file(self, file_path, **kwargs) -> None:
+        raise NotImplementedError
+
+    def log_error(self, error_string, **kwargs) -> None:
+        raise NotImplementedError
+
+    def get_resume_data(self, **kwargs) -> dict:
+        """
+        Sets tracker attributes that along with { "resume": True } will be used to resume training.
+        It is assumed that after init is called this data will be complete.
+        If the logger does not have any resume functionality, it should return an empty dict.
+        """
+        raise NotImplementedError
+
+class ConsoleLogger(BaseLogger):
+    def init(self, full_config: BaseModel, extra_config: dict, **kwargs) -> None:
+        print("Logging to console")
+
+    def log(self, log, **kwargs) -> None:
+        print(log)
+
+    def log_images(self, images, captions=[], image_section="images", **kwargs) -> None:
+        pass
+
+    def log_file(self, file_path, **kwargs) -> None:
+        pass
+
+    def log_error(self, error_string, **kwargs) -> None:
+        print(error_string)
+
+    def get_resume_data(self, **kwargs) -> dict:
+        return {}
+
+class WandbLogger(BaseLogger):
+    """
+    Logs to a wandb run.
+    Parameters:
+        data_path (str): A file path for storing temporary data.
+        wandb_entity (str): The wandb entity to log to.
+        wandb_project (str): The wandb project to log to.
+        wandb_run_id (str): The wandb run id to resume.
+        wandb_run_name (str): The wandb run name to use.
+    """
+    def __init__(self,
+        data_path: str,
+        wandb_entity: str,
+        wandb_project: str,
+        wandb_run_id: Optional[str] = None,
+        wandb_run_name: Optional[str] = None,
+        **kwargs
+    ):
+        super().__init__(data_path, **kwargs)
+        self.entity = wandb_entity
+        self.project = wandb_project
+        self.run_id = wandb_run_id
+        self.run_name = wandb_run_name
+
+    def init(self, full_config: BaseModel, extra_config: dict, **kwargs) -> None:
+        assert self.entity is not None, "wandb_entity must be specified for wandb logger"
+        assert self.project is not None, "wandb_project must be specified for wandb logger"
+        self.wandb = import_or_print_error('wandb', '`pip install wandb` to use the wandb logger')
+        os.environ["WANDB_SILENT"] = "true"
+        # Initializes the wandb run
+        init_object = {
+            "entity": self.entity,
+            "project": self.project,
+            "config": {**full_config.dict(), **extra_config}
+        }
+        if self.run_name is not None:
+            init_object['name'] = self.run_name
+        if self.resume:
+            assert self.run_id is not None, '`wandb_run_id` must be provided if `wandb_resume` is True'
+            if self.run_name is not None:
+                print("You are renaming a run. I hope that is what you intended.")
+            init_object['resume'] = 'must'
+            init_object['id'] = self.run_id
+
+        self.wandb.init(**init_object)
+        print(f"Logging to wandb run {self.wandb.run.path}-{self.wandb.run.name}")
+
+    def log(self, log, **kwargs) -> None:
+        if self.verbose:
+            print(log)
+        self.wandb.log(log, **kwargs)
+
+    def log_images(self, images, captions=[], image_section="images", **kwargs) -> None:
+        """
+        Takes a tensor of images and a list of captions and logs them to wandb.
+        """
+        wandb_images = [self.wandb.Image(image, caption=caption) for image, caption in zip_longest(images, captions)]
+        self.wandb.log({ image_section: wandb_images }, **kwargs)
+
+    def log_file(self, file_path, base_path: Optional[str] = None, **kwargs) -> None:
+        if base_path is None:
+            # Then we take the basepath as the parent of the file_path
+            base_path = Path(file_path).parent
+        self.wandb.save(str(file_path), base_path = str(base_path))
+
+    def log_error(self, error_string, step=None, **kwargs) -> None:
+        if self.verbose:
+            print(error_string)
+        self.wandb.log({"error": error_string, **kwargs}, step=step)
+
+    def get_resume_data(self, **kwargs) -> dict:
+        # In order to resume, we need wandb_entity, wandb_project, and wandb_run_id
+        return {
+            "entity": self.entity,
+            "project": self.project,
+            "run_id": self.wandb.run.id
+        }
+
+logger_type_map = {
+    'console': ConsoleLogger,
+    'wandb': WandbLogger,
+}
+def create_logger(logger_type: str, data_path: str, **kwargs) -> BaseLogger:
+    if logger_type == 'custom':
+        raise NotImplementedError('Custom loggers are not supported yet. Please use a different logger type.')
+    try:
+        logger_class = logger_type_map[logger_type]
+    except KeyError:
+        raise ValueError(f'Unknown logger type: {logger_type}. Must be one of {list(logger_type_map.keys())}')
+    return logger_class(data_path, **kwargs)
+
+class BaseLoader:
+    """
+    An abstract class representing an object that can load a model checkpoint.
+    Parameters:
+        data_path (str): A file path for storing temporary data.
+    """
+    def __init__(self, data_path: str, only_auto_resume: bool = False, **kwargs):
+        self.data_path = Path(data_path)
+        self.only_auto_resume = only_auto_resume
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        raise NotImplementedError
+
+    def recall() -> dict:
+        raise NotImplementedError
+
+class UrlLoader(BaseLoader):
+    """
+    A loader that downloads the file from a url and loads it
+    Parameters:
+        data_path (str): A file path for storing temporary data.
+        url (str): The url to download the file from.
+    """
+    def __init__(self, data_path: str, url: str, **kwargs):
+        super().__init__(data_path, **kwargs)
+        self.url = url
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        # Makes sure the file exists to be downloaded
+        pass  # TODO: Actually implement that
+
+    def recall(self) -> dict:
+        # Download the file
+        save_path = self.data_path / 'loaded_checkpoint.pth'
+        urllib.request.urlretrieve(self.url, str(save_path))
+        # Load the file
+        return torch.load(str(save_path), map_location='cpu')
+        
+
+class LocalLoader(BaseLoader):
+    """
+    A loader that loads a file from a local path
+    Parameters:
+        data_path (str): A file path for storing temporary data.
+        file_path (str): The path to the file to load.
+    """
+    def __init__(self, data_path: str, file_path: str, **kwargs):
+        super().__init__(data_path, **kwargs)
+        self.file_path = Path(file_path)
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        # Makes sure the file exists to be loaded
+        if not self.file_path.exists() and not self.only_auto_resume:
+            raise FileNotFoundError(f'Model not found at {self.file_path}')
+
+    def recall(self) -> dict:
+        # Load the file
+        return torch.load(str(self.file_path), map_location='cpu')
+
+class WandbLoader(BaseLoader):
+    """
+    A loader that loads a model from an existing wandb run
+    """
+    def __init__(self, data_path: str, wandb_file_path: str, wandb_run_path: Optional[str] = None, **kwargs):
+        super().__init__(data_path, **kwargs)
+        self.run_path = wandb_run_path
+        self.file_path = wandb_file_path
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        self.wandb = import_or_print_error('wandb', '`pip install wandb` to use the wandb recall function')
+        # Make sure the file can be downloaded
+        if self.wandb.run is not None and self.run_path is None:
+            self.run_path = self.wandb.run.path
+            assert self.run_path is not None, 'wandb run was not found to load from. If not using the wandb logger must specify the `wandb_run_path`.'
+        assert self.run_path is not None, '`wandb_run_path` must be provided for the wandb loader'
+        assert self.file_path is not None, '`wandb_file_path` must be provided for the wandb loader'
+        
+        os.environ["WANDB_SILENT"] = "true"
+        pass  # TODO: Actually implement that
+
+    def recall(self) -> dict:
+        file_reference = self.wandb.restore(self.file_path, run_path=self.run_path)
+        return torch.load(file_reference.name, map_location='cpu')
+
+loader_type_map = {
+    'url': UrlLoader,
+    'local': LocalLoader,
+    'wandb': WandbLoader,
+}
+def create_loader(loader_type: str, data_path: str, **kwargs) -> BaseLoader:
+    if loader_type == 'custom':
+        raise NotImplementedError('Custom loaders are not supported yet. Please use a different loader type.')
+    try:
+        loader_class = loader_type_map[loader_type]
+    except KeyError:
+        raise ValueError(f'Unknown loader type: {loader_type}. Must be one of {list(loader_type_map.keys())}')
+    return loader_class(data_path, **kwargs)
+
+class BaseSaver:
+    def __init__(self,
+        data_path: str,
+        save_latest_to: Optional[Union[str, bool]] = None,
+        save_best_to: Optional[Union[str, bool]] = None,
+        save_meta_to: Optional[str] = None,
+        save_type: str = 'checkpoint',
+        **kwargs
+    ):
+        self.data_path = Path(data_path)
+        self.save_latest_to = save_latest_to
+        self.saving_latest = save_latest_to is not None and save_latest_to is not False
+        self.save_best_to = save_best_to
+        self.saving_best = save_best_to is not None and save_best_to is not False
+        self.save_meta_to = save_meta_to
+        self.saving_meta = save_meta_to is not None
+        self.save_type = save_type
+        assert save_type in ['checkpoint', 'model'], '`save_type` must be one of `checkpoint` or `model`'
+        assert self.saving_latest or self.saving_best or self.saving_meta, 'At least one saving option must be specified'
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        raise NotImplementedError
+
+    def save_file(self, local_path: Path, save_path: str, is_best=False, is_latest=False, **kwargs) -> None:
+        """
+        Save a general file under save_meta_to
+        """
+        raise NotImplementedError
+
+class LocalSaver(BaseSaver):
+    def __init__(self,
+        data_path: str,
+        **kwargs
+    ):
+        super().__init__(data_path, **kwargs)
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        # Makes sure the directory exists to be saved to
+        print(f"Saving {self.save_type} locally")
+        if not self.data_path.exists():
+            self.data_path.mkdir(parents=True)
+
+    def save_file(self, local_path: str, save_path: str, **kwargs) -> None:
+        # Copy the file to save_path
+        save_path_file_name = Path(save_path).name
+        # Make sure parent directory exists
+        save_path_parent = Path(save_path).parent
+        if not save_path_parent.exists():
+            save_path_parent.mkdir(parents=True)
+        print(f"Saving {save_path_file_name} {self.save_type} to local path {save_path}")
+        shutil.copy(local_path, save_path)
+
+class WandbSaver(BaseSaver):
+    def __init__(self, data_path: str, wandb_run_path: Optional[str] = None, **kwargs):
+        super().__init__(data_path, **kwargs)
+        self.run_path = wandb_run_path
+
+    def init(self, logger: BaseLogger, **kwargs) -> None:
+        self.wandb = import_or_print_error('wandb', '`pip install wandb` to use the wandb logger')
+        os.environ["WANDB_SILENT"] = "true"
+        # Makes sure that the user can upload tot his run
+        if self.run_path is not None:
+            entity, project, run_id = self.run_path.split("/")
+            self.run = self.wandb.init(entity=entity, project=project, id=run_id)
+        else:
+            assert self.wandb.run is not None, 'You must be using the wandb logger if you are saving to wandb and have not set `wandb_run_path`'
+            self.run = self.wandb.run
+        # TODO: Now actually check if upload is possible
+        print(f"Saving to wandb run {self.run.path}-{self.run.name}")
+
+    def save_file(self, local_path: Path, save_path: str, **kwargs) -> None:
+        # In order to log something in the correct place in wandb, we need to have the same file structure here
+        save_path_file_name = Path(save_path).name
+        print(f"Saving {save_path_file_name} {self.save_type} to wandb run {self.run.path}-{self.run.name}")
+        save_path = Path(self.data_path) / save_path
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy(local_path, save_path)
+        self.run.save(str(save_path), base_path = str(self.data_path), policy='now')
+
+class HuggingfaceSaver(BaseSaver):
+    def __init__(self, data_path: str, huggingface_repo: str, token_path: Optional[str] = None, **kwargs):
+        super().__init__(data_path, **kwargs)
+        self.huggingface_repo = huggingface_repo
+        self.token_path = token_path
+
+    def init(self, logger: BaseLogger, **kwargs):
+        # Makes sure this user can upload to the repo
+        self.hub = import_or_print_error('huggingface_hub', '`pip install huggingface_hub` to use the huggingface saver')
+        try:
+            identity = self.hub.whoami()  # Errors if not logged in
+            # Then we are logged in
+        except:
+            # We are not logged in. Use the token_path to set the token.
+            if not os.path.exists(self.token_path):
+                raise Exception("Not logged in to huggingface and no token_path specified. Please login with `huggingface-cli login` or if that does not work set the token_path.")
+            with open(self.token_path, "r") as f:
+                token = f.read().strip()
+            self.hub.HfApi.set_access_token(token)
+            identity = self.hub.whoami()
+        print(f"Saving to huggingface repo {self.huggingface_repo}")
+
+    def save_file(self, local_path: Path, save_path: str, **kwargs) -> None:
+        # Saving to huggingface is easy, we just need to upload the file with the correct name
+        save_path_file_name = Path(save_path).name
+        print(f"Saving {save_path_file_name} {self.save_type} to huggingface repo {self.huggingface_repo}")
+        self.hub.upload_file(
+            path_or_fileobj=str(local_path),
+            path_in_repo=str(save_path),
+            repo_id=self.huggingface_repo
+        )
+        
+saver_type_map = {
+    'local': LocalSaver,
+    'wandb': WandbSaver,
+    'huggingface': HuggingfaceSaver
+}
+def create_saver(saver_type: str, data_path: str, **kwargs) -> BaseSaver:
+    if saver_type == 'custom':
+        raise NotImplementedError('Custom savers are not supported yet. Please use a different saver type.')
+    try:
+        saver_class = saver_type_map[saver_type]
+    except KeyError:
+        raise ValueError(f'Unknown saver type: {saver_type}. Must be one of {list(saver_type_map.keys())}')
+    return saver_class(data_path, **kwargs)
+
+
+class Tracker:
+    def __init__(self, data_path: Optional[str] = DEFAULT_DATA_PATH, overwrite_data_path: bool = False, dummy_mode: bool = False):
+        self.data_path = Path(data_path)
+        if not dummy_mode:
+            if not overwrite_data_path:
+                assert not self.data_path.exists(), f'Data path {self.data_path} already exists. Set overwrite_data_path to True to overwrite.'
+                if not self.data_path.exists():
+                    self.data_path.mkdir(parents=True)
+        self.logger: BaseLogger = None
+        self.loader: Optional[BaseLoader] = None
+        self.savers: List[BaseSaver]= []
+        self.dummy_mode = dummy_mode
+
+    def _load_auto_resume(self) -> bool:
+        # If the file does not exist, we return False. If autoresume is enabled we print a warning so that the user can know that this is the first run.
+        if not self.auto_resume_path.exists():
+            if self.logger.auto_resume:
+                print("Auto_resume is enabled but no auto_resume.json file exists. Assuming this is the first run.")
+            return False
+
+        # Now we know that the autoresume file exists, but if we are not auto resuming we should remove it so that we don't accidentally load it next time
+        if not self.logger.auto_resume:
+            print(f'Removing auto_resume.json because auto_resume is not enabled in the config')
+            self.auto_resume_path.unlink()
+            return False
+
+        # Otherwise we read the json into a dictionary will will override parts of logger.__dict__
+        with open(self.auto_resume_path, 'r') as f:
+            auto_resume_dict = json.load(f)
+        # Check if the logger is of the same type as the autoresume save
+        if auto_resume_dict["logger_type"] != self.logger.__class__.__name__:
+            raise Exception(f'The logger type in the auto_resume file is {auto_resume_dict["logger_type"]} but the current logger is {self.logger.__class__.__name__}. Either use the original logger type, set `auto_resume` to `False`, or delete your existing tracker-data folder.')
+        # Then we are ready to override the logger with the autoresume save
+        self.logger.__dict__["resume"] = True
+        print(f"Updating {self.logger.__dict__} with {auto_resume_dict}")
+        self.logger.__dict__.update(auto_resume_dict)
+        return True
+
+    def _save_auto_resume(self):
+        # Gets the autoresume dict from the logger and adds "logger_type" to it then saves it to the auto_resume file
+        auto_resume_dict = self.logger.get_resume_data()
+        auto_resume_dict['logger_type'] = self.logger.__class__.__name__
+        with open(self.auto_resume_path, 'w') as f:
+            json.dump(auto_resume_dict, f)
+
+    def init(self, full_config: BaseModel, extra_config: dict):
+        self.auto_resume_path = self.data_path / 'auto_resume.json'
+        # Check for resuming the run
+        self.did_auto_resume = self._load_auto_resume()
+        if self.did_auto_resume:
+            print(f'\n\nWARNING: RUN HAS BEEN AUTO-RESUMED WITH THE LOGGER TYPE {self.logger.__class__.__name__}.\nIf this was not your intention, stop this run and set `auto_resume` to `False` in the config.\n\n')
+            print(f"New logger config: {self.logger.__dict__}")
+        
+        self.save_metadata = dict(
+            version = version.parse(__version__)
+        )  # Data that will be saved alongside the checkpoint or model
+        self.blacklisted_checkpoint_metadata_keys = ['scaler', 'optimizer', 'model', 'version', 'step', 'steps']  # These keys would cause us to error if we try to save them as metadata
+
+        assert self.logger is not None, '`logger` must be set before `init` is called'
+        if self.dummy_mode:
+            # The only thing we need is a loader
+            if self.loader is not None:
+                self.loader.init(self.logger)
+            return
+        assert len(self.savers) > 0, '`savers` must be set before `init` is called'
+
+        self.logger.init(full_config, extra_config)
+        if self.loader is not None:
+            self.loader.init(self.logger)
+        for saver in self.savers:
+            saver.init(self.logger)
+
+        if self.logger.auto_resume:
+            # Then we need to save the autoresume file. It is assumed after logger.init is called that the logger is ready to be saved.
+            self._save_auto_resume()
+
+    def add_logger(self, logger: BaseLogger):
+        self.logger = logger
+
+    def add_loader(self, loader: BaseLoader):
+        self.loader = loader
+
+    def add_saver(self, saver: BaseSaver):
+        self.savers.append(saver)
+
+    def log(self, *args, **kwargs):
+        if self.dummy_mode:
+            return
+        self.logger.log(*args, **kwargs)
+    
+    def log_images(self, *args, **kwargs):
+        if self.dummy_mode:
+            return
+        self.logger.log_images(*args, **kwargs)
+
+    def log_file(self, *args, **kwargs):
+        if self.dummy_mode:
+            return
+        self.logger.log_file(*args, **kwargs)
+
+    def save_config(self, current_config_path: str, config_name = 'config.json'):
+        if self.dummy_mode:
+            return
+        # Save the config under config_name in the root folder of data_path
+        shutil.copy(current_config_path, self.data_path / config_name)
+        for saver in self.savers:
+            if saver.saving_meta:
+                remote_path = Path(saver.save_meta_to) / config_name
+                saver.save_file(current_config_path, str(remote_path))
+
+    def add_save_metadata(self, state_dict_key: str, metadata: Any):
+        """
+        Adds a new piece of metadata that will be saved along with the model or decoder.
+        """
+        self.save_metadata[state_dict_key] = metadata
+
+    def _save_state_dict(self, trainer: Union[DiffusionPriorTrainer, DecoderTrainer], save_type: str, file_path: str, **kwargs) -> Path:
+        """
+        Gets the state dict to be saved and writes it to file_path.
+        If save_type is 'checkpoint', we save the entire trainer state dict.
+        If save_type is 'model', we save only the model state dict.
+        """
+        assert save_type in ['checkpoint', 'model']
+        if save_type == 'checkpoint':
+            # Create a metadata dict without the blacklisted keys so we do not error when we create the state dict
+            metadata = {k: v for k, v in self.save_metadata.items() if k not in self.blacklisted_checkpoint_metadata_keys}
+            trainer.save(file_path, overwrite=True, **kwargs, **metadata)
+        elif save_type == 'model':
+            if isinstance(trainer, DiffusionPriorTrainer):
+                prior = trainer.ema_diffusion_prior.ema_model if trainer.use_ema else trainer.diffusion_prior
+                prior: DiffusionPrior = trainer.accelerator.unwrap_model(prior)
+                # Remove CLIP if it is part of the model
+                original_clip = prior.clip
+                prior.clip = None
+                model_state_dict = prior.state_dict()
+                prior.clip = original_clip
+            elif isinstance(trainer, DecoderTrainer):
+                decoder: Decoder = trainer.accelerator.unwrap_model(trainer.decoder)
+                # Remove CLIP if it is part of the model
+                original_clip = decoder.clip
+                decoder.clip = None
+                if trainer.use_ema:
+                    trainable_unets = decoder.unets
+                    decoder.unets = trainer.unets  # Swap EMA unets in
+                    model_state_dict = decoder.state_dict()
+                    decoder.unets = trainable_unets  # Swap back
+                else:
+                    model_state_dict = decoder.state_dict()
+                decoder.clip = original_clip
+            else:
+                raise NotImplementedError('Saving this type of model with EMA mode enabled is not yet implemented. Actually, how did you get here?')
+            state_dict = {
+                **self.save_metadata,
+                'model': model_state_dict
+            }
+            torch.save(state_dict, file_path)
+        return Path(file_path)
+
+    def save(self, trainer, is_best: bool, is_latest: bool, **kwargs):
+        if self.dummy_mode:
+            return
+        if not is_best and not is_latest:
+            # Nothing to do
+            return
+        # Save the checkpoint and model to data_path
+        checkpoint_path = self.data_path / 'checkpoint.pth'
+        self._save_state_dict(trainer, 'checkpoint', checkpoint_path, **kwargs)
+        model_path = self.data_path / 'model.pth'
+        self._save_state_dict(trainer, 'model', model_path, **kwargs)
+        print("Saved cached models")
+        # Call the save methods on the savers
+        for saver in self.savers:
+            local_path = checkpoint_path if saver.save_type == 'checkpoint' else model_path
+            if saver.saving_latest and is_latest:
+                latest_checkpoint_path = saver.save_latest_to.format(**kwargs)
+                try:
+                    saver.save_file(local_path, latest_checkpoint_path, is_latest=True, **kwargs)
+                except Exception as e:
+                    self.logger.log_error(f'Error saving checkpoint: {e}', **kwargs)
+                    print(f'Error saving checkpoint: {e}')
+            if saver.saving_best and is_best:
+                best_checkpoint_path = saver.save_best_to.format(**kwargs)
+                try:
+                    saver.save_file(local_path, best_checkpoint_path, is_best=True, **kwargs)
+                except Exception as e:
+                    self.logger.log_error(f'Error saving checkpoint: {e}', **kwargs)
+                    print(f'Error saving checkpoint: {e}')
+    
+    @property
+    def can_recall(self):
+        # Defines whether a recall can be performed.
+        return self.loader is not None and (not self.loader.only_auto_resume or self.did_auto_resume)
+    
+    def recall(self):
+        if self.can_recall:
+            return self.loader.recall()
+        else:
+            raise ValueError('Tried to recall, but no loader was set or auto-resume was not performed.')
+
+
+    

dalle2_pytorch/train.py

@@ -1,199 +0,0 @@
-import copy
-from functools import partial
-
-import torch
-from torch import nn
-from torch.cuda.amp import autocast, GradScaler
-
-from dalle2_pytorch.dalle2_pytorch import Decoder
-from dalle2_pytorch.optimizer import get_optimizer
-
-# helper functions
-
-def exists(val):
-    return val is not None
-
-def cast_tuple(val, length = 1):
-    return val if isinstance(val, tuple) else ((val,) * length)
-
-def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
-    return dict(zip(keys, values))
-
-def group_dict_by_key(cond, d):
-    return_val = [dict(),dict()]
-    for key in d.keys():
-        match = bool(cond(key))
-        ind = int(not match)
-        return_val[ind][key] = d[key]
-    return (*return_val,)
-
-def string_begins_with(prefix, str):
-    return str.startswith(prefix)
-
-def group_by_key_prefix(prefix, d):
-    return group_dict_by_key(partial(string_begins_with, prefix), d)
-
-def groupby_prefix_and_trim(prefix, d):
-    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
-    return kwargs_without_prefix, kwargs
-
-# exponential moving average wrapper
-
-class EMA(nn.Module):
-    def __init__(
-        self,
-        model,
-        beta = 0.99,
-        update_after_step = 1000,
-        update_every = 10,
-    ):
-        super().__init__()
-        self.beta = beta
-        self.online_model = model
-        self.ema_model = copy.deepcopy(model)
-
-        self.update_after_step = update_after_step # only start EMA after this step number, starting at 0
-        self.update_every = update_every
-
-        self.register_buffer('initted', torch.Tensor([False]))
-        self.register_buffer('step', torch.tensor([0.]))
-
-    def update(self):
-        self.step += 1
-
-        if self.step <= self.update_after_step or (self.step % self.update_every) != 0:
-            return
-
-        if not self.initted:
-            self.ema_model.state_dict(self.online_model.state_dict())
-            self.initted.data.copy_(torch.Tensor([True]))
-
-        self.update_moving_average(self.ema_model, self.online_model)
-
-    def update_moving_average(self, ma_model, current_model):
-        def calculate_ema(beta, old, new):
-            if not exists(old):
-                return new
-            return old * beta + (1 - beta) * new
-
-        for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
-            old_weight, up_weight = ma_params.data, current_params.data
-            ma_params.data = calculate_ema(self.beta, old_weight, up_weight)
-
-        for current_buffer, ma_buffer in zip(current_model.buffers(), ma_model.buffers()):
-            new_buffer_value = calculate_ema(self.beta, ma_buffer, current_buffer)
-            ma_buffer.copy_(new_buffer_value)
-
-    def __call__(self, *args, **kwargs):
-        return self.ema_model(*args, **kwargs)
-
-# trainers
-
-class DecoderTrainer(nn.Module):
-    def __init__(
-        self,
-        decoder,
-        use_ema = True,
-        lr = 3e-4,
-        wd = 1e-2,
-        max_grad_norm = None,
-        amp = False,
-        **kwargs
-    ):
-        super().__init__()
-        assert isinstance(decoder, Decoder)
-        ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
-
-        self.decoder = decoder
-        self.num_unets = len(self.decoder.unets)
-
-        self.use_ema = use_ema
-
-        if use_ema:
-            has_lazy_linear = any([type(module) == nn.LazyLinear for module in decoder.modules()])
-            assert not has_lazy_linear, 'you must set the text_embed_dim on your u-nets if you plan on doing automatic exponential moving average'
-
-        self.ema_unets = nn.ModuleList([])
-
-        self.amp = amp
-
-        # be able to finely customize learning rate, weight decay
-        # per unet
-
-        lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
-
-        for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
-            optimizer = get_optimizer(
-                unet.parameters(),
-                lr = unet_lr,
-                wd = unet_wd,
-                **kwargs
-            )
-
-            setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
-
-            if self.use_ema:
-                self.ema_unets.append(EMA(unet, **ema_kwargs))
-
-            scaler = GradScaler(enabled = amp)
-            setattr(self, f'scaler{ind}', scaler)
-
-        # gradient clipping if needed
-
-        self.max_grad_norm = max_grad_norm
-
-    @property
-    def unets(self):
-        return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
-
-    def scale(self, loss, *, unet_number):
-        assert 1 <= unet_number <= self.num_unets
-        index = unet_number - 1
-        scaler = getattr(self, f'scaler{index}')
-        return scaler.scale(loss)
-
-    def update(self, unet_number):
-        assert 1 <= unet_number <= self.num_unets
-        index = unet_number - 1
-        unet = self.decoder.unets[index]
-
-        optimizer = getattr(self, f'optim{index}')
-        scaler = getattr(self, f'scaler{index}')
-
-        if exists(self.max_grad_norm):
-            scaler.unscale_(optimizer)
-            nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
-
-        scaler.step(optimizer)
-        scaler.update()
-        optimizer.zero_grad()
-
-        if self.use_ema:
-            ema_unet = self.ema_unets[index]
-            ema_unet.update()
-
-    @torch.no_grad()
-    def sample(self, *args, **kwargs):
-        if self.use_ema:
-            trainable_unets = self.decoder.unets
-            self.decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
-
-        output = self.decoder.sample(*args, **kwargs)
-
-        if self.use_ema:
-            self.decoder.unets = trainable_unets             # restore original training unets
-        return output
-
-    def forward(
-        self,
-        x,
-        *,
-        unet_number,
-        divisor = 1,
-        **kwargs
-    ):
-        with autocast(enabled = self.amp):
-            loss = self.decoder(x, unet_number = unet_number, **kwargs)
-        return self.scale(loss / divisor, unet_number = unet_number)

dalle2_pytorch/train_configs.py

@@ -0,0 +1,371 @@
+import json
+from torchvision import transforms as T
+from pydantic import BaseModel, validator, root_validator
+from typing import List, Optional, Union, Tuple, Dict, Any, TypeVar
+
+from x_clip import CLIP as XCLIP
+from coca_pytorch import CoCa
+
+from dalle2_pytorch.dalle2_pytorch import (
+    CoCaAdapter,
+    OpenAIClipAdapter,
+    Unet,
+    Decoder,
+    DiffusionPrior,
+    DiffusionPriorNetwork,
+    XClipAdapter
+)
+from dalle2_pytorch.trackers import Tracker, create_loader, create_logger, create_saver
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+InnerType = TypeVar('InnerType')
+ListOrTuple = Union[List[InnerType], Tuple[InnerType]]
+SingularOrIterable = Union[InnerType, ListOrTuple[InnerType]]
+
+# general pydantic classes
+
+class TrainSplitConfig(BaseModel):
+    train: float = 0.75
+    val: float = 0.15
+    test: float = 0.1
+
+    @root_validator
+    def validate_all(cls, fields):
+        actual_sum = sum([*fields.values()])
+        if actual_sum != 1.:
+            raise ValueError(f'{fields.keys()} must sum to 1.0. Found: {actual_sum}')
+        return fields
+
+class TrackerLogConfig(BaseModel):
+    log_type: str = 'console'
+    resume: bool = False  # For logs that are saved to unique locations, resume a previous run
+    auto_resume: bool = False  # If the process crashes and restarts, resume from the run that crashed
+    verbose: bool = False
+
+    class Config:
+        # Each individual log type has it's own arguments that will be passed through the config
+        extra = "allow"
+
+    def create(self, data_path: str):
+        kwargs = self.dict()
+        return create_logger(self.log_type, data_path, **kwargs)
+
+class TrackerLoadConfig(BaseModel):
+    load_from: Optional[str] = None
+    only_auto_resume: bool = False  # Only attempt to load if the logger is auto-resuming
+
+    class Config:
+        extra = "allow"
+
+    def create(self, data_path: str):
+        kwargs = self.dict()
+        if self.load_from is None:
+            return None
+        return create_loader(self.load_from, data_path, **kwargs)
+
+class TrackerSaveConfig(BaseModel):
+    save_to: str = 'local'
+    save_all: bool = False
+    save_latest: bool = True
+    save_best: bool = True
+
+    class Config:
+        extra = "allow"
+
+    def create(self, data_path: str):
+        kwargs = self.dict()
+        return create_saver(self.save_to, data_path, **kwargs)
+
+class TrackerConfig(BaseModel):
+    data_path: str = '.tracker_data'
+    overwrite_data_path: bool = False
+    log: TrackerLogConfig
+    load: Optional[TrackerLoadConfig]
+    save: Union[List[TrackerSaveConfig], TrackerSaveConfig]
+
+    def create(self, full_config: BaseModel, extra_config: dict, dummy_mode: bool = False) -> Tracker:
+        tracker = Tracker(self.data_path, dummy_mode=dummy_mode, overwrite_data_path=self.overwrite_data_path)
+        # Add the logger
+        tracker.add_logger(self.log.create(self.data_path))
+        # Add the loader
+        if self.load is not None:
+            tracker.add_loader(self.load.create(self.data_path))
+        # Add the saver or savers
+        if isinstance(self.save, list):
+            for save_config in self.save:
+                tracker.add_saver(save_config.create(self.data_path))
+        else:
+            tracker.add_saver(self.save.create(self.data_path))
+        # Initialize all the components and verify that all data is valid
+        tracker.init(full_config, extra_config)
+        return tracker
+
+# diffusion prior pydantic classes
+
+class AdapterConfig(BaseModel):
+    make: str = "openai"
+    model: str = "ViT-L/14"
+    base_model_kwargs: Dict[str, Any] = None
+
+    def create(self):
+        if self.make == "openai":
+            return OpenAIClipAdapter(self.model)
+        elif self.make == "x-clip":
+            return XClipAdapter(XCLIP(**self.base_model_kwargs))
+        elif self.make == "coca":
+            return CoCaAdapter(CoCa(**self.base_model_kwargs))
+        else:
+            raise AttributeError("No adapter with that name is available.")
+
+class DiffusionPriorNetworkConfig(BaseModel):
+    dim: int
+    depth: int
+    max_text_len: int = None
+    num_timesteps: int = None
+    num_time_embeds: int = 1
+    num_image_embeds: int = 1
+    num_text_embeds: int = 1
+    dim_head: int = 64
+    heads: int = 8
+    ff_mult: int = 4
+    norm_in: bool = False
+    norm_out: bool = True
+    attn_dropout: float = 0.
+    ff_dropout: float = 0.
+    final_proj: bool = True
+    normformer: bool = False
+    rotary_emb: bool = True
+
+    class Config:
+        extra = "allow"
+
+    def create(self):
+        kwargs = self.dict()
+        return DiffusionPriorNetwork(**kwargs)
+
+class DiffusionPriorConfig(BaseModel):
+    clip: AdapterConfig = None
+    net: DiffusionPriorNetworkConfig
+    image_embed_dim: int
+    image_size: int
+    image_channels: int = 3
+    timesteps: int = 1000
+    sample_timesteps: Optional[int] = None
+    cond_drop_prob: float = 0.
+    loss_type: str = 'l2'
+    predict_x_start: bool = True
+    beta_schedule: str = 'cosine'
+    condition_on_text_encodings: bool = True
+
+    class Config:
+        extra = "allow"
+
+    def create(self):
+        kwargs = self.dict()
+
+        has_clip = exists(kwargs.pop('clip'))
+        kwargs.pop('net')
+
+        clip = None
+        if has_clip:
+            clip = self.clip.create()
+
+        diffusion_prior_network = self.net.create()
+        return DiffusionPrior(net = diffusion_prior_network, clip = clip, **kwargs)
+
+class DiffusionPriorTrainConfig(BaseModel):
+    epochs: int = 1
+    lr: float = 1.1e-4
+    wd: float = 6.02e-2
+    max_grad_norm: float = 0.5
+    use_ema: bool = True
+    ema_beta: float = 0.99
+    amp: bool = False
+    warmup_steps: int = None             # number of warmup steps
+    save_every_seconds: int = 3600       # how often to save
+    eval_timesteps: List[int] = [64]     # which sampling timesteps to evaluate with
+    best_validation_loss: float = 1e9    # the current best valudation loss observed
+    current_epoch: int = 0               # the current epoch
+    num_samples_seen: int = 0            # the current number of samples seen
+    random_seed: int = 0                 # manual seed for torch
+
+class DiffusionPriorDataConfig(BaseModel):
+    image_url: str                   # path to embeddings folder
+    meta_url: str                    # path to metadata (captions) for images
+    splits: TrainSplitConfig         # define train, validation, test splits for your dataset
+    batch_size: int                  # per-gpu batch size used to train the model
+    num_data_points: int = 25e7      # total number of datapoints to train on
+    eval_every_seconds: int = 3600   # validation statistics will be performed this often
+
+class TrainDiffusionPriorConfig(BaseModel):
+    prior: DiffusionPriorConfig
+    data: DiffusionPriorDataConfig
+    train: DiffusionPriorTrainConfig
+    tracker: TrackerConfig
+
+    @classmethod
+    def from_json_path(cls, json_path):
+        with open(json_path) as f:
+            config = json.load(f)
+        return cls(**config)
+
+# decoder pydantic classes
+
+class UnetConfig(BaseModel):
+    dim: int
+    dim_mults: ListOrTuple[int]
+    image_embed_dim: int = None
+    text_embed_dim: int = None
+    cond_on_text_encodings: bool = None
+    cond_dim: int = None
+    channels: int = 3
+    self_attn: ListOrTuple[int]
+    attn_dim_head: int = 32
+    attn_heads: int = 16
+    init_cross_embed: bool = True
+
+    class Config:
+        extra = "allow"
+
+class DecoderConfig(BaseModel):
+    unets: ListOrTuple[UnetConfig]
+    image_size: int = None
+    image_sizes: ListOrTuple[int] = None
+    clip: Optional[AdapterConfig]   # The clip model to use if embeddings are not provided
+    channels: int = 3
+    timesteps: int = 1000
+    sample_timesteps: Optional[SingularOrIterable[Optional[int]]] = None
+    loss_type: str = 'l2'
+    beta_schedule: ListOrTuple[str] = None  # None means all cosine
+    learned_variance: SingularOrIterable[bool] = True
+    image_cond_drop_prob: float = 0.1
+    text_cond_drop_prob: float = 0.5
+
+    def create(self):
+        decoder_kwargs = self.dict()
+
+        unet_configs = decoder_kwargs.pop('unets')
+        unets = [Unet(**config) for config in unet_configs]
+
+        has_clip = exists(decoder_kwargs.pop('clip'))
+        clip = None
+        if has_clip:
+            clip = self.clip.create()
+
+        return Decoder(unets, clip=clip, **decoder_kwargs)
+
+    @validator('image_sizes')
+    def check_image_sizes(cls, image_sizes, values):
+        if exists(values.get('image_size')) ^ exists(image_sizes):
+            return image_sizes
+        raise ValueError('either image_size or image_sizes is required, but not both')
+
+    class Config:
+        extra = "allow"
+
+class DecoderDataConfig(BaseModel):
+    webdataset_base_url: str               # path to a webdataset with jpg images
+    img_embeddings_url: Optional[str]      # path to .npy files with embeddings
+    text_embeddings_url: Optional[str]     # path to .npy files with embeddings
+    num_workers: int = 4
+    batch_size: int = 64
+    start_shard: int = 0
+    end_shard: int = 9999999
+    shard_width: int = 6
+    index_width: int = 4
+    splits: TrainSplitConfig
+    shuffle_train: bool = True
+    resample_train: bool = False
+    preprocessing: Dict[str, Any] = {'ToTensor': True}
+
+    @property
+    def img_preproc(self):
+        def _get_transformation(transformation_name, **kwargs):
+            if transformation_name == "RandomResizedCrop":
+                return T.RandomResizedCrop(**kwargs)
+            elif transformation_name == "RandomHorizontalFlip":
+                return T.RandomHorizontalFlip()
+            elif transformation_name == "ToTensor":
+                return T.ToTensor()
+
+        transforms = []
+        for transform_name, transform_kwargs_or_bool in self.preprocessing.items():
+            transform_kwargs = {} if not isinstance(transform_kwargs_or_bool, dict) else transform_kwargs_or_bool
+            transforms.append(_get_transformation(transform_name, **transform_kwargs))
+        return T.Compose(transforms)
+
+class DecoderTrainConfig(BaseModel):
+    epochs: int = 20
+    lr: SingularOrIterable[float] = 1e-4
+    wd: SingularOrIterable[float] = 0.01
+    warmup_steps: Optional[SingularOrIterable[int]] = None
+    find_unused_parameters: bool = True
+    max_grad_norm: SingularOrIterable[float] = 0.5
+    save_every_n_samples: int = 100000
+    n_sample_images: int = 6                       # The number of example images to produce when sampling the train and test dataset
+    cond_scale: Union[float, List[float]] = 1.0
+    device: str = 'cuda:0'
+    epoch_samples: int = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
+    validation_samples: int = None                 # Same as above but for validation.
+    save_immediately: bool = False
+    use_ema: bool = True
+    ema_beta: float = 0.999
+    amp: bool = False
+    unet_training_mask: ListOrTuple[bool] = None   # If None, use all unets
+
+class DecoderEvaluateConfig(BaseModel):
+    n_evaluation_samples: int = 1000
+    FID: Dict[str, Any] = None
+    IS: Dict[str, Any] = None
+    KID: Dict[str, Any] = None
+    LPIPS: Dict[str, Any] = None
+
+class TrainDecoderConfig(BaseModel):
+    decoder: DecoderConfig
+    data: DecoderDataConfig
+    train: DecoderTrainConfig
+    evaluate: DecoderEvaluateConfig
+    tracker: TrackerConfig
+    seed: int = 0
+
+    @classmethod
+    def from_json_path(cls, json_path):
+        with open(json_path) as f:
+            config = json.load(f)
+        return cls(**config)
+    
+    @root_validator
+    def check_has_embeddings(cls, values):
+        # Makes sure that enough information is provided to get the embeddings specified for training
+        data_config, decoder_config = values.get('data'), values.get('decoder')
+
+        if not exists(data_config) or not exists(decoder_config):
+            # Then something else errored and we should just pass through
+            return values
+
+        using_text_embeddings = any([unet.cond_on_text_encodings for unet in decoder_config.unets])
+        using_clip = exists(decoder_config.clip)
+        img_emb_url = data_config.img_embeddings_url
+        text_emb_url = data_config.text_embeddings_url
+
+        if using_text_embeddings:
+            # Then we need some way to get the embeddings
+            assert using_clip or exists(text_emb_url), 'If text conditioning, either clip or text_embeddings_url must be provided'
+
+        if using_clip:
+            if using_text_embeddings:
+                assert not exists(text_emb_url) or not exists(img_emb_url), 'Loaded clip, but also provided text_embeddings_url and img_embeddings_url. This is redundant. Remove the clip model or the text embeddings'
+            else:
+                assert not exists(img_emb_url), 'Loaded clip, but also provided img_embeddings_url. This is redundant. Remove the clip model or the embeddings'
+
+        if text_emb_url:
+            assert using_text_embeddings, "Text embeddings are being loaded, but text embeddings are not being conditioned on. This will slow down the dataloader for no reason."
+
+        return values

dalle2_pytorch/trainer.py

@@ -0,0 +1,742 @@
+import time
+import copy
+from pathlib import Path
+from math import ceil
+from functools import partial, wraps
+from contextlib import nullcontext
+from collections.abc import Iterable
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR
+from torch.cuda.amp import autocast, GradScaler
+
+from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
+from dalle2_pytorch.optimizer import get_optimizer
+from dalle2_pytorch.version import __version__
+from packaging import version
+
+import pytorch_warmup as warmup
+
+from ema_pytorch import EMA
+
+from accelerate import Accelerator, DistributedType
+
+import numpy as np
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if callable(d) else d
+
+def cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(),dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
+
+def num_to_groups(num, divisor):
+    groups = num // divisor
+    remainder = num % divisor
+    arr = [divisor] * groups
+    if remainder > 0:
+        arr.append(remainder)
+    return arr
+
+# decorators
+
+def cast_torch_tensor(fn):
+    @wraps(fn)
+    def inner(model, *args, **kwargs):
+        device = kwargs.pop('_device', next(model.parameters()).device)
+        cast_device = kwargs.pop('_cast_device', True)
+        cast_deepspeed_precision = kwargs.pop('_cast_deepspeed_precision', True)
+
+        kwargs_keys = kwargs.keys()
+        all_args = (*args, *kwargs.values())
+        split_kwargs_index = len(all_args) - len(kwargs_keys)
+        all_args = tuple(map(lambda t: torch.from_numpy(t) if exists(t) and isinstance(t, np.ndarray) else t, all_args))
+
+        if cast_device:
+            all_args = tuple(map(lambda t: t.to(device) if exists(t) and isinstance(t, torch.Tensor) else t, all_args))
+
+        if cast_deepspeed_precision:
+            try:
+                accelerator = model.accelerator
+                if accelerator is not None and accelerator.distributed_type == DistributedType.DEEPSPEED:
+                    cast_type_map = {
+                        "fp16": torch.half,
+                        "bf16": torch.bfloat16,
+                        "no": torch.float
+                    }
+                    precision_type = cast_type_map[accelerator.mixed_precision]
+                    all_args = tuple(map(lambda t: t.to(precision_type) if exists(t) and isinstance(t, torch.Tensor) else t, all_args))
+            except AttributeError:
+                # Then this model doesn't have an accelerator
+                pass
+
+        args, kwargs_values = all_args[:split_kwargs_index], all_args[split_kwargs_index:]
+        kwargs = dict(tuple(zip(kwargs_keys, kwargs_values)))
+
+        out = fn(model, *args, **kwargs)
+        return out
+    return inner
+
+# gradient accumulation functions
+
+def split_iterable(it, split_size):
+    accum = []
+    for ind in range(ceil(len(it) / split_size)):
+        start_index = ind * split_size
+        accum.append(it[start_index: (start_index + split_size)])
+    return accum
+
+def split(t, split_size = None):
+    if not exists(split_size):
+        return t
+
+    if isinstance(t, torch.Tensor):
+        return t.split(split_size, dim = 0)
+
+    if isinstance(t, Iterable):
+        return split_iterable(t, split_size)
+
+    return TypeError
+
+def find_first(cond, arr):
+    for el in arr:
+        if cond(el):
+            return el
+    return None
+
+def split_args_and_kwargs(*args, split_size = None, **kwargs):
+    all_args = (*args, *kwargs.values())
+    len_all_args = len(all_args)
+    first_tensor = find_first(lambda t: isinstance(t, torch.Tensor), all_args)
+    assert exists(first_tensor)
+
+    batch_size = len(first_tensor)
+    split_size = default(split_size, batch_size)
+    num_chunks = ceil(batch_size / split_size)
+
+    dict_len = len(kwargs)
+    dict_keys = kwargs.keys()
+    split_kwargs_index = len_all_args - dict_len
+
+    split_all_args = [split(arg, split_size = split_size) if exists(arg) and isinstance(arg, (torch.Tensor, Iterable)) else ((arg,) * num_chunks) for arg in all_args]
+    chunk_sizes = tuple(map(len, split_all_args[0]))
+
+    for (chunk_size, *chunked_all_args) in tuple(zip(chunk_sizes, *split_all_args)):
+        chunked_args, chunked_kwargs_values = chunked_all_args[:split_kwargs_index], chunked_all_args[split_kwargs_index:]
+        chunked_kwargs = dict(tuple(zip(dict_keys, chunked_kwargs_values)))
+        chunk_size_frac = chunk_size / batch_size
+        yield chunk_size_frac, (chunked_args, chunked_kwargs)
+
+# diffusion prior trainer
+
+def prior_sample_in_chunks(fn):
+    @wraps(fn)
+    def inner(self, *args, max_batch_size = None, **kwargs):
+        if not exists(max_batch_size):
+            return fn(self, *args, **kwargs)
+
+        outputs = [fn(self, *chunked_args, **chunked_kwargs) for _, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs)]
+        return torch.cat(outputs, dim = 0)
+    return inner
+
+class DiffusionPriorTrainer(nn.Module):
+    def __init__(
+        self,
+        diffusion_prior,
+        accelerator = None,
+        use_ema = True,
+        lr = 3e-4,
+        wd = 1e-2,
+        eps = 1e-6,
+        max_grad_norm = None,
+        group_wd_params = True,
+        warmup_steps = None,
+        cosine_decay_max_steps = None,
+        **kwargs
+    ):
+        super().__init__()
+        assert isinstance(diffusion_prior, DiffusionPrior)
+
+        ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
+        accelerator_kwargs, kwargs = groupby_prefix_and_trim('accelerator_', kwargs)
+
+        if not exists(accelerator):
+            accelerator = Accelerator(**accelerator_kwargs)
+
+        # assign some helpful member vars
+
+        self.accelerator = accelerator
+        self.text_conditioned = diffusion_prior.condition_on_text_encodings
+
+        # setting the device
+
+        self.device = accelerator.device
+        diffusion_prior.to(self.device)
+
+        # save model
+
+        self.diffusion_prior = diffusion_prior
+
+        # mixed precision checks
+
+        if (
+            exists(self.accelerator) 
+            and self.accelerator.distributed_type == DistributedType.DEEPSPEED 
+            and self.diffusion_prior.clip is not None
+            ):
+            # Then we need to make sure clip is using the correct precision or else deepspeed will error
+            cast_type_map = {
+                "fp16": torch.half,
+                "bf16": torch.bfloat16,
+                "no": torch.float
+            }
+            precision_type = cast_type_map[accelerator.mixed_precision]
+            assert precision_type == torch.float, "DeepSpeed currently only supports float32 precision when using on the fly embedding generation from clip"
+            self.diffusion_prior.clip.to(precision_type)
+
+        # optimizer stuff
+
+        self.optim_kwargs = dict(lr=lr, wd=wd, eps=eps, group_wd_params=group_wd_params)
+
+        self.optimizer = get_optimizer(
+            self.diffusion_prior.parameters(),
+            **self.optim_kwargs,
+            **kwargs
+        )
+
+        if exists(cosine_decay_max_steps):
+            self.scheduler = CosineAnnealingLR(optimizer, T_max = cosine_decay_max_steps)
+        else:
+            self.scheduler = LambdaLR(self.optimizer, lr_lambda = lambda _: 1.0)
+        
+        self.warmup_scheduler = warmup.LinearWarmup(self.optimizer, warmup_period = warmup_steps) if exists(warmup_steps) else None
+
+        # distribute the model if using HFA
+
+        self.diffusion_prior, self.optimizer, self.scheduler = self.accelerator.prepare(self.diffusion_prior, self.optimizer, self.scheduler)
+
+        # exponential moving average stuff
+
+        self.use_ema = use_ema
+
+        if self.use_ema:
+            self.ema_diffusion_prior = EMA(self.accelerator.unwrap_model(self.diffusion_prior), **ema_kwargs)
+
+        # gradient clipping if needed
+
+        self.max_grad_norm = max_grad_norm
+
+        # track steps internally
+
+        self.register_buffer('step', torch.tensor([0], device = self.device))
+
+    # utility
+
+    def save(self, path, overwrite = True, **kwargs):
+
+        # only save on the main process
+        if self.accelerator.is_main_process:
+            print(f"Saving checkpoint at step: {self.step.item()}")
+            path = Path(path)
+            assert not (path.exists() and not overwrite)
+            path.parent.mkdir(parents = True, exist_ok = True)
+
+            # FIXME: LambdaLR can't be saved due to pickling issues
+            save_obj = dict(
+                optimizer = self.optimizer.state_dict(),
+                scheduler = self.scheduler.state_dict(),
+                warmup_scheduler = self.warmup_scheduler,
+                model = self.accelerator.unwrap_model(self.diffusion_prior).state_dict(),
+                version = version.parse(__version__),
+                step = self.step,
+                **kwargs
+            )
+
+            if self.use_ema:
+                save_obj = {
+                    **save_obj,
+                    'ema': self.ema_diffusion_prior.state_dict(),
+                    'ema_model': self.ema_diffusion_prior.ema_model.state_dict() # save the ema model specifically for easy ema-only reload
+                }
+
+            torch.save(save_obj, str(path))
+
+    def load(self, path_or_state, overwrite_lr = True, strict = True):
+        """
+        Load a checkpoint of a diffusion prior trainer.
+
+        Will load the entire trainer, including the optimizer and EMA.
+
+        Params:
+            - path_or_state (str | torch): a path to the DiffusionPriorTrainer checkpoint file
+            - overwrite_lr (bool): wether or not to overwrite the stored LR with the LR specified in the new trainer
+            - strict (bool): kwarg for `torch.nn.Module.load_state_dict`, will force an exact checkpoint match
+
+        Returns:
+            loaded_obj (dict): The loaded checkpoint dictionary
+        """
+
+        # all processes need to load checkpoint. no restriction here
+        if isinstance(path_or_state, str):
+            path = Path(path_or_state)
+            assert path.exists()
+            loaded_obj = torch.load(str(path), map_location=self.device)
+
+        elif isinstance(path_or_state, dict):
+            loaded_obj = path_or_state
+
+        if version.parse(__version__) != loaded_obj['version']:
+            print(f'loading saved diffusion prior at version {loaded_obj["version"]} but current package version is at {__version__}')
+
+        # unwrap the model when loading from checkpoint
+        self.accelerator.unwrap_model(self.diffusion_prior).load_state_dict(loaded_obj['model'], strict = strict)
+        self.step.copy_(torch.ones_like(self.step, device=self.device) * loaded_obj['step'].to(self.device))
+
+        self.optimizer.load_state_dict(loaded_obj['optimizer'])
+        self.scheduler.load_state_dict(loaded_obj['scheduler'])
+
+        # set warmupstep
+        if exists(self.warmup_scheduler):
+            self.warmup_scheduler.last_step = self.step.item()
+
+        # ensure new lr is used if different from old one
+        if overwrite_lr:
+            new_lr = self.optim_kwargs["lr"]
+
+            for group in self.optimizer.param_groups:
+                group["lr"] = new_lr if group["lr"] > 0.0 else 0.0
+
+        if self.use_ema:
+            assert 'ema' in loaded_obj
+            self.ema_diffusion_prior.load_state_dict(loaded_obj['ema'], strict = strict)
+            # below might not be necessary, but I had a suspicion that this wasn't being loaded correctly
+            self.ema_diffusion_prior.ema_model.load_state_dict(loaded_obj["ema_model"])
+
+        return loaded_obj
+
+    # model functionality
+
+    def update(self):
+
+        if exists(self.max_grad_norm):
+            self.accelerator.clip_grad_norm_(self.diffusion_prior.parameters(), self.max_grad_norm)
+        
+        self.optimizer.step()
+        self.optimizer.zero_grad()
+
+        # accelerator will ocassionally skip optimizer steps in a "dynamic loss scaling strategy"
+        if not self.accelerator.optimizer_step_was_skipped:
+            sched_context = self.warmup_scheduler.dampening if exists(self.warmup_scheduler) else nullcontext
+            with sched_context():
+                self.scheduler.step()
+
+        if self.use_ema:
+            self.ema_diffusion_prior.update()
+
+        self.step += 1
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @prior_sample_in_chunks
+    def p_sample_loop(self, *args, **kwargs):
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
+        return model.p_sample_loop(*args, **kwargs)
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @prior_sample_in_chunks
+    def sample(self, *args, **kwargs):
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
+        return model.sample(*args, **kwargs)
+
+    @torch.no_grad()
+    def sample_batch_size(self, *args, **kwargs):
+        model = self.ema_diffusion_prior.ema_model if self.use_ema else self.diffusion_prior
+        return model.sample_batch_size(*args, **kwargs)
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @prior_sample_in_chunks
+    def embed_text(self, *args, **kwargs):
+        return self.accelerator.unwrap_model(self.diffusion_prior).clip.embed_text(*args, **kwargs)
+
+    @cast_torch_tensor
+    def forward(
+        self,
+        *args,
+        max_batch_size = None,
+        **kwargs
+    ):
+        total_loss = 0.
+
+        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
+            with self.accelerator.autocast():
+                loss = self.diffusion_prior(*chunked_args, **chunked_kwargs)
+                loss = loss * chunk_size_frac
+
+            total_loss += loss.item()
+
+            if self.training:
+                self.accelerator.backward(loss)
+
+        return total_loss
+
+# decoder trainer
+
+def decoder_sample_in_chunks(fn):
+    @wraps(fn)
+    def inner(self, *args, max_batch_size = None, **kwargs):
+        if not exists(max_batch_size):
+            return fn(self, *args, **kwargs)
+
+        if self.decoder.unconditional:
+            batch_size = kwargs.get('batch_size')
+            batch_sizes = num_to_groups(batch_size, max_batch_size)
+            outputs = [fn(self, *args, **{**kwargs, 'batch_size': sub_batch_size}) for sub_batch_size in batch_sizes]
+        else:
+            outputs = [fn(self, *chunked_args, **chunked_kwargs) for _, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs)]
+
+        return torch.cat(outputs, dim = 0)
+    return inner
+
+class DecoderTrainer(nn.Module):
+    def __init__(
+        self,
+        decoder,
+        accelerator = None,
+        dataloaders = None,
+        use_ema = True,
+        lr = 1e-4,
+        wd = 1e-2,
+        eps = 1e-8,
+        warmup_steps = None,
+        cosine_decay_max_steps = None,
+        max_grad_norm = 0.5,
+        amp = False,
+        group_wd_params = True,
+        **kwargs
+    ):
+        super().__init__()
+        assert isinstance(decoder, Decoder)
+        ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
+
+        self.accelerator = default(accelerator, Accelerator)
+
+        self.num_unets = len(decoder.unets)
+
+        self.use_ema = use_ema
+        self.ema_unets = nn.ModuleList([])
+
+        self.amp = amp
+
+        # be able to finely customize learning rate, weight decay
+        # per unet
+
+        lr, wd, eps, warmup_steps, cosine_decay_max_steps = map(partial(cast_tuple, length = self.num_unets), (lr, wd, eps, warmup_steps, cosine_decay_max_steps))
+
+        assert all([unet_lr <= 1e-2 for unet_lr in lr]), 'your learning rate is too high, recommend sticking with 1e-4, at most 5e-4'
+
+        optimizers = []
+        schedulers = []
+        warmup_schedulers = []
+
+        for unet, unet_lr, unet_wd, unet_eps, unet_warmup_steps, unet_cosine_decay_max_steps in zip(decoder.unets, lr, wd, eps, warmup_steps, cosine_decay_max_steps):
+            if isinstance(unet, nn.Identity):
+                optimizers.append(None)
+                schedulers.append(None)
+                warmup_schedulers.append(None)
+            else:
+                optimizer = get_optimizer(
+                    unet.parameters(),
+                    lr = unet_lr,
+                    wd = unet_wd,
+                    eps = unet_eps,
+                    group_wd_params = group_wd_params,
+                    **kwargs
+                )
+
+                optimizers.append(optimizer)
+
+                if exists(unet_cosine_decay_max_steps):
+                    scheduler = CosineAnnealingLR(optimizer, T_max = unet_cosine_decay_max_steps)
+                else:
+                    scheduler = LambdaLR(optimizer, lr_lambda = lambda step: 1.0)
+
+                warmup_scheduler = warmup.LinearWarmup(optimizer, warmup_period = unet_warmup_steps) if exists(unet_warmup_steps) else None
+                warmup_schedulers.append(warmup_scheduler)
+
+                schedulers.append(scheduler)
+
+            if self.use_ema:
+                self.ema_unets.append(EMA(unet, **ema_kwargs))
+
+        # gradient clipping if needed
+
+        self.max_grad_norm = max_grad_norm
+
+        self.register_buffer('steps', torch.tensor([0] * self.num_unets))
+
+        if self.accelerator.distributed_type == DistributedType.DEEPSPEED and decoder.clip is not None:
+            # Then we need to make sure clip is using the correct precision or else deepspeed will error
+            cast_type_map = {
+                "fp16": torch.half,
+                "bf16": torch.bfloat16,
+                "no": torch.float
+            }
+            precision_type = cast_type_map[accelerator.mixed_precision]
+            assert precision_type == torch.float, "DeepSpeed currently only supports float32 precision when using on the fly embedding generation from clip"
+            clip = decoder.clip
+            clip.to(precision_type)
+
+        decoder, train_dataloader, *optimizers = list(self.accelerator.prepare(decoder, dataloaders['train'], *optimizers))
+
+        self.decoder = decoder
+
+        # prepare dataloaders
+
+        train_loader = val_loader = None
+        if exists(dataloaders):
+            train_loader, val_loader = self.accelerator.prepare(dataloaders["train"], dataloaders["val"])
+
+        self.train_loader = train_loader
+        self.val_loader = val_loader
+
+        # store optimizers
+
+        for opt_ind, optimizer in zip(range(len(optimizers)), optimizers):
+            setattr(self, f'optim{opt_ind}', optimizer)
+
+        # store schedulers
+
+        for sched_ind, scheduler in zip(range(len(schedulers)), schedulers):
+            setattr(self, f'sched{sched_ind}', scheduler)
+
+        # store warmup schedulers
+
+        self.warmup_schedulers = warmup_schedulers
+
+    def validate_and_return_unet_number(self, unet_number = None):
+        if self.num_unets == 1:
+            unet_number = default(unet_number, 1)
+
+        assert exists(unet_number) and 1 <= unet_number <= self.num_unets
+        return unet_number
+
+    def num_steps_taken(self, unet_number = None):
+        unet_number = self.validate_and_return_unet_number(unet_number)
+        return self.steps[unet_number - 1].item()
+
+    def save(self, path, overwrite = True, **kwargs):
+        path = Path(path)
+        assert not (path.exists() and not overwrite)
+        path.parent.mkdir(parents = True, exist_ok = True)
+
+        save_obj = dict(
+            model = self.accelerator.unwrap_model(self.decoder).state_dict(),
+            version = __version__,
+            steps = self.steps.cpu(),
+            **kwargs
+        )
+
+        for ind in range(0, self.num_unets):
+            optimizer_key = f'optim{ind}'
+            scheduler_key = f'sched{ind}'
+
+            optimizer = getattr(self, optimizer_key)
+            scheduler = getattr(self, scheduler_key)
+
+            optimizer_state_dict = optimizer.state_dict() if exists(optimizer) else None
+            scheduler_state_dict = scheduler.state_dict() if exists(scheduler) else None
+
+            save_obj = {**save_obj, optimizer_key: optimizer_state_dict, scheduler_key: scheduler_state_dict}
+
+        if self.use_ema:
+            save_obj = {**save_obj, 'ema': self.ema_unets.state_dict()}
+
+        self.accelerator.save(save_obj, str(path))
+
+    def load_state_dict(self, loaded_obj, only_model = False, strict = True):
+        if version.parse(__version__) != version.parse(loaded_obj['version']):
+            self.accelerator.print(f'loading saved decoder at version {loaded_obj["version"]}, but current package version is {__version__}')
+
+        self.accelerator.unwrap_model(self.decoder).load_state_dict(loaded_obj['model'], strict = strict)
+        self.steps.copy_(loaded_obj['steps'])
+
+        if only_model:
+            return loaded_obj
+
+        for ind, last_step in zip(range(0, self.num_unets), self.steps.tolist()):
+
+            optimizer_key = f'optim{ind}'
+            optimizer = getattr(self, optimizer_key)
+
+            scheduler_key = f'sched{ind}'
+            scheduler = getattr(self, scheduler_key)
+
+            warmup_scheduler = self.warmup_schedulers[ind]
+
+            if exists(optimizer):
+                optimizer.load_state_dict(loaded_obj[optimizer_key])
+
+            if exists(scheduler):
+                scheduler.load_state_dict(loaded_obj[scheduler_key])
+
+            if exists(warmup_scheduler):
+                warmup_scheduler.last_step = last_step
+
+        if self.use_ema:
+            assert 'ema' in loaded_obj
+            self.ema_unets.load_state_dict(loaded_obj['ema'], strict = strict)
+
+    def load(self, path, only_model = False, strict = True):
+        path = Path(path)
+        assert path.exists()
+
+        loaded_obj = torch.load(str(path), map_location = 'cpu')
+
+        self.load_state_dict(loaded_obj, only_model = only_model, strict = strict)
+
+        return loaded_obj
+
+    @property
+    def unets(self):
+        return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
+
+    def increment_step(self, unet_number):
+        assert 1 <= unet_number <= self.num_unets
+
+        unet_index_tensor = torch.tensor(unet_number - 1, device = self.steps.device)
+        self.steps += F.one_hot(unet_index_tensor, num_classes = len(self.steps))
+
+    def update(self, unet_number = None):
+        unet_number = self.validate_and_return_unet_number(unet_number)
+        index = unet_number - 1
+
+        optimizer = getattr(self, f'optim{index}')
+        scheduler = getattr(self, f'sched{index}')
+
+        if exists(self.max_grad_norm):
+            self.accelerator.clip_grad_norm_(self.decoder.parameters(), self.max_grad_norm)  # Automatically unscales gradients
+
+        optimizer.step()
+        optimizer.zero_grad()
+
+        warmup_scheduler = self.warmup_schedulers[index]
+        scheduler_context = warmup_scheduler.dampening if exists(warmup_scheduler) else nullcontext
+
+        with scheduler_context():
+            scheduler.step()
+
+        if self.use_ema:
+            ema_unet = self.ema_unets[index]
+            ema_unet.update()
+
+        self.increment_step(unet_number)
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @decoder_sample_in_chunks
+    def sample(self, *args, **kwargs):
+        distributed = self.accelerator.num_processes > 1
+        base_decoder = self.accelerator.unwrap_model(self.decoder)
+
+        was_training = base_decoder.training
+        base_decoder.eval()
+
+        if kwargs.pop('use_non_ema', False) or not self.use_ema:
+            out = base_decoder.sample(*args, **kwargs, distributed = distributed)
+            base_decoder.train(was_training)
+            return out
+
+        trainable_unets = self.accelerator.unwrap_model(self.decoder).unets
+        base_decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
+
+        output = base_decoder.sample(*args, **kwargs, distributed = distributed)
+
+        base_decoder.unets = trainable_unets             # restore original training unets
+
+        # cast the ema_model unets back to original device
+        for ema in self.ema_unets:
+            ema.restore_ema_model_device()
+
+        base_decoder.train(was_training)
+        return output
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @prior_sample_in_chunks
+    def embed_text(self, *args, **kwargs):
+        return self.accelerator.unwrap_model(self.decoder).clip.embed_text(*args, **kwargs)
+
+    @torch.no_grad()
+    @cast_torch_tensor
+    @prior_sample_in_chunks
+    def embed_image(self, *args, **kwargs):
+        return self.accelerator.unwrap_model(self.decoder).clip.embed_image(*args, **kwargs)
+
+    @cast_torch_tensor
+    def forward(
+        self,
+        *args,
+        unet_number = None,
+        max_batch_size = None,
+        return_lowres_cond_image=False,
+        **kwargs
+    ):
+        unet_number = self.validate_and_return_unet_number(unet_number)
+
+        total_loss = 0.
+        cond_images = []
+        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
+            with self.accelerator.autocast():
+                loss_obj = self.decoder(*chunked_args, unet_number = unet_number, return_lowres_cond_image=return_lowres_cond_image, **chunked_kwargs)
+                # loss_obj may be a tuple with loss and cond_image
+                if return_lowres_cond_image:
+                    loss, cond_image = loss_obj
+                else:
+                    loss = loss_obj
+                    cond_image = None
+                loss = loss * chunk_size_frac
+                if cond_image is not None:
+                    cond_images.append(cond_image)
+
+            total_loss += loss.item()
+
+            if self.training:
+                self.accelerator.backward(loss)
+
+        if return_lowres_cond_image:
+            return total_loss, torch.stack(cond_images)
+        else:
+            return total_loss

dalle2_pytorch/utils.py

@@ -0,0 +1,35 @@
+import time
+import importlib
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+# time helpers
+
+class Timer:
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.last_time = time.time()
+
+    def elapsed(self):
+        return time.time() - self.last_time
+
+# print helpers
+
+def print_ribbon(s, symbol = '=', repeat = 40):
+    flank = symbol * repeat
+    return f'{flank} {s} {flank}'
+
+# import helpers
+
+def import_or_print_error(pkg_name, err_str = None):
+    try:
+        return importlib.import_module(pkg_name)
+    except ModuleNotFoundError as e:
+        if exists(err_str):
+            print(err_str)
+        exit()

dalle2_pytorch/version.py

@@ -0,0 +1 @@
+__version__ = '1.9.0'

dalle2_pytorch/vqgan_vae.py

@@ -68,8 +68,8 @@ def group_dict_by_key(cond, d):
         return_val[ind][key] = d[key]
     return (*return_val,)
 
-def string_begins_with(prefix, str):
-    return str.startswith(prefix)
+def string_begins_with(prefix, string_input):
+    return string_input.startswith(prefix)
 
 def group_by_key_prefix(prefix, d):
     return group_dict_by_key(partial(string_begins_with, prefix), d)
@@ -331,112 +331,6 @@ class ResBlock(nn.Module):
     def forward(self, x):
         return self.net(x) + x
 
-# convnext enc dec
-
-class ChanLayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5):
-        super().__init__()
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
-
-    def forward(self, x):
-        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
-        mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (var + self.eps).sqrt() * self.g
-
-class ConvNext(nn.Module):
-    def __init__(self, dim, mult = 4, kernel_size = 3, ds_kernel_size = 7):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.net = nn.Sequential(
-            nn.Conv2d(dim, dim, ds_kernel_size, padding = ds_kernel_size // 2, groups = dim),
-            ChanLayerNorm(dim),
-            nn.Conv2d(dim, inner_dim, kernel_size, padding = kernel_size // 2),
-            nn.GELU(),
-            nn.Conv2d(inner_dim, dim, kernel_size, padding = kernel_size // 2)
-        )
-
-    def forward(self, x):
-        return self.net(x) + x
-
-class ConvNextEncDec(nn.Module):
-    def __init__(
-        self,
-        dim,
-        *,
-        channels = 3,
-        layers = 4,
-        layer_mults = None,
-        num_blocks = 1,
-        first_conv_kernel_size = 5,
-        use_attn = True,
-        attn_dim_head = 64,
-        attn_heads = 8,
-        attn_dropout = 0.,
-    ):
-        super().__init__()
-
-        self.layers = layers
-
-        self.encoders = MList([])
-        self.decoders = MList([])
-
-        layer_mults = default(layer_mults, list(map(lambda t: 2 ** t, range(layers))))
-        assert len(layer_mults) == layers, 'layer multipliers must be equal to designated number of layers'
-
-        layer_dims = [dim * mult for mult in layer_mults]
-        dims = (dim, *layer_dims)
-
-        self.encoded_dim = dims[-1]
-
-        dim_pairs = zip(dims[:-1], dims[1:])
-
-        append = lambda arr, t: arr.append(t)
-        prepend = lambda arr, t: arr.insert(0, t)
-
-        if not isinstance(num_blocks, tuple):
-            num_blocks = (*((0,) * (layers - 1)), num_blocks)
-
-        if not isinstance(use_attn, tuple):
-            use_attn = (*((False,) * (layers - 1)), use_attn)
-
-        assert len(num_blocks) == layers, 'number of blocks config must be equal to number of layers'
-        assert len(use_attn) == layers
-
-        for layer_index, (dim_in, dim_out), layer_num_blocks, layer_use_attn in zip(range(layers), dim_pairs, num_blocks, use_attn):
-            append(self.encoders, nn.Sequential(nn.Conv2d(dim_in, dim_out, 4, stride = 2, padding = 1), leaky_relu()))
-            prepend(self.decoders, nn.Sequential(nn.ConvTranspose2d(dim_out, dim_in, 4, 2, 1), leaky_relu()))
-
-            if layer_use_attn:
-                prepend(self.decoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
-
-            for _ in range(layer_num_blocks):
-                append(self.encoders, ConvNext(dim_out))
-                prepend(self.decoders, ConvNext(dim_out))
-
-            if layer_use_attn:
-                append(self.encoders, VQGanAttention(dim = dim_out, heads = attn_heads, dim_head = attn_dim_head, dropout = attn_dropout))
-
-        prepend(self.encoders, nn.Conv2d(channels, dim, first_conv_kernel_size, padding = first_conv_kernel_size // 2))
-        append(self.decoders, nn.Conv2d(dim, channels, 1))
-
-    def get_encoded_fmap_size(self, image_size):
-        return image_size // (2 ** self.layers)
-
-    @property
-    def last_dec_layer(self):
-        return self.decoders[-1].weight
-
-    def encode(self, x):
-        for enc in self.encoders:
-            x = enc(x)
-        return x
-
-    def decode(self, x):
-        for dec in self.decoders:
-            x = dec(x)
-        return x
-
 # vqgan attention layer
 
 class VQGanAttention(nn.Module):
@@ -682,8 +576,6 @@ class VQGanVAE(nn.Module):
             enc_dec_klass = ResnetEncDec
         elif vae_type == 'vit':
             enc_dec_klass = ViTEncDec
-        elif vae_type == 'convnext':
-            enc_dec_klass = ConvNextEncDec
         else:
             raise ValueError(f'{vae_type} not valid')
 

dalle2_pytorch/vqgan_vae_trainer.py

@@ -3,22 +3,24 @@ import copy
 from random import choice
 from pathlib import Path
 from shutil import rmtree
+from PIL import Image
 
 import torch
 from torch import nn
-
-from PIL import Image
-from torchvision.datasets import ImageFolder
-import torchvision.transforms as T
+from torch.cuda.amp import autocast, GradScaler
 from torch.utils.data import Dataset, DataLoader, random_split
+
+import torchvision.transforms as T
+from torchvision.datasets import ImageFolder
 from torchvision.utils import make_grid, save_image
 
 from einops import rearrange
 
-from dalle2_pytorch.train import EMA
 from dalle2_pytorch.vqgan_vae import VQGanVAE
 from dalle2_pytorch.optimizer import get_optimizer
 
+from ema_pytorch import EMA
+
 # helpers
 
 def exists(val):
@@ -96,9 +98,10 @@ class VQGanVAETrainer(nn.Module):
         valid_frac = 0.05,
         random_split_seed = 42,
         ema_beta = 0.995,
-        ema_update_after_step = 2000,
+        ema_update_after_step = 500,
         ema_update_every = 10,
         apply_grad_penalty_every = 4,
+        amp = False
     ):
         super().__init__()
         assert isinstance(vae, VQGanVAE), 'vae must be instance of VQGanVAE'
@@ -120,6 +123,10 @@ class VQGanVAETrainer(nn.Module):
         self.optim = get_optimizer(vae_parameters, lr = lr, wd = wd)
         self.discr_optim = get_optimizer(discr_parameters, lr = lr, wd = wd)
 
+        self.amp = amp
+        self.scaler = GradScaler(enabled = amp)
+        self.discr_scaler = GradScaler(enabled = amp)
+
         # create dataset
 
         self.ds = ImageDataset(folder, image_size = image_size)
@@ -178,20 +185,22 @@ class VQGanVAETrainer(nn.Module):
             img = next(self.dl)
             img = img.to(device)
 
-            loss = self.vae(
-                img,
-                return_loss = True,
-                apply_grad_penalty = apply_grad_penalty
-            )
+            with autocast(enabled = self.amp):
+                loss = self.vae(
+                    img,
+                    return_loss = True,
+                    apply_grad_penalty = apply_grad_penalty
+                )
+
+
+                self.scaler.scale(loss / self.grad_accum_every).backward()
 
             accum_log(logs, {'loss': loss.item() / self.grad_accum_every})
 
-            (loss / self.grad_accum_every).backward()
-
-        self.optim.step()
+        self.scaler.step(self.optim)
+        self.scaler.update()
         self.optim.zero_grad()
 
-
         # update discriminator
 
         if exists(self.vae.discr):
@@ -200,12 +209,15 @@ class VQGanVAETrainer(nn.Module):
                 img = next(self.dl)
                 img = img.to(device)
 
-                loss = self.vae(img, return_discr_loss = True)
+                with autocast(enabled = self.amp):
+                    loss = self.vae(img, return_discr_loss = True)
+
+                    self.discr_scaler.scale(loss / self.grad_accum_every).backward()
+
                 accum_log(logs, {'discr_loss': loss.item() / self.grad_accum_every})
 
-                (loss / self.grad_accum_every).backward()
-
-            self.discr_optim.step()
+            self.discr_scaler.step(self.discr_optim)
+            self.discr_scaler.update()
             self.discr_optim.zero_grad()
 
             # log

prior.md

@@ -0,0 +1,183 @@
+# Diffusion Prior
+This readme serves as an introduction to the diffusion prior.
+
+## Intro
+
+A properly trained prior will allow you to translate between two embedding spaces. If you know *a priori* that two embeddings are connected some way—then ability the translate between them could extremely helpful.
+
+### Motivation
+
+Before we dive into the model, let’s look at a quick example of where the model may be helpful.
+
+For demonstration purposes we will imagine that we wish to generate images from text using CLIP and a Decoder.
+
+> [CLIP](https://openai.com/blog/clip/) is a contrastive model that learns to maximize the cosine similarity between a given image and caption, however, there is no guarantee that these embeddings are in the same space. While the embeddings generated are ***close*** the image and text embeddings occupy two disjoint sets.
+
+```python
+# Load Models
+clip_model = clip.load("ViT-L/14")
+decoder = Decoder(checkpoint="best.pth") # A decoder trained on CLIP Image embeddings
+
+# Retrieve prompt from user and encode with CLIP
+prompt = "A corgi wearing sunglasses"
+tokenized_text = tokenize(prompt)
+text_embedding = clip_model.encode_text(tokenized_text)
+
+# Now, pass the text embedding to the decoder
+predicted_image = decoder.sample(text_embedding)
+```
+
+> **Question**: *Can you spot the issue here?*
+>
+> **Answer**: *We’re trying to generate an image from a text embedding!*
+
+Unfortunately, we run into the issue previously mentioned--the image embeddings and the text embeddings are not interchangeable! Now let's look at a better solution
+
+```python
+# Load Models
+prior= Prior(checkpoint="prior.pth") # A decoder trained to go from: text-> clip text emb -> clip img emb
+decoder = Decoder(checkpoint="decoder.pth") # A decoder trained on CLIP Image embeddings
+
+# Retrieve prompt from user and encode with a prior
+prompt = "A corgi wearing sunglasses"
+tokenized_text = tokenize(prompt)
+text_embedding = prior.sample(tokenized_text) # <-- now we get an embedding in the same space as images!
+
+# Now, pass the predicted image embedding to the decoder
+predicted_image = decoder.sample(text_embedding)
+```
+
+With the prior we are able to successfully generate embeddings *within* CLIP's image space! For this reason, the decoder will perform much better as it receives input that is much closer to its training data.
+
+> **You may be asking yourself the following question:**
+>
+> *"Why don't you just train the decoder on clip text embeddings instead of image embeddings?"*
+>
+> OpenAI covers this topic in their [DALLE-2 paper](https://arxiv.org/abs/2204.06125). The TL;DR is *"it doesn't work as well as decoders trained on image embeddings"*...also...its just an example :smile:
+
+## Usage
+
+To utilize a pre-trained prior, it’s quite simple.
+
+### Loading Checkpoints
+```python
+import torch
+from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork, OpenAIClipAdapter
+from dalle2_pytorch.trainer import DiffusionPriorTrainer
+
+def load_diffusion_model(dprior_path):
+
+    prior_network = DiffusionPriorNetwork(
+        dim=768,
+        depth=24,
+        dim_head=64,
+        heads=32,
+        normformer=True,
+        attn_dropout=5e-2,
+        ff_dropout=5e-2,
+        num_time_embeds=1,
+        num_image_embeds=1,
+        num_text_embeds=1,
+        num_timesteps=1000,
+        ff_mult=4
+    )
+
+    diffusion_prior = DiffusionPrior(
+        net=prior_network,
+        clip=OpenAIClipAdapter("ViT-L/14"),
+        image_embed_dim=768,
+        timesteps=1000,
+        cond_drop_prob=0.1,
+        loss_type="l2",
+        condition_on_text_encodings=True,
+
+    )
+
+    trainer = DiffusionPriorTrainer(
+        diffusion_prior=diffusion_prior,
+        lr=1.1e-4,
+        wd=6.02e-2,
+        max_grad_norm=0.5,
+        amp=False,
+        group_wd_params=True,
+        use_ema=True,
+        device=device,
+        accelerator=None,
+    )
+
+    trainer.load(dprior_path)
+
+    return trainer
+```
+
+ Here we instantiate a model matches the configuration it was trained with, and then load the weights (*just like any other PyTorch model!*)
+
+### Sampling
+Once we have a pre-trained model, generating embeddings is quite simple!
+```python
+# tokenize the text
+tokenized_text = clip.tokenize("<your amazing prompt>")
+# predict an embedding
+predicted_embedding = prior.sample(tokenized_text, n_samples_per_batch=2, cond_scale=1.0)
+```
+
+The resulting tensor returned from `.sample()` is of the same shape as your training data along the non-batch dimension(s). For example, a prior trained on `ViT-L/14` embeddings will predict an embedding of shape (1, 768).
+
+> For CLIP priors, this is quite handy as it means that you can use prior.sample(tokenizer_text) as a drop in replacement for clip.encode_text().
+
+**Some things to note:**
+* It is possible to specify the number of embeddings to sample from (the default suggested by OpenAI is `n=2`). Put simply, the idea here is that you avoid getting unlucky with a bad embedding generation by creating two; and selecting the one with the higher cosine similarity with the prompt.
+* You may specify a higher conditioning scale than the default (`1.0`). It is unclear whether OpenAI uses a higher value for the prior specifically, or only on the decoder. Local testing has shown poor results with anything higher than `1.0` but *ymmv*.
+
+---
+
+## Training
+
+### Overview
+
+Training the prior is a relatively straightforward process thanks to the Trainer base class. The major step that is required of you is preparing a dataset in the format that EmbeddingReader expects. Having pre-computed embeddings massively increases training efficiency and is generally recommended as you will likely benefit from having them on hand for other tasks as well. Once you have a dataset, you are ready to move onto configuration
+
+## Dataset
+
+To train the prior, it is highly recommended to use precomputed embeddings for the images. To obtain these for a custom dataset, you can leverage [img2datset](https://github.com/rom1504/img2dataset) to pull images from a list of URLs and [clip_retrieval](https://github.com/rom1504/clip-retrieval#clip-inference) for generating the actual embeddings that can be used in the prior's dataloader.
+
+## Configuration
+
+The configuration file allows for you to easily track and reproduce experiments. It is a simple JSON file that will specify the architecture, dataset, and training parameters. For more information and specifics please see the configuration README.
+
+## Distributed Training
+
+If you would like to train in a distributed manner we have opted to leverage huggingface’ new Accelerate library. HFA makes it extremely simple to distribute work across multiple GPU’s and nodes. All that is required of you is to follow the simple CLI configuration tool [more information here](https://huggingface.co/docs/accelerate/accelerator).
+
+## Evaluation
+
+There are a variety of metrics available to you when training the prior. You can read a brief description of each in the table below:
+| Metric                              | Description                                                                                                                                                                                                                                                  | Comments                                                                                                                                                                                                                                                                                                                                                |
+| ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Online Model Validation             | The validation loss associated with your online model.                                                                                                                                                                                                       | Ideally validation loss will be as low as possible. Using L2 loss, values as low as `0.1` and lower are possible after around 1 Billion samples seen.                                                                                                                                                                                                |
+| EMA Validation                      | This metric measures the validation loss associated with your EMA model.                                                                                                                                                                                     | This will likely lag behind your "online" model's validation loss, but should outperform in the long-term.                                                                                                                                                                                                                                              |
+| Baseline Similarity                 | Baseline similarity refers to the similarity between your dataset's prompts and associated image embeddings. This will serve as a guide for your prior's performance in cosine similarity.                                                                    | Generally `0.3` is considered a good cosine similarity for caption similarity.                                                                                                                                                                                                                                                                         |
+| Similarity With Original Image      | This metric will measure the cosine similarity between your prior's predicted image embedding and the actual image that the caption was associated with. This is useful for determining wether your prior is generating images with the right contents.      | Values around `0.75`+ are obtainable. This metric should improve rapidly in the early stages of training and plateau with diminishing increases over time. If it takes hundreds of millions of samples to reach above `0.5`/`0.6` similarity--then you likely are suffering from some kind of training error or inefficiency (i.e. not using EMA) |
+| Difference From Baseline Similarity | Sometimes its useful to visualize a metric in another light. This metric will show you how your prior's predicted image embeddings match up with the baseline similarity measured in your dataset.                                                           | This value should float around `0.0` with some room for variation. After a billion samples seen, values are within `0.01`+/- of `0.0`. If this climbs to high, (~>`0.02`) then this may be a sign that your model is overfitting somehow.                                                                                                       |
+| Similarity With Text                | This metric is your bread and butter cosine similarity between the predicted image embedding and the original caption given to the prior. Monitoring this metric will be on of your main focuses and is probably the second most important behind your loss. | As mentioned, this value should be close to baseline similarity. We have observed early rapid increase with diminishing returns as the prior learns to generate valid image embeddings. If this value increases too far beyond the baseline similarity--it could be an indication that your model is overfitting.                                       |
+| Similarity With Unrelated Caption   | This metric will attempt to exposed an overfit prior by feeding it arbitrary prompts (from your dataset) and then measure the similarity of this predicted embedding with some other image.                                                                   | Early on we found that a poorly trained/modeled prior could effectively fool CLIP into believing that the cosine similarity between two images were high (when in fact the caption and image were completely unrelated). With this in mind--a low value is ideal, anything below `0.1` is probably safe.                                              |
+
+## Launching the script
+
+Now that you’ve done all the prep it’s time for the easy part! 🚀
+
+To actually launch the script, you will either use `accelerate launch train_diffusion_prior.py --config_path <path to your config>` to launch with distributed training & huggingface accelerate or `python train_diffusion_prior.py` if you would like to train on your gpu/cpu without huggingface accelerate.
+
+## Checkpointing
+
+Checkpoints will be saved to the directory specified in your configuration file.
+
+Additionally, a final checkpoint is saved before running the test split. This file will be saved to the same directory and titled “latest.pth”. This is to avoid problems where your `save_every` configuration does not overlap with the number of steps required to do a complete pass through the data.
+
+## Things To Keep In Mind
+
+The prior has not been trained for tasks other than the traditional CLIP embedding translation…at least yet.
+
+As we finalize the replication of unCLIP, there will almost assuredly be experiments attempting to apply the prior network to other tasks.
+
+With that in mind, you are more or less a pioneer in embedding-translation if you are reading this and attempting something you don’t see documentation for!

setup.py

@@ -1,4 +1,5 @@
 from setuptools import setup, find_packages
+exec(open('dalle2_pytorch/version.py').read())
 
 setup(
   name = 'dalle2-pytorch',
@@ -10,11 +11,12 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.95',
+  version = __version__,
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
   author_email = 'lucidrains@gmail.com',
+  long_description_content_type = 'text/markdown',
   url = 'https://github.com/lucidrains/dalle2-pytorch',
   keywords = [
     'artificial intelligence',
@@ -22,20 +24,30 @@ setup(
     'text to image'
   ],
   install_requires=[
+    'accelerate',
     'click',
-    'clip-anytorch',
+    'clip-anytorch>=2.4.0',
+    'coca-pytorch>=0.0.5',
+    'ema-pytorch>=0.0.7',
     'einops>=0.4',
     'einops-exts>=0.0.3',
     'embedding-reader',
     'kornia>=0.5.4',
+    'numpy',
+    'packaging',
     'pillow',
+    'pydantic',
+    'pytorch-warmup',
+    'resize-right>=0.0.2',
+    'rotary-embedding-torch',
     'torch>=1.10',
     'torchvision',
     'tqdm',
     'vector-quantize-pytorch',
-    'webdataset',
-    'x-clip>=0.5.1',
-    'youtokentome'
+    'x-clip>=0.4.4',
+    'webdataset>=0.2.5',
+    'fsspec>=2022.1.0',
+    'torchmetrics[image]>=0.8.0'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',

train_decoder.py

@@ -0,0 +1,650 @@
+from pathlib import Path
+from typing import List
+from datetime import timedelta
+
+from dalle2_pytorch.trainer import DecoderTrainer
+from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
+from dalle2_pytorch.trackers import Tracker
+from dalle2_pytorch.train_configs import DecoderConfig, TrainDecoderConfig
+from dalle2_pytorch.utils import Timer, print_ribbon
+from dalle2_pytorch.dalle2_pytorch import Decoder, resize_image_to
+from clip import tokenize
+
+import torchvision
+import torch
+from torch import nn
+from torchmetrics.image.fid import FrechetInceptionDistance
+from torchmetrics.image.inception import InceptionScore
+from torchmetrics.image.kid import KernelInceptionDistance
+from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+from accelerate import Accelerator, DistributedDataParallelKwargs, InitProcessGroupKwargs
+from accelerate.utils import dataclasses as accelerate_dataclasses
+import webdataset as wds
+import click
+
+# constants
+
+TRAIN_CALC_LOSS_EVERY_ITERS = 10
+VALID_CALC_LOSS_EVERY_ITERS = 10
+
+# helpers functions
+
+def exists(val):
+    return val is not None
+
+# main functions
+
+def create_dataloaders(
+    available_shards,
+    webdataset_base_url,
+    img_embeddings_url=None,
+    text_embeddings_url=None,
+    shard_width=6,
+    num_workers=4,
+    batch_size=32,
+    n_sample_images=6,
+    shuffle_train=True,
+    resample_train=False,
+    img_preproc = None,
+    index_width=4,
+    train_prop = 0.75,
+    val_prop = 0.15,
+    test_prop = 0.10,
+    seed = 0,
+    **kwargs
+):
+    """
+    Randomly splits the available shards into train, val, and test sets and returns a dataloader for each
+    """
+    assert train_prop + test_prop + val_prop == 1
+    num_train = round(train_prop*len(available_shards))
+    num_test = round(test_prop*len(available_shards))
+    num_val = len(available_shards) - num_train - num_test
+    assert num_train + num_test + num_val == len(available_shards), f"{num_train} + {num_test} + {num_val} = {num_train + num_test + num_val} != {len(available_shards)}"
+    train_split, test_split, val_split = torch.utils.data.random_split(available_shards, [num_train, num_test, num_val], generator=torch.Generator().manual_seed(seed))
+
+    # The shard number in the webdataset file names has a fixed width. We zero pad the shard numbers so they correspond to a filename.
+    train_urls = [webdataset_base_url.format(str(shard).zfill(shard_width)) for shard in train_split]
+    test_urls = [webdataset_base_url.format(str(shard).zfill(shard_width)) for shard in test_split]
+    val_urls = [webdataset_base_url.format(str(shard).zfill(shard_width)) for shard in val_split]
+    
+    create_dataloader = lambda tar_urls, shuffle=False, resample=False, for_sampling=False: create_image_embedding_dataloader(
+        tar_url=tar_urls,
+        num_workers=num_workers,
+        batch_size=batch_size if not for_sampling else n_sample_images,
+        img_embeddings_url=img_embeddings_url,
+        text_embeddings_url=text_embeddings_url,
+        index_width=index_width,
+        shuffle_num = None,
+        extra_keys= ["txt"],
+        shuffle_shards = shuffle,
+        resample_shards = resample, 
+        img_preproc=img_preproc,
+        handler=wds.handlers.warn_and_continue
+    )
+
+    train_dataloader = create_dataloader(train_urls, shuffle=shuffle_train, resample=resample_train)
+    train_sampling_dataloader = create_dataloader(train_urls, shuffle=False, for_sampling=True)
+    val_dataloader = create_dataloader(val_urls, shuffle=False)
+    test_dataloader = create_dataloader(test_urls, shuffle=False)
+    test_sampling_dataloader = create_dataloader(test_urls, shuffle=False, for_sampling=True)
+    return {
+        "train": train_dataloader,
+        "train_sampling": train_sampling_dataloader,
+        "val": val_dataloader,
+        "test": test_dataloader,
+        "test_sampling": test_sampling_dataloader
+    }
+
+def get_dataset_keys(dataloader):
+    """
+    It is sometimes neccesary to get the keys the dataloader is returning. Since the dataset is burried in the dataloader, we need to do a process to recover it.
+    """
+    # If the dataloader is actually a WebLoader, we need to extract the real dataloader
+    if isinstance(dataloader, wds.WebLoader):
+        dataloader = dataloader.pipeline[0]
+    return dataloader.dataset.key_map
+
+def get_example_data(dataloader, device, n=5):
+    """
+    Samples the dataloader and returns a zipped list of examples
+    """
+    images = []
+    img_embeddings = []
+    text_embeddings = []
+    captions = []
+    for img, emb, txt in dataloader:
+        img_emb, text_emb = emb.get('img'), emb.get('text')
+        if img_emb is not None:
+            img_emb = img_emb.to(device=device, dtype=torch.float)
+            img_embeddings.extend(list(img_emb))
+        else:
+            # Then we add None img.shape[0] times
+            img_embeddings.extend([None]*img.shape[0])
+        if text_emb is not None:
+            text_emb = text_emb.to(device=device, dtype=torch.float)
+            text_embeddings.extend(list(text_emb))
+        else:
+            # Then we add None img.shape[0] times
+            text_embeddings.extend([None]*img.shape[0])
+        img = img.to(device=device, dtype=torch.float)
+        images.extend(list(img))
+        captions.extend(list(txt))
+        if len(images) >= n:
+            break
+    return list(zip(images[:n], img_embeddings[:n], text_embeddings[:n], captions[:n]))
+
+def generate_samples(trainer, example_data, clip=None, start_unet=1, end_unet=None, condition_on_text_encodings=False, cond_scale=1.0, device=None, text_prepend="", match_image_size=True):
+    """
+    Takes example data and generates images from the embeddings
+    Returns three lists: real images, generated images, and captions
+    """
+    real_images, img_embeddings, text_embeddings, txts = zip(*example_data)
+    sample_params = {}
+    if img_embeddings[0] is None:
+        # Generate image embeddings from clip
+        imgs_tensor = torch.stack(real_images)
+        assert clip is not None, "clip is None, but img_embeddings is None"
+        imgs_tensor.to(device=device)
+        img_embeddings, img_encoding = clip.embed_image(imgs_tensor)
+        sample_params["image_embed"] = img_embeddings
+    else:
+        # Then we are using precomputed image embeddings
+        img_embeddings = torch.stack(img_embeddings)
+        sample_params["image_embed"] = img_embeddings
+    if condition_on_text_encodings:
+        if text_embeddings[0] is None:
+            # Generate text embeddings from text
+            assert clip is not None, "clip is None, but text_embeddings is None"
+            tokenized_texts = tokenize(txts, truncate=True)
+            text_embed, text_encodings = clip.embed_text(tokenized_texts)
+            sample_params["text_encodings"] = text_encodings
+        else:
+            # Then we are using precomputed text embeddings
+            text_embeddings = torch.stack(text_embeddings)
+            sample_params["text_encodings"] = text_embeddings
+    sample_params["start_at_unet_number"] = start_unet
+    sample_params["stop_at_unet_number"] = end_unet
+    if start_unet > 1:
+        # If we are only training upsamplers
+        sample_params["image"] = torch.stack(real_images)
+    if device is not None:
+        sample_params["_device"] = device
+    samples = trainer.sample(**sample_params, _cast_deepspeed_precision=False)  # At sampling time we don't want to cast to FP16
+    generated_images = list(samples)
+    captions = [text_prepend + txt for txt in txts]
+    if match_image_size:
+        generated_image_size = generated_images[0].shape[-1]
+        real_images = [resize_image_to(image, generated_image_size, clamp_range=(0, 1)) for image in real_images]
+    return real_images, generated_images, captions
+
+def generate_grid_samples(trainer, examples, clip=None, start_unet=1, end_unet=None, condition_on_text_encodings=False, cond_scale=1.0, device=None, text_prepend=""):
+    """
+    Generates samples and uses torchvision to put them in a side by side grid for easy viewing
+    """
+    real_images, generated_images, captions = generate_samples(trainer, examples, clip, start_unet, end_unet, condition_on_text_encodings, cond_scale, device, text_prepend)
+    grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)]
+    return grid_images, captions
+                    
+def evaluate_trainer(trainer, dataloader, device, start_unet, end_unet, clip=None, condition_on_text_encodings=False, cond_scale=1.0, inference_device=None, n_evaluation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
+    """
+    Computes evaluation metrics for the decoder
+    """
+    metrics = {}
+    # Prepare the data
+    examples = get_example_data(dataloader, device, n_evaluation_samples)
+    if len(examples) == 0:
+        print("No data to evaluate. Check that your dataloader has shards.")
+        return metrics
+    real_images, generated_images, captions = generate_samples(trainer, examples, clip, start_unet, end_unet, condition_on_text_encodings, cond_scale, inference_device)
+    real_images = torch.stack(real_images).to(device=device, dtype=torch.float)
+    generated_images = torch.stack(generated_images).to(device=device, dtype=torch.float)
+    # Convert from [0, 1] to [0, 255] and from torch.float to torch.uint8
+    int_real_images = real_images.mul(255).add(0.5).clamp(0, 255).type(torch.uint8)
+    int_generated_images = generated_images.mul(255).add(0.5).clamp(0, 255).type(torch.uint8)
+
+    def null_sync(t, *args, **kwargs):
+        return [t]
+
+    if exists(FID):
+        fid = FrechetInceptionDistance(**FID, dist_sync_fn=null_sync)
+        fid.to(device=device)
+        fid.update(int_real_images, real=True)
+        fid.update(int_generated_images, real=False)
+        metrics["FID"] = fid.compute().item()
+    if exists(IS):
+        inception = InceptionScore(**IS, dist_sync_fn=null_sync)
+        inception.to(device=device)
+        inception.update(int_real_images)
+        is_mean, is_std = inception.compute()
+        metrics["IS_mean"] = is_mean.item()
+        metrics["IS_std"] = is_std.item()
+    if exists(KID):
+        kernel_inception = KernelInceptionDistance(**KID, dist_sync_fn=null_sync)
+        kernel_inception.to(device=device)
+        kernel_inception.update(int_real_images, real=True)
+        kernel_inception.update(int_generated_images, real=False)
+        kid_mean, kid_std = kernel_inception.compute()
+        metrics["KID_mean"] = kid_mean.item()
+        metrics["KID_std"] = kid_std.item()
+    if exists(LPIPS):
+        # Convert from [0, 1] to [-1, 1]
+        renorm_real_images = real_images.mul(2).sub(1)
+        renorm_generated_images = generated_images.mul(2).sub(1)
+        lpips = LearnedPerceptualImagePatchSimilarity(**LPIPS, dist_sync_fn=null_sync)
+        lpips.to(device=device)
+        lpips.update(renorm_real_images, renorm_generated_images)
+        metrics["LPIPS"] = lpips.compute().item()
+
+    if trainer.accelerator.num_processes > 1:
+        # Then we should sync the metrics
+        metrics_order = sorted(metrics.keys())
+        metrics_tensor = torch.zeros(1, len(metrics), device=device, dtype=torch.float)
+        for i, metric_name in enumerate(metrics_order):
+            metrics_tensor[0, i] = metrics[metric_name]
+        metrics_tensor = trainer.accelerator.gather(metrics_tensor)
+        metrics_tensor = metrics_tensor.mean(dim=0)
+        for i, metric_name in enumerate(metrics_order):
+            metrics[metric_name] = metrics_tensor[i].item()
+    return metrics
+
+def save_trainer(tracker: Tracker, trainer: DecoderTrainer, epoch: int, sample: int, next_task: str, validation_losses: List[float], samples_seen: int, is_latest=True, is_best=False):
+    """
+    Logs the model with an appropriate method depending on the tracker
+    """
+    tracker.save(trainer, is_best=is_best, is_latest=is_latest, epoch=epoch, sample=sample, next_task=next_task, validation_losses=validation_losses, samples_seen=samples_seen)
+    
+def recall_trainer(tracker: Tracker, trainer: DecoderTrainer):
+    """
+    Loads the model with an appropriate method depending on the tracker
+    """
+    trainer.accelerator.print(print_ribbon(f"Loading model from {type(tracker.loader).__name__}"))
+    state_dict = tracker.recall()
+    trainer.load_state_dict(state_dict, only_model=False, strict=True)
+    return state_dict.get("epoch", 0), state_dict.get("validation_losses", []), state_dict.get("next_task", "train"), state_dict.get("sample", 0), state_dict.get("samples_seen", 0)
+
+def train(
+    dataloaders,
+    decoder: Decoder,
+    accelerator: Accelerator,
+    tracker: Tracker,
+    inference_device,
+    clip=None,
+    evaluate_config=None,
+    epoch_samples = None,  # If the training dataset is resampling, we have to manually stop an epoch
+    validation_samples = None,
+    save_immediately=False,
+    epochs = 20,
+    n_sample_images = 5,
+    save_every_n_samples = 100000,
+    unet_training_mask=None,
+    condition_on_text_encodings=False,
+    cond_scale=1.0,
+    **kwargs
+):
+    """
+    Trains a decoder on a dataset.
+    """
+    is_master = accelerator.process_index == 0
+
+    if not exists(unet_training_mask):
+        # Then the unet mask should be true for all unets in the decoder
+        unet_training_mask = [True] * len(decoder.unets)
+    assert len(unet_training_mask) == len(decoder.unets), f"The unet training mask should be the same length as the number of unets in the decoder. Got {len(unet_training_mask)} and {trainer.num_unets}"
+    trainable_unet_numbers = [i+1 for i, trainable in enumerate(unet_training_mask) if trainable]
+    first_trainable_unet = trainable_unet_numbers[0]
+    last_trainable_unet = trainable_unet_numbers[-1]
+    def move_unets(unet_training_mask):
+        for i in range(len(decoder.unets)):
+            if not unet_training_mask[i]:
+                # Replace the unet from the module list with a nn.Identity(). This training script never uses unets that aren't being trained so this is fine.
+                decoder.unets[i] = nn.Identity().to(inference_device)
+    # Remove non-trainable unets
+    move_unets(unet_training_mask)
+
+    trainer = DecoderTrainer(
+        decoder=decoder,
+        accelerator=accelerator,
+        dataloaders=dataloaders,
+        **kwargs
+    )
+
+    # Set up starting model and parameters based on a recalled state dict
+    start_epoch = 0
+    validation_losses = []
+    next_task = 'train'
+    sample = 0
+    samples_seen = 0
+    val_sample = 0
+    step = lambda: int(trainer.num_steps_taken(unet_number=first_trainable_unet))
+
+    if tracker.can_recall:
+        start_epoch, validation_losses, next_task, recalled_sample, samples_seen = recall_trainer(tracker, trainer)
+        if next_task == 'train':
+            sample = recalled_sample
+        if next_task == 'val':
+            val_sample = recalled_sample
+        accelerator.print(f"Loaded model from {type(tracker.loader).__name__} on epoch {start_epoch} having seen {samples_seen} samples with minimum validation loss {min(validation_losses) if len(validation_losses) > 0 else 'N/A'}")
+        accelerator.print(f"Starting training from task {next_task} at sample {sample} and validation sample {val_sample}")
+    trainer.to(device=inference_device)
+
+    accelerator.print(print_ribbon("Generating Example Data", repeat=40))
+    accelerator.print("This can take a while to load the shard lists...")
+    if is_master:
+        train_example_data = get_example_data(dataloaders["train_sampling"], inference_device, n_sample_images)
+        accelerator.print("Generated training examples")
+        test_example_data = get_example_data(dataloaders["test_sampling"], inference_device, n_sample_images)
+        accelerator.print("Generated testing examples")
+    
+    send_to_device = lambda arr: [x.to(device=inference_device, dtype=torch.float) for x in arr]
+
+    sample_length_tensor = torch.zeros(1, dtype=torch.int, device=inference_device)
+    unet_losses_tensor = torch.zeros(TRAIN_CALC_LOSS_EVERY_ITERS, trainer.num_unets, dtype=torch.float, device=inference_device)
+    for epoch in range(start_epoch, epochs):
+        accelerator.print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
+
+        timer = Timer()
+        last_sample = sample
+        last_snapshot = sample
+
+        if next_task == 'train':
+            for i, (img, emb, txt) in enumerate(dataloaders["train"]):
+                # We want to count the total number of samples across all processes
+                sample_length_tensor[0] = len(img)
+                all_samples = accelerator.gather(sample_length_tensor)  # TODO: accelerator.reduce is broken when this was written. If it is fixed replace this.
+                total_samples = all_samples.sum().item()
+                sample += total_samples
+                samples_seen += total_samples
+                img_emb = emb.get('img')
+                has_img_embedding = img_emb is not None
+                if has_img_embedding:
+                    img_emb, = send_to_device((img_emb,))
+                text_emb = emb.get('text')
+                has_text_embedding = text_emb is not None
+                if has_text_embedding:
+                    text_emb, = send_to_device((text_emb,))
+                img, = send_to_device((img,))
+
+                trainer.train()
+                for unet in range(1, trainer.num_unets+1):
+                    # Check if this is a unet we are training
+                    if not unet_training_mask[unet-1]: # Unet index is the unet number - 1
+                        continue
+
+                    forward_params = {}
+                    if has_img_embedding:
+                        forward_params['image_embed'] = img_emb
+                    else:
+                        # Forward pass automatically generates embedding
+                        assert clip is not None
+                        img_embed, img_encoding = clip.embed_image(img)
+                        forward_params['image_embed'] = img_embed
+                    if condition_on_text_encodings:
+                        if has_text_embedding:
+                            forward_params['text_encodings'] = text_emb
+                        else:
+                            # Then we need to pass the text instead
+                            assert clip is not None
+                            tokenized_texts = tokenize(txt, truncate=True).to(inference_device)
+                            assert tokenized_texts.shape[0] == len(img), f"The number of texts ({tokenized_texts.shape[0]}) should be the same as the number of images ({len(img)})"
+                            text_embed, text_encodings = clip.embed_text(tokenized_texts)
+                            forward_params['text_encodings'] = text_encodings
+                    loss = trainer.forward(img, **forward_params, unet_number=unet, _device=inference_device)
+                    trainer.update(unet_number=unet)
+                    unet_losses_tensor[i % TRAIN_CALC_LOSS_EVERY_ITERS, unet-1] = loss
+                
+                samples_per_sec = (sample - last_sample) / timer.elapsed()
+                timer.reset()
+                last_sample = sample
+
+                if i % TRAIN_CALC_LOSS_EVERY_ITERS == 0:
+                    # We want to average losses across all processes
+                    unet_all_losses = accelerator.gather(unet_losses_tensor)
+                    mask = unet_all_losses != 0
+                    unet_average_loss = (unet_all_losses * mask).sum(dim=0) / mask.sum(dim=0)
+                    loss_map = { f"Unet {index} Training Loss": loss.item() for index, loss in enumerate(unet_average_loss) if unet_training_mask[index] }
+
+                    # gather decay rate on each UNet
+                    ema_decay_list = {f"Unet {index} EMA Decay": ema_unet.get_current_decay() for index, ema_unet in enumerate(trainer.ema_unets) if unet_training_mask[index]}
+
+                    log_data = {
+                        "Epoch": epoch,
+                        "Sample": sample,
+                        "Step": i,
+                        "Samples per second": samples_per_sec,
+                        "Samples Seen": samples_seen,
+                        **ema_decay_list,
+                        **loss_map
+                    }
+
+                    if is_master:
+                        tracker.log(log_data, step=step())
+
+                if is_master and (last_snapshot + save_every_n_samples < sample or (save_immediately and i == 0)):  # This will miss by some amount every time, but it's not a big deal... I hope
+                    # It is difficult to gather this kind of info on the accelerator, so we have to do it on the master
+                    print("Saving snapshot")
+                    last_snapshot = sample
+                    # We need to know where the model should be saved
+                    save_trainer(tracker, trainer, epoch, sample, next_task, validation_losses, samples_seen)
+                    if exists(n_sample_images) and n_sample_images > 0:
+                        trainer.eval()
+                        train_images, train_captions = generate_grid_samples(trainer, train_example_data, clip, first_trainable_unet, last_trainable_unet, condition_on_text_encodings, cond_scale, inference_device, "Train: ")
+                        tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step())
+                
+                if epoch_samples is not None and sample >= epoch_samples:
+                    break
+            next_task = 'val'
+            sample = 0
+
+        all_average_val_losses = None
+        if next_task == 'val':
+            trainer.eval()
+            accelerator.print(print_ribbon(f"Starting Validation {epoch}", repeat=40))
+            last_val_sample = val_sample
+            val_sample_length_tensor = torch.zeros(1, dtype=torch.int, device=inference_device)
+            average_val_loss_tensor = torch.zeros(1, trainer.num_unets, dtype=torch.float, device=inference_device)
+            timer = Timer()
+            accelerator.wait_for_everyone()
+            i = 0
+            for i, (img, emb, txt) in enumerate(dataloaders['val']):  # Use the accelerate prepared loader
+                val_sample_length_tensor[0] = len(img)
+                all_samples = accelerator.gather(val_sample_length_tensor)
+                total_samples = all_samples.sum().item()
+                val_sample += total_samples
+                img_emb = emb.get('img')
+                has_img_embedding = img_emb is not None
+                if has_img_embedding:
+                    img_emb, = send_to_device((img_emb,))
+                text_emb = emb.get('text')
+                has_text_embedding = text_emb is not None
+                if has_text_embedding:
+                    text_emb, = send_to_device((text_emb,))
+                img, = send_to_device((img,))
+
+                for unet in range(1, len(decoder.unets)+1):
+                    if not unet_training_mask[unet-1]: # Unet index is the unet number - 1
+                        # No need to evaluate an unchanging unet
+                        continue
+                        
+                    forward_params = {}
+                    if has_img_embedding:
+                        forward_params['image_embed'] = img_emb.float()
+                    else:
+                        # Forward pass automatically generates embedding
+                        assert clip is not None
+                        img_embed, img_encoding = clip.embed_image(img)
+                        forward_params['image_embed'] = img_embed
+                    if condition_on_text_encodings:
+                        if has_text_embedding:
+                            forward_params['text_encodings'] = text_emb.float()
+                        else:
+                            # Then we need to pass the text instead
+                            assert clip is not None
+                            tokenized_texts = tokenize(txt, truncate=True)
+                            assert tokenized_texts.shape[0] == len(img), f"The number of texts ({tokenized_texts.shape[0]}) should be the same as the number of images ({len(img)})"
+                            text_embed, text_encodings = clip.embed_text(tokenized_texts)
+                            forward_params['text_encodings'] = text_encodings
+                    loss = trainer.forward(img.float(), **forward_params, unet_number=unet, _device=inference_device)
+                    average_val_loss_tensor[0, unet-1] += loss
+
+                if i % VALID_CALC_LOSS_EVERY_ITERS == 0:
+                    samples_per_sec = (val_sample - last_val_sample) / timer.elapsed()
+                    timer.reset()
+                    last_val_sample = val_sample
+                    accelerator.print(f"Epoch {epoch}/{epochs} Val Step {i} -  Sample {val_sample} - {samples_per_sec:.2f} samples/sec")
+                    accelerator.print(f"Loss: {(average_val_loss_tensor / (i+1))}")
+                    accelerator.print("")
+                
+                if validation_samples is not None and val_sample >= validation_samples:
+                    break
+            print(f"Rank {accelerator.state.process_index} finished validation after {i} steps")
+            accelerator.wait_for_everyone()
+            average_val_loss_tensor /= i+1
+            # Gather all the average loss tensors
+            all_average_val_losses = accelerator.gather(average_val_loss_tensor)
+            if is_master:
+                unet_average_val_loss = all_average_val_losses.mean(dim=0)
+                val_loss_map = { f"Unet {index} Validation Loss": loss.item() for index, loss in enumerate(unet_average_val_loss) if loss != 0 }
+                tracker.log(val_loss_map, step=step())
+            next_task = 'eval'
+
+        if next_task == 'eval':
+            if exists(evaluate_config):
+                accelerator.print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
+                evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, first_trainable_unet, last_trainable_unet, clip=clip, inference_device=inference_device, **evaluate_config.dict(), condition_on_text_encodings=condition_on_text_encodings, cond_scale=cond_scale)
+                if is_master:
+                    tracker.log(evaluation, step=step())
+            next_task = 'sample'
+            val_sample = 0
+
+        if next_task == 'sample':
+            if is_master:
+                # Generate examples and save the model if we are the master
+                # Generate sample images
+                print(print_ribbon(f"Sampling Set {epoch}", repeat=40))
+                test_images, test_captions = generate_grid_samples(trainer, test_example_data, clip, first_trainable_unet, last_trainable_unet, condition_on_text_encodings, cond_scale, inference_device, "Test: ")
+                train_images, train_captions = generate_grid_samples(trainer, train_example_data, clip, first_trainable_unet, last_trainable_unet, condition_on_text_encodings, cond_scale, inference_device, "Train: ")
+                tracker.log_images(test_images, captions=test_captions, image_section="Test Samples", step=step())
+                tracker.log_images(train_images, captions=train_captions, image_section="Train Samples", step=step())
+
+                print(print_ribbon(f"Starting Saving {epoch}", repeat=40))
+                is_best = False
+                if all_average_val_losses is not None:
+                    average_loss = all_average_val_losses.mean(dim=0).sum() / sum(unet_training_mask)
+                    if len(validation_losses) == 0 or average_loss < min(validation_losses):
+                        is_best = True
+                    validation_losses.append(average_loss)
+                save_trainer(tracker, trainer, epoch, sample, next_task, validation_losses, samples_seen, is_best=is_best)
+            next_task = 'train'
+
+def create_tracker(accelerator: Accelerator, config: TrainDecoderConfig, config_path: str, dummy: bool = False) -> Tracker:
+    tracker_config = config.tracker
+    accelerator_config = {
+        "Distributed": accelerator.distributed_type != accelerate_dataclasses.DistributedType.NO,
+        "DistributedType": accelerator.distributed_type,
+        "NumProcesses": accelerator.num_processes,
+        "MixedPrecision": accelerator.mixed_precision
+    }
+    accelerator.wait_for_everyone()  # If nodes arrive at this point at different times they might try to autoresume the current run which makes no sense and will cause errors
+    tracker: Tracker = tracker_config.create(config, accelerator_config, dummy_mode=dummy)
+    tracker.save_config(config_path, config_name='decoder_config.json')
+    tracker.add_save_metadata(state_dict_key='config', metadata=config.dict())
+    return tracker
+    
+def initialize_training(config: TrainDecoderConfig, config_path):
+    # Make sure if we are not loading, distributed models are initialized to the same values
+    torch.manual_seed(config.seed)
+
+    # Set up accelerator for configurable distributed training
+    ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=config.train.find_unused_parameters)
+    init_kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=60*60))
+    accelerator = Accelerator(kwargs_handlers=[ddp_kwargs, init_kwargs])
+
+    if accelerator.num_processes > 1:
+        # We are using distributed training and want to immediately ensure all can connect
+        accelerator.print("Waiting for all processes to connect...")
+        accelerator.wait_for_everyone()
+        accelerator.print("All processes online and connected")
+
+    # If we are in deepspeed fp16 mode, we must ensure learned variance is off
+    if accelerator.mixed_precision == "fp16" and accelerator.distributed_type == accelerate_dataclasses.DistributedType.DEEPSPEED and config.decoder.learned_variance:
+        raise ValueError("DeepSpeed fp16 mode does not support learned variance")
+    
+    # Set up data
+    all_shards = list(range(config.data.start_shard, config.data.end_shard + 1))
+    world_size = accelerator.num_processes
+    rank = accelerator.process_index
+    shards_per_process = len(all_shards) // world_size
+    assert shards_per_process > 0, "Not enough shards to split evenly"
+    my_shards = all_shards[rank * shards_per_process: (rank + 1) * shards_per_process]
+    dataloaders = create_dataloaders (
+        available_shards=my_shards,
+        img_preproc = config.data.img_preproc,
+        train_prop = config.data.splits.train,
+        val_prop = config.data.splits.val,
+        test_prop = config.data.splits.test,
+        n_sample_images=config.train.n_sample_images,
+        **config.data.dict(),
+        rank = rank,
+        seed = config.seed,
+    )
+
+    # If clip is in the model, we need to remove it for compatibility with deepspeed
+    clip = None
+    if config.decoder.clip is not None:
+        clip = config.decoder.clip.create()  # Of course we keep it to use it during training, just not in the decoder as that causes issues
+        config.decoder.clip = None
+    # Create the decoder model and print basic info
+    decoder = config.decoder.create()
+    get_num_parameters = lambda model, only_training=False: sum(p.numel() for p in model.parameters() if (p.requires_grad or not only_training))
+
+    # Create and initialize the tracker if we are the master
+    tracker = create_tracker(accelerator, config, config_path, dummy = rank!=0)
+
+    has_img_embeddings = config.data.img_embeddings_url is not None
+    has_text_embeddings = config.data.text_embeddings_url is not None
+    conditioning_on_text = any([unet.cond_on_text_encodings for unet in config.decoder.unets])
+
+    has_clip_model = clip is not None
+    data_source_string = ""
+
+    if has_img_embeddings:
+        data_source_string += "precomputed image embeddings"
+    elif has_clip_model:
+        data_source_string += "clip image embeddings generation"
+    else:
+        raise ValueError("No image embeddings source specified")
+    if conditioning_on_text:
+        if has_text_embeddings:
+            data_source_string += " and precomputed text embeddings"
+        elif has_clip_model:
+            data_source_string += " and clip text encoding generation"
+        else:
+            raise ValueError("No text embeddings source specified")
+
+    accelerator.print(print_ribbon("Loaded Config", repeat=40))
+    accelerator.print(f"Running training with {accelerator.num_processes} processes and {accelerator.distributed_type} distributed training")
+    accelerator.print(f"Training using {data_source_string}. {'conditioned on text' if conditioning_on_text else 'not conditioned on text'}")
+    accelerator.print(f"Number of parameters: {get_num_parameters(decoder)} total; {get_num_parameters(decoder, only_training=True)} training")
+    for i, unet in enumerate(decoder.unets):
+        accelerator.print(f"Unet {i} has {get_num_parameters(unet)} total; {get_num_parameters(unet, only_training=True)} training")
+
+    train(dataloaders, decoder, accelerator,
+        clip=clip,
+        tracker=tracker,
+        inference_device=accelerator.device,
+        evaluate_config=config.evaluate,
+        condition_on_text_encodings=conditioning_on_text,
+        **config.train.dict(),
+    )
+    
+# Create a simple click command line interface to load the config and start the training
+@click.command()
+@click.option("--config_file", default="./train_decoder_config.json", help="Path to config file")
+def main(config_file):
+    config_file_path = Path(config_file)
+    config = TrainDecoderConfig.from_json_path(str(config_file_path))
+    initialize_training(config, config_path=config_file_path)
+
+if __name__ == "__main__":
+    main()