update

block in a section that links to available pre-trained models for those who are interested

README.md

@@ -12,7 +12,7 @@ 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 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
 
@@ -24,6 +24,11 @@ There was enough interest for a <a href="https://github.com/lucidrains/dalle2-ja
 
 *ongoing at 21k steps*
 
+## 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> 🚧
+- DALL-E 2 🚧
+
 ## Install
 
 ```bash
@@ -1034,6 +1039,18 @@ Once built, images will be saved to the same directory the command is invoked
 
 <a href="https://github.com/lucidrains/stylegan2-pytorch">template</a>
 
+## Appreciation
+
+This library would not have gotten to this working state without the help of
+
+- <a href="https://github.com/nousr">Zion</a> and <a href="https://github.com/krish240574">Kumar</a> for the diffusion training script
+- <a href="https://github.com/Veldrovive">Aidan</a> for the decoder training script and dataloaders
+- <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/crowsonkb">Katherine</a> for her advice
+
+... and many others. Thank you! 🙏
+
 ## Todo
 
 - [x] finish off gaussian diffusion class for latent embedding - allow for prediction of epsilon
@@ -1064,6 +1081,10 @@ Once built, images will be saved to the same directory the command is invoked
 - [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
 - [ ] 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) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
 - [ ] train on a toy task, offer in colab
@@ -1073,12 +1094,9 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] test out grid attention in cascading ddpm locally, decide whether to keep or remove
 - [ ] 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
-- [ ] 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
 - [ ] bring in skip-layer excitatons (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training
 - [ ] decoder needs one day worth of refactor for tech debt
 - [ ] allow for unet to be able to condition non-cross attention style as well
-- [ ] for all model classes with hyperparameters that changes the network architecture, make it requirement that they must expose a config property, and write a simple function that asserts that it restores the object correctly
-- [ ] 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)
 - [ ] read the paper, figure it out, and build it https://github.com/lucidrains/DALLE2-pytorch/issues/89
 
 ## Citations

configs/README.md

@@ -4,11 +4,12 @@ For more complex configuration, we provide the option of using a configuration f
 
 ### 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.json.example).
+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>Unets</ins>:**
+**<ins>Unet</ins>:**
+
+This is a single unet config, which belongs as an array nested under the decoder config as a list of `unets`
 
-Each member of this array defines a single unet that will be added to the decoder.
 | Option | Required | Default | Description |
 | ------ | -------- | ------- | ----------- |
 | `dim`  | Yes      | N/A     | The starting channels of the unet. |
@@ -22,6 +23,7 @@ Any parameter from the `Unet` constructor can also be given here.
 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. |

configs/decoder_defaults.py

@@ -1,82 +0,0 @@
-"""
-Defines the default values for the decoder config
-"""
-
-from enum import Enum
-class ConfigField(Enum):
-    REQUIRED = 0  # This had more options. It's a bit unnecessary now, but I can't think of a better way to do it.
-
-default_config = {
-    "unets": ConfigField.REQUIRED,
-    "decoder": {
-        "image_sizes": ConfigField.REQUIRED,  # The side lengths of the upsampled image at the end of each unet
-        "image_size": ConfigField.REQUIRED,  # Usually the same as image_sizes[-1] I think
-        "channels": 3,
-        "timesteps": 1000,
-        "loss_type": "l2",
-        "beta_schedule": "cosine",
-        "learned_variance": True
-    },
-    "data": {
-        "webdataset_base_url": ConfigField.REQUIRED,  # Path to a webdataset with jpg images
-        "embeddings_url": ConfigField.REQUIRED,  # Path to .npy files with embeddings
-        "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": {
-            "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,  # The number of example images to produce when sampling the train and test dataset
-        "device": "cuda:0",
-        "epoch_samples": 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": None,  # Same as above but for validation.
-        "use_ema": True,
-        "ema_beta": 0.99,
-        "amp": False,
-        "save_all": False,  # Whether to preserve all checkpoints
-        "save_latest": True,  # Whether to always save the latest checkpoint
-        "save_best": True,  # Whether to save the best checkpoint
-        "unet_training_mask": None  # If None, use all unets
-    },
-    "evaluate": {
-        "n_evalation_samples": 1000,
-        "FID": None,
-        "IS": None,
-        "KID": None,
-        "LPIPS": None
-    },
-    "tracker": {
-        "tracker_type": "console",  # Decoder currently supports console and wandb
-        "data_path": "./models",  # The path where files will be saved locally
-
-        "wandb_entity": "",  # Only needs to be set if tracker_type is wandb
-        "wandb_project": "",
-
-        "verbose": False  # Whether to print console logging for non-console trackers
-    },
-    "load": {
-        "source": None,  # Supports file and wandb
-
-        "run_path": "",  # Used only if source is wandb
-        "file_path": "",  # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
-
-        "resume": False  # If using wandb, whether to resume the run
-    }
-}

configs/train_decoder_config.example.json

@@ -1,18 +1,17 @@
 {
-    "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
-        }
-    ],
     "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],
-        "image_size": [64],
         "channels": 3,
         "timesteps": 1000,
         "loss_type": "l2",
@@ -63,7 +62,7 @@
         "unet_training_mask": [true]
     },
     "evaluate": {
-        "n_evalation_samples": 1000,
+        "n_evaluation_samples": 1000,
         "FID": {
             "feature": 64
         },

dalle2_pytorch/dalle2_pytorch.py

@@ -59,6 +59,9 @@ def default(val, d):
     return d() if isfunction(d) else d
 
 def cast_tuple(val, length = 1):
+    if isinstance(val, list):
+        val = tuple(val)
+
     return val if isinstance(val, tuple) else ((val,) * length)
 
 def module_device(module):
@@ -887,6 +890,8 @@ class DiffusionPrior(BaseGaussianDiffusion):
         )
 
         if exists(clip):
+            assert image_channels == clip.image_channels, f'channels of image ({image_channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
             elif isinstance(clip, CoCa):
@@ -1707,12 +1712,19 @@ class Decoder(BaseGaussianDiffusion):
         )
 
         self.unconditional = unconditional
-        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
 
-        assert self.unconditional or (exists(clip) ^ exists(image_size)), 'either CLIP is supplied, or you must give the image_size and channels (usually 3 for RGB)'
+        # text conditioning
+
+        assert not (condition_on_text_encodings and unconditional), 'unconditional decoder image generation cannot be set to True if conditioning on text is present'
+        self.condition_on_text_encodings = condition_on_text_encodings
+
+        # clip
 
         self.clip = None
         if exists(clip):
+            assert not unconditional, 'clip must not be given if doing unconditional image training'
+            assert channels == clip.image_channels, f'channels of image ({channels}) should be equal to the channels that CLIP accepts ({clip.image_channels})'
+
             if isinstance(clip, CLIP):
                 clip = XClipAdapter(clip, **clip_adapter_overrides)
             elif isinstance(clip, CoCa):
@@ -1722,13 +1734,20 @@ class Decoder(BaseGaussianDiffusion):
             assert isinstance(clip, BaseClipAdapter)
 
             self.clip = clip
-            self.clip_image_size = clip.image_size
-            self.channels = clip.image_channels
-        else:
-            self.clip_image_size = image_size
-            self.channels = channels
 
-        self.condition_on_text_encodings = condition_on_text_encodings
+        # determine image size, with image_size and image_sizes taking precedence
+
+        if exists(image_size) or exists(image_sizes):
+            assert exists(image_size) ^ exists(image_sizes), 'only one of image_size or image_sizes must be given'
+            image_size = default(image_size, lambda: image_sizes[-1])
+        elif exists(clip):
+            image_size = clip.image_size
+        else:
+            raise Error('either image_size, image_sizes, or clip must be given to decoder')
+
+        # channels
+
+        self.channels = channels
 
         # automatically take care of ensuring that first unet is unconditional
         # while the rest of the unets are conditioned on the low resolution image produced by previous unet
@@ -1770,7 +1789,7 @@ class Decoder(BaseGaussianDiffusion):
 
         # unet image sizes
 
-        image_sizes = default(image_sizes, (self.clip_image_size,))
+        image_sizes = default(image_sizes, (image_size,))
         image_sizes = tuple(sorted(set(image_sizes)))
 
         assert len(self.unets) == len(image_sizes), f'you did not supply the correct number of u-nets ({len(self.unets)}) for resolutions {image_sizes}'
@@ -1808,6 +1827,7 @@ class Decoder(BaseGaussianDiffusion):
         self.clip_x_start = clip_x_start
 
         # normalize and unnormalize image functions
+
         self.normalize_img = normalize_neg_one_to_one if auto_normalize_img else identity
         self.unnormalize_img = unnormalize_zero_to_one if auto_normalize_img else identity
 

dalle2_pytorch/train_configs.py

@@ -0,0 +1,189 @@
+import json
+from torchvision import transforms as T
+from pydantic import BaseModel, validator, root_validator
+from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
+
+from dalle2_pytorch.dalle2_pytorch import Unet, Decoder, DiffusionPrior, DiffusionPriorNetwork
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def ListOrTuple(inner_type):
+    return Union[List[inner_type], Tuple[inner_type]]
+
+# pydantic classes
+
+class DiffusionPriorNetworkConfig(BaseModel):
+    dim: int
+    depth: int
+    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_out: bool = True
+    attn_dropout: float = 0.
+    ff_dropout: float = 0.
+    final_proj: bool = True
+    normformer: bool = False
+    rotary_emb: bool = True
+
+class DiffusionPriorConfig(BaseModel):
+    # only clip-less diffusion prior config for now
+    net: DiffusionPriorNetworkConfig
+    image_embed_dim: int
+    image_size: int
+    image_channels: int = 3
+    timesteps: int = 1000
+    cond_drop_prob: float = 0.
+    loss_type: str = 'l2'
+    predict_x_start: bool = True
+    beta_schedule: str = 'cosine'
+
+    def create(self):
+        kwargs = self.dict()
+        diffusion_prior_network = DiffusionPriorNetwork(**kwargs.pop('net'))
+        return DiffusionPrior(net = diffusion_prior_network, **kwargs)
+
+    class Config:
+        extra = "allow"
+
+class UnetConfig(BaseModel):
+    dim: int
+    dim_mults: ListOrTuple(int)
+    image_embed_dim: int = None
+    cond_dim: int = None
+    channels: int = 3
+    attn_dim_head: int = 32
+    attn_heads: int = 16
+
+    class Config:
+        extra = "allow"
+
+class DecoderConfig(BaseModel):
+    unets: ListOrTuple(UnetConfig)
+    image_size: int = None
+    image_sizes: ListOrTuple(int) = None
+    channels: int = 3
+    timesteps: int = 1000
+    loss_type: str = 'l2'
+    beta_schedule: str = 'cosine'
+    learned_variance: 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]
+        return Decoder(unets, **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 TrainSplitConfig(BaseModel):
+    train: float = 0.75
+    val: float = 0.15
+    test: float = 0.1
+
+    @root_validator
+    def validate_all(cls, fields):
+        if sum([*fields.values()]) != 1.:
+            raise ValueError(f'{fields.keys()} must sum to 1.0')
+        return fields
+
+class DecoderDataConfig(BaseModel):
+    webdataset_base_url: str     # path to a webdataset with jpg images
+    embeddings_url: 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: float = 1e-4
+    wd: float = 0.01
+    max_grad_norm: 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
+    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.
+    use_ema: bool = True
+    ema_beta: float = 0.99
+    amp: bool = False
+    save_all: bool = False                         # Whether to preserve all checkpoints
+    save_latest: bool = True                       # Whether to always save the latest checkpoint
+    save_best: bool = True                         # Whether to save the best checkpoint
+    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 TrackerConfig(BaseModel):
+    tracker_type: str = 'console'           # Decoder currently supports console and wandb
+    data_path: str = './models'             # The path where files will be saved locally
+    init_config: Dict[str, Any] = None
+    wandb_entity: str = ''                  # Only needs to be set if tracker_type is wandb
+    wandb_project: str = ''
+    verbose: bool = False                   # Whether to print console logging for non-console trackers
+
+class DecoderLoadConfig(BaseModel):
+    source: str = None                      # Supports file and wandb
+    run_path: str = ''                      # Used only if source is wandb
+    file_path: str = ''                     # The local filepath if source is file. If source is wandb, the relative path to the model file in wandb.
+    resume: bool = False                    # If using wandb, whether to resume the run
+
+class TrainDecoderConfig(BaseModel):
+    decoder: DecoderConfig
+    data: DecoderDataConfig
+    train: DecoderTrainConfig
+    evaluate: DecoderEvaluateConfig
+    tracker: TrackerConfig
+    load: DecoderLoadConfig
+
+    @classmethod
+    def from_json_path(cls, json_path):
+        with open(json_path) as f:
+            config = json.load(f)
+        return cls(**config)

dalle2_pytorch/trainer.py

@@ -1,5 +1,6 @@
 import time
 import copy
+from pathlib import Path
 from math import ceil
 from functools import partial, wraps
 from collections.abc import Iterable
@@ -55,6 +56,10 @@ def num_to_groups(num, divisor):
         arr.append(remainder)
     return arr
 
+def get_pkg_version():
+    from pkg_resources import get_distribution
+    return get_distribution('dalle2_pytorch').version
+
 # decorators
 
 def cast_torch_tensor(fn):
@@ -128,12 +133,6 @@ def split_args_and_kwargs(*args, split_size = None, **kwargs):
         chunk_size_frac = chunk_size / batch_size
         yield chunk_size_frac, (chunked_args, chunked_kwargs)
 
-# print helpers
-
-def print_ribbon(s, symbol = '=', repeat = 40):
-    flank = symbol * repeat
-    return f'{flank} {s} {flank}'
-
 # saving and loading functions
 
 # for diffusion prior
@@ -191,7 +190,7 @@ class EMA(nn.Module):
         self.update_after_step = update_after_step  // update_every # only start EMA after this step number, starting at 0
 
         self.register_buffer('initted', torch.Tensor([False]))
-        self.register_buffer('step', torch.tensor([0.]))
+        self.register_buffer('step', torch.tensor([0]))
 
     def restore_ema_model_device(self):
         device = self.initted.device
@@ -287,7 +286,50 @@ class DiffusionPriorTrainer(nn.Module):
 
         self.max_grad_norm = max_grad_norm
 
-        self.register_buffer('step', torch.tensor([0.]))
+        self.register_buffer('step', torch.tensor([0]))
+
+    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(
+            scaler = self.scaler.state_dict(),
+            optimizer = self.optimizer.state_dict(),
+            model = self.diffusion_prior.state_dict(),
+            version = get_pkg_version(),
+            step = self.step.item(),
+            **kwargs
+        )
+
+        if self.use_ema:
+            save_obj = {**save_obj, 'ema': self.ema_diffusion_prior.state_dict()}
+
+        torch.save(save_obj, str(path))
+
+    def load(self, path, only_model = False, strict = True):
+        path = Path(path)
+        assert path.exists()
+
+        loaded_obj = torch.load(str(path))
+
+        if get_pkg_version() != loaded_obj['version']:
+            print(f'loading saved diffusion prior at version {loaded_obj["version"]} but current package version is at {get_pkg_version()}')
+
+        self.diffusion_prior.load_state_dict(loaded_obj['model'], strict = strict)
+        self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
+
+        if only_model:
+            return loaded_obj
+
+        self.scaler.load_state_dict(loaded_obj['scaler'])
+        self.optimizer.load_state_dict(loaded_obj['optimizer'])
+
+        if self.use_ema:
+            assert 'ema' in loaded_obj
+            self.ema_diffusion_prior.load_state_dict(loaded_obj['ema'], strict = strict)
+
+        return loaded_obj
 
     def update(self):
         if exists(self.max_grad_norm):
@@ -410,6 +452,60 @@ class DecoderTrainer(nn.Module):
 
         self.register_buffer('step', torch.tensor([0.]))
 
+    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.decoder.state_dict(),
+            version = get_pkg_version(),
+            step = self.step.item(),
+            **kwargs
+        )
+
+        for ind in range(0, self.num_unets):
+            scaler_key = f'scaler{ind}'
+            optimizer_key = f'scaler{ind}'
+            scaler = getattr(self, scaler_key)
+            optimizer = getattr(self, optimizer_key)
+            save_obj = {**save_obj, scaler_key: scaler.state_dict(), optimizer_key: optimizer.state_dict()}
+
+        if self.use_ema:
+            save_obj = {**save_obj, 'ema': self.ema_unets.state_dict()}
+
+        torch.save(save_obj, str(path))
+
+    def load(self, path, only_model = False, strict = True):
+        path = Path(path)
+        assert path.exists()
+
+        loaded_obj = torch.load(str(path))
+
+        if get_pkg_version() != loaded_obj['version']:
+            print(f'loading saved decoder at version {loaded_obj["version"]}, but current package version is {get_pkg_version()}')
+
+        self.decoder.load_state_dict(loaded_obj['model'], strict = strict)
+        self.step.copy_(torch.ones_like(self.step) * loaded_obj['step'])
+
+        if only_model:
+            return loaded_obj
+
+        for ind in range(0, self.num_unets):
+            scaler_key = f'scaler{ind}'
+            optimizer_key = f'scaler{ind}'
+            scaler = getattr(self, scaler_key)
+            optimizer = getattr(self, optimizer_key)
+
+            scaler.load_state_dict(loaded_obj[scaler_key])
+            optimizer.load_state_dict(loaded_obj[optimizer_key])
+
+        if self.use_ema:
+            assert 'ema' in loaded_obj
+            self.ema_unets.load_state_dict(loaded_obj['ema'], strict = strict)
+
+        return loaded_obj
+
     @property
     def unets(self):
         return nn.ModuleList([ema.ema_model for ema in self.ema_unets])

dalle2_pytorch/utils.py

@@ -1,5 +1,7 @@
 import time
 
+# time helpers
+
 class Timer:
     def __init__(self):
         self.reset()
@@ -9,3 +11,9 @@ class Timer:
 
     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}'

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.3.6',
+  version = '0.4.14',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -32,6 +32,7 @@ setup(
     'kornia>=0.5.4',
     'numpy',
     'pillow',
+    'pydantic',
     'resize-right>=0.0.2',
     'rotary-embedding-torch',
     'torch>=1.10',

train_decoder.py

@@ -1,13 +1,11 @@
 from dalle2_pytorch import Unet, Decoder
-from dalle2_pytorch.trainer import DecoderTrainer, print_ribbon
+from dalle2_pytorch.trainer import DecoderTrainer
 from dalle2_pytorch.dataloaders import create_image_embedding_dataloader
 from dalle2_pytorch.trackers import WandbTracker, ConsoleTracker
-from dalle2_pytorch.utils import Timer
+from dalle2_pytorch.train_configs import TrainDecoderConfig
+from dalle2_pytorch.utils import Timer, print_ribbon
 
-from configs.decoder_defaults import default_config, ConfigField
-import json
 import torchvision
-from torchvision import transforms as T
 import torch
 from torchmetrics.image.fid import FrechetInceptionDistance
 from torchmetrics.image.inception import InceptionScore
@@ -16,6 +14,17 @@ from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
 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,
@@ -76,23 +85,6 @@ def create_dataloaders(
         "test_sampling": test_sampling_dataloader
     }
 
-
-def create_decoder(device, decoder_config, unets_config):
-    """Creates a sample decoder"""
-    unets = []
-    for i in range(0, len(unets_config)):
-        unets.append(Unet(
-            **unets_config[i]
-        ))
-    
-    decoder = Decoder(
-        unet=tuple(unets),  # Must be tuple because of cast_tuple
-        **decoder_config
-    )
-    decoder.to(device=device)
-
-    return decoder
-
 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.
@@ -147,33 +139,33 @@ def generate_grid_samples(trainer, examples, 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, n_evalation_samples=1000, FID=None, IS=None, KID=None, LPIPS=None):
+def evaluate_trainer(trainer, dataloader, device, 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_evalation_samples)
+    examples = get_example_data(dataloader, device, n_evaluation_samples)
     real_images, generated_images, captions = generate_samples(trainer, examples)
     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)
-    if FID is not None:
+    if exists(FID):
         fid = FrechetInceptionDistance(**FID)
         fid.to(device=device)
         fid.update(int_real_images, real=True)
         fid.update(int_generated_images, real=False)
         metrics["FID"] = fid.compute().item()
-    if IS is not None:
+    if exists(IS):
         inception = InceptionScore(**IS)
         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 KID is not None:
+    if exists(KID):
         kernel_inception = KernelInceptionDistance(**KID)
         kernel_inception.to(device=device)
         kernel_inception.update(int_real_images, real=True)
@@ -181,7 +173,7 @@ def evaluate_trainer(trainer, dataloader, device, n_evalation_samples=1000, FID=
         kid_mean, kid_std = kernel_inception.compute()
         metrics["KID_mean"] = kid_mean.item()
         metrics["KID_std"] = kid_std.item()
-    if LPIPS is not None:
+    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)
@@ -245,11 +237,11 @@ def train(
     start_epoch = 0
     validation_losses = []
 
-    if load_config is not None and load_config["source"] is not None:
-        start_epoch, start_step, validation_losses = recall_trainer(tracker, trainer, recall_source=load_config["source"], **load_config)
+    if exists(load_config) and exists(load_config.source):
+        start_epoch, start_step, validation_losses = recall_trainer(tracker, trainer, recall_source=load_config.source, **load_config)
     trainer.to(device=inference_device)
 
-    if unet_training_mask is None:
+    if not exists(unet_training_mask):
         # Then the unet mask should be true for all unets in the decoder
         unet_training_mask = [True] * trainer.num_unets
     assert len(unet_training_mask) == trainer.num_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}"
@@ -264,7 +256,6 @@ def train(
 
     for epoch in range(start_epoch, epochs):
         print(print_ribbon(f"Starting epoch {epoch}", repeat=40))
-        trainer.train()
 
         timer = Timer()
 
@@ -273,24 +264,28 @@ def train(
         last_snapshot = 0
 
         losses = []
+
         for i, (img, emb) in enumerate(dataloaders["train"]):
             step += 1
             sample += img.shape[0]
             img, emb = send_to_device((img, emb))
             
+            trainer.train()
             for unet in range(1, trainer.num_unets+1):
                 # Check if this is a unet we are training
-                if unet_training_mask[unet-1]: # Unet index is the unet number - 1
-                    loss = trainer.forward(img, image_embed=emb, unet_number=unet)
-                    trainer.update(unet_number=unet)
-                    losses.append(loss)
+                if not unet_training_mask[unet-1]: # Unet index is the unet number - 1
+                    continue
+
+                loss = trainer.forward(img, image_embed=emb, unet_number=unet)
+                trainer.update(unet_number=unet)
+                losses.append(loss)
 
             samples_per_sec = (sample - last_sample) / timer.elapsed()
 
             timer.reset()
             last_sample = sample
 
-            if i % 10 == 0:
+            if i % TRAIN_CALC_LOSS_EVERY_ITERS == 0:
                 average_loss = sum(losses) / len(losses)
                 log_data = {
                     "Training loss": average_loss,
@@ -310,14 +305,15 @@ def train(
                     save_paths.append("latest.pth")
                 if save_all:
                     save_paths.append(f"checkpoints/epoch_{epoch}_step_{step}.pth")
+
                 save_trainer(tracker, trainer, epoch, step, validation_losses, save_paths)
-                if n_sample_images is not None and n_sample_images > 0:
+
+                if exists(n_sample_images) and n_sample_images > 0:
                     trainer.eval()
                     train_images, train_captions = generate_grid_samples(trainer, train_example_data, "Train: ")
-                    trainer.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:
+            if exists(epoch_samples) and sample >= epoch_samples:
                 break
 
         trainer.eval()
@@ -334,12 +330,12 @@ def train(
                     loss = trainer.forward(img.float(), image_embed=emb.float(), unet_number=unet)
                     average_loss += loss
 
-                if i % 10 == 0:
+                if i % VALID_CALC_LOSS_EVERY_ITERS == 0:
                     print(f"Epoch {epoch}/{epochs} - {sample / timer.elapsed():.2f} samples/sec")
                     print(f"Loss: {average_loss / (i+1)}")
                     print("")
 
-                if validation_samples is not None and sample >= validation_samples:
+                if exists(validation_samples) and sample >= validation_samples:
                     break
 
             average_loss /= i+1
@@ -349,8 +345,7 @@ def train(
             tracker.log(log_data, step=step, verbose=True)
 
         # Compute evaluation metrics
-        trainer.eval()
-        if evaluate_config is not None:
+        if exists(evaluate_config):
             print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
             evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, **evaluate_config)
             tracker.log(evaluation, step=step, verbose=True)
@@ -376,21 +371,25 @@ def create_tracker(config, tracker_type=None, data_path=None, **kwargs):
     """
     Creates a tracker of the specified type and initializes special features based on the full config
     """
-    tracker_config = config["tracker"]
+    tracker_config = config.tracker
     init_config = {}
-    init_config["config"] = config.config
+
+    if exists(tracker_config.init_config):
+        init_config["config"] = tracker_config.init_config
+
     if tracker_type == "console":
         tracker = ConsoleTracker(**init_config)
     elif tracker_type == "wandb":
         # We need to initialize the resume state here
-        load_config = config["load"]
-        if load_config["source"] == "wandb" and load_config["resume"]:
+        load_config = config.load
+        if load_config.source == "wandb" and load_config.resume:
             # Then we are resuming the run load_config["run_path"]
-            run_id = config["resume"]["wandb_run_path"].split("/")[-1]
+            run_id = load_config.run_path.split("/")[-1]
             init_config["id"] = run_id
             init_config["resume"] = "must"
-        init_config["entity"] = tracker_config["wandb_entity"]
-        init_config["project"] = tracker_config["wandb_project"]
+
+        init_config["entity"] = tracker_config.wandb_entity
+        init_config["project"] = tracker_config.wandb_project
         tracker = WandbTracker(data_path)
         tracker.init(**init_config)
     else:
@@ -399,106 +398,43 @@ def create_tracker(config, tracker_type=None, data_path=None, **kwargs):
     
 def initialize_training(config):
     # Create the save path
-    if "cuda" in config["train"]["device"]:
+    if "cuda" in config.train.device:
         assert torch.cuda.is_available(), "CUDA is not available"
-    device = torch.device(config["train"]["device"])
+    device = torch.device(config.train.device)
     torch.cuda.set_device(device)
-    all_shards = list(range(config["data"]["start_shard"], config["data"]["end_shard"] + 1))
+    all_shards = list(range(config.data.start_shard, config.data.end_shard + 1))
 
     dataloaders = create_dataloaders (
         available_shards=all_shards,
-        img_preproc = config.get_preprocessing(),
-        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"]
+        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()
     )
 
-    decoder = create_decoder(device, config["decoder"], config["unets"])
+    decoder = config.decoder.create().to(device = device)
     num_parameters = sum(p.numel() for p in decoder.parameters())
     print(print_ribbon("Loaded Config", repeat=40))
     print(f"Number of parameters: {num_parameters}")
 
-    tracker = create_tracker(config, **config["tracker"])
+    tracker = create_tracker(config, **config.tracker.dict())
 
     train(dataloaders, decoder, 
         tracker=tracker,
         inference_device=device,
-        load_config=config["load"],
-        evaluate_config=config["evaluate"],
-        **config["train"],
+        load_config=config.load,
+        evaluate_config=config.evaluate,
+        **config.train.dict(),
     )
 
-
-class TrainDecoderConfig:
-    def __init__(self, config):
-        self.config = self.map_config(config, default_config)
-
-    def map_config(self, config, defaults):
-        """
-        Returns a dictionary containing all config options in the union of config and defaults.
-        If the config value is an array, apply the default value to each element.
-        If the default values dict has a value of ConfigField.REQUIRED for a key, it is required and a runtime error should be thrown if a value is not supplied from config
-        """
-        def _check_option(option, option_config, option_defaults):
-            for key, value in option_defaults.items():
-                if key not in option_config:
-                    if value == ConfigField.REQUIRED:
-                        raise RuntimeError("Required config value '{}' of option '{}' not supplied".format(key, option))
-                    option_config[key] = value
-        
-        for key, value in defaults.items():
-            if key not in config:
-                # Then they did not pass in one of the main configs. If the default is an array or object, then we can fill it in. If is a required object, we must error
-                if value == ConfigField.REQUIRED:
-                    raise RuntimeError("Required config value '{}' not supplied".format(key))
-                elif isinstance(value, dict):
-                    config[key] = {}
-                elif isinstance(value, list):
-                    config[key] = [{}]
-            # Config[key] is now either a dict, list of dicts, or an object that cannot be checked. 
-            # If it is a list, then we need to check each element
-            if isinstance(value, list):
-                assert isinstance(config[key], list)
-                for element in config[key]:
-                    _check_option(key, element, value[0])
-            elif isinstance(value, dict):
-                _check_option(key, config[key], value)
-            # This object does not support checking
-        return config
-
-    def get_preprocessing(self):
-        """
-        Takes the preprocessing dictionary and converts it to a composition of torchvision transforms
-        """
-        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()
-        
-        transformations = []
-        for transformation_name, transformation_kwargs in self.config["data"]["preprocessing"].items():
-            if isinstance(transformation_kwargs, dict):
-                transformations.append(_get_transformation(transformation_name, **transformation_kwargs))
-            else:
-                transformations.append(_get_transformation(transformation_name))
-        return T.Compose(transformations)
-    
-    def __getitem__(self, key):
-        return self.config[key]
-
 # 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):
     print("Recalling config from {}".format(config_file))
-    with open(config_file) as f:
-        config = json.load(f)
-    config = TrainDecoderConfig(config)
+    config = TrainDecoderConfig.from_json_path(config_file)
     initialize_training(config)
 
 

train_diffusion_prior.py

@@ -9,10 +9,10 @@ from torch import nn
 
 from dalle2_pytorch.dataloaders import make_splits
 from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork, OpenAIClipAdapter
-from dalle2_pytorch.trainer import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model, print_ribbon
+from dalle2_pytorch.trainer import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model
 
 from dalle2_pytorch.trackers import ConsoleTracker, WandbTracker
-from dalle2_pytorch.utils import Timer
+from dalle2_pytorch.utils import Timer, print_ribbon
 
 from embedding_reader import EmbeddingReader