make sure classifier free guidance is used only if conditional dropout is present on the DiffusionPrior and Decoder classes. also make sure prior can have a different conditional scale than decoder

README.md

@@ -706,7 +706,7 @@ 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 +732,8 @@ 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)
 
@@ -774,8 +774,12 @@ 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
 
@@ -810,8 +814,8 @@ 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()
 
 # prior networks (with transformer)
 
@@ -838,8 +842,7 @@ diffusion_prior_trainer = DiffusionPriorTrainer(
     ema_update_every = 10,
 )
 
-loss = diffusion_prior_trainer(text, images)
-loss.backward()
+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
@@ -848,6 +851,57 @@ diffusion_prior_trainer.update()  # this will update the optimizer as well as th
 image_embeds = diffusion_prior_trainer.sample(text) # (4, 512) - exponential moving averaged image embeddings
 ```
 
+## Bonus
+
+### Unconditional Training
+
+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`
+
+ex.
+
+```python
+import torch
+from dalle2_pytorch import Unet, Decoder
+
+# 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()
+
+# mock 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(images, unet_number = i)
+    loss.backward()
+
+# do the above for many many many many steps
+# then it will learn to generate images
+
+images = decoder.sample(batch_size = 2) # (2, 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.
@@ -892,14 +946,14 @@ dataset = ImageEmbeddingDataset(
 )
 ```
 
-## Scripts
+### Scripts (wip)
 
-### Using the `train_diffusion_prior.py` script
+#### `train_diffusion_prior.py`
 
 This script allows training the DiffusionPrior on pre-computed text and image embeddings. The working example below elucidates this process.
 Please note that the script internally passes text_embed and image_embed to the DiffusionPrior, unlike the example below.
 
-### Usage 
+#### Usage
 
 ```bash
 $ python train_diffusion_prior.py
@@ -907,58 +961,49 @@ $ python train_diffusion_prior.py
 
 The most significant parameters for the script are as follows:
 
---image-embed-url, default = "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
+- `image-embed-url`, default = `"https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/"`
 
---text-embed-url, default = "https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
+- `text-embed-url`, default = `"https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/"`
 
---image-embed-dim, default=768 - 768 corresponds to the ViT iL/14 embedding size,change it to what your chosen ViT generates
+- `image-embed-dim`, default = `768` - 768 corresponds to the ViT iL/14 embedding size,change it to what your chosen ViT generates
 
---learning-rate, default=1.1e-4
+- `learning-rate`, default = `1.1e-4`
 
---weight-decay,  default=6.02e-2
+- `weight-decay`,  default = `6.02e-2`
 
---max-grad-norm, default=0.5
+- `max-grad-norm`, default = `0.5`
 
---batch-size, default=10 ** 4
+- `batch-size`, default = `10 ** 4`
 
---num-epochs, default=5
+- `num-epochs`, default = `5`
 
---clip, default=None # Signals the prior to use pre-computed embeddings
+- `clip`, default = `None` # Signals the prior to use pre-computed embeddings
 
-### Sample wandb run log
-
-Please find a sample wandb run log at : https://wandb.ai/laion/diffusion-prior/runs/1blxu24j
-
-### Loading and saving the Diffusion Prior model
+#### Loading and Saving the DiffusionPrior model
 
 Two methods are provided, load_diffusion_model and save_diffusion_model, the names being self-explanatory. 
 
-## from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model
+```python
+from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model
+```
+
+##### Loading
 
     load_diffusion_model(dprior_path, device) 
-
         dprior_path : path to saved model(.pth)
-    
         device      : the cuda device you're running on
     
+##### Saving
+
     save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim)
-    
         save_path : path to save at
-    
         model     : object of Diffusion_Prior
-    
         optimizer : optimizer object - see train_diffusion_prior.py for how to create one. 
-    
             e.g: optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
-    
         scaler    : a GradScaler object.
-    
             e.g: scaler = GradScaler(enabled=amp)
-    
         config    : config object created in train_diffusion_prior.py - see file for example. 
-    
         image_embed_dim - the dimension of the image_embedding
-    
             e.g: 768
 
 ## CLI (wip)
@@ -1004,6 +1049,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [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>
 - [ ] 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
@@ -1011,12 +1057,15 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
 - [ ] 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>
 - [ ] 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
 
@@ -1105,4 +1154,13 @@ Once built, images will be saved to the same directory the command is invoked
 }
 ```
 
+```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}
+}
+```
+
 *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>

dalle2_pytorch/dalle2_pytorch.py

@@ -794,7 +794,7 @@ class DiffusionPriorNetwork(nn.Module):
         text_embed,
         text_encodings = None,
         mask = None,
-        cond_drop_prob = 0.2
+        cond_drop_prob = 0.
     ):
         batch, dim, device, dtype = *image_embed.shape, image_embed.device, image_embed.dtype
 
@@ -901,6 +901,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         self.channels = default(image_channels, lambda: clip.image_channels)
 
         self.cond_drop_prob = cond_drop_prob
+        self.can_classifier_guidance = cond_drop_prob > 0.
         self.condition_on_text_encodings = condition_on_text_encodings
 
         # in paper, they do not predict the noise, but predict x0 directly for image embedding, claiming empirically better results. I'll just offer both.
@@ -914,8 +915,10 @@ class DiffusionPrior(BaseGaussianDiffusion):
         self.training_clamp_l2norm = training_clamp_l2norm
         self.init_image_embed_l2norm = init_image_embed_l2norm
 
-    def p_mean_variance(self, x, t, text_cond, clip_denoised: bool):
-        pred = self.net(x, t, **text_cond)
+    def p_mean_variance(self, x, t, text_cond, clip_denoised = False, cond_scale = 1.):
+        assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the model was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
+
+        pred = self.net.forward_with_cond_scale(x, t, cond_scale = cond_scale, **text_cond)
 
         if self.predict_x_start:
             x_recon = pred
@@ -934,16 +937,16 @@ class DiffusionPrior(BaseGaussianDiffusion):
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.inference_mode()
-    def p_sample(self, x, t, text_cond = None, clip_denoised = True, repeat_noise = False):
+    def p_sample(self, x, t, text_cond = None, clip_denoised = True, repeat_noise = False, cond_scale = 1.):
         b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, text_cond = text_cond, clip_denoised = clip_denoised)
+        model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, text_cond = text_cond, clip_denoised = clip_denoised, cond_scale = cond_scale)
         noise = noise_like(x.shape, device, repeat_noise)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
 
     @torch.inference_mode()
-    def p_sample_loop(self, shape, text_cond):
+    def p_sample_loop(self, shape, text_cond, cond_scale = 1.):
         device = self.betas.device
 
         b = shape[0]
@@ -954,7 +957,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
 
         for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
             times = torch.full((b,), i, device = device, dtype = torch.long)
-            image_embed = self.p_sample(image_embed, times, text_cond = text_cond)
+            image_embed = self.p_sample(image_embed, times, text_cond = text_cond, cond_scale = cond_scale)
 
         return image_embed
 
@@ -980,19 +983,19 @@ class DiffusionPrior(BaseGaussianDiffusion):
 
     @torch.inference_mode()
     @eval_decorator
-    def sample_batch_size(self, batch_size, text_cond):
+    def sample_batch_size(self, batch_size, text_cond, cond_scale = 1.):
         device = self.betas.device
         shape = (batch_size, self.image_embed_dim)
 
         img = torch.randn(shape, device = device)
 
         for i in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
-            img = self.p_sample(img, torch.full((batch_size,), i, device = device, dtype = torch.long), text_cond = text_cond)
+            img = self.p_sample(img, torch.full((batch_size,), i, device = device, dtype = torch.long), text_cond = text_cond, cond_scale = cond_scale)
         return img
 
     @torch.inference_mode()
     @eval_decorator
-    def sample(self, text, num_samples_per_batch = 2):
+    def sample(self, text, num_samples_per_batch = 2, cond_scale = 1.):
         # in the paper, what they did was
         # sample 2 image embeddings, choose the top 1 similarity, as judged by CLIP
         text = repeat(text, 'b ... -> (b r) ...', r = num_samples_per_batch)
@@ -1007,7 +1010,7 @@ class DiffusionPrior(BaseGaussianDiffusion):
         if self.condition_on_text_encodings:
             text_cond = {**text_cond, 'text_encodings': text_encodings, 'mask': text_mask}
 
-        image_embeds = self.p_sample_loop((batch_size, image_embed_dim), text_cond = text_cond)
+        image_embeds = self.p_sample_loop((batch_size, image_embed_dim), text_cond = text_cond, cond_scale = cond_scale)
 
         # retrieve original unscaled image embed
 
@@ -1163,6 +1166,7 @@ class CrossAttention(nn.Module):
         dim_head = 64,
         heads = 8,
         dropout = 0.,
+        norm_context = False
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
@@ -1172,7 +1176,7 @@ class CrossAttention(nn.Module):
         context_dim = default(context_dim, dim)
 
         self.norm = LayerNorm(dim)
-        self.norm_context = LayerNorm(context_dim)
+        self.norm_context = LayerNorm(context_dim) if norm_context else nn.Identity()
         self.dropout = nn.Dropout(dropout)
 
         self.null_kv = nn.Parameter(torch.randn(2, dim_head))
@@ -1304,7 +1308,7 @@ class Unet(nn.Module):
         self,
         dim,
         *,
-        image_embed_dim,
+        image_embed_dim = None,
         text_embed_dim = None,
         cond_dim = None,
         num_image_tokens = 4,
@@ -1376,14 +1380,18 @@ class Unet(nn.Module):
         self.image_to_cond = nn.Sequential(
             nn.Linear(image_embed_dim, cond_dim * num_image_tokens),
             Rearrange('b (n d) -> b n d', n = num_image_tokens)
-        ) if image_embed_dim != cond_dim else nn.Identity()
+        ) if cond_on_image_embeds and image_embed_dim != cond_dim else nn.Identity()
+
+        self.norm_cond = nn.LayerNorm(cond_dim)
+        self.norm_mid_cond = nn.LayerNorm(cond_dim)
 
         # text encoding conditioning (optional)
 
         self.text_to_cond = None
 
         if cond_on_text_encodings:
-            self.text_to_cond = nn.LazyLinear(cond_dim) if not exists(text_embed_dim) else nn.Linear(text_embed_dim, cond_dim)
+            assert exists(text_embed_dim), 'text_embed_dim must be given to the unet if cond_on_text_encodings is True'
+            self.text_to_cond = nn.Linear(text_embed_dim, cond_dim)
 
         # finer control over whether to condition on image embeddings and text encodings
         # so one can have the latter unets in the cascading DDPMs only focus on super-resoluting
@@ -1593,6 +1601,11 @@ class Unet(nn.Module):
 
         mid_c = c if not exists(text_tokens) else torch.cat((c, text_tokens), dim = -2)
 
+        # normalize conditioning tokens
+
+        c = self.norm_cond(c)
+        mid_c = self.norm_mid_cond(mid_c)
+
         # go through the layers of the unet, down and up
 
         hiddens = []
@@ -1692,7 +1705,7 @@ 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 exists(clip) ^ exists(image_size), 'either CLIP is supplied, or you must give the image_size and channels (usually 3 for RGB)'
+        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)'
 
         self.clip = None
         if exists(clip):
@@ -1783,6 +1796,7 @@ class Decoder(BaseGaussianDiffusion):
 
         self.image_cond_drop_prob = image_cond_drop_prob
         self.text_cond_drop_prob = text_cond_drop_prob
+        self.can_classifier_guidance = image_cond_drop_prob > 0. or text_cond_drop_prob > 0.
 
         # whether to clip when sampling
 
@@ -1809,6 +1823,8 @@ class Decoder(BaseGaussianDiffusion):
         unet.cpu()
 
     def p_mean_variance(self, unet, x, t, image_embed, text_encodings = None, text_mask = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None):
+        assert not (cond_scale != 1. and not self.can_classifier_guidance), 'the decoder was not trained with conditional dropout, and thus one cannot use classifier free guidance (cond_scale anything other than 1)'
+
         pred = default(model_output, lambda: unet.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img))
 
         if learned_variance:
@@ -1979,8 +1995,7 @@ class Decoder(BaseGaussianDiffusion):
                 image_size = vae.get_encoded_fmap_size(image_size)
                 shape = (batch_size, vae.encoded_dim, image_size, image_size)
 
-                if exists(lowres_cond_img):
-                    lowres_cond_img = vae.encode(lowres_cond_img)
+                lowres_cond_img = maybe(vae.encode)(lowres_cond_img)
 
                 img = self.p_sample_loop(
                     unet,
@@ -2028,12 +2043,12 @@ class Decoder(BaseGaussianDiffusion):
 
         times = torch.randint(0, self.num_timesteps, (b,), device = device, dtype = torch.long)
 
-        if not exists(image_embed):
+        if not exists(image_embed) and not self.unconditional:
             assert exists(self.clip), 'if you want to derive CLIP image embeddings automatically, you must supply `clip` to the decoder on init'
             image_embed, _ = self.clip.embed_image(image)
 
         text_encodings = text_mask = None
-        if exists(text) and not exists(text_encodings):
+        if exists(text) and not exists(text_encodings) and not self.unconditional:
             assert exists(self.clip), 'if you are passing in raw text, you need to supply `clip` to the decoder'
             _, text_encodings, text_mask = self.clip.embed_text(text)
 
@@ -2054,9 +2069,7 @@ class Decoder(BaseGaussianDiffusion):
         vae.eval()
         with torch.no_grad():
             image = vae.encode(image)
-
-            if exists(lowres_cond_img):
-                lowres_cond_img = vae.encode(lowres_cond_img)
+            lowres_cond_img = maybe(vae.encode)(lowres_cond_img)
 
         return self.p_losses(unet, image, times, image_embed = image_embed, text_encodings = text_encodings, text_mask = text_mask, lowres_cond_img = lowres_cond_img, predict_x_start = predict_x_start, learned_variance = learned_variance)
 
@@ -2087,6 +2100,7 @@ class DALLE2(nn.Module):
         self,
         text,
         cond_scale = 1.,
+        prior_cond_scale = 1.,
         return_pil_images = False
     ):
         device = next(self.parameters()).device
@@ -2096,7 +2110,7 @@ class DALLE2(nn.Module):
             text = [text] if not isinstance(text, (list, tuple)) else text
             text = tokenizer.tokenize(text).to(device)
 
-        image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples)
+        image_embed = self.prior.sample(text, num_samples_per_batch = self.prior_num_samples, cond_scale = prior_cond_scale)
 
         text_cond = text if self.decoder_need_text_cond else None
         images = self.decoder.sample(image_embed, text = text_cond, cond_scale = cond_scale)

dalle2_pytorch/optimizer.py

@@ -10,13 +10,14 @@ def get_optimizer(
     lr = 2e-5,
     wd = 1e-2,
     betas = (0.9, 0.999),
+    eps = 1e-8,
     filter_by_requires_grad = False
 ):
     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)
@@ -26,4 +27,4 @@ def get_optimizer(
         {'params': list(no_wd_params), 'weight_decay': 0},
     ]
 
-    return AdamW(param_groups, lr = lr, weight_decay = wd, betas = betas)
+    return AdamW(param_groups, lr = lr, weight_decay = wd, betas = betas, eps = eps)

dalle2_pytorch/trackers.py

@@ -0,0 +1,49 @@
+import os
+import torch
+from torch import nn
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+# base class
+
+class BaseTracker(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def init(self, config, **kwargs):
+        raise NotImplementedError
+
+    def log(self, log, **kwargs):
+        raise NotImplementedError
+
+# basic stdout class
+
+class ConsoleTracker(BaseTracker):
+    def init(self, **config):
+        print(config)
+
+    def log(self, log, **kwargs):
+        print(log)
+
+# basic wandb class
+
+class WandbTracker(BaseTracker):
+    def __init__(self):
+        super().__init__()
+        try:
+            import wandb
+        except ImportError as e:
+            print('`pip install wandb` to use the wandb experiment tracker')
+            raise e
+
+        os.environ["WANDB_SILENT"] = "true"
+        self.wandb = wandb
+
+    def init(self, **config):
+        self.wandb.init(**config)
+
+    def log(self, log, **kwargs):
+        self.wandb.log(log, **kwargs)

dalle2_pytorch/train.py

@@ -1,6 +1,8 @@
 import time
 import copy
-from functools import partial
+from math import ceil
+from functools import partial, wraps
+from collections.abc import Iterable
 
 import torch
 from torch import nn
@@ -9,11 +11,16 @@ from torch.cuda.amp import autocast, GradScaler
 from dalle2_pytorch.dalle2_pytorch import Decoder, DiffusionPrior
 from dalle2_pytorch.optimizer import get_optimizer
 
+import numpy as np
+
 # helper functions
 
 def exists(val):
     return val is not None
 
+def default(val, d):
+    return val if exists(val) else d
+
 def cast_tuple(val, length = 1):
     return val if isinstance(val, tuple) else ((val,) * length)
 
@@ -40,6 +47,79 @@ def groupby_prefix_and_trim(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
 
+# 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)
+
+        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))
+
+        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)
+
 # print helpers
 
 def print_ribbon(s, symbol = '=', repeat = 40):
@@ -71,7 +151,7 @@ def load_diffusion_model(dprior_path, device):
     # Load state dict from saved model
     diffusion_prior.load_state_dict(loaded_obj['model'])
 
-    return diffusion_prior
+    return diffusion_prior, loaded_obj
 
 def save_diffusion_model(save_path, model, optimizer, scaler, config, image_embed_dim):
     # Saving State Dict
@@ -90,7 +170,7 @@ class EMA(nn.Module):
     def __init__(
         self,
         model,
-        beta = 0.99,
+        beta = 0.9999,
         update_after_step = 1000,
         update_every = 10,
     ):
@@ -147,6 +227,7 @@ class DiffusionPriorTrainer(nn.Module):
         use_ema = True,
         lr = 3e-4,
         wd = 1e-2,
+        eps = 1e-6,
         max_grad_norm = None,
         amp = False,
         **kwargs
@@ -173,6 +254,7 @@ class DiffusionPriorTrainer(nn.Module):
             diffusion_prior.parameters(),
             lr = lr,
             wd = wd,
+            eps = eps,
             **kwargs
         )
 
@@ -180,6 +262,8 @@ class DiffusionPriorTrainer(nn.Module):
 
         self.max_grad_norm = max_grad_norm
 
+        self.register_buffer('step', torch.tensor([0.]))
+
     def update(self):
         if exists(self.max_grad_norm):
             self.scaler.unscale_(self.optimizer)
@@ -192,11 +276,15 @@ class DiffusionPriorTrainer(nn.Module):
         if self.use_ema:
             self.ema_diffusion_prior.update()
 
+        self.step += 1
+
     @torch.inference_mode()
+    @cast_torch_tensor
     def p_sample_loop(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.p_sample_loop(*args, **kwargs)
 
     @torch.inference_mode()
+    @cast_torch_tensor
     def sample(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.sample(*args, **kwargs)
 
@@ -204,15 +292,26 @@ class DiffusionPriorTrainer(nn.Module):
     def sample_batch_size(self, *args, **kwargs):
         return self.ema_diffusion_prior.ema_model.sample_batch_size(*args, **kwargs)
 
+    @cast_torch_tensor
     def forward(
         self,
         *args,
-        divisor = 1,
+        max_batch_size = None,
         **kwargs
     ):
-        with autocast(enabled = self.amp):
-            loss = self.diffusion_prior(*args, **kwargs)
-        return self.scaler.scale(loss / divisor)
+        total_loss = 0.
+
+        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
+            with autocast(enabled = self.amp):
+                loss = self.diffusion_prior(*chunked_args, **chunked_kwargs)
+                loss = loss * chunk_size_frac
+
+            total_loss += loss.item()
+
+            if self.training:
+                self.scaler.scale(loss).backward()
+
+        return total_loss
 
 # decoder trainer
 
@@ -223,6 +322,7 @@ class DecoderTrainer(nn.Module):
         use_ema = True,
         lr = 2e-5,
         wd = 1e-2,
+        eps = 1e-8,
         max_grad_norm = None,
         amp = False,
         **kwargs
@@ -235,11 +335,6 @@ class DecoderTrainer(nn.Module):
         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
@@ -247,13 +342,14 @@ class DecoderTrainer(nn.Module):
         # be able to finely customize learning rate, weight decay
         # per unet
 
-        lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
+        lr, wd, eps = map(partial(cast_tuple, length = self.num_unets), (lr, wd, eps))
 
-        for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
+        for ind, (unet, unet_lr, unet_wd, unet_eps) in enumerate(zip(self.decoder.unets, lr, wd, eps)):
             optimizer = get_optimizer(
                 unet.parameters(),
                 lr = unet_lr,
                 wd = unet_wd,
+                eps = unet_eps,
                 **kwargs
             )
 
@@ -269,6 +365,8 @@ class DecoderTrainer(nn.Module):
 
         self.max_grad_norm = max_grad_norm
 
+        self.register_buffer('step', torch.tensor([0.]))
+
     @property
     def unets(self):
         return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
@@ -299,7 +397,10 @@ class DecoderTrainer(nn.Module):
             ema_unet = self.ema_unets[index]
             ema_unet.update()
 
+        self.step += 1
+
     @torch.no_grad()
+    @cast_torch_tensor
     def sample(self, *args, **kwargs):
         if self.use_ema:
             trainable_unets = self.decoder.unets
@@ -316,14 +417,24 @@ class DecoderTrainer(nn.Module):
 
         return output
 
+    @cast_torch_tensor
     def forward(
         self,
-        x,
-        *,
+        *args,
         unet_number,
-        divisor = 1,
+        max_batch_size = None,
         **kwargs
     ):
-        with autocast(enabled = self.amp):
-            loss = self.decoder(x, unet_number = unet_number, **kwargs)
-        return self.scale(loss / divisor, unet_number = unet_number)
+        total_loss = 0.
+
+        for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
+            with autocast(enabled = self.amp):
+                loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)
+                loss = loss * chunk_size_frac
+
+            total_loss += loss.item()
+
+            if self.training:
+                self.scale(loss, unet_number = unet_number).backward()
+
+        return total_loss

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.20',
+  version = '0.2.38',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',
@@ -30,6 +30,7 @@ setup(
     'einops-exts>=0.0.3',
     'embedding-reader',
     'kornia>=0.5.4',
+    'numpy',
     'pillow',
     'resize-right>=0.0.2',
     'rotary-embedding-torch',

train_diffusion_prior.py

@@ -1,24 +1,42 @@
-import os
+from pathlib import Path
+import click
 import math
-import argparse
+import time
 import numpy as np
 
 import torch
 from torch import nn
-from embedding_reader import EmbeddingReader
-from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork
-from dalle2_pytorch.train import load_diffusion_model, save_diffusion_model, print_ribbon
-from dalle2_pytorch.optimizer import get_optimizer
-from torch.cuda.amp import autocast,GradScaler
 
-import time
+from dalle2_pytorch import DiffusionPrior, DiffusionPriorNetwork
+from dalle2_pytorch.train import DiffusionPriorTrainer, load_diffusion_model, save_diffusion_model, print_ribbon
+from dalle2_pytorch.trackers import ConsoleTracker, WandbTracker
+
+from embedding_reader import EmbeddingReader
+
 from tqdm import tqdm
 
-import wandb
-os.environ["WANDB_SILENT"] = "true"
+# constants
+
 NUM_TEST_EMBEDDINGS = 100 # for cosine similarity reporting during training
 REPORT_METRICS_EVERY = 100 # for cosine similarity and other metric reporting during training
 
+tracker = WandbTracker()
+
+# helpers functions
+
+def exists(val):
+    val is not None
+
+class Timer:
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.last_time = time.time()
+
+    def elapsed(self):
+        return time.time() - self.last_time
+# functions
 
 def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_type,phase="Validation"):
     model.eval()
@@ -40,7 +58,7 @@ def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_t
             total_samples += batches
 
         avg_loss = (total_loss / total_samples)
-        wandb.log({f'{phase} {loss_type}': avg_loss})
+        tracker.log({f'{phase} {loss_type}': avg_loss})
 
 def report_cosine_sims(diffusion_prior,image_reader,text_reader,train_set_size,NUM_TEST_EMBEDDINGS,device):
     diffusion_prior.eval()
@@ -87,85 +105,177 @@ def report_cosine_sims(diffusion_prior,image_reader,text_reader,train_set_size,N
            text_embed, predicted_unrelated_embeddings).cpu().numpy()
        predicted_img_similarity = cos(
            test_image_embeddings, predicted_image_embeddings).cpu().numpy()
-       wandb.log({"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity),
+       tracker.log({"CosineSimilarity(text_embed,image_embed)": np.mean(original_similarity),
             "CosineSimilarity(text_embed,predicted_image_embed)":np.mean(predicted_similarity),
             "CosineSimilarity(orig_image_embed,predicted_image_embed)":np.mean(predicted_img_similarity),
             "CosineSimilarity(text_embed,predicted_unrelated_embed)": np.mean(unrelated_similarity),
             "Cosine similarity difference":np.mean(predicted_similarity - original_similarity)})
 
-def train(image_embed_dim,
-          image_embed_url,
-          text_embed_url,
-          batch_size,
-          train_percent,
-          val_percent,
-          test_percent,
-          num_epochs,
-          dp_loss_type,
-          clip,
-          dp_condition_on_text_encodings,
-          dp_timesteps,
-          dp_normformer,
-          dp_cond_drop_prob,
-          dpn_depth,
-          dpn_dim_head,
-          dpn_heads,
-          save_interval,
-          save_path,
-          device,
-          RESUME,
-          DPRIOR_PATH,
-          config,
-          wandb_entity,
-          wandb_project,
-          learning_rate=0.001,
-          max_grad_norm=0.5,
-          weight_decay=0.01,
-          dropout=0.05,
-          amp=False):
+@click.command()
+@click.option("--wandb-entity", default="laion")
+@click.option("--wandb-project", default="diffusion-prior")
+@click.option("--wandb-dataset", default="LAION-5B")
+@click.option("--wandb-arch", default="DiffusionPrior")
+@click.option("--image-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
+@click.option("--text-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
+@click.option("--learning-rate", default=1.1e-4)
+@click.option("--weight-decay", default=6.02e-2)
+@click.option("--dropout", default=5e-2)
+@click.option("--max-grad-norm", default=0.5)
+@click.option("--batch-size", default=10**4)
+@click.option("--num-epochs", default=5)
+@click.option("--image-embed-dim", default=768)
+@click.option("--train-percent", default=0.7)
+@click.option("--val-percent", default=0.2)
+@click.option("--test-percent", default=0.1)
+@click.option("--dpn-depth", default=6)
+@click.option("--dpn-dim-head", default=64)
+@click.option("--dpn-heads", default=8)
+@click.option("--dp-condition-on-text-encodings", default=False)
+@click.option("--dp-timesteps", default=100)
+@click.option("--dp-normformer", default=False)
+@click.option("--dp-cond-drop-prob", default=0.1)
+@click.option("--dp-loss-type", default="l2")
+@click.option("--clip", default=None)
+@click.option("--amp", default=False)
+@click.option("--save-interval", default=30)
+@click.option("--save-path", default="./diffusion_prior_checkpoints")
+@click.option("--pretrained-model-path", default=None)
+def train(
+    wandb_entity,
+    wandb_project,
+    wandb_dataset,
+    wandb_arch,
+    image_embed_url,
+    text_embed_url,
+    learning_rate,
+    weight_decay,
+    dropout,
+    max_grad_norm,
+    batch_size,
+    num_epochs,
+    image_embed_dim,
+    train_percent,
+    val_percent,
+    test_percent,
+    dpn_depth,
+    dpn_dim_head,
+    dpn_heads,
+    dp_condition_on_text_encodings,
+    dp_timesteps,
+    dp_normformer,
+    dp_cond_drop_prob,
+    dp_loss_type,
+    clip,
+    amp,
+    save_interval,
+    save_path,
+    pretrained_model_path
+):
+    config = {
+        "learning_rate": learning_rate,
+        "architecture": wandb_arch,
+        "dataset": wandb_dataset,
+        "weight_decay": weight_decay,
+        "max_gradient_clipping_norm": max_grad_norm,
+        "batch_size": batch_size,
+        "epochs": num_epochs,
+        "diffusion_prior_network": {
+            "depth": dpn_depth,
+            "dim_head": dpn_dim_head,
+            "heads": dpn_heads,
+            "normformer": dp_normformer
+        },
+        "diffusion_prior": {
+            "condition_on_text_encodings": dp_condition_on_text_encodings,
+            "timesteps": dp_timesteps,
+            "cond_drop_prob": dp_cond_drop_prob,
+            "loss_type": dp_loss_type,
+            "clip": clip
+        }
+    }
+
+    # Check if DPRIOR_PATH exists(saved model path)
+
+    DPRIOR_PATH = args.pretrained_model_path
+    RESUME = exists(DPRIOR_PATH)
+
+    if not RESUME:
+        tracker.init(
+            entity = wandb_entity,
+            project = wandb_project,
+            config = config
+        )
+
+    # Obtain the utilized device.
+
+    has_cuda = torch.cuda.is_available()
+    if has_cuda:
+        device = torch.device("cuda:0")
+        torch.cuda.set_device(device)
+
+    # Training loop
+    # diffusion prior network
 
-    # DiffusionPriorNetwork 
     prior_network = DiffusionPriorNetwork( 
-            dim = image_embed_dim, 
-            depth = dpn_depth, 
-            dim_head = dpn_dim_head, 
-            heads = dpn_heads,
-            attn_dropout = dropout,
-            ff_dropout = dropout,
-            normformer = dp_normformer).to(device)
+        dim = image_embed_dim,
+        depth = dpn_depth,
+        dim_head = dpn_dim_head,
+        heads = dpn_heads,
+        attn_dropout = dropout,
+        ff_dropout = dropout,
+        normformer = dp_normformer
+    )
     
-    # DiffusionPrior with text embeddings and image embeddings pre-computed
+    # diffusion prior with text embeddings and image embeddings pre-computed
+
     diffusion_prior = DiffusionPrior( 
-            net = prior_network, 
-            clip = clip, 
-            image_embed_dim = image_embed_dim, 
-            timesteps = dp_timesteps,
-            cond_drop_prob = dp_cond_drop_prob, 
-            loss_type = dp_loss_type, 
-            condition_on_text_encodings = dp_condition_on_text_encodings).to(device)
+        net = prior_network,
+        clip = clip,
+        image_embed_dim = image_embed_dim,
+        timesteps = dp_timesteps,
+        cond_drop_prob = dp_cond_drop_prob,
+        loss_type = dp_loss_type,
+        condition_on_text_encodings = dp_condition_on_text_encodings
+    )
 
     # Load pre-trained model from DPRIOR_PATH
+
     if RESUME:
-        diffusion_prior=load_diffusion_model(DPRIOR_PATH,device)   
-        wandb.init( entity=wandb_entity, project=wandb_project, config=config) 
+        diffusion_prior, loaded_obj = load_diffusion_model(DPRIOR_PATH, device)
+        tracker.init(entity = wandb_entity, project = wandb_project, config = config)
+
+    # diffusion prior trainer
+
+    trainer = DiffusionPriorTrainer(
+        diffusion_prior = diffusion_prior,
+        lr = learning_rate,
+        wd = weight_decay,
+        max_grad_norm = max_grad_norm,
+        amp = amp,
+    ).to(device)
+
+    # load optimizer and scaler
+
+    if RESUME:
+        trainer.optimizer.load_state_dict(loaded_obj['optimizer'])
+        trainer.scaler.load_state_dict(loaded_obj['scaler'])
 
     # Create save_path if it doesn't exist
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
+
+    Path(save_path).mkdir(exist_ok = True, parents = True)
 
     # Get image and text embeddings from the servers
+
     print_ribbon("Downloading embeddings - image and text")
     image_reader = EmbeddingReader(embeddings_folder=image_embed_url, file_format="npy")
     text_reader  = EmbeddingReader(embeddings_folder=text_embed_url, file_format="npy")
     num_data_points = text_reader.count
 
     ### Training code ###
-    scaler = GradScaler(enabled=amp)
-    optimizer = get_optimizer(diffusion_prior.net.parameters(), wd=weight_decay, lr=learning_rate)
-    epochs = num_epochs
 
-    step = 0
-    t = time.time()
+    timer = Timer()
+    epochs = num_epochs
 
     train_set_size = int(train_percent*num_data_points)
     val_set_size = int(val_percent*num_data_points)
@@ -176,32 +286,31 @@ def train(image_embed_dim,
         for emb_images,emb_text in zip(image_reader(batch_size=batch_size, start=0, end=train_set_size),
                 text_reader(batch_size=batch_size, start=0, end=train_set_size)):
 
-            diffusion_prior.train()
+            trainer.train()
             
             emb_images_tensor = torch.tensor(emb_images[0]).to(device)
             emb_text_tensor = torch.tensor(emb_text[0]).to(device)
 
-            with autocast(enabled=amp):
-                loss = diffusion_prior(text_embed = emb_text_tensor,image_embed = emb_images_tensor)
-                scaler.scale(loss).backward()
+            loss = trainer(text_embed = emb_text_tensor, image_embed = emb_images_tensor)
 
             # Samples per second
-            step+=1
-            samples_per_sec = batch_size*step/(time.time()-t)
+
+            samples_per_sec = batch_size * step / timer.elapsed()
+
             # Save checkpoint every save_interval minutes
-            if(int(time.time()-t) >= 60*save_interval):
-                t = time.time()
+            if(int(timer.elapsed()) >= 60 * save_interval):
+                timer.reset()
 
                 save_diffusion_model(
                     save_path,
                     diffusion_prior,
-                    optimizer,
-                    scaler,
+                    trainer.optimizer,
+                    trainer.scaler,
                     config,
                     image_embed_dim)
 
             # Log to wandb
-            wandb.log({"Training loss": loss.item(),
+            tracker.log({"Training loss": loss.item(),
                         "Steps": step,
                         "Samples per second": samples_per_sec})
             # Log cosineSim(text_embed,predicted_image_embed) - cosineSim(text_embed,image_embed)
@@ -225,130 +334,13 @@ def train(image_embed_dim,
                         dp_loss_type,
                         phase="Validation")
 
-            scaler.unscale_(optimizer)
-            nn.utils.clip_grad_norm_(diffusion_prior.parameters(), max_grad_norm)
-
-            scaler.step(optimizer)
-            scaler.update()
-            optimizer.zero_grad()
+            trainer.update()
 
     ### Test run ###
     test_set_size = int(test_percent*train_set_size) 
-    start=train_set_size+val_set_size
-    end=num_data_points
+    start = train_set_size+val_set_size
+    end = num_data_points
     eval_model(diffusion_prior,device,image_reader,text_reader,start,end,batch_size,dp_loss_type,phase="Test")
 
-def main():
-    parser = argparse.ArgumentParser()
-    # Logging
-    parser.add_argument("--wandb-entity", type=str, default="laion")
-    parser.add_argument("--wandb-project", type=str, default="diffusion-prior")
-    parser.add_argument("--wandb-dataset", type=str, default="LAION-5B")
-    parser.add_argument("--wandb-arch", type=str, default="DiffusionPrior")
-    # URLs for embeddings 
-    parser.add_argument("--image-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
-    parser.add_argument("--text-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
-    # Hyperparameters
-    parser.add_argument("--learning-rate", type=float, default=1.1e-4)
-    parser.add_argument("--weight-decay", type=float, default=6.02e-2)
-    parser.add_argument("--dropout", type=float, default=5e-2)
-    parser.add_argument("--max-grad-norm", type=float, default=0.5)
-    parser.add_argument("--batch-size", type=int, default=10**4)
-    parser.add_argument("--num-epochs", type=int, default=5)
-    # Image embed dimension
-    parser.add_argument("--image-embed-dim", type=int, default=768)
-    # Train-test split
-    parser.add_argument("--train-percent", type=float, default=0.7)
-    parser.add_argument("--val-percent", type=float, default=0.2)
-    parser.add_argument("--test-percent", type=float, default=0.1)
-    # LAION training(pre-computed embeddings)
-    # DiffusionPriorNetwork(dpn) parameters
-    parser.add_argument("--dpn-depth", type=int, default=6)
-    parser.add_argument("--dpn-dim-head", type=int, default=64)
-    parser.add_argument("--dpn-heads", type=int, default=8)
-    # DiffusionPrior(dp) parameters
-    parser.add_argument("--dp-condition-on-text-encodings", type=bool, default=False)
-    parser.add_argument("--dp-timesteps", type=int, default=100)
-    parser.add_argument("--dp-normformer", type=bool, default=False)
-    parser.add_argument("--dp-cond-drop-prob", type=float, default=0.1)
-    parser.add_argument("--dp-loss-type", type=str, default="l2")
-    parser.add_argument("--clip", type=str, default=None)
-    parser.add_argument("--amp", type=bool, default=False)
-    # Model checkpointing interval(minutes)
-    parser.add_argument("--save-interval", type=int, default=30)
-    parser.add_argument("--save-path", type=str, default="./diffusion_prior_checkpoints")
-    # Saved model path 
-    parser.add_argument("--pretrained-model-path", type=str, default=None)
-
-    args = parser.parse_args()
-
-    config = ({"learning_rate": args.learning_rate,
-        "architecture": args.wandb_arch,
-        "dataset": args.wandb_dataset,
-        "weight_decay":args.weight_decay,
-        "max_gradient_clipping_norm":args.max_grad_norm,
-        "batch_size":args.batch_size,
-        "epochs": args.num_epochs,
-        "diffusion_prior_network":{"depth":args.dpn_depth,
-        "dim_head":args.dpn_dim_head,
-        "heads":args.dpn_heads,
-        "normformer":args.dp_normformer},
-        "diffusion_prior":{"condition_on_text_encodings": args.dp_condition_on_text_encodings,
-        "timesteps": args.dp_timesteps,
-        "cond_drop_prob":args.dp_cond_drop_prob,
-        "loss_type":args.dp_loss_type,
-        "clip":args.clip}
-        })
-
-    RESUME = False
-    # Check if DPRIOR_PATH exists(saved model path)
-    DPRIOR_PATH = args.pretrained_model_path
-    if(DPRIOR_PATH is not None):
-        RESUME = True
-    else:
-        wandb.init(
-          entity=args.wandb_entity,
-          project=args.wandb_project,
-          config=config)
-
-    # Obtain the utilized device.
-
-    has_cuda = torch.cuda.is_available()
-    if has_cuda:
-        device = torch.device("cuda:0")
-        torch.cuda.set_device(device)
-
-    # Training loop
-    train(args.image_embed_dim,
-          args.image_embed_url,
-          args.text_embed_url,
-          args.batch_size,
-          args.train_percent,
-          args.val_percent,
-          args.test_percent,
-          args.num_epochs,
-          args.dp_loss_type,
-          args.clip,
-          args.dp_condition_on_text_encodings,
-          args.dp_timesteps,
-          args.dp_normformer,
-          args.dp_cond_drop_prob,
-          args.dpn_depth,
-          args.dpn_dim_head,
-          args.dpn_heads,
-          args.save_interval,
-          args.save_path,
-          device,
-          RESUME,
-          DPRIOR_PATH,
-          config,
-          args.wandb_entity,
-          args.wandb_project,
-          args.learning_rate,
-          args.max_grad_norm,
-          args.weight_decay,
-          args.dropout,
-          args.amp)
-
 if __name__ == "__main__":
-  main()
+    train()