let researchers do the hyperparameter search

README.md

@@ -760,7 +760,7 @@ decoder = Decoder(
     unet = (unet1, unet2),
     image_sizes = (128, 256),
     clip = clip,
-    timesteps = 1,
+    timesteps = 1000,
     condition_on_text_encodings = True
 ).cuda()
 
@@ -778,6 +778,12 @@ for unet_number in (1, 2):
     loss.backward()
 
     decoder_trainer.update(unet_number) # update the specific unet as well as its exponential moving average
+
+# after much training
+# you can sample from the exponentially moving averaged unets as so
+
+mock_image_embed = torch.randn(4, 512).cuda()
+images = decoder_trainer.sample(mock_image_embed, text = text) # (4, 3, 256, 256)
 ```
 
 ## CLI (wip)
@@ -811,15 +817,16 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] use inheritance just this once for sharing logic between decoder and prior network ddpms
 - [x] bring in vit-vqgan https://arxiv.org/abs/2110.04627 for the latent diffusion
 - [x] abstract interface for CLIP adapter class, so other CLIPs can be brought in
-- [ ] take care of mixed precision as well as gradient accumulation within decoder trainer
+- [x] take care of mixed precision as well as gradient accumulation within decoder trainer
+- [x] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
+- [x] bring in tools to train vqgan-vae
 - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
 - [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
 - [ ] transcribe code to Jax, which lowers the activation energy for distributed training, given access to TPUs
-- [ ] just take care of the training for the decoder in a wrapper class, as each unet in the cascade will need its own optimizer
 - [ ] train on a toy task, offer in colab
 - [ ] think about how best to design a declarative training config that handles preencoding for prior and training of multiple networks in decoder
 - [ ] extend diffusion head to use diffusion-gan (potentially using lightweight-gan) to speed up inference
-- [ ] bring in tools to train vqgan-vae
+- [ ] add convnext backbone for vqgan-vae (in addition to vit [vit-vqgan] + resnet)
 
 ## Citations
 

dalle2_pytorch/dalle2_pytorch.py

@@ -922,6 +922,7 @@ class ConvNextBlock(nn.Module):
         dim_out,
         *,
         cond_dim = None,
+        time_cond_dim = None,
         mult = 2,
         norm = True
     ):
@@ -940,6 +941,14 @@ class ConvNextBlock(nn.Module):
                 )
             )
 
+        self.time_mlp = None
+
+        if exists(time_cond_dim):
+            self.time_mlp = nn.Sequential(
+                nn.GELU(),
+                nn.Linear(time_cond_dim, dim)
+            )
+
         self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
 
         inner_dim = int(dim_out * mult)
@@ -952,9 +961,13 @@ class ConvNextBlock(nn.Module):
 
         self.res_conv = nn.Conv2d(dim, dim_out, 1) if need_projection else nn.Identity()
 
-    def forward(self, x, cond = None):
+    def forward(self, x, cond = None, time = None):
         h = self.ds_conv(x)
 
+        if exists(time) and exists(self.time_mlp):
+            t = self.time_mlp(time)
+            h = rearrange(t, 'b c -> b c 1 1') + h
+
         if exists(self.cross_attn):
             assert exists(cond)
             h = self.cross_attn(h, context = cond) + h
@@ -1059,6 +1072,8 @@ class Unet(nn.Module):
         cond_on_text_encodings = False,
         max_text_len = 256,
         cond_on_image_embeds = False,
+        init_dim = None,
+        init_conv_kernel_size = 7
     ):
         super().__init__()
         # save locals to take care of some hyperparameters for cascading DDPM
@@ -1076,22 +1091,34 @@ class Unet(nn.Module):
         self.channels = channels
 
         init_channels = channels if not lowres_cond else channels * 2 # in cascading diffusion, one concats the low resolution image, blurred, for conditioning the higher resolution synthesis
+        init_dim = default(init_dim, dim // 2)
 
-        dims = [init_channels, *map(lambda m: dim * m, dim_mults)]
+        assert (init_conv_kernel_size % 2) == 1
+        self.init_conv = nn.Conv2d(init_channels, init_dim, init_conv_kernel_size, padding = init_conv_kernel_size // 2)
+
+        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
 
         # time, image embeddings, and optional text encoding
 
         cond_dim = default(cond_dim, dim)
+        time_cond_dim = dim * 4
 
-        self.time_mlp = nn.Sequential(
+        self.to_time_hiddens = nn.Sequential(
             SinusoidalPosEmb(dim),
-            nn.Linear(dim, dim * 4),
-            nn.GELU(),
-            nn.Linear(dim * 4, cond_dim * num_time_tokens),
+            nn.Linear(dim, time_cond_dim),
+            nn.GELU()
+        )
+
+        self.to_time_tokens = nn.Sequential(
+            nn.Linear(time_cond_dim, cond_dim * num_time_tokens),
             Rearrange('b (r d) -> b r d', r = num_time_tokens)
         )
 
+        self.to_time_cond = nn.Sequential(
+            nn.Linear(time_cond_dim, time_cond_dim)
+        )
+
         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)
@@ -1133,26 +1160,26 @@ class Unet(nn.Module):
             layer_cond_dim = cond_dim if not is_first else None
 
             self.downs.append(nn.ModuleList([
-                ConvNextBlock(dim_in, dim_out, norm = ind != 0),
+                ConvNextBlock(dim_in, dim_out, time_cond_dim = time_cond_dim, norm = ind != 0),
                 Residual(GridAttention(dim_out, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim),
+                ConvNextBlock(dim_out, dim_out, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
                 Downsample(dim_out) if not is_last else nn.Identity()
             ]))
 
         mid_dim = dims[-1]
 
-        self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
+        self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim)
         self.mid_attn = EinopsToAndFrom('b c h w', 'b (h w) c', Residual(Attention(mid_dim, **attn_kwargs))) if attend_at_middle else None
-        self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim)
+        self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, cond_dim = cond_dim, time_cond_dim = time_cond_dim)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             is_last = ind >= (num_resolutions - 2)
             layer_cond_dim = cond_dim if not is_last else None
 
             self.ups.append(nn.ModuleList([
-                ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim),
+                ConvNextBlock(dim_out * 2, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
                 Residual(GridAttention(dim_in, window_size = sparse_attn_window, **attn_kwargs)) if sparse_attn else nn.Identity(),
-                ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim),
+                ConvNextBlock(dim_in, dim_in, cond_dim = layer_cond_dim, time_cond_dim = time_cond_dim),
                 Upsample(dim_in)
             ]))
 
@@ -1214,9 +1241,16 @@ class Unet(nn.Module):
         if exists(lowres_cond_img):
             x = torch.cat((x, lowres_cond_img), dim = 1)
 
+        # initial convolution
+
+        x = self.init_conv(x)
+
         # time conditioning
 
-        time_tokens = self.time_mlp(time)
+        time_hiddens = self.to_time_hiddens(time)
+
+        time_tokens = self.to_time_tokens(time_hiddens)
+        t = self.to_time_cond(time_hiddens)
 
         # conditional dropout
 
@@ -1283,24 +1317,24 @@ class Unet(nn.Module):
         hiddens = []
 
         for convnext, sparse_attn, convnext2, downsample in self.downs:
-            x = convnext(x, c)
+            x = convnext(x, c, t)
             x = sparse_attn(x)
-            x = convnext2(x, c)
+            x = convnext2(x, c, t)
             hiddens.append(x)
             x = downsample(x)
 
-        x = self.mid_block1(x, mid_c)
+        x = self.mid_block1(x, mid_c, t)
 
         if exists(self.mid_attn):
             x = self.mid_attn(x)
 
-        x = self.mid_block2(x, mid_c)
+        x = self.mid_block2(x, mid_c, t)
 
         for convnext, sparse_attn, convnext2, upsample in self.ups:
             x = torch.cat((x, hiddens.pop()), dim=1)
-            x = convnext(x, c)
+            x = convnext(x, c, t)
             x = sparse_attn(x)
-            x = convnext2(x, c)
+            x = convnext2(x, c, t)
             x = upsample(x)
 
         return self.final_conv(x)
@@ -1540,7 +1574,13 @@ class Decoder(BaseGaussianDiffusion):
 
     @torch.no_grad()
     @eval_decorator
-    def sample(self, image_embed, text = None, cond_scale = 1.):
+    def sample(
+        self,
+        image_embed,
+        text = None,
+        cond_scale = 1.,
+        stop_at_unet_number = None
+    ):
         batch_size = image_embed.shape[0]
 
         text_encodings = text_mask = None
@@ -1552,7 +1592,7 @@ class Decoder(BaseGaussianDiffusion):
 
         img = None
 
-        for unet, vae, channel, image_size, predict_x_start in tqdm(zip(self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start)):
+        for unet_number, unet, vae, channel, image_size, predict_x_start in tqdm(zip(range(1, len(self.unets) + 1), self.unets, self.vaes, self.sample_channels, self.image_sizes, self.predict_x_start)):
 
             context = self.one_unet_in_gpu(unet = unet) if image_embed.is_cuda else null_context()
 
@@ -1584,6 +1624,9 @@ class Decoder(BaseGaussianDiffusion):
 
                 img = vae.decode(img)
 
+            if exists(stop_at_unet_number) and stop_at_unet_number == unet_number:
+                break
+
         return img
 
     def forward(

dalle2_pytorch/train.py

@@ -3,12 +3,19 @@ from functools import partial
 
 import torch
 from torch import nn
+from torch.cuda.amp import autocast, GradScaler
 
 from dalle2_pytorch.dalle2_pytorch import Decoder
 from dalle2_pytorch.optimizer import get_optimizer
 
 # helper functions
 
+def exists(val):
+    return val is not None
+
+def cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
 def pick_and_pop(keys, d):
     values = list(map(lambda key: d.pop(key), keys))
     return dict(zip(keys, values))
@@ -89,6 +96,10 @@ class DecoderTrainer(nn.Module):
         self,
         decoder,
         use_ema = True,
+        lr = 3e-4,
+        wd = 1e-2,
+        max_grad_norm = None,
+        amp = False,
         **kwargs
     ):
         super().__init__()
@@ -106,24 +117,82 @@ class DecoderTrainer(nn.Module):
 
         self.ema_unets = nn.ModuleList([])
 
-        for ind, unet in enumerate(self.decoder.unets):
-            optimizer = get_optimizer(unet.parameters(), **kwargs)
+        self.amp = amp
+
+        # be able to finely customize learning rate, weight decay
+        # per unet
+
+        lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
+
+        for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
+            optimizer = get_optimizer(
+                unet.parameters(),
+                lr = unet_lr,
+                wd = unet_wd,
+                **kwargs
+            )
+
             setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
 
             if self.use_ema:
                 self.ema_unets.append(EMA(unet, **ema_kwargs))
 
+            scaler = GradScaler(enabled = amp)
+            setattr(self, f'scaler{ind}', scaler)
+
+        # gradient clipping if needed
+
+        self.max_grad_norm = max_grad_norm
+
+    @property
+    def unets(self):
+        return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
+
+    def scale(self, loss, *, unet_number):
+        assert 1 <= unet_number <= self.num_unets
+        index = unet_number - 1
+        scaler = getattr(self, f'scaler{index}')
+        return scaler.scale(loss)
+
     def update(self, unet_number):
         assert 1 <= unet_number <= self.num_unets
         index = unet_number - 1
+        unet = self.decoder.unets[index]
+
+        if exists(self.max_grad_norm):
+            nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
 
         optimizer = getattr(self, f'optim{index}')
-        optimizer.step()
+        scaler = getattr(self, f'scaler{index}')
+
+        scaler.step(optimizer)
+        scaler.update()
         optimizer.zero_grad()
 
         if self.use_ema:
             ema_unet = self.ema_unets[index]
             ema_unet.update()
 
-    def forward(self, x, *, unet_number, **kwargs):
-        return self.decoder(x, unet_number = unet_number, **kwargs)
+    @torch.no_grad()
+    def sample(self, *args, **kwargs):
+        if self.use_ema:
+            trainable_unets = self.decoder.unets
+            self.decoder.unets = self.unets                  # swap in exponential moving averaged unets for sampling
+
+        output = self.decoder.sample(*args, **kwargs)
+
+        if self.use_ema:
+            self.decoder.unets = trainable_unets             # restore original training unets
+        return output
+
+    def forward(
+        self,
+        x,
+        *,
+        unet_number,
+        divisor = 1,
+        **kwargs
+    ):
+        with autocast(enabled = self.amp):
+            loss = self.decoder(x, unet_number = unet_number, **kwargs)
+        return self.scale(loss / divisor, unet_number = unet_number)

dalle2_pytorch/train_vqgan_vae.py

@@ -0,0 +1,266 @@
+from math import sqrt
+import copy
+from random import choice
+from pathlib import Path
+from shutil import rmtree
+
+import torch
+from torch import nn
+
+from PIL import Image
+from torchvision.datasets import ImageFolder
+import torchvision.transforms as T
+from torch.utils.data import Dataset, DataLoader, random_split
+from torchvision.utils import make_grid, save_image
+
+from einops import rearrange
+
+from dalle2_pytorch.train import EMA
+from dalle2_pytorch.vqgan_vae import VQGanVAE
+from dalle2_pytorch.optimizer import get_optimizer
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def noop(*args, **kwargs):
+    pass
+
+def cycle(dl):
+    while True:
+        for data in dl:
+            yield data
+
+def cast_tuple(t):
+    return t if isinstance(t, (tuple, list)) else (t,)
+
+def yes_or_no(question):
+    answer = input(f'{question} (y/n) ')
+    return answer.lower() in ('yes', 'y')
+
+def accum_log(log, new_logs):
+    for key, new_value in new_logs.items():
+        old_value = log.get(key, 0.)
+        log[key] = old_value + new_value
+    return log
+
+# classes
+
+class ImageDataset(Dataset):
+    def __init__(
+        self,
+        folder,
+        image_size,
+        exts = ['jpg', 'jpeg', 'png']
+    ):
+        super().__init__()
+        self.folder = folder
+        self.image_size = image_size
+        self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
+
+        print(f'{len(self.paths)} training samples found at {folder}')
+
+        self.transform = T.Compose([
+            T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+            T.Resize(image_size),
+            T.RandomHorizontalFlip(),
+            T.CenterCrop(image_size),
+            T.ToTensor()
+        ])
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, index):
+        path = self.paths[index]
+        img = Image.open(path)
+        return self.transform(img)
+
+# main trainer class
+
+class VQGanVAETrainer(nn.Module):
+    def __init__(
+        self,
+        vae,
+        *,
+        num_train_steps,
+        lr,
+        batch_size,
+        folder,
+        grad_accum_every,
+        wd = 0.,
+        save_results_every = 100,
+        save_model_every = 1000,
+        results_folder = './results',
+        valid_frac = 0.05,
+        random_split_seed = 42,
+        ema_beta = 0.995,
+        ema_update_after_step = 2000,
+        ema_update_every = 10,
+        apply_grad_penalty_every = 4,
+    ):
+        super().__init__()
+        assert isinstance(vae, VQGanVAE), 'vae must be instance of VQGanVAE'
+        image_size = vae.image_size
+
+        self.vae = vae
+        self.ema_vae = EMA(vae, update_after_step = ema_update_after_step, update_every = ema_update_every)
+
+        self.register_buffer('steps', torch.Tensor([0]))
+
+        self.num_train_steps = num_train_steps
+        self.batch_size = batch_size
+        self.grad_accum_every = grad_accum_every
+
+        all_parameters = set(vae.parameters())
+        discr_parameters = set(vae.discr.parameters())
+        vae_parameters = all_parameters - discr_parameters
+
+        self.optim = get_optimizer(vae_parameters, lr = lr, wd = wd)
+        self.discr_optim = get_optimizer(discr_parameters, lr = lr, wd = wd)
+
+        # create dataset
+
+        self.ds = ImageDataset(folder, image_size = image_size)
+
+        # split for validation
+
+        if valid_frac > 0:
+            train_size = int((1 - valid_frac) * len(self.ds))
+            valid_size = len(self.ds) - train_size
+            self.ds, self.valid_ds = random_split(self.ds, [train_size, valid_size], generator = torch.Generator().manual_seed(random_split_seed))
+            print(f'training with dataset of {len(self.ds)} samples and validating with randomly splitted {len(self.valid_ds)} samples')
+        else:
+            self.valid_ds = self.ds
+            print(f'training with shared training and valid dataset of {len(self.ds)} samples')
+
+        # dataloader
+
+        self.dl = cycle(DataLoader(
+            self.ds,
+            batch_size = batch_size,
+            shuffle = True
+        ))
+
+        self.valid_dl = cycle(DataLoader(
+            self.valid_ds,
+            batch_size = batch_size,
+            shuffle = True
+        ))
+
+        self.save_model_every = save_model_every
+        self.save_results_every = save_results_every
+
+        self.apply_grad_penalty_every = apply_grad_penalty_every
+
+        self.results_folder = Path(results_folder)
+
+        if len([*self.results_folder.glob('**/*')]) > 0 and yes_or_no('do you want to clear previous experiment checkpoints and results?'):
+            rmtree(str(self.results_folder))
+
+        self.results_folder.mkdir(parents = True, exist_ok = True)
+
+    def train_step(self):
+        device = next(self.vae.parameters()).device
+        steps = int(self.steps.item())
+        apply_grad_penalty = not (steps % self.apply_grad_penalty_every)
+
+        self.vae.train()
+
+        # logs
+
+        logs = {}
+
+        # update vae (generator)
+
+        for _ in range(self.grad_accum_every):
+            img = next(self.dl)
+            img = img.to(device)
+
+            loss = self.vae(
+                img,
+                return_loss = True,
+                apply_grad_penalty = apply_grad_penalty
+            )
+
+            accum_log(logs, {'loss': loss.item() / self.grad_accum_every})
+
+            (loss / self.grad_accum_every).backward()
+
+        self.optim.step()
+        self.optim.zero_grad()
+
+
+        # update discriminator
+
+        if exists(self.vae.discr):
+            discr_loss = 0
+            for _ in range(self.grad_accum_every):
+                img = next(self.dl)
+                img = img.to(device)
+
+                loss = self.vae(img, return_discr_loss = True)
+                accum_log(logs, {'discr_loss': loss.item() / self.grad_accum_every})
+
+                (loss / self.grad_accum_every).backward()
+
+            self.discr_optim.step()
+            self.discr_optim.zero_grad()
+
+            # log
+
+            print(f"{steps}: vae loss: {logs['loss']} - discr loss: {logs['discr_loss']}")
+
+        # update exponential moving averaged generator
+
+        self.ema_vae.update()
+
+        # sample results every so often
+
+        if not (steps % self.save_results_every):
+            for model, filename in ((self.ema_vae.ema_model, f'{steps}.ema'), (self.vae, str(steps))):
+                model.eval()
+
+                imgs = next(self.dl)
+                imgs = imgs.to(device)
+
+                recons = model(imgs)
+                nrows = int(sqrt(self.batch_size))
+
+                imgs_and_recons = torch.stack((imgs, recons), dim = 0)
+                imgs_and_recons = rearrange(imgs_and_recons, 'r b ... -> (b r) ...')
+
+                imgs_and_recons = imgs_and_recons.detach().cpu().float().clamp(0., 1.)
+                grid = make_grid(imgs_and_recons, nrow = 2, normalize = True, value_range = (0, 1))
+
+                logs['reconstructions'] = grid
+
+                save_image(grid, str(self.results_folder / f'{filename}.png'))
+
+            print(f'{steps}: saving to {str(self.results_folder)}')
+
+        # save model every so often
+
+        if not (steps % self.save_model_every):
+            state_dict = self.vae.state_dict()
+            model_path = str(self.results_folder / f'vae.{steps}.pt')
+            torch.save(state_dict, model_path)
+
+            ema_state_dict = self.ema_vae.state_dict()
+            model_path = str(self.results_folder / f'vae.{steps}.ema.pt')
+            torch.save(ema_state_dict, model_path)
+
+            print(f'{steps}: saving model to {str(self.results_folder)}')
+
+        self.steps += 1
+        return logs
+
+    def train(self, log_fn = noop):
+        device = next(self.vae.parameters()).device
+
+        while self.steps < self.num_train_steps:
+            logs = self.train_step()
+            log_fn(logs)
+
+        print('training complete')

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.77',
+  version = '0.0.85',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',