take care of gradient accumulation automatically for researchers, by passing in a max_batch_size on the decoder or diffusion prior trainer forward

README.md

@@ -732,8 +732,8 @@ clip = CLIP(
 
 # mock data
 
-text = torch.randint(0, 49408, (4, 256)).cuda()
+text = torch.randint(0, 49408, (32, 256)).cuda()
-images = torch.randn(4, 3, 256, 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 = decoder_trainer(
-    loss.backward()
+        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
 
@@ -839,7 +843,6 @@ diffusion_prior_trainer = DiffusionPriorTrainer(
 )
 
 loss = diffusion_prior_trainer(text, images)
-loss.backward()
 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
@@ -1017,6 +1020,7 @@ Once built, images will be saved to the same directory the command is invoked
 - [ ] 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
 
 ## Citations
 

dalle2_pytorch/dalle2_pytorch.py

@@ -1,7 +1,7 @@
 import math
 from tqdm import tqdm
 from inspect import isfunction
-from functools import partial
+from functools import partial, wraps
 from contextlib import contextmanager
 from collections import namedtuple
 from pathlib import Path
@@ -45,6 +45,14 @@ def exists(val):
 def identity(t, *args, **kwargs):
     return t
 
+def maybe(fn):
+    @wraps(fn)
+    def inner(x):
+        if not exists(x):
+            return x
+        return fn(x)
+    return inner
+
 def default(val, d):
     if exists(val):
         return val
@@ -114,10 +122,10 @@ def resize_image_to(image, target_image_size):
 # ddpms expect images to be in the range of -1 to 1
 # but CLIP may otherwise
 
-def normalize_img(img):
+def normalize_neg_one_to_one(img):
     return img * 2 - 1
 
-def unnormalize_img(normed_img):
+def unnormalize_zero_to_one(normed_img):
     return (normed_img + 1) * 0.5
 
 # clip related adapters
@@ -278,7 +286,7 @@ class OpenAIClipAdapter(BaseClipAdapter):
     def embed_image(self, image):
         assert not self.cleared
         image = resize_image_to(image, self.image_size)
-        image = self.clip_normalize(unnormalize_img(image))
+        image = self.clip_normalize(image)
         image_embed = self.clip.encode_image(image)
         return EmbeddedImage(l2norm(image_embed.float()), None)
 
@@ -606,7 +614,6 @@ class Attention(nn.Module):
         heads = 8,
         dropout = 0.,
         causal = False,
-        post_norm = False,
         rotary_emb = None
     ):
         super().__init__()
@@ -616,7 +623,6 @@ class Attention(nn.Module):
 
         self.causal = causal
         self.norm = LayerNorm(dim)
-        self.post_norm = LayerNorm(dim)     # sandwich norm from Coqview paper + Normformer
         self.dropout = nn.Dropout(dropout)
 
         self.null_kv = nn.Parameter(torch.randn(2, dim_head))
@@ -627,7 +633,7 @@ class Attention(nn.Module):
 
         self.to_out = nn.Sequential(
             nn.Linear(inner_dim, dim, bias = False),
-            LayerNorm(dim) if post_norm else nn.Identity()
+            LayerNorm(dim)
         )
 
     def forward(self, x, mask = None, attn_bias = None):
@@ -684,8 +690,7 @@ class Attention(nn.Module):
         out = einsum('b h i j, b j d -> b h i d', attn, v)
 
         out = rearrange(out, 'b h n d -> b n (h d)')
-        out = self.to_out(out)
+        return self.to_out(out)
-        return self.post_norm(out)
 
 class CausalTransformer(nn.Module):
     def __init__(
@@ -711,7 +716,7 @@ class CausalTransformer(nn.Module):
         self.layers = nn.ModuleList([])
         for _ in range(depth):
             self.layers.append(nn.ModuleList([
-                Attention(dim = dim, causal = True, dim_head = dim_head, heads = heads, dropout = attn_dropout, post_norm = normformer, rotary_emb = rotary_emb),
+                Attention(dim = dim, causal = True, dim_head = dim_head, heads = heads, dropout = attn_dropout, rotary_emb = rotary_emb),
                 FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout, post_activation_norm = normformer)
             ]))
 
@@ -1158,6 +1163,7 @@ class CrossAttention(nn.Module):
         dim_head = 64,
         heads = 8,
         dropout = 0.,
+        norm_context = False
     ):
         super().__init__()
         self.scale = dim_head ** -0.5
@@ -1167,13 +1173,17 @@ 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))
         self.to_q = nn.Linear(dim, inner_dim, bias = False)
         self.to_kv = nn.Linear(context_dim, inner_dim * 2, bias = False)
-        self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim, bias = False),
+            LayerNorm(dim)
+        )
 
     def forward(self, x, context, mask = None):
         b, n, device = *x.shape[:2], x.device
@@ -1369,6 +1379,9 @@ class Unet(nn.Module):
             Rearrange('b (n d) -> b n d', n = num_image_tokens)
         ) if 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
@@ -1584,6 +1597,11 @@ 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 = []
@@ -1821,7 +1839,7 @@ class Decoder(BaseGaussianDiffusion):
             # eq 15 - https://arxiv.org/abs/2102.09672
             min_log = extract(self.posterior_log_variance_clipped, t, x.shape)
             max_log = extract(torch.log(self.betas), t, x.shape)
-            var_interp_frac = unnormalize_img(var_interp_frac_unnormalized)
+            var_interp_frac = unnormalize_zero_to_one(var_interp_frac_unnormalized)
 
             posterior_log_variance = var_interp_frac * max_log + (1 - var_interp_frac) * min_log
             posterior_variance = posterior_log_variance.exp()
@@ -1844,6 +1862,8 @@ class Decoder(BaseGaussianDiffusion):
         b = shape[0]
         img = torch.randn(shape, device = device)
 
+        lowres_cond_img = maybe(normalize_neg_one_to_one)(lowres_cond_img)
+
         for i in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
             img = self.p_sample(
                 unet,
@@ -1859,11 +1879,19 @@ class Decoder(BaseGaussianDiffusion):
                 clip_denoised = clip_denoised
             )
 
-        return img
+        unnormalize_img = unnormalize_zero_to_one(img)
+        return unnormalize_img
 
     def p_losses(self, unet, x_start, times, *, image_embed, lowres_cond_img = None, text_encodings = None, text_mask = None, predict_x_start = False, noise = None, learned_variance = False, clip_denoised = False):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
+        # normalize to [-1, 1]
+
+        x_start = normalize_neg_one_to_one(x_start)
+        lowres_cond_img = maybe(normalize_neg_one_to_one)(lowres_cond_img)
+
+        # get x_t
+
         x_noisy = self.q_sample(x_start = x_start, t = times, noise = noise)
 
         model_output = unet(
@@ -1890,6 +1918,11 @@ class Decoder(BaseGaussianDiffusion):
             # return simple loss if not using learned variance
             return loss
 
+        # most of the code below is transcribed from
+        # https://github.com/hojonathanho/diffusion/blob/master/diffusion_tf/diffusion_utils_2.py
+        # the Improved DDPM paper then further modified it so that the mean is detached (shown a couple lines before), and weighted to be smaller than the l1 or l2 "simple" loss
+        # it is questionable whether this is really needed, looking at some of the figures in the paper, but may as well stay faithful to their implementation
+
         # if learning the variance, also include the extra weight kl loss
 
         true_mean, _, true_log_variance_clipped = self.q_posterior(x_start = x_start, x_t = x_noisy, t = times)

dalle2_pytorch/optimizer.py

@@ -7,16 +7,17 @@ def separate_weight_decayable_params(params):
 
 def get_optimizer(
     params,
-    lr = 3e-4,
+    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/train.py

@@ -1,6 +1,8 @@
 import time
 import copy
+from math import ceil
 from functools import partial
+from collections.abc import Iterable
 
 import torch
 from torch import nn
@@ -14,6 +16,9 @@ from dalle2_pytorch.optimizer import get_optimizer
 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 +45,46 @@ 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
 
+# 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 split_args_and_kwargs(x, *args, split_size = None, **kwargs):
+    batch_size = len(x)
+    split_size = default(split_size, batch_size)
+    chunk_size = ceil(batch_size / split_size)
+
+    dict_len = len(kwargs)
+    dict_keys = kwargs.keys()
+    all_args = (x, *args, *kwargs.values())
+    len_all_args = len(all_args)
+    split_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,) * chunk_size) 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_index], chunked_all_args[split_index:]
+        chunked_kwargs = dict(tuple(zip(dict_keys, chunked_kwargs_values)))
+        yield chunk_size, (chunked_args, chunked_kwargs)
+
 # print helpers
 
 def print_ribbon(s, symbol = '=', repeat = 40):
@@ -90,7 +135,7 @@ class EMA(nn.Module):
     def __init__(
         self,
         model,
-        beta = 0.99,
+        beta = 0.9999,
         update_after_step = 1000,
         update_every = 10,
     ):
@@ -147,6 +192,7 @@ 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 +219,7 @@ class DiffusionPriorTrainer(nn.Module):
             diffusion_prior.parameters(),
             lr = lr,
             wd = wd,
+            eps = eps,
             **kwargs
         )
 
@@ -206,13 +253,25 @@ class DiffusionPriorTrainer(nn.Module):
 
     def forward(
         self,
+        x,
         *args,
-        divisor = 1,
+        max_batch_size = None,
         **kwargs
     ):
-        with autocast(enabled = self.amp):
+        batch_size = x.shape[0]
-            loss = self.diffusion_prior(*args, **kwargs)
+        total_samples = 0
-        return self.scaler.scale(loss / divisor)
+        total_loss = 0.
+
+        for chunk_size, (chunked_args, chunked_kwargs) in split_args_and_kwargs(x, *args, split_size = max_batch_size, **kwargs):
+            with autocast(enabled = self.amp):
+                loss = self.diffusion_prior(*chunked_args, **chunked_kwargs)
+
+                total_loss += loss.item() * chunk_size
+                total_samples += chunk_size
+
+                self.scaler.scale(loss * (chunk_size / batch_size)).backward()
+
+        return total_loss / total_samples
 
 # decoder trainer
 
@@ -221,8 +280,9 @@ class DecoderTrainer(nn.Module):
         self,
         decoder,
         use_ema = True,
-        lr = 3e-4,
+        lr = 2e-5,
         wd = 1e-2,
+        eps = 1e-8,
         max_grad_norm = None,
         amp = False,
         **kwargs
@@ -247,13 +307,14 @@ 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
             )
 
@@ -321,9 +382,20 @@ class DecoderTrainer(nn.Module):
         x,
         *,
         unet_number,
-        divisor = 1,
+        max_batch_size = None,
         **kwargs
     ):
-        with autocast(enabled = self.amp):
+        batch_size = x.shape[0]
-            loss = self.decoder(x, unet_number = unet_number, **kwargs)
+        total_samples = 0
-        return self.scale(loss / divisor, unet_number = unet_number)
+        total_loss = 0.
+
+        for chunk_size, (chunked_args, chunked_kwargs) in split_args_and_kwargs(x, split_size = max_batch_size, **kwargs):
+            with autocast(enabled = self.amp):
+                loss = self.decoder(*chunked_args, unet_number = unet_number, **chunked_kwargs)
+
+                total_loss += loss.item() * chunk_size
+                total_samples += chunk_size
+
+                self.scale(loss * (chunk_size / batch_size), unet_number = unet_number).backward()
+
+        return total_loss / total_samples

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.14',
+  version = '0.2.26',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',