bet on the new self-conditioning technique out of geoffrey hintons group

README.md

@@ -1253,4 +1253,15 @@ For detailed information on training the diffusion prior, please refer to the [d
 }
 ```
 
+```bibtex
+@misc{chen2022analog,
+    title   = {Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning},
+    author  = {Ting Chen and Ruixiang Zhang and Geoffrey Hinton},
+    year    = {2022},
+    eprint  = {2208.04202},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
 *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

@@ -8,6 +8,7 @@ from pathlib import Path
 
 import torch
 import torch.nn.functional as F
+from torch.utils.checkpoint import checkpoint
 from torch import nn, einsum
 import torchvision.transforms as T
 
@@ -108,6 +109,28 @@ def pad_tuple_to_length(t, length, fillvalue = None):
         return t
     return (*t, *((fillvalue,) * remain_length))
 
+# checkpointing helper function
+
+def make_checkpointable(fn, **kwargs):
+    if isinstance(fn, nn.ModuleList):
+        return [maybe(make_checkpointable)(el, **kwargs) for el in fn]
+
+    condition = kwargs.pop('condition', None)
+
+    if exists(condition) and not condition(fn):
+        return fn
+
+    @wraps(fn)
+    def inner(*args):
+        input_needs_grad = any([isinstance(el, torch.Tensor) and el.requires_grad for el in args])
+
+        if not input_needs_grad:
+            return fn(*args)
+
+        return checkpoint(fn, *args)
+
+    return inner
+
 # for controlling freezing of CLIP
 
 def set_module_requires_grad_(module, requires_grad):
@@ -847,7 +870,7 @@ class Attention(nn.Module):
 
         # attention
 
-        attn = sim.softmax(dim = -1)
+        attn = sim.softmax(dim = -1, dtype = torch.float32)
         attn = self.dropout(attn)
 
         # aggregate values
@@ -1134,17 +1157,17 @@ class DiffusionPrior(nn.Module):
         pred = self.net.forward_with_cond_scale(x, t, cond_scale = cond_scale, **text_cond)
 
         if self.predict_x_start:
-            x_recon = pred
+            x_start = pred
         else:
-            x_recon = self.noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
+            x_start = self.noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
 
         if clip_denoised and not self.predict_x_start:
-            x_recon.clamp_(-1., 1.)
+            x_start.clamp_(-1., 1.)
 
         if self.predict_x_start and self.sampling_clamp_l2norm:
-            x_recon = l2norm(x_recon) * self.image_embed_scale
+            x_start = l2norm(x_start) * self.image_embed_scale
 
-        model_mean, posterior_variance, posterior_log_variance = self.noise_scheduler.q_posterior(x_start=x_recon, x_t=x, t=t)
+        model_mean, posterior_variance, posterior_log_variance = self.noise_scheduler.q_posterior(x_start=x_start, x_t=x, t=t)
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
@@ -1548,7 +1571,7 @@ class CrossAttention(nn.Module):
             mask = rearrange(mask, 'b j -> b 1 1 j')
             sim = sim.masked_fill(~mask, max_neg_value)
 
-        attn = sim.softmax(dim = -1)
+        attn = sim.softmax(dim = -1, dtype = torch.float32)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')
@@ -1677,6 +1700,7 @@ class Unet(nn.Module):
         attn_heads = 16,
         lowres_cond = False,             # for cascading diffusion - https://cascaded-diffusion.github.io/
         lowres_noise_cond = False,       # for conditioning on low resolution noising, based on Imagen
+        self_cond = False,
         sparse_attn = False,
         cosine_sim_cross_attn = False,
         cosine_sim_self_attn = False,
@@ -1698,6 +1722,7 @@ class Unet(nn.Module):
         pixel_shuffle_upsample = True,
         final_conv_kernel_size = 1,
         combine_upsample_fmaps = False, # whether to combine the outputs of all upsample blocks, as in unet squared paper
+        checkpoint_during_training = False,
         **kwargs
     ):
         super().__init__()
@@ -1711,12 +1736,21 @@ class Unet(nn.Module):
 
         self.lowres_cond = lowres_cond
 
+        # whether to do self conditioning
+
+        self.self_cond = self_cond
+
         # determine dimensions
 
         self.channels = channels
         self.channels_out = default(channels_out, 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
+         # initial number of channels depends on
+         # (1) low resolution conditioning from cascading ddpm paper, conditioned on previous unet output in the cascade
+         # (2) self conditioning (bit diffusion paper)
+
+        init_channels = channels * (1 + int(lowres_cond) + int(self_cond))
+
         init_dim = default(init_dim, dim)
 
         self.init_conv = CrossEmbedLayer(init_channels, dim_out = init_dim, kernel_sizes = init_cross_embed_kernel_sizes, stride = 1) if init_cross_embed else nn.Conv2d(init_channels, init_dim, init_conv_kernel_size, padding = init_conv_kernel_size // 2)
@@ -1908,6 +1942,10 @@ class Unet(nn.Module):
 
         zero_init_(self.to_out) # since both OpenAI and @crowsonkb are doing it
 
+        # whether to checkpoint during training
+
+        self.checkpoint_during_training = checkpoint_during_training
+
     # if the current settings for the unet are not correct
     # for cascading DDPM, then reinit the unet with the right settings
     def cast_model_parameters(
@@ -1965,7 +2003,9 @@ class Unet(nn.Module):
         image_cond_drop_prob = 0.,
         text_cond_drop_prob = 0.,
         blur_sigma = None,
-        blur_kernel_size = None
+        blur_kernel_size = None,
+        disable_checkpoint = False,
+        self_cond = None
     ):
         batch_size, device = x.shape[0], x.device
 
@@ -1973,6 +2013,14 @@ class Unet(nn.Module):
 
         assert not (self.lowres_cond and not exists(lowres_cond_img)), 'low resolution conditioning image must be present'
 
+        # concat self conditioning, if needed
+
+        if self.self_cond:
+            self_cond = default(self_cond, lambda: torch.zeros_like(x))
+            x = torch.cat((x, self_cond), dim = 1)
+
+        # concat low resolution conditioning
+
         if exists(lowres_cond_img):
             x = torch.cat((x, lowres_cond_img), dim = 1)
 
@@ -2087,17 +2135,29 @@ class Unet(nn.Module):
         c = self.norm_cond(c)
         mid_c = self.norm_mid_cond(mid_c)
 
+        # gradient checkpointing
+
+        can_checkpoint = self.training and self.checkpoint_during_training and not disable_checkpoint
+        apply_checkpoint_fn = make_checkpointable if can_checkpoint else identity
+
+        # make checkpointable modules
+
+        init_resnet_block, mid_block1, mid_attn, mid_block2, final_resnet_block = [maybe(apply_checkpoint_fn)(module) for module in (self.init_resnet_block, self.mid_block1, self.mid_attn, self.mid_block2, self.final_resnet_block)]
+
+        can_checkpoint_cond = lambda m: isinstance(m, ResnetBlock)
+        downs, ups = [maybe(apply_checkpoint_fn)(m, condition = can_checkpoint_cond) for m in (self.downs, self.ups)]
+
         # initial resnet block
 
-        if exists(self.init_resnet_block):
-            x = self.init_resnet_block(x, t)
+        if exists(init_resnet_block):
+            x = init_resnet_block(x, t)
 
         # go through the layers of the unet, down and up
 
         down_hiddens = []
         up_hiddens = []
 
-        for pre_downsample, init_block, resnet_blocks, attn, post_downsample in self.downs:
+        for pre_downsample, init_block, resnet_blocks, attn, post_downsample in downs:
             if exists(pre_downsample):
                 x = pre_downsample(x)
 
@@ -2113,16 +2173,16 @@ class Unet(nn.Module):
             if exists(post_downsample):
                 x = post_downsample(x)
 
-        x = self.mid_block1(x, t, mid_c)
+        x = mid_block1(x, t, mid_c)
 
-        if exists(self.mid_attn):
-            x = self.mid_attn(x)
+        if exists(mid_attn):
+            x = mid_attn(x)
 
-        x = self.mid_block2(x, t, mid_c)
+        x = mid_block2(x, t, mid_c)
 
         connect_skip = lambda fmap: torch.cat((fmap, down_hiddens.pop() * self.skip_connect_scale), dim = 1)
 
-        for init_block, resnet_blocks, attn, upsample in self.ups:
+        for init_block, resnet_blocks, attn, upsample in ups:
             x = connect_skip(x)
             x = init_block(x, t, c)
 
@@ -2139,7 +2199,7 @@ class Unet(nn.Module):
 
         x = torch.cat((x, r), dim = 1)
 
-        x = self.final_resnet_block(x, t)
+        x = final_resnet_block(x, t)
 
         if exists(lowres_cond_img):
             x = torch.cat((x, lowres_cond_img), dim = 1)
@@ -2530,23 +2590,23 @@ class Decoder(nn.Module):
         x = x.clamp(-s, s) / s
         return x
 
-    def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, lowres_cond_img = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None, lowres_noise_level = None):
+    def p_mean_variance(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, lowres_cond_img = None, self_cond = None, clip_denoised = True, predict_x_start = False, learned_variance = False, cond_scale = 1., model_output = None, lowres_noise_level = 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, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, lowres_noise_level = lowres_noise_level))
+        pred = default(model_output, lambda: unet.forward_with_cond_scale(x, t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, self_cond = self_cond, lowres_noise_level = lowres_noise_level))
 
         if learned_variance:
             pred, var_interp_frac_unnormalized = pred.chunk(2, dim = 1)
 
         if predict_x_start:
-            x_recon = pred
+            x_start = pred
         else:
-            x_recon = noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
+            x_start = noise_scheduler.predict_start_from_noise(x, t = t, noise = pred)
 
         if clip_denoised:
-            x_recon = self.dynamic_threshold(x_recon)
+            x_start = self.dynamic_threshold(x_start)
 
-        model_mean, posterior_variance, posterior_log_variance = noise_scheduler.q_posterior(x_start=x_recon, x_t=x, t=t)
+        model_mean, posterior_variance, posterior_log_variance = noise_scheduler.q_posterior(x_start=x_start, x_t=x, t=t)
 
         if learned_variance:
             # if learned variance, posterio variance and posterior log variance are predicted by the network
@@ -2562,16 +2622,17 @@ class Decoder(nn.Module):
             posterior_log_variance = var_interp_frac * max_log + (1 - var_interp_frac) * min_log
             posterior_variance = posterior_log_variance.exp()
 
-        return model_mean, posterior_variance, posterior_log_variance
+        return model_mean, posterior_variance, posterior_log_variance, x_start
 
     @torch.no_grad()
-    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, cond_scale = 1., lowres_cond_img = None, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_noise_level = None):
+    def p_sample(self, unet, x, t, image_embed, noise_scheduler, text_encodings = None, cond_scale = 1., lowres_cond_img = None, self_cond = None, predict_x_start = False, learned_variance = False, clip_denoised = True, lowres_noise_level = None):
         b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, clip_denoised = clip_denoised, predict_x_start = predict_x_start, noise_scheduler = noise_scheduler, learned_variance = learned_variance, lowres_noise_level = lowres_noise_level)
+        model_mean, _, model_log_variance, x_start = self.p_mean_variance(unet, x = x, t = t, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, self_cond = self_cond, clip_denoised = clip_denoised, predict_x_start = predict_x_start, noise_scheduler = noise_scheduler, learned_variance = learned_variance, lowres_noise_level = lowres_noise_level)
         noise = torch.randn_like(x)
         # 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
+        pred = model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+        return pred, x_start
 
     @torch.no_grad()
     def p_sample_loop_ddpm(
@@ -2597,6 +2658,8 @@ class Decoder(nn.Module):
         b = shape[0]
         img = torch.randn(shape, device = device)
 
+        x_start = None # for self-conditioning
+
         is_inpaint = exists(inpaint_image)
         resample_times = inpaint_resample_times if is_inpaint else 1
 
@@ -2624,13 +2687,16 @@ class Decoder(nn.Module):
                     noised_inpaint_image = noise_scheduler.q_sample(inpaint_image, t = times)
                     img = (img * ~inpaint_mask) + (noised_inpaint_image * inpaint_mask)
 
-                img = self.p_sample(
+                self_cond = x_start if unet.self_cond else None
+
+                img, x_start = self.p_sample(
                     unet,
                     img,
                     times,
                     image_embed = image_embed,
                     text_encodings = text_encodings,
                     cond_scale = cond_scale,
+                    self_cond = self_cond,
                     lowres_cond_img = lowres_cond_img,
                     lowres_noise_level = lowres_noise_level,
                     predict_x_start = predict_x_start,
@@ -2689,6 +2755,8 @@ class Decoder(nn.Module):
 
         img = torch.randn(shape, device = device)
 
+        x_start = None # for self-conditioning
+
         if not is_latent_diffusion:
             lowres_cond_img = maybe(self.normalize_img)(lowres_cond_img)
 
@@ -2709,7 +2777,9 @@ class Decoder(nn.Module):
                     noised_inpaint_image = noise_scheduler.q_sample(inpaint_image, t = time_cond)
                     img = (img * ~inpaint_mask) + (noised_inpaint_image * inpaint_mask)
 
-                pred = unet.forward_with_cond_scale(img, time_cond, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, lowres_cond_img = lowres_cond_img, lowres_noise_level = lowres_noise_level)
+                self_cond = x_start if unet.self_cond else None
+
+                pred = unet.forward_with_cond_scale(img, time_cond, image_embed = image_embed, text_encodings = text_encodings, cond_scale = cond_scale, self_cond = self_cond, lowres_cond_img = lowres_cond_img, lowres_noise_level = lowres_noise_level)
 
                 if learned_variance:
                     pred, _ = pred.chunk(2, dim = 1)
@@ -2769,13 +2839,35 @@ class Decoder(nn.Module):
 
         x_noisy = noise_scheduler.q_sample(x_start = x_start, t = times, noise = noise)
 
-        model_output = unet(
-            x_noisy,
-            times,
+        # unet kwargs
+
+        unet_kwargs = dict(
             image_embed = image_embed,
             text_encodings = text_encodings,
             lowres_cond_img = lowres_cond_img,
             lowres_noise_level = lowres_noise_level,
+        )
+
+        # self conditioning
+
+        self_cond = None
+
+        if unet.self_cond and random.random() < 0.5:
+            with torch.no_grad():
+                self_cond = unet(x_noisy, times, **unet_kwargs)
+
+                if learned_variance:
+                    self_cond, _ = self_cond.chunk(2, dim = 1)
+
+                self_cond = self_cond.detach()
+
+        # forward to get model prediction
+
+        model_output = unet(
+            x_noisy,
+            times,
+            **unet_kwargs,
+            self_cond = self_cond,
             image_cond_drop_prob = self.image_cond_drop_prob,
             text_cond_drop_prob = self.text_cond_drop_prob,
         )
@@ -2806,7 +2898,7 @@ class Decoder(nn.Module):
         # if learning the variance, also include the extra weight kl loss
 
         true_mean, _, true_log_variance_clipped = noise_scheduler.q_posterior(x_start = x_start, x_t = x_noisy, t = times)
-        model_mean, _, model_log_variance = self.p_mean_variance(unet, x = x_noisy, t = times, image_embed = image_embed, noise_scheduler = noise_scheduler, clip_denoised = clip_denoised, learned_variance = True, model_output = model_output)
+        model_mean, _, model_log_variance, _ = self.p_mean_variance(unet, x = x_noisy, t = times, image_embed = image_embed, noise_scheduler = noise_scheduler, clip_denoised = clip_denoised, learned_variance = True, model_output = model_output)
 
         # kl loss with detached model predicted mean, for stability reasons as in paper
 

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.4.6'
+__version__ = '1.6.0'