bring in two tricks from the cogview paper for reducing the chances of overflow, for attention and layernorm

README.md

@@ -20,18 +20,20 @@ As of 5/23/22, it is no longer SOTA. SOTA will be <a href="https://github.com/lu
 
 - Decoder is now verified working for unconditional generation on my experimental setup for Oxford flowers. 2 researchers have also confirmed Decoder is working for them.
 
-<img src="./samples/oxford.png" width="600px" />
+<img src="./samples/oxford.png" width="450px" />
 
 *ongoing at 21k steps*
 
 - <a href="https://twitter.com/Buntworthy/status/1529475416775434240?t=0GEge3Kr9I36cjcUVCQUTg">Justin Pinkney</a> successfully trained the diffusion prior in the repository for his CLIP to Stylegan2 text-to-image application
 
+- <a href="https://github.com/rom1504">Romain</a> has scaled up training to 800 GPUs with the available scripts without any issues
+
 ## Pre-Trained Models
 
 - LAION is training prior models. Checkpoints are available on <a href="https://huggingface.co/zenglishuci/conditioned-prior">🤗huggingface</a> and the training statistics are available on <a href="https://wandb.ai/nousr_laion/conditioned-prior/reports/LAION-DALLE2-PyTorch-Prior--VmlldzoyMDI2OTIx">🐝WANDB</a>.
 - Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/jkrtg0so?workspace=user-veldrovive">In-progress test run</a> 🚧
 - Decoder - <a href="https://wandb.ai/veldrovive/dalle2_train_decoder/runs/3d5rytsa?workspace=">Another test run with sparse attention</a>
-- DALL-E 2 🚧
+- DALL-E 2 🚧 - <a href="https://github.com/LAION-AI/dalle2-laion">DALL-E 2 Laion repository</a>
 
 ## Appreciation
 

dalle2_pytorch/dalle2_pytorch.py

@@ -490,14 +490,15 @@ class NoiseScheduler(nn.Module):
 # diffusion prior
 
 class LayerNorm(nn.Module):
-    def __init__(self, dim):
+    def __init__(self, dim, eps = 1e-5):
         super().__init__()
-        self.gamma = nn.Parameter(torch.ones(dim))
-        self.register_buffer("beta", torch.zeros(dim))
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
 
     def forward(self, x):
-        return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
-
+        var = torch.var(x, dim = -1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = -1, keepdim = True)
+        return (x - mean) * (var + self.eps).rsqrt() * self.g
 
 class ChanLayerNorm(nn.Module):
     def __init__(self, dim, eps = 1e-5):
@@ -508,8 +509,7 @@ class ChanLayerNorm(nn.Module):
     def forward(self, x):
         var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (var + self.eps).sqrt() * self.g
-
+        return (x - mean) * (var + self.eps).rsqrt() * self.g
 
 class Residual(nn.Module):
     def __init__(self, fn):
@@ -629,10 +629,13 @@ class Attention(nn.Module):
         heads = 8,
         dropout = 0.,
         causal = False,
-        rotary_emb = None
+        rotary_emb = None,
+        pb_relax_alpha = 32 ** 2
     ):
         super().__init__()
-        self.scale = dim_head ** -0.5
+        self.pb_relax_alpha = pb_relax_alpha
+        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
+
         self.heads = heads
         inner_dim = dim_head * heads
 
@@ -696,6 +699,9 @@ class Attention(nn.Module):
 
         # attention
 
+        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        sim = sim * self.pb_relax_alpha
+
         attn = sim.softmax(dim = -1, dtype = torch.float32)
         attn = self.dropout(attn)
 
@@ -1210,10 +1216,12 @@ class CrossAttention(nn.Module):
         dim_head = 64,
         heads = 8,
         dropout = 0.,
-        norm_context = False
+        norm_context = False,
+        pb_relax_alpha = 32 ** 2
     ):
         super().__init__()
-        self.scale = dim_head ** -0.5
+        self.pb_relax_alpha = pb_relax_alpha
+        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
         self.heads = heads
         inner_dim = dim_head * heads
 
@@ -1259,6 +1267,9 @@ class CrossAttention(nn.Module):
             mask = rearrange(mask, 'b j -> b 1 1 j')
             sim = sim.masked_fill(~mask, max_neg_value)
 
+        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        sim = sim * self.pb_relax_alpha
+
         attn = sim.softmax(dim = -1, dtype = torch.float32)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)

dalle2_pytorch/train_configs.py

@@ -346,17 +346,17 @@ class TrainDecoderConfig(BaseModel):
         img_emb_url = data_config.img_embeddings_url
         text_emb_url = data_config.text_embeddings_url
 
-        if using_text_encodings:
+        if using_text_embeddings:
             # Then we need some way to get the embeddings
             assert using_clip or exists(text_emb_url), 'If text conditioning, either clip or text_embeddings_url must be provided'
 
         if using_clip:
-            if using_text_encodings:
+            if using_text_embeddings:
                 assert not exists(text_emb_url) or not exists(img_emb_url), 'Loaded clip, but also provided text_embeddings_url and img_embeddings_url. This is redundant. Remove the clip model or the text embeddings'
             else:
                 assert not exists(img_emb_url), 'Loaded clip, but also provided img_embeddings_url. This is redundant. Remove the clip model or the embeddings'
 
         if text_emb_url:
-            assert using_text_encodings, "Text embeddings are being loaded, but text embeddings are not being conditioned on. This will slow down the dataloader for no reason."
+            assert using_text_embeddings, "Text embeddings are being loaded, but text embeddings are not being conditioned on. This will slow down the dataloader for no reason."
 
         return values

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.15.3'
+__version__ = '0.16.1'