bring in the simple tokenizer released by openai, but also plan on le…

…aving room for custom tokenizer with yttm

MANIFEST.in

@@ -0,0 +1 @@
+recursive-include dalle2_pytorch *.txt

dalle2_pytorch/dalle2_pytorch.py

@@ -15,6 +15,15 @@ def exists(val):
 def default(val, d):
     return val if exists(val) else d
 
+def eval_decorator(fn):
+    def inner(model, *args, **kwargs):
+        was_training = model.training
+        model.eval()
+        out = fn(model, *args, **kwargs)
+        model.train(was_training)
+        return out
+    return inner
+
 # for controlling freezing of CLIP
 
 def set_module_requires_grad_(module, requires_grad):
@@ -30,24 +39,61 @@ def unfreeze_all_layers_(module):
 # diffusion prior
 
 class DiffusionPrior(nn.Module):
-    def __init__(self):
+    def __init__(
+        self,
+        *,
+        clip
+    ):
         super().__init__()
-    def forward(self, x):
-        return x
+        assert isinstance(clip, CLIP)
+
+    def forward(
+        self,
+        *,
+        text,
+        image
+    ):
+        return text
 
 # decoder
 
 class Decoder(nn.Module):
-    def __init__(self):
+    def __init__(
+        self,
+        *,
+        clip,
+        prior
+    ):
         super().__init__()
-    def forward(self, x):
-        return x
+        assert isinstance(clip, CLIP)
+        assert isinstance(prior, DiffusionPrior)
+
+    def forward(
+        self,
+        *,
+        image
+    ):
+        return image
 
 # main class
 
 class DALLE2(nn.Module):
-    def __init__(self):
+    def __init__(
+        self,
+        *,
+        clip,
+        prior,
+        decoder
+    ):
         super().__init__()
-
-    def forward(self, x):
-        return x
+        assert isinstance(clip), CLIP
+        assert isinstance(prior), DiffusionPrior
+        assert isinstance(decoder), Decoder
+
+    @torch.no_grad()
+    def forward(
+        self,
+        *,
+        text
+    ):
+        return text