clamp tokens to appropriate bounds

cog.yaml

@@ -6,7 +6,7 @@ build:
     - "libgl1-mesa-glx"
     - "libglib2.0-0"
   python_packages:
-    - "min-dalle==0.3.3"
+    - "min-dalle==0.3.4"
   run:
     - pip install torch==1.11.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
 

min_dalle/models/dalle_bart_decoder.py

@@ -117,6 +117,7 @@ class DalleBartDecoder(nn.Module):
         super().__init__()
         self.layer_count = layer_count
         self.embed_count = embed_count
+        self.image_vocab_count = image_vocab_count
         self.embed_tokens = nn.Embedding(image_vocab_count + 1, embed_count)
         self.embed_positions = nn.Embedding(IMAGE_TOKEN_COUNT, embed_count)
         self.layers: List[DecoderLayer] = nn.ModuleList([
@@ -152,6 +153,7 @@ class DalleBartDecoder(nn.Module):
         image_count = encoder_state.shape[0] // 2
         token_index_batched = token_index[[0] * image_count * 2]
         prev_tokens = prev_tokens[list(range(image_count)) * 2]
+        prev_tokens = prev_tokens.clamp(0, self.image_vocab_count)
         decoder_state = self.embed_tokens.forward(prev_tokens)
         decoder_state += self.embed_positions.forward(token_index_batched)
         decoder_state = self.layernorm_embedding.forward(decoder_state)

min_dalle/models/dalle_bart_encoder.py

@@ -119,6 +119,7 @@ class DalleBartEncoder(nn.Module):
         glu_embed_count: int
     ):
         super().__init__()
+        self.text_vocab_count = text_vocab_count
         self.embed_tokens = nn.Embedding(text_vocab_count, embed_count)
         self.embed_positions = nn.Embedding(text_token_count, embed_count)
         self.layers: List[EncoderLayer] = nn.ModuleList([
@@ -138,6 +139,7 @@ class DalleBartEncoder(nn.Module):
     def forward(self, text_tokens: LongTensor) -> FloatTensor:
         attention_mask = text_tokens.not_equal(1)
         pose_tokens = self.token_indices[None][[0] * text_tokens.shape[0]]
+        text_tokens = text_tokens.clamp(0, self.text_vocab_count - 1)
         encoder_state = (
             self.embed_tokens.forward(text_tokens) +
             self.embed_positions.forward(pose_tokens)

min_dalle/models/vqgan_detokenizer.py

@@ -1,5 +1,5 @@
 import torch
-from torch import FloatTensor
+from torch import FloatTensor, LongTensor
 from torch.nn import Module, ModuleList, GroupNorm, Conv2d, Embedding
 torch.set_grad_enabled(False)
 
@@ -160,12 +160,14 @@ class Decoder(Module):
 class VQGanDetokenizer(Module):
     def __init__(self):
         super().__init__()
-        m, n = 2 ** 14, 2 ** 8
-        self.embedding = Embedding(m, n)
-        self.post_quant_conv = Conv2d(n, n, 1)
+        vocab_count, embed_count = 2 ** 14, 2 ** 8
+        self.vocab_count = vocab_count
+        self.embedding = Embedding(vocab_count, embed_count)
+        self.post_quant_conv = Conv2d(embed_count, embed_count, 1)
         self.decoder = Decoder()
 
-    def forward(self, z: FloatTensor) -> FloatTensor:
+    def forward(self, z: LongTensor) -> FloatTensor:
+        z = z.clamp(0, self.vocab_count - 1)
         z = self.embedding.forward(z)
         z = z.view((z.shape[0], 2 ** 4, 2 ** 4, 2 ** 8))
         z = z.permute(0, 3, 1, 2).contiguous()

setup.py

@@ -5,7 +5,7 @@ setuptools.setup(
     name='min-dalle',
     description = 'min(DALL·E)',
     # long_description=(Path(__file__).parent / "README.rst").read_text(),
-    version='0.3.3',
+    version='0.3.4',
     author='Brett Kuprel',
     author_email='brkuprel@gmail.com',
     url='https://github.com/kuprel/min-dalle',