Add truncate option to tokenize (#126)

* Add truncate_text option to tokenize

This makes it possible to run tokenize on texts that are longer than the number of tokens
that fit the context length without having to try to guess how to cut in number of 
characters beforehand

* add doc, rename to just "truncate", use eot_token

Co-authored-by: Jong Wook Kim <[email protected]>

clip/clip.py

@@ -180,7 +180,7 @@ def patch_float(module):
     return model, _transform(model.input_resolution.item())
 
 
-def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
+def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> torch.LongTensor:
     """
     Returns the tokenized representation of given input string(s)
 
@@ -192,6 +192,9 @@ def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.Lo
     context_length : int
         The context length to use; all CLIP models use 77 as the context length
 
+    truncate: bool
+        Whether to truncate the text in case its encoding is longer than the context length
+
     Returns
     -------
     A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
@@ -206,7 +209,11 @@ def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.Lo
 
     for i, tokens in enumerate(all_tokens):
         if len(tokens) > context_length:
-            raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
+            if truncate:
+                tokens = tokens[:context_length]
+                tokens[-1] = eot_token
+            else:
+                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
         result[i, :len(tokens)] = torch.tensor(tokens)
 
     return result