Ignoring special tokens + updating 1 test case.

server/tests/models/test_seq2seq_lm.py

@@ -168,7 +168,7 @@ def test_seq2seq_lm_generate_token_completion(
     assert next_batch is None
 
     assert len(generations) == 1
-    assert generations[0].generated_text.text == "a few weeks"
+    assert generations[0].generated_text.text == " a few weeks"
     assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id
     assert generations[0].generated_text.generated_tokens == 7
 
@@ -186,7 +186,7 @@ def test_seq2seq_lm_generate_token_completion_multi(
     assert next_batch is not None
 
     assert len(generations) == 2
-    assert generations[1].generated_text.text == "a few "
+    assert generations[1].generated_text.text == " a few "
     assert (
         generations[1].request_id
         == default_multi_requests_seq2seq_lm_batch.requests[1].id

server/text_generation_server/models/causal_lm.py

@@ -645,6 +645,7 @@ class CausalLM(Model):
                         all_input_ids[:, 0],
                         prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1,
                         read_offset=len(all_input_ids) - stopping_criteria.current_tokens,
+                        skip_special_tokens=True
                     )
                     # Get seed
                     if isinstance(next_token_chooser.choice, Sampling):

server/text_generation_server/models/flash_causal_lm.py

@@ -793,11 +793,6 @@ class FlashCausalLM(Model):
 
         return int(num_blocks * BLOCK_SIZE)
 
-    def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
-        return self.tokenizer.decode(
-            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-        )
-
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -1012,6 +1007,7 @@ class FlashCausalLM(Model):
                         all_input_ids,
                         prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1,
                         read_offset=len(all_input_ids) - stopping_criteria.current_tokens,
+                        skip_special_tokens=True
                     )
                     generated_text = GeneratedText(
                         output_text,

server/text_generation_server/models/idefics_causal_lm.py

@@ -727,6 +727,7 @@ class IdeficsCausalLM(Model):
                         all_input_ids[:, 0],
                         prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1,
                         read_offset=len(all_input_ids) - stopping_criteria.current_tokens,
+                        skip_special_tokens=True
                     )
                     # Get seed
                     if isinstance(next_token_chooser.choice, Sampling):

server/text_generation_server/models/model.py

@@ -64,16 +64,17 @@ class Model(ABC):
         all_input_ids: List[int],
         prefix_offset: int = 0,
         read_offset: int = 0,
+        skip_special_tokens: bool = False,
     ) -> Tuple[str, int, int]:
         """Hack to hopefully support generate_stream for the maximum number of tokenizers"""
 
         # The prefix text is necessary only to defeat cleanup algorithms in the decode
         # which decide to add a space or not depending on the surrounding ids.
         prefix_text = self.tokenizer.decode(
-            all_input_ids[prefix_offset:read_offset], skip_special_tokens=False
+            all_input_ids[prefix_offset:read_offset], skip_special_tokens=skip_special_tokens
         )
         new_text = self.tokenizer.decode(
-            all_input_ids[prefix_offset:], skip_special_tokens=False
+            all_input_ids[prefix_offset:], skip_special_tokens=skip_special_tokens
         )
 
         if len(new_text) > len(prefix_text) and not new_text.endswith("<EFBFBD>"):

server/text_generation_server/models/seq2seq_lm.py

@@ -714,6 +714,7 @@ class Seq2SeqLM(Model):
                         all_decoder_input_ids,
                         prefix_offset=len(all_decoder_input_ids) - decoder_input_length - 1,
                         read_offset=len(all_decoder_input_ids) - decoder_input_length,
+                        skip_special_tokens=True
                     )
 
                     # Get seed