[V1][Perf] Faster incremental detokenization (#15137)

Signed-off-by: Nick Hill <nhill@redhat.com>

requirements/common.txt

@@ -8,7 +8,7 @@ blake3
 py-cpuinfo
 transformers >= 4.51.1
 huggingface-hub[hf_xet] >= 0.30.0  # Required for Xet downloads.
-tokenizers >= 0.19.1  # Required for Llama 3.
+tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 protobuf # Required by LlamaTokenizer.
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
 aiohttp

requirements/test.in

@@ -35,6 +35,7 @@ opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]==0.4.8 # required for model evaluation test
 transformers==4.51.1
+tokenizers==0.21.1
 huggingface-hub[hf_xet]>=0.30.0  # Required for Xet downloads.
 # quantization
 bitsandbytes>=0.45.3

requirements/test.txt

@@ -624,8 +624,10 @@ tiktoken==0.7.0
     #   mistral-common
 timm==1.0.11
     # via -r requirements/test.in
-tokenizers==0.21.0
-    # via transformers
+tokenizers==0.21.1
+    # via
+    #   -r requirements/test.in
+    #   transformers
 torch==2.6.0
     # via
     #   -r requirements/test.in

tests/lora/test_llama_tp.py

@@ -47,6 +47,7 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     ]
     sampling_params = vllm.SamplingParams(temperature=0,
                                           max_tokens=256,
+                                          skip_special_tokens=False,
                                           stop=["[/assistant]"])
     outputs = llm.generate(
         prompts,

tests/tokenization/test_detokenize.py

@@ -4,14 +4,22 @@ from collections.abc import Generator
 from typing import Any, Optional
 
 import pytest
-from transformers import AutoTokenizer
+from transformers import (AutoTokenizer, PreTrainedTokenizer,
+                          PreTrainedTokenizerFast)
 
 from vllm.inputs import token_inputs
 from vllm.sequence import Logprob, SamplingParams, Sequence, SequenceGroup
-from vllm.transformers_utils.detokenizer import (Detokenizer,
-                                                 detokenize_incrementally)
+from vllm.transformers_utils.detokenizer import Detokenizer
 from vllm.transformers_utils.tokenizer_group import get_tokenizer_group
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.engine.detokenizer import (FastIncrementalDetokenizer,
+                                        IncrementalDetokenizer,
+                                        SlowIncrementalDetokenizer)
+
+SPECIAL_TOKS_TRUTH = [
+    "Some text with adjacent special tokens                <|padding|><|padding|><fim_prefix><fim_middle><fim_suffix>other text<fim_pad>",  # noqa
+]
 
 TRUTH = [
     "Hello here, this is a simple test",
@@ -22,7 +30,8 @@ TRUTH = [
     # incomplete UTF-8 characters
     # see https://github.com/vllm-project/vllm/pull/9625
     "ပုံပြင်လေးပြောပြပါ်",
-]
+] + SPECIAL_TOKS_TRUTH
+
 TOKENIZERS = [
     "facebook/opt-125m",
     "gpt2",
@@ -38,26 +47,37 @@ TOKENIZERS = [
 ]
 
 
-def _run_incremental_decode(tokenizer, all_input_ids,
-                            skip_special_tokens: bool, starting_index: int):
-    decoded_text = ""
-    offset = 0
-    token_offset = 0
-    prev_tokens = None
-    for i in range(starting_index, len(all_input_ids)):
-        new_tokens, text, offset, token_offset = detokenize_incrementally(
-            tokenizer,
-            all_input_ids[:i + 1],
-            prev_tokens,
-            offset,
-            token_offset,
-            skip_special_tokens=skip_special_tokens)
-        decoded_text += text
-        if prev_tokens is None:
-            prev_tokens = new_tokens
-        else:
-            prev_tokens += new_tokens
-    return decoded_text
+def _run_incremental_decode(tokenizer,
+                            all_input_ids,
+                            skip_special_tokens: bool,
+                            starting_index: int,
+                            spaces_between_special_tokens: bool = True,
+                            fast: Optional[bool] = None):
+
+    prompt_token_ids = all_input_ids[:starting_index]
+
+    params = SamplingParams(
+        skip_special_tokens=skip_special_tokens,
+        spaces_between_special_tokens=spaces_between_special_tokens,
+    )
+    request = EngineCoreRequest("", "", prompt_token_ids, None, None, None,
+                                params, None, 0.0, None)
+
+    if fast is None:
+        detokenizer = IncrementalDetokenizer.from_new_request(
+            tokenizer, request)
+    elif fast:
+        detokenizer = FastIncrementalDetokenizer(tokenizer, request)
+    else:
+        detokenizer = SlowIncrementalDetokenizer(tokenizer, request)
+
+    output_text = ""
+    for i, token_id in enumerate(all_input_ids[starting_index:]):
+        detokenizer.update([token_id], False)
+        finished = i == len(all_input_ids) - 1
+        output_text += detokenizer.get_next_output_text(finished, delta=True)
+
+    return output_text, detokenizer.output_token_ids
 
 
 @pytest.fixture
@@ -85,11 +105,13 @@ def test_mistral_edge_case(tokenizer, truth):
     starting_index = 0
     all_input_ids = tokenizer(truth, add_special_tokens=False).input_ids
 
-    decoded_text = _run_incremental_decode(tokenizer,
-                                           all_input_ids,
-                                           skip_special_tokens=True,
-                                           starting_index=starting_index)
+    decoded_text, out_ids = _run_incremental_decode(
+        tokenizer,
+        all_input_ids,
+        skip_special_tokens=True,
+        starting_index=starting_index)
     assert decoded_text == truth
+    assert out_ids == all_input_ids[starting_index:]
 
 
 @pytest.fixture
@@ -106,40 +128,86 @@ def skip_special_tokens(request, tokenizer_name) -> Generator[bool, Any, None]:
 @pytest.mark.parametrize("with_prompt", [True, False])
 @pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
 @pytest.mark.parametrize("skip_special_tokens", (True, False), indirect=True)
-def test_decode_streaming(tokenizer, truth, with_prompt, skip_special_tokens):
+@pytest.mark.parametrize("spaces_between_special_tokens", (True, False))
+@pytest.mark.parametrize("fast", (True, False))
+def test_decode_streaming(tokenizer, truth, with_prompt, skip_special_tokens,
+                          spaces_between_special_tokens, fast):
+    if fast and not isinstance(tokenizer, PreTrainedTokenizerFast):
+        pytest.skip()
+
+    if skip_special_tokens and not spaces_between_special_tokens:
+        pytest.skip()
+
+    if not fast and isinstance(tokenizer, PreTrainedTokenizerFast):
+        # Fix up inconsistency in fast/slow tokenizer behaviour.
+        tokenizer.add_special_tokens({
+            "additional_special_tokens": [
+                at for at in
+                tokenizer._tokenizer.get_added_tokens_decoder().values()
+                if at.special
+            ]
+        })
+
+    extra_decode_args = {} if not isinstance(tokenizer,  PreTrainedTokenizer) \
+        else {"spaces_between_special_tokens": spaces_between_special_tokens}
+
+    truth_tokens = tokenizer(truth, add_special_tokens=False).input_ids
+    if tokenizer.bos_token_id is not None:
+        truth_tokens.insert(0, tokenizer.bos_token_id)
+    truth_tokens.append(tokenizer.eos_token_id)
+
+    new_truth = tokenizer.decode(truth_tokens,
+                                 skip_special_tokens=skip_special_tokens,
+                                 **extra_decode_args)
+
     if with_prompt:
-        truth_tokens = tokenizer(truth, add_special_tokens=False).input_ids
-        prompt_input_ids = truth_tokens[:len(truth) // 2]
-        generated_input_ids = truth_tokens[len(truth) // 2:]
+        num_prompt_tokens = len(
+            tokenizer(truth[:len(truth) // 2],
+                      add_special_tokens=False).input_ids)
+        if tokenizer.bos_token_id is not None:
+            num_prompt_tokens += 1
+
+        prompt_input_ids = truth_tokens[:num_prompt_tokens]
+        generated_input_ids = truth_tokens[num_prompt_tokens:]
         all_input_ids = prompt_input_ids + generated_input_ids
         starting_index = len(prompt_input_ids)
         prompt = tokenizer.decode(prompt_input_ids,
-                                  skip_special_tokens=skip_special_tokens)
-        generated = truth[len(prompt):]
+                                  skip_special_tokens=skip_special_tokens,
+                                  **extra_decode_args)
+
+        generated = new_truth[len(prompt):]
     else:
-        generated = truth
+        generated = new_truth
         starting_index = 0
-        all_input_ids = tokenizer(truth, add_special_tokens=False).input_ids
-    if skip_special_tokens:
-        if tokenizer.bos_token_id is not None:
-            all_input_ids = [tokenizer.bos_token_id] + all_input_ids
-            starting_index += 1
-        all_input_ids = all_input_ids + [tokenizer.eos_token_id]
+        all_input_ids = truth_tokens
 
-    decoded_text = _run_incremental_decode(
+    decoded_text, out_ids = _run_incremental_decode(
         tokenizer,
         all_input_ids,
         skip_special_tokens=skip_special_tokens,
-        starting_index=starting_index)
+        starting_index=starting_index,
+        spaces_between_special_tokens=spaces_between_special_tokens,
+        fast=fast)
 
     assert decoded_text == generated
+    assert out_ids == all_input_ids[starting_index:]
 
-    decoded_text = _run_incremental_decode(
+
+@pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
+@pytest.mark.parametrize("fast", (True, False))
+def test_oov_decode(tokenizer, fast):
+    if fast and not isinstance(tokenizer, PreTrainedTokenizerFast):
+        pytest.skip()
+
+    decoded_text, out_ids = _run_incremental_decode(
         tokenizer, [len(tokenizer)],
-        skip_special_tokens=skip_special_tokens,
-        starting_index=starting_index)
+        skip_special_tokens=True,
+        starting_index=0,
+        spaces_between_special_tokens=True,
+        fast=fast)
 
     assert decoded_text == ''
+    assert out_ids == [len(tokenizer)]
 
 
 @pytest.fixture
@@ -165,15 +233,14 @@ def detokenizer(tokenizer_name: str) -> Detokenizer:
 @pytest.fixture(name="complete_sequence_token_ids")
 def create_complete_sequence_token_ids(complete_sequence: str,
                                        tokenizer) -> list[int]:
-    complete_sequence_token_ids = tokenizer(complete_sequence).input_ids
-    return complete_sequence_token_ids
+    return tokenizer(complete_sequence, add_special_tokens=False).input_ids
 
 
 def create_sequence(prompt_token_ids=None):
-    prompt_token_ids = prompt_token_ids or [1]
+    prompt_token_ids = prompt_token_ids or []
     return Sequence(
         seq_id=0,
-        inputs=token_inputs(prompt_token_ids, prompt="<s>"),
+        inputs=token_inputs(prompt_token_ids),
         block_size=16,
     )
 
@@ -224,7 +291,7 @@ def test_decode_sequence_logprobs(complete_sequence: str,
     assert sequential_result == "".join(sequential_logprobs_text_chosen_token)
     assert sequential_result != "".join(sequential_logprobs_text_other_token)
 
-    if skip_special_tokens:
+    if not skip_special_tokens:
         # Text for logprobs for the chosen token should be the same as the
         # generated text. Note that this will only be true if we skip
         # special tokens.
@@ -233,10 +300,23 @@ def test_decode_sequence_logprobs(complete_sequence: str,
 
 @pytest.mark.parametrize("complete_sequence", TRUTH)
 @pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
-def test_decode_prompt_logprobs(complete_sequence_token_ids: list[int],
+def test_decode_prompt_logprobs(complete_sequence: str,
+                                complete_sequence_token_ids: list[int],
                                 detokenizer: Detokenizer):
+
+    # We want to use skip_special_tokens=False here but Mistral tokenizers
+    # don't support that.
+    if complete_sequence not in SPECIAL_TOKS_TRUTH:
+        skip_special_tokens = True
+    elif not isinstance(detokenizer.tokenizer_group.get_lora_tokenizer(None),
+                        MistralTokenizer):
+        skip_special_tokens = False
+    else:
+        pytest.skip("MistralTokenizers don't support "
+                    "skip_special_tokens=False")
+        return
     """Verify Detokenizer decodes prompt logprobs correctly."""
-    sampling_params = SamplingParams(skip_special_tokens=True,
+    sampling_params = SamplingParams(skip_special_tokens=skip_special_tokens,
                                      prompt_logprobs=1)
 
     # Run sequentially.
@@ -256,8 +336,10 @@ def test_decode_prompt_logprobs(complete_sequence_token_ids: list[int],
     # decoded_prompt_logprobs doesn't contain the first token.
     token_ids = complete_sequence_token_ids
     tokenizer = detokenizer.get_tokenizer_for_seq(seq)
-    text_full = tokenizer.decode(token_ids, skip_special_tokens=True)
-    text_first = tokenizer.decode(token_ids[0], skip_special_tokens=True)
+    text_full = tokenizer.decode(token_ids,
+                                 skip_special_tokens=skip_special_tokens)
+    text_first = tokenizer.decode(token_ids[0],
+                                  skip_special_tokens=skip_special_tokens)
     text = text_full[len(text_first):]
 
     # Text for logprobs for the chosen token should be the same as the

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -70,6 +70,15 @@ class MistralToolParser(ToolParser):
                 "Mistral Tool Parser could not locate the tool call token in "
                 "the tokenizer!")
 
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        if request.tools and request.tool_choice != 'none':
+            # do not skip special tokens because mistral uses the special
+            # tokens to indicate the start and end of the tool calls
+            # information.
+            request.skip_special_tokens = False
+        return request
+
     def extract_tool_calls(
         self,
         model_output: str,

vllm/v1/engine/detokenizer.py

 # SPDX-License-Identifier: Apache-2.0
-
-from dataclasses import dataclass, field
+from abc import ABC, abstractmethod
 from typing import Optional
 
+from tokenizers import Tokenizer
+from tokenizers.decoders import DecodeStream
+from transformers import PreTrainedTokenizerFast
+
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.logger import init_logger
 from vllm.transformers_utils.detokenizer_utils import (
@@ -12,39 +15,22 @@ from vllm.v1.engine import EngineCoreRequest
 logger = init_logger(__name__)
 
 
-@dataclass
 class IncrementalDetokenizer:
 
-    # Generation data
-    token_ids: list[int]
-    output_text: str = ""
-    tokens: list[str] = field(default_factory=list)
-    prompt_len: int = 0
-
-    # Stop strings
-    stop: list[str] = field(default_factory=list)
-    include_stop_str_in_output: bool = False
-
-    # Metadata for incremental detokenization
-    prefix_offset: int = 0
-    read_offset: int = 0
-
-    # Parameters for detokenization
-    skip_special_tokens: bool = True
-    spaces_between_special_tokens: bool = True
-
-    # Tokenizer for this request,
-    # None if detokenization is disabled.
-    tokenizer: Optional[AnyTokenizer] = None
-
-    # Accounting for stop string buffering
-    stop_buffer_length: int = 0
-    _last_output_text_offset: int = 0
+    def __init__(self):
+        self.token_ids: list[int] = []
 
     @property
     def output_token_ids(self) -> list[int]:
-        return self.token_ids if not self.prompt_len else (
-            self.token_ids[self.prompt_len:])
+        return self.token_ids
+
+    def update(self, new_token_ids: list[int],
+               stop_terminated: bool) -> Optional[str]:
+        self.token_ids.extend(new_token_ids)
+        return None
+
+    def get_next_output_text(self, finished: bool, delta: bool) -> str:
+        return ""
 
     @classmethod
     def from_new_request(
@@ -54,39 +40,37 @@ class IncrementalDetokenizer:
     ) -> "IncrementalDetokenizer":
 
         if tokenizer is None:
-            return cls(token_ids=[])
+            # No tokenizer => skipping detokenization.
+            return IncrementalDetokenizer()
+
+        if isinstance(tokenizer, PreTrainedTokenizerFast):
+            # Fast tokenizer => use tokenizers library DecodeStream.
+            return FastIncrementalDetokenizer(tokenizer, request)
+
+        # Fall back to slow python-based incremental detokenization.
+        return SlowIncrementalDetokenizer(tokenizer, request)
+
+
+class BaseIncrementalDetokenizer(IncrementalDetokenizer, ABC):
+
+    def __init__(self, request: EngineCoreRequest):
+        super().__init__()
 
-        tokens, prefix_offset, read_offset = convert_prompt_ids_to_tokens(
-            tokenizer=tokenizer,
-            prompt_ids=request.prompt_token_ids,
-            skip_special_tokens=request.sampling_params.skip_special_tokens,
-        )
+        # Stop strings
+        params = request.sampling_params
+        self.stop = stop = params.stop
+        self.include_stop_str_in_output = params.include_stop_str_in_output
 
-        stops = request.sampling_params.stop
         # Number of chars to hold back when stop strings are to be excluded
         # from streamed output.
-        if stops and not request.sampling_params.include_stop_str_in_output:
-            stop_buffer_length = max(len(s) for s in stops) - 1
+        if stop and not self.include_stop_str_in_output:
+            self.stop_buffer_length = max(len(s) for s in stop) - 1
         else:
-            stop_buffer_length = 0
-
-        return cls(
-            tokens=tokens,
-            # Detokenizer mutates this list, so need a unique copy.
-            # NOTE(Nick): could we take ownership of it though?
-            token_ids=request.prompt_token_ids.copy(),
-            stop=stops,
-            include_stop_str_in_output=request.sampling_params.
-            include_stop_str_in_output,
-            prefix_offset=prefix_offset,
-            read_offset=read_offset,
-            skip_special_tokens=request.sampling_params.skip_special_tokens,
-            spaces_between_special_tokens=request.sampling_params.
-            spaces_between_special_tokens,
-            prompt_len=len(request.prompt_token_ids),
-            tokenizer=tokenizer,
-            stop_buffer_length=stop_buffer_length,
-        )
+            self.stop_buffer_length = 0
+        self._last_output_text_offset: int = 0
+
+        # Generation data
+        self.output_text = ""
 
     def update(self, new_token_ids: list[int],
                stop_terminated: bool) -> Optional[str]:
@@ -98,11 +82,7 @@ class IncrementalDetokenizer:
         Return matched stop string or None.
         """
         if not new_token_ids:
-            # Skip detokenization if no new token ids
-            return None
-        if self.tokenizer is None:
-            # Skip detokenization if no tokenizer
-            self.token_ids.extend(new_token_ids)
+            # Skip detokenization if no new token ids.
             return None
 
         if stop_terminated and not self.include_stop_str_in_output:
@@ -116,34 +96,16 @@ class IncrementalDetokenizer:
         # 1) Detokenize the new token ids incrementally.
         # TODO(woosuk): This method becomes very inefficient when the number of
         # new_token_ids is more than 1. We need to optimize this.
-        decoded_text = ""
+        offset_before = len(self.output_text)
         for new_token_id in new_token_ids:
             self.token_ids.append(new_token_id)
-            (new_tokens, new_decoded_token_text, prefix_offset,
-             read_offset) = detokenize_incrementally(
-                 tokenizer=self.tokenizer,
-                 all_input_ids=self.token_ids,
-                 prev_tokens=self.tokens,
-                 prefix_offset=self.prefix_offset,
-                 read_offset=self.read_offset,
-                 skip_special_tokens=self.skip_special_tokens,
-                 spaces_between_special_tokens=self.
-                 spaces_between_special_tokens,
-             )
-
-            self.tokens.extend(new_tokens)
-            self.prefix_offset = prefix_offset
-            self.read_offset = read_offset
-
-            decoded_text += new_decoded_token_text
-
-        self.output_text += decoded_text
+            self.output_text += self.decode_next(new_token_id)
 
         if stop_terminated:
             if skipped_stop_token_id is not None:
-                # Cleanup after skipping detokenization
+                # Cleanup after skipping detokenization.
                 self.token_ids.append(skipped_stop_token_id)
-            # Stop token triggered; skip stop string check
+            # Stop token triggered; skip stop string check.
             return None
 
         # 2) Evaluate stop strings.
@@ -151,7 +113,7 @@ class IncrementalDetokenizer:
         if self.stop:
             stop = StopChecker.check_stop_strings(
                 output_text=self.output_text,
-                new_char_count=len(decoded_text),
+                new_char_count=len(self.output_text) - offset_before,
                 stop=self.stop,
                 include_in_output=self.include_stop_str_in_output,
             )
@@ -162,6 +124,10 @@ class IncrementalDetokenizer:
 
         return stop_string
 
+    @abstractmethod
+    def decode_next(self, next_token_id: int) -> str:
+        raise NotImplementedError
+
     def get_next_output_text(self, finished: bool, delta: bool) -> str:
         """If delta is True, only new text since the last call to
         this method is returned"""
@@ -177,3 +143,114 @@ class IncrementalDetokenizer:
             self._last_output_text_offset = length
             return self.output_text[last_offset:length]
         return ""
+
+
+class FastIncrementalDetokenizer(BaseIncrementalDetokenizer):
+
+    def __init__(self, tokenizer: PreTrainedTokenizerFast,
+                 request: EngineCoreRequest):
+        super().__init__(request)
+
+        sampling_params = request.sampling_params
+        self.stream = DecodeStream(
+            skip_special_tokens=sampling_params.skip_special_tokens)
+
+        self.tokenizer: Tokenizer = tokenizer._tokenizer
+
+        # Find a safe place to start.
+        prompt_suffix = request.prompt_token_ids
+        prompt_len = len(prompt_suffix)
+        if prompt_len > 4:
+            for i in range(4, max(prompt_len + 1, 32)):
+                suffix = request.prompt_token_ids[-i:]
+                if '�' not in self.tokenizer.decode(suffix):
+                    prompt_suffix = suffix
+                    break
+
+        # Prime the stream.
+        for tid in prompt_suffix:
+            self.stream.step(self.tokenizer, tid)
+
+        self.spaces_between_special_tokens = (
+            sampling_params.skip_special_tokens
+            or sampling_params.spaces_between_special_tokens)
+
+        if not self.spaces_between_special_tokens:
+            # Store dict of added token ids so that we can suppress
+            # the spaces between them.
+            if (added_token_ids := getattr(self.tokenizer, "added_token_ids",
+                                           None)) is None:
+                self.tokenizer.added_token_ids = added_token_ids = {
+                    tid: tok.content
+                    for tid, tok in
+                    self.tokenizer.get_added_tokens_decoder().items()
+                }
+
+            if added_token_ids:
+                self.last_special = False
+                self.added_token_ids = added_token_ids
+            else:
+                # No added tokens.
+                self.spaces_between_special_tokens = True
+
+    def decode_next(self, next_token_id: int) -> str:
+        token = self.stream.step(self.tokenizer, next_token_id)
+
+        if not self.spaces_between_special_tokens:
+            special_token = self.added_token_ids.get(next_token_id)
+            is_special = special_token is not None
+            if is_special and self.last_special:
+                # Return raw token string without any prefixed spaces.
+                token = special_token
+            self.last_special = is_special
+
+        return token or ""
+
+
+class SlowIncrementalDetokenizer(BaseIncrementalDetokenizer):
+
+    def __init__(self, tokenizer: AnyTokenizer, request: EngineCoreRequest):
+        super().__init__(request)
+
+        self.tokenizer = tokenizer
+
+        # Metadata for incremental detokenization.
+        self.tokens, self.prefix_offset, self.read_offset = (
+            convert_prompt_ids_to_tokens(
+                tokenizer=tokenizer,
+                prompt_ids=request.prompt_token_ids,
+                skip_special_tokens=request.sampling_params.
+                skip_special_tokens,
+            ))
+
+        self.token_ids.extend(request.prompt_token_ids)
+        self.prompt_len = len(request.prompt_token_ids)
+
+        params = request.sampling_params
+        self.skip_special_tokens = params.skip_special_tokens
+        self.spaces_between_special_tokens = (
+            params.spaces_between_special_tokens)
+
+    @property
+    def output_token_ids(self) -> list[int]:
+        return self.token_ids if not self.prompt_len else (
+            self.token_ids[self.prompt_len:])
+
+    def decode_next(self, next_token_id: int) -> str:
+        new_tokens, decoded_text, prefix_offset, read_offset = (
+            detokenize_incrementally(
+                tokenizer=self.tokenizer,
+                all_input_ids=self.token_ids,
+                prev_tokens=self.tokens,
+                prefix_offset=self.prefix_offset,
+                read_offset=self.read_offset,
+                skip_special_tokens=self.skip_special_tokens,
+                spaces_between_special_tokens=self.
+                spaces_between_special_tokens,
+            ))
+
+        self.tokens.extend(new_tokens)
+        self.prefix_offset = prefix_offset
+        self.read_offset = read_offset
+
+        return decoded_text