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

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

Author: njhill

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 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 @@
     # 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 @@
 ]
 
 
-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 @@ def __init__(self, tokenizer: AnyTokenizer):
                 "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,