[V0 Deprecation] Remove V0 Sequence class & Sampler (vllm-project#25332)

Signed-off-by: Woosuk Kwon <[email protected]>
Signed-off-by: Woosuk Kwon <[email protected]>

Author: WoosukKwon

tests/conftest.py

@@ -48,10 +48,10 @@
                               initialize_model_parallel)
 from vllm.inputs import TextPrompt
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob
 from vllm.multimodal.utils import fetch_image
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
-from vllm.sequence import Logprob
 from vllm.transformers_utils.utils import maybe_model_redirect
 from vllm.utils import set_default_torch_num_threads
 

tests/models/multimodal/generation/test_granite_speech.py

@@ -7,8 +7,8 @@
 import pytest
 from transformers import AutoModelForSpeechSeq2Seq
 
+from vllm.logprobs import SampleLogprobs
 from vllm.lora.request import LoRARequest
-from vllm.sequence import SampleLogprobs
 
 from ....conftest import (AudioTestAssets, HfRunner, PromptAudioInput,
                           VllmRunner)

tests/models/multimodal/generation/test_phi4mm.py

@@ -12,10 +12,10 @@
 from transformers import AutoTokenizer
 
 from vllm.assets.image import ImageAsset
+from vllm.logprobs import SampleLogprobs
 from vllm.lora.request import LoRARequest
 from vllm.multimodal.image import convert_image_mode, rescale_image_size
 from vllm.platforms import current_platform
-from vllm.sequence import SampleLogprobs
 
 from ....conftest import (IMAGE_ASSETS, HfRunner, PromptAudioInput,
                           PromptImageInput, VllmRunner)

tests/models/multimodal/generation/test_pixtral.py

@@ -13,8 +13,8 @@
 from transformers import AutoProcessor
 
 from vllm import SamplingParams, TextPrompt, TokensPrompt
+from vllm.logprobs import Logprob, SampleLogprobs
 from vllm.multimodal import MultiModalDataBuiltins
-from vllm.sequence import Logprob, SampleLogprobs
 
 from ....utils import VLLM_PATH, large_gpu_test
 from ...utils import check_logprobs_close

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -19,7 +19,7 @@
                           GenerationConfig, GenerationMixin)
 from transformers.video_utils import VideoMetadata
 
-from vllm.sequence import SampleLogprobs
+from vllm.logprobs import SampleLogprobs
 from vllm.utils import is_list_of
 
 from .....conftest import HfRunner, ImageAsset, ImageTestAssets

tests/models/multimodal/generation/vlm_utils/types.py

@@ -12,7 +12,7 @@
 from transformers.models.auto.auto_factory import _BaseAutoModelClass
 
 from vllm.config import RunnerOption
-from vllm.sequence import SampleLogprobs
+from vllm.logprobs import SampleLogprobs
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from .....conftest import (AUDIO_ASSETS, IMAGE_ASSETS, HfRunner, ImageAsset,

tests/models/utils.py

@@ -12,7 +12,7 @@
 
 from vllm.config import ModelConfig, ModelDType, RunnerOption
 from vllm.inputs import InputContext
-from vllm.sequence import Logprob, PromptLogprobs, SampleLogprobs
+from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 
 from .registry import HF_EXAMPLE_MODELS
 

tests/tokenization/test_detokenize.py

@@ -8,10 +8,7 @@
 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
-from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.detokenizer import (FastIncrementalDetokenizer,
@@ -217,138 +214,3 @@ def test_oov_decode(tokenizer, fast):
 
     assert decoded_text == ''
     assert out_ids == [len(tokenizer)]
-
-
-@pytest.fixture
-def detokenizer(tokenizer_name: str) -> Detokenizer:
-    tokenizer = get_tokenizer(
-        tokenizer_name,
-        tokenizer_mode="mistral" if "mistral" in tokenizer_name else "auto",
-        trust_remote_code=False,
-        revision=None,
-    )
-
-    return Detokenizer(tokenizer)
-
-
-@pytest.fixture(name="complete_sequence_token_ids")
-def create_complete_sequence_token_ids(complete_sequence: str,
-                                       tokenizer) -> list[int]:
-    return tokenizer(complete_sequence, add_special_tokens=False).input_ids
-
-
-def create_sequence(prompt_token_ids=None):
-    prompt_token_ids = prompt_token_ids or []
-    return Sequence(
-        seq_id=0,
-        inputs=token_inputs(prompt_token_ids),
-        block_size=16,
-    )
-
-
-def create_dummy_logprobs(
-        complete_sequence_token_ids: list[int]) -> list[dict[int, Logprob]]:
-    return [{
-        token_id: Logprob(logprob=0.0),
-        token_id + 1: Logprob(logprob=0.1)
-    } for token_id in complete_sequence_token_ids]
-
-
-def create_dummy_prompt_logprobs(
-        complete_sequence_token_ids: list[int]
-) -> list[Optional[dict[int, Any]]]:
-    # logprob for the first prompt token is None.
-    logprobs: list[Optional[dict[int, Any]]] = [None]
-    logprobs.extend(create_dummy_logprobs(complete_sequence_token_ids)[1:])
-    return logprobs
-
-
-@pytest.mark.parametrize("complete_sequence", TRUTH)
-@pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
-@pytest.mark.parametrize("skip_special_tokens", [True, False], indirect=True)
-def test_decode_sequence_logprobs(complete_sequence: str,
-                                  complete_sequence_token_ids: list[int],
-                                  detokenizer: Detokenizer,
-                                  skip_special_tokens: bool):
-    """Verify Detokenizer decodes logprobs correctly."""
-    sampling_params = SamplingParams(skip_special_tokens=skip_special_tokens,
-                                     logprobs=2)
-
-    # Run sequentially.
-    seq = create_sequence()
-    dummy_logprobs = create_dummy_logprobs(complete_sequence_token_ids)
-    sequential_logprobs_text_chosen_token: list[str] = []
-    sequential_logprobs_text_other_token: list[str] = []
-    for new_token, logprobs in zip(complete_sequence_token_ids,
-                                   dummy_logprobs):
-        seq.append_token_id(new_token, logprobs)
-        detokenizer.decode_sequence_inplace(seq, sampling_params)
-        sequential_logprobs_text_chosen_token.append(
-            seq.output_logprobs[-1][new_token].decoded_token)
-        sequential_logprobs_text_other_token.append(
-            seq.output_logprobs[-1][new_token + 1].decoded_token)
-    sequential_result = seq.output_text
-
-    assert sequential_result == "".join(sequential_logprobs_text_chosen_token)
-    assert sequential_result != "".join(sequential_logprobs_text_other_token)
-
-    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.
-        assert sequential_result == complete_sequence
-
-
-@pytest.mark.parametrize("complete_sequence", TRUTH)
-@pytest.mark.parametrize("tokenizer_name", TOKENIZERS)
-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, 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=skip_special_tokens,
-                                     prompt_logprobs=1)
-
-    # Run sequentially.
-    seq = create_sequence(complete_sequence_token_ids)
-    seq_group = SequenceGroup(request_id="1",
-                              seqs=[seq],
-                              sampling_params=sampling_params,
-                              arrival_time=0.0)
-    dummy_logprobs = create_dummy_prompt_logprobs(complete_sequence_token_ids)
-    detokenizer.decode_prompt_logprobs_inplace(seq_group,
-                                               dummy_logprobs,
-                                               position_offset=0)
-    # First logprob is None.
-    decoded_prompt_logprobs: list[dict[int, Any]] = dummy_logprobs[
-        1:]  # type: ignore
-
-    # decoded_prompt_logprobs doesn't contain the first token.
-    token_ids = complete_sequence_token_ids
-    tokenizer = detokenizer.tokenizer
-    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
-    # prompt text. Note that the first logprob is None.
-    assert text == "".join([
-        logprobs[token_id].decoded_token
-        for token_id, logprobs in zip(token_ids[1:], decoded_prompt_logprobs)
-    ])
-    assert text != "".join([
-        logprobs[token_id + 1].decoded_token
-        for token_id, logprobs in zip(token_ids[1:], decoded_prompt_logprobs)
-    ])

tests/tool_use/test_jamba_tool_parser.py

@@ -12,7 +12,7 @@
 from vllm.entrypoints.openai.protocol import (DeltaMessage, FunctionCall,
                                               ToolCall)
 from vllm.entrypoints.openai.tool_parsers import JambaToolParser
-from vllm.transformers_utils.detokenizer import detokenize_incrementally
+from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
 
 MODEL = "ai21labs/Jamba-tiny-dev"

tests/tool_use/test_qwen3coder_tool_parser.py

@@ -13,7 +13,7 @@
                                               ToolCall)
 from vllm.entrypoints.openai.tool_parsers.qwen3coder_tool_parser import (
     Qwen3CoderToolParser)
-from vllm.transformers_utils.detokenizer import detokenize_incrementally
+from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
 
 MODEL = "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8"