[Chore][V0 Deprecation] Move LogProb to a separate file (#24055)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

vllm/beam_search.py

@@ -4,8 +4,8 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Optional, Union
 
+from vllm.logprobs import Logprob
 from vllm.lora.request import LoRARequest
-from vllm.sequence import Logprob
 
 if TYPE_CHECKING:
     from vllm.multimodal import MultiModalDataDict

vllm/entrypoints/openai/protocol.py

@@ -43,10 +43,10 @@ from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
 from vllm.entrypoints.score_utils import (ScoreContentPartParam,
                                           ScoreMultiModalParam)
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
                                   RequestOutputKind, SamplingParams)
-from vllm.sequence import Logprob
 from vllm.utils import random_uuid, resolve_obj_by_qualname
 
 logger = init_logger(__name__)

vllm/entrypoints/openai/serving_chat.py

@@ -43,10 +43,10 @@ from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
 from vllm.entrypoints.utils import get_max_tokens
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.reasoning import ReasoningParser, ReasoningParserManager
 from vllm.sampling_params import BeamSearchParams, SamplingParams
-from vllm.sequence import Logprob
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
 from vllm.transformers_utils.tokenizers import (maybe_serialize_tool_calls,
                                                 truncate_tool_call_ids,

vllm/entrypoints/openai/serving_completion.py

@@ -38,9 +38,9 @@ from vllm.entrypoints.utils import get_max_tokens
 from vllm.inputs.data import (EmbedsPrompt, TokensPrompt, is_embeds_prompt,
                               is_tokens_prompt)
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams, SamplingParams
-from vllm.sequence import Logprob
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import as_list, merge_async_iterators
 

vllm/entrypoints/openai/serving_engine.py

@@ -67,13 +67,13 @@ from vllm.inputs.data import EmbedsPrompt as EngineEmbedsPrompt
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.inputs.parse import parse_and_batch_prompt
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob, PromptLogprobs
 from vllm.lora.request import LoRARequest
 from vllm.multimodal import (  # noqa: F401 - Required to resolve Pydantic error in RequestProcessingMixin
     MultiModalDataDict, MultiModalUUIDDict)
 from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import BeamSearchParams, SamplingParams
-from vllm.sequence import Logprob, PromptLogprobs
 from vllm.tracing import (contains_trace_headers, extract_trace_headers,
                           log_tracing_disabled_warning)
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer

vllm/entrypoints/openai/serving_responses.py

@@ -58,11 +58,11 @@ from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.tool_server import MCPToolServer, ToolServer
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob as SampleLogprob
+from vllm.logprobs import SampleLogprobs
 from vllm.outputs import CompletionOutput
 from vllm.reasoning import ReasoningParser, ReasoningParserManager
 from vllm.sampling_params import SamplingParams
-from vllm.sequence import Logprob as SampleLogprob
-from vllm.sequence import SampleLogprobs
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import random_uuid
 

vllm/logprobs.py

@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import Optional
+
+
+# We use dataclass for now because it is used for
+# openai server output, and msgspec is not serializable.
+# TODO(sang): Fix it.
+@dataclass
+class Logprob:
+    """Infos for supporting OpenAI compatible logprobs and token ranks.
+
+    Attributes:
+        logprob: The logprob of chosen token
+        rank: The vocab rank of chosen token (>=1)
+        decoded_token: The decoded chosen token index
+    """
+    logprob: float
+    rank: Optional[int] = None
+    decoded_token: Optional[str] = None
+
+
+# {token_id -> logprob} per each sequence group. None if the corresponding
+# sequence group doesn't require prompt logprob.
+PromptLogprobs = list[Optional[dict[int, Logprob]]]
+# {token_id -> logprob} for each sequence group.
+SampleLogprobs = list[dict[int, Logprob]]

vllm/model_executor/layers/sampler.py

@@ -13,14 +13,14 @@ import torch
 import torch.nn as nn
 
 import vllm.envs as envs
+from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.model_executor.layers.utils import apply_penalties
 from vllm.model_executor.sampling_metadata import (SamplingMetadata,
                                                    SamplingTensors,
                                                    SequenceGroupToSample)
 from vllm.sampling_params import SamplingType
 from vllm.sequence import (VLLM_INVALID_TOKEN_ID,
-                           CompletionSequenceGroupOutput, Logprob,
+                           CompletionSequenceGroupOutput, SequenceOutput)
-                           PromptLogprobs, SampleLogprobs, SequenceOutput)
 
 if envs.VLLM_USE_FLASHINFER_SAMPLER and find_spec("flashinfer"):
     # yapf: disable

vllm/model_executor/model_loader/neuron.py

@@ -14,12 +14,12 @@ from transformers import PretrainedConfig
 
 from vllm.config import (ModelConfig, ParallelConfig, SchedulerConfig,
                          SpeculativeConfig)
+from vllm.logprobs import Logprob
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import get_quantization_config
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+from vllm.sequence import CompletionSequenceGroupOutput, SequenceOutput
-                           SequenceOutput)
 
 TORCH_DTYPE_TO_NEURON_AMP = {
     "auto": "f32",

vllm/model_executor/model_loader/neuronx_distributed.py

@@ -27,11 +27,11 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, PretrainedConfig
 from vllm.config import (ModelConfig, ParallelConfig, SchedulerConfig,
                          SpeculativeConfig)
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+from vllm.sequence import CompletionSequenceGroupOutput, SequenceOutput
-                           SequenceOutput)
 
 # yapf: enable
 logger = init_logger(__name__)

vllm/outputs.py

@@ -11,11 +11,12 @@ import torch
 from typing_extensions import TypeVar
 
 from vllm.logger import init_logger
+from vllm.logprobs import PromptLogprobs, SampleLogprobs
 from vllm.lora.request import LoRARequest
 from vllm.multimodal.inputs import MultiModalPlaceholderDict
 from vllm.sampling_params import RequestOutputKind
-from vllm.sequence import (PromptLogprobs, RequestMetrics, SampleLogprobs,
+from vllm.sequence import (RequestMetrics, SequenceGroup, SequenceGroupBase,
-                           SequenceGroup, SequenceGroupBase, SequenceStatus)
+                           SequenceStatus)
 
 logger = init_logger(__name__)
 

vllm/sequence.py

@@ -16,6 +16,7 @@ import msgspec
 import torch
 
 from vllm.inputs import SingletonInputs
+from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.multimodal import MultiModalKwargs, MultiModalPlaceholderDict
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import RequestOutputKind, SamplingParams
@@ -38,30 +39,6 @@ def array_full(token_id: int, count: int):
     return array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
 
 
-# We use dataclass for now because it is used for
-# openai server output, and msgspec is not serializable.
-# TODO(sang): Fix it.
-@dataclass
-class Logprob:
-    """Infos for supporting OpenAI compatible logprobs and token ranks.
-
-    Attributes:
-        logprob: The logprob of chosen token
-        rank: The vocab rank of chosen token (>=1)
-        decoded_token: The decoded chosen token index
-    """
-    logprob: float
-    rank: Optional[int] = None
-    decoded_token: Optional[str] = None
-
-
-# {token_id -> logprob} per each sequence group. None if the corresponding
-# sequence group doesn't require prompt logprob.
-PromptLogprobs = list[Optional[dict[int, Logprob]]]
-# {token_id -> logprob} for each sequence group.
-SampleLogprobs = list[dict[int, Logprob]]
-
-
 class SequenceStatus(enum.IntEnum):
     """Status of a sequence."""
     WAITING = 0

vllm/transformers_utils/detokenizer.py

@@ -3,8 +3,9 @@
 
 from typing import Optional
 
-from vllm.sequence import (VLLM_INVALID_TOKEN_ID, Logprob, SamplingParams,
+from vllm.logprobs import Logprob
-                           Sequence, SequenceGroup)
+from vllm.sequence import (VLLM_INVALID_TOKEN_ID, SamplingParams, Sequence,
+                           SequenceGroup)
 
 from .detokenizer_utils import (convert_prompt_ids_to_tokens,
                                 detokenize_incrementally)

vllm/v1/engine/logprobs.py

@@ -7,7 +7,7 @@ from dataclasses import dataclass
 from typing import Optional
 
 from vllm.logger import init_logger
-from vllm.sequence import Logprob, PromptLogprobs, SampleLogprobs
+from vllm.logprobs import Logprob, PromptLogprobs, SampleLogprobs
 from vllm.transformers_utils.detokenizer_utils import (
     AnyTokenizer, convert_ids_list_to_tokens)
 from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest