Revert "Support for guided decoding for offline LLM (vllm-project#6878)"

This reverts commit 654bc5c.

docs/source/conf.py

@@ -111,7 +111,6 @@ def setup(app):
     "tqdm",
     "tensorizer",
     "pynvml",
-    "outlines",
 ]
 
 for mock_target in autodoc_mock_imports:

tests/entrypoints/conftest.py → tests/entrypoints/openai/conftest.py

@@ -1,26 +1,6 @@
 import pytest
 
 
-@pytest.fixture
-def sample_prompts():
-    return [
-        "Hello, my name is",
-        "The president of the United States is",
-        "The capital of France is",
-        "The future of AI is",
-    ]
-
-
-@pytest.fixture
-def sample_token_ids():
-    return [
-        [0],
-        [0, 1],
-        [0, 2, 1],
-        [0, 3, 1, 2],
-    ]
-
-
 @pytest.fixture
 def sample_regex():
     return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
@@ -86,4 +66,4 @@ def sample_sql_statements():
 table: "table_1" | "table_2"
 condition: column "=" number
 number: "1" | "2"
-""")
+""")

vllm/entrypoints/llm.py

@@ -10,9 +10,6 @@
                          parse_and_batch_prompt)
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.model_executor.guided_decoding import (
-    GuidedDecodingRequest, get_local_guided_decoding_logits_processor)
-from vllm.model_executor.guided_decoding.guided_fields import LLMGuidedOptions
 from vllm.outputs import EmbeddingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
@@ -265,8 +262,6 @@ def generate(
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
-        guided_options_request: Optional[Union[LLMGuidedOptions,
-                                               GuidedDecodingRequest]] = None
     ) -> List[RequestOutput]:
         """Generates the completions for the input prompts.
 
@@ -308,14 +303,6 @@ def generate(
         else:
             inputs = cast(Union[PromptInputs, Sequence[PromptInputs]], prompts)
 
-        if isinstance(guided_options_request, dict):
-            if len(guided_options_request) > 1:
-                raise ValueError(
-                    "You can only use one guided decoding but multiple is "
-                    f"specified: {guided_options_request}")
-            guided_options_request = GuidedDecodingRequest(
-                **guided_options_request)
-
         if sampling_params is None:
             # Use default sampling params.
             sampling_params = SamplingParams()
@@ -324,8 +311,7 @@ def generate(
             inputs=inputs,
             params=sampling_params,
             lora_request=lora_request,
-            prompt_adapter_request=prompt_adapter_request,
-            guided_options=guided_options_request)
+            prompt_adapter_request=prompt_adapter_request)
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
         return LLMEngine.validate_outputs(outputs, RequestOutput)
@@ -522,7 +508,6 @@ def _validate_and_add_requests(
                       Sequence[PoolingParams]],
         lora_request: Optional[Union[Sequence[LoRARequest], LoRARequest]],
         prompt_adapter_request: Optional[PromptAdapterRequest],
-        guided_options: Optional[GuidedDecodingRequest] = None,
     ) -> None:
         if isinstance(inputs, (str, dict)):
             # Convert a single prompt to a list.
@@ -538,15 +523,6 @@ def _validate_and_add_requests(
             raise ValueError("The lengths of prompts and lora_request "
                              "must be the same.")
 
-        if isinstance(params, list):
-            params = [
-                self._add_guided_processor(param, guided_options)
-                if isinstance(param, SamplingParams) else param
-                for param in params
-            ]
-        elif isinstance(params, SamplingParams):
-            params = self._add_guided_processor(params, guided_options)
-
         # Add requests to the engine.
         for i, request_inputs in enumerate(inputs):
             self._add_request(
@@ -572,24 +548,6 @@ def _add_request(
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request)
 
-    def _add_guided_processor(
-            self,
-            params: SamplingParams,
-            guided_options: Optional[GuidedDecodingRequest] = None):
-        if guided_options:
-            if guided_options.guided_decoding_backend is None:
-                decoding_config = self.llm_engine.get_decoding_config()
-                guided_options.guided_decoding_backend = (
-                    decoding_config.guided_decoding_backend)
-            guided_logits_processor = get_local_guided_decoding_logits_processor(  #noqa
-                guided_options.guided_decoding_backend, guided_options,
-                self.get_tokenizer())
-            if guided_logits_processor:
-                if params.logits_processors is None:
-                    params.logits_processors = []
-                params.logits_processors.append(guided_logits_processor)
-        return params
-
     def _run_engine(
             self, *, use_tqdm: bool
     ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:

vllm/entrypoints/openai/protocol.py

@@ -1,7 +1,6 @@
 # Adapted from
 # https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
 import time
-from argparse import Namespace
 from typing import Any, Dict, List, Literal, Optional, Union
 
 import torch
@@ -15,23 +14,6 @@
 from vllm.sampling_params import LogitsProcessor, SamplingParams
 from vllm.utils import random_uuid
 
-# torch is mocked during docs generation,
-# so we have to provide the values as literals
-_MOCK_LONG_INFO = Namespace(min=-9223372036854775808, max=9223372036854775807)
-
-try:
-    from sphinx.ext.autodoc.mock import _MockModule
-
-    if isinstance(torch, _MockModule):
-        _LONG_INFO = _MOCK_LONG_INFO
-    else:
-        _LONG_INFO = torch.iinfo(torch.long)
-except ModuleNotFoundError:
-    _LONG_INFO = torch.iinfo(torch.long)
-
-assert _LONG_INFO.min == _MOCK_LONG_INFO.min
-assert _LONG_INFO.max == _MOCK_LONG_INFO.max
-
 
 class OpenAIBaseModel(BaseModel):
     # OpenAI API does not allow extra fields
@@ -126,7 +108,9 @@ class ChatCompletionRequest(OpenAIBaseModel):
     n: Optional[int] = 1
     presence_penalty: Optional[float] = 0.0
     response_format: Optional[ResponseFormat] = None
-    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    seed: Optional[int] = Field(None,
+                                ge=torch.iinfo(torch.long).min,
+                                le=torch.iinfo(torch.long).max)
     stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None
@@ -343,7 +327,9 @@ class CompletionRequest(OpenAIBaseModel):
     max_tokens: Optional[int] = 16
     n: int = 1
     presence_penalty: Optional[float] = 0.0
-    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    seed: Optional[int] = Field(None,
+                                ge=torch.iinfo(torch.long).min,
+                                le=torch.iinfo(torch.long).max)
     stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None

vllm/model_executor/guided_decoding/__init__.py

@@ -3,10 +3,9 @@
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionNamedToolChoiceParam, ChatCompletionRequest,
     CompletionRequest)
-from vllm.model_executor.guided_decoding.guided_fields import (
-    GuidedDecodingRequest)
+from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (
+    get_lm_format_enforcer_guided_decoding_logits_processor)
 from vllm.model_executor.guided_decoding.outlines_decoding import (
-    get_local_outlines_guided_decoding_logits_processor,
     get_outlines_guided_decoding_logits_processor)
 from vllm.sampling_params import LogitsProcessor
 
@@ -21,8 +20,6 @@ async def get_guided_decoding_logits_processor(
         return await get_outlines_guided_decoding_logits_processor(
             request, tokenizer)
     if guided_decoding_backend == 'lm-format-enforcer':
-        from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (  # noqa
-            get_lm_format_enforcer_guided_decoding_logits_processor)
         return await get_lm_format_enforcer_guided_decoding_logits_processor(
             request, tokenizer)
 
@@ -31,25 +28,6 @@ async def get_guided_decoding_logits_processor(
         "Must be one of 'outlines, 'lm-format-enforcer'")
 
 
-def get_local_guided_decoding_logits_processor(
-        guided_decoding_backend: str, guided_options: GuidedDecodingRequest,
-        tokenizer) -> Optional[LogitsProcessor]:
-    # request = _adapt_request_for_tool_use(request)
-
-    if guided_decoding_backend == 'outlines':
-        return get_local_outlines_guided_decoding_logits_processor(
-            guided_options, tokenizer)
-    if guided_decoding_backend == 'lm-format-enforcer':
-        from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (  # noqa
-            get_local_lm_format_enforcer_guided_decoding_logits_processor)
-        return get_local_lm_format_enforcer_guided_decoding_logits_processor(
-            guided_options, tokenizer)
-
-    raise ValueError(
-        f"Unknown guided decoding backend '{guided_decoding_backend}'. "
-        "Must be one of 'outlines, 'lm-format-enforcer'")
-
-
 def _adapt_request_for_tool_use(request: Union[CompletionRequest,
                                                ChatCompletionRequest]):
     # the legacy completion API does not support tool use