Support for guided decoding for offline LLM

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

@@ -1,5 +1,23 @@
 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():

tests/entrypoints/llm/test_guided_generate.py

@@ -0,0 +1,142 @@
+import json
+import re
+import weakref
+
+import jsonschema
+import pytest
+
+from vllm.entrypoints.llm import LLM
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+
+from ...conftest import cleanup
+
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(model=MODEL_NAME, max_model_len=1024)
+
+    with llm.deprecate_legacy_api():
+        yield weakref.proxy(llm)
+        del llm
+    cleanup()
+
+
+@pytest.mark.skip_global_cleanup
+def test_guided_regex(sample_regex, llm):
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+    )
+    outputs = llm.generate(
+        prompts=[
+            f"Give an example IPv4 address with this regex: {sample_regex}"
+        ] * 2,
+        sampling_params=sampling_params,
+        use_tqdm=True,
+        guided_options_request=dict(guided_regex=sample_regex))
+
+    assert outputs is not None
+    for output in outputs:
+        assert output is not None
+        assert isinstance(output, RequestOutput)
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(generated_text)
+        assert generated_text is not None
+        assert re.fullmatch(sample_regex, generated_text) is not None
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+
+@pytest.mark.skip_global_cleanup
+def test_guided_json_completion(sample_json_schema, llm):
+    sampling_params = SamplingParams(
+        temperature=1.0,
+        max_tokens=1000,
+    )
+    outputs = llm.generate(
+        prompts=[
+            f"Give an example JSON for an employee profile "
+            f"that fits this schema: {sample_json_schema}"
+        ] * 2,
+        sampling_params=sampling_params,
+        use_tqdm=True,
+        guided_options_request=dict(guided_json=sample_json_schema))
+
+    assert outputs is not None
+
+    for output in outputs:
+        assert output is not None
+        assert isinstance(output, RequestOutput)
+        prompt = output.prompt
+
+        generated_text = output.outputs[0].text
+        assert generated_text is not None
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+        output_json = json.loads(generated_text)
+        jsonschema.validate(instance=output_json, schema=sample_json_schema)
+
+
+@pytest.mark.skip_global_cleanup
+def test_guided_choice_completion(sample_guided_choice, llm):
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+    )
+    outputs = llm.generate(
+        prompts="The best language for type-safe systems programming is ",
+        sampling_params=sampling_params,
+        use_tqdm=True,
+        guided_options_request=dict(guided_choice=sample_guided_choice))
+
+    assert outputs is not None
+    for output in outputs:
+        assert output is not None
+        assert isinstance(output, RequestOutput)
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(generated_text)
+        assert generated_text is not None
+        assert generated_text in sample_guided_choice
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+
+@pytest.mark.skip_global_cleanup
+def test_guided_grammar(sample_sql_statements, llm):
+
+    sampling_params = SamplingParams(
+        temperature=0.8,
+        top_p=0.95,
+    )
+    outputs = llm.generate(
+        prompts=("Generate a sql state that select col_1 from "
+                 "table_1 where it is equals to 1"),
+        sampling_params=sampling_params,
+        use_tqdm=True,
+        guided_options_request=dict(guided_grammar=sample_sql_statements))
+
+    assert outputs is not None
+    for output in outputs:
+        assert output is not None
+        assert isinstance(output, RequestOutput)
+        prompt = output.prompt
+
+        generated_text = output.outputs[0].text
+        assert generated_text is not None
+
+        # use Lark to parse the output, and make sure it's a valid parse tree
+        from lark import Lark
+        parser = Lark(sample_sql_statements)
+        parser.parse(generated_text)
+
+        # remove spaces for comparison b/c we removed them in the grammar
+        ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
+            " ", "")
+
+        assert generated_text.strip() == ground_truth
+
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

vllm/entrypoints/llm.py

@@ -10,6 +10,9 @@
                          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
@@ -262,6 +265,8 @@ 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.
 
@@ -303,6 +308,14 @@ 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()
@@ -311,7 +324,8 @@ def generate(
             inputs=inputs,
             params=sampling_params,
             lora_request=lora_request,
-            prompt_adapter_request=prompt_adapter_request)
+            prompt_adapter_request=prompt_adapter_request,
+            guided_options=guided_options_request)
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
         return LLMEngine.validate_outputs(outputs, RequestOutput)
@@ -508,6 +522,7 @@ 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.
@@ -523,6 +538,14 @@ 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)
+                for param in params if isinstance(param, SamplingParams)
+            ]
+        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(
@@ -548,6 +571,24 @@ 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/model_executor/guided_decoding/__init__.py

@@ -3,9 +3,10 @@
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionNamedToolChoiceParam, ChatCompletionRequest,
     CompletionRequest)
-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.guided_fields import (
+    GuidedDecodingRequest)
 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
 
@@ -20,6 +21,8 @@ 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)
 
@@ -28,6 +31,25 @@ 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

vllm/model_executor/guided_decoding/guided_fields.py

@@ -0,0 +1,38 @@
+from dataclasses import dataclass
+from typing import Dict, List, Optional, TypedDict, Union
+
+from pydantic import BaseModel
+
+
+class LLMGuidedOptions(TypedDict, total=False):
+    guided_json: Union[Dict, BaseModel, str]
+    guided_regex: str
+    guided_choice: List[str]
+    guided_grammar: str
+    guided_decoding_backend: str
+    guided_whitespace_pattern: str
+    guided_json_object: bool
+
+
+@dataclass
+class GuidedDecodingRequest:
+    """One of the fields will be used to retrieve the logit processor."""
+    guided_json: Optional[Union[Dict, BaseModel, str]] = None
+    guided_regex: Optional[str] = None
+    guided_choice: Optional[List[str]] = None
+    guided_grammar: Optional[str] = None
+    guided_decoding_backend: Optional[str] = None
+    guided_whitespace_pattern: Optional[str] = None
+    guided_json_object: Optional[bool] = None
+
+    def __post_init__(self):
+        """Validate that some fields are mutually exclusive."""
+        guide_count = sum([
+            self.guided_json is not None, self.guided_regex is not None,
+            self.guided_choice is not None, self.guided_grammar is not None,
+            self.guided_json_object is not None
+        ])
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding but multiple are "
+                f"specified: {self.__dict__}")

vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py

@@ -12,7 +12,10 @@
 
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               CompletionRequest)
+from vllm.model_executor.guided_decoding.guided_fields import (
+    GuidedDecodingRequest)
 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
 
@@ -54,6 +57,42 @@ async def get_lm_format_enforcer_guided_decoding_logits_processor(
     return logits_processor
 
 
+def get_local_lm_format_enforcer_guided_decoding_logits_processor(
+        guided_options: GuidedDecodingRequest,
+        tokenizer) -> Optional[LogitsProcessor]:
+    """
+    Given an OpenAI-compatible request, check for guided decoding parameters
+    and get the necessary logits processor for the given guide.
+    We cache logit processors by (guide, tokenizer), and on cache hit
+    we make a shallow copy to reuse the same underlying FSM.
+    """
+
+    tokenizer_data = _cached_build_vllm_token_enforcer_tokenizer_data(
+        tokenizer)
+    character_level_parser: CharacterLevelParser
+    if guided_options.guided_json:
+        schema = _normalize_json_schema_object(guided_options.guided_json)
+        character_level_parser = JsonSchemaParser(schema)
+    elif guided_options.guided_choice:
+        character_level_parser = UnionParser(
+            [StringParser(choice) for choice in guided_options.guided_choice])
+    elif guided_options.guided_regex:
+        character_level_parser = RegexParser(guided_options.guided_regex)
+    elif guided_options.guided_grammar:
+        # CFG grammar not supported by LMFE, revert to outlines
+        return get_local_outlines_guided_decoding_logits_processor(
+            guided_options, tokenizer)
+    elif guided_options.guided_json_object:
+        # None means any json object
+        character_level_parser = JsonSchemaParser(None)
+    else:
+        return None
+
+    logits_processor = build_vllm_logits_processor(tokenizer_data,
+                                                   character_level_parser)
+    return logits_processor
+
+
 def _normalize_json_schema_object(schema: Union[str, dict, BaseModel]) -> dict:
     if isinstance(schema, str):
         return json_loads(schema)