Support for guided decoding for offline LLM

tests/entrypoints/llm/conftest.py

@@ -0,0 +1,69 @@
+import pytest
+
+
+@pytest.fixture
+def sample_regex():
+    return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
+            r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
+
+
+@pytest.fixture
+def sample_json_schema():
+    return {
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": "string"
+            },
+            "age": {
+                "type": "integer"
+            },
+            "skills": {
+                "type": "array",
+                "items": {
+                    "type": "string",
+                    "maxLength": 10
+                },
+                "minItems": 3
+            },
+            "work_history": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "company": {
+                            "type": "string"
+                        },
+                        "duration": {
+                            "type": "number"
+                        },
+                        "position": {
+                            "type": "string"
+                        }
+                    },
+                    "required": ["company", "position"]
+                }
+            }
+        },
+        "required": ["name", "age", "skills", "work_history"]
+    }
+
+
+@pytest.fixture
+def sample_guided_choice():
+    return [
+        "Python", "Java", "JavaScript", "C++", "C#", "PHP", "TypeScript",
+        "Ruby", "Swift", "Kotlin"
+    ]
+
+
+@pytest.fixture
+def sample_sql_statements():
+    return ("""
+start: select_statement
+select_statement: "SELECT" column "from" table "where" condition
+column: "col_1" | "col_2"
+table: "table_1" | "table_2"
+condition: column "=" number
+number: "1" | "2"
+""")

tests/entrypoints/llm/test_guided_generate.py

@@ -0,0 +1,154 @@
+import json
+import re
+import weakref
+from typing import List
+
+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"
+
+PROMPTS = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+TOKEN_IDS = [
+    [0],
+    [0, 1],
+    [0, 2, 1],
+    [0, 3, 1, 2],
+]
+
+@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=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=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=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=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

@@ -1,5 +1,6 @@
 from contextlib import contextmanager
-from typing import ClassVar, List, Optional, Sequence, Union, cast, overload
+from typing import (ClassVar, Dict, List, Optional, Sequence, Union, cast,
+                    overload)
 
 from tqdm import tqdm
 from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
@@ -10,6 +11,8 @@
                          parse_and_batch_prompt)
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.guided_decoding import (
+    GuidedDecodingFields, get_local_guided_decoding_logits_processor)
 from vllm.outputs import EmbeddingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
@@ -262,6 +265,7 @@ def generate(
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options: Optional[Union[Dict, GuidedDecodingFields]] = None
     ) -> List[RequestOutput]:
         """Generates the completions for the input prompts.
 
@@ -303,6 +307,13 @@ def generate(
         else:
             inputs = cast(Union[PromptInputs, Sequence[PromptInputs]], prompts)
 
+        if isinstance(guided_options, Dict):
+            if len(guided_options) > 1:
+                raise ValueError(
+                    "You can only use one guided decoding but multiple is "
+                    f"specified: {self.__dict__}")
+            guided_options = GuidedDecodingFields(**guided_options)
+
         if sampling_params is None:
             # Use default sampling params.
             sampling_params = SamplingParams()
@@ -311,7 +322,8 @@ 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)
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
         return LLMEngine.validate_outputs(outputs, RequestOutput)
@@ -508,6 +520,7 @@ def _validate_and_add_requests(
                       Sequence[PoolingParams]],
         lora_request: Optional[Union[Sequence[LoRARequest], LoRARequest]],
         prompt_adapter_request: Optional[PromptAdapterRequest],
+        guided_options: Optional[GuidedDecodingFields] = None,
     ) -> None:
         if isinstance(inputs, (str, dict)):
             # Convert a single prompt to a list.
@@ -523,6 +536,18 @@ def _validate_and_add_requests(
             raise ValueError("The lengths of prompts and lora_request "
                              "must be the same.")
 
+        if isinstance(params, list):
+            if len(params) != num_requests:
+                raise ValueError("The lengths of prompts and params "
+                                 "must be the same.")
+
+            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 +573,24 @@ def _add_request(
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request)
 
+    def _add_guided_processor(
+            self,
+            params: SamplingParams,
+            guided_options: Optional[GuidedDecodingFields] = 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 (
+    GuidedDecodingFields)
 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: GuidedDecodingFields,
+        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