Guided decoding

clients/python/llmengine/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.0.0b27"
+__version__ = "0.0.0b28"
 
 import os
 from typing import Sequence

clients/python/llmengine/completion.py

@@ -1,4 +1,4 @@
-from typing import AsyncIterable, Iterator, List, Optional, Union
+from typing import Any, AsyncIterable, Dict, Iterator, List, Optional, Union
 
 from llmengine.api_engine import APIEngine
 from llmengine.data_types import (
@@ -43,6 +43,10 @@ async def acreate(
         frequency_penalty: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
+        include_stop_str_in_output: Optional[bool] = False,
+        guided_json: Optional[Dict[str, Any]] = None,
+        guided_regex: Optional[str] = None,
+        guided_choice: Optional[List[str]] = None,
         timeout: int = COMPLETION_TIMEOUT,
         stream: bool = False,
     ) -> Union[CompletionSyncResponse, AsyncIterable[CompletionStreamResponse]]:
@@ -102,6 +106,18 @@ async def acreate(
                 Float that controls the cumulative probability of the top tokens to consider.
                 Range: (0.0, 1.0]. 1.0 means consider all tokens.
 
+            include_stop_str_in_output (Optional[bool]):
+                Whether to include the stop sequence in the output. Default to False.
+
+            guided_json (Optional[Dict[str, Any]]):
+                If specified, the output will follow the JSON schema. For examples see https://json-schema.org/learn/miscellaneous-examples.
+
+            guided_regex (Optional[str]):
+                If specified, the output will follow the regex pattern.
+
+            guided_choice (Optional[List[str]]):
+                If specified, the output will be exactly one of the choices.
+
             timeout (int):
                 Timeout in seconds. This is the maximum amount of time you are willing to wait for a response.
 
@@ -198,6 +214,10 @@ async def _acreate_stream(
                 frequency_penalty=frequency_penalty,
                 top_k=top_k,
                 top_p=top_p,
+                include_stop_str_in_output=include_stop_str_in_output,
+                guided_json=guided_json,
+                guided_regex=guided_regex,
+                guided_choice=guided_choice,
                 timeout=timeout,
             )
 
@@ -237,6 +257,10 @@ def create(
         frequency_penalty: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
+        include_stop_str_in_output: Optional[bool] = False,
+        guided_json: Optional[Dict[str, Any]] = None,
+        guided_regex: Optional[str] = None,
+        guided_choice: Optional[List[str]] = None,
         timeout: int = COMPLETION_TIMEOUT,
         stream: bool = False,
     ) -> Union[CompletionSyncResponse, Iterator[CompletionStreamResponse]]:
@@ -297,6 +321,18 @@ def create(
                 Float that controls the cumulative probability of the top tokens to consider.
                 Range: (0.0, 1.0]. 1.0 means consider all tokens.
 
+            include_stop_str_in_output (Optional[bool]):
+                Whether to include the stop sequence in the output. Default to False.
+
+            guided_json (Optional[Dict[str, Any]]):
+                If specified, the output will follow the JSON schema.
+
+            guided_regex (Optional[str]):
+                If specified, the output will follow the regex pattern.
+
+            guided_choice (Optional[List[str]]):
+                If specified, the output will be exactly one of the choices.
+
             timeout (int):
                 Timeout in seconds. This is the maximum amount of time you are willing to wait for a response.
 
@@ -396,6 +432,10 @@ def _create_stream(**kwargs):
                 frequency_penalty=frequency_penalty,
                 top_k=top_k,
                 top_p=top_p,
+                include_stop_str_in_output=include_stop_str_in_output,
+                guided_json=guided_json,
+                guided_regex=guided_regex,
+                guided_choice=guided_choice,
             ).dict()
             response = cls.post_sync(
                 resource_name=f"v1/llm/completions-sync?model_endpoint_name={model}",

clients/python/llmengine/data_types.py

@@ -279,6 +279,10 @@ class CompletionSyncV1Request(BaseModel):
     frequency_penalty: Optional[float] = Field(default=None, ge=0.0, le=2.0)
     top_k: Optional[int] = Field(default=None, ge=-1)
     top_p: Optional[float] = Field(default=None, gt=0.0, le=1.0)
+    include_stop_str_in_output: Optional[bool] = Field(default=False)
+    guided_json: Optional[Dict[str, Any]] = Field(default=None)
+    guided_regex: Optional[str] = Field(default=None)
+    guided_choice: Optional[List[str]] = Field(default=None)
 
 
 class TokenOutput(BaseModel):
@@ -349,6 +353,10 @@ class CompletionStreamV1Request(BaseModel):
     frequency_penalty: Optional[float] = Field(default=None, ge=0.0, le=2.0)
     top_k: Optional[int] = Field(default=None, ge=-1)
     top_p: Optional[float] = Field(default=None, gt=0.0, le=1.0)
+    include_stop_str_in_output: Optional[bool] = Field(default=False)
+    guided_json: Optional[Dict[str, Any]] = Field(default=None)
+    guided_regex: Optional[str] = Field(default=None)
+    guided_choice: Optional[List[str]] = Field(default=None)
 
 
 class CompletionStreamOutput(BaseModel):

clients/python/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "scale-llm-engine"
-version = "0.0.0.beta27"
+version = "0.0.0.beta28"
 description = "Scale LLM Engine Python client"
 license = "Apache-2.0"
 authors = ["Phil Chen <[email protected]>"]

clients/python/setup.py

@@ -3,6 +3,6 @@
 setup(
     name="scale-llm-engine",
     python_requires=">=3.7",
-    version="0.0.0.beta27",
+    version="0.0.0.beta28",
     packages=find_packages(),
 )

docs/guides/completions.md

@@ -193,6 +193,59 @@ response = Completion.batch_create(
 print(response.json())
 ```
 
+## Guided decoding
+
+Guided decoding is supported by vLLM and backed by [Outlines](https://github.com/outlines-dev/outlines).
+It enforces certain token generation patterns by tinkering with the sampling logits.
+
+=== "Guided decoding with regex"
+```python
+from llmengine import Completion
+
+response = Completion.create(
+    model="llama-2-7b",
+    prompt="Hello, my name is",
+    max_new_tokens=10,
+    temperature=0.2,
+    guided_regex="Sean.*",
+)
+
+print(response.json())
+# {"request_id":"c19f0fae-317e-4f69-8e06-c04189299b9c","output":{"text":"Sean. I'm a 2","num_prompt_tokens":6,"num_completion_tokens":10,"tokens":null}}
+```
+
+=== "Guided decoding with choice"
+```python
+from llmengine import Completion
+
+response = Completion.create(
+    model="llama-2-7b",
+    prompt="Hello, my name is",
+    max_new_tokens=10,
+    temperature=0.2,
+    guided_choice=["Sean", "Brian", "Tim"],
+)
+
+print(response.json())
+# {"request_id":"641e2af3-a3e3-4493-98b9-d38115ba0d22","output":{"text":"Sean","num_prompt_tokens":6,"num_completion_tokens":4,"tokens":null}}
+```
+
+=== "Guided decoding with JSON schema"
+```python
+from llmengine import Completion
+
+response = Completion.create(
+    model="llama-2-7b",
+    prompt="Hello, my name is",
+    max_new_tokens=10,
+    temperature=0.2,
+    guided_json={"properties":{"myString":{"type":"string"}},"required":["myString"]},
+)
+
+print(response.json())
+# {"request_id":"5b184654-96b6-4932-9eb6-382a51fdb3d5","output":{"text":"{\"myString\" : \"John Doe","num_prompt_tokens":6,"num_completion_tokens":10,"tokens":null}}
+```
+
 ## Which model should I use?
 
 See the [Model Zoo](../../model_zoo) for more information on best practices for which model to use for Completions.

model-engine/model_engine_server/common/dtos/llms.py

@@ -184,6 +184,18 @@ class CompletionSyncV1Request(BaseModel):
     """
     Whether to include the stop strings in output text.
     """
+    guided_json: Optional[Dict[str, Any]] = None
+    """
+    JSON schema for guided decoding.
+    """
+    guided_regex: Optional[str] = None
+    """
+    Regex for guided decoding.
+    """
+    guided_choice: Optional[List[str]] = None
+    """
+    Choices for guided decoding.
+    """
 
 
 class TokenOutput(BaseModel):
@@ -248,6 +260,18 @@ class CompletionStreamV1Request(BaseModel):
     """
     Whether to include the stop strings in output text.
     """
+    guided_json: Optional[Dict[str, Any]] = None
+    """
+    JSON schema for guided decoding. Only supported in vllm.
+    """
+    guided_regex: Optional[str] = None
+    """
+    Regex for guided decoding. Only supported in vllm.
+    """
+    guided_choice: Optional[List[str]] = None
+    """
+    Choices for guided decoding. Only supported in vllm.
+    """
 
 
 class CompletionStreamOutput(BaseModel):

model-engine/model_engine_server/domain/use_cases/llm_model_endpoint_use_cases.py

@@ -1365,6 +1365,26 @@ def validate_and_update_completion_params(
                 "include_stop_str_in_output is only supported in vllm."
             )
 
+    guided_count = 0
+    if request.guided_choice is not None:
+        guided_count += 1
+    if request.guided_json is not None:
+        guided_count += 1
+    if request.guided_regex is not None:
+        guided_count += 1
+
+    if guided_count > 1:
+        raise ObjectHasInvalidValueException(
+            "Only one of guided_json, guided_choice, guided_regex can be enabled."
+        )
+
+    if (
+        request.guided_choice is not None
+        or request.guided_regex is not None
+        or request.guided_json is not None
+    ) and not inference_framework == LLMInferenceFramework.VLLM:
+        raise ObjectHasInvalidValueException("Guided decoding is only supported in vllm.")
+
     return request
 
 
@@ -1656,6 +1676,12 @@ async def execute(
                 vllm_args["logprobs"] = 1
             if request.include_stop_str_in_output is not None:
                 vllm_args["include_stop_str_in_output"] = request.include_stop_str_in_output
+            if request.guided_choice is not None:
+                vllm_args["guided_choice"] = request.guided_choice
+            if request.guided_regex is not None:
+                vllm_args["guided_regex"] = request.guided_regex
+            if request.guided_json is not None:
+                vllm_args["guided_json"] = request.guided_json
 
             inference_request = SyncEndpointPredictV1Request(
                 args=vllm_args,
@@ -1918,6 +1944,12 @@ async def execute(
                 args["logprobs"] = 1
             if request.include_stop_str_in_output is not None:
                 args["include_stop_str_in_output"] = request.include_stop_str_in_output
+            if request.guided_choice is not None:
+                args["guided_choice"] = request.guided_choice
+            if request.guided_regex is not None:
+                args["guided_regex"] = request.guided_regex
+            if request.guided_json is not None:
+                args["guided_json"] = request.guided_json
             args["stream"] = True
         elif model_content.inference_framework == LLMInferenceFramework.LIGHTLLM:
             args = {

model-engine/model_engine_server/inference/vllm/requirements.txt

@@ -1,3 +1,2 @@
-ray>=2.9
-vllm==0.3.2
+vllm==0.3.3
 pydantic>=2.0

model-engine/model_engine_server/inference/vllm/vllm_server.py

@@ -7,10 +7,12 @@
 from typing import AsyncGenerator
 
 import uvicorn
-from fastapi import BackgroundTasks, FastAPI, Request
+from fastapi import BackgroundTasks, FastAPI, HTTPException, Request
 from fastapi.responses import Response, StreamingResponse
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.entrypoints.openai.protocol import CompletionRequest as OpenAICompletionRequest
+from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
 from vllm.sampling_params import SamplingParams
 from vllm.utils import random_uuid
 
@@ -38,7 +40,35 @@ async def generate(request: Request) -> Response:
     request_dict = await request.json()
     prompt = request_dict.pop("prompt")
     stream = request_dict.pop("stream", False)
+    guided_json = request_dict.pop("guided_json", None)
+    guided_regex = request_dict.pop("guided_regex", None)
+    guided_choice = request_dict.pop("guided_choice", None)
     sampling_params = SamplingParams(**request_dict)
+
+    # Dummy request to get guided decode logit processor
+    try:
+        partial_openai_request = OpenAICompletionRequest.model_validate(
+            {
+                "model": "",
+                "prompt": "",
+                "guided_json": guided_json,
+                "guided_regex": guided_regex,
+                "guided_choice": guided_choice,
+            }
+        )
+    except Exception:
+        raise HTTPException(
+            status_code=400, detail="Bad request: failed to parse guided decoding parameters."
+        )
+
+    guided_decode_logit_processor = await get_guided_decoding_logits_processor(
+        partial_openai_request, engine.get_tokenizer()
+    )
+    if guided_decode_logit_processor is not None:
+        if sampling_params.logits_processors is None:
+            sampling_params.logits_processors = []
+        sampling_params.logits_processors.append(guided_decode_logit_processor)
+
     request_id = random_uuid()
     results_generator = engine.generate(prompt, sampling_params, request_id)