[REFACTOR] Move GenerationConfig to protocol (#2427)

This PR moves GenerationConfig to protocol.
As we move towards OAI style API GenerationConfig becomes more like an internal API.

This change reflects that and also removes duplicated definition of ResponseFormat
and DebugConfig

python/mlc_llm/__init__.py

@@ -4,6 +4,5 @@
 """
 
 from . import protocol, serve
-from .chat_module import ChatConfig, ChatModule, ConvConfig, GenerationConfig
 from .libinfo import __version__
 from .serve import AsyncMLCEngine, MLCEngine

python/mlc_llm/protocol/__init__.py

@@ -1,4 +1,9 @@
-"""Definitions of pydantic models for API entry points and configurations"""
-from . import openai_api_protocol
+"""Definitions of pydantic models for API entry points and configurations
 
-RequestProtocol = openai_api_protocol.CompletionRequest
+Note
+----
+We use the following convention
+
+- filename_protocol If the classes can appear in an API endpoint
+- filename_config For other config classes
+"""

python/mlc_llm/protocol/generation_config.py

@@ -0,0 +1,32 @@
+"""Low-level generation config class"""
+# pylint: disable=missing-class-docstring, disable=too-many-instance-attributes
+from typing import Dict, List, Optional
+
+from pydantic import BaseModel
+
+from .debug_protocol import DebugConfig
+from .openai_api_protocol import RequestResponseFormat
+
+
+class GenerationConfig(BaseModel):  # pylint:
+    """The generation configuration dataclass.
+
+    This is a config class used by Engine internally.
+    """
+
+    n: int = 1
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+    repetition_penalty: Optional[float] = None
+    logprobs: bool = False
+    top_logprobs: int = 0
+    logit_bias: Optional[Dict[int, float]] = None
+    # internally we use -1 to represent infinite
+    max_tokens: int = -1
+    seed: Optional[int] = None
+    stop_strs: Optional[List[str]] = None
+    stop_token_ids: Optional[List[int]] = None
+    response_format: Optional[RequestResponseFormat] = None
+    debug_config: Optional[Optional[DebugConfig]] = None

python/mlc_llm/serve/__init__.py

@@ -2,7 +2,7 @@
 
 # Load MLC LLM library by importing base
 from .. import base
-from .config import DebugConfig, EngineConfig, GenerationConfig
+from .config import EngineConfig
 from .data import Data, ImageData, RequestStreamOutput, TextData, TokenData
 from .engine import AsyncMLCEngine, MLCEngine
 from .grammar import BNFGrammar, GrammarStateMatcher

python/mlc_llm/serve/config.py

@@ -2,147 +2,7 @@
 
 import json
 from dataclasses import asdict, dataclass, field
-from typing import Dict, List, Literal, Optional, Tuple, Union
-
-
-@dataclass
-class ResponseFormat:
-    """The response format dataclass.
-
-    Parameters
-    ----------
-    type : Literal["text", "json_object"]
-        The type of response format. Default: "text".
-
-    schema : Optional[str]
-        The JSON schema string for the JSON response format. If None, a legal json string without
-        special restrictions will be generated.
-
-        Could be specified when the response format is "json_object". Default: None.
-    """
-
-    type: Literal["text", "json_object"] = "text"
-    schema: Optional[str] = None
-
-    def __post_init__(self):
-        if self.schema is not None and self.type != "json_object":
-            raise ValueError("JSON schema is only supported in JSON response format")
-
-
-@dataclass
-class DebugConfig:
-    """The debug configuration dataclass.Parameters
-    ----------
-    ignore_eos : bool
-        When it is true, ignore the eos token and generate tokens until `max_tokens`.
-        Default is set to False.
-
-    pinned_system_prompt : bool
-        Whether the input and generated data pinned in engine. Default is set to False.
-        This can be used for system prompt or other purpose, if the data is aimed to be
-        kept all the time.
-
-    special_request: Optional[string]
-        Special requests to send to engine
-    """
-
-    ignore_eos: bool = False
-    pinned_system_prompt: bool = False
-    special_request: Optional[Literal["query_engine_metrics"]] = None
-
-
-@dataclass
-class GenerationConfig:  # pylint: disable=too-many-instance-attributes
-    """The generation configuration dataclass.
-
-    Parameters
-    ----------
-    n : int
-        How many chat completion choices to generate for each input message.
-
-    temperature : Optional[float]
-        The value that applies to logits and modulates the next token probabilities.
-
-    top_p : Optional[float]
-        In sampling, only the most probable tokens with probabilities summed up to
-        `top_p` are kept for sampling.
-
-    frequency_penalty : Optional[float]
-        Positive values penalize new tokens based on their existing frequency
-        in the text so far, decreasing the model's likelihood to repeat the same
-        line verbatim.
-
-    presence_penalty : Optional[float]
-        Positive values penalize new tokens based on whether they appear in the text
-        so far, increasing the model's likelihood to talk about new topics.
-
-    repetition_penalty : float
-        The penalty term that applies to logits to control token repetition in generation.
-        It will be suppressed when any of frequency_penalty and presence_penalty is
-        non-zero.
-
-    logprobs : bool
-        Whether to return log probabilities of the output tokens or not.
-        If true, the log probabilities of each output token will be returned.
-
-    top_logprobs : int
-        An integer between 0 and 5 specifying the number of most likely
-        tokens to return at each token position, each with an associated
-        log probability.
-        `logprobs` must be set to True if this parameter is used.
-
-    logit_bias : Optional[Dict[int, float]]
-        The bias logit value added to selected tokens prior to sampling.
-
-    max_tokens : Optional[int]
-        The maximum number of generated tokens,
-        or None, in which case the generation will not stop
-        until exceeding model capability or hit any stop criteria.
-
-    seed : Optional[int]
-        The random seed of the generation.
-        The seed will be a random value if not specified.
-
-    stop_strs : List[str]
-        The list of strings that mark the end of generation.
-
-    stop_token_ids : List[int]
-        The list of token ids that mark the end of generation.
-
-    response_format : ResponseFormat
-        The response format of the generation output.
-
-    debug_config : Optional[DebugConfig]
-        The optional debug configuration.
-    """
-
-    n: int = 1
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
-    frequency_penalty: Optional[float] = None
-    presence_penalty: Optional[float] = None
-    repetition_penalty: float = 1.0
-    logprobs: bool = False
-    top_logprobs: int = 0
-    logit_bias: Optional[Dict[int, float]] = field(default_factory=dict)  # type: ignore
-
-    max_tokens: Optional[int] = 128
-    seed: Optional[int] = None
-    stop_strs: List[str] = field(default_factory=list)
-    stop_token_ids: List[int] = field(default_factory=list)
-
-    response_format: ResponseFormat = field(default_factory=ResponseFormat)
-
-    debug_config: Optional[DebugConfig] = field(default_factory=DebugConfig)
-
-    def asjson(self) -> str:
-        """Return the config in string of JSON format."""
-        return json.dumps(asdict(self))
-
-    @staticmethod
-    def from_json(json_str: str) -> "GenerationConfig":
-        """Construct a config from JSON string."""
-        return GenerationConfig(**json.loads(json_str))
+from typing import List, Literal, Optional, Tuple, Union
 
 
 @dataclass

python/mlc_llm/serve/engine.py

@@ -22,8 +22,9 @@
 from tvm.runtime import Device
 
 from mlc_llm.protocol import debug_protocol, openai_api_protocol
+from mlc_llm.protocol.generation_config import GenerationConfig
 from mlc_llm.serve import data, engine_utils
-from mlc_llm.serve.config import EngineConfig, GenerationConfig
+from mlc_llm.serve.config import EngineConfig
 from mlc_llm.streamer import TextStreamer
 from mlc_llm.support import logging
 
@@ -1372,7 +1373,9 @@ async def _generate(
         # Create the request with the given id, input data, generation
         # config and the created callback.
         input_data = engine_utils.convert_prompts_to_data(prompt)
-        request = self._ffi["create_request"](request_id, input_data, generation_config.asjson())
+        request = self._ffi["create_request"](
+            request_id, input_data, generation_config.model_dump_json()
+        )
 
         # Create the unique async request stream of the request.
         stream = engine_base.AsyncRequestStream()
@@ -1898,7 +1901,9 @@ def _generate(  # pylint: disable=too-many-locals
         # Create the request with the given id, input data, generation
         # config and the created callback.
         input_data = engine_utils.convert_prompts_to_data(prompt)
-        request = self._ffi["create_request"](request_id, input_data, generation_config.asjson())
+        request = self._ffi["create_request"](
+            request_id, input_data, generation_config.model_dump_json()
+        )
 
         # Record the stream in the tracker
         self.state.sync_output_queue = queue.Queue()

python/mlc_llm/serve/engine_base.py

@@ -18,9 +18,10 @@
 
 from mlc_llm.protocol import openai_api_protocol
 from mlc_llm.protocol.conversation_protocol import Conversation
+from mlc_llm.protocol.generation_config import GenerationConfig
 from mlc_llm.protocol.mlc_chat_config import MLCChatConfig
 from mlc_llm.serve import data, engine_utils
-from mlc_llm.serve.config import EngineConfig, GenerationConfig
+from mlc_llm.serve.config import EngineConfig
 from mlc_llm.serve.event_trace_recorder import EventTraceRecorder
 from mlc_llm.streamer import TextStreamer
 from mlc_llm.support import download_cache, logging

python/mlc_llm/serve/engine_utils.py

@@ -3,10 +3,13 @@
 import uuid
 from typing import Any, Callable, Dict, List, Optional, Union
 
-from mlc_llm.protocol import RequestProtocol, error_protocol, openai_api_protocol
+from mlc_llm.protocol import error_protocol, openai_api_protocol
+from mlc_llm.protocol.generation_config import GenerationConfig
 from mlc_llm.serve import data
 
-from .config import DebugConfig, GenerationConfig, ResponseFormat
+RequestProtocol = Union[
+    openai_api_protocol.CompletionRequest, openai_api_protocol.ChatCompletionRequest
+]
 
 
 def get_unsupported_fields(request: RequestProtocol) -> List[str]:
@@ -20,9 +23,7 @@ def get_unsupported_fields(request: RequestProtocol) -> List[str]:
     raise RuntimeError("Cannot reach here")
 
 
-def openai_api_get_generation_config(
-    request: Union[openai_api_protocol.CompletionRequest, openai_api_protocol.ChatCompletionRequest]
-) -> Dict[str, Any]:
+def openai_api_get_generation_config(request: RequestProtocol) -> Dict[str, Any]:
     """Create the generation config from the given request."""
     kwargs: Dict[str, Any] = {}
     arg_names = [
@@ -36,6 +37,8 @@ def openai_api_get_generation_config(
         "top_logprobs",
         "logit_bias",
         "seed",
+        "response_format",
+        "debug_config",
     ]
     for arg_name in arg_names:
         kwargs[arg_name] = getattr(request, arg_name)
@@ -45,12 +48,6 @@ def openai_api_get_generation_config(
         kwargs["max_tokens"] = -1
     if request.stop is not None:
         kwargs["stop_strs"] = [request.stop] if isinstance(request.stop, str) else request.stop
-    if request.response_format is not None:
-        kwargs["response_format"] = ResponseFormat(
-            **request.response_format.model_dump(by_alias=True)
-        )
-    if request.debug_config is not None:
-        kwargs["debug_config"] = DebugConfig(**request.debug_config.model_dump())
     return kwargs
 
 

python/mlc_llm/serve/request.py

@@ -4,8 +4,9 @@
 import tvm._ffi
 from tvm.runtime import Object
 
+from mlc_llm.protocol.generation_config import GenerationConfig
+
 from . import _ffi_api
-from .config import GenerationConfig
 from .data import Data
 
 
@@ -29,6 +30,6 @@ def inputs(self) -> List[Data]:
     @property
     def generation_config(self) -> GenerationConfig:
         """The generation config of the request."""
-        return GenerationConfig.from_json(
+        return GenerationConfig.model_validate_json(
             _ffi_api.RequestGetGenerationConfigJSON(self)  # type: ignore  # pylint: disable=no-member
         )

python/mlc_llm/serve/sync_engine.py

@@ -13,8 +13,9 @@
 
 import tvm
 
+from mlc_llm.protocol.generation_config import GenerationConfig
 from mlc_llm.serve import data
-from mlc_llm.serve.config import EngineConfig, GenerationConfig
+from mlc_llm.serve.config import EngineConfig
 from mlc_llm.serve.engine_base import (
     EngineMetrics,
     _check_engine_config,
@@ -307,7 +308,7 @@ def create_request(
         """
         if not isinstance(inputs, list):
             inputs = [inputs]
-        return self._ffi["create_request"](request_id, inputs, generation_config.asjson())
+        return self._ffi["create_request"](request_id, inputs, generation_config.model_dump_json())
 
     def add_request(self, request: Request) -> None:
         """Add a new request to the engine.

python/mlc_llm/testing/debug_chat.py

@@ -373,9 +373,6 @@ def generate(
 
         generate_length : int
             How many tokens to generate.
-
-        generation_config : Optional[GenerationConfig]
-            Will be used to override the GenerationConfig in ``mlc-chat-config.json``.
         """
         out_tokens = []
 

tests/python/serve/evaluate_engine.py

@@ -4,7 +4,7 @@
 import random
 from typing import List, Tuple
 
-from mlc_llm.serve import GenerationConfig
+from mlc_llm.protocol.generation_config import GenerationConfig
 from mlc_llm.serve.sync_engine import EngineConfig, SyncMLCEngine