Improve configs - SpeculativeConfig

vllm/config.py

@@ -2125,139 +2125,113 @@ def __post_init__(self):
             self.device = torch.device(self.device_type)
 
 
+SpeculativeMethod = Literal["ngram", "eagle", "medusa", "mlp_speculator",
+                            "draft_model"]
+SpeculativeAcceptanceMethod = Literal["rejection_sampler",
+                                      "typical_acceptance_sampler"]
+
+
+@config
 @dataclass
 class SpeculativeConfig:
-    """
-    Configuration for speculative decoding.
-    Configurable parameters include:
-    - General Speculative Decoding Control:
-        - num_speculative_tokens (int): The number of speculative
-            tokens, if provided. It will default to the number in the draft
-            model config if present, otherwise, it is required.
-        - model (Optional[str]): The name of the draft model, eagle head,
-            or additional weights, if provided.
-        - method (Optional[str]): The name of the speculative method to use.
-            If users provide and set the `model` param, the speculative method
-            type will be detected automatically if possible, if `model` param
-            is not provided, the method name must be provided.
-            - Possible values:
-                - ngram
-                    Related additional configuration:
-                    - prompt_lookup_max (Optional[int]):
-                        Maximum size of ngram token window when using Ngram
-                        proposer, required when method is set to ngram.
-                    - prompt_lookup_min (Optional[int]):
-                        Minimum size of ngram token window when using Ngram
-                        proposer, if provided. Defaults to 1.
-                - eagle
-                - medusa
-                - mlp_speculator
-                - draft_model
-        - acceptance_method (str): The method to use for accepting draft
-            tokens. This can take two possible values: 'rejection_sampler' and
-            'typical_acceptance_sampler' for RejectionSampler and
-            TypicalAcceptanceSampler respectively. If not specified, it
-            defaults to 'rejection_sampler'.
-            - Possible values:
-                - rejection_sampler
-                - typical_acceptance_sampler
-                    Related additional configuration:
-                    - posterior_threshold (Optional[float]):
-                        A threshold value that sets a lower bound on the
-                        posterior probability of a token in the target model
-                        for it to be accepted. This threshold is used only
-                        when we use the TypicalAcceptanceSampler for token
-                        acceptance.
-                    - posterior_alpha (Optional[float]):
-                        Scaling factor for entropy-based threshold, applied
-                        when using TypicalAcceptanceSampler.
-        - draft_tensor_parallel_size (Optional[int]): The degree of the tensor
-            parallelism for the draft model. Can only be 1 or the same as the
-            target model's tensor parallel size.
-        - disable_logprobs (bool): If set to True, token log probabilities are
-            not returned during speculative decoding. If set to False, token
-            log probabilities are returned according to the log probability
-            settings in SamplingParams. If not specified, it defaults to True.
-
-    - Draft Model Configuration:
-        - quantization (Optional[str]): Quantization method that was used to
-            quantize the draft model weights. If None, we assume the
-            model weights are not quantized. Note that it only takes effect
-            when using the draft model-based speculative method.
-        - max_model_len (Optional[int]): The maximum model length of the
-            draft model. Used when testing the ability to skip
-            speculation for some sequences.
-        - revision: The specific model version to use for the draft model. It
-            can be a branch name, a tag name, or a commit id. If unspecified,
-            will use the default version.
-        - code_revision: The specific revision to use for the draft model code
-            on Hugging Face Hub. It can be a branch name, a tag name, or a
-            commit id. If unspecified, will use the default version.
+    """Configuration for speculative decoding."""
 
-    - Advanced Control:
-        - disable_mqa_scorer (bool): Disable the MQA scorer and fall back to
-            batch expansion for scoring proposals. If not specified, it
-            defaults to False.
-        - disable_by_batch_size (Optional[int]): Disable speculative decoding
-            for new incoming requests when the number of enqueued requests is
-            larger than this value, if provided.
-
-    Although the parameters above are structured hierarchically, there is no
-    need to nest them during configuration.
-
-    Non-configurable internal parameters include:
-    - Model Configuration:
-        - target_model_config (ModelConfig): The configuration of the target
-            model.
-        - draft_model_config (ModelConfig): The configuration of the draft
-            model initialized internal.
-    - Parallelism Configuration:
-        - target_parallel_config (ParallelConfig): The parallel configuration
-            for the target model.
-        - draft_parallel_config (ParallelConfig): The parallel configuration
-            for the draft model initialized internal.
-    - Execution Control:
-        - enable_chunked_prefill (bool): Whether vLLM is configured to use
-            chunked prefill or not. Used for raising an error since it's not
-            yet compatible with speculative decode.
-        - disable_log_stats (bool): Whether to disable the periodic printing of
-            stage times in speculative decoding.
-    """
-    # speculative configs from cli args
+    # General speculative decoding control
     num_speculative_tokens: int = field(default=None,
                                         init=True)  # type: ignore
-    method: Optional[str] = None
-    acceptance_method: str = "rejection_sampler"
+    """The number of speculative tokens, if provided. It will default to the
+    number in the draft model config if present, otherwise, it is required."""
+    model: Optional[str] = None
+    """The name of the draft model, eagle head, or additional weights, if
+    provided."""
+    method: Optional[SpeculativeMethod] = None
+    """The name of the speculative method to use. If users provide and set the
+    `model` param, the speculative method type will be detected automatically
+    if possible, if `model` param is not provided, the method name must be
+    provided.
+
+    If using `ngram` method, the related configuration `prompt_lookup_max` and
+    `prompt_lookup_min` should be considered."""
+    acceptance_method: SpeculativeAcceptanceMethod = "rejection_sampler"
+    """The method to use for accepting draft tokens:\n
+    - "rejection_sampler" maps to `RejectionSampler`.\n
+    - "typical_acceptance_sampler" maps to `TypicalAcceptanceSampler`.
+
+    If using `typical_acceptance_sampler`, the related configuration
+    `posterior_threshold` and `posterior_alpha` should be considered."""
     draft_tensor_parallel_size: Optional[int] = None
+    """The degree of the tensor parallelism for the draft model. Can only be 1
+    or the same as the target model's tensor parallel size."""
     disable_logprobs: bool = True
+    """If set to True, token log probabilities are not returned during
+    speculative decoding. If set to False, token log probabilities are returned
+    according to the log probability settings in SamplingParams."""
 
-    model: Optional[str] = None
+    # Draft model configuration
     quantization: Optional[str] = None
+    """Quantization method that was used to quantize the draft model weights.
+    If `None`, we assume the model weights are not quantized. Note that it only
+    takes effect when using the draft model-based speculative method."""
     max_model_len: Optional[int] = None
+    """The maximum model length of the draft model. Used when testing the
+    ability to skip speculation for some sequences."""
     revision: Optional[str] = None
+    """The specific model version to use for the draft model. It can be a
+    branch name, a tag name, or a commit id. If unspecified, will use the
+    default version."""
     code_revision: Optional[str] = None
+    """The specific revision to use for the draft model code on Hugging Face
+    Hub. It can be a branch name, a tag name, or a commit id. If unspecified,
+    will use the default version."""
 
+    # Advanced control
     disable_mqa_scorer: bool = False
+    """Disable the MQA scorer and fall back to batch expansion for scoring
+    proposals."""
     disable_by_batch_size: Optional[int] = None
+    """Disable speculative decoding for new incoming requests when the number
+    of enqueued requests is larger than this value, if provided."""
+
+    # Ngram proposer configuration
     prompt_lookup_max: Optional[int] = None
+    """Maximum size of ngram token window when using Ngram proposer, required
+    when method is set to ngram."""
     prompt_lookup_min: Optional[int] = None
+    """Minimum size of ngram token window when using Ngram proposer, if
+    provided. Defaults to 1."""
+
+    # Typical acceptance sampler configuration
     posterior_threshold: Optional[float] = None
+    """A threshold value that sets a lower bound on the posterior probability
+    of a token in the target model for it to be accepted. This threshold is
+    used only when we use the `TypicalAcceptanceSampler` for token acceptance.
+    """
     posterior_alpha: Optional[float] = None
+    """Scaling factor for entropy-based threshold, applied when using
+    `TypicalAcceptanceSampler`."""
 
     # required configuration params passed from engine
     target_model_config: ModelConfig = field(default=None,
                                              init=True)  # type: ignore
+    """The configuration of the target model."""
     target_parallel_config: ParallelConfig = field(default=None,
                                                    init=True)  # type: ignore
+    """The parallel configuration for the target model."""
     enable_chunked_prefill: bool = field(default=None,
                                          init=True)  # type: ignore
+    """Whether vLLM is configured to use chunked prefill or not. Used for
+    raising an error since it's not yet compatible with speculative decode."""
     disable_log_stats: bool = field(default=None, init=True)  # type: ignore
+    """Whether to disable the periodic printing of stage times in speculative
+    decoding."""
 
     # params generated in the post-init stage
     draft_model_config: ModelConfig = field(default=None,
                                             init=True)  # type: ignore
+    """The configuration of the draft model initialized internal."""
     draft_parallel_config: ParallelConfig = field(default=None,
                                                   init=True)  # type: ignore
+    """The parallel configuration for the draft model initialized internal."""
 
     def compute_hash(self) -> str:
         """

vllm/engine/arg_utils.py

@@ -766,11 +766,18 @@ def get_kwargs(cls: type[Config]) -> dict[str, Any]:
                             help=('Maximum number of forward steps per '
                                   'scheduler call.'))
 
-        parser.add_argument('--speculative-config',
-                            type=json.loads,
-                            default=None,
-                            help='The configurations for speculative decoding.'
-                            ' Should be a JSON string.')
+        # Speculative arguments
+        speculative_group = parser.add_argument_group(
+            title="SpeculativeConfig",
+            description=SpeculativeConfig.__doc__,
+        )
+        speculative_group.add_argument(
+            '--speculative-config',
+            type=json.loads,
+            default=None,
+            help='The configurations for speculative decoding.'
+            ' Should be a JSON string.')
+
         parser.add_argument(
             '--ignore-patterns',
             action="append",