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234
vllm_npu/patch/platform/patch_config.py
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234
vllm_npu/patch/platform/patch_config.py
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import ast
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import vllm.envs as envs
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from vllm.config.speculative import SpeculativeConfig
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from vllm.logger import logger
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def __post_init__(self):
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# Note: "method" is a new parameter that helps to extend the
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# configuration of non-model-based proposers, and the "model" parameter
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# will be used to set the draft model, eagle head, or additional weight
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# when needed. If users do not specify "method", the speculative method
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# will be detected automatically if possible. If the speculative method
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# can not be detected, it will be considered as the "draft_model" by
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# default.
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if self.model is None and self.num_speculative_tokens is not None:
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# TODO(Shangming): Refactor mtp configuration logic when supporting
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if (self.target_model_config
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and self.target_model_config.hf_text_config.model_type
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in ("deepseek_v3", "deepseek_v32", "mimo", "ernie4_5_moe",
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"qwen3_next")):
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# use the draft model from the same model:
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self.model = self.target_model_config.model
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# Align the quantization of draft model for cases such as
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# --quantization fp8 with a bf16 checkpoint.
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if not self.quantization:
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self.quantization = self.target_model_config.quantization
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elif self.method in ("ngram", "[ngram]"):
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self.model = "ngram"
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else:
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raise ValueError("num_speculative_tokens was provided but without "
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"speculative model.")
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# Automatically configure the method for ngram when "model" is used
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# instead of "method"
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if self.method is None and (self.model is not None
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and self.model in ("ngram", "[ngram]")):
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self.method = "ngram"
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if self.method in ("ngram", "[ngram]"):
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# Unified to "ngram" internally
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self.method = "ngram"
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# Set default values if not provided
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if (self.prompt_lookup_min is None and self.prompt_lookup_max is None):
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# TODO(woosuk): Tune these values. They are arbitrarily chosen.
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self.prompt_lookup_min = 5
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self.prompt_lookup_max = 5
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elif self.prompt_lookup_min is None:
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assert self.prompt_lookup_max is not None
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self.prompt_lookup_min = self.prompt_lookup_max
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elif self.prompt_lookup_max is None:
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assert self.prompt_lookup_min is not None
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self.prompt_lookup_max = self.prompt_lookup_min
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# Validate values
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if self.prompt_lookup_min < 1:
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raise ValueError(
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f"prompt_lookup_min={self.prompt_lookup_min} must be > 0")
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if self.prompt_lookup_max < 1:
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raise ValueError(
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f"prompt_lookup_max={self.prompt_lookup_max} must be > 0")
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if self.prompt_lookup_min > self.prompt_lookup_max:
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raise ValueError(
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f"prompt_lookup_min={self.prompt_lookup_min} must "
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f"be <= prompt_lookup_max={self.prompt_lookup_max}")
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# TODO: current we still need extract vocab_size from target model
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# config, in future, we may try refactor it out, and set
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# draft related config as None here.
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self.draft_model_config = self.target_model_config
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self.draft_parallel_config = self.target_parallel_config
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else:
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self.prompt_lookup_max = 0
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self.prompt_lookup_min = 0
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if self.model is not None:
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# TODO: Move this import to the top once `ModelConfig`
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# lives in `vllm.config.model`.
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from vllm.config import ModelConfig
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self.draft_model_config = ModelConfig(
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model=self.model,
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runner="draft",
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tokenizer=self.target_model_config.tokenizer,
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tokenizer_mode=self.target_model_config.tokenizer_mode,
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trust_remote_code=self.target_model_config.trust_remote_code,
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allowed_local_media_path=self.target_model_config.
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allowed_local_media_path,
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allowed_media_domains=self.target_model_config.
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allowed_media_domains,
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dtype=self.target_model_config.dtype,
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seed=self.target_model_config.seed,
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revision=self.revision,
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code_revision=self.code_revision,
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tokenizer_revision=self.target_model_config.tokenizer_revision,
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spec_target_max_model_len=self.target_model_config.
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max_model_len,
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quantization=self.quantization,
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enforce_eager=self.target_model_config.enforce_eager,
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max_logprobs=self.target_model_config.max_logprobs,
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hf_overrides=SpeculativeConfig.hf_config_override,
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)
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# Automatically detect the method
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if self.method in ('eagle', 'eagle3'):
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pass
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# examples:
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# yuhuili/EAGLE-LLaMA3-Instruct-8B
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# yuhuili/EAGLE3-LLaMA3.1-Instruct-8B
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# AngelSlim/Qwen3-8B_eagle3
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elif "eagle-" in self.draft_model_config.model.lower():
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self.method = "eagle"
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elif "eagle3" in self.draft_model_config.model.lower():
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self.method = "eagle3"
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elif self.draft_model_config.hf_config.model_type == "medusa":
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self.method = "medusa"
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elif (self.draft_model_config.hf_config.model_type ==
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"mlp_speculator"):
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self.method = "mlp_speculator"
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elif (self.draft_model_config.hf_config.model_type
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in ("deepseek_mtp", "mimo_mtp", "glm4_moe_mtp")):
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self.method = "deepseek_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"All Deepseek MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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elif (self.draft_model_config.hf_config.model_type == "ernie_mtp"):
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self.method = "ernie_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"All Ernie MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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elif (self.draft_model_config.hf_config.model_type ==
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"qwen3_next_mtp"):
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self.method = "qwen3_next_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"All Qwen3Next MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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elif (self.draft_model_config.hf_config.model_type
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in ("longcat_flash_mtp")):
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self.method = "longcat_flash_mtp"
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if self.num_speculative_tokens > 1:
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logger.warning(
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"LongCat MTP models only have " \
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"one layer. Might need some code changes " \
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"to support multiple layers."
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)
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else:
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self.method = "draft_model"
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raise NotImplementedError(
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"Speculative decoding with draft model is not "
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"supported yet. Please consider using other "
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"speculative decoding methods such as ngram, medusa, "
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"eagle, or deepseek_mtp.")
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# Replace hf_config for EAGLE draft_model
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if self.method in ("eagle", "eagle3"):
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if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
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raise ValueError(
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"Chunked prefill and EAGLE are not compatible "
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"when using V0.")
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from vllm.transformers_utils.configs import SpeculatorsConfig
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from vllm.transformers_utils.configs.eagle import EAGLEConfig
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if isinstance(self.draft_model_config.hf_config,
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(EAGLEConfig, SpeculatorsConfig)):
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pass
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else:
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eagle_config = EAGLEConfig(
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self.draft_model_config.hf_config,
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method=self.method,
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model_type="eagle")
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self.draft_model_config.hf_config = eagle_config
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if (self.num_speculative_tokens is not None
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and hasattr(self.draft_model_config.hf_config,
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"num_lookahead_tokens")):
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self.draft_model_config.hf_config.num_lookahead_tokens = \
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self.num_speculative_tokens
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n_predict = getattr(self.draft_model_config.hf_config, "n_predict",
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None)
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if n_predict is not None:
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if self.num_speculative_tokens is None:
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# Default to max value defined in draft model config.
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self.num_speculative_tokens = n_predict
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elif self.num_speculative_tokens > n_predict and \
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self.num_speculative_tokens % n_predict != 0:
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# Ensure divisibility for MTP module reuse.
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raise ValueError(
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f"num_speculative_tokens:{self.num_speculative_tokens}"
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f" must be divisible by {n_predict=}")
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if self.speculative_token_tree is None:
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# Generate chain of tokens.
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self.speculative_token_tree = str([
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(i + 1) * (0, ) for i in range(self.num_speculative_tokens)
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])
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else:
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# Sort the token tree breadth-first.
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tree_choices = ast.literal_eval(self.speculative_token_tree)
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self.speculative_token_tree = str(
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sorted(tree_choices, key=lambda t: (len(t), t)))
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self.draft_tensor_parallel_size = \
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SpeculativeConfig._verify_and_get_draft_tp(
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self.target_parallel_config,
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self.draft_tensor_parallel_size,
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self.draft_model_config.hf_config
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)
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self.draft_model_config.max_model_len = (
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SpeculativeConfig._maybe_override_draft_max_model_len(
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self.max_model_len,
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self.draft_model_config.max_model_len,
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self.target_model_config.max_model_len,
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))
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self.draft_parallel_config = (
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SpeculativeConfig.create_draft_parallel_config(
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self.target_parallel_config,
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self.draft_tensor_parallel_size))
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SpeculativeConfig.__post_init__ = __post_init__
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