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synced 2025-12-10 10:40:44 +08:00
Provide default max model length (#1224)
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@ -164,9 +164,6 @@ class ModelConfig:
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total_num_attention_heads = self.hf_config.num_attention_heads
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return total_num_attention_heads // parallel_config.tensor_parallel_size
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def get_max_model_len(self) -> int:
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return self.max_model_len
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def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
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total_num_hidden_layers = self.hf_config.num_hidden_layers
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return total_num_hidden_layers // parallel_config.pipeline_parallel_size
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@ -378,10 +375,17 @@ def _get_and_verify_max_len(
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if max_len_key is not None:
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derived_max_model_len = min(derived_max_model_len, max_len_key)
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if derived_max_model_len == float("inf"):
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raise ValueError(
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"The model's config.json must contain one of the following keys "
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"to determine the original maximum length of the model: "
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f"{possible_keys}")
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if max_model_len is not None:
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# If max_model_len is specified, we use it.
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return max_model_len
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default_max_len = 2048
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logger.warning(
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"The model's config.json does not contain any of the following "
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"keys to determine the original maximum length of the model: "
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f"{possible_keys}. Assuming the model's maximum length is "
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f"{default_max_len}.")
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derived_max_model_len = default_max_len
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rope_scaling = getattr(hf_config, "rope_scaling", None)
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if rope_scaling is not None:
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@ -184,7 +184,7 @@ class EngineArgs:
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self.worker_use_ray)
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scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
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self.max_num_seqs,
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model_config.get_max_model_len())
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model_config.max_model_len)
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return model_config, cache_config, parallel_config, scheduler_config
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@ -77,6 +77,7 @@ class LLMEngine:
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f"revision={model_config.revision}, "
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f"trust_remote_code={model_config.trust_remote_code}, "
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f"dtype={model_config.dtype}, "
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f"max_seq_len={model_config.max_model_len}, "
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f"download_dir={model_config.download_dir!r}, "
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f"load_format={model_config.load_format}, "
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f"tensor_parallel_size={parallel_config.tensor_parallel_size}, "
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@ -615,7 +615,7 @@ if __name__ == "__main__":
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engine_args = AsyncEngineArgs.from_cli_args(args)
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engine = AsyncLLMEngine.from_engine_args(engine_args)
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engine_model_config = asyncio.run(engine.get_model_config())
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max_model_len = engine_model_config.get_max_model_len()
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max_model_len = engine_model_config.max_model_len
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# A separate tokenizer to map token IDs to strings.
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tokenizer = get_tokenizer(engine_args.tokenizer,
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