seed_parameter_behavior.md

Seed Parameter Behavior in vLLM

Overview

The seed parameter in vLLM is used to control the random states for various random number generators. This parameter can affect the behavior of random operations in user code, especially when working with models in vLLM.

Default Behavior

By default, the seed parameter is set to None. When the seed parameter is None, the global random states for random, np.random, and torch.manual_seed are not set. This means that the random operations will behave as expected, without any fixed random states.

Specifying a Seed

If a specific seed value is provided, the global random states for random, np.random, and torch.manual_seed will be set accordingly. This can be useful for reproducibility, as it ensures that the random operations produce the same results across multiple runs.

Example Usage

Without Specifying a Seed

import random
from vllm import LLM

# Initialize a vLLM model without specifying a seed
model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")

# Try generating random numbers
print(random.randint(0, 100))  # Outputs different numbers across runs

Specifying a Seed

import random
from vllm import LLM

# Initialize a vLLM model with a specific seed
model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct", seed=42)

# Try generating random numbers
print(random.randint(0, 100))  # Outputs the same number across runs

Important Notes

• If the seed parameter is not specified, the behavior of global random states remains unaffected.
• If a specific seed value is provided, the global random states for random, np.random, and torch.manual_seed will be set to that value.
• This behavior can be useful for reproducibility but may lead to non-intuitive behavior if the user is not explicitly aware of it.

Conclusion

Understanding the behavior of the seed parameter in vLLM is crucial for ensuring the expected behavior of random operations in your code. By default, the seed parameter is set to None, which means that the global random states are not affected. However, specifying a seed value can help achieve reproducibility in your experiments.