llm runtime_error ai_generated partial

Warning: seed parameter may not produce deterministic results with temperature close to 0

ID: llm/seed-parameter-ignored-with-low-temp

Also available as: JSON · Markdown · 中文
75%Fix Rate
85%Confidence
1Evidence
2024-02-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
openai-python>=1.0.0 active — — —
gpt-4-turbo-2024-04-09 active — — —
gpt-3.5-turbo-0125 active — — —

Root Cause

Even with temperature=0, some LLM providers (e.g., OpenAI) do not guarantee full determinism due to GPU non-determinism, batching, or model updates, and seed is only a best-effort hint.

generic

中文

即使 temperature=0，某些 LLM 提供商（如 OpenAI）也不能保证完全确定性，因为 GPU 非确定性、批处理或模型更新，seed 仅作为尽力而为的提示。

Official Documentation

https://platform.openai.com/docs/guides/text-generation/reproducible-outputs

Workarounds

  1. 85% success Accept non-determinism and implement idempotency in your application logic. For testing, compare outputs using fuzzy matching or semantic similarity instead of exact equality. 
    Accept non-determinism and implement idempotency in your application logic. For testing, compare outputs using fuzzy matching or semantic similarity instead of exact equality.
  2. 90% success Use a self-hosted model (e.g., Llama 3 with vLLM) where you can control CUDA determinism flags: `export CUBLAS_WORKSPACE_CONFIG=:4096:8` and set `torch.use_deterministic_algorithms(True)`. 
    Use a self-hosted model (e.g., Llama 3 with vLLM) where you can control CUDA determinism flags: `export CUBLAS_WORKSPACE_CONFIG=:4096:8` and set `torch.use_deterministic_algorithms(True)`.
  3. 70% success Log the full request parameters and response ID for reproducibility; retry with same parameters if output is anomalous. 
    Log the full request parameters and response ID for reproducibility; retry with same parameters if output is anomalous.

中文步骤

  1. Accept non-determinism and implement idempotency in your application logic. For testing, compare outputs using fuzzy matching or semantic similarity instead of exact equality.
  2. Use a self-hosted model (e.g., Llama 3 with vLLM) where you can control CUDA determinism flags: `export CUBLAS_WORKSPACE_CONFIG=:4096:8` and set `torch.use_deterministic_algorithms(True)`.
  3. Log the full request parameters and response ID for reproducibility; retry with same parameters if output is anomalous.

Dead Ends

Common approaches that don't work:

  1. 60% fail

    This is the standard approach but still fails; the warning indicates it's not a configuration issue but a platform limitation.

  2. 90% fail

    All seeds behave identically; the non-determinism is inherent to the API, not seed-specific.

  3. 80% fail

    Streaming vs non-streaming both exhibit the same non-determinism at the output level.