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[PyTorch] Avoid using LRU cache for cu_seqlens - #798
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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
timmoon10
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We speculate the bug is because torch.device("cuda") != torch.device("cuda", 0) != "cuda".
ksivaman
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Apr 24, 2024
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* Try using global buffer for cu_seqlens Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> * Avoid using functools.lru_cache Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> * fixes Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> --------- Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> Co-authored-by: Vasudevan Rengasamy <vrengasamy@nvidia.com>
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* Try using global buffer for cu_seqlens Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> * Avoid using functools.lru_cache Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> * fixes Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> --------- Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com> Co-authored-by: Vasudevan Rengasamy <vrengasamy@nvidia.com> Signed-off-by: Pawel Gadzinski <pgadzinski@nvidia.com>
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LRU cache usage is leading to IMAs in certain LLM workloads
@vasunvidia