[GH-ISSUE #48] Drop Token #46

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opened 2026-10-26 21:07:11 +00:00 by bsakaguchi · 2 comments
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Originally created by @Richie-yan on GitHub (May 30, 2024).
Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V2/issues/48

hello @DeepSeekDDM @luofuli
I have some question in drop token deepseek v2
Is the capacity in the token-dropping strategy based on the expert dimension or the device dimension?
If it's on the expert dimension, then the capacity is calculated as capacity = math.ceil(num_tokens * topk) / num_experts * capacity_factor, and then each expert processes its own tokens, dropping the least scored tokens if token > capacity, and padding if token < capacity.
If it's on the device dimension, is the capacity calculated as capacity = math.ceil(num_tokens * topk) / num_groups * capacity_factor? How is the token dropping executed in this case?
Because the paper mentions device-level token dropping, I have the above confusion.

Originally created by @Richie-yan on GitHub (May 30, 2024). Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V2/issues/48 hello @DeepSeekDDM @luofuli I have some question in drop token deepseek v2 Is the capacity in the token-dropping strategy based on the expert dimension or the device dimension? If it's on the expert dimension, then the capacity is calculated as capacity = math.ceil(num_tokens * topk) / num_experts * capacity_factor, and then each expert processes its own tokens, dropping the least scored tokens if token > capacity, and padding if token < capacity. If it's on the device dimension, is the capacity calculated as capacity = math.ceil(num_tokens * topk) / num_groups * capacity_factor? How is the token dropping executed in this case? Because the paper mentions device-level token dropping, I have the above confusion.
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@Richie-yan commented on GitHub (May 30, 2024):

Adding another question: How should I understand the statement from the paper that "we ensure that the tokens belonging to approximately 10% of the training sequences will never be dropped"? Is there a specific strategy implemented during token dropping to enforce this?
@DeepSeekDDM @luofuli

<!-- gh-comment-id:2138530535 --> @Richie-yan commented on GitHub (May 30, 2024): Adding another question: How should I understand the statement from the paper that "we ensure that the tokens belonging to approximately 10% of the training sequences will never be dropped"? Is there a specific strategy implemented during token dropping to enforce this? @DeepSeekDDM @luofuli
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@DeepSeekDDM commented on GitHub (May 30, 2024):

Refer to this issue: https://github.com/deepseek-ai/DeepSeek-V2/issues/5

<!-- gh-comment-id:2139322626 --> @DeepSeekDDM commented on GitHub (May 30, 2024): Refer to this issue: https://github.com/deepseek-ai/DeepSeek-V2/issues/5
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