Reduce the device bubble introduced by heavy loop synchronization in coalesced fetch/release(z3_leaf_module) - #6694
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loadams merged 48 commits intoJan 6, 2025
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Hi , @tjruwase. Some models use a large number of MoE experts, but the total number of parameters for MoE remains unchanged. This PR handles such cases as a whole to optimize memory management and reduce bubbles caused by long loops. when Do you have any suggestions? |
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Dec 24, 2024
tjruwase
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Dec 26, 2024
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hi, @tjruwase ,It seems that the OOM in the CI is not caused by this PR. Could you please help retrigger it? Thanks! |
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Hi @inkcherry - thanks, this is a known issue we are working on resolving with our CI nodes. I'll re-trigger the CI. |
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…coalesced fetch/release(z3_leaf_module) (deepspeedai#6694) depend on deepspeedai#6649 When performing fetch/release operations on Z3 leaf modules, the loop time is excessively long in fine-grained module. Compared to non-leaf modules, Z3 leaf modules may include a larger number of parameters. Although each loop unit does not consume much time, the overall loop length can be significant.  **The fetch time is impacted by:** Post-allgather operations (narrow， slice ，cat, difficult to avoid) Memory pressure（record_stream/fetch event create&sync） **The release time is impacted by:** slice Free parameter record_stream Considering the fine-grained leaf modules, where each parameter is relatively small, we can treat the parameters within each leaf module as a unified entity to handle memory pressure. This approach can approximately halve the CPU time required for fetch/release operations. --------- Co-authored-by: Ma, Guokai <guokai.ma@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com> Signed-off-by: siqi <siqi@tecorigin.com>
traincheck-team
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…coalesced fetch/release(z3_leaf_module) (deepspeedai#6694) depend on deepspeedai#6649 When performing fetch/release operations on Z3 leaf modules, the loop time is excessively long in fine-grained module. Compared to non-leaf modules, Z3 leaf modules may include a larger number of parameters. Although each loop unit does not consume much time, the overall loop length can be significant.  **The fetch time is impacted by:** Post-allgather operations (narrow， slice ，cat, difficult to avoid) Memory pressure（record_stream/fetch event create&sync） **The release time is impacted by:** slice Free parameter record_stream Considering the fine-grained leaf modules, where each parameter is relatively small, we can treat the parameters within each leaf module as a unified entity to handle memory pressure. This approach can approximately halve the CPU time required for fetch/release operations. --------- Co-authored-by: Ma, Guokai <guokai.ma@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
mauryaavinash95
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Mar 20, 2025
…coalesced fetch/release(z3_leaf_module) (deepspeedai#6694) depend on deepspeedai#6649 When performing fetch/release operations on Z3 leaf modules, the loop time is excessively long in fine-grained module. Compared to non-leaf modules, Z3 leaf modules may include a larger number of parameters. Although each loop unit does not consume much time, the overall loop length can be significant.  **The fetch time is impacted by:** Post-allgather operations (narrow， slice ，cat, difficult to avoid) Memory pressure（record_stream/fetch event create&sync） **The release time is impacted by:** slice Free parameter record_stream Considering the fine-grained leaf modules, where each parameter is relatively small, we can treat the parameters within each leaf module as a unified entity to handle memory pressure. This approach can approximately halve the CPU time required for fetch/release operations. --------- Co-authored-by: Ma, Guokai <guokai.ma@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
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depend on #6649
When performing fetch/release operations on Z3 leaf modules, the loop time is excessively long in fine-grained module. Compared to non-leaf modules, Z3 leaf modules may include a larger number of parameters. Although each loop unit does not consume much time, the overall loop length can be significant.
The fetch time is impacted by:
Post-allgather operations (narrow， slice ，cat, difficult to avoid)
Memory pressure（record_stream/fetch event create&sync）
The release time is impacted by:
slice
Free parameter record_stream
Considering the fine-grained leaf modules, where each parameter is relatively small, we can treat the parameters within each leaf module as a unified entity to handle memory pressure. This approach can approximately halve the CPU time required for fetch/release operations.