[CI]Moe alltoall communication optimization #1067
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
for unquantized sence. Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
for unquantized sence. Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
76 tasks
|
This pull request has conflicts, please resolve those before we can evaluate the pull request. |
for unquantized sence. Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
for unquantized sence. Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
…-ascend into moe_alltoall_v6 # Conflicts: # vllm_ascend/ops/fused_moe.py
wangxiyuan
approved these changes
Jun 6, 2025
|
If this change may cause the accuracy regression, then please add flags to make this behaviour controllable. We should give choice back to the user. |
added 4 commits
June 6, 2025 17:29
…-ascend into moe_alltoall_v6 # Conflicts: # vllm_ascend/ops/fused_moe.py
|
This pull request has conflicts, please resolve those before we can evaluate the pull request. |
Yuxiao-Xu
pushed a commit
to Yuxiao-Xu/vllm-ascend
that referenced
this pull request
Jun 7, 2025
[CI]Moe alltoall communication optimization The DeepSeek V3/R1 model has 256 routing experts. During parallel inference, if the load of an EP rank is high, the overall communication and computing time is slowed down, which becomes a weakness of parallel inference because the load is unevenly distributed. However, the data volume in the prefill phase is large, and the inter-card communication time consumption/calculation time consumption and the data volume are closely related to each other. Therefore, less non-linear precision loss can be used to obtain a near-linear performance improvement. During parallel inference, global synchronization occurs during communication. As a result, the card with low load completes the calculation first and waits for the card with the highest load to complete the calculation. Therefore, if the load is unbalanced, the card with high load slows down the overall time consumption. Significant performance gains can be achieved by discarding a small number of tokens, which is unacceptable in some precision-sensitive scenarios. However, similar to quantification, it is a solution that uses an acceptable precision loss in some scenarios for performance. In addition, a trade-off between performance and precision can be achieved by configuring a proportion of discarded tokens. Perform the test on A3. The batch size is 8 (B), the prompt length is 3.5K tokens (S), and the parallel configuration is as follows: AttnDP=2, AttnTP=8, MoeTP=1, and MoeEP=16. In this sence, we got a 10%-15% performance gain. Plus, the next version, we'll have an alltoallv moe. --------- Signed-off-by: weijinqian_v1 <weijinqian@huawei.com> Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
Yuxiao-Xu
pushed a commit
to Yuxiao-Xu/vllm-ascend
that referenced
this pull request
Jun 7, 2025
[CI]Moe alltoall communication optimization The DeepSeek V3/R1 model has 256 routing experts. During parallel inference, if the load of an EP rank is high, the overall communication and computing time is slowed down, which becomes a weakness of parallel inference because the load is unevenly distributed. However, the data volume in the prefill phase is large, and the inter-card communication time consumption/calculation time consumption and the data volume are closely related to each other. Therefore, less non-linear precision loss can be used to obtain a near-linear performance improvement. During parallel inference, global synchronization occurs during communication. As a result, the card with low load completes the calculation first and waits for the card with the highest load to complete the calculation. Therefore, if the load is unbalanced, the card with high load slows down the overall time consumption. Significant performance gains can be achieved by discarding a small number of tokens, which is unacceptable in some precision-sensitive scenarios. However, similar to quantification, it is a solution that uses an acceptable precision loss in some scenarios for performance. In addition, a trade-off between performance and precision can be achieved by configuring a proportion of discarded tokens. Perform the test on A3. The batch size is 8 (B), the prompt length is 3.5K tokens (S), and the parallel configuration is as follows: AttnDP=2, AttnTP=8, MoeTP=1, and MoeEP=16. In this sence, we got a 10%-15% performance gain. Plus, the next version, we'll have an alltoallv moe. --------- Signed-off-by: weijinqian_v1 <weijinqian@huawei.com> Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
chopper0126
pushed a commit
to chopper0126/vllm-ascend
that referenced
this pull request
Oct 16, 2025
[CI]Moe alltoall communication optimization The DeepSeek V3/R1 model has 256 routing experts. During parallel inference, if the load of an EP rank is high, the overall communication and computing time is slowed down, which becomes a weakness of parallel inference because the load is unevenly distributed. However, the data volume in the prefill phase is large, and the inter-card communication time consumption/calculation time consumption and the data volume are closely related to each other. Therefore, less non-linear precision loss can be used to obtain a near-linear performance improvement. During parallel inference, global synchronization occurs during communication. As a result, the card with low load completes the calculation first and waits for the card with the highest load to complete the calculation. Therefore, if the load is unbalanced, the card with high load slows down the overall time consumption. Significant performance gains can be achieved by discarding a small number of tokens, which is unacceptable in some precision-sensitive scenarios. However, similar to quantification, it is a solution that uses an acceptable precision loss in some scenarios for performance. In addition, a trade-off between performance and precision can be achieved by configuring a proportion of discarded tokens. Perform the test on A3. The batch size is 8 (B), the prompt length is 3.5K tokens (S), and the parallel configuration is as follows: AttnDP=2, AttnTP=8, MoeTP=1, and MoeEP=16. In this sence, we got a 10%-15% performance gain. Plus, the next version, we'll have an alltoallv moe. --------- Signed-off-by: weijinqian_v1 <weijinqian@huawei.com> Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
Angazenn
pushed a commit
to Angazenn/vllm-ascend
that referenced
this pull request
Oct 21, 2025
[CI]Moe alltoall communication optimization The DeepSeek V3/R1 model has 256 routing experts. During parallel inference, if the load of an EP rank is high, the overall communication and computing time is slowed down, which becomes a weakness of parallel inference because the load is unevenly distributed. However, the data volume in the prefill phase is large, and the inter-card communication time consumption/calculation time consumption and the data volume are closely related to each other. Therefore, less non-linear precision loss can be used to obtain a near-linear performance improvement. During parallel inference, global synchronization occurs during communication. As a result, the card with low load completes the calculation first and waits for the card with the highest load to complete the calculation. Therefore, if the load is unbalanced, the card with high load slows down the overall time consumption. Significant performance gains can be achieved by discarding a small number of tokens, which is unacceptable in some precision-sensitive scenarios. However, similar to quantification, it is a solution that uses an acceptable precision loss in some scenarios for performance. In addition, a trade-off between performance and precision can be achieved by configuring a proportion of discarded tokens. Perform the test on A3. The batch size is 8 (B), the prompt length is 3.5K tokens (S), and the parallel configuration is as follows: AttnDP=2, AttnTP=8, MoeTP=1, and MoeEP=16. In this sence, we got a 10%-15% performance gain. Plus, the next version, we'll have an alltoallv moe. --------- Signed-off-by: weijinqian_v1 <weijinqian@huawei.com> Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
[CI]Moe alltoall communication optimization
The DeepSeek V3/R1 model has 256 routing experts. During parallel inference, if the load of an EP rank is high, the overall communication and computing time is slowed down, which becomes a weakness of parallel inference because the load is unevenly distributed. However, the data volume in the prefill phase is large, and the inter-card communication time consumption/calculation time consumption and the data volume are closely related to each other. Therefore, less non-linear precision loss can be used to obtain a near-linear performance improvement.
During parallel inference, global synchronization occurs during communication. As a result, the card with low load completes the calculation first and waits for the card with the highest load to complete the calculation. Therefore, if the load is unbalanced, the card with high load slows down the overall time consumption. Significant performance gains can be achieved by discarding a small number of tokens, which is unacceptable in some precision-sensitive scenarios. However, similar to quantification, it is a solution that uses an acceptable precision loss in some scenarios for performance. In addition, a trade-off between performance and precision can be achieved by configuring a proportion of discarded tokens.
Perform the test on A3. The batch size is 8 (B), the prompt length is 3.5K tokens (S), and the parallel configuration is as follows: AttnDP=2, AttnTP=8, MoeTP=1, and MoeEP=16. In this sence, we got a 10%-15% performance gain.
Plus, the next version, we'll have an alltoallv moe.