Conversation
Signed-off-by: oliver könig <okoenig@nvidia.com>
📝 WalkthroughWalkthroughA configuration parameter Changes
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~2 minutes Possibly related PRs
Suggested reviewers
🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches
🧪 Generate unit tests (beta)
Tip Issue Planner is now in beta. Read the docs and try it out! Share your feedback on Discord. Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
There was a problem hiding this comment.
🧹 Nitpick comments (1)
scripts/performance/configs/qwen/qwen3_llm_pretrain.py (1)
337-344: Explicitly setmoe_token_dispatcher_typefor all qwen3_next_80b_a3b GPU variants for consistency.All
qwen3_235b_a22bandqwen3_30b_a3bper-GPU configs explicitly setmoe_token_dispatcher_type. The fourqwen3_next_80b_a3bvariants (gb200, b300, b200, gb300) silently rely on the default"alltoall"fromqwen3_next_80b_a3b_pretrain_config(). While the H100 variant does override it explicitly, adding explicit assignments to the other four variants improves consistency, auditability, and makes platform-specific choices visible at the config level.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@scripts/performance/configs/qwen/qwen3_llm_pretrain.py` around lines 337 - 344, The qwen3_next_80b_a3b GPU variant configs should explicitly set cfg.moe_token_dispatcher_type instead of relying on the default from qwen3_next_80b_a3b_pretrain_config(); update each qwen3_next_80b_a3b variant function (the gb200, b300, b200, gb300 variants that build cfg via qwen3_next_80b_a3b_pretrain_config()) to assign cfg.moe_token_dispatcher_type = "<appropriate_dispatcher>" (use the same string used by the H100 variant or the intended dispatcher like "alltoall" or whichever platform-specific choice is required) before calling set_qwen3_next_common_configs(cfg) / set_workload_base_configs(cfg, base_cfg) and returning cfg so the dispatcher choice is explicit and consistent across all variants.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Nitpick comments:
In `@scripts/performance/configs/qwen/qwen3_llm_pretrain.py`:
- Around line 337-344: The qwen3_next_80b_a3b GPU variant configs should
explicitly set cfg.moe_token_dispatcher_type instead of relying on the default
from qwen3_next_80b_a3b_pretrain_config(); update each qwen3_next_80b_a3b
variant function (the gb200, b300, b200, gb300 variants that build cfg via
qwen3_next_80b_a3b_pretrain_config()) to assign cfg.moe_token_dispatcher_type =
"<appropriate_dispatcher>" (use the same string used by the H100 variant or the
intended dispatcher like "alltoall" or whichever platform-specific choice is
required) before calling set_qwen3_next_common_configs(cfg) /
set_workload_base_configs(cfg, base_cfg) and returning cfg so the dispatcher
choice is explicit and consistent across all variants.
Signed-off-by: oliver könig <okoenig@nvidia.com>
Signed-off-by: oliver könig <okoenig@nvidia.com> Signed-off-by: pengdurice <pengduhit@gmail.com>
Signed-off-by: oliver könig <okoenig@nvidia.com>
What does this PR do ?
Add a one line overview of what this PR aims to accomplish.
Changelog
GitHub Actions CI
See the CI sectionin the Contributing doc for how to trigger the CI. A Nvidia developer will need to approve and trigger the CI for external contributors.
Before your PR is "Ready for review"
Pre checks:
If you haven't finished some of the above items you can still open "Draft" PR.
Additional Information
Summary by CodeRabbit