Optimize tp fp8 moe for small average router per expert - #4751
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Pull request overview
This PR adds a “compact routed-block” scheduling path for blocked-FP8 MoE (targeting cases with small average routed tokens per expert) and makes the origin blocked-FP8 launch configuration depend on average routes per expert, aiming to reduce wasted CTAs and improve efficiency.
Changes:
- Added compact routed-block metadata generation (
_get_sorted_idx_blocks) and a new Triton kernel/launcher for compact down-projection. - Switched blocked-FP8 MoE to select gate/down configs based on average routes per expert and to conditionally use the compact down path.
- Added unit tests for config selection, CTA estimation, and compact-down gating logic.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| tests/pytorch/kernel/test_fused_moe.py | Adds unit tests for blocked-FP8 MoE config selection and compact-down gating/CTA estimates. |
| lmdeploy/pytorch/kernels/cuda/fused_moe.py | Adds _get_sorted_idx_blocks and Triton kernel to build compact block metadata from routing histograms. |
| lmdeploy/pytorch/kernels/cuda/blocked_fp8_fused_moe.py | Adds compact down-projection kernel/launcher, config selection helpers, and integrates compact down path into fused_moe_blocked_fp8. |
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| if w1.size(0) != w2.size(0): | ||
| return False | ||
| if w1.size(0) != num_experts: | ||
| return False |
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| gate_moe_cfg, down_moe_cfg = _origin_blocked_fp8_moe_configs(M, topk_ids.numel(), num_experts, E) | ||
| use_compact_down = _should_use_compact_blocked_fp8_moe_down(input, input_scale, w1, w1_scale, w2, w2_scale, | ||
| topk_ids, num_experts) | ||
| if use_compact_down: |
lvhan028
reviewed
Jul 16, 2026
| return 128 | ||
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| def _origin_blocked_fp8_moe_configs(num_tokens: int, num_routes: int, num_experts: int, local_experts: int): |
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local_experts is not used.
lvhan028
approved these changes
Jul 16, 2026
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