Overlap two kernels in DeepSeek with communication

[fzyzcjy]

(cherry picked from commit bd6cd8e)

Motivation

Modifications

Checklist

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[gemini-code-assist]

Hello @fzyzcjy, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

Summary of Changes

As gemini-code-assist, I've reviewed this pull request. The primary goal, based on the title, appears to be optimizing the execution strategy for the DeepSeek model, specifically by enabling the overlapping of computation kernels (likely within the MLP) with communication operations. This is achieved by adjusting the scheduling of operations within the decode phase for the DeepSeek MoE architecture.

Highlights

• DeepSeek MoE Optimization: The core change targets the execution strategy for the DeepSeek Mixture-of-Experts (MoE) model during the decode phase.
• Kernel and Communication Overlap: The modification aims to allow computation kernels (specifically related to MLP operations) to overlap with communication operations, which can improve performance by reducing idle time.
• Operation Scheduling Adjustment: The change involves adjusting the sequence and placement of YieldOperation calls within the defined operation strategy, altering when the execution flow yields control.

Changelog

• python/sglang/srt/operations_strategy.py
  • Modified the _compute_moe_deepseek_blog_decode function.
  • Moved a operations.YieldOperation() from after layer.op_comm_postprocess_layer (line 132 before change) to after layer.mlp.op_combine_b (line 130 after change).
  • This change alters the yielding points in the DeepSeek MoE decode strategy, facilitating the intended kernel/communication overlap.

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---

Kernels run, then data flies,
Overlap them, performance rise.
Yielding here and there,
Efficiency we share,
Beneath the AI skies.

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[gemini-code-assist]

Code Review

This PR modifies the operation staging strategy for DeepSeek model decoding, aiming to improve kernel overlap by adjusting YieldOperation placements. The code change itself is concise and appears to be a targeted optimization.

To help with the review and ensure the quality of this performance-related change, could you please:

1. Update the PR Description: The current description is a template. Please fill in the "Motivation" and "Modifications" sections to explain the problem this PR solves and how the changes achieve that.
2. Provide Benchmark Results: Since this is a performance optimization, it's crucial to see benchmark data (throughput/latency) comparing the performance before and after this change. This will help quantify the improvement and ensure no regressions.
3. Add/Mention Tests: Are there specific unit or integration tests that verify the correctness of this new staging strategy? Given the complexity of operation scheduling, ensuring correctness under various conditions is important.
4. Checklist: Please update the checklist in the PR description.

Addressing these points will provide more confidence in the effectiveness and correctness of this optimization.

The change itself, which isolates op_combine_b into its own stage, seems plausible for enabling finer-grained pipelining, especially if op_combine_b involves significant communication that can be overlapped. The execute_overlapped_operations logic appears to handle the increased number of stages correctly with the existing tbo_delta_stages=2.

Summary of Findings

• Rationale and Impact of Staging Change: The PR modifies operation staging in DeepSeek decode. The specific reasons for isolating op_combine_b and the expected performance gains are not detailed. This makes it harder to fully assess the change.
• Missing Validation and Documentation for Performance Change: The PR lacks a filled description, benchmark results, and information about tests. For a performance-focused change, these are crucial for verifying improvements and correctness.

Merge Readiness

This pull request proposes a change to the operation staging for DeepSeek model decoding, likely for performance optimization. While the code modification is small and seems structurally compatible with the existing overlap mechanism, crucial information is missing to fully assess its impact and correctness. Specifically, the PR description needs to be completed, benchmark results demonstrating the performance improvement should be provided, and details on testing for this new strategy would be beneficial.

Due to the lack of this supporting information for a performance-critical change, I recommend that these aspects be addressed before merging. I am unable to approve PRs directly; further review and approval by maintainers will be necessary once these points are clarified.

[gemini-code-assist]

This change refines the operation staging for the DeepSeek decode path:

• A YieldOperation is introduced after layer.mlp.op_combine_b.
• The final YieldOperation after layer.op_comm_postprocess_layer is removed.

This effectively isolates op_combine_b into its own stage and groups op_output with op_comm_postprocess_layer in the new final stage. The total number of stages for decode operations increases from 5 to 6.

Could you provide more details on the specific kernels being overlapped and the expected performance benefits from this new staging? For instance, is op_combine_b (which involves deepep_dispatcher.combine_b) a communication-heavy step where yielding immediately after offers significant overlap opportunities with other batch processing?

Understanding the rationale will help in assessing the impact, especially since tbo_delta_stages remains 2.

[ch-wan]

LGTM