[None][fix] Adjust default moe_max_num_tokens to fix OOM.

[yuxianq]

Summary by CodeRabbit

• New Features

  • Added a new property to improve configuration of data parallelism settings.

• Refactor

  • Simplified and standardized the logic for determining the maximum number of tokens in MoE modules.
  • Improved consistency by using the new data parallelism property across multiple modules.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

The changes update the logic for determining maximum token counts in several MoE (Mixture of Experts) module initializations, replacing conditional scaling by world_size with unconditional scaling by a new dp_size property. The Mapping class is extended with a dp_size property to support this new logic. No public APIs are changed.

Changes

Cohort / File(s)	Change Summary
MoE Token Count Initialization Update tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py, tensorrt_llm/_torch/modules/fused_moe/fused_moe_vanilla.py, tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py	Refactored initialization of max_num_tokens and moe_max_num_tokens to use dp_size instead of world_size, removed conditional logic, and standardized fallback assignments for MoE token counts.
Mapping Class Enhancement tensorrt_llm/mapping.py	Added a new dp_size property to the Mapping class, returning tp_size if attention data parallelism is enabled, otherwise 1.

Sequence Diagram(s)

sequenceDiagram
    participant ModelConfig
    participant Mapping
    participant MoE_Module

    ModelConfig->>Mapping: Access mapping.dp_size
    Mapping-->>ModelConfig: Return tp_size or 1
    ModelConfig->>MoE_Module: Provide max_num_tokens, moe_max_num_tokens, dp_size
    MoE_Module->>MoE_Module: Compute max_num_tokens = max_num_tokens * dp_size
    MoE_Module->>MoE_Module: Set moe_max_num_tokens (with fallback)

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Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

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• liji-nv
• hlu1

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📥 Commits

Reviewing files that changed from the base of the PR and between a178cea and 35fdebe.

📒 Files selected for processing (4)

• tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py (1 hunks)
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_vanilla.py (1 hunks)
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py (1 hunks)
• tensorrt_llm/mapping.py (1 hunks)

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Files:

• tensorrt_llm/mapping.py
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_vanilla.py
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py

**/*.{cpp,h,hpp,cc,cxx,cu,py}

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All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the current year. This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.

Files:

• tensorrt_llm/mapping.py
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_vanilla.py
• tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py

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🔇 Additional comments (4)

tensorrt_llm/mapping.py (1)

375-377: LGTM! Clean abstraction for data parallel size calculation.

The new dp_size property provides a clean abstraction that encapsulates the logic for determining the effective data parallel size based on whether attention data parallelism is enabled. This centralizes what was previously conditional logic scattered across multiple MoE implementations.

tensorrt_llm/_torch/modules/fused_moe/fused_moe_wide_ep.py (1)

142-143: LGTM! Consistent token scaling using the new dp_size property.

The changes successfully replace the conditional world_size multiplication with the new dp_size property, which provides cleaner and more consistent logic for scaling token counts based on data parallelism configuration. The fallback assignment for moe_max_num_tokens is also simplified and correct.

tensorrt_llm/_torch/modules/fused_moe/fused_moe_cutlass.py (1)

89-90: LGTM! Unified token scaling logic across MoE implementations.

The changes align perfectly with the updates in other MoE modules, replacing conditional world_size multiplication with the new dp_size property. This provides consistent behavior across all fused MoE implementations and should help address the OOM issues mentioned in the PR.

tensorrt_llm/_torch/modules/fused_moe/fused_moe_vanilla.py (1)

85-86: LGTM! Consistent application of dp_size with appropriate fallback.

The changes maintain consistency with other MoE implementations by using the new dp_size property. The fallback logic correctly uses the scaled max_num_tokens value, which preserves the existing behavior for this vanilla MoE implementation while leveraging the new centralized data parallel size calculation.

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[yuxianq]

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[yuxianq]

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[yuxianq]

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[yuxianq]

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[yuxianq]

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[yuxianq]

Close this PR since it has been included in the merged PR #6811.