[https://nvbugs/5532248][fix] Fix fused_moe OOM #7931
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📝 WalkthroughWalkthroughThe change updates MoE operator memory management by altering the sequence of allocations. The workspace is now allocated (and, if needed, explicitly released and reallocated) before the output tensor is created. This applies to both the standard execution path and the min-latency path to avoid allocation conflicts and potential OOM. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor Caller
participant MoEOp
participant WorkspaceMgr as Workspace Manager
rect rgb(245,245,255)
note over MoEOp: Old flow (before change)
Caller->>MoEOp: run()
MoEOp->>MoEOp: Create output tensor
MoEOp->>WorkspaceMgr: Ensure/resize workspace
WorkspaceMgr-->>MoEOp: Workspace ready (may OOM)
MoEOp-->>Caller: Return with results
end
rect rgb(240,255,240)
note over MoEOp: New flow (after change)
Caller->>MoEOp: run()
MoEOp->>WorkspaceMgr: Release existing workspace (if resizing)
MoEOp->>WorkspaceMgr: Allocate/resize workspace
WorkspaceMgr-->>MoEOp: Workspace ready
MoEOp->>MoEOp: Create output tensor
MoEOp-->>Caller: Return with results
end
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
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Actionable comments posted: 0
🧹 Nitpick comments (2)
cpp/tensorrt_llm/thop/moeOp.cpp (2)
395-399: Allocate output after workspace: good change to reduce OOM risk.This ordering helps avoid allocator fragmentation. Optional: mirror this in runMoeMinLatency (allocate output after getWorkspaceInfo) for consistency, unless you intentionally deprioritize min-latency.
790-794: Release-before-realloc is good; fix 32-bit cast risk.Casting total_workspace_size to long can truncate on LLP64 (Windows). Use int64_t for the tensor shape.
Apply this diff:
- workspace_info.workspace = torch::empty({static_cast<long>(total_workspace_size)}, + workspace_info.workspace = torch::empty({static_cast<int64_t>(total_workspace_size)}, torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
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🧠 Learnings (5)
📓 Common learnings
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.
Learnt from: sklevtsov-nvidia
PR: NVIDIA/TensorRT-LLM#3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu:118-127
Timestamp: 2025-08-09T20:57:04.084Z
Learning: In the CUTLASS MoE finalize fusion implementation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu), when setting `fused_finalize_epilogue.stride_final_output` with shape `(hidden_size, num_output_tokens, 1)`, the `num_rows_in_final_output` should be set to `num_output_tokens` (not `hidden_size`) because of a swap+transpose operation that maps rows of the output tensor to `hidden_size` and columns to `num_output_tokens`.
📚 Learning: 2025-08-19T03:35:20.866Z
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4616-4626
Timestamp: 2025-08-19T03:35:20.866Z
Learning: In the MOE profiler TMA workspace preparation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu), the overlapping of TMA WS regions for NONE and FINALIZE variants is deliberate design to save memory space, as confirmed by djns99. The comment "reuse the same pointers to save space" reflects this intentional behavior.
Applied to files:
cpp/tensorrt_llm/thop/moeOp.cpp
📚 Learning: 2025-08-14T23:23:27.449Z
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.
Applied to files:
cpp/tensorrt_llm/thop/moeOp.cpp
📚 Learning: 2025-08-09T20:57:04.084Z
Learnt from: sklevtsov-nvidia
PR: NVIDIA/TensorRT-LLM#3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu:118-127
Timestamp: 2025-08-09T20:57:04.084Z
Learning: In the CUTLASS MoE finalize fusion implementation (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_gemm_tma_warp_specialized_input.cu), when setting `fused_finalize_epilogue.stride_final_output` with shape `(hidden_size, num_output_tokens, 1)`, the `num_rows_in_final_output` should be set to `num_output_tokens` (not `hidden_size`) because of a swap+transpose operation that maps rows of the output tensor to `hidden_size` and columns to `num_output_tokens`.
Applied to files:
cpp/tensorrt_llm/thop/moeOp.cpp
📚 Learning: 2025-08-21T02:39:12.009Z
Learnt from: djns99
PR: NVIDIA/TensorRT-LLM#7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.
Applied to files:
cpp/tensorrt_llm/thop/moeOp.cpp
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Both unwaived test cases passed. The failed case is accuracy/test_llm_api_pytorch.py::TestDeepSeekR1::test_fp8_blockscale[throughput_mtp_trtllm] and it's waived in #7946
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Signed-off-by: Hui Gao <huig@nvidia.com>
Signed-off-by: Hui Gao <huig@nvidia.com>
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