[TRTLLM-7263][fix] Prevent recreation of cublas handles in lora_grouped_gemm every call

[amitz-nv]

Cherrypick of merge commit of #6968

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Summary by CodeRabbit

• Refactor
  • Optimized LoRA grouped GEMM execution with thread-local, lazy initialization of GPU compute resources, reducing per-call overhead.
  • Improves latency and throughput, especially under multi-threaded workloads.
  • Enhances stability by avoiding repeated initialization in concurrent scenarios.
  • Minor naming consistency cleanup for maintainability.

[coderabbitai]

📝 Walkthrough

Walkthrough

Thread-local, lazy initialization of CublasMMWrapper is introduced in lora_grouped_gemm. A thread_local std::shared_ptr is created on first use via getCublasHandle and getCublasLtHandle, and then reused. Variable name corrected to cublasWrapper. Applied to both constructor sites for LoraImpl.

Changes

Cohort / File(s)	Summary
Thread-local CublasMMWrapper init in LoRA op cpp/tensorrt_llm/thop/loraOp.cpp	Replace per-call CublasMMWrapper creation with thread_local, lazily-initialized shared_ptr; initialize on first use with getCublasHandle/getCublasLtHandle; use cublasWrapper for LoraImpl construction in both call sites; fix variable name typo.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  participant Caller
  participant lora_grouped_gemm
  participant TLS as thread_local cublasWrapper
  participant CUDA as getCublasHandle/getCublasLtHandle
  participant Lora as LoraImpl

  Caller->>lora_grouped_gemm: invoke(...)
  alt cublasWrapper not initialized in this thread
    lora_grouped_gemm->>TLS: check cublasWrapper
    lora_grouped_gemm->>CUDA: getCublasHandle(), getCublasLtHandle()
    CUDA-->>lora_grouped_gemm: handles
    lora_grouped_gemm->>TLS: create and store CublasMMWrapper
    Note right of TLS: Lazy init per thread
  else already initialized
    lora_grouped_gemm->>TLS: reuse cublasWrapper
  end
  lora_grouped_gemm->>Lora: construct LoraImpl(cublasWrapper)
  Lora-->>Caller: perform GEMM (LoRA grouped)
  Note over lora_grouped_gemm,Lora: Subsequent calls reuse wrapper in the same thread

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Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• [TRTLLM-7263][fix] Prevent recreation of cublas handles in lora_grouped_gemm every call #6968: Mirrors the same change in loraOp.cpp, switching to thread_local lazy CublasMMWrapper and fixing the variable name.

Suggested reviewers

• Naveassaf
• shaharmor98

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[amitz-nv]

/bot run

[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (2)

cpp/tensorrt_llm/thop/loraOp.cpp (2)

133-140: Thread-local lazy init: good improvement; add device guard and align with static naming convention

Great call moving to thread-local lazy initialization to avoid per-call handle churn. Two follow-ups:

• Guard against device switches on the same thread (e.g., multi-GPU scenarios) so cached handles always match the current device.
• Per guidelines, locally visible statics should use sPrefix; also mark locals const when not modified.

Proposed change:

-thread_local std::shared_ptr<tensorrt_llm::common::CublasMMWrapper> cublasWrapper;
-if (cublasWrapper == nullptr)
-{
-    auto cublasHandle = getCublasHandle();
-    auto cublasLtHandle = getCublasLtHandle();
-    cublasWrapper
-        = std::make_shared<tensorrt_llm::common::CublasMMWrapper>(cublasHandle, cublasLtHandle, nullptr, nullptr);
-}
+thread_local int sCublasWrapperDevice = -1;
+thread_local std::shared_ptr<tensorrt_llm::common::CublasMMWrapper> sCublasWrapper;
+int const currentDevice = at::cuda::current_device();
+if (!sCublasWrapper || sCublasWrapperDevice != currentDevice)
+{
+    auto const cublasHandle = getCublasHandle();
+    auto const cublasLtHandle = getCublasLtHandle();
+    sCublasWrapper = std::make_shared<tensorrt_llm::common::CublasMMWrapper>(
+        cublasHandle, cublasLtHandle, nullptr, nullptr);
+    sCublasWrapperDevice = currentDevice;
+}

Notes:

• This keeps behavior identical for single-device runs while making it device-aware.
• Confirm availability/semantics of at::cuda::current_device() in your PyTorch version; if your getCublasHandle/getCublasLtHandle are already per-device/thread and safe across device switches, we can skip the guard.

Also, it’s good that you did not add setStream() here; as learned in prior PRs, LoraImpl::run() handles setStream()/setWorkspace internally in _runGemm().

If you want, I can factor this into a small helper (e.g., getThreadLocalCublasWrapper()) and update call sites.

---

158-158: Pass the renamed thread-local wrapper variable at construction (if you adopt the sPrefix change)

Update the argument to use sCublasWrapper to match the suggested rename above:

-    auto mLoraImpl = std::make_shared<tensorrt_llm::kernels::LoraImpl>(
-        inHiddenSize, outHiddenSizes, transA, transB, numLoraModules, loraRuntimeDataType, max_low_rank, cublasWrapper);
+    auto mLoraImpl = std::make_shared<tensorrt_llm::kernels::LoraImpl>(
+        inHiddenSize, outHiddenSizes, transA, transB, numLoraModules, loraRuntimeDataType, max_low_rank, sCublasWrapper);

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🧠 Learnings (2)

📚 Learning: 2025-08-17T15:07:01.380Z

Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#6968
File: cpp/tensorrt_llm/thop/loraOp.cpp:133-141
Timestamp: 2025-08-17T15:07:01.380Z
Learning: In TensorRT-LLM's LoRA implementation, the LoraImpl::run() method handles setStream() internally in _runGemm(), along with setWorkspace(). Both stream and workspace are passed as arguments to run(), so there's no need to call setStream() explicitly in loraOp.cpp - this avoids redundancy and follows the intended architectural separation.

Applied to files:

• cpp/tensorrt_llm/thop/loraOp.cpp

📚 Learning: 2025-08-17T15:07:01.380Z

Learnt from: amitz-nv
PR: NVIDIA/TensorRT-LLM#6968
File: cpp/tensorrt_llm/thop/loraOp.cpp:133-141
Timestamp: 2025-08-17T15:07:01.380Z
Learning: In TensorRT-LLM's LoRA implementation, the LoraImpl::run() method handles setStream() internally in _runGemm() (line 51 in lora.cpp), along with setWorkspace(). The stream parameter flows from loraOp.cpp through LoraImpl::run() to _runGemm() where setStream() is called appropriately. Adding setStream() in loraOp.cpp would be redundant and goes against the intended architectural design.

Applied to files:

• cpp/tensorrt_llm/thop/loraOp.cpp

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