[None][chore] Update tinygemm kernel name #10248
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Signed-off-by: Jonas Li <[email protected]>
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📝 WalkthroughWalkthroughA CUDA kernel is renamed from Changes
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~3 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
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🧹 Nitpick comments (1)
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh (1)
175-178: LGTM! Kernel renamed to a more descriptive name.The rename from the generic
kerneltotinygemm_kernelsignificantly improves code clarity and maintainability. The function signature and all attributes remain unchanged, ensuring functional equivalence.Optional: The coding guidelines specify camelCase for function names (e.g.,
tinygemmKernel), though CUDA kernel naming conventions often vary. Consider aligning with the project's naming guidelines if consistency is a priority.
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📒 Files selected for processing (2)
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cucpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh
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📓 Path-based instructions (2)
**/*.{cpp,h,cu,cuh}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.{cpp,h,cu,cuh}: Closing braces of namespaces should have a comment saying the namespace it closes:} // namespace foo
Preferconstorconstexprvariables over#definewhenever possible, as the latter are not visible to the compiler
A variable that is not modified after its initialization should be declared asconst
For naming of constants in C++, follow the naming section conventions
Except0(only used in comparison for checking signness/existence/emptiness) andnullptr,true,false, all other literals should only be used for variable initialization in C++
Use the Allman indentation style in C++
Put the semicolon for an emptyfororwhileloop in a new line in C++
The statement forming the body of aswitch,while,do .. whileorforstatement shall be a compound statement (use brace-delimited statements) in C++
If and else should always be followed by brace-delimited statements, even if empty or a single statement in C++
C++ filenames should use camel case with first letter lowercase:thisIsASubDirandthisIsAFilename.cpp
All files involved in the compilation of a compilation target (.exe/.so) must have filenames that are case-insensitive unique in C++
All types (including class names) in C++ should use camel case with uppercase first letter:FooBarClass
Local variables, methods and namespaces in C++ should use camel case with first letter lowercase:localFooBar
Non-magic-number global variables that are non-static and not defined in anonymous namespace in C++ should use camel case prefixed by a lower case 'g':gDontUseGlobalFoos
Non-magic-number global variables that are static or defined in an anonymous namespace in C++ should use camel case prefixed by a lower case 's':sMutableStaticGlobal
Locally visible static variables in C++ should use camel case with lowercase prefix 's' as the first letter:static std::once_flag sFlag;
Public, private and protected class member variables in C++ should use camel case prefi...
Files:
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cucpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh
**/*.{cpp,h,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the year of its latest meaningful modification
Files:
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cucpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_kernel.cuh
🧠 Learnings (5)
📓 Common learnings
Learnt from: nzmora-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 9163
File: tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py:107-113
Timestamp: 2025-11-14T11:22:03.729Z
Learning: In TensorRT-LLM AutoDeploy custom ops, when adding hardware capability checks to select between kernel implementations (e.g., cuBLAS vs. CUDA kernel), use descriptive variable names that identify the specific GPU architectures or families being targeted (e.g., `is_blackwell_geforce_or_ada`) rather than generic names like `enable_cuda_core`. This makes it clear that the code is selecting an implementation path based on hardware capabilities, not enabling/disabling hardware features.
📚 Learning: 2025-11-14T11:22:03.729Z
Learnt from: nzmora-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 9163
File: tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py:107-113
Timestamp: 2025-11-14T11:22:03.729Z
Learning: In TensorRT-LLM AutoDeploy custom ops, when adding hardware capability checks to select between kernel implementations (e.g., cuBLAS vs. CUDA kernel), use descriptive variable names that identify the specific GPU architectures or families being targeted (e.g., `is_blackwell_geforce_or_ada`) rather than generic names like `enable_cuda_core`. This makes it clear that the code is selecting an implementation path based on hardware capabilities, not enabling/disabling hardware features.
Applied to files:
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cu
📚 Learning: 2025-09-23T15:01:00.070Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/config.cu:15-17
Timestamp: 2025-09-23T15:01:00.070Z
Learning: In TensorRT-LLM NCCL device kernels, the <sstream> header is not needed as an explicit include in config.cu because it's provided transitively through other headers. Local compilation testing confirms this works without the explicit include.
Applied to files:
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cu
📚 Learning: 2025-08-19T03:35:20.866Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 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/kernels/tinygemm2/tinygemm2_cuda.cu
📚 Learning: 2025-08-08T22:03:40.707Z
Learnt from: sklevtsov-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 3294
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1198-1209
Timestamp: 2025-08-08T22:03:40.707Z
Learning: In the CUTLASS MoE kernels (cpp/tensorrt_llm/cutlass_extensions), when `layout_info.fusion` is set to `TmaWarpSpecializedGroupedGemmInput::EpilogueFusion::FINALIZE`, the `router_scales` parameter must be non-null by design. The fused finalize kernel epilogue does not perform nullptr checks and requires valid router scales to function correctly. This is an implicit contract that callers must satisfy when enabling the FINALIZE fusion mode.
Applied to files:
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cu
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🔇 Additional comments (2)
cpp/tensorrt_llm/kernels/tinygemm2/tinygemm2_cuda.cu (2)
64-65: LGTM! Kernel name updated correctly.The
cudaFuncSetAttributecall has been properly updated to reference the renamedtinygemm_kernel. The more descriptive name improves code clarity.
85-86: LGTM! Kernel launch updated correctly.The
cudaLaunchKernelExcall has been properly updated to reference the renamedtinygemm_kernel. All template parameters and arguments remain unchanged, ensuring functional equivalence.
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