[TRTLLM-9752][fix] disable PDL for quant kernels #10288
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Signed-off-by: Bo Deng <[email protected]>
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📝 WalkthroughWalkthroughThis change replaces environment variable checks for programmatic stream serialization with hardcoded Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
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⚠️ Outside diff range comments (2)
cpp/tensorrt_llm/kernels/quantization.cu (2)
20-20: Remove unusedenvUtils.hinclude.The
envUtils.hheader is no longer used in this file. All environment variable checks have been replaced with hardcoded values (e.g.,programmaticStreamSerializationAllowed = falseon lines 180, 215, 390).
173-185: Remove unusedenvUtils.hinclude and update copyright year.Line 20 includes
tensorrt_llm/common/envUtils.h, but this header is never used in the file and should be removed. Additionally, update the copyright header on line 2 from2019-2023to2019-2025to reflect the current modification year per coding guidelines.
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cpp/tensorrt_llm/kernels/quantization.cu
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**/*.{cpp,h,cu,cuh}
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Files:
cpp/tensorrt_llm/kernels/quantization.cu
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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/quantization.cu
🧠 Learnings (9)
📓 Common learnings
Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.
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.
📚 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/quantization.cu
📚 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/quantization.cu
📚 Learning: 2025-09-23T15:13:48.819Z
Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/multimem.h:20-30
Timestamp: 2025-09-23T15:13:48.819Z
Learning: TRT-LLM targets modern CUDA toolkits that support FP8 datatypes, so cuda_fp8.h can be included unconditionally without version guards in TRT-LLM code.
Applied to files:
cpp/tensorrt_llm/kernels/quantization.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 (cpp/tensorrt_llm/kernels/nccl_device/config.cu), std::ostringstream is used but <sstream> doesn't need to be explicitly included because it's provided transitively through other headers like tensorrt_llm/common/cudaUtils.h or config.h. Local compilation testing confirms this works without the explicit include.
Applied to files:
cpp/tensorrt_llm/kernels/quantization.cu
📚 Learning: 2025-08-17T15:07:01.420Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 6968
File: cpp/tensorrt_llm/thop/loraOp.cpp:133-141
Timestamp: 2025-08-17T15:07:01.420Z
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/kernels/quantization.cu
📚 Learning: 2025-08-14T15:43:23.107Z
Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: tensorrt_llm/_torch/attention_backend/trtllm.py:259-262
Timestamp: 2025-08-14T15:43:23.107Z
Learning: In TensorRT-LLM's attention backend, tensor parameters in the plan() method are assigned directly without validation (dtype, device, contiguity checks). This maintains consistency across all tensor inputs and follows the pattern of trusting callers to provide correctly formatted tensors.
Applied to files:
cpp/tensorrt_llm/kernels/quantization.cu
📚 Learning: 2025-08-26T09:37:10.463Z
Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.
Applied to files:
cpp/tensorrt_llm/kernels/quantization.cu
📚 Learning: 2025-08-17T15:07:01.420Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 6968
File: cpp/tensorrt_llm/thop/loraOp.cpp:133-141
Timestamp: 2025-08-17T15:07:01.420Z
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/kernels/quantization.cu
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🔇 Additional comments (2)
cpp/tensorrt_llm/kernels/quantization.cu (2)
208-220: Consistent with FP4 quantization changes.The change correctly disables programmatic stream serialization for the MXFP8 quantization kernel launch, consistent with the FP4 quantization path.
383-394: Consistent with other quantization kernel changes.The change correctly disables programmatic stream serialization for the per-token global scale computation, maintaining consistency with the FP4 and MXFP8 quantization paths.
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PR_Github #29891 [ run ] triggered by Bot. Commit: |
…VIDIA#9006) Signed-off-by: Yiqing Yan <[email protected]> Signed-off-by: Yanchao Lu <[email protected]> Co-authored-by: Yanchao Lu <[email protected]>
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/bot skip --comment "The failed cases are not related to the changes, test_llama4_vlm_patch has been waived on main (https://nvbugspro.nvidia.com/bug/5747878), test_trtllm_serve_examples[bash-genai_perf_client.sh] failed because perf_analyzer was not installed." |
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