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环境
If applicable, please include the following: CPU architecture: x86_64 GPU properties GPU name: NVIDIA A10 Clock frequencies used: None Libraries TensorRT branch: 9.0.0 TensorRT LLM: 0.1.3 Cuda: 12.1.66 Cudnn: unknown Container: registry.cn-hangzhou.aliyuncs.com%2ftrt-hackathon%2ftrt-hackathon%3afinal_v1 NVIDIA driver version: 525.105.17 OS: Ubuntu 5.15.0-73
复现步骤: 进入trtllm根目录,cd tests/quantization 执行: python -m unittest test_smooth_quant_gemm.py TestSmoothQuantGemm.test_matmul 即可看到cuda runtime error:what(): [TensorRT-LLM Error][int8gemm Runner] Failed to run cutlass int8 gemm. Error: Error Internal 报错代码位于 ./3rdparty/cutlass/include/cutlass/gemm/device/gemm_universal_base.h 的initialize函数,cudaFuncSetAttribute返回的cudaerror_t为1, 没有继续检察后续代码,更改一些参数重新编译仍然报错
Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) { CUTLASS_TRACE_HOST("GemmUniversalBase::initialize() - workspace " << workspace << ", stream: " << (stream ? "non-null" : "null")); size_t workspace_bytes = get_workspace_size(args); CUTLASS_TRACE_HOST(" workspace_bytes: " << workspace_bytes); if (workspace_bytes) { if (!workspace) { CUTLASS_TRACE_HOST(" error: device workspace must not be null"); return Status::kErrorWorkspaceNull; } if (args.mode == GemmUniversalMode::kGemm) { CUTLASS_TRACE_HOST(" clearing device workspace"); cudaError_t result = cudaMemsetAsync(workspace, 0, workspace_bytes, stream); if (result != cudaSuccess) { CUTLASS_TRACE_HOST(" cudaMemsetAsync() returned error " << cudaGetErrorString(result)); return Status::kErrorInternal; } } } // Get CUDA grid shape cutlass::gemm::GemmCoord grid_tiled_shape; int gemm_k_size = 0; get_grid_shape_(grid_tiled_shape, gemm_k_size, args); // Initialize the Params structure params_ = typename GemmKernel::Params( args, grid_tiled_shape, gemm_k_size, static_cast<int *>(workspace) ); // Specify shared memory capacity for kernel. int smem_size = int(sizeof(typename GemmKernel::SharedStorage)); if (smem_size >= (48 << 10)) { cudaError_t result = cudaFuncSetAttribute(Kernel<GemmKernel>, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size); if (result != cudaSuccess) { return Status::kErrorInternal; } } return Status::kSuccess; }
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把CUTLASS_TRACE_HOST这个宏打开,详细看一下错误位置
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环境
If applicable, please include the following:
CPU architecture: x86_64
GPU properties
GPU name: NVIDIA A10
Clock frequencies used: None
Libraries
TensorRT branch: 9.0.0
TensorRT LLM: 0.1.3
Cuda: 12.1.66
Cudnn: unknown
Container: registry.cn-hangzhou.aliyuncs.com%2ftrt-hackathon%2ftrt-hackathon%3afinal_v1
NVIDIA driver version: 525.105.17
OS: Ubuntu 5.15.0-73
复现步骤:
进入trtllm根目录,cd tests/quantization
执行: python -m unittest test_smooth_quant_gemm.py TestSmoothQuantGemm.test_matmul
即可看到cuda runtime error:what(): [TensorRT-LLM Error][int8gemm Runner] Failed to run cutlass int8 gemm. Error: Error Internal
报错代码位于 ./3rdparty/cutlass/include/cutlass/gemm/device/gemm_universal_base.h 的initialize函数,cudaFuncSetAttribute返回的cudaerror_t为1, 没有继续检察后续代码,更改一些参数重新编译仍然报错
Tasks
The text was updated successfully, but these errors were encountered: