编译时启用TORCH_USE_CUDA_DSA主要用于调试CUDA设备端代码。 TORCH_USE_CUDA_DSA是一个编译选项,用于启用PyTorch中的设备端断言(device-side assertions)。这些断言可以帮助开发者在GPU上执行代码时捕获错误。当你在使用PyTorch进行深度学习模型训练,尤其是在依赖GPU加速的情况下,如果遇到与CUDA相关的错误或警告,启用这个...
192.168.37.6: For debugging consider passing CUDA_LAUNCH_BLOCKING=1. 192.168.37.6: Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. export TORCH_USE_CUDA_DSA=1 以上train在V100-32GB*16,大概率显存不足。 发布于 2024-01-14 13:51・广东...
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Traceback (most recent call last): File "/home/ma-user/work/pretrain/peft-baichuan2-13b-1/train.py", line 285, in <module> main() File "/home/ma-user/work/pretrain/peft-baichuan2-13b-1/train.py", line 268, ...
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Any ideas on what needs to be done to fix this a...
No Commentson Solve Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. When doing finetuning model, you may encountered with this error message that telling CUDA Out of Memory (OOM) with detail RuntimeError: CUDA error: out of memory; Compile with TORCH_USE_CUDA_DSA to ...
nitialization error CUDA kernel errors CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA`,x传入的不是list,而是tensor。原因是pytorch。改成list就没有这个问题。
[rank0]: return t.to( [rank0]: ^^^ [rank0]: RuntimeError: CUDA error: out of memory [rank0]: Compile withTORCH_USE_CUDA_DSAto enable device-side assertions.
export CUDA_LAUNCH_BLOCKING=1```然后再运行你的程序。2. 编译PyTorch时,使用`TORCH_USE_CUDA_DSA`选项,这会启用设备端断言(device-side assertions),有助于定位CUDA内核中的错误。重新编译PyTorch时,可以这样设置:```bashTORCH_USE_CUDA_DSA=1 python setup.py install```通过这两个方法,你可以更准确地定位...
一位用户在 A100 GPU 上启用 max-autotune 时看到警告:“Not enough SMs to use max_autotune_gemm mode”(SM 数量不足)。这种警告可能与启用 MIG(多实例 GPU)或 PyTorch 内部阈值设置有关。总之,max-autotune 模式需要较新版本的 PyTorch 和 CUDA 支持,例如 CUDA 11.4+ 支持的 Triton 和 CUDA Graph 功能...
For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. 2024-03-29 18:28:51,875 xinference.api.restful_api 8 ERROR [address=0.0.0.0:43266, pid=897] CUDA error: invalid argument ...