缺点是工作量巨大，还有 rebase 的问题；第三种比较适合那种 NPU/XPU，一般这种硬件的调度方式和 GPU ...
目前世界上排前三位的GPGPU生态是CUDA（NV）、ROCm（AMD）、oneAPI（Intel）。国内GPGPU主力在用的imagin...
(dml) ---> 8 tensor1 = tensor1.new(tensor1.shape[0]).fill_(0) 9 tensor2 = tensor2.new(tensor2.shape[0]).fill_(0) 11 print("sum:", (tensor1 + tensor2).item()) RuntimeError: new(): expected key in DispatchKeySet(CPU, CUDA, HIP, XLA, MPS, IPU, XPU, HPU, Lazy, Me...
Support for CUDA Multi Process Service (MPS) Support for additional error detection with cudaMemcpy and cudaMemset C.11. New Features in 5.5 Analysis mode in racecheck tool. For more information, see Racecheck Tool Support for racecheck on SM 3.5 GPUs. C.12. New Features in 5.0 Repor...
MPS - Implemented a programmatic API to configure SM partitions. MPS - Also allow non-uniform SM partitioning using either MPS control daemon command set_active_thread_percentage or the environment variable CUDA_MPS_ACTIVE_THREAD_PERCENTAGE. MPS - Improve the information provided by the error codes...
[CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastCUDA...
See the nvidia-cuda-mps-control man page for more information on how to configure an MPS environment. ‣ The CUDA 5.5 Toolkit adds support for Linux on the ARMv7 Architecture. The toolkit comes with a comprehensive set of tools to develop applications for Linux on ARMv7, either natively ...
1 CUDA device(s) found [0] GeForce 8800 GT (14 MPs; 1500 MHz) cuda-cap: 1.1 using GeForce 8800 GT (14 MPs; 1500 MHz) cuda-cap: 1.1 called makes copying lookup-table to device 180029184 bytes free, 356645120 bytes used of 536674304 bytes initAP26 invoking kernel invoking kernel invokin...
[CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, Tracer, AutocastCPU, AutocastCUDA, FuncTorch...
runtimeerror: expected one of cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone device type at start of device string: auto 以下是我的分析和解决方案: 1. 分析错误信息 错误信息表明,在尝试将模型或张...