a ha @sdddlZddlZddlZddlmZmZgdZgdZdddZd d Z d d Z ej d dZ dS)N) check_errorcudart)initstartstopprofile)ZgpustarttimestampZgpuendtimestampZ gridsize3dZthreadblocksizeZstreamidzenableonstart 0Zconckerneltrace key_valuecCst}t|dstdttjdrRtjjdurRttjjdddkrRtd|dur^tn|}|dkrt|j j }n|d kr|j j }nt d t jd d F}|d dd|D|t||j||Wdn1s0YdS)NcudaOutputModez-HIP does not support profiler initialization!cuda.r z.CUDA12+ does not need profiler initialization!r csvz;supported CUDA profiler output modes are: key_value and csvT)delete css|]}|dVqdS)asciiN)encode).0frA/var/www/auris/lib/python3.9/site-packages/torch/cuda/profiler.py -zinit..)rhasattrAssertionErrortorchversionr intsplit DEFAULT_FLAGSr Z KeyValuePairZCSV RuntimeErrortempfileNamedTemporaryFilewritejoinflushrZcudaProfilerInitializename)Z output_fileflagsZ output_modertZoutput_mode_enumrrrrrs,     rcCsttdS)zStarts cuda profiler data collection. .. warning:: Raises CudaError in case of it is unable to start the profiler. N)rrZcudaProfilerStartrrrrr2srcCsttdS)zStops cuda profiler data collection. .. warning:: Raises CudaError in case of it is unable to stop the profiler. N)rrZcudaProfilerStoprrrrr;srccs$ztdVWtnt0dS)a Enable profiling. Context Manager to enabling profile collection by the active profiling tool from CUDA backend. Example: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> import torch >>> model = torch.nn.Linear(20, 30).cuda() >>> inputs = torch.randn(128, 20).cuda() >>> with torch.cuda.profiler.profile() as prof: ... model(inputs) N)rrrrrrrDsr)Nr ) contextlibr!rrr__all__rrrrcontextmanagerrrrrrs