![]() # Space separated output with columns index,direction,kernel name,parameters,silicon time. Python -m -csv -c idx,dir,kernel,params,sil net.dict # CSV output with columns index,direction,kernel name,parameters,silicon time. Python -m -w 130 -c idx,dir,kernel,params,sil net.dict # Columnated output of width 130 with columns index,direction,kernel name,parameters,silicon time. # Columnated output of width 150 with some default columns. Assuming the directory is `prof`, here are a few examples of how to use `prof.py`. Note that a few columns might have the value "na" implying either its a work in progress or the tool was unable to extract that information. The tool produces 20 columns of information for every GPU kernel but you can select a subset of columns using the `-c` flag. The tool can produce a CSV output, a columnated output (similar to `column -t` for terminal readability) and a space separated output (for post processing by AWK for instance). ![]() The input is the python dictionary created above. This file can be used as input to other custom Is a python dictionary which contains information about the kernel name,ĭuration, parameters etc. **Note:** if you're experiencing issues with hardware counters and you get a message such as `**_ERR_NVGPUCTRPERM The user running does not have permission to access NVIDIA GPU Performance Counters on the target device_**`, please follow the steps described in (#hardware-counters).ģ. Nvprof -f -o net.sql -profile-from-start off - python net.py # If you used profiler.start() and profiler.stop() in net.py This file can be opened with NVVP, as usual. Run NVprof to generate a SQL (NVVP) file. # Define network, loss function, optimizer etc. (say after warm-up) for which you would like to capture data. Use `profiler.start()` and `profiler.stop()` to pick an iteration Run the training/inference loop with the () Add the following lines to your PyTorch network: Since many PyTorch kernels are open-source (or even custom written by the user, as in (())), this provides the user with information that helps root cause performance issues and prioritize optimization work.ġ. Other useful information might include knowing that a particular kernel did not exploit much thread parallelism, as determined by the grid/block dimensions. In fact, PyProf comes with a flag that lets the user obtain information regarding whether Tensor Cores were used by the kernel. For instance, according to the (()), the M, N and K dimensions that result in Tensor Core usage need to be divisible by 8. For more details, see NVIDIA's ().Īrmed with such information, the user can determine various issues to help them tune the network. Note that these numbers are based on the algorithm, not the actual performance of the specific kernel. For example, for matrices AMxK and BKxN, the FLOP count for a matrix multiplication is 2 * M * N * K, and bandwidth is M * K + N * K + M * N. Regarding FLOP and bandwidth implementations, these are usually quite straightforward. In addition, extra information from the profile is added for use by CUDA professionals, such as CUDA launch parameters (block/grid dimensions). Querying the record produced by the profiler to correlate the kernel name and duration with PyTorch API/layer name, tensor dimensions, tensor precision, as well as calculating FLOPs and bandwidth for common operations. This information is recorded at profile capture time, e.g. Instrumenting PyTorch operations to capture the tensor dimensions and precision using (). PyProf addresses all of the issues above by:ġ. Which line in the user's code resulted in launching this particular kernel (program trace)? Forward-backward correlation: currently it's very hard to determine what the forward pass step was that resulted in the particular weight and data gradients (wgrad, dgrad), which makes it difficult to determine the tensor dimensions required by these backprop steps to assess their performance. Knowing the tensor dimensions and precision, we can figure out the FLOPs and bandwidth required by a layer, and then determine how close to maximum performance the kernel is for that operation. What the tensor dimensions and precision were: without knowing the tensor dimensions and precision, it's impossible to reason about whether the actual (silicon) kernel time is close to maximum performance of such a kernel on the GPU. the association of `ComputeOffsetsKernel` with a concrete PyTorch layer or API is not obvious. Getting kernels out of (()) or (()) provides some generic kernel name and its execution time, but not detailed information regarding the following: 1,252 +0,0 PyProf - PyTorch Profiling toolĪnalyzing the performance of deep neural networks is hard.
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