CPU Optimization PyTorch Strategies
Hi all,I’m working on developing a model for production, but the machine I’ll use to perform the inferences doesn’t have a GPU (as usual in these cases). So, I’m testing different pytorch-native optimization strategies and I’m making a benchmark graph to make a decision about the best strategies to use. In case someone finds it interesting, this is the benchmark graph and below the graph you can find the description of each optimization. The model is a medium BERT with a LSTM layer on top. The x-axis represents the number of splits of 512-tokens in each doc (it means if you have one document with 1024 tokens, this number would be 2. You can understand this number as a metric of the document length.)Normal: No optimization applied.Quantized: Dinamic Quantization applied. You can read more about this techniquehere. I don’t know why It ins’t working well, I’ve applied the process to the complete model, maybe I should be more selective with the layers to quantize…N_threads: Decrease the number of threads using by PyTorch internally (128 by default) I’ve founded that 64 is a good number.Prune: Basically it is a technique that apply a binary mask to model weights to converting some of them to zero (the strategy to select which weights to convert it is selected by user) You can read more bout this techniquehereAny suggestions are welcome!
2022-02-01T12:32:04Z
Hi all,I’ve made some progress. First of all, I want to explain the new things I’ve learnt about the number of threads.There are two different possible multiprocesses in PyTorch, “intra_op” and “inter_op”. The difference between them are the elements involved in the operations. If I’ve understood it correctly, “intra_op” refers to the operations between elements in the same minibatch and “inter_op” refers to the operations between elements in different minibatches. You can read about thathere. So, I’ve separated the election of number of threads in two “intra_op” and “inter_op”. Blelow you can see the new bechmark graph: