Graph Capture (Experimental)

Feature Description

This feature automatically applies a combination of TorchScript trace technique and TorchDynamo to try to generate a graph model, for providing a good user experience while keeping execution fast. Specifically, the process tries to generate a graph with TorchScript trace functionality first. In case of generation failure or incorrect results detected, it changes to TorchDynamo with TorchScript backend. Failure of the graph generation with TorchDynamo triggers a warning message. Meanwhile the generated graph model falls back to the original one. I.e. the inference workload runs in eager mode. Users can take advantage of this feature through a new knob --graph_mode of the ipex.optimize() function to automatically run into graph mode.

Usage Example

import torch
import torchvision.models as models

model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, graph_mode=True)
######################################################

with torch.no_grad():
  model(data)