PyTorch Interface

Users can use FlexFlow to optimize the parallelization performance of existing PyTorch models in two steps. The PyTorch support requires the PyTorch FX module, so make sure your PyTorch is up to date.

1. Export a PyTorch Model to an external file

A PyTorch model can be exported to the FlexFlow model format and saved into an external file:

import torch
import flexflow.torch.fx as fx

# create a PyTorch Model
class MyPyTorchModule(nn.Module):

# export the PyTorch Model to an external file
model = MyPyTorchModule()
fx.torch_to_flexflow(model, "filename")

2. Import a FlexFlow model from a external file

A FlexFlow program can directly import a previously saved PyTorch model and autotune the parallelization performance for a given parallel machine:

from flexflow.torch.model import PyTorchModel

# create input tensors
dims_input = [ffconfig.get_batch_size(), 3, 32, 32]
input_tensor = ffmodel.create_tensor(dims_input, DataType.DT_FLOAT)

# create a flexflow model from the file
torch_model = PyTorchModel("filename")
output_tensor = torch_model.apply(ffmodel, [input_tensor])

# use the Python API to train the model
ffoptimizer = SGDOptimizer(ffmodel, 0.01)
..., y=dataloader_label, epochs=epochs)

More FlexFlow PyTorch examples are available on GitHub.