v20.20 (Dec 20, 20)
- Build
- FlexFlow now supports both Makefile and CMake build. More details are available in this instruction.
- Frontend Supports
- PyTorch. FlexFlow now supports training existing PyTorch models with minimal changes to the source code. To run PyTorch models in FlexFlow, users can first export a model to the ONNX format using
torch.onnxand then load an ONNX model in FlexFlow for distributed training. More examples: https://github.com/flexflow/FlexFlow/tree/master/examples/python/pytorch - ONNX. FlexFlow supports training existing ONNX models through
flexflow.onnx.model. More examples: https://github.com/flexflow/FlexFlow/tree/master/examples/python/onnx - TensorFlow Keras. Similar to the PyTorch support.
flexflow.kerasenables distributed training of existing TensorFlow Keras models. See this bootcamp talk for more details.
- PyTorch. FlexFlow now supports training existing PyTorch models with minimal changes to the source code. To run PyTorch models in FlexFlow, users can first export a model to the ONNX format using
- Parallelization Optimizer
- Integrated the parallelization optimizer into the FlexFlow runtime. Users can now use the
--search-budgetand--search-alphato control the FlexFlow parallelization optimizer for searching for optimized strategies. See this post for the usage of the optimizer.
- Integrated the parallelization optimizer into the FlexFlow runtime. Users can now use the
- Examples
- More PyTorch, ONNX, TensorFlow Keras examples have been added to the
/examples/pythonfolder. - Updated the cpp examples to use the new runtime interface.
- More PyTorch, ONNX, TensorFlow Keras examples have been added to the
- Mapper
- Implemented a new mapper with improved runtime performance.
- Legion
- Updated the Legion version with improved runtime performance