DAPPLE is a distributed training framework which combines pipeline parallelism and data parallelism to address aforementioned scheduling and planning challenges with synchronous training. This framework features a profiler, a planner and a runtime system. The profiler takes a user’s DNN model as input, and profiles execution time, activation and parameter sizes for each layer. Sample profiling results for some models are given in profiling results. Taking profiling results as input, DAPPLE planner generates an optimized hybrid parallelization plan on a given global batch size, which is further split into multiple micro-batches and scheduled for execution by DAPPLE runtime.

This repository contains the source code implementation of DAPPLE's planning results on 5 typical models: VGG19, AmoebaNet, BERT, GNMT, and XLNET.

All the planner-related experiments can be reproduced on any machine, regardless of the environment. We've provided a detailed how-to in PLANNER_REPRODUCTION.md.

Please see the launch script run.sh for each model for details.

PyPI: https://pypi.org/project/HPGO/

pip3 install HPGO

rustup default nightly
cargo build --release
maturin build --release
pip3 install xxx.whl

# Import HPGO Python API
import HPGO
# Construct the Conductor object
# conductor_from_torch_graph_and_seps(profile_filename, profile_batch_size, global_batch_size, devices)
conductor = HPGO.conductor_from_torch_graph_and_seps("./profiling_results/xlnet-36-pbs-1.txt", 1, 128, [8, 16])
result = conductor.py_orchestrate()
print(result)

The DAPPLE Planner is open sourced under the terms of BSD-3-Clause, details of which can be found in the src/LICENSE.md file

The file src/input/torch_graph_py.rs contains Python source code from PipeDream, which is licensed under the MIT License.