TASO: The Tensor Algebra SuperOptimizer for Deep Learning

TASO optimizes the computation graphs of DNN models using automatically generated and verified graph transformations. For an arbitrary DNN model, TASO uses the auto-generated graph transformations to build a large search space of potential computation graphs that are equivalent to the original DNN model. TASO employs a cost-based search algorithm to explore the space, and automatically discovers highly optimized computation graphs. TASO outperforms the graph optimizers in existing deep learning frameworks by up to 3x.

End-to-end inference performance comparison on a NVIDIA V100 GPU.

Install TASO

See instructions to install TASO from source. We also provide prebuilt docker images with all dependencies pre-installed.

Use TASO

TASO can directly optimize any pre-trained DNN models in ONNX, TensorFlow, and PyTorch graph formats. TASO also provides a Python interface for optimizing arbitrary DNN architectures. TASO supports exporting the optimized computation graphs to ONNX, which can be directly used as inputs by most existing deep learning frameworks.

Optimize ONNX Models

TASO can directly optimize pre-trained ONNX models, and this can be done in just a few lines of Python code. The following code snippet shows how to load a pre-trained DNN model from ONNX, optimize the model, and save the optimized model into a ONNX file.

import taso
import onnx

old_model = taso.load_onnx("/path/to/load/onnx/model")
taso_graph = taso.optimize(old_model)
new_model = taso.export_onnx(taso_graph)
onnx.save(new_model, "/path/to/save/new/onnx/model")

The optimized model has the same accuracy as the original and can be directly used by existing deep learning frameworks. Some original and TASO-optimized ONNX files are available in the onnx folder.

Optimize TensorFlow Models

TASO can optimize TensorFlow models by converting the model to ONNX using tf2onnx.

• First, install tf2onnx from PyPi as follows or from source.

pip install -U tf2onnx

• Second, convert a TensorFlow model to ONNX using tf2onnx.

python -m tf2onnx.convert \
       --saved-model /path/to/tensorflow/saved/model \
       --output /path/to/onnx/model/file

• Third, use TASO to optimize the model in ONNX by following the above instructions.

Optimize PyTorch Models

PyTorch has built-in support for ONNX as a part of the torch.onnx package. TASO can directly optimize PyTorch models in the ONNX format.

Optimize Arbitrary DNN Models using the Python Interface

TASO can also optimize arbitrary DNN architectures using the TASO Python interface. The following code snippet builds the left-most DNN graph depicted in the figure. TASO automatically performs a series of non-trivial transformations, and eventually discovers the right-most DNN graph, which is 1.3x faster on a V100 GPU. More DNN examples are available in the examples folder.

import taso
import onnx

#Build DNN model
graph = taso.new_graph()
input = graph.new_input(dims=(1,128,56,56))
w1 = graph.new_weight(dims=(128,128,3,3))
w2 = graph.new_weight(dims=(128,128,1,1))
w3 = graph.new_weight(dims=(128,128,3,3))
left = graph.conv2d(input=input, weight=w1, strides=(1,1), padding="SAME", activation="RELU")
left = graph.conv2d(input=left, weight=w3, strides=(1,1), padding="SAME")
right = graph.conv2d(input=input, weight=w2, strides=(1,1), padding="SAME", activation="RELU")
output = graph.add(left, right)
output = graph.relu(output)

#Optimize DNN model
new_graph = taso.optimize(graph)
onnx_model = taso.export_onnx(new_graph)
onnx.save(onnx_model, "/path/to/save/new/onnx/model")

Publication

• Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken. TASO: Optimizing Deep Learning Computation with Automated Generation of Graph Substitutions. In Proceedings of the Symposium on Operating Systems Principles (SOSP), Ontario, Canada, October 2019.

• Zhihao Jia, James Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, and Alex Aiken. Optimizing DNN Computation with Relaxed Graph Substitutions. In Proceedings of the Conference on Systems and Machine Learning (SysML), Palo Alto, CA, April 2019.