pku-liang / MAGIS

MAGIS: Memory Optimization via Coordinated Graph Transformation and Scheduling for DNN (ASPLOS'24)

MAGIS: Memory Optimization via Coordinated Graph Transformation and Scheduling for DNN

[Paper] [Poster]

Install

python -m pip install -r requirements.txt
cd python && python3 setup.py [develop|install]

Simple Example

from magis.testing import nn
from magis.testing import setup_training_graph, run_optimization 

G, y = nn.bert("large", batch_size=64)
G = setup_training_graph(G, y, update_weight=True, inplace=False)

run_optimization(
	G,
	"name",
	mem_limit_ratio=0.8, # 80% memory ratio limit
	# lat_limit_ratio=1.1, # 10% latency overhead limit
	time_budget=3 * 60, # 3 minutes optimization time 
	save_graph=True, # save result graph + sched to "./data/name.pkl"
	dump_file=open("results.csv", "a"), # save profile result to "results.csv"
	dtype="float32", # data type 
)

The columns in results.csv represent: "name", "device memory limit", "latency limit", "memory limit", "latency limit ratio", "memory limit ratio", "weight memory", "opt-is-prof-result", "opt-latency", "opt-memory", "opt-simul-latency", "opt-simul-memory", "ori-is-prof-result", "ori-latency", "ori-memory", "ori-simul-latency", "ori-simul-memory"

Note that:

• "memory" means peak-memory-usage (divided by data-type-bytes).
• "opt" means "optimization" and "ori" means "origin".
• "opt-is-prof-result" is True meaning that "opt-latency" and "opt-memory" are from real hardware profiling.
• "opt-is-prof-result" is False meaning that "opt-latency" & "opt-memory" equals to "opt-simul-latency" & "opt-simul-memory", which are from simulation based on single-operator profiling results and memory analysis.
• Generally, only "ori-is-prof-result" can be False since the original memory footprint may exceed the device memory limit.

Code Organization

• python/magis/
  • utils/: Utilities for other components
    • base_graph.py: Basic graph data structure (using rustworkx library) for computation graph and dimension graph.
    • conv_utils.py: Utilities for shape calculation of convolution
    • logging.py: Utilities for logging
    • timing.py: Utilities for recording python-code execution time
    • union_find_set.py: Union-find-set data structure used for the construction of dimension graph
  • operators/: Definitions of various operators
  • op_graph.py: Computation graph (MAGIS Graph IR)
  • dim_graph.py: Dimension graph
  • scheduler.py: Schedulers to schedule the computation graph (with only re-reordering).
  • simulator.py: Simulators to estimate the latency & memory based on the given computation graph and its schedule.
  • transform/: Transformations
    • rewrite_rules/: Pattern-match based rewriting rules
      • base.py: Basic definitions
      • taso_rules/: Rules from TASO
      • sched_rules.py: Rules derived from scheduling methods like Re-materialization and Swapping.
    • misc.py: Transformations other than rewriting rules
    • mutator.py: An abstraction of rewriting rules and other transformations. A mutator accepts a computation graph as input and generates a sequence of new graphs. Different mutators can be composited via combinators like "chain", "zip", "truncate" etc.
  • backend/: Backends to compile/execute given graph + schedule
    • base.py: Basic declarations. A backend provides interfaces to measure latency for a single operator and memory & latency for a whole graph. A codegen backend additionally provides interfaces to generate code for each type of operators.
    • torch_cuda.py: A backend to generate python-code invoking PyTorch API.
  • optimizer.py: Optimizer to optimize graph's memory & latency with the help of scheduler, simulator, and mutator.
  • testing/: Utilities for testing
    • nn/: Some neural networks defined using MAGIS Graph IR
    • bench.py: Some utilities for running testing
    • config.py: Some configurations

We may provide more detailed explanations of our code in the future updates.

Citation

If you find MAGIS useful or relevant to your project and research, please kindly cite our paper:

@inproceedings{10.1145/3620666.3651330,
	author = {Chen, Renze and Ding, Zijian and Zheng, Size and Zhang, Chengrui and Leng, Jingwen and Liu, Xuanzhe and Liang, Yun},
	title = {MAGIS: Memory Optimization via Coordinated Graph Transformation and Scheduling for DNN},
	year = {2024},
	isbn = {9798400703867},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3620666.3651330},
	doi = {10.1145/3620666.3651330},
	pages = {607–621},
	numpages = {15},
	location = {<conf-loc>, <city>La Jolla</city>, <state>CA</state>, <country>USA</country>, </conf-loc>},
	series = {ASPLOS '24}
}