a. Trace:
# `func` is the function to be traced
traced_func = parrots.aot.trace(func, input, target)b. Profiling:
parrots.runtime.profile()
parrots.runtime.profile_memory()c. Run it:
loss, acc = traced_func(input, target)d. Save IR and profiling information:
parrots.aot.save(traced_func, 'function.json')
# Quit parrots and get `profile_memory.json` and `profile.txt`a. Put original IR JSON function.json, timeline profiling result profile.txt (please rename to timeline.txt) and memory profiling result profile_memory.json (please rename to memory.json) into one directory under data, like:
├── data
│ ├── resnet152-32
│ │ ├── function.json
│ │ ├── memory.json
│ │ └── timeline.txt
│ ├── resnet50-32
│ │ ├── function.json
│ │ ├── memory.json
│ │ └── timeline.txt
│ ├── processor.py
b. Run processor.py (maybe failed, please debug by yourself)
python3 processor.pyc. You should get like this:
├── data
│ ├── resnet152-32
│ │ ├── function.json
│ │ ├── memory.json
│ │ ├── pattern.json
│ │ └── timeline.txt
│ ├── resnet50-32
│ │ ├── function.json
│ │ ├── memory.json
│ │ ├── pattern.json
│ │ └── timeline.txt
│ ├── processor.py
a. Compile with CMake:
mkdir build
cd build
# Debug
cmake .. -DCMAKE_BUILD_TYPE=Debug
# Release (about 10x faster than debug binary)
cmake .. -DCMAKE_BUILD_TYPE=Release
makeb. Run:
# Usage: dlmo <input> <output> <limit>
./dlmo ../data/resnet152-32/pattern.json optimized.json 2.1GiBc. The output should be like (you may have to read the source code and adjust the parameters):
Running case ../data/resnet152-32/pattern.json (2356 operators) with optimizer (limit 2.100000 GiB) ...
> Start back-tracing search from source (peak memory: 8.019928 GiB, total time: 229.738614 ms)
> Progress (300): 2.660553 GiB, 256.848531 ms
> Progress (600): 2.094578 GiB, 276.179152 ms
> Progress (900): 2.094578 GiB, 276.605152 ms
> Progress (1200): 2.082615 GiB, 276.739483 ms
> Reach search limit, stop searching
> Result:
> Schedules searched: 1500
> Time used: 26677.157000 ms
> Best: {peak memory: 2.094578 GiB, total time: 274.821156 ms}
> Satisfy memory: true
> Writing result into path optimized.json ... OK!
d. The newly generated IR JSON is named as <output>.
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