autograd.cpp contains several examples of doing autograd in PyTorch C++ frontend.
To build the code, run the following commands from your terminal:
$ cd autograd
$ mkdir build
$ cd build
$ cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
$ makewhere /path/to/libtorch should be the path to the unzipped LibTorch
distribution, which you can get from the PyTorch
homepage.
Execute the compiled binary to run:
$ ./autograd
====== Running: "Basic autograd operations" ======
1 1
1 1
[ CPUFloatType{2,2} ]
3 3
3 3
[ CPUFloatType{2,2} ]
AddBackward1
27 27
27 27
[ CPUFloatType{2,2} ]
MulBackward1
27
[ CPUFloatType{} ]
MeanBackward0
false
true
SumBackward0
4.5000 4.5000
4.5000 4.5000
[ CPUFloatType{2,2} ]
813.6625
1015.0142
-664.8849
[ CPUFloatType{3} ]
MulBackward1
204.8000
2048.0000
0.2048
[ CPUFloatType{3} ]
true
true
false
true
false
true
====== Running "Computing higher-order gradients in C++" ======
0.0025 0.0946 0.1474 0.1387
0.0238 -0.0018 0.0259 0.0094
0.0513 -0.0549 -0.0604 0.0210
[ CPUFloatType{3,4} ]
====== Running "Using custom autograd function in C++" ======
-3.5513 3.7160 3.6477
-3.5513 3.7160 3.6477
[ CPUFloatType{2,3} ]
0.3095 1.4035 -0.0349
0.3095 1.4035 -0.0349
0.3095 1.4035 -0.0349
0.3095 1.4035 -0.0349
[ CPUFloatType{4,3} ]
5.5000
5.5000
[ CPUFloatType{2} ]