grain 0.0.6

grain

autograd mir and CUDA library for dynamic neural networks in D.

• documentaion https://shigekikarita.github.io/grain/grain.html

features

• dynamic computation graph like chainer or pytorch
• statically typed tensor Variable(T, size_t dim, alias Storage) unlike numpy
• CPU (mir) and CUDA (cublas/cudnn) backend
• extensible (i.e., user-defined) autograd function

how to run MNIST

$ dub --config=example-mnist -b=cuda-release # with cuda
$ dub --config=example-mnist -b=release      # without cuda

it results as following (may take several seconds without cuda)

Running ./grain-example-mnist
loading data/train-images-idx3-ubyte.gz
loading data/train-labels-idx1-ubyte.gz
loading data/t10k-images-idx3-ubyte.gz
loading data/t10k-labels-idx1-ubyte.gz
train loss: 0.538635, acc: 0.864311
test loss: 0.299959, acc: 0.915264
train loss: 0.277901, acc: 0.920858
test loss: 0.241783, acc: 0.930589
train loss: 0.229879, acc: 0.934999
test loss: 0.206087, acc: 0.939704
train loss: 0.198716, acc: 0.943937
test loss: 0.181938, acc: 0.945613
train loss: 0.175066, acc: 0.950957
test loss: 0.163919, acc: 0.951022

how to test

$ curl -fsS https://dlang.org/install.sh | bash -s ldc-1.9.0
$ source ~/dlang/ldc-1.9.0/activate
$ dub test -b=cuda-unittest # with cuda
$ dub test                  # without cuda

I have tested with

• LDC1.9.0 (prebuilt binary)
• CUDA9.1 (optional)
• CUDNN7 (optional but required if you use CUDA)
• NVidia GTX760, GTX1080 (https://grain.dpldocs.info/grain.htmloptional)

links

• CUDA in D
• https://github.com/ldc-developers/ldc
• https://llvm.org/docs/NVPTXUsage.html
• https://llvm.org/docs/CompileCudaWithLLVM.html
• Referenced autograd libraries
• Pytorch https://github.com/pytorch/pytorch
• Chainer v1 https://github.com/chainer/chainer/tree/v1

todo

sorted by higher priority for me

• practical examples (MNIST, CIFAR10, WordLM). see example/
• dub --config=example-mnist
• (wip) dub --config=example-char-rnn
• more autograd functions. see source/grain/functions/ TODO
• multi GPU
• curand wrappers
• statically linked kernel module instead of ptx string
• dmd support
• double backward (implement Function.backward with Chain)