Pytorch OpenCL backend - simplified

Now installation of opencl backend for pytorch is really simple.

Install nighly version of pytorch for CPU in virtual environment
Clone dlrpim_backend repository and checkout true_out_of_tree_support branch
Update submodules

Run few commands inside repo

 mkdir build
 cd build
 cmake -DCMAKE_PREFIX_PATH=$VIRTUAL_ENV/lib/python3.8/site-packages/torch/share/cmake/Torch ..
 make
 cd ..

Run mnist training:
```
 python mnist.py --device=ocl:0
```

That's it.

Keep updated

Inference of ONNX Models using DLPrimitives

I worked on integration of inference of ONNX models using DLPrimitives. It isn't a simple task since ONNX operator set is very reach and many things can be implemented in different ways.

After many revisions and improvements I managed to validate multiple imagenet pretrained networks from pytorch, mxnet and few based on TensorFlow (see about issues with TF later)

How do you create a dlprimitives network using ONNX Model?

// load and parse ONNX Model
dp::ONNXModel model;
model.load(onnx_path);

// create network
dp::Context ctx(device_id);
dp::Net net(ctx);


// load parameters
net.load_model(model);

And you are ready to go.

I validated following networks and frameworks:

Pytorch, op-sets 9, 11, 13, nets alexnet, vgg16, resnet18, resnext50_32x4d, wide_resnet50_2, efficientnet_b0, efficientnet_b4, regnet_y_400mf, squeezenet1_0, mobilenet_v2, densenet121
MXNet: vgg11_bn, alexnet, mobilenetv2_0.25, mobilenet0.25, densenet121, resnet18_v1, squeezenet1.0
Tensorflow: op-sets 9, and 11 limited initial support, channel first format: resnet50, densenet121

Some networks on pytorch don't pass due to lack of some of the operators. The situation with TensorFlow is more complicated and only few networks worked ok.

TensorFlow

When I stated validated pretrained keras networks I discovered very surprising thing. TensorFlow uses asymmetrical padding in some cases, since in TF/Keras you don't explicitly provide padding but rather give some vague definition of "same" or "valid" for the padding, in some cases padding may differ on start and end of the image.

Interestingly, cuDNN does not even provide asymmetrical padding option for convolutions. Looking into the code TF does padding manually is such case (that is actually huge waste of memory and memory bandwidth)

So implementing these convolutions will require implementing of new simple padding layer just to make sure we can use dlprimitives for inference of TF models.

To be continued...

Documentation is Online

I published recent documentation online:

http://dlprimitives.org/docs

DLPrimitives Blog :: Releases

Pytorch OpenCL backend - simplified

Inference of ONNX Models using DLPrimitives

TensorFlow

Documentation is Online