Pysyft: Implement cumprod Functionality in FloatTensor with CPU/GPU Backend Support

[iamtrask]

User Story: As a Data Scientist using PySyft's FloatTensor type, I want to leverage a wide range of methods which use our new Unity backend. For this ticket to be complete, the cumprod() should be added to our FloatTensor class with the appropriate functionality, returning a new tensor.

Furthermore, the function should automatically determine which backend to use (CPU/GPU) based on where the data is located. If the data is located on the CPU, a performant CPU implementation should run but if the data for a given FloatTensor is located on a GPU, it should be run using an HLSL kernel where appropriate. Obviously, if no GPU is available, it should automatically fall back to the CPU implementation.

Every Reference You Might Need for this Issue:

• For a reference on the operation this performs check out PyTorch's documentation.
• For a reference on how to program in Unity, check out this basic tutorial
• For a reference on how to write HLSL code, check out this basic tutorial
• For a complete tutorial on how to add functions to FloatTensor (step by step guide) see this Google Document
• For a reference on how other functions like this have been implemented check out the functions in this notebook as well as the corresponding files that made it possible (SyftController, FloatTensor.Ops, FloatTensor.ShaderOps, FloatTensorShaders, and FloatTensorTest).
• And of course, please consider our Contributor Guidelines for all contributions.

Acceptance Criteria:

• [ ] an integration test in PySyft demonstrating the correct CPU and GPU operation implemented over a FloatTensor while connected to a Unity backend
• [ ] a Unit Test in OpenMined/OpenMined demonstrating the correct operation on a FloatTensor
• [ ] inline documentation in the python code. For inspiration on inline documentation, please check out PyTorch's documentation for this operator.
• [ ] Link your Pull Request back to this Issue so that it gets closed appropriately when the PR is merged.

[anishpdalal]

@iamtrask I just joined OpenMined and am looking for a first contribution on the Deep Learning portion of the architecture. Would love to work on this!

[iamtrask]

All you @anishpdalal !!! Go for it!