tfopgen 0.2.0

Requirements

A tensorflow installation, required for building the operator.

pip install tensorflow

Installation

pip install tfopgen

Usage

The user should provide a YAML configuration file defining the operator:

• inputs and optionally, their shapes.

• outputs and optionally, their outputs.

• polymorphic type attributes.

• other attributes.

• documentation.

For example, we can define the outline for a ComplexPhase operator in the complex_phase.yml file.

---
project: astronomy
library: fourier
name: ComplexPhase
type_attrs:
  - "FT: {float, double} = DT_FLOAT"
  - "CT: {complex64, complex128} = DT_COMPLEX64"
inputs:
  - ["uvw: FT", [null, null, 3]]   # (ntime, nbl, 3)
  - ["frequency: FT", [null]]      # (nchan, )
  - ["lm: FT", [null, 2]]          # (nsrc, 2)
outputs:
  - ["complex_phase: CT", [null, null, null, null]]
doc: >
  Given tensors
    (1) of (U, V, W) baseline coordinates with shape (ntime, nbl, 3)
    (2) of (L, M) sky coordinates with shape (nsrc, 2)
    (3) of frequencies,
  compute the complex phase with shape (nsrc, ntime, nbl, nchan)

We can then run:

$ tfopgen complex_phase.yml

to create the following directory structure and files:

$ tree fourier/
fourier/
├── complex_phase_op_cpu.cpp
├── complex_phase_op_cpu.h
├── complex_phase_op_gpu.cu
├── complex_phase_op_gpu.cuh
├── complex_phase_op.h
├── Makefile
└── test_complex_phase.py

The project and library options specify C++ namespaces within which the operator is created. Additionally, the Makefile will create a fourier.so shared library that can be loaded with tf.load_op_library('fourier.so').

Any polymorphic type attributes should be supplied. The generator will template the operators on type attributes. It will also generate concrete permutations of REGISTER_KERNEL_BUILDER for both the CPU and GPU op using the actual types supplied in the type attributes (float, double, complex64 and complex128) below:

type_attrs:
  - "FT: {float, double} = DT_FLOAT"
  - "CT: {complex64, complex128} = DT_COMPLEX64"

The operator inputs and their optional shapes should be specified as a list containing a string defining the .Input directive, and a list describing the shape of the input tensor. A null value in the shape will be translated into a python None. If concrete dimensions are specified, corresponding checks will be generated in the Shape Function associated with the operator.

inputs:
  - ["uvw: FT", [null, null, 3]]   # (ntime, nbl, 3)
  - ["frequency: FT", [null]]      # (nchan, )
  - ["lm: FT", [null, 2]]          # (nsrc, 2)

The operator outputs should similarly defined.

outputs:
  - ["complex_phase: CT", [null, null, null, null]]

Given these inputs and outputs, CPU and GPU operators are created with named variables corresponding to the inputs and outputs. Additionally, a CUDA kernel with the given inputs and outputs is created, as well as a shape function checking the rank and dimensions of the supplied inputs.

Other attributes may be specified (and will be output in the REGISTER_OP) directive, but are not catered for automatically by the generator code as the range of attribute behaviour is complex.

op_other_attrs:
    - "iterations: int32 >= 2",

Finally operator documentation may also be supplied.

doc: >
  Given tensors
    (1) of (U, V, W) baseline coordinates with shape (ntime, nbl, 3)
    (2) of (L, M) sky coordinates with shape (nsrc, 2)
    (3) of frequencies,
  compute the complex phase with shape (nsrc, ntime, nbl, nchan)