- Split SpecialFunctions files in to a separate BesselFunctions file.
In particular add:
- Modified bessel functions of the second kind k0, k1, k0e, k1e
- Bessel functions of the first kind j0, j1
- Bessel functions of the second kind y0, y1
The change caused the device struct to be copied for each expression evaluation, and caused, e.g., a 10% regression in the TensorFlow multinomial op on GPU:
Benchmark Time(ns) CPU(ns) Iterations
----------------------------------------------------------------------
BM_Multinomial_gpu_1_100000_4 128173 231326 2922 1.610G items/s
VS
Benchmark Time(ns) CPU(ns) Iterations
----------------------------------------------------------------------
BM_Multinomial_gpu_1_100000_4 146683 246914 2719 1.509G items/s
Not having this attribute results in the following failures in the `--config=rocm` TF build.
```
In file included from tensorflow/core/kernels/cross_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/framework/register_types.h:20:
In file included from ./tensorflow/core/framework/numeric_types.h:20:
In file included from ./third_party/eigen3/unsupported/Eigen/CXX11/Tensor:1:
In file included from external/eigen_archive/unsupported/Eigen/CXX11/Tensor:140:
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h:356:37: error: 'Eigen::constCast': no overloaded function has restriction specifiers that are compatible with the ambient context 'data'
typename Storage::Type result = constCast(m_impl.data());
^
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h:356:37: error: 'Eigen::constCast': no overloaded function has restriction specifiers that are compatible with the ambient context 'data'
external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h:148:56: note: in instantiation of member function 'Eigen::TensorEvaluator<const Eigen::TensorChippingOp<1, Eigen::TensorMap<Eigen::Tensor<int, 2, 1, long>, 16, MakePointer> >, Eigen::Gpu\
Device>::data' requested here
return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
```
Adding the EIGEN_DEVICE_FUNC attribute resolves those errors
* Modifying TensorDeviceSYCL to use `EIGEN_THROW_X`.
* Modifying TensorMacro to use `EIGEN_TRY/CATCH(X)` macro.
* Modifying TensorReverse.h to use `EIGEN_DEVICE_REF` instead of `&`.
* Fixing the SYCL device macro in SpecialFunctionsImpl.h.
* Abstracting the pointer type so that both SYCL memory and pointer can be captured.
* Converting SYCL virtual pointer to SYCL device memory in Eigen evaluator class.
* Binding SYCL placeholder accessor to command group handler by using bind method in Eigen evaluator node.
* Adding SYCL macro for controlling loop unrolling.
* Modifying the TensorDeviceSycl.h and SYCL executor method to adopt the above changes.
