The following commit introduces compile errors when running eigen with hipcc
2918f85ba9
hipcc errors out because it requies the device attribute on the methods within the TensorBlockV2ResourceRequirements struct instroduced by the commit above. The fix is to add the device attribute to those methods
* Force-inline implementations. They pass around pointers to shared memory
blocks. Without inlining compiler must operate via generic pointers.
Inlining allows compiler to detect that we're operating on shared memory
which allows generation of substantially faster code.
* Fixed a long-standing typo which resulted in launching 8x more kernels
than we needed (.z dimension of the block is unused by the kernel).
77b447c24e
While providing a 50% speedup on Haswell+ processors, the large relative error outside [-18, 18] in this approximation causes problems, e.g., when computing gradients of activation functions like softplus in neural networks.
Recent changes have introduced the following build error when compiling with HIPCC
---------
unsupported/test/../../Eigen/src/Core/GenericPacketMath.h:254:58: error: 'ldexp': no overloaded function has restriction specifiers that are compatible with the ambient context 'pldexp'
---------
The fix for the error is to pick the math function(s) from the global namespace (where they are declared as device functions in the HIP header files) when compiling with HIPCC.
* Unifying all loadLocalTile from lhs and rhs to an extract_block function.
* Adding get_tensor operation which was missing in TensorContractionMapper.
* Adding the -D method missing from cmake for Disable_Skinny Contraction operation.
* Wrapping all the indices in TensorScanSycl into Scan parameter struct.
* Fixing typo in Device SYCL
* Unifying load to private register for tall/skinny no shared
* Unifying load to vector tile for tensor-vector/vector-tensor operation
* Removing all the LHS/RHS class for extracting data from global
* Removing Outputfunction from TensorContractionSkinnyNoshared.
* Combining the local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining General Tensor-Vector and VectorTensor contraction into one kernel.
* Making double buffering optional for Tensor contraction when local memory is version is used.
* Modifying benchmark to accept custom Reduction Sizes
* Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host
* Adding Test for SYCL
* Modifying SYCL CMake
Confirm that install directory is identical
before and after this simplifying patch.
```bash
hg clone <<Eigen>>
mkdir eigen-bld
cd eigen-bld
cmake ../Eigen -DCMAKE_INSTALL_PREFIX:PATH=/tmp/bef
make install
find /tmp/pre_eigen_modernize >/tmp/bef
# Apply this patch
cmake ../Eigen -DCMAKE_INSTALL_PREFIX:PATH=/tmp/aft
make install
find /tmp/post_eigen_modernize |sed 's/post_e/pre_e/g' >/tmp/aft
diff /tmp/bef /tmp/aft
```
Features committed in 2016 have required cmake verison 2.8.11.
`sergiu Tue Nov 22 12:25:06 2016 +0100: target_compile_definitions`
Set the minimum cmake version to the minimum version that
is capable of compiling or installing the code base.
Add a new EIGEN_HAS_INTRINSIC_INT128 macro, and use this instead of __SIZEOF_INT128__. This fixes related issues with TensorIntDiv.h when building with Clang for Windows, where support for 128-bit integer arithmetic is advertised but broken in practice.
Ancient versions of CMake required else(), endif(), and similar block
termination commands to have arguments matching the command starting the block.
This is no longer the preferred style.
2. Simplify handling of special cases by taking advantage of the fact that the
builtin vrsqrt approximation handles negative, zero and +inf arguments correctly.
This speeds up the SSE and AVX implementations by ~20%.
3. Make the Newton-Raphson formula used for rsqrt more numerically robust:
Before: y = y * (1.5 - x/2 * y^2)
After: y = y * (1.5 - y * (x/2) * y)
Forming y^2 can overflow for very large or very small (denormalized) values of x, while x*y ~= 1. For AVX512, this makes it possible to compute accurate results for denormal inputs down to ~1e-42 in single precision.
4. Add a faster double precision implementation for Knights Landing using the vrsqrt28 instruction and a single Newton-Raphson iteration.
Benchmark results: https://bitbucket.org/snippets/rmlarsen/5LBq9o
