The sum accuracy test currently uses the default test precision for
the given scalar type. However, scalars are generated via a normal
distribution, and given a large enough count and strong enough random
generator, the expected sum is zero. This causes the test to
periodically fail.
Here we estimate an upper-bound for the error as `sqrt(N) * prec` for
summing N values, with each having an approximate epsilon of `prec`.
Also fixed a few warnings generated by MSVC when compiling the
reduction test.
reducer0.reducePacket(accum1, accum0);
reducer0.reducePacket(accum2, accum0);
reducer0.reducePacket(accum3, accum0);
For the mean reducer this will increment the count as well as adding together the accumulators and result in the wrong count being divided into the sum at the end.
For moderately sized inputs, running the Tree reduction quickly
fills/overflows the GPU thread stack space, leading to memory errors.
This was happening in the `cxx11_tensor_complex_gpu` test, for example.
Disabling tree reduction on GPU fixes this.
PR 181 ( https://gitlab.com/libeigen/eigen/-/merge_requests/181 ) adds `__launch_bounds__(1024)` attribute to GPU kernels, that did not have that attribute explicitly specified.
That PR seems to cause regressions on the CUDA platform. This PR/commit makes the changes in PR 181, to be applicable for HIP only
Starting with ROCm 3.5, the HIP compiler will change from HCC to hip-clang.
This compiler change introduce a change in the default value of the `__launch_bounds__` attribute associated with a GPU kernel. (default value means the value assumed by the compiler as the `__launch_bounds attribute__` value, when it is not explicitly specified by the user)
Currently (i.e. for HIP with ROCm 3.3 and older), the default value is 1024. That changes to 256 with ROCm 3.5 (i.e. hip-clang compiler). As a consequence of this change, if a GPU kernel with a `__luanch_bounds__` attribute of 256 is launched at runtime with a threads_per_block value > 256, it leads to a runtime error. This is leading to a couple of Eigen unit test failures with ROCm 3.5.
This commit adds an explicit `__launch_bounds(1024)__` attribute to every GPU kernel that currently does not have it explicitly specified (and hence will end up getting the default value of 256 with the change to hip-clang)
* 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
* 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.
1. Eigen/src/Core/arch/GPU/Half.h
Updating the HIPCC implementation half so that it can declared as a __shared__ variable
2. Eigen/src/Core/util/Macros.h, Eigen/src/Core/util/Memory.h
introducing a EIGEN_USE_STD(func) macro that calls
- std::func be default
- ::func when eigen is being compiled with HIPCC
This change was requested in the previous HIP PR
(https://bitbucket.org/eigen/eigen/pull-requests/518/pr-with-hip-specific-fixes-for-the-eigen/diff)
3. unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
Removing EIGEN_DEVICE_FUNC attribute from pure virtual methods as it is not supported by HIPCC
4. unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
Disabling the template specializations of InnerMostDimReducer as they run into HIPCC link errors
- unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
Changing "pass-by-reference" argument to be "pass-by-value" instead
(in a __global__ function decl).
"pass-by-reference" arguments to __global__ functions are unwise,
and will be explicitly flagged as errors by the newer versions of HIP.
- Eigen/src/Core/util/Memory.h
- unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
Changes introduced in recent commits breaks the HIP compile.
Adding EIGEN_DEVICE_FUNC attribute to some functions and
calling ::malloc/free instead of the corresponding std:: versions
to get the HIP compile working again
- unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
Change introduced a recent commit breaks the HIP compile
(link stage errors out due to failure to inline a function).
Disabling the recently introduced code (only for HIP compile), to get
the eigen nightly testing going again.
Will submit another PR once we have te proper fix.
- Eigen/src/Core/util/ConfigureVectorization.h
Enabling GPU VECTOR support when HIP compiler is in use
(for both the host and device compile phases)
Compiling the eigen unittests with hip-clang (HIP with clang as the underlying compiler instead of hcc or nvcc), results in compile errors. The changes in this commit fix those compile errors. The main change is to convert a few instances of "__device__" to "EIGEN_DEVICE_FUNC"
