| Commit message (Collapse) | Author | Age |
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Time-dependence prevents tests from being repeatable. This has long
been an issue with debugging the tensor tests. Removing this will allow
future tests to be repeatable in the usual way.
Also, the recently added macros in !476 are causing headaches across different
platforms. For example, checking `_XOPEN_SOURCE` is leading to multiple
ambiguous macro errors across Google, and `_DEFAULT_SOURCE`/`_SVID_SOURCE`/`_BSD_SOURCE`
are sometimes defined with values, sometimes defined as empty, and sometimes
not defined at all when they probably should be. This is leading to
multiple build breakages.
The simplest approach is to generate a seed via
`Eigen::internal::random<uint64_t>()` if on CPU. For GPU, we use a
hash based on the current thread ID (since `rand()` isn't supported
on GPU).
Fixes #1602.
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`TensorRandom` currently relies on BSD `random()`, which is not always
available. The [linux manpage](https://man7.org/linux/man-pages/man3/srandom.3.html)
gives the glibc condition:
```
_XOPEN_SOURCE >= 500
|| /* Glibc since 2.19: */ _DEFAULT_SOURCE
|| /* Glibc <= 2.19: */ _SVID_SOURCE || _BSD_SOURCE
```
In particular, this was failing to compile for MinGW via msys2. If not
available, we fall back to using `rand()`.
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The existing `TensorRandom.h` implementation makes the assumption that
`half` (`bfloat16`) has a `uint16_t` member `x` (`value`), which is not
always true. This currently fails on arm64, where `x` has type `__fp16`.
Added `bit_cast` specializations to allow casting to/from `uint16_t`
for both `half` and `bfloat16`. Also added tests in
`half_float`, `bfloat16_float`, and `cxx11_tensor_random` to catch
these errors in the future.
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Newlib in Native Client SDK does not provide ::random function.
Implement get_random_seed for NaCl using ::rand, similarly to Windows version.
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module required to run it on devices supporting SYCL.
* 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.
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clang-cuda.
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There are two major changes (and a few minor ones which are not listed here...see PR discussion for details)
1. Eigen::half implementations for HIP and CUDA have been merged.
This means that
- `CUDA/Half.h` and `HIP/hcc/Half.h` got merged to a new file `GPU/Half.h`
- `CUDA/PacketMathHalf.h` and `HIP/hcc/PacketMathHalf.h` got merged to a new file `GPU/PacketMathHalf.h`
- `CUDA/TypeCasting.h` and `HIP/hcc/TypeCasting.h` got merged to a new file `GPU/TypeCasting.h`
After this change the `HIP/hcc` directory only contains one file `math_constants.h`. That will go away too once that file becomes a part of the HIP install.
2. new macros EIGEN_GPUCC, EIGEN_GPU_COMPILE_PHASE and EIGEN_HAS_GPU_FP16 have been added and the code has been updated to use them where appropriate.
- `EIGEN_GPUCC` is the same as `(EIGEN_CUDACC || EIGEN_HIPCC)`
- `EIGEN_GPU_DEVICE_COMPILE` is the same as `(EIGEN_CUDA_ARCH || EIGEN_HIP_DEVICE_COMPILE)`
- `EIGEN_HAS_GPU_FP16` is the same as `(EIGEN_HAS_CUDA_FP16 or EIGEN_HAS_HIP_FP16)`
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This commit enables the use of Eigen on HIP kernels / AMD GPUs. Support has been added along the same lines as what already exists for using Eigen in CUDA kernels / NVidia GPUs.
Application code needs to explicitly define EIGEN_USE_HIP when using Eigen in HIP kernels. This is because some of the CUDA headers get picked up by default during Eigen compile (irrespective of whether or not the underlying compiler is CUDACC/NVCC, for e.g. Eigen/src/Core/arch/CUDA/Half.h). In order to maintain this behavior, the EIGEN_USE_HIP macro is used to switch to using the HIP version of those header files (see Eigen/Core and unsupported/Eigen/CXX11/Tensor)
Use the "-DEIGEN_TEST_HIP" cmake option to enable the HIP specific unit tests.
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aliases
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Antonio Sanchez
Antonio Sanchez
Teng Lu
maratek
Mehdi Goli
Gael Guennebaud
Deven Desai
Benoit Steiner