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branch.
* 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
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Add async evaluation to a number of ops.
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c++11 functionality with older compilers.
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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
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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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From 68d431b4c14ad60a778ee93c1f59ecc4b931950e Mon Sep 17 00:00:00 2001
Found via `codespell -q 3 -I ../eigen-word-whitelist.txt` where the whitelists consists of:
```
als
ans
cas
dum
lastr
lowd
nd
overfl
pres
preverse
substraction
te
uint
whch
```
---
CMakeLists.txt | 26 +++++++++----------
Eigen/src/Core/GenericPacketMath.h | 2 +-
Eigen/src/SparseLU/SparseLU.h | 2 +-
bench/bench_norm.cpp | 2 +-
doc/HiPerformance.dox | 2 +-
doc/QuickStartGuide.dox | 2 +-
.../Eigen/CXX11/src/Tensor/TensorChipping.h | 6 ++---
.../Eigen/CXX11/src/Tensor/TensorDeviceGpu.h | 2 +-
.../src/Tensor/TensorForwardDeclarations.h | 4 +--
.../src/Tensor/TensorGpuHipCudaDefines.h | 2 +-
.../Eigen/CXX11/src/Tensor/TensorReduction.h | 2 +-
.../CXX11/src/Tensor/TensorReductionGpu.h | 2 +-
.../test/cxx11_tensor_concatenation.cpp | 2 +-
unsupported/test/cxx11_tensor_executor.cpp | 2 +-
14 files changed, 29 insertions(+), 29 deletions(-)
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evaluators
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here should be based on CoeffreturnType not the Scalar type. Therefore, Similar to reduction and evalTo function, it should have its own MakePointer class. In this case, for other device the type is defaulted to CoeffReturnType and no changes is required on users' code. However, in SYCL, on the device, we can recunstruct the device Type.
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consistent with implementation.
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Improved support for OpenCL
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Tensor Trace op
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DataDependancy
* Wrapping data type to the pointer class for sycl in non-terminal nodes; not having that breaks Tensorflow Conv2d code.
* Applying Ronnan's Comments.
* Applying benoit's comments
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Tensor Contractsycl to be located in any place in the expression tree.
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duplication name error; adding tensorConcatinationOp backend for sycl.
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tensor_broadcast_sycl on GPU; adding get_sycl_supported_devices() on syclDevice.h.
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Unsupported/Eigen/CXX11/Tensor; added TensorReduction for sycl (full reduction and partial reduction); added TensorReduction test case for sycl (full reduction and partial reduction); fixed the tile size on TensorSyclRun.h based on the device max work group size;
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TernaryFunctors and their executors allow operations on 3-tuples of inputs.
API fully implemented for Arrays and Tensors based on binary functors.
Ported the cephes betainc function (regularized incomplete beta
integral) to Eigen, with support for CPU and GPU, floats, doubles, and
half types.
Added unit tests in array.cpp and cxx11_tensor_cuda.cu
Collapsed revision
* Merged helper methods for betainc across floats and doubles.
* Added TensorGlobalFunctions with betainc(). Removed betainc() from TensorBase.
* Clean up CwiseTernaryOp checks, change igamma_helper to cephes_helper.
* betainc: merge incbcf and incbd into incbeta_cfe. and more cleanup.
* Update TernaryOp and SpecialFunctions (betainc) based on review comments.
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This is the initial implementation a generic scan operation.
Based on this, cumsum and cumprod method have been added to TensorBase.
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the Tensor class since most of the code currently already use integers.
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previously disabled by mistake
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'generator'. This simplifies the creation of constant tensors initialized using specific regular patterns.
Created a gaussian window generator as a first use case.
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encoding it in the options.
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Eugene Zhulenev
Mehdi Goli
Rasmus Munk Larsen
Deven Desai
luz.paz"
Gael Guennebaud
Benoit Steiner
Luke Iwanski
Eugene Brevdo
Igor Babuschkin