Antonio Sánchez
c079ee5e44
Fix tensor documentation.
2025-02-05 17:36:00 +00:00
Tobias Wood
f38e16c193
Apply clang-format
2023-11-29 11:12:48 +00:00
Antonio Sánchez
6e4d5d4832
Add IWYU private pragmas to internal headers.
2023-08-21 16:25:22 +00:00
Mehdi Goli
0623791930
[SYCL-2020] Enabling USM support for SYCL. SYCL-1.2.1 did not have support for USM.
2023-05-05 17:30:36 +00:00
Mehdi Goli
b523120687
[SYCL-2020 Support] Enabling Intel DPCPP Compiler support to Eigen
2023-01-16 07:04:08 +00:00
Erik Schultheis
64909b82bd
static const class members turned into constexpr
2022-04-04 17:33:33 +00:00
Erik Schultheis
421cbf0866
Replace Eigen type metaprogramming with corresponding std types and make use of alias templates
2022-03-16 16:43:40 +00:00
Kolja Brix
8d81a2339c
Reduce usage of reserved names
2022-01-10 20:53:29 +00:00
Rasmus Munk Larsen
d7d0bf832d
Issue an error in case of direct inclusion of internal headers.
2021-09-10 19:12:26 +00:00
Rasmus Munk Larsen
13fb5ab92c
Fix more enum arithmetic.
2021-06-15 09:09:31 -07:00
Nathan Luehr
972cf0c28a
Fix calls to device functions from host code
2021-05-11 22:47:49 +00:00
Eugene Zhulenev
1c879eb010
Remove V2 suffix from TensorBlock
2019-12-10 15:40:23 -08:00
Eugene Zhulenev
dbca11e880
Remove TensorBlock.h and old TensorBlock/BlockMapper
2019-12-10 14:31:44 -08:00
Eugene Zhulenev
2918f85ba9
Do not use std::vector in getResourceRequirements
2019-12-09 16:19:55 -08:00
Eugene Zhulenev
13c3327f5c
Remove legacy block evaluation support
2019-11-12 10:12:28 -08:00
Eugene Zhulenev
0d2a14ce11
Cleanup Tensor block destination and materialized block storage allocation
2019-10-16 17:14:37 -07:00
Eugene Zhulenev
d380c23b2c
Block evaluation for TensorGenerator/TensorReverse/TensorShuffling
2019-10-14 14:31:59 -07:00
Eugene Zhulenev
ef9dfee7bd
Tensor block evaluation V2 support for unary/binary/broadcsting
2019-09-24 12:52:45 -07:00
Eugene Zhulenev
f0b36fb9a4
evalSubExprsIfNeededAsync + async TensorContractionThreadPool
2019-08-30 15:13:38 -07:00
Mehdi Goli
7d08fa805a
[SYCL] This PR adds the minimum modifications to the Eigen unsupported 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.
2019-06-28 10:08:23 +01:00
Eugene Zhulenev
c144bb355b
Merge with upstream eigen/default
2018-08-27 14:34:07 -07:00
Eugene Zhulenev
f2209d06e4
Add block evaluationto CwiseUnaryOp and add PreferBlockAccess enum to all evaluators
2018-08-10 16:53:36 -07:00
Benoit Steiner
26239ee580
Use NULL instead of nullptr to avoid adding a cxx11 requirement.
2018-08-13 11:05:51 -07:00
Mehdi Goli
b512a9536f
Enabling per device specialisation of packetsize.
2018-08-01 13:39:13 +01:00
Eugene Zhulenev
6913221c43
Add tiled evaluation support to TensorExecutor
2018-07-25 13:51:10 -07:00
Benoit Steiner
53725c10b8
Merged in mehdi_goli/opencl/DataDependancy (pull request PR-10)
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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
2017-06-28 17:55:23 +00:00
Luke Iwanski
cb81975714
Partial OpenCL support via SYCL compatible with ComputeCpp CE.
2016-09-19 12:44:13 +01:00
Benoit Steiner
e5f71aa6b2
Deleted useless trailing commas
2016-04-29 18:36:10 -07:00
Rasmus Munk Larsen
235e83aba6
Eigen cost model part 1. This implements a basic recursive framework to estimate the cost of evaluating tensor expressions.
2016-04-14 13:57:35 -07:00
Benoit Steiner
e09eb835db
Decoupled the packet type definition from the definition of the tensor ops. All the vectorization is now defined in the tensor evaluators. This will make it possible to relialably support devices with different packet types in the same compilation unit.
2016-03-08 12:07:33 -08:00
Benoit Steiner
5b7713dd33
Record whether the underlying tensor storage can be accessed directly during the evaluation of an expression.
2016-01-19 17:05:10 -08:00
Benoit Steiner
f84417d97b
Removed an incorrect assertion.
2015-07-27 09:25:22 -07:00
Benoit Steiner
f6282e451a
Fixed a typo in an assertion.
2015-07-24 17:35:47 -07:00
Benoit Steiner
410895a7e4
Silenced several compilation warnings
2015-02-10 12:13:19 -08:00
Benoit Steiner
f697df7237
Improved support for RowMajor tensors
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Misc fixes and API cleanups.
2015-01-14 15:38:48 -08:00
Benoit Steiner
99d75235a9
Misc improvements and cleanups
2014-10-13 17:02:09 -07:00
Benoit Steiner
b1892ab14d
Added suppor for in place evaluation to simple tensor expressions.
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Use mempy to speedup tensor copies whenever possible.
2014-08-13 08:26:44 -07:00
Benoit Steiner
38ab7e6ed0
Reworked the expression evaluation mechanism in order to make it possible to efficiently compute convolutions and contractions in the future:
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* The scheduling of computation is moved out the the assignment code and into a new TensorExecutor class
* The assignment itself is now a regular node on the expression tree
* The expression evaluators start by recursively evaluating all their subexpressions if needed
2014-06-13 09:56:51 -07:00
Benoit Steiner
925fb6b937
TensorEval are now typed on the device: this will make it possible to use partial template specialization to optimize the strategy of each evaluator for each device type.
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Started work on partial evaluations.
2014-06-10 09:14:44 -07:00
Benoit Steiner
6fa6cdd2b9
Added support for tensor contractions
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Updated expression evaluation mechanism to also compute the size of the tensor result
Misc fixes and improvements.
2014-06-04 09:21:48 -07:00
Benoit Steiner
7402fea0a8
Vectorized the evaluation of tensor expression (using SSE, AVX, NEON, ...)
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Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores.
Added the ability to offload the evaluation of a tensor expression to a GPU.
2014-05-16 15:08:05 -07:00
Benoit Steiner
c0f2cb016e
Extended support for Tensors:
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* Added ability to map a region of the memory to a tensor
* Added basic support for unary and binary coefficient wise expressions, such as addition or square root
* Provided an emulation layer to make it possible to compile the code with compilers (such as nvcc) that don't support cxx11.
2014-04-28 10:32:27 -07:00
