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38 Commits

Author SHA1 Message Date
Eisuke Kawashima
ac5c83a3f5 unset executable flag 2022-05-22 22:47:43 +09:00
Sean McBride
f1b9692d63 Removed EIGEN_UNUSED decorations from many functions that are in fact used 2022-03-03 20:19:33 +00:00
Kolja Brix
8d81a2339c Reduce usage of reserved names 2022-01-10 20:53:29 +00:00
Shiva Ghose
a4098ac676 Fix duplicate include guard *ALTIVEC_H -> *ZVECTOR_H 
Some some header guards were repeated between the `AltiVec` package and the
`ZVector` packages. This could cause a problem if (for whatever reason) someone
attempts to include headers for both architectures.
 2021-12-31 08:43:24 +00:00
Antonio Sanchez
40bbe8a4d0 Fix ZVector build. 
Cross-compiled via `s390x-linux-gnu-g++`, run via qemu.  This allows the
packetmath tests to pass.
 2021-10-27 16:30:15 +00:00
Andreas Krebbel
8faafc3aaa ZVector: Move alignas qualifier to come first 
We currently have plenty of type definitions with the alignment
qualifier coming after the type.  The compiler warns about ignoring
them:
int EIGEN_ALIGN16 ai[4];

Turn this into:
EIGEN_ALIGN16 int ai[4];
 2021-10-26 15:33:47 +02:00
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
9312a5bf5c Implement a generic vectorized version of Smith's algorithms for complex division. 2021-07-01 23:31:12 +00:00
Rasmus Munk Larsen
bffd267d17 Small cleanup: Get rid of the macros EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD and CJMADD, which were effectively unused, apart from on x86, where the change results in identically performing code. 2021-06-24 18:52:17 -07:00
Rasmus Munk Larsen
52a5f98212 Get rid of code duplication for conj_helper. For packets where LhsType=RhsType a single generic implementation suffices. For scalars, the generic implementation of pconj automatically forwards to numext::conj, so much of the existing specialization can be avoided. For mixed types we still need specializations. 2021-06-24 15:47:48 -07:00
Rasmus Munk Larsen
fc87e2cbaa Use bit_cast to create -0.0 for floating point types to avoid compiler optimization changing sign with --ffast-math enabled. 2021-06-11 02:35:53 +00:00
Antonio Sanchez
3f4684f87d Include <cstdint> in one place, remove custom typedefs 
Originating from
[this SO issue](https://stackoverflow.com/questions/65901014/how-to-solve-this-all-error-2-in-this-case),
some win32 compilers define `__int32` as a `long`, but MinGW defines
`std::int32_t` as an `int`, leading to a type conflict.

To avoid this, we remove the custom `typedef` definitions for win32.  The
Tensor module requires C++11 anyways, so we are guaranteed to have
included `<cstdint>` already in `Eigen/Core`.

Also re-arranged the headers to only include `<cstdint>` in one place to
avoid this type of error again.
 2021-01-26 14:23:05 -08:00
Andreas Krebbel
1e74f93d55 Fix some packet-functions in the IBM ZVector packet-math. 2020-11-25 14:11:23 +00:00
Rasmus Munk Larsen
225ab040e0 Remove unused packet op "palign". 
Clean up a compiler warning in c++03 mode in AVX512/Complex.h.
 2020-05-07 17:14:26 -07:00
Rasmus Munk Larsen
e80ec24357 Remove unused packet op "preduxp". 2020-04-23 18:17:14 +00:00
Rasmus Munk Larsen
13ef08e5ac Move implementation of vectorized error function erf() to SpecialFunctionsImpl.h. 2019-09-27 13:56:04 -07:00
Rasmus Munk Larsen
6de5ed08d8 Add generic PacketMath implementation of the Error Function (erf). 2019-09-19 12:48:30 -07:00
Eugene Zhulenev
e9f0eb8a5e Add masked_store_available to unpacket_traits 2019-05-02 14:52:58 -07:00
Anuj Rawat
8c7a6feb8e Adding lowlevel APIs for optimized RHS packet load in TensorFlow 
SpatialConvolution

Low-level APIs are added in order to optimized packet load in gemm_pack_rhs
in TensorFlow SpatialConvolution. The optimization is for scenario when a
packet is split across 2 adjacent columns. In this case we read it as two
'partial' packets and then merge these into 1. Currently this only works for
Packet16f (AVX512) and Packet8f (AVX2). We plan to add this for other
packet types (such as Packet8d) also.

This optimization shows significant speedup in SpatialConvolution with
certain parameters. Some examples are below.

Benchmark parameters are specified as:
Batch size, Input dim, Depth, Num of filters, Filter dim

Speedup numbers are specified for number of threads 1, 2, 4, 8, 16.

AVX512:

Parameters                  | Speedup (Num of threads: 1, 2, 4, 8, 16)
----------------------------|------------------------------------------
128,   24x24,  3, 64,   5x5 |2.18X, 2.13X, 1.73X, 1.64X, 1.66X
128,   24x24,  1, 64,   8x8 |2.00X, 1.98X, 1.93X, 1.91X, 1.91X
 32,   24x24,  3, 64,   5x5 |2.26X, 2.14X, 2.17X, 2.22X, 2.33X
128,   24x24,  3, 64,   3x3 |1.51X, 1.45X, 1.45X, 1.67X, 1.57X
 32,   14x14, 24, 64,   5x5 |1.21X, 1.19X, 1.16X, 1.70X, 1.17X
128, 128x128,  3, 96, 11x11 |2.17X, 2.18X, 2.19X, 2.20X, 2.18X

AVX2:

Parameters                  | Speedup (Num of threads: 1, 2, 4, 8, 16)
----------------------------|------------------------------------------
128,   24x24,  3, 64,   5x5 | 1.66X, 1.65X, 1.61X, 1.56X, 1.49X
 32,   24x24,  3, 64,   5x5 | 1.71X, 1.63X, 1.77X, 1.58X, 1.68X
128,   24x24,  1, 64,   5x5 | 1.44X, 1.40X, 1.38X, 1.37X, 1.33X
128,   24x24,  3, 64,   3x3 | 1.68X, 1.63X, 1.58X, 1.56X, 1.62X
128, 128x128,  3, 96, 11x11 | 1.36X, 1.36X, 1.37X, 1.37X, 1.37X

In the higher level benchmark cifar10, we observe a runtime improvement
of around 6% for AVX512 on Intel Skylake server (8 cores).

On lower level PackRhs micro-benchmarks specified in TensorFlow
tensorflow/core/kernels/eigen_spatial_convolutions_test.cc, we observe
the following runtime numbers:

AVX512:

Parameters                                                     | Runtime without patch (ns) | Runtime with patch (ns) | Speedup
---------------------------------------------------------------|----------------------------|-------------------------|---------
BM_RHS_NAME(PackRhs, 128, 24, 24, 3, 64, 5, 5, 1, 1, 256, 56)  |  41350                     | 15073                   | 2.74X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 1, 1, 256, 56)  |   7277                     |  7341                   | 0.99X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 2, 2, 256, 56)  |   8675                     |  8681                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 1, 1, 256, 56)  |  24155                     | 16079                   | 1.50X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 2, 2, 256, 56)  |  25052                     | 17152                   | 1.46X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 1, 1, 256, 56) |  18269                     | 18345                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 2, 4, 256, 56) |  19468                     | 19872                   | 0.98X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 1, 1, 36, 432)   | 156060                     | 42432                   | 3.68X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 2, 2, 36, 432)   | 132701                     | 36944                   | 3.59X

AVX2:

Parameters                                                     | Runtime without patch (ns) | Runtime with patch (ns) | Speedup
---------------------------------------------------------------|----------------------------|-------------------------|---------
BM_RHS_NAME(PackRhs, 128, 24, 24, 3, 64, 5, 5, 1, 1, 256, 56)  | 26233                      | 12393                   | 2.12X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 1, 1, 256, 56)  |  6091                      |  6062                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 32, 64, 5, 5, 2, 2, 256, 56)  |  7427                      |  7408                   | 1.00X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 1, 1, 256, 56)  | 23453                      | 20826                   | 1.13X
BM_RHS_NAME(PackRhs, 32, 64, 64, 30, 64, 5, 5, 2, 2, 256, 56)  | 23167                      | 22091                   | 1.09X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 1, 1, 256, 56) | 23422                      | 23682                   | 0.99X
BM_RHS_NAME(PackRhs, 32, 256, 256, 4, 16, 8, 8, 2, 4, 256, 56) | 23165                      | 23663                   | 0.98X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 1, 1, 36, 432)   | 72689                      | 44969                   | 1.62X
BM_RHS_NAME(PackRhs, 32, 64, 64, 4, 16, 3, 3, 2, 2, 36, 432)   | 61732                      | 39779                   | 1.55X

All benchmarks on Intel Skylake server with 8 cores.
 2019-04-20 06:46:43 +00:00
Gustavo Lima Chaves
e763fcd09e Introducing "vectorized" byte on unpacket_traits structs 
This is a preparation to a change on gebp_traits, where a new template
argument will be introduced to dictate the packet size, so it won't be
bound to the current/max packet size only anymore.

By having packet types defined early on gebp_traits, one has now to
act on packet types, not scalars anymore, for the enum values defined
on that class. One approach for reaching the vectorizable/size
properties one needs there could be getting the packet's scalar again
with unpacket_traits<>, then the size/Vectorizable enum entries from
packet_traits<>. It turns out guards like "#ifndef
EIGEN_VECTORIZE_AVX512" at AVX/PacketMath.h will hide smaller packet
variations of packet_traits<> for some types (and it makes sense to
keep that). In other words, one can't go back to the scalar and create
a new PacketType, as this will always lead to the maximum packet type
for the architecture.

The less costly/invasive solution for that, thus, is to add the
vectorizable info on every unpacket_traits struct as well.
 2018-12-19 14:24:44 -08:00
Konstantinos Margaritis
6c3475f110 remove debugging 2017-10-12 15:34:55 -04:00
Konstantinos Margaritis
c4ad358565 explicitly set conjugate mask 2017-10-11 11:05:29 -04:00
Konstantinos Margaritis
380d41fd76 added some extra debugging 2017-10-11 10:40:12 -04:00
Konstantinos Margaritis
d0b7b9d0d3 some Packet2cf pmul fixes 2017-10-11 10:17:22 -04:00
Konstantinos Margaritis
df173f5620 initial pexp() for 32-bit floats, commented out due to vec_cts() 2017-10-11 09:40:49 -04:00
Konstantinos Margaritis
3dcae2a27f initial pexp() for 32-bit floats, commented out due to vec_cts() 2017-10-11 09:40:45 -04:00
Konstantinos Margaritis
c2a2246489 fix predux_mul for z14/float 2017-10-10 13:38:32 -04:00
Konstantinos Margaritis
bc30305d29 complete z14 port 2017-10-09 16:55:10 -04:00
Konstantinos Margaritis
e1e71ca4e4 initial support for z14 2017-08-06 19:53:18 -04:00
Gael Guennebaud
b240080e64 bug #1436: fix compilation of Jacobi rotations with ARM NEON, some specializations of internal::conj_helper were missing. 2017-06-15 10:16:30 +02:00
Gael Guennebaud
ca79c1545a Add std:: namespace prefix to all (hopefully) instances if size_t/ptrdfiff_t 2017-01-23 22:02:53 +01:00
Konstantinos Margaritis
672aa97d4d implement float/std::complex<float> for ZVector as well, minor fixes to ZVector 2016-11-17 13:27:33 -05:00
Gael Guennebaud
35a8e94577 bug #1167: simplify installation of header files using cmake's install(DIRECTORY ...) command. 2016-08-29 10:59:37 +02:00
Konstantinos Margaritis
0e8fc31087 remove pgather/pscatter for std::complex<double> for s390x 2016-04-15 07:08:57 -04:00
Konstantinos Margaritis
317384b397 complete the port, remove float support 2016-04-05 14:56:45 -04:00
Konstantinos Margaritis
bc0ad363c6 add remaining includes 2016-04-05 06:01:17 -04:00
Konstantinos Margaritis
2d41dc9622 complete int/double specialized traits for ZVector 2016-04-05 06:00:51 -04:00
Konstantinos Margaritis
01e7298fe6 actually include ZVector files, passes most basic tests (float still fails) 2016-03-28 10:58:02 -04:00