-Currently the values (from buffer) are added/multiplied as is to the
output registers while performing the matrix add/mul post-ops. Support
is added for scaling these values before using them in the post-ops.
Both scalar and vector scale_factors are supported.
AMD-Internal: [SWLCSG-3181]
Change-Id: Ifdb7160a1ea4f5ecccfa3ef31ecfed432898c14d
-A light-weight mechanism/framework to log input details and a
stringified version of the post-ops structure is added to LPGEMM.
Additionally the runtime of the API is also logged.
The logging framework logs to a file with filename following the format
aocl_gemm_log_<PID>_<TID>.txt.
-To enable this feature, the AOCL_LPGEMM_LOGGER_SUPPORT=1 macro needs to
be defined when compiling BLIS (with aocl_gemm addon enabled) by passing
CFLAGS="-DAOCL_LPGEMM_LOGGER_SUPPORT=1" to ./configure. Additionally
AOCL_ENABLE_LPGEMM_LOGGER=1 has to be exported in the environment during
LPGEMM runtime.
AMD-Internal: [SWLCSG-3280]
Change-Id: I30bfb35b2dc412df70044601b335938fc9f49cfb
Details:
- The batch matmul performs a series of matmuls, processing
more than one GEMM problem at once.
- Introduced a new parameter called batch_size for the user
to indicate number of GEMM problems in a batch/group.
- This operation supports processing GEMM problems with
different parameters including dims,post-ops,stor-schemes etc.,
- This operation is optimized for problems where all the
GEMMs in a batch are of same size and shape.
- For now, the threads are distributed among different GEMM
problems equally irrespective of their dimensions which
leads to better performance for batches with identical GEMMs
but performs sub-optimally for batches with non-identical GEMMs.
- Optimizations for batches with non-identical GEMMs is in progress.
- Added bench and input files for batch_matmul.
AMD-Internal: [SWLCSG-2944]
Change-Id: Idc59db5b8c5794bf19f6f86bcb8455cd2599c155
Description
-In enum AOCL_PARAMS_STORAGE_TYPES the member FLOAT was declared and the
clang 18 compiler in msvc throwing issue with multiple definition. We
replace FLOAT and BFLOAT16 to AOCL_GEMM_<F32/BF16>.
AMD-Internal: CPUPL-6174
Change-Id: Ic061af068854d51629b82b495efd0eb54543f329
Description:
1. AutoAWQ use a int32 buffer to store 8 elements each of 4 bits in this
format [0, 2, 4, 6, 1, 3, 5, 7].
2. Support is added to convert above format back to the original
sequential order [0, 1, 2, 3, 4, 5, 6, 7] before reordering
in the AWQ API.
AMD-Internal: SWLCSG-3169
Change-Id: I5395766060c200ab81d0b8be94356678a169ac13
Description:
1. Added group quantization and zero-point (zp) in
aocl_gemm_bf16s4f32o<bf16|f32> API.
2. Group quantization is technique to improve accuracy
where scale factors to dequantize weights varies at group
level instead of per channel and per tensor level.
3. Added zp and scaling in woq packb kernels so that for
large M values zp and scaling are performed at pack-b
stage and bf16 kernels are called
4. Adding zp support and scaling to default path in WoQ kernels
created some performance overhead when M value is very small.
5. Added string group_size to lpgemm bench to read
group size from bench_input.txt and tested for
various combinations of matrix dimensions.
6. The scalefactors could be of type float or bf16
and the zeropoint values are expected to be
in int8 format.
AMD-Internal: [SWLCSG-3168, SWLCSG-3172]
Change-Id: Iff07b54d76edc7408eb2ea0b29ce8b4a04a38f57
Description:
1. The bias type was supported only based on output data type.
2. The option is added in the pre-ops structure to select the bias data
type irrespective of the storage data type in bf16 and WoQ API's
AMD-Internal: SWLCSG-3171
Change-Id: Iac10b946c2d4a5c405b2dc857362be0058615abf
Description:
Implemented sigmoid, tanh as fused post-ops in
aocl_gemm_<s8|u8>s8<s32|s16>o<s8|u8|s32> API's
Sigmoid(x) = 1/1+e^(-x)
Tanh(x) = (1-e^(-2x))/(1+e^(2x))
Updated bench_lpgemm to recognize sigmod, tanh
as options for post-ops from bench_input and verified.
AMD-Internal: [SWLCSG-3178]
Change-Id: I9df3aab02222f728ff9d1f292c7bc549f30176f0
Description:
Implemented sigmoid, tanh as fused post-ops in
aocl_gemm_f32f32f32of32 API's
Sigmoid(x) = 1/1+e^(-x)
Tanh(x) = (1-e^(-2x))/(1+e^(2x))
Updated bench_lpgemm to recognize sigmod, tanh
as options for post-ops from bench_input and verified.
AMD-Internal: [SWLCSG-3178]
Change-Id: Iac0a907f6dea1d9cb82d9fd8716bfdbf1c33921d
Description:
Implemented sigmoid, tanh as fused post-ops in
aocl_gemm_bf16bf16f32o<f32|bf16) API's
Sigmoid(x) = 1/1+e^(-x)
Tanh(x) = (1-e^(-2x))/(1+e^(2x))
Updated bench_lpgemm to recognize sigmod, tanh
as options for post-ops from bench_input and verified.
AMD-Internal: [SWLCSG-3178]
Change-Id: I78a3ba4a67ab63f9d671fbe315f977b016a0d969
-Currently lpgemm sets the context (block sizes and micro-kernels) based
on the ISA of the machine it is being executed on. However this approach
does not give the flexibility to select a different context at runtime.
In order to enable runtime selection of context, the context
initialization is modified to read the AOCL_ENABLE_INSTRUCTIONS env
variable and set the context based on the same. As part of this commit,
only f32 context selection is enabled.
-Bug fixes in scale ops in f32 micro-kernels and GEMV path selection.
-Added vectorized f32 packing kernels for NR=16(AVX2) and NR=64(AVX512).
This is only for B matrix and helps remove dependency of f32 lpgemm api
on the BLIS packing framework.
AMD Internal: [CPUPL-5959]
Change-Id: I4b459aaf33c54423952f89905ba43cf119ce20f6
Details:
- Added a new API called unreorder that converts a matrix from
reordered format to it's original format( row-major or col-major ).
- Currently this API only supports bf16 datatype.
- Added corresponding bench and input file to test accuracy of the
API.
- The new API is only supported for 'B' matrix.
- Modified input validation checks in reorder API to account for
row Vs col storage of matrix and transposes for bf16 datatype.
Change-Id: Ifb9c53b7e6da6f607939c164eb016e82514581b7
- Implemented the Scale post-op for the F32 API for all kernels
- f32_scale = (f32 * scale_factor) + offset
- Added the bench inputs
Change-Id: Ib0f25f870eafe695d8b2a2c434c8cb3ec4f7db4c
-Added new pack kernels that packs/reorders B matrix from column-major
input format. This also supports the transB scenario if input B matrix
is row major.
Change-Id: I4c75b6e81016331fd7e7f95ad4212e6d38dc586f
Updated logic to use "%ld" and "%lld" format specifiers to read
64-bit integer from input files using fscanf function on Linux and
Windows respectively when the user set INT_SIZE='auto' on 64-bit
machine or INT_SIZE='64'. Otherwise "%d" on both windows and Linux
for benchmarking blis and LPGEMM.
Change-Id: I4762c4c1b3fcd09cf66d0cc9572d38766be6be60
Updated format specifier to read signed double("%lld") and unsigned
double("%llu") from file using fscanf from both windows and Linux.
AMD-Internal: [CPUPL-5787]
Change-Id: Ibef50b0df708f474e22f703240e264eff1de3994
For the bf16bf16of32bf16 lpgemm api, inside the micro-kernels in order
to convert the accumulated float values to bfloat16 before storing,
the _mm512_cvtneps_pbh intrinsic (vcvtneps2bf16) is used. This
intrinsic rounds the value based on a rounding bias logic. Replicating
the same rounding logic inside the bf16 bench accuracy check function
to get proper one to one comparison of output values.
AMD Internal: [SWLCSG-2948]
Change-Id: I135ac39ac8484769b6c0fe5b3e351dd22d7ca1d8
Details:
- In WOQ, if m = 4, special case kernels are added where
s4->bf16 conversion happens inside the compute kernel and
packing is avoided. For all other cases, B matrix is
dequantized and packed at KC loop level and native bf16
kernels are re-used at compute level.
- Fixes in bench to avoid accuracy failures when datatype of
output is bf16.
Change-Id: Ie8db42da536891693d5e82a5336b66514a50ccb2
This API supports applying element wise operations (eg: post-ops) on a
float(f32) input matrix to get an output matrix of the same (float(f32)).
Change-Id: I387a544f0d33d2231f5f6a92e212f17b1103dd24
AMD Internal: [SWLCSG-2947]
Change-Id: I387a544f0d33d2231f5f6a92e212f17b1103dd24
- removed the duplicate check for col-major inputs in s8s8s32/u8s8s32
APIs
- Fixed the print in bench_lpgemm
Change-Id: If40837b89927dd82d8aa6f620d1a7f2c24aed53c
- Standardize formatting (spacing etc).
- Add full copyright to cmake files (excluding .json)
- Correct copyright and disclaimer text for frame and
zen, skx and a couple of other kernels to cover all
contributors, as is commonly used in other files.
- Fixed some typos and missing lines in copyright
statements.
AMD-Internal: [CPUPL-4415]
Change-Id: Ib248bb6033c4d0b408773cf0e2a2cda6c2a74371
- Remove execute file permission from source and make files.
- dos2unix conversion.
- Add missing eol at end of files.
Also update .gitignore to not exclude build directory but to
exclude any build_* created by cmake builds.
AMD-Internal: [CPUPL-4415]
Change-Id: I5403290d49fe212659a8015d5e94281fe41eb124
-Matrix MUL op support added in main as well as fringe bfloat16 element
wise operations kernels.
-Benchmarking/testing framework for the same is added.
-Fixed issues in setting up post-ops node index.
AMD Internal: [SWLCSG-2947, SWLCSG-2953]
Change-Id: Iba7561a6a60df41211efbf06fab1b4900207bcf8
This post-operation computes C = (beta*C + alpha*A*B) * D, where D
is a matrix with dimensions and data type the same as that of C matrix.
AMD-Internal: [SWLCSG-2953]
Change-Id: Id4df2ca76a8f696cb16edbd02c25f621f9a828fd
-This API supports applying element wise operations (eg: post-ops) on a
bfloat16 input matrix to get an output matrix of the same(bfloat16) or
upscaled data type (float).
-Benchmarking/testing framework for the same is added.
AMD Internal: SWLCSG-2947
Change-Id: I43f1c269be1a1997d4912d8a3a97be5e5f3442d2
- Fixed framework of bf16s4f32of32 API to correct
pointer updations.
- Modified pre_op structure to exclude pre-op-offset.
Now offset is passed as a separate parameter to the
scale-pack functions.
- Fixed work-distribution among threads in MT scenario.
- Added Blocksizes and kernel-pointers and verified
functionality for the new API.
AMD-Internal: [SWLCSG-2943]
Change-Id: I58fece240d62c798c880a2b2b7fa64e560cc753d
-To enable Weight-only-Quantization (WOQ) workflow, new LPGEMM APIs
have been developed where data types are A:bf16, B:int4 and C:f32/bf16.
The testing and benchmarking framework for the same are added.
AMD-Internal: [SWLCSG-2943]
Change-Id: Icdc1d60819a23dd9f41382499d1a3c055c5edc17
-Quantization of f32 to bf16 (bf16 = (f32 * scale_factor) + zero_point)
instead of just type conversion in aocl_gemm_bf16bf16f32obf16.
-Support for multiple scale/sum/matrix_add/bias post-ops in a single
LPGEMM api call.
-Post-ops mask related fixes in lpgemv kernels .
-Additional scale post-ops sanity checks.
AMD-Internal: [SWLCSG-2945]
Change-Id: I3b35cc413c176bb50bfdbd6acd4839a5ba7e94bb
Description:
In recent changes bli_print_msg is used in lpgemm
test application file bench_lpgemm.c for printing error
message. bli_print_msg is a blis library function which
is not exported for the usage of applications, because
of which linking failed when blis shared library is used
to build.
Updated bli_print_msg with printf in the bench_lpgemm.c
AMD Internal: CPUPL-5326
Change-Id: I021849baa6881bd997013e42013db1c5c711627f
Initailized c_save instead of 'c" and then removed copying c to c_save.
Because at the start every n_repeats iteration we are copying back c_save to c.
Therefore if we initialize c_save, we can avoid extra copy of "c" to c_save before calling
GEMM. For very large sizes matrix initialization takes considerable amount of time. This can
be reduced now.
Change-Id: I2c6ffe169e991607314897cb0c1fbfc0d74ef179
Details:
- Corrected the usage of vpdpbusd instruction in
GEMV implementation for INT8 APIs.
- Modified bench to fill matrices with values
ranging between -5 and +5 whenever the datatype is
a signed integer.
Change-Id: I457462b888b667d8a34c53de762e9b4aee784ecc
Support for reordering B matrix of datatype int4 as per the pack schema
requirements of u8s8s32 kernel. Vectorized int4_t -> int8_t conversion
implemented via leveraging the vpmultishiftqb instruction. The reordered
B matrix will then be used in the u8s8s32o<s32|s8> api.
AMD-Internal: [SWLCSG-2390]
Change-Id: I3a8f8aba30cac0c4828a31f1d27fa1b45ea07bba
Enabled command line option to link libiomp5.so or libomp.so or libgomp.so libraries using cmake.
Eg:- -DOpenMP_libomp_LIBRARY=<path to openmp library including library name>.
If we not set above variable, by default openmp library will be libomp.so for clang and libgomp.so for gcc compiler.
Change-Id: I5bffa10ff8351f5d10f0d543cbdf55aa16c84c90
-As it stands the bf16bf16f32ob16 API expects bias array to be of type
float. However actual use case requires the usage of bias array of bf16
type. The bf16 micro-kernels are updated to work with bf16 bias array by
upscaling it to float type and then using it in the post-ops workflow.
-Corrected register usage in bf16 JIT generator for bf16bf16f32ob16 API
when k > KC.
AMD-Internal: [SWLCSG-2604]
Change-Id: I404e566ff59d1f3730b569eb8bef865cb7a3b4a1
Description:
--Added support for tranB in u8s8s32o<s32|s8> and
s8s8s32o<s32|s8> API's
--Updated the bench_lpgemm by adding options to
support transpose of B matrix
--Updated data_gen_script.py in lpgemm bench
according to latest input format.
AMD-Internal: [SWLCSG-2582]
Change-Id: I4a05cc390ae11440d6ff86da281dbafbeb907048
- Implemented the feature to benchmark ?AXPYV APIs
for the supported datatypes. The feature allows to
benchmark BLAS, CBLAS or the native BLIS API, based
on the macro definition.
- Added a sample input file to provide examples to benchmark
AXPYV for all its datatype supports.
- Updated the sample input file for SCALV to provide examples
to benchmark all of its datatype supports.
AMD-Internal: [CPUPL-4805]
Change-Id: I550920e3a57fcc2e4900e9e698330d8b8595bdee
- Added support for ?DOTC in bench.
- Updated DTL to accept conjx as a parameter:
- 'N', i.e., no conjugate for DOTU
- 'C', i.e., conjugate for DOTC
- Updated DTL calls in the interface with respective values of
conjx.
AMD-Internal: [CPUPL-4804]
Change-Id: I447b19a6273566c6021c1721ce173bac4a59142c
- Added BUILD_STATIC_LIBS option which is on by default, only on Linux.
- Added TEST_WITH_SHARED option which is off by default, only on Linux.
- If only shared or static lib is being built, that's the one that will be used for testing.
- If both are being built, TEST_WITH_SHARED determins which library wil be used for testing.
- Set linux workflows so that they build both static and shared libs, and use linux-static and linux-shared to denote which one should be used for testing.
- Set -fPIC for both static and shared builds to fix issues faced when building blis using AOCC 4.0.0 and gtestsuite using gcc 9.4.0.
AMD-Internal: [CPUPL-2748]
Change-Id: I4227bab97ff31ecddfe218e18499f33b4e4ee63e
CMakelists.txt is updated to support aocl_gemm on windows.
On windows, BLIS library(blis+aocl_gemm) is built successfully
only with AOCC Compiler. (Clang has an issue with optimizing
VNNI instructions).
$cmake .. -DENABLE_ADDON="aocl_gemm" ....
AMD-Internal: [CPUPL-2748]
Change-Id: I9620878ab6934233fadc9ddc5d5e82ad85be9209
- Updated the existing benchmarking file for SCALV API, to include
support to call the BLAS and CBLAS mixed-precision SCALV, namely
cblas_csscalv(), csscalv_(), cblas_zdscalv(), zdscalv_().
- The input is expected to be given with the datatype 'ZD' and 'CS'
in order to benchmark the associated mixed-precision APIs.
AMD-Internal: [CPUPL-4722]
Change-Id: I4ab0fb19fe1949468cf707d0a857e8a1681addeb
Description
1. when mr0=1 case the accumulator register and operand
registers for an fma instruction got swapped. Corrected
the copy paste error.
2. Removed fill array for c_ref in bench_lpgemm.c and used
memcpy from c buf, because fill array now using rand()
function to initialize data which can be different
when c_ref and c called separately, this was working
because data was fixed (i=0 ... i%5).
Change-Id: Ia513331ba49d28adc7bcdc0ec78d443abe66780b
1. The 5 LOOP LPGEMM path is in-efficient when A or B is a vector
(i.e, m == 1 or n == 1).
2. An efficient implementation of lpgemv_rowvar_f32 is developed
considering the b matrix reorder in case of m=1 and post-ops fusion.
3. When m = 1 the algorithm divide the GEMM workload in n dimension
intelligently at a granularity of NR. Each thread work on A:1xk
B:kx(>=NR) and produce C=1x(>NR). K is unrolled by 4 along with
remainder loop.
4. When n = 1 the algorithm divide the GEMM workload in m dimension
intelligently at a granularity of MR. Each thread work on A:(>=MR)xk
B:kx1 and produce C = (>=MR)x1. When n=1 reordering of B is avoided
to efficiently process in n one kernel.
5. Fixed few warnings while loading 2 f32 bias elements using
_mm_load_sd using float pointer. Typecasted to (const double *)
AMD-Internal: [SWLCSG-2391, SWLCSG-2353]
Change-Id: If1d0b8d59e0278f5f16b499de1d629e63da5b599
-This post-operation computes C = (beta*C + alpha*A*B) + D, where D is
a matrix with dimensions and data type the same as that of C matrix.
AMD-Internal: [SWLCSG-2424]
Change-Id: I9464d1f514e3b04275fe93441489b4503a08937a
-This post-operation computes C = (beta*C + alpha*A*B) + D, where D is
a matrix with dimensions and data type the same as that of C matrix.
-For clang compilers (including aocc), -march=znver1 is not enabled for
zen kernels. Have updated CKVECFLAGS to capture the same.
AMD-Internal: [SWLCSG-2424]
Change-Id: Ie369f7ea5c80ab69eea3f3e03a8d9546e14f5c09
CMakelists.txt is added in bench.
Steps are provided to build for different targets.
AMD-Internal: [CPUPL-2748]
Change-Id: I58027f4e42d1323cafb151224c45868bc8337ff4
