-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
Description:
Loop count was taken as 16 instead of n0_partial_rem in packb_nrlt16_bf16bf16f32of32_col_major_ref function.
Updated comments on reference reorder functionality.
AMD Internal: SWLCSG-3279
Change-Id: Idfc3b92906bc2b24651c7923e395fe10db56166b
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:
Implemented a reference version for
aocl_gemm_reorder_bf16bf16f32of32 function
to make the code cpu architecture independent.
AMD-Internal: [ SWLCSG-3279 ]
Change-Id: I0c715864c0ab3e5afea2ee6ee9546b75c3dbf9ec
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_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
Description:
aocl_reorder_f32obf16 function is implemented to
reorder input weight matrix of data type float to
bfloat16.
The reordering is done to match the input requirements
of API aocl_gemm_bf16bf16f32o<f32|bf16>.
The objective of the API is to convert a model/matrix
of type f32 to bf16 and process when machine supports
bf16 FMA instruction _mm512_dpbf16_ps but the model
is still in float
Change-Id: Ib7c743d52d01a1ac09e84ac120577ec9e02f90f5
-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 AVX512 packA kernel for col major inputs in F32 API
- Removed the work arounds for n = 1, mtag_a = PACK case, where the execution was
being directed to GEMM instead of GEMV.
Change-Id: I6fb700d96069213a762e8a83a209c5388a91050f
- added the missing stride updates in B reorder case in GEMV
- added the missing stride updates for the cast of transA with B
reordered case.
Change-Id: Ic89781dfa7c0d9380ea523796958f795828a1ade
Description:
1. Written 6x64 main and other fringe kernels for WoQ where scaling s4
weights into bf16 performed in the kernel itself to reduce bandwidth.
2. These kernels are performing better compared to bf16 weights when m
is small and n is large.
3. Established a threshold to do quantization support at packing of
B (KCXNC) level or WoQ kernel level.
Change-Id: I4f8265b8b58c276ff2590cc948d1f920aa0bb289
- Added support for TransA and transB in f32f32of32 APIs
- Modified the GEMV case(m == 1) to support PACKB feature
- Redirecting the operations to GEMM instead of GEMV in case of n == 1
conditions, with storage scheme r/transA and c/transB to avoid the
packing errors which would lead to failures in computation.
Change-Id: I0eb8c31485af4e33c53fd36b5e5788d75d3a67a9
-It has been observed that reduction of threads as part of smart
threading for smaller input dimensions hampers the performance of the
other inputs with larger dimensions due to lower operating frequency of
the newly launched threads (apart from the existing ones). Disabling
smart threading for these bandwidth bound input patterns (small m and n)
fixes this issue.
-Bug fixes related to work split in LPGEMV for n < NR and m < MR cases.
AMD Internal: [SWLCSG-2948]
Change-Id: I0117dc0ea6820a9fac8e14f93374b54a7d80c121
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
For some applications, one of the input dimension is mostly m < MR or
n < NR with the other dimension being small for the most part, with
intermittent large ones. Currently in these cases (m < MR or n < NR),
the number of threads used is reduced (as part of smart threading) if
the other dimension (n or m) is also small. For larger dimensions all
the threads are used.
However its been observed that this reduction of threads hampers the
performance of the larger inputs due to lower operating frequency of
the newly launched threads (apart from the existing ones). Disabling
smart threading for these bandwidth bound input patterns (m < MR or
n < NR) fixes this issue.
AMD Internal: [SWLCSG-2948]
Change-Id: I5334860cf4411ea4504d2e6bc598b9904780bbbf
- Revert of patch 1110983 - Duplicate check removal and early return for
s8s8s32/u8s8s32
- Add fix - Added check to see if post-ops is enabled with col-major
storage and return early in that case.
Change-Id: Id3b8c97b6d1425dfb06f3b196e5acd60caee8fca
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
- When n=1, reorder of B matrix is avoided to efficiently
process data. A dot-product based kernel is implemented to
perform gemv when n==1.
AMD-Internal: [SWLCSG-2354]
Change-Id: I6b73dfddd9a15e7b914d031646a1d913a7ab4761
- 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
Description:
1. Added a new API aocl_gemm_bf16s4f32of32 to support
for WoQ (Weight-only-Quantization) in LLM's
2. The API supports only reordered B matrix of data
size signed 4 bits (S4).
3. Substracting zero point and multiplying with scale
on B matrix is performed in packing B.
4. zero point and scale data should be passed by user
through pre-ops data structure.
5. The API is still in experimental state and NOT tested.
AMD-Internal: SWLCSG-2943
Change-Id: I10b159b64c2e2aaf39da5462685618ba8cc800ee
Details:
- To enable Weight-only-Quantization(WOQ) workflow,
new LPGEMM APIs are added where datatypes are A: bf16,
B: int4, C: f32/bf16. To support this, B matrix will
be reordered with type still being int4. New pack
kernels that packs the reordered B matrix after
converting the data from int4 to bf16 and applying
zero-point and scale are added.
AMD-Internal: [SWLCSG-2943]
Change-Id: Iabe23dab607913c0114b97cb2b91248babeaac03
-To enable Weight-only-Quantization (WOQ) workflow, new LPGEMM APIs
are required where data types are A:bf16, B:int4 and C:f32/bf16. It
is expected that the BF16 kernels will be reused within this API and
subsequently the B matrix needs to be reordered following the BF16
kernel schema, but with the reordered matrix type still being int4. To
address this, new BF16 reorder kernels enabling the same are added.
AMD-Internal: [SWLCSG-2943]
Change-Id: Ib770ecbf90a3d906deafece94b1a96e0b9412738
-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
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
Details:
- For a variable x, Using address of x in an instruction throws
exception if the difference between &x and access position is
larger than 2 GiB. To solve this issue all variables are stored
within the JIT code section and are accessed using relative addressing.
- Fixed a bug in B matrix pack function for s8s8s32os32 API.
- Fixed a bug in JIT code to apply bias on col-major matrices.
AMD-Internal: [SWLCSG-2820]
Change-Id: I82f117a0422c794cb9b1a4d65a89d60de4adfd96
- When n=1, reorder of B matrix is avoided to efficiently
process data. A dot-product based kernel is implemented to
perform gemv when n=1.
AMD-Internal: [SWLCSG-2354]
Change-Id: If5f74651ab11232d0b87d34bd05f65aacaea94f1
-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 optimized lpgemv for both m == 1 and n == 1 cases.
- Fixed few bugs in LPGEMV for bf16 and f32 datatypes.
- Fixed few bugs in JIT-based implementation of LPGEMM for BF16
datatype.
AMD-Internal: [SWLCSG-2354]
Change-Id: I245fd97c8f160b148656f782d241f86097a0cf38
SWISH post-op computes swish(x) = x / (1 + exp(-1 * alpha * x)).
SiLU = SWISH with alpha = 1. Adding the support for swish in JIT
based BF16 kernels.
AMD-Internal: [SWLCSG-2387]
Change-Id: I9eea0c801f5f067a5cfbd2941bc991708b86e45e
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 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.
AMD-Internal: [SWLCSG-2355]
Change-Id: I7497dad4c293587cbc171a5998b9f2817a4db880
- Modified all structs that are passed to JIT-generated code to use
integer of type uint64_t rather than dim_t so that functionality
is not affected when size of BLIS-internal integer is modified
during configure time.
Change-Id: Ib81c088072badf13da4ca73be2d4af4551b713d8
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
Details:
- Added new folder named JIT/ under addon/aocl_gemm/. This folder
will contain all the JIT related code.
- Modified lpgemm_cntx_init code to generate main and fringe kernels
for 6x64 bf16 microkernel and store function pointers to all the
generated kernels in a global function pointer array. This happens
only when gcc version is < 11.2
- When gcc version < 11.2, microkernel uses JIT-generated kernels.
otherwise, microkernel uses the intrinsics based implementation.
AMD-Internal: [SWLCSG-2622]
Change-Id: I16256c797b2546a8cd2049680001947346260461
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.
-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
1. Added Trans A feature to handle column major inputs
for A matrix.
2. Trans A is enabled by on-the-go pack of A matrix.
3. The on-the-go pack of A converts a column storage
MCxKC block of A into row storage MCxKC block as
LPGEMM kernels are row major kernels.
4. New pack routines are added for conversion of A matrix
from column major storage to row major storage.
5. LPGEMM Cntx is updated with pack kernel function
pointers.
6. Packing of A matrix:
- Converts column major input A to row major
in blocks of MCxKC with newly added pack A
functions when cs_a > 1.
7. Pack routines are added for AVX512 and AVX2
INT8 LPGEMM APIs.
8. Trans A feature is now supported in:
1. u8s8s32os32/os8
2. u8s8s16os16/os8/ou8
3. s8s8s32os32/os8
4. s8s8s16os16/os8
AMD-Internal: SWLCSG-2582
Change-Id: I7ce331545525a9a09f3853280615b55fcf2edabf
-As it stands, in LPGEMM, users are expected to pass an array of values
with length the same as N dimension as inputs for zero point or scale
factor. However at times, a single scalar value is used as zero point
or scale factor for the entire downscaling operation. The mandate to
pass an array requires the user to allocate extra memory and fill it
with the scalar value so as to be used in downscaling. This limitation
is lifted as part of this commit, and now scalar values can be passed
as zero point or scale factor.
-LPGEMM bench enhancements along with new input format to improve
readability as well as flexibility.
AMD-Internal: [SWLCSG-2581]
Change-Id: Ibd0d89f03e1acadd099382dffcabfec324ceb50f
