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
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
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
-Currently lpgemm can only be built using either zen3 or zen4 config.
The lpgemm kernel code is re-structured to support amdzen, and thus
multi machine deployment.
-The micro-kernel calls (gemm and pack) are currently hardcoded in the
lpgemm framework. This is removed and a new lpgemm_cntx based dispatch
mechanism is designed to support runtime configurability for
micro-kernels.
AMD-Internal: [CPUPL-2965]
Change-Id: I4bbcb4e5db767def1663caf5481f0b4c988149ef
-As of now, memcpy is used in u8s8s32 micro-kernel for copying in k
fringe loop (( k % 4 )!= 0) and NR' < 16 fringe kernels. However for
small k/n dimensions, memcpy invocation has high overhead.
-This issue is fixed by replacing memcpy with a MACRO based
implementation of copy routine, specifically optimized for the sizes
that will be encountered in fringe cases (k < 4, NR' < 16).
AMD-Internal: [CPUPL-3008]
Change-Id: I376bab0aac325832e42e370b291614e5fd5272dc
1. Added Tanh approximation based GeLU Post-Op for S16, S32 and BF16
2. Changes are done at frame and micro-kernel level to
implement this post-op.
3. Efficient AVX-512 and AVX-2 vector versions of TANHF and EXPF
functions are implemented for the GeLU post-operation.
4. TANH and EXPF math functions are efficiently implemented in
macro-based fashion to exploit register level fusion of GeLU
with GEMM operations for improved performance
5. LPGEMM bench is changed to pass GeLU post-op as input and
support accuracy check to verify functional correctness
AMD-Internal: [CPUPL-2978]
Change-Id: I472ac35c00a4ea1ab983cc5f6ff6a123c8035f28
-The post operations attributes are moved to a new struct
lpgemm_post_op_attr, and an object of this struct is passed to the
low precision gemm kernels in place of the multiple parameters.
-The u8s8s32s8 api (downscale api) performance is low when the k
value is less (k < KC). Two scenarios are observed here:
a. beta = 0: Currently, for downscale api, a temporary buffer is
used to accumulate intermediate s32 output, so that it can be used
in later iterations of pc loop (k dim). The usage of this buffer
(store) can be avoided if k < KC. Here intermediate accumulation
is not required, since the after the first iteration of the pc loop,
the output can be downscaled and stored.
b. beta != 0: In this case the existing values of the original s8 C
output matrix needs to be converted to s32 and beta scaled. Currently
the s8 values are converted to s32 and stored in temporary buffer in
pc loop (5 loop algorithm) in blocks of mxNC. This temporary buffer
is passed to the micro kernel and beta scaling is applied on this.
However the mxNC block copy is costly and can be avoided if a new
condition is introduced for beta scaling in the micro kernel, whereby
the original s8 data is loaded instead of from the temporary buffer
to a register, converted to s32 and beta scaling applied on it.
AMD-Internal: [CPUPL-2884]
Change-Id: Id9b4650d500e1b553e48c4f1e4c902b3f553211c
Clipping is done during the downscaling of the accumulated result
from s32 to s8 for u8s8s32os8 and from s16 to s8 for u8s8s16os8,
to saturate the final output values between [-128,127]
AMD-Internal: [LWPZENDNN-493]
Change-Id: Ica9bba5044e87b815e2b4e35809bf440bb9dd41f
- BFloat16 flags added to zen4 make_defs in order to enable
compilation of low precision gemm by using zen4 config.
- Avoid -ftree-partial-pre optimization flag with gcc due to
non optimal code generation for intrinsics based kernels in
low precision gemm.
- Enable only Zen3 specific low precision gemm kernels (s16)
compilation when aocl_gemm addon is compiled on Zen3 machines.
AMD-Internal: [CPUPL-1545]
Change-Id: Id3be3410bfbf141bb6fc4b4e3391115a4e0bb79f
- Downscaling is used when GEMM output is accumulated at a higher
precision and needs to be converted to a lower precision afterwards.
This is required in AI workloads where quantization/dequantization
routines are used.
- New GEMM APIs are introduced specifically to support this use case.
Currently downscaling support is added for s32, s16 and bfloat16 GEMM.
AMD-Internal: [CPUPL-2475]
Change-Id: I81c3ee1ba5414f62427a7a0abb6ecef0c5ff71bf
-Parametric ReLU is the generalization of leaky ReLU in which the
leakage coefficient is tunable. The support for the same is added
following the register-level fusion technique.
-Low precision bench enhancement to check accuracy/performance of
low precision gemm with PReLU.
-Bug fixes in low precision gemm kernels.
AMD-Internal: [CPUPL-2442]
Change-Id: I81336405b185a994297d122b2d868b758ae6dad5
-The micro-kernel function signatures follow a common pattern. These
functions can be represented as an instantiation of a MACRO as is done
in BLIS, and thus the number of micro-kernel header files can be brought
down. A new single header file containing all the MACRO definitions with
the instantiation is added, and the existing unnecessary header files
are removed.
-The bias addition in micro-kernel for n remaining < 16 reads the bias
array assuming it contains 16 elements. This can result in seg-faults,
since out of bound memory is accessed. It is fixed by copying required
elements to an intermediate buffer and using that buffer for loading.
-Input matrix storage type parameter is added to lpgemm APIs. It can be
either row or column major, denoted by r and c respectively. Currently
only row major input matrices are supported.
-Bug fix in s16 fringe micro-kernel to use correct offset while storing
output.
AMD-Internal: [CPUPL-2386]
Change-Id: Idfa23e69d54ad7e06a67b1e36a5b5558fbff03a3
- Low precision gemm is often used in ML/DNN workloads and is used
in conjunction with pre and post operations. Performing gemm and ops
together at the micro kernel level results in better overall performance
due to cache/register reuse of output matrix. The provision for defining
the post-operations and invoking the micro-kernel with it from the
framework is added as part of this change. This includes adding new data
structures/functions to define the post-ops to be applied and an
extensible template using which new post-ops can easily be integrated.
As for the post-operations, RELU and Bias Add for u8s8s32 is implemented
in this first cut.
- aocl_gemm bench modifications to test/benchmark RELU and Bias Add.
AMD-Internal: [CPUPL-2316]
Change-Id: Iad5fe9e54965bb52d5381ae459a69800946c7d18
- Multi-Threaded int8 GEMM (Input - uint8_t, int8_t, Output - int32_t).
AVX512_vnni based micro-kernel for int8 gemm. Paralellization supported
along m and n dimensions.
- Multi-Threaded B matrix reorder support for sgemm. Reordering B matrix
is packing entire B matrix upfront before sgemm. It allows sgemm to
take advantage of packed B matrix without incurring packing costs during
runtime.
- Makefile updates to addon make rules to compile avx512 code for
selected files in addon folder.
- CPU features query enhancements to check for AVX512_VNNI flag.
- Bench for int8 gemm and sgemm with B matrix reorder. Supports
performance mode for benchmarking and accuracy mode for testing code
correctness.
AMD-Internal: [CPUPL-2102]
Change-Id: I8fb25f5c2fbd97d756f95b623332cb29e3b8d182
