Added Matrix-mul and Matrix-add postops in FP32 AVX512_256 GEMV kernels
- Matrix-add and Matrix-mul post ops in FP32 AVX512_256 GEMV m = 1 and
n = 1 kernels has been added.
Co-authored-by: VarshaV <varshav2@amd.com>
- Implemented multithreading framework for the DGEMV API on Zen architectures. Architecture specific AOCL-dynamic logic determines the optimal number of threads for improved performance.
- The condition check for the value of beta is optimized by utilizing masked operations. The mask value is set based on value of beta, and the masked operations are applied when the vector y is loaded or scaled with beta.
AMD-Internal: [CPUPL-6746]
* Bug Fixes in FP32 Kernels:
- The current implementation lets m=1 tiny cases inside LPGEMV_TINY loop,
but the m=1 GEMV kernel call doesn't have the call to GEMV_M_ONE kernels.
Added the m=1 path in LPGEMV_TINY loop by handling the pack A/Pack B/reorder B
conditions.
- Added BF16 support for BIAS, Matrix-Add and Matrix-Mul for AVX512 F32
main and GEMV kernels
- Added BF16 Matrix-Add and Matrix-Mul support for AVX512_256 F32 kernels.
- Modified the condition check in FP32 Zero point in AVX512 kernels, and
fixed few bugs in Col-major Zero point evaluation.
AMD Internal: [ CPUPL - 6748 ]
* Bug Fixes in FP32 Kernels:
- The current implementation lets m=1 tiny cases inside LPGEMV_TINY loop,
but doesn't have the call to GEMV_M_ONE kernels. Added the m=1 path in
LPGEMV_TINY loop by handling the pack A/Pack B/reorder B conditions.
- Added BF16 support for BIAS, Matrix-Add and Matrix-Mul for AVX512 F32
main and GEMV kernels.
- Added BF16 Downscale, BIAS, Matrix-Add and Matrix-Mul support in AVX2 GEMV_N
and AVX512_256 GEMV kernels.
- Added BF16 Matrix-Add and Matrix-Mul support for AVX512_256 F32 kernels.
- Modified the condition check in FP32 Zero point in AVX512 kernels, and
fixed few bugs in Col-major Zero point evaluation and instruction usage.
AMD Internal: [ CPUPL - 6748 ]
* Bug Fixes in FP32 Kernels:
- The current implementation lets m=1 tiny cases inside LPGEMV_TINY loop,
but doesn't have the call to GEMV_M_ONE kernels. Added the m=1 path in
LPGEMV_TINY loop by handling the pack A/Pack B/reorder B conditions.
- Added BF16 support for BIAS, Matrix-Add and Matrix-Mul for AVX512 F32
main and GEMV kernels.
- Added BF16 Downscale, BIAS, Matrix-Add and Matrix-Mul support in AVX2 GEMV_N
and AVX512_256 GEMV kernels.
- Added BF16 Matrix-Add and Matrix-Mul support for AVX512_256 F32 kernels.
- Modified the condition check in FP32 Zero point in AVX512 kernels, and
fixed few bugs in Col-major Zero point evaluation and instruction usage.
AMD Internal: [ CPUPL - 6748 ]
* Bug Fixes in FP32 Kernels:
- The current implementation lets m=1 tiny cases inside LPGEMV_TINY loop,
but doesn't have the call to GEMV_M_ONE kernels. Added the m=1 path in
LPGEMV_TINY loop by handling the pack A/Pack B/reorder B conditions.
- Added BF16 support for BIAS, Matrix-Add and Matrix-Mul for AVX512 F32
main and GEMV kernels.
- Added BF16 Downscale, BIAS, Matrix-Add and Matrix-Mul support in AVX2 GEMV_N
and AVX512_256 GEMV kernels.
- Added BF16 Matrix-Add and Matrix-Mul support for AVX512_256 F32 kernels.
- Modified the condition check in FP32 Zero point in AVX512 kernels, and
fixed few bugs in Col-major Zero point evaluation and instruction usage.
AMD Internal: [ CPUPL - 6748 ]
---------
Co-authored-by: VarshaV <varshav2@amd.com>
* Implemented 6xlt8 AVX2 kernel for n<8 inputs
* Implemented fringe kernels for 6x16 and 6xlt16 AVX512 kernels for FP32
* Implemented m-fringe kernels for 6xlt8 kernel for AVX2
* Implemented m-fringe kernels for 6xlt8 kernel for AVX2
* Added the deleted kernels and fixed bias bug
AMD-Internal: SWLCSG-3556
* Bug Fixes in LPGEMM for AVX512(SkyLake) machine
- B-matrix in bf16bf16f32obf16/f32 API is re-ordered. For machines that
doesn't support BF16 instructions, the BF16 input is unre-ordered and
converted to FP32 to use FP32 kernels.
- For n = 1 and k = 1 sized matrices, re-ordering in BF16 is copying the
matrix to the re-ordered buffer array. But the un-reordering to FP32
requires the matrix to have size multiple of 16 along n and multiple
of 2 along k dimension.
- The entry condition to the above has been modified for AVX512 configuration.
- In bf16 API, the tiny path entry check has been modified to prevent
seg fault while AOCL_ENABLE_INSTRUCTIONS=AVX2 is set in BF16 supporting
machines.
- Modified existing store instructions in FP32 AVX512 kernels to support
execution in machines that has AVX512 support but not BF16/VNNI(SkyLake).
- Added Bf16 beta and store types in FP32 avx512_256 kernels
AMD Internal: [SWLCSG-3552]
* Bug Fixes in LPGEMM for AVX512(SkyLake) machine
- B-matrix in bf16bf16f32obf16/f32 API is re-ordered. For machines that
doesn't support BF16 instructions, the BF16 input is unre-ordered and
converted to FP32 to use FP32 kernels.
- For n = 1 and k = 1 sized matrices, re-ordering in BF16 is copying the
matrix to the re-ordered buffer array. But the un-reordering to FP32
requires the matrix to have size multiple of 16 along n and multiple
of 2 along k dimension.
- The entry condition to the above has been modified for AVX512 configuration.
- In bf16 API, the tiny path entry check has been modified to prevent
seg fault while AOCL_ENABLE_INSTRUCTIONS=AVX2 is set in BF16 supporting
machines.
- Modified existing store instructions in FP32 AVX512 kernels to support
execution in machines that has AVX512 support but not BF16/VNNI(SkyLake).
- Added Bf16 beta and store types, along with BIAS and ZP in FP32 avx512_256
kernels
AMD Internal: [SWLCSG-3552]
* Bug Fixes in LPGEMM for AVX512(SkyLake) machine
- Support added in FP32 512_256 kerenls for : Beta, BIAS, Zero-point and
BF16 store types for bf16bf16f32obf16 API execution in AVX2 mode.
- B-matrix in bf16bf16f32obf16/f32 API is re-ordered. For machines that
doesn't support BF16 instructions, the BF16 input is unre-ordered and
converted to FP32 type to use FP32 kernels.
- For n = 1 and k = 1 sized matrices, re-ordering in BF16 is copying the
matrix to the re-ordered buffer array. But the un-reordering to FP32
requires the matrix to have size multiple of 16 along n and multiple
of 2 along k dimension. The entry condition here has been modified for
AVX512 configuration.
- Fix for seg fault with AOCL_ENABLE_INSTRUCTIONS=AVX2 mode in BF16/VNNI
ISA supporting configruations:
- BF16 tiny path entry check has been modified to take into account arch_id
to ensure improper entry into the tiny kernel.
- The store in BF16->FP32 col-major for m = 1 conditions were updated to
correct storage pattern,
- BF16 beta load macro was modified to account for data in unaligned memory.
- Modified existing store instructions in FP32 AVX512 kernels to support
execution in machines that has AVX512 support but not BF16/VNNI(SkyLake)
AMD Internal: [SWLCSG-3552]
---------
Co-authored-by: VarshaV <varshav2@amd.com>
* Implemented GEMV kernel for m=1 case.
Description:
- Added a new GEMV kernel for AVX2 where m=1.
- Added a new GEMV kernel for AVX512 with ymm registers where m=1.
Details:
- This implementation is picked form cntx when GEMM is invoked on
machines that support AVX512 instructions by forcing the
AVX2 path using AOCL_ENABLE_INSTRUCTIONS=AVX2 during run-time.
- This implementation uses MR=16 for GEMV.
AMD-Internal: [SWLCSG-3519]
Change-Id: I8598ce6b05c3d5a96c764d96089171570fbb9e1a
- Added a new GEMV kernel with MR = 8 which will be used
for cases where n=1.
- Modified GEMM and GEMV framework to choose right GEMV kernel
based on compile-time and run-time architecture parameters. This
had to be done since GEMV kernels are not stored-in/retrieved-from
the cntx.
- Added a pack kernel that packs A matrix from col-major to row-major
using AVX2 instructions.
AMD-Internal: [SWLCSG-3519]
Change-Id: Ibf7a8121d0bde37660eac58a160c5b9c9ebd2b5c
Details:
- These kernels are picked from cntx when GEMM is invoked
on machines that support AVX512 instructions by forcing the
AVX2 path using AOCL_ENABLE_INSTRUCTIONS=AVX2 during run-time.
- This path uses the same blocksizes and pack kernels as AVX512
path.
- GEMV is disabled currently as AVX2 kernels for GEMV are not
implemented.
AMD-Internal: [SWLCSG-3519]
Change-Id: I75401fac48478fe99edb8e71fa44d36dd7513ae5
Description
- To enable AVX512 VNNI support without native BF16 in BF16 kernels, the
BF16 C_type is converted to F32 for computation and then cast back to
BF16 before storing the result.
- Added support for handling BF16 zero-point values of BF16 type.
- Added a condition to disable the tiny path for the BF16 code path
where native BF16 is not supported.
AMD Internal : [CPUPL-6627]
Change-Id: I1e0cfefd24c5ffbcc95db73e7f5784a957c79ab9
- For single-threaded configuration of BLIS, packing of A and B matrices
are enabled by default. But, packing of A is only supported for RV
kernels where elements from matrix A are being broadcasted. Since
elements are being loaded in RD kernels, packing of A results in
failures. Hence, disabled packing of matrix A for RD kernels.
- Fixed the issue where c_i index pointer was incorrectly being reset
when exceeding MC block thus, resulting in failures for certain
Post-Ops.
- Fixed the FP32 reoder case were for n == 1 and rs_b == 1 condition, it
was incorrectly using sizeof(BLIS_FLOAT) instead of sizeof(float).
AMD-Internal: [SWLCSG-3497]
Change-Id: I6d18afa996c253d79f666ea9789270bb59b629dd
- Support for Post-Ops has been added for all F32 RD AVX512 and AVX2
kernels.
AMD-Internal: [SWLCSG-3497]
Change-Id: Ia2967417303d8278c547957878d93c42c887109e
- Added FP32 RD (dot-product) kernels for both, AVX512 and AVX2 ISAs.
- The FP32 AVX512 primary RD kernel has blocking of dimensions 6x64
(MRxNR) whereas it is 6x16 (MRxNR) for the AVX2 primary RD kernel.
- Updatd f32 framework to accomodate rd kernels in case of B trans
with thresholds
- Updated data gen python script
TODO:
- Post-Ops not yet supported.
Change-Id: Ibf282741f58a1446321273d5b8044db993f23714
- Included a new code section to handle input having non-unit strided y
vector for dgemv transpose case. Removed the same from the respective
kernels to avoid repeated branching caused by condition checks within
the 'for' loop.
- The condition check for beta is equal to zero in the primary kernels
are moved outside the for loop to avoid repeated branching.
- The '_mm512_reduce_pd' operations in the primary kernel is replaced by
a series of operations to reduce the number of instructions required
to reduce the 8 registers.
- Changing naming convention for DGEMV transpose kernels.
- Modified unit kernel test to avoid y increment for dgemv tranpose
kernels during the test.
AMD-Internal: [CPUPL-6565]
Change-Id: I1ac516d6b8f156ac53ac9f6eb18badd50e152e05
- Updated the bli_dgemv_zen_ref( ... ) kernel to support general stride.
- Since the latest dgemv kernels don't support general stride, added
checks to invoke bli_dgemv_zen_ref( ... ) when A matrix has a general
stride.
- Thanks to Vignesh Balasubramanian <vignesh.balasubramanian@amd.com>
for finding this issue.
AMD-Internal: [CPUPL-6492]
Change-Id: Ia987ce7674cb26cb32eea4a6e9bd6623f2027328
-New packing kernels for A matrix, both based on AVX512 and AVX2 ISA,
for both row and column major storage are added as part of this change.
Dependency on haswell A packing kernels are removed by this.
-Tiny GEMM thresholds are further tuned for BF16 and F32 APIs.
AMD-Internal: [SWLCSG-3380, SWLCSG-3415]
Change-Id: I7330defacbacc9d07037ce1baf4a441f941e59be
- Bug fix in Matrix Mul post op.
- Updated the config in AVX512_VNNI_BF16 context
to work in AVX2 kernels
Change-Id: I25980508facc38606596402dba4cfce88f4eb173
- Added column major pack kernels, which will transpose and store the
BF16 matrix input to F32 input matrix
- Added BF16 Zero point Downscale support to F32 main and fringe
kernels.
- Updated Matrix Add and Matrix Mul post-ops in f32-AVX2 main and
fringe kernels to support BF16 input.
- Modified the f32 tiny kernels loop to update the buf_downscale
parameter.
- Modified bf16bf16f32obf16 framework to work with AVX-2 system.
- Added wrapper in bf16 5-Loop to call the corresponding AVX-2/AVX-512
5 Loop functions.
- Bug fixes in the f32-AVX2 kernels BIAS post-ops.
- Bug fixes in the Convert function, and the bf16 5-loop
for multi-threaded inputs.
AMD-Internal:[SWLCSG-3281 , CPUPL-6447]
Change-Id: I4191fbe6f79119410c2328cd61d9b4d87b7a2bcd
- Currently the BF16 kernels uses the AVX512 VNNI instructions.
In order to support AVX2 kernels, the BF16 input has to be converted
to F32 and then the F32 kernels has to be executed.
- Added un-pack function for the B-Matrix, which does the unpacking of
the Re-ordered BF16 B-Matrix and converts it to Float.
- Added a kernel, to convert the matrix data from Bf16 to F32 for the
give input.
- Added a new path to the BF16 5LOOP to work with the BF16 data, where
the packed/unpacked A matrix is converted from BF16 to F32. The
packed B matrix is converted from BF16 to F32 and the re-ordered B
matrix is unre-ordered and converted to F32 before feeding to the
F32 micro kernels.
- Removed AVX512 condition checks in BF16 code path.
- Added the Re-order reference code path to support BF16 AVX2.
- Currently the F32 AVX-2 kernels supports only F32 BIAS support.
Added BF16 support for BIAS post-op in F32 AVX2 kernels.
- Bug fix in the test input generation script.
AMD Internal : [SWLCSG - 3281]
Change-Id: I1f9d59bfae4d874bf9fdab9bcfec5da91eadb0fb
-The following S16 APIs are removed:
1. aocl_gemm_u8s8s16os16
2. aocl_gemm_u8s8s16os8
3. aocl_gemm_u8s8s16ou8
4. aocl_gemm_s8s8s16os16
5. aocl_gemm_s8s8s16os8
along with the associated reorder APIs and corresponding
framework elements.
AMD-Internal: [CPUPL-6412]
Change-Id: I251f8b02a4cba5110615ddeb977d86f5c949363b
More changes to standardize copyright formatting and correct years
for some files modified in recent commits.
AMD-Internal: [CPUPL-5895]
Change-Id: Ie95d599710c1e0605f14bbf71467ca5f5352af12
- Reduced the blocking size of 'bli_ddotv_zen_int10'
kernel from 40 elements to 20 elements for better
utilization of vector registers
- Replaced redundant 'for' loops in 'bli_ddotv_zen_int10'
kernel with 'if' conditions to handle reminder
iterations. As only a single iteration is used when
reminder is less than the primary unroll factor.
- Added a conditional check to invoke the vectorized
DDOTV kernels directly(fast-path), without incurring
any additional framework overhead.
- The fast-path is taken when the input size is ideal
for single-threaded execution. Thus, we avoid the
call to bli_nthreads_l1() function to set the ideal
number of threads.
- Updated getestsuite ukr tests for 'bli_ddotv_zen_int10'
kernel.
AMD-Internal: [CPUPL-4877]
Change-Id: If43f0fcff1c5b1563ad233005717398b5b6fb8f2
Description:
1. Support has been added to scale buffer values using both scalar and
vector scale factors before matrix add or matrix mul post-ops.
AMD-Internal: CPUPL-6340
Change-Id: Ie023d5963689897509ef3d5784c3592791e57125
- As part of AOCL-BLAS, there exists a set of vectorized
SUP kernels for GEMM, that are performant when invoked
in a bare-metal fashion.
- Designed a macro-based interface for handling tiny
sizes in GEMM, that would utilize there kernels. This
is currently instantiated for 'Z' datatype(double-precision
complex).
- Design breakdown :
- Tiny path requires the usage of AVX2 and/or AVX512
SUP kernels, based on the micro-architecture. The
decision logic for invoking tiny-path is specific
to the micro-architecture. These thresholds are defined
in their respective configuration directories(header files).
- List of AVX2/AVX512 SUP kernels(lookup table), and their
lookup functions are defined in the base-architecture from
which the support starts. Since we need to support backward
compatibility when defining the lookup table/functions, they
are present in the kernels folder(base-architecture).
- Defined a new type to be used to create the lookup table and its
entries. This type holds the kernel pointer, blocking dimensions
and the storage preference.
- This design would only require the appropriate thresholds and
the associated lookup table to be defined for the other datatypes
and micro-architecture support. Thus, is it extensible.
- NOTE : The SUP kernels that are listed for Tiny GEMM are m-var
kernels. Thus, the blocking in framework is done accordingly.
In case of adding the support for n-var, the variant
information could be encoded in the object definition.
- Added test-cases to validate the interface for functionality(API
level tests). Also added exception value tests, which have been
disabled due to the SUP kernel optimizations.
AMD-Internal: [CPUPL-6040][CPUPL-6018][CPUPL-5319][CPUPL-3799]
Change-Id: I84f734f8e683c90efa63f2fa79d2c03484e07956
- Using 'if' condition instead of 'for'loop to handle fringe
cases. 'for' loop is redundant for handling reminder iterations
as only a single iteration is used when reminder is less than
primary unroll factor.
AMD-Internal: [CPUPL-5594]
Change-Id: I8cebc037742ee47961869e22e2471e550fcd99e9
- Developed new AVX512 DGEMV kernels for Zen4/5 architectures and
AVX2 kernels for Zen1/2/3 architectures. These kernels are written
from the ground up and are independent of fused kernels.
- The DGEMV primary kernel processes the calculation in chunks of
8 columns. Fringe columns (sizes 1 to 7) are handled by fringe
kernels, which are invoked by the primary kernel as needed.
- Implemented the kernels by computing the dot product of matrix A
columns with vector x in chunks of 32 elements, storing the results
in accumulator registers. Fringe elements are handled in chunks
of 16, 8, etc. The data in the accumulator registers is then reduced
and added to vector y.
AMD-Internal: [CPUPL-5835]
Change-Id: I5cb9eb1330db095931586a7028fd7676fbbecc61
- Updated the existing AVX2 {S/D}AMAXV kernels to comply
to the standard when having exception values. This makes
it exhibit the same behaviour as it AVX512 variants.
Provided additional optimizations with loop unrolling.
- Removed redundant early return checks inside the kernels,
since they have been abstracted to a higher layer.
- Updated the unit-tests(micro-kernel) and exception value
tests for appropriate code-coverage. Also re-enabled the
exception value tests.
AMD-Internal: [CPUPL-4745]
Change-Id: I36c793220bd4977a00281af9737c51cd1e5c60d9
Some kernel file names were the same for different sub-configurations,
which could result in duplicate copies of the same object being archived
depending upon the order of (re-)compiling the source files. Rename the
files to be specific to each sub-configuration to avoid this problem.
AMD-Internal: [CPUPL-5895]
Change-Id: I182ac706e04a364f1df20fd0fb5b633eb10eeafb
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.
- Added logger functionality for batch_matmul APIs.
AMD-Internal: [SWLCSG-2944]
Change-Id: I83e26c1f30a5dd5a31139f6706ac74be0aa6bd9a
Details:
- Fixed few bugs in downscale post-op for f32 datatype.
- Fixed a bug in setting strides of packB buffer in
int8 APIs.
Change-Id: Idb3019cc4593eace3bd5475dd1463dea32dbe75c
This patch introduces comprehensive optimizations to the DGEMM kernel, focusing on loop
efficiency and edge kernel performance. The following technical improvements have been implemented:
1. **IR Loop Optimization:**
- The IR loop has been re-implemented in hand-written assembly to eliminate the overhead associated
with `begin_asm` and `end_asm` calls, resulting in more efficient execution.
2. **JR Loop Integration:**
- The JR loop is now incorporated into the micro kernel. This integration avoids the repetitive overhead
of stack frame management for each JR iteration, thereby enhancing loop performance.
3. **Kernel Decomposition Strategy:**
- The m dimension is decomposed into specific sizes: 20, 18, 17, 16, 12, 11, 10, 9, 8, 4, 2, and 1.
- For remaining cases, masked variants of edge kernels are utilized to handle the decomposition efficiently.
1. **Interleaved Scaling by Alpha:**
- Scaling by the alpha factor is interleaved with load instructions to optimize the instruction pipeline
and reduce latency.
2. **Efficient Mask Preparation:**
- Masks are prepared within inline assembly code only at points where masked load-store operations are necessary,
minimizing unnecessary overhead.
3. **Broadcast Instruction Optimization:**
- In edge kernels where each FMA (Fused Multiply-Add) operation requires a broadcast without subsequent reuse,
the broadcast instruction is replaced with `mem_1to8`.
- This allows the compiler to optimize by assigning separate vector registers for broadcasting, thus avoiding
dependency chains and improving execution efficiency.
4. **C Matrix Update Optimization:**
- During the update of the C matrix in edge kernels, columns are pre-loaded into multiple vector registers.
This approach breaks dependency chains during FMA operations following the scaling by alpha, thereby mitigating
performance bottlenecks and enhancing throughput.
These optimizations collectively improve the performance of the DGEMM kernel, particularly in handling edge cases and
reducing overhead in critical loops. The changes are expected to yield significant performance gains in matrix multiplication
operations.
This patch also involves changes for tiny gemm interface. A light
interface for calling kernels and removing calls to avx2 dgemm kernels
as we use avx512 dgemm kernels for all the sizes for zen4 and zen5.
For zen4 and zen5 when A matrix transposed(CRC, RRC), tiny kernel does not have
the support to handle such inputs and thus such inputs are routed to
gemm_small path.
AMD-Internal: [CPUPL-6054]
Change-Id: I57b430f9969ca39aa111b54fa169e4225b900c4a
- AVX512 specific DGEMV native kernels are added for Zen4/5
architectures to handle the NO_TRANSPOSE cases and are independent of
the AXPYF fused kernels.
- The following set of kernels biased towards the n-dimension perform
beta scaling of y vector within the kernel itself and handle cases
where n is less than 5:
- bli_dgemv_n_zen_int_32x8n_avx512( ... )
- bli_dgemv_n_zen_int_32x4n_avx512( ... )
- bli_dgemv_n_zen_int_32x2n_avx512( ... )
- bli_dgemv_n_zen_int_32x1n_avx512( ... )
- The bli_dgemv_n_zen_int_16mx8_avx512( ... ) is biased towards the
m-dimension and for this kernel beta scaling is handled beforehand
within the framework.
- Added unit-tests for the new kernels.
- AVX2 path for Zen/2/3 architectures still follows the old approach of
using fused kernel, namely AXPYF, to perform the GEMV operation.
AMD-Internal: [CPUPL-5560]
Change-Id: I22bc2a865cd28b9cdcb383e17d1ff38bdd28de79
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
-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
- 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
SCALV is used directly by BLAS, CBLAS and BLIS scal{v} APIs but
also within many other APIs to handle special cases. In general
it is preferred to use SETV when alpha=0, but BLAS and CBLAS
continue to multiple all vector element by alpha. This has
different behaviour for propagating NaNs or Infs.
Changes in this commit:
- Standardize early returns from SCALV reference and optimized
kernels.
- User supplied N<0 is handled at the top level API layer. Use
negative values of N in kernel calls to signify that SETV
should _not_ be used when alpha=0. This should only be
required in SCALV.
- Include serial threshold in zdscal (as in dscal) to reduce
overhead for small problem sizes.
- Code tidying to make different variants more consistent.
- More standardization of tests in SCALV gtestsuite programs.
- Remove scalv_extreme_cases.cpp as it is now redundant.
AMD-Internal: [CPUPL-4415]
Change-Id: I42e98875ceaea224cc98d0cdfe0133c9abc3edae
- Added explicit typecast to the pointers that are passed
to the _mm_prefetch( ... ) intrinsic, to avoid compiler
warnings.
AMD-Internal: [CPUPL-4415]
Change-Id: I1c1398b7b5abe81848d33cb6df107f7f077588ea
1. Updated datatype from __int64_t to int64_t. Since
__int64_t was not defined for Windows
2. Updated CMake build system to build lpgemm on windows
Change-Id: I5fc5ed93ecc54e4a9931b7b40b790d37c7ead4b8
- Added the attribute to export symbols, in the header file that
contains the L1 kernel declarations. This attribute was previously
added as part of the kernel definitions.
AMD-Internal: [CPUPL-4415]
Change-Id: I375246f47d53c220f885644f9b75c7d7991ae710
- 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
- Delete unused cmake files.
- Add guards around call to bli_cpuid_is_avx2fma3_supported
in frame/3/bli_l3_sup.c, currently assumes that non-x86
platforms will not use bli_gemmtsup.
- Correct variable in frame/base/bli_arch.c on non-x86
builds.
- Add guards around omp pragma to avoid possible gcc
compiler warning in kernels/zen/2/bli_gemv_zen_int_4.c.
- Add missing registers in clobber list in
kernels/zen4/1/bli_dotv_zen_int_avx512.c.
- Add gtestsuite ERS_IIT tests for TRMV, copied from TRSV.
- Correct calls to cblas_{c,z}swap in gtestsuite.
- Correct test name in ddotxf gtestsuite program.
AMD-Internal: [CPUPL-4415]
Change-Id: I69ad56390017676cc609b4d3aba3244a2df6a6b5
Corrections for spelling and other mistakes in code comments
and doc files.
AMD-Internal: [CPUPL-4500]
Change-Id: I33e28932b0e26bbed850c55602dee12fd002da7f
- 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
- Added reference kernel for dgemv that handles computation for tiny
sizes (m < 8 && n < 8).
- The reference kernel, bli_dgemv_zen_ref( ... ), supports both
row/column storage schemes as well as transpose and no transpose
cases.
- Added additional unit-tests for functional verification.
AMD-Internal: [CPUPL-5098]
Change-Id: I66fdf0a40e90bdb3fed40152c45ab28a17a87ada
- Removed some of the unrolling factors that affected the
performance of AVX2 DAXPYV kernel. In addition to improving
the current performance on sizes compatible to single-threaded
runs, this will now perform better for tiny sizes as well
since the overhead to reach the computation is less.
- Updated the vector partitioning logic, by using
bli_thread_range_sub( ... ), which ensures that there is no
false sharing among multiple threads.
- Updated the AOCL-DYNAMIC logic for the API, to include thresholds
or zen4 and zen5 micro-architectures.
AMD-Internal: [CPUPL-5514]
Change-Id: Iee9edddac685334213cd6694421ab3df3547e930
