EngineX-Ascend/enginex-ascend-910-llama.cpp
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ggml-cpu: enable IBM NNPA Vector Intrinsics (#14317)

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* ggml-cpu: add nnpa compile flag

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 4a9f60c201573128f73a65999b3e5cc497fae5c1)

* ggml-cpu: add fp16->fp32 nnpa first

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 8d4a7987f9c1887f716be96250f2caeee0253929)

* ggml-cpu: add fp32->fp16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 0ff0d6516247a41d2ade42b42cf0d676a4dd1627)

* ggml-cpu: better variable names

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 2f58bbcbb89c183340e252362b2a40651f573f1f)

* docs: update s390x docs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 01b929491b50071a5d0572235dcf5a449da70aa7)

* ggml-cpu: add debugging prints to see if dlf16 is correct

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix print vs printf

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix float placeholder

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: ensure fp16 and fp32 load and stores are called

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fp16 load ensured to hit

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: remove sigint from fp16 store

for some reason, the function is not getting a hit when debugged with
    gdb. we will need to investigate further

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: activate nnpa for ggml_cpu_fp16_to_fp32

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: nnpa activate ggml_cpu_fp16_to_fp32 for 8 elements

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: nnpa switch to vec_xst test

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: switch to vec_xst for 4 element loops also

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: rework noop

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: remove noop, general code cleanup

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: clarify variable naming

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: activate nnpa for ggml_cpu_fp32_to_fp16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add breakpoint for debugging

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: test fix for conversion failure

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: disable fp32->fp16 nnpa conversions for now

there are some conversion failures in nnpa that requires the eyes of an
ibm stsm. will create a separate pr to introduce the fp32->fp16 change.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: switch to elif macro

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: reattempt fp32->fp16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix typo

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: reattempt fp32->fp16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix compiler types

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: change to typedef vector types

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add 4 element loops for fp32->fp16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: clarified vector naming

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: bring back fp32->fp16 store nnpa

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: activate nnpa fp32->fp16 or fp16->fp32 compute

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add nnpa macro check in ggml-impl

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add missing __func__

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: diagnose why __NNPA__ macro is not being defined

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: import vecintrin.h to fix compiler errors

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: update macro tests

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: move s390x typedef to own header file

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml-cpu: move s390x typedef to own header file"

This reverts commit 157f856c34589566151630e294563a420702db39.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: switch to importing ggml-cpu-impl instead

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix macro declaration

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: test more macros

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add debug prints

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: bruteforce macro definitions

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: move macro definitions

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add ggml-impl.h to cmakelists

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: switch to private macros

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: move s390x typedef to own header file

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 157f856c34589566151630e294563a420702db39)

* ggml-cpu: move things around

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: bring back compile macros

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: switch to quotes for import

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add compiler error macro

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add s390x detection in ggml-src

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: bring back compile definitions

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: undo cmakelists work

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml-cpu: move s390x typedef to own header file"

This reverts commit 18d79e1a30b39d9aaa0bd58400c5cf2c32135c9a.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: remove typedefs.h

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: remove typedef from cmakelists

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add ggml-impl.h future notes

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: add todo comment for future reference

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: clarify naming of dlf16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: remove unnecessary target compile definitions

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: move nnpa fp16->fp32 and fp32->fp16 to simd-mappings

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml: refactor fp32->fp16 and fp16->fp32 simd to ggml-cpu

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* docs: update broken huggingface link for s390x

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix duplicate func names during compile

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml-cpu: fix duplicate func names during compile"

This reverts commit fbb733451f27677063b914d4f6c9a9841d45b38d.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml: refactor fp32->fp16 and fp16->fp32 simd to ggml-cpu"

This reverts commit bd288e8fa52b5244f65cee21cb61062f1a9e0ca5.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml: refactor fp16<->fp32 simd to ggml-cpu

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix missing simd-mappings.h import in quants.c

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix missing simd-mappings.h within repack

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix amx mmq missing simd-mappings.h

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: attempt at fixing loongarch failing build

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: move nnpa together with other fp16<->fp32 simd

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: fix wrong refactor of ggml-base

ref: https://github.com/ggml-org/llama.cpp/pull/14317#discussion_r2164176555

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml: remove dependency on ggml-cpu from ggml-base

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: rename all fp16<->fp32 macros to prefix with ggml_cpu

ref: https://github.com/ggml-org/llama.cpp/pull/14317#discussion_r2164449406

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: remove mistaken fallback macro

fallback logic was already implemented but i was too sleepy to realise

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml: move ggml_table_f32_f16 to ggml-cpu

ref: https://github.com/ggml-org/llama.cpp/pull/14317#discussion_r2164775006

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: move ggml_table_f32_f16 back to ggml-base due to ci failures

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml-cpu: move ggml_table_f32_f16 back to ggml-base due to ci failures"

This reverts commit 32a3533564bdb7902cefb9c89b1c9e956a81ce29.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml: move ggml_table_f32_f16 to ggml-cpu"

This reverts commit 9e40d984ad27d7b60392fb2b7548885201864fe4.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml: move ggml_table_f32_f16 to ggml-cpu

ref: https://github.com/ggml-org/llama.cpp/pull/14317#discussion_r2164775006

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
(cherry picked from commit 9e40d984ad27d7b60392fb2b7548885201864fe4)

* ggml: move ggml_table_f32_f16 to ggml-cpu.c

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: extern c ggml_table_f32_f16 + chore docs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: dedup ggml_table_f32_f16 from simd-mappings.h

we rely on the variable declaration in ggml-cpu.c instead

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml-cpu: dedup ggml_table_f32_f16 from simd-mappings.h"

This reverts commit f71b21d2f74f5e03ec0c2b4fefd3cbf395aecf16.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-cpu: bring back ggml_table_f32_f16

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* Revert "ggml-cpu: bring back ggml_table_f32_f16"

This reverts commit 2dce119178bed5ef5c8398c4230ddd14fef80e49.

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* fix ggml time initialization

* fix f32_f16 table init

* remove extra line

---------

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Aaron Teo
2025-06-26 05:49:04 +08:00
committed by GitHub
parent b193d53069
commit 60ef23d6c1
29 changed files with 1005 additions and 862 deletions
Download Patch File Download Diff File Expand all files Collapse all files

105
ggml/src/ggml-cpu/arch/loongarch/quants.c
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@@ -3,6 +3,7 @@
#include "ggml-quants.h"
#include "ggml-impl.h"
#include "ggml-cpu.h"
#include "simd-mappings.h"
#include "../../quants.h"
#include "../../ggml-cpu-impl.h"
@@ -474,7 +475,7 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
// Quantize these floats
const float d = max_scalar / 127.f;
y[i].d = GGML_FP32_TO_FP16(d);
y[i].d = GGML_CPU_FP32_TO_FP16(d);
const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f;
const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id );
@@ -548,7 +549,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
// Quantize these floats
const float d = max_scalar / 127.f;
y[i].d = GGML_FP32_TO_FP16(d);
y[i].d = GGML_CPU_FP32_TO_FP16(d);
const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f;
const __m256 mul = __lasx_xvreplfr2vr_s( id );
@@ -576,7 +577,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
// Compute the sum of the quants and set y[i].s
const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3));
const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7));
y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1)));
y[i].s = GGML_CPU_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1)));
// Convert int32 to int16
ni0 = lsx_packs_w( ni0, ni1 );
@@ -667,7 +668,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
// Main loop
for (; ib < nb; ++ib) {
/* Compute combined scale for the block */
const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
const __m256 d = __lasx_xvreplfr2vr_s( GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d) );
__m256i qx = bytes_from_nibbles_32(x[ib].qs);
@@ -699,7 +700,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (; ib + 1 < nb; ib += 2) {
// Compute combined scale for the block 0 and 1
const __m128 d_0_1 = (__m128)__lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
const __m128 d_0_1 = (__m128)__lsx_vreplgr2vr_w( GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d) );
const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0);
@@ -717,7 +718,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
//_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 2 and 3
const __m128 d_2_3 = (__m128)__lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
const __m128 d_2_3 = (__m128)__lsx_vreplgr2vr_w( GGML_CPU_FP16_TO_FP32(x[ib + 1].d) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) );
const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0);
@@ -766,7 +767,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
}
int sumi = sumi0 + sumi1;
sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d);
sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
}
*s = sumf;
@@ -797,10 +798,10 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
// Main loop
for (; ib < nb; ++ib) {
const float d0 = GGML_FP16_TO_FP32(x[ib].d);
const float d1 = GGML_FP16_TO_FP32(y[ib].d);
const float d0 = GGML_CPU_FP16_TO_FP32(x[ib].d);
const float d1 = GGML_CPU_FP16_TO_FP32(y[ib].d);
summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
summs += GGML_CPU_FP16_TO_FP32(x[ib].m) * GGML_CPU_FP16_TO_FP32(y[ib].s);
const __m256 d0v = __lasx_xvreplfr2vr_s( d0 );
const __m256 d1v = __lasx_xvreplfr2vr_s( d1 );
@@ -834,7 +835,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
}
int sumi = sumi0 + sumi1;
sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
}
*s = sumf;
@@ -865,7 +866,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
// Main loop
for (; ib < nb; ++ib) {
/* Compute combined scale for the block */
const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME
const __m256 d = __lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d)); //FIXME
__m256i qx = bytes_from_nibbles_32(x[ib].qs);
__m256i bxhi = bytes_from_bits_32(x[ib].qh);
@@ -902,7 +903,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
}
int sumi = sumi0 + sumi1;
sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi;
sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
}
*s = sumf;
@@ -934,16 +935,16 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
// Main loop
for (; ib < nb; ++ib) {
const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d));
const __m256 dx = __lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(x[ib].d));
summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
summs += GGML_CPU_FP16_TO_FP32(x[ib].m) * GGML_CPU_FP16_TO_FP32(y[ib].s);
__m256i qx = bytes_from_nibbles_32(x[ib].qs);
__m256i bxhi = bytes_from_bits_32(x[ib].qh);
bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10));
qx = __lasx_xvor_v(qx, bxhi);
const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d));
const __m256 dy = __lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(y[ib].d));
const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
const __m256 q = mul_sum_us8_pairs_float(qx, qy);
@@ -973,7 +974,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
}
int sumi = sumi0 + sumi1;
sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
}
*s = sumf;
@@ -1003,7 +1004,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
// Main loop
for (; ib < nb; ++ib) {
// Compute combined scale for the block
const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
const __m256 d = __lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d));
__m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0);
__m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
@@ -1023,7 +1024,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
sumi += x[ib].qs[j]*y[ib].qs[j];
}
sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d));
sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
}
*s = sumf;
@@ -1047,8 +1048,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
const uint8_t * GGML_RESTRICT q2 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
@@ -1116,8 +1117,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
summs += y[i].bsums[j] * (sc[j] >> 4);
}
const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
int isum = 0;
int is = 0;
@@ -1170,7 +1171,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
const uint8_t * GGML_RESTRICT q3 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
// Set up scales
@@ -1294,7 +1295,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
q8 += 8; a += 8;
}
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
}
for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -1330,8 +1331,8 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
memcpy(utmp, x[i].scales, 12);
utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
@@ -1438,9 +1439,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
q8 += 8; a += 8;
}
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
sumf -= dmin * sumi;
}
for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -1477,8 +1478,8 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
const uint8_t * GGML_RESTRICT q5 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
memcpy(utmp, x[i].scales, 12);
utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
@@ -1593,9 +1594,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
q8 += 8; a += 8;
}
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d;
const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
sumf -= dmin * sumi;
}
for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -1624,7 +1625,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
const uint8_t * GGML_RESTRICT q4 = x[i].ql;
const uint8_t * GGML_RESTRICT qh = x[i].qh;
@@ -1713,7 +1714,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
q8 += 8; a += 8;
}
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
}
for (int l = 0; l < 8; ++l) sumf += sums[l];
@@ -1780,7 +1781,7 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
__m256 accumf = (__m256)__lasx_xvldi(0);
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint16_t * GGML_RESTRICT q2 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
__m256i sumi1 = __lasx_xvldi(0);
@@ -1820,7 +1821,7 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
float sumf = 0.f;
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint16_t * GGML_RESTRICT q2 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
int32_t bsum = 0;
@@ -1895,7 +1896,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
__m256 accumf = (__m256)__lasx_xvldi(0);
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint16_t * GGML_RESTRICT q2 = x[i].qs;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
@@ -1980,7 +1981,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
float sumf = 0.f;
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint16_t * GGML_RESTRICT q2 = x[i].qs;
const uint8_t * GGML_RESTRICT sc = x[i].scales;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
@@ -2049,7 +2050,7 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
__m256 accumf = (__m256)__lasx_xvldi(0);
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * GGML_RESTRICT qs = x[i].qs;
const uint8_t * GGML_RESTRICT qh = x[i].qh;
const uint16_t * GGML_RESTRICT signs = (const uint16_t *)(x[i].qs + QK_K/8);
@@ -2108,7 +2109,7 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
float sumf = 0;
for (int i = 0; i < nb; i++) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
@@ -2168,7 +2169,7 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
__m256 accumf = (__m256)__lasx_xvldi(0);
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * GGML_RESTRICT q3 = x[i].qs;
const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
@@ -2213,7 +2214,7 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
float sumf = 0.f;
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * GGML_RESTRICT q3 = x[i].qs;
const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
const int8_t * GGML_RESTRICT q8 = y[i].qs;
@@ -2279,7 +2280,7 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
__m256 accumf = (__m256)__lasx_xvldi(0);
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * GGML_RESTRICT qs = x[i].qs;
const uint8_t * GGML_RESTRICT qh = x[i].qh;
const uint16_t * GGML_RESTRICT signs = (const uint16_t *)x[i].signs;
@@ -2340,7 +2341,7 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
float sumf = 0.f;
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * GGML_RESTRICT qs = x[i].qs;
const uint8_t * GGML_RESTRICT qh = x[i].qh;
const uint8_t * GGML_RESTRICT signs = x[i].signs;
@@ -2451,7 +2452,7 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
+ (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2;
}
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum);
accum1 += d * sumi1;
}
@@ -2484,7 +2485,7 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
qs += 4;
}
sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
}
*s = sumf;
@@ -2530,9 +2531,9 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
const __m256i p_1 = lasx_madd_h(p16_1, mone);
const __m256i p_2 = lasx_madd_h(p16_2, mone);
accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(y[ib + 0].d)*GGML_CPU_FP16_TO_FP32(x[ib + 0].d)),
__lasx_xvffint_s_w(p_1), accum1);
accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(y[ib + 1].d)*GGML_CPU_FP16_TO_FP32(x[ib + 1].d)),
__lasx_xvffint_s_w(p_2), accum2);
}
@@ -2540,7 +2541,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
#endif
for (; ib < nb; ++ib) {
const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d);
const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_CPU_FP16_TO_FP32(x[ib].d);
int sumi1 = 0, sumi2 = 0;
for (int j = 0; j < QK4_NL/2; ++j) {
sumi1 += y[ib].qs[j+ 0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
@@ -2595,7 +2596,7 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
sumi1 = __lasx_xvadd_w(p_1, sumi1);
sumi2 = __lasx_xvadd_w(p_2, sumi2);
}
accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_CPU_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
__lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum);
}
@@ -2604,7 +2605,7 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
#else
float sumf = 0;
for (int ibl = 0; ibl < nb; ++ibl) {
const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
uint16_t h = x[ibl].scales_h;
const uint8_t * qs = x[ibl].qs;
const int8_t * q8 = y[ibl].qs;