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llamafile: PowerPC Sgemm Optimization (#15558)

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This patch improves GEMM for FP32 Data Type on PowerPC

Implements GEMM on large blocks with configurable block size mc, nc, kc
(default: 256, 256, 256).
Packing Function optimized to access blocks as per memory layout.
GEMM Optimized to work on larger blocks.
Isolated Packing from GEMM Operations for better MMA utilization.

Verified functionality and correctness uing llama-cli and stand alone
test case (performs matmul and compares final mattrix C result with base).

Minor code refactoring changes:
Replace macro with inline function
Code Indent made consistent with 4 spaces

Performance Testing:

Observed 50% ~ 70% improvement in Prompt Processing Speed mesured using
llama-bench with Meta-Llama3-8B FP32 Model.  Similar gains observed with
Mistral-7b-Instruct-v0.3 Model.

model                   Size                Params     Backend       Threads   Test    Patch   Base
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp512   98.58   60.3
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp1024  95.88   57.36
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp2048  85.46   53.26
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp4096  68.66   45.78
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp6144  57.35   40.44

25 ~ 30% improvement in llama-batched-bench with Metla-Llama3-8B in
Prompt Processing Speed for large prompts (256, 512, 1024, 2048, 4096)tokens with various batch
sizes ( 1, 2, 4, 8, 16)

Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
This commit is contained in:
shalinib-ibm
2025-08-26 21:05:25 +05:30
committed by GitHub
parent 0373486dbc
commit a6a58d6478
1 changed files with 241 additions and 132 deletions
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157
ggml/src/ggml-cpu/llamafile/sgemm.cpp
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@@ -2177,12 +2177,36 @@ class tinyBLAS_PPC {
}
void matmul(int64_t m, int64_t n) {
int64_t mc = 256; int64_t nc = 256; int64_t kc = 256;
if (m % mc == 0 && n % nc == 0 && k % kc == 0) {
matmul_tiled(m, n, mc, nc, kc);
} else {
mnpack(0, m, 0, n);
}
}
private:
void (tinyBLAS_PPC::*kernel)(int64_t, int64_t);
inline void save_acc(acc_t * ACC, int64_t ii, int64_t jj) {
vec_t vec_C[4];
__builtin_mma_disassemble_acc(vec_C, ACC);
for (int I = 0; I < 4; I++) {
for (int J = 0; J < 4; J++) {
*((float *)(C+ii+((jj+J)*ldc)+I)) = *((float *)&vec_C[I]+J);
}
}
}
inline void add_save_acc(acc_t * ACC, int64_t ii, int64_t jj) {
vec_t vec_C[4];
__builtin_mma_disassemble_acc(vec_C, ACC);
for (int I = 0; I < 4; I++) {
for (int J = 0; J < 4; J++) {
float * c_ptr = (float *)(C+ii+((jj+J)*ldc)+I);
*c_ptr += *((float *)&vec_C[I]+J);
}
}
}
inline void vector_permute_store_4(vector float * src, float * vecOffset) {
vector float t1, t2, t3, t4, t5, t6, t7, t8;
@@ -2247,7 +2271,6 @@ class tinyBLAS_PPC {
boffset = vec;
j = (rows >> 3);
if (j > 0) {
do {
aoffsets[0] = aoffset;
for (int it = 1; it < 8; it++)
@@ -2265,10 +2288,13 @@ class tinyBLAS_PPC {
vector_permute_store_8(c1, boffset);
vector_permute_store_8(c2, boffset + 32);
for (int it = 0; it < 4; it++)
aoffsets[it] = aoffsets[it] + 8*lda;
boffset += 64;
i--;
if (i > 0) {
for (int it = 0; it < 8; it++) {
aoffsets[it] = aoffsets[it] + 8;
}
}
} while(i > 0);
}
if (cols & 4) {
@@ -2333,7 +2359,7 @@ class tinyBLAS_PPC {
__builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]);
__builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]);
}
SAVE_ACC(&acc_0, ii, jj);
save_acc(&acc_0, ii, jj);
}
void KERNEL_4x8(int64_t ii, int64_t jj) {
@@ -2353,8 +2379,8 @@ class tinyBLAS_PPC {
__builtin_mma_xvf32gerpp(&acc_0, vec_A[3], (vec_t)vec_B[6]);
__builtin_mma_xvf32gerpp(&acc_1, vec_A[3], (vec_t)vec_B[7]);
}
SAVE_ACC(&acc_0, ii, jj);
SAVE_ACC(&acc_1, ii, jj+4);
save_acc(&acc_0, ii, jj);
save_acc(&acc_1, ii, jj + 4);
}
void KERNEL_8x4(int64_t ii, int64_t jj) {
@@ -2374,8 +2400,8 @@ class tinyBLAS_PPC {
__builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[6], vec_B[3]);
__builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[7], vec_B[3]);
}
SAVE_ACC(&acc_0, ii, jj);
SAVE_ACC(&acc_1, ii+4, jj);
save_acc(&acc_0, ii, jj);
save_acc(&acc_1, ii + 4, jj);
}
void KERNEL_8x8(int64_t ii, int64_t jj) {
@@ -2395,10 +2421,87 @@ class tinyBLAS_PPC {
__builtin_mma_xvf32gerpp(&acc_3, (vec_t)vec_A[x + 1], vec_B[x + 1]);
}
}
SAVE_ACC(&acc_0, ii, jj);
SAVE_ACC(&acc_1, ii, jj+4);
SAVE_ACC(&acc_2, ii+4, jj);
SAVE_ACC(&acc_3, ii+4, jj+4);
save_acc(&acc_0, ii, jj);
save_acc(&acc_1, ii, jj + 4);
save_acc(&acc_2, ii + 4, jj);
save_acc(&acc_3, ii + 4, jj + 4);
}
inline void MMA_16x8(vec_t * vec_A0, vec_t * vec_A1, vec_t * vec_B, acc_t * acc) {
for (int x = 0; x < 16; x += 2) {
__builtin_mma_xvf32gerpp(&acc[0], vec_A0[x + 0], vec_B[x]);
__builtin_mma_xvf32gerpp(&acc[1], vec_A0[x + 0], vec_B[x + 1]);
__builtin_mma_xvf32gerpp(&acc[2], vec_A0[x + 1], vec_B[x]);
__builtin_mma_xvf32gerpp(&acc[3], vec_A0[x + 1], vec_B[x + 1]);
__builtin_mma_xvf32gerpp(&acc[4], vec_A1[x + 0], vec_B[x]);
__builtin_mma_xvf32gerpp(&acc[5], vec_A1[x + 0], vec_B[x + 1]);
__builtin_mma_xvf32gerpp(&acc[6], vec_A1[x + 1], vec_B[x]);
__builtin_mma_xvf32gerpp(&acc[7], vec_A1[x + 1], vec_B[x + 1]);
}
}
void KERNEL(int64_t ii, int64_t jj, int64_t mc, int64_t nc, int64_t kc, vec_t * vec_A, vec_t * vec_B, int64_t kk) {
for (int64_t i = 0; i < mc; i += 16) {
int A_base_addr = (mc / 8) * (i / 8) * 16;
for (int64_t j = 0; j < nc; j += 8) {
int B_base_addr = (nc / 8) * (j / 8) * 16;
acc_t acc[8];
vec_t A0_block[16]; vec_t A1_block[16];
for (int x = 0; x < 8; x++)
__builtin_mma_xxsetaccz(&acc[x]);
for (int64_t l = 0; l < kc; l += 8) {
int A0_block_idx = A_base_addr + (l / 8) * 16;
int A1_block_idx = A0_block_idx + (mc / 8) * 16;
int B_block_idx = B_base_addr + (l / 8) * 16;
vec_t* A0_block = &vec_A[A0_block_idx];
vec_t* A1_block = &vec_A[A1_block_idx];
vec_t* B_block = &vec_B[B_block_idx];
MMA_16x8(A0_block, A1_block, B_block, acc);
}
if (kk == 0) {
save_acc(&acc[0], ii + i, jj + j);
save_acc(&acc[1], ii + i, jj + j + 4);
save_acc(&acc[2], ii + i + 4, jj + j);
save_acc(&acc[3], ii + i + 4, jj + j + 4);
save_acc(&acc[4], ii + i + 8, jj + j);
save_acc(&acc[5], ii + i + 8, jj + j + 4);
save_acc(&acc[6], ii + i + 12, jj + j);
save_acc(&acc[7], ii + i + 12, jj + j + 4);
} else {
add_save_acc(&acc[0], ii + i, jj + j);
add_save_acc(&acc[1], ii + i, jj + j + 4);
add_save_acc(&acc[2], ii + i + 4, jj + j);
add_save_acc(&acc[3], ii + i + 4, jj + j + 4);
add_save_acc(&acc[4], ii + i + 8, jj + j);
add_save_acc(&acc[5], ii + i + 8, jj + j + 4);
add_save_acc(&acc[6], ii + i + 12, jj + j);
add_save_acc(&acc[7], ii + i + 12, jj + j + 4);
}
}
}
}
void matmul_tiled(int64_t m , int64_t n, int64_t mc, int64_t nc, int64_t kc) {
int64_t ytiles = m / mc;
int64_t xtiles = n / nc;
int64_t tiles = xtiles * ytiles;
int64_t duty = (tiles + nth - 1) / nth;
int64_t start = duty * ith;
int64_t end = start + duty;
if (end > tiles) {
end = tiles;
}
for (int64_t job = start; job < end; ++job) {
int64_t ii = (job / xtiles) * mc;
int64_t jj = (job % xtiles) * nc;
for (int64_t kk = 0; kk < k; kk += kc) {
vec_t A_pack[kc * mc / 4];
vec_t B_pack[kc * nc / 4];
packTranspose(A + (ii * lda) + kk, lda, kc, mc, (float *)A_pack);
packTranspose(B + (jj * ldb) + kk, ldb, kc, nc, (float *)B_pack);
KERNEL(ii, jj, mc, nc, kc, A_pack, B_pack, kk);
}
}
}
void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) {
@@ -2449,7 +2552,7 @@ class tinyBLAS_PPC {
vec_t vec_C[4];
acc_t acc_0;
__builtin_mma_xxsetaccz(&acc_0);
vec_t vec_A[4] {0}, vec_B[4] = {0};
vec_t vec_A[4] = {0}, vec_B[4] = {0};
for (int l = 0; l < k; l += 4) {
/* 'GEMV Forwarding' concept is used in first two conditional loops.
* when one of the matrix has a single row/column, the elements are
@@ -2488,6 +2591,21 @@ class tinyBLAS_PPC {
}
}
template<int RM, int RN>
inline void kernel(int64_t ii, int64_t jj) {
if constexpr(RM == 4 && RN == 4) {
KERNEL_4x4(ii, jj);
} else if constexpr(RM == 4 && RN == 8) {
KERNEL_4x8(ii, jj);
} else if constexpr(RM == 8 && RN == 4) {
KERNEL_8x4(ii, jj);
} else if constexpr(RM == 8 && RN == 8) {
KERNEL_8x8(ii, jj);
} else {
static_assert(false, "RN/RM values not supported");
}
}
template <int RM, int RN>
NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) {
int64_t ytiles = (m - m0) / RM;
@@ -2496,21 +2614,12 @@ class tinyBLAS_PPC {
int64_t duty = (tiles + nth - 1) / nth;
int64_t start = duty * ith;
int64_t end = start + duty;
if (RM == 4 && RN == 4) {
kernel = &tinyBLAS_PPC::KERNEL_4x4;
} else if (RM == 4 && RN == 8) {
kernel = &tinyBLAS_PPC::KERNEL_4x8;
} else if (RM == 8 && RN == 4) {
kernel = &tinyBLAS_PPC::KERNEL_8x4;
} else if (RM == 8 && RN == 8) {
kernel = &tinyBLAS_PPC::KERNEL_8x8;
}
if (end > tiles)
end = tiles;
for (int64_t job = start; job < end; ++job) {
int64_t ii = m0 + job / xtiles * RM;
int64_t jj = n0 + job % xtiles * RN;
(this->*kernel)(ii, jj);
kernel<RM, RN>(ii, jj);
}
}