8000 Refactor/online repacking by Djip007 · Pull Request #10446 · ggml-org/llama.cpp · GitHub
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Refactor/online repacking #10446

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Merged
merged 9 commits into from
Dec 7, 2024
Merged

Refactor/online repacking #10446

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9ac05c1
rename ggml-cpu-aarch64.c to .cpp
Djip007 Dec 1, 2024
98ea414
reformat extra cpu backend.
Djip007 Nov 19, 2024
95322e9
clang-format
Djip007 Dec 2, 2024
3a042b4
Clean Q4_0_N_M ref
Djip007 Dec 4, 2024
0a2be72
add op GGML_OP_MUL_MAT_ID for Q4_0_N_M with runtime repack
Djip007 Dec 5, 2024
b14b471
added/corrected control on tensor size for Q4 repacking.
Djip007 Dec 6, 2024
7dc8a3e
Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
Djip007 Dec 7, 2024
e115f6f
Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
Djip007 Dec 7, 2024
1221d13
add debug logs on repacks.
Djip007 Dec 7, 2024
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added/corrected control on tensor size for Q4 repacking.
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@Djip007
Djip007 committed Dec 6, 2024
commit b14b47132a14ffceabc42240b465ff3ed97ae23d
50 changes: 32 additions & 18 deletions ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -3680,17 +3680,17 @@ static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_in
static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
constexpr int nrows_interleaved = 4;

block_q4_0x4 * dst = (block_q4_0x4 *)t->data;
const block_q4_0 * src = (const block_q4_0 *)data;
block_q4_0 dst_tmp[4];
int nrow = t->ne[1]*t->ne[2]*t->ne[3]; // Number of rows
int nrows_interleaved = 4;
int nrow = ggml_nrows(t);
int nblocks = t->ne[0] / QK4_0;

GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));

if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
return -1;
}

Expand All @@ -3711,17 +3711,17 @@ static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block
static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
GGML_ASSERT(interleave_block == 8);
constexpr int nrows_interleaved = 8;

block_q4_0x8 * dst = (block_q4_0x8*)t->data;
const block_q4_0 * src = (const block_q4_0*) data;
block_q4_0 dst_tmp[8];
int nrow = t->ne[1]*t->ne[2]*t->ne[3]; // Number of rows
int nrows_interleaved = 8;
int nrow = ggml_nrows(t);
int nblocks = t->ne[0] / QK4_0;

GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));

if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
return -1;
}

Expand Down Expand Up @@ -3779,13 +3779,13 @@ static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_b
block_iq4_nlx4 * dst = (block_iq4_nlx4 *)t->data;
const block_iq4_nl * src = (const block_iq4_nl *)data;
block_iq4_nl dst_tmp[4];
int nrow = t->ne[1]*t->ne[2]*t->ne[3]; // Number of rows
int nrow = ggml_nrows(t);
int nrows_interleaved = 4;
int nblocks = t->ne[0] / QK4_0;

GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl));

if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
return -1;
}

Expand Down Expand Up @@ -4121,17 +4121,25 @@ static const tensor_traits<block_iq4_nl, 4, 4> iq4_nl_4x4_q8_0;
static const ggml::cpu::tensor_traits * ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur) {
if (cur->type == GGML_TYPE_Q4_0) {
if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
return &ggml::cpu::aarch64::q4_0_8x8_q8_0;
if (cur->ne[1] % 8==0) {
return &ggml::cpu::aarch64::q4_0_8x8_q8_0;
}
}
if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
return &ggml::cpu::aarch64::q4_0_4x8_q8_0;
if (cur->ne[1] % 4 == 0) {
return &ggml::cpu::aarch64::q4_0_4x8_q8_0;
}
}
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
return &ggml::cpu::aarch64::q4_0_4x4_q8_0;
if (cur->ne[1] % 4 == 0) {
return &ggml::cpu::aarch64::q4_0_4x4_q8_0;
}
}
} else if (cur->type == GGML_TYPE_IQ4_NL) {
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
return &ggml::cpu::aarch64::iq4_nl_4x4_q8_0;
if (cur->ne[1] % 4 == 0) {
return &ggml::cpu::aarch64::iq4_nl_4x4_q8_0;
}
}
}

Expand Down Expand Up @@ -4184,9 +4192,12 @@ static size_t ggml_backend_cpu_aarch64_buffer_type_get_alignment(ggml_backend_bu
namespace ggml::cpu::aarch64 {
class extra_buffer_type : ggml::cpu::extra_buffer_type {
bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
if (op->op == GGML_OP_MUL_MAT && op->src[0]->buffer && (ggml_n_dims(op->src[0]) == 2) &&
op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type() &&
ggml_aarch64_get_optimal_repack_type(op->src[0])) {
if ( op->op == GGML_OP_MUL_MAT &&
op->src[0]->buffer &&
(ggml_n_dims(op->src[0]) == 2) &&
op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type() &&
ggml_aarch64_get_optimal_repack_type(op->src[0])
) {
if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
return false;
}
Expand All @@ -4197,9 +4208,12 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
// return true;
//}
// may be possible if Q8_0 packed...
} else if (op->op == GGML_OP_MUL_MAT_ID && op->src[0]->buffer && (ggml_n_dims(op->src[0]) == 3) &&
op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type() &&
ggml_aarch64_get_optimal_repack_type(op->src[0])) {
} else if (op->op == GGML_OP_MUL_MAT_ID
&& op->src[0]->buffer
&& (ggml_n_dims(op->src[0]) == 3)
&& op->src[0]->buffer->buft == ggml_backend_cpu_aarch64_buffer_type()
&& ggml_aarch64_get_optimal_repack_type(op->src[0])
) {
if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
return false;
}
Expand Down
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