vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

add check for tensor dimensions

Browse source
This commit is contained in:
Charles Xu 2024-11-08 17:01:51 +01:00
parent 5947d72c84
commit 871036d236
8 changed files with 30 additions and 27 deletions
Download patch file Download diff file Expand all files Collapse all files

6
Makefile
View file

@ -874,9 +874,9 @@ ggml/src/ggml-cuda/%.o: \
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
endif # GGML_HIPBLAS
ifdef GGML_CPU_AARCH64
MK_CPPFLAGS += -DGGML_USE_CPU_AARCH64
MK_CFLAGS += -DGGML_USE_CPU_AARCH64
ifdef GGML_RUNTIME_REPACK
MK_CPPFLAGS += -DGGML_USE_RUNTIME_REPACK
MK_CFLAGS += -DGGML_USE_RUNTIME_REPACK
endif
ifdef GGML_METAL

2
ggml/CMakeLists.txt
View file

@ -92,7 +92,7 @@ else()
endif()
option(GGML_CPU_HBM "ggml: use memkind for CPU HBM" OFF)
option(GGML_CPU_AARCH64 "ggml: use runtime weight quantization to enable optimized GEMM/GEMV kernels for AARCH64 cpu" OFF)
option(GGML_RUNTIME_REPACK "ggml: use runtime weight quantization to enable optimized GEMM/GEMV kernels for AARCH64 cpu" OFF)
option(GGML_AVX "ggml: enable AVX" ${INS_ENB})
option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB})

2
ggml/include/ggml-cpu.h
View file

@ -145,7 +145,7 @@ extern "C" {
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
#endif
#ifdef GGML_USE_CPU_AARCH64
#ifdef GGML_USE_RUNTIME_REPACK
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void);
#endif

4
ggml/src/CMakeLists.txt
View file

@ -880,10 +880,10 @@ if (GGML_CPU_HBM)
target_link_libraries(ggml PUBLIC memkind)
endif()
if (GGML_CPU_AARCH64)
if (GGML_RUNTIME_REPACK)
message(STATUS "Using runtime weight quantization to enable optimized GEMM/GEMV kernels for AARCH64 cpu")
add_compile_definitions(GGML_USE_CPU_AARCH64)
add_compile_definitions(GGML_USE_RUNTIME_REPACK)
endif()
if (GGML_CANN)

33
ggml/src/ggml-aarch64.c
View file

@ -3477,10 +3477,9 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
}
}
#ifdef GGML_USE_CPU_AARCH64
static void repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * data, size_t data_size) {
#ifdef GGML_USE_RUNTIME_REPACK
static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * data, size_t data_size) {
GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
GGML_ASSERT(t->ne[0] % 8 == 0);
GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
block_q4_0x4 *dst = (block_q4_0x4 *)t->data;
@ -3492,9 +3491,12 @@ static void repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_bloc
GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
return -1;
}
for (int b = 0; b < nrow; b += nrows_interleaved) {
for (int64_t x = 0; x < nblocks; x++)
{
for (int64_t x = 0; x < nblocks; x++) {
for (int i = 0; i < nrows_interleaved; i++) {
dst_tmp[i] = src[x + i * nblocks];
}
@ -3502,13 +3504,13 @@ static void repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_bloc
}
src += nrows_interleaved * nblocks;
}
return 0;
GGML_UNUSED(data_size);
}
static void repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, const void * data, size_t data_size) {
static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, const void * data, size_t data_size) {
GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
GGML_ASSERT(t->ne[0] % 8 == 0);
GGML_ASSERT(interleave_block == 8);
block_q4_0x8 *dst = (block_q4_0x8*)t->data;
@ -3520,6 +3522,10 @@ static void repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block
GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0));
if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
return -1;
}
for (int b = 0; b < nrow; b += nrows_interleaved) {
for (int64_t x = 0; x < nblocks; x++) {
for (int i = 0; i < nrows_interleaved; i++ ) {
@ -3529,6 +3535,7 @@ static void repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block
}
src += nrows_interleaved * nblocks;
}
return 0;
GGML_UNUSED(data_size);
}
@ -3536,22 +3543,18 @@ static void repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block
// Prepare for optimized kernels if applicable
int ggml_prepare_optimal_kernel(struct ggml_tensor * cur, const void * data, size_t data_size) {
GGML_ASSERT(cur->type == GGML_TYPE_Q4_0);
int ret = -1;
#if defined(__ARM_ARCH)
if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) {
repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size);
ret = 0;
return repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size);
}
else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size);
ret = 0;
return repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size);
}
else if (ggml_cpu_has_neon()) {
repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size);
ret = 0;
return repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size);
}
#endif
return ret;
return -1;
GGML_UNUSED(cur);
GGML_UNUSED(data);

2
ggml/src/ggml-aarch64.h
View file

@ -33,7 +33,7 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
#ifdef GGML_USE_CPU_AARCH64
#ifdef GGML_USE_RUNTIME_REPACK
int ggml_prepare_optimal_kernel(struct ggml_tensor * cur, const void * data, size_t data_size);
enum ggml_type ggml_get_optimal_type(const struct ggml_tensor * cur);
#endif

6
ggml/src/ggml-backend.cpp
View file

@ -2239,7 +2239,7 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
}
#endif
#ifdef GGML_USE_CPU_AARCH64
#ifdef GGML_USE_RUNTIME_REPACK
// buffer type AARCH64
@ -2316,7 +2316,7 @@ static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backen
bufts[index++] = ggml_backend_cpu_hbm_buffer_type();
#endif
#ifdef GGML_USE_CPU_AARCH64
#ifdef GGML_USE_RUNTIME_REPACK
if (ggml_cpu_has_neon() || ggml_cpu_has_matmul_int8() || ggml_cpu_has_sve()) {
bufts[index++] = ggml_backend_cpu_aarch64_buffer_type();
}
@ -2635,7 +2635,7 @@ static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_b
}
static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
#ifdef GGML_USE_CPU_AARCH64
#ifdef GGML_USE_RUNTIME_REPACK
const struct ggml_tensor *tensor = op->src[0];
if (tensor && tensor->buffer && (strcmp(tensor->buffer->buft->iface.get_name(tensor->buffer->buft),"CPU_AARCH64") == 0)) {
if (op->op == GGML_OP_MUL_MAT && tensor->type == GGML_TYPE_Q4_0) {

2
ggml/src/ggml-cpu.c
View file

@ -7427,7 +7427,7 @@ static void ggml_compute_forward_mul_mat(
enum ggml_type type = src0->type;
#ifdef GGML_USE_CPU_AARCH64
#ifdef GGML_USE_RUNTIME_REPACK
if (strcmp(src0->buffer->buft->iface.get_name(src0->buffer->buft),"CPU_AARCH64") == 0) {
type = ggml_get_optimal_type(src0);
}