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whisper.cpp/ggml/src/ggml-cpu/ggml-cpu.c
Acly d87dfcf7c0 ggml : Depthwise 2D convolution (ggml/1152) 
* ggml-cpu : kernels for faster depthwise 2D convolution

* fix compile: remove static after moving to ops.cpp

* add dilation for depthwise_conv_2d

* review: rename to ggml_conv_2d_dw_direct, remove redundant struct keywords, pass by ref, whitespace

* review: rename depthwise_conv_2d -> conv_2d_dw everywhere
2025-04-24 20:39:16 +03:00

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#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows
#define _USE_MATH_DEFINES // For M_PI on MSVC
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-cpu-traits.h"
#include "ggml-cpu-impl.h"
#include "ggml-cpu.h"
#include "ggml-impl.h"
#include "ggml-cpu-quants.h"
#include "ggml-threading.h"
#include "unary-ops.h"
#include "binary-ops.h"
#include "vec.h"
#include "ops.h"
#include "ggml.h"
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
#include <alloca.h>
#endif
#include <assert.h>
#include <errno.h>
#include <time.h>
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <stdint.h>
#include <inttypes.h>
#include <stdio.h>
#include <float.h>
#include <limits.h>
#include <stdarg.h>
#include <signal.h>
#if defined(__gnu_linux__)
#include <syscall.h>
#endif
#ifdef GGML_USE_OPENMP
#include <omp.h>
#endif
#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
#undef GGML_USE_LLAMAFILE
#endif
#ifdef GGML_USE_LLAMAFILE
#include "llamafile/sgemm.h"
#endif
#if defined(_MSC_VER)
// disable "possible loss of data" to avoid hundreds of casts
// we should just be careful :)
#pragma warning(disable: 4244 4267)
// disable POSIX deprecation warnings
// these functions are never going away, anyway
#pragma warning(disable: 4996)
// unreachable code because of multiple instances of code after GGML_ABORT
#pragma warning(disable: 4702)
#endif
// Note: once we move threading into a separate C++ file
// will use std::hardware_destructive_interference_size instead of hardcoding it here
// and we'll use C++ attribute syntax.
#define GGML_CACHE_LINE 64
#if defined(__clang__) || defined(__GNUC__)
#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE)))
#endif
#if defined(__has_feature)
#if __has_feature(thread_sanitizer)
#define GGML_TSAN_ENABLED 1
#endif
#else // __has_feature
#if defined(__SANITIZE_THREAD__)
#define GGML_TSAN_ENABLED 1
#endif
#endif // __has_feature
#define UNUSED GGML_UNUSED
#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0)
#if defined(__ARM_ARCH)
struct ggml_arm_arch_features_type {
int has_neon;
int has_dotprod;
int has_i8mm;
int has_sve;
int sve_cnt;
int has_sme;
} ggml_arm_arch_features = {-1, -1, -1, -1, 0, -1};
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#if defined(_MSC_VER) && !defined(__clang__)
#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE))
typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;
typedef atomic_int atomic_flag;
#define ATOMIC_FLAG_INIT 0
typedef enum {
memory_order_relaxed,
memory_order_consume,
memory_order_acquire,
memory_order_release,
memory_order_acq_rel,
memory_order_seq_cst
} memory_order;
static void atomic_store(atomic_int * ptr, LONG val) {
InterlockedExchange(ptr, val);
}
static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) {
// TODO: add support for explicit memory order
InterlockedExchange(ptr, val);
}
static LONG atomic_load(atomic_int * ptr) {
return InterlockedCompareExchange(ptr, 0, 0);
}
static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) {
// TODO: add support for explicit memory order
return InterlockedCompareExchange(ptr, 0, 0);
}
static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) {
return InterlockedExchangeAdd(ptr, inc);
}
static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) {
// TODO: add support for explicit memory order
return InterlockedExchangeAdd(ptr, inc);
}
static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) {
return InterlockedExchange(ptr, 1);
}
static void atomic_flag_clear(atomic_flag * ptr) {
InterlockedExchange(ptr, 0);
}
static void atomic_thread_fence(memory_order mo) {
MemoryBarrier();
}
#else // clang
#include <stdatomic.h>
#endif
typedef HANDLE pthread_t;
typedef DWORD thread_ret_t;
static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) {
(void) unused;
HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
if (handle == NULL)
{
return EAGAIN;
}
*out = handle;
return 0;
}
static int pthread_join(pthread_t thread, void * unused) {
(void) unused;
int ret = (int) WaitForSingleObject(thread, INFINITE);
CloseHandle(thread);
return ret;
}
static int sched_yield (void) {
Sleep (0);
return 0;
}
#else
#include <pthread.h>
#include <stdatomic.h>
#include <sched.h>
#if defined(__FreeBSD__)
#include <pthread_np.h>
#endif
typedef void * thread_ret_t;
#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>
#endif
typedef pthread_t ggml_thread_t;
#if defined(__APPLE__)
#include <unistd.h>
#include <mach/mach.h>
#include <TargetConditionals.h>
#endif
static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = {
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32,
.vec_dot_type = GGML_TYPE_F32,
.nrows = 1,
},
[GGML_TYPE_F16] = {
.from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row,
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16,
.vec_dot_type = GGML_TYPE_F16,
.nrows = 1,
},
[GGML_TYPE_Q4_0] = {
.from_float = quantize_row_q4_0,
.vec_dot = ggml_vec_dot_q4_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
#if defined (__ARM_FEATURE_MATMUL_INT8)
.nrows = 2,
#else
.nrows = 1,
#endif
},
[GGML_TYPE_Q4_1] = {
.from_float = quantize_row_q4_1,
.vec_dot = ggml_vec_dot_q4_1_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
#if defined (__ARM_FEATURE_MATMUL_INT8)
.nrows = 2,
#else
.nrows = 1,
#endif
},
[GGML_TYPE_Q5_0] = {
.from_float = quantize_row_q5_0,
.vec_dot = ggml_vec_dot_q5_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
},
[GGML_TYPE_Q5_1] = {
.from_float = quantize_row_q5_1,
.vec_dot = ggml_vec_dot_q5_1_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
.nrows = 1,
},
[GGML_TYPE_Q8_0] = {
.from_float = quantize_row_q8_0,
.vec_dot = ggml_vec_dot_q8_0_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
#if defined (__ARM_FEATURE_MATMUL_INT8)
.nrows = 2,
#else
.nrows = 1,
#endif
},
[GGML_TYPE_Q8_1] = {
.from_float = quantize_row_q8_1,
.vec_dot_type = GGML_TYPE_Q8_1,
.nrows = 1,
},
[GGML_TYPE_Q2_K] = {
.from_float = quantize_row_q2_K,
.vec_dot = ggml_vec_dot_q2_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q3_K] = {
.from_float = quantize_row_q3_K,
.vec_dot = ggml_vec_dot_q3_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q4_K] = {
.from_float = quantize_row_q4_K,
.vec_dot = ggml_vec_dot_q4_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q5_K] = {
.from_float = quantize_row_q5_K,
.vec_dot = ggml_vec_dot_q5_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q6_K] = {
.from_float = quantize_row_q6_K,
.vec_dot = ggml_vec_dot_q6_K_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ2_XXS] = {
.from_float = NULL,
.vec_dot = ggml_vec_dot_iq2_xxs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ2_XS] = {
.from_float = NULL,
.vec_dot = ggml_vec_dot_iq2_xs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ3_XXS] = {
// NOTE: from_float for iq3 and iq2_s was removed because these quants require initialization in ggml_quantize_init
//.from_float = quantize_row_iq3_xxs,
.vec_dot = ggml_vec_dot_iq3_xxs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ3_S] = {
//.from_float = quantize_row_iq3_s,
.vec_dot = ggml_vec_dot_iq3_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ2_S] = {
//.from_float = quantize_row_iq2_s,
.vec_dot = ggml_vec_dot_iq2_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ1_S] = {
.from_float = NULL,
.vec_dot = ggml_vec_dot_iq1_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ1_M] = {
.from_float = NULL,
.vec_dot = ggml_vec_dot_iq1_m_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ4_NL] = {
.from_float = quantize_row_iq4_nl,
.vec_dot = ggml_vec_dot_iq4_nl_q8_0,
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
},
[GGML_TYPE_IQ4_XS] = {
.from_float = quantize_row_iq4_xs,
.vec_dot = ggml_vec_dot_iq4_xs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q8_K] = {
.from_float = quantize_row_q8_K,
},
[GGML_TYPE_BF16] = {
.from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row,
.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16,
.vec_dot_type = GGML_TYPE_BF16,
.nrows = 1,
},
[GGML_TYPE_TQ1_0] = {
.from_float = quantize_row_tq1_0,
.vec_dot = ggml_vec_dot_tq1_0_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_TQ2_0] = {
.from_float = quantize_row_tq2_0,
.vec_dot = ggml_vec_dot_tq2_0_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
};
const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) {
return &type_traits_cpu[type];
}
//
// Threading defs
//
typedef pthread_t ggml_thread_t;
#if defined(_WIN32)
typedef CONDITION_VARIABLE ggml_cond_t;
typedef SRWLOCK ggml_mutex_t;
#define ggml_mutex_init(m) InitializeSRWLock(m)
#define ggml_mutex_destroy(m)
#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m)
#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m)
#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m)
#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m)
#define ggml_cond_init(c) InitializeConditionVariable(c)
#define ggml_cond_destroy(c)
#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED)
#define ggml_cond_broadcast(c) WakeAllConditionVariable(c)
#define ggml_thread_create pthread_create
#define ggml_thread_join pthread_join
#else
typedef pthread_cond_t ggml_cond_t;
typedef pthread_mutex_t ggml_mutex_t;
#define ggml_mutex_init(m) pthread_mutex_init(m, NULL)
#define ggml_mutex_destroy(m) pthread_mutex_destroy(m)
#define ggml_mutex_lock(m) pthread_mutex_lock(m)
#define ggml_mutex_unlock(m) pthread_mutex_unlock(m)
#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m)
#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m)
#define ggml_lock_init(x) UNUSED(x)
#define ggml_lock_destroy(x) UNUSED(x)
#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64))
#define ggml_lock_lock(x) _mm_pause()
#else
#define ggml_lock_lock(x) UNUSED(x)
#endif
#define ggml_lock_unlock(x) UNUSED(x)
#define GGML_LOCK_INITIALIZER 0
#define ggml_cond_init(c) pthread_cond_init(c, NULL)
#define ggml_cond_destroy(c) pthread_cond_destroy(c)
#define ggml_cond_wait(c, m) pthread_cond_wait(c, m)
#define ggml_cond_broadcast(c) pthread_cond_broadcast(c)
#define ggml_thread_create pthread_create
#define ggml_thread_join pthread_join
#endif
// Threadpool def
struct ggml_threadpool {
ggml_mutex_t mutex; // mutex for cond.var
ggml_cond_t cond; // cond.var for waiting for new work
struct ggml_cgraph * cgraph;
struct ggml_cplan * cplan;
// synchronization primitives
atomic_int n_graph; // incremented when there is work to be done (i.e each graph)
atomic_int GGML_CACHE_ALIGN n_barrier;
atomic_int GGML_CACHE_ALIGN n_barrier_passed;
atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
// these are atomic as an annotation for thread-sanitizer
atomic_bool stop; // Used for stopping the threadpool altogether
atomic_bool pause; // Used for pausing the threadpool or individual threads
atomic_int abort; // Used for aborting processing of a graph
struct ggml_compute_state * workers; // per thread state
int n_threads_max; // number of threads in the pool
atomic_int n_threads_cur; // number of threads used in the current graph
int32_t prio; // Scheduling priority
uint32_t poll; // Polling level (0 - no polling)
enum ggml_status ec;
};
// Per-thread state
struct ggml_compute_state {
#ifndef GGML_USE_OPENMP
ggml_thread_t thrd;
bool cpumask[GGML_MAX_N_THREADS];
int last_graph;
bool pending;
#endif
struct ggml_threadpool * threadpool;
int ith;
};
// Helpers for polling loops
#if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) )
static inline void ggml_thread_cpu_relax(void) {
__asm__ volatile("yield" ::: "memory");
}
#elif defined(__x86_64__)
static inline void ggml_thread_cpu_relax(void) {
_mm_pause();
}
#else
static inline void ggml_thread_cpu_relax(void) {;}
#endif
//
// NUMA support
//
#define GGML_NUMA_MAX_NODES 8
#define GGML_NUMA_MAX_CPUS 512
struct ggml_numa_node {
uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node
uint32_t n_cpus;
};
struct ggml_numa_nodes {
enum ggml_numa_strategy numa_strategy;
struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES];
uint32_t n_nodes;
uint32_t total_cpus; // hardware threads on system
uint32_t current_node; // node on which main process is execting
#if defined(__gnu_linux__)
cpu_set_t cpuset; // cpuset from numactl
#else
uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype
#endif
};
//
// ggml state
//
struct ggml_state {
struct ggml_numa_nodes numa;
};
static struct ggml_state g_state = {0};
void ggml_barrier(struct ggml_threadpool * tp) {
int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed);
if (n_threads == 1) {
return;
}
#ifdef GGML_USE_OPENMP
#pragma omp barrier
#else
int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed);
// enter barrier (full seq-cst fence)
int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst);
if (n_barrier == (n_threads - 1)) {
// last thread
atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed);
// exit barrier (fill seq-cst fence)
atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst);
return;
}
// wait for other threads
while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) {
ggml_thread_cpu_relax();
}
// exit barrier (full seq-cst fence)
// TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead
#ifdef GGML_TSAN_ENABLED
atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst);
#else
atomic_thread_fence(memory_order_seq_cst);
#endif
#endif
}
#if defined(__gnu_linux__)
static cpu_set_t ggml_get_numa_affinity(void) {
cpu_set_t cpuset;
pthread_t thread;
thread = pthread_self();
CPU_ZERO(&cpuset);
pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset);
return cpuset;
}
#else
static uint32_t ggml_get_numa_affinity(void) {
return 0; // no NUMA support
}
#endif
void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
if (g_state.numa.n_nodes > 0) {
fprintf(stderr, "ggml_numa_init: NUMA already initialized\n");
return;
}
#if defined(__gnu_linux__)
struct stat st;
char path[256];
int rv;
// set numa scheme
g_state.numa.numa_strategy = numa_flag;
GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy);
g_state.numa.cpuset = ggml_get_numa_affinity();
// enumerate nodes
while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) {
rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes);
GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
if (stat(path, &st) != 0) { break; }
++g_state.numa.n_nodes;
}
// enumerate CPUs
while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) {
rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus);
GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
if (stat(path, &st) != 0) { break; }
++g_state.numa.total_cpus;
}
GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus);
// figure out which node we're on
uint current_cpu;
int getcpu_ret = 0;
#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 33) || defined(__COSMOPOLITAN__)
getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node);
#else
// old glibc doesn't have a wrapper for this call. Fall back on direct syscall
# if !defined(SYS_getcpu) && defined(SYS_get_cpu)
# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name
# endif
getcpu_ret = syscall(SYS_getcpu, &current_cpu, &g_state.numa.current_node);
#endif
if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) {
g_state.numa.n_nodes = 0;
return;
}
GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu);
for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) {
struct ggml_numa_node * node = &g_state.numa.nodes[n];
GGML_PRINT_DEBUG("CPUs on node %u:", n);
node->n_cpus = 0;
for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) {
rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c);
GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path));
if (stat(path, &st) == 0) {
node->cpus[node->n_cpus++] = c;
GGML_PRINT_DEBUG(" %u", c);
}
}
GGML_PRINT_DEBUG("\n");
}
if (ggml_is_numa()) {
FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r");
if (fptr != NULL) {
char buf[42];
if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) {
GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n");
}
fclose(fptr);
}
}
#else
UNUSED(numa_flag);
// TODO
#endif
}
bool ggml_is_numa(void) {
return g_state.numa.n_nodes > 1;
}
#if defined(__ARM_ARCH)
#if defined(__linux__) && defined(__aarch64__)
#include <sys/auxv.h>
#elif defined(__APPLE__)
#include <sys/sysctl.h>
#endif
#if !defined(HWCAP2_I8MM)
#define HWCAP2_I8MM (1 << 13)
#endif
#if !defined(HWCAP2_SME)
#define HWCAP2_SME (1 << 23)
#endif
static void ggml_init_arm_arch_features(void) {
#if defined(__linux__) && defined(__aarch64__)
uint32_t hwcap = getauxval(AT_HWCAP);
uint32_t hwcap2 = getauxval(AT_HWCAP2);
ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD);
ggml_arm_arch_features.has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM);
ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE);
ggml_arm_arch_features.has_sme = !!(hwcap2 & HWCAP2_SME);
#if defined(__ARM_FEATURE_SVE)
ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL);
#endif
#elif defined(__APPLE__)
int oldp = 0;
size_t size = sizeof(oldp);
if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_neon = oldp;
if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_dotprod = oldp;
if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_i8mm = oldp;
if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) != 0) {
oldp = 0;
}
ggml_arm_arch_features.has_sme = oldp;
ggml_arm_arch_features.has_sve = 0;
ggml_arm_arch_features.sve_cnt = 0;
#else
// Run-time CPU feature detection not implemented for this platform, fallback to compile time
#if defined(__ARM_NEON)
ggml_arm_arch_features.has_neon = 1;
#else
ggml_arm_arch_features.has_neon = 0;
#endif
#if defined(__ARM_FEATURE_MATMUL_INT8)
ggml_arm_arch_features.has_i8mm = 1;
#else
ggml_arm_arch_features.has_i8mm = 0;
#endif
#if defined(__ARM_FEATURE_SVE)
ggml_arm_arch_features.has_sve = 1;
ggml_arm_arch_features.sve_cnt = 16;
#else
ggml_arm_arch_features.has_sve = 0;
ggml_arm_arch_features.sve_cnt = 0;
#endif
#if defined(__ARM_FEATURE_SME) || defined(__ARM_FEATURE_SME2)
ggml_arm_arch_features.has_sme = 1;
#else
ggml_arm_arch_features.has_sme = 0;
#endif
#endif
}
#endif
struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
GGML_ASSERT(!ggml_get_no_alloc(ctx));
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1);
ggml_set_i32(result, value);
return result;
}
struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) {
GGML_ASSERT(!ggml_get_no_alloc(ctx));
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_set_f32(result, value);
return result;
}
struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
const int n = ggml_nrows(tensor);
const int nc = tensor->ne[0];
const size_t n1 = tensor->nb[1];
char * const data = tensor->data;
switch (tensor->type) {
case GGML_TYPE_I8:
{
assert(tensor->nb[0] == sizeof(int8_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value);
}
} break;
case GGML_TYPE_I16:
{
assert(tensor->nb[0] == sizeof(int16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value);
}
} break;
case GGML_TYPE_I32:
{
assert(tensor->nb[0] == sizeof(int32_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value);
}
} break;
case GGML_TYPE_F16:
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
}
} break;
case GGML_TYPE_BF16:
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value));
}
} break;
case GGML_TYPE_F32:
{
assert(tensor->nb[0] == sizeof(float));
for (int i = 0; i < n; i++) {
ggml_vec_set_f32(nc, (float *)(data + i*n1), value);
}
} break;
default:
{
GGML_ABORT("fatal error");
}
}
return tensor;
}
struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
const int n = ggml_nrows(tensor);
const int nc = tensor->ne[0];
const size_t n1 = tensor->nb[1];
char * const data = tensor->data;
switch (tensor->type) {
case GGML_TYPE_I8:
{
assert(tensor->nb[0] == sizeof(int8_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value);
}
} break;
case GGML_TYPE_I16:
{
assert(tensor->nb[0] == sizeof(int16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value);
}
} break;
case GGML_TYPE_I32:
{
assert(tensor->nb[0] == sizeof(int32_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value);
}
} break;
case GGML_TYPE_F16:
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
}
} break;
case GGML_TYPE_BF16:
{
assert(tensor->nb[0] == sizeof(ggml_bf16_t));
for (int i = 0; i < n; i++) {
ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value));
}
} break;
case GGML_TYPE_F32:
{
assert(tensor->nb[0] == sizeof(float));
for (int i = 0; i < n; i++) {
ggml_vec_set_f32(nc, (float *)(data + i*n1), value);
}
} break;
default:
{
GGML_ABORT("fatal error");
}
}
return tensor;
}
int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) {
if (!ggml_is_contiguous(tensor)) {
int64_t id[4] = { 0, 0, 0, 0 };
ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]);
}
switch (tensor->type) {
case GGML_TYPE_I8:
{
GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
return ((int8_t *)(tensor->data))[i];
}
case GGML_TYPE_I16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
return ((int16_t *)(tensor->data))[i];
}
case GGML_TYPE_I32:
{
GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
return ((int32_t *)(tensor->data))[i];
}
case GGML_TYPE_F16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
}
case GGML_TYPE_BF16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
}
case GGML_TYPE_F32:
{
GGML_ASSERT(tensor->nb[0] == sizeof(float));
return ((float *)(tensor->data))[i];
}
default:
{
GGML_ABORT("fatal error");
}
}
}
void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) {
if (!ggml_is_contiguous(tensor)) {
int64_t id[4] = { 0, 0, 0, 0 };
ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value);
return;
}
switch (tensor->type) {
case GGML_TYPE_I8:
{
GGML_ASSERT(tensor->nb[0] == sizeof(int8_t));
((int8_t *)(tensor->data))[i] = value;
} break;
case GGML_TYPE_I16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(int16_t));
((int16_t *)(tensor->data))[i] = value;
} break;
case GGML_TYPE_I32:
{
GGML_ASSERT(tensor->nb[0] == sizeof(int32_t));
((int32_t *)(tensor->data))[i] = value;
} break;
case GGML_TYPE_F16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t));
((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
} break;
case GGML_TYPE_F32:
{
GGML_ASSERT(tensor->nb[0] == sizeof(float));
((float *)(tensor->data))[i] = value;
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
switch (tensor->type) {
case GGML_TYPE_I8:
return ((int8_t *) data)[0];
case GGML_TYPE_I16:
return ((int16_t *) data)[0];
case GGML_TYPE_I32:
return ((int32_t *) data)[0];
case GGML_TYPE_F16:
return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
case GGML_TYPE_BF16:
return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
case GGML_TYPE_F32:
return ((float *) data)[0];
default:
GGML_ABORT("fatal error");
}
}
void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) {
void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
switch (tensor->type) {
case GGML_TYPE_I8:
{
((int8_t *)(data))[0] = value;
} break;
case GGML_TYPE_I16:
{
((int16_t *)(data))[0] = value;
} break;
case GGML_TYPE_I32:
{
((int32_t *)(data))[0] = value;
} break;
case GGML_TYPE_F16:
{
((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value);
} break;
case GGML_TYPE_F32:
{
((float *)(data))[0] = value;
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) {
if (!ggml_is_contiguous(tensor)) {
int64_t id[4] = { 0, 0, 0, 0 };
ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]);
}
switch (tensor->type) {
case GGML_TYPE_I8:
{
return ((int8_t *)(tensor->data))[i];
}
case GGML_TYPE_I16:
{
return ((int16_t *)(tensor->data))[i];
}
case GGML_TYPE_I32:
{
return ((int32_t *)(tensor->data))[i];
}
case GGML_TYPE_F16:
{
return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]);
}
case GGML_TYPE_BF16:
{
return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]);
}
case GGML_TYPE_F32:
{
return ((float *)(tensor->data))[i];
}
default:
{
GGML_ABORT("fatal error");
}
}
}
void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) {
if (!ggml_is_contiguous(tensor)) {
int64_t id[4] = { 0, 0, 0, 0 };
ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value);
return;
}
switch (tensor->type) {
case GGML_TYPE_I8:
{
((int8_t *)(tensor->data))[i] = value;
} break;
case GGML_TYPE_I16:
{
((int16_t *)(tensor->data))[i] = value;
} break;
case GGML_TYPE_I32:
{
((int32_t *)(tensor->data))[i] = value;
} break;
case GGML_TYPE_F16:
{
((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value);
} break;
case GGML_TYPE_F32:
{
((float *)(tensor->data))[i] = value;
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
switch (tensor->type) {
case GGML_TYPE_I8:
return ((int8_t *) data)[0];
case GGML_TYPE_I16:
return ((int16_t *) data)[0];
case GGML_TYPE_I32:
return ((int32_t *) data)[0];
case GGML_TYPE_F16:
return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]);
case GGML_TYPE_BF16:
return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]);
case GGML_TYPE_F32:
return ((float *) data)[0];
default:
GGML_ABORT("fatal error");
}
}
void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) {
void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
switch (tensor->type) {
case GGML_TYPE_I8:
{
((int8_t *)(data))[0] = value;
} break;
case GGML_TYPE_I16:
{
((int16_t *)(data))[0] = value;
} break;
case GGML_TYPE_I32:
{
((int32_t *)(data))[0] = value;
} break;
case GGML_TYPE_F16:
{
((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value);
} break;
case GGML_TYPE_BF16:
{
((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value);
} break;
case GGML_TYPE_F32:
{
((float *)(data))[0] = value;
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
////////////////////////////////////////////////////////////////////////////////
// ggml_compute_forward_mul_mat
static void ggml_compute_forward_mul_mat_one_chunk(
const struct ggml_compute_params * params,
struct ggml_tensor * dst,
const enum ggml_type type,
const int64_t num_rows_per_vec_dot,
const int64_t ir0_start,
const int64_t ir0_end,
const int64_t ir1_start,
const int64_t ir1_end) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
const bool src1_cont = ggml_is_contiguous(src1);
ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
// broadcast factors
const int64_t r2 = ne12 / ne02;
const int64_t r3 = ne13 / ne03;
//printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end);
// threads with no work simply yield (not sure if it helps)
if (ir0_start >= ir0_end || ir1_start >= ir1_end) {
return;
}
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
assert(ne12 % ne02 == 0);
assert(ne13 % ne03 == 0);
// block-tiling attempt
const int64_t blck_0 = 16;
const int64_t blck_1 = 16;
const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11;
// attempt to reduce false-sharing (does not seem to make a difference)
// 16 * 2, accounting for mmla kernels
float tmp[32];
for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) {
const int64_t i13 = (ir1 / (ne12 * ne1));
const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1;
const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1);
// broadcast src0 into src1
const int64_t i03 = i13 / r3;
const int64_t i02 = i12 / r2;
const int64_t i1 = i11;
const int64_t i2 = i12;
const int64_t i3 = i13;
const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03);
// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
// the original src1 data pointer, so we should index using the indices directly
// TODO: this is a bit of a hack, we should probably have a better way to handle this
const char * src1_col = (const char*)wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size
: (i11 * nb11 + i12 * nb12 + i13 * nb13));
float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));
//for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
// vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
//}
for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) {
vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot);
}
for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) {
memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float));
}
}
}
}
}
static void ggml_compute_forward_mul_mat(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
const int ith = params->ith;
const int nth = params->nth;
enum ggml_type const vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
int64_t const vec_dot_num_rows = type_traits_cpu[src0->type].nrows;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(src0->type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
// TODO: extract to "extra_op"
#if GGML_USE_LLAMAFILE
// broadcast factors
const int64_t r2 = ne12 / ne02;
const int64_t r3 = ne13 / ne03;
const bool src1_cont = ggml_is_contiguous(src1);
if (src1_cont) {
for (int64_t i13 = 0; i13 < ne13; i13++)
for (int64_t i12 = 0; i12 < ne12; i12++)
if (!llamafile_sgemm(params,
ne01, ne11, ne00/ggml_blck_size(src0->type),
(const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
nb01/ggml_type_size(src0->type),
(const char *)src1->data + i12*nb12 + i13*nb13,
nb11/ggml_type_size(src1->type),
(char *)dst->data + i12*nb2 + i13*nb3,
nb1/ggml_type_size(dst->type),
src0->type,
src1->type,
dst->type))
goto UseGgmlGemm1;
return;
}
UseGgmlGemm1:;
#endif
if (src1->type != vec_dot_type) {
char * wdata = params->wdata;
const size_t nbw0 = ggml_type_size(vec_dot_type);
const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
const size_t nbw2 = nbw1*ne11;
const size_t nbw3 = nbw2*ne12;
assert(params->wsize >= ne13*nbw3);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
#if 0
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
#else
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
size_t bs = ggml_blck_size(vec_dot_type);
int64_t ne10_block_start = (ith * ne10/bs) / nth;
int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + ne10_block_start*bs*nb10),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1 + ne10_block_start*nbw0),
(ne10_block_end - ne10_block_start) * bs);
}
}
}
#endif
}
if (ith == 0) {
// Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start.
atomic_store_explicit(&params->threadpool->current_chunk, nth, memory_order_relaxed);
}
ggml_barrier(params->threadpool);
#if GGML_USE_LLAMAFILE
if (src1->type != vec_dot_type) {
const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
for (int64_t i13 = 0; i13 < ne13; i13++)
for (int64_t i12 = 0; i12 < ne12; i12++)
if (!llamafile_sgemm(params,
ne01, ne11, ne00/ggml_blck_size(src0->type),
(const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
nb01/ggml_type_size(src0->type),
(const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size,
row_size/ggml_type_size(vec_dot_type),
(char *)dst->data + i12*nb2 + i13*nb3,
nb1/ggml_type_size(dst->type),
src0->type,
vec_dot_type,
dst->type))
goto UseGgmlGemm2;
return;
}
UseGgmlGemm2:;
#endif
// This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers)
const int64_t nr0 = ne0;
// This is the size of the rest of the dimensions of the result
const int64_t nr1 = ne1 * ne2 * ne3;
// Now select a reasonable chunk size.
int chunk_size = 16;
// We need to step up the size if it's small
if (nr0 == 1 || nr1 == 1) {
chunk_size = 64;
}
// distribute the work across the inner or outer loop based on which one is larger
// The number of chunks in the 0/1 dim.
// CEIL(nr0/chunk_size)
int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;
// If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread.
// Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggml-org/llama.cpp/pull/6915
// In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that.
if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) {
// distribute the thread work across the inner or outer loop based on which one is larger
nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows
}
// The number of elements in each chunk
const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
// The first chunk comes from our thread_id, the rest will get auto-assigned.
int current_chunk = ith;
while (current_chunk < nchunk0 * nchunk1) {
const int64_t ith0 = current_chunk % nchunk0;
const int64_t ith1 = current_chunk / nchunk0;
const int64_t ir0_start = dr0 * ith0;
const int64_t ir0_end = MIN(ir0_start + dr0, nr0);
const int64_t ir1_start = dr1 * ith1;
const int64_t ir1_end = MIN(ir1_start + dr1, nr1);
// dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols
int64_t num_rows_per_vec_dot = vec_dot_num_rows;
// these checks are needed to avoid crossing dim1 boundaries
// can be optimized, but the logic would become more complicated, so keeping it like this for simplicity
if ((nr0 % 2 != 0) || (ne11 % 2 != 0) || ((ir0_end - ir0_start) % 2 != 0) || ((ir1_end - ir1_start) % 2 != 0)) {
num_rows_per_vec_dot = 1;
}
ggml_compute_forward_mul_mat_one_chunk(params, dst, src0->type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end);
if (nth >= nchunk0 * nchunk1) {
break;
}
current_chunk = atomic_fetch_add_explicit(&params->threadpool->current_chunk, 1, memory_order_relaxed);
}
}
// ggml_compute_forward_mul_mat_id
#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ids->ne[0]*ids->ne[1] + (i1)]
struct mmid_row_mapping {
int32_t i1;
int32_t i2;
};
static void ggml_compute_forward_mul_mat_id_one_chunk(
struct ggml_tensor * dst,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
const struct ggml_tensor * ids,
const int64_t cur_a,
const int64_t ir0_start,
const int64_t ir0_end,
const int64_t ir1_start,
const int64_t ir1_end,
const char * src0_cur,
const struct mmid_row_mapping * matrix_rows,
const size_t row_size,
const bool src1_cont,
const void * wdata) {
GGML_TENSOR_BINARY_OP_LOCALS
const enum ggml_type type = src0->type;
ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot;
enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
const int64_t blck_0 = 16;
const int64_t blck_1 = 16;
float tmp[16];
for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ++ir1) {
const int64_t _i12 = ir1; // logical row index for this expert
struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
const int id = row_mapping.i1; // selected expert index
const int64_t i11 = id % ne11;
const int64_t i12 = row_mapping.i2; // row index in src1
const int64_t i1 = id; // selected expert index
const int64_t i2 = i12; // row
// desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
// if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
// the original src1 data pointer, so we should index using the indices directly
// TODO: this is a bit of a hack, we should probably have a better way to handle this
const char * src1_col = (const char *) wdata +
(src1_cont || src1->type != vec_dot_type
? (i11 + i12*ne11)*row_size
: (i11*nb11 + i12*nb12));
float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));
for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) {
vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
}
memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir0_end) - iir0)*sizeof(float));
}
}
}
}
static void * incr_ptr_aligned(void ** p, size_t size, size_t align) {
void * ptr = *p;
ptr = (void *) GGML_PAD((uintptr_t) ptr, align);
*p = (void *) ((char *) ptr + size);
return ptr;
}
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const struct ggml_tensor * ids = dst->src[2];
GGML_TENSOR_BINARY_OP_LOCALS
const int ith = params->ith;
const int nth = params->nth;
const enum ggml_type type = src0->type;
const bool src1_cont = ggml_is_contiguous(src1);
enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type;
ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// row groups
const int n_ids = ids->ne[0]; // n_expert_used
const int n_as = ne02; // n_expert
void * wdata_cur = params->wdata;
if (src1->type != vec_dot_type) {
incr_ptr_aligned(&wdata_cur, ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));
}
int64_t * matrix_row_counts = // [n_as]
incr_ptr_aligned(&wdata_cur, n_as*sizeof(int64_t), sizeof(int64_t));
struct mmid_row_mapping * matrix_rows = // [n_as][ids->ne[0]*ids->ne[1]]
incr_ptr_aligned(&wdata_cur, n_as*ids->ne[0]*ids->ne[1]*sizeof(struct mmid_row_mapping), sizeof(int64_t));
char (*atomic_current_chunk)[CACHE_LINE_SIZE] = // [n_as]
incr_ptr_aligned(&wdata_cur, CACHE_LINE_SIZE * n_as, CACHE_LINE_SIZE);
GGML_ASSERT(params->wsize >= (size_t)((char *) wdata_cur - (char *) params->wdata));
if (src1->type != vec_dot_type) {
char * wdata = params->wdata;
const size_t nbw0 = ggml_type_size(vec_dot_type);
const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
const size_t nbw2 = nbw1*ne11;
const size_t nbw3 = nbw2*ne12;
assert(params->wsize >= ne13*nbw3);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
#if 0
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = ith; i12 < ne12; i12 += nth) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
#else
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
size_t bs = ggml_blck_size(vec_dot_type);
int64_t ne10_block_start = (ith * ne10/bs) / nth;
int64_t ne10_block_end = ((ith + 1) * ne10/bs) / nth;
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + ne10_block_start*bs*nb10),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1 + ne10_block_start*nbw0),
(ne10_block_end - ne10_block_start) * bs);
}
}
}
#endif
}
if (ith == 0) {
// initialize matrix_row_counts
memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
// group rows by src0 matrix
for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
for (int id = 0; id < n_ids; ++id) {
const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]);
assert(i02 >= 0 && i02 < n_as);
MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1};
matrix_row_counts[i02] += 1;
}
}
}
// reset current_chunk
for (int cur_a = ith; cur_a < n_as; cur_a += nth) {
atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
*current_chunk_ctr = nth;
}
ggml_barrier(params->threadpool);
for (int cur_a = 0; cur_a < n_as; ++cur_a) {
const int64_t cne1 = matrix_row_counts[cur_a];
if (cne1 == 0) {
continue;
}
const char * src0_cur = (const char *) src0->data + cur_a * nb02;
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
const int64_t nr0 = ne01;
const int64_t nr1 = cne1;
int chunk_size = 16;
if (nr0 == 1 || nr1 == 1) {
chunk_size = 64;
}
#if defined(__aarch64__)
// disable for ARM
const bool disable_chunking = true;
#else
// disable for NUMA
const bool disable_chunking = ggml_is_numa();
#endif // defined(__aarch64__)
int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;
if (nchunk0 * nchunk1 < nth * 4 || disable_chunking) {
nchunk0 = nr0 > nr1 ? nth : 1;
nchunk1 = nr0 > nr1 ? 1 : nth;
}
const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
int current_chunk = ith;
atomic_int * current_chunk_ctr = (atomic_int *)(atomic_current_chunk + cur_a);
while (current_chunk < nchunk0 * nchunk1) {
const int64_t ith0 = current_chunk % nchunk0;
const int64_t ith1 = current_chunk / nchunk0;
const int64_t ir0_start = dr0 * ith0;
const int64_t ir0_end = MIN(ir0_start + dr0, nr0);
const int64_t ir1_start = dr1 * ith1;
const int64_t ir1_end = MIN(ir1_start + dr1, nr1);
ggml_compute_forward_mul_mat_id_one_chunk(
dst, src0, src1, ids, cur_a,
ir0_start, ir0_end, ir1_start, ir1_end,
src0_cur, matrix_rows, row_size, src1_cont, wdata
);
if (nth >= nchunk0 * nchunk1) {
break;
}
current_chunk = atomic_fetch_add_explicit(current_chunk_ctr, 1, memory_order_relaxed);
}
}
}
/////////////////////////////////
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
GGML_ASSERT(params);
if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) {
return;
}
// extra_buffer op?
if (ggml_cpu_extra_compute_forward(params, tensor)) {
return;
}
switch (tensor->op) {
case GGML_OP_DUP:
{
ggml_compute_forward_dup(params, tensor);
} break;
case GGML_OP_ADD:
{
ggml_compute_forward_add(params, tensor);
} break;
case GGML_OP_ADD1:
{
ggml_compute_forward_add1(params, tensor);
} break;
case GGML_OP_ACC:
{
ggml_compute_forward_acc(params, tensor);
} break;
case GGML_OP_SUB:
{
ggml_compute_forward_sub(params, tensor);
} break;
case GGML_OP_MUL:
{
ggml_compute_forward_mul(params, tensor);
} break;
case GGML_OP_DIV:
{
ggml_compute_forward_div(params, tensor);
} break;
case GGML_OP_SQR:
{
ggml_compute_forward_sqr(params, tensor);
} break;
case GGML_OP_SQRT:
{
ggml_compute_forward_sqrt(params, tensor);
} break;
case GGML_OP_LOG:
{
ggml_compute_forward_log(params, tensor);
} break;
case GGML_OP_SIN:
{
ggml_compute_forward_sin(params, tensor);
} break;
case GGML_OP_COS:
{
ggml_compute_forward_cos(params, tensor);
} break;
case GGML_OP_SUM:
{
ggml_compute_forward_sum(params, tensor);
} break;
case GGML_OP_SUM_ROWS:
{
ggml_compute_forward_sum_rows(params, tensor);
} break;
case GGML_OP_MEAN:
{
ggml_compute_forward_mean(params, tensor);
} break;
case GGML_OP_ARGMAX:
{
ggml_compute_forward_argmax(params, tensor);
} break;
case GGML_OP_COUNT_EQUAL:
{
ggml_compute_forward_count_equal(params, tensor);
} break;
case GGML_OP_REPEAT:
{
ggml_compute_forward_repeat(params, tensor);
} break;
case GGML_OP_REPEAT_BACK:
{
ggml_compute_forward_repeat_back(params, tensor);
} break;
case GGML_OP_CONCAT:
{
ggml_compute_forward_concat(params, tensor);
} break;
case GGML_OP_SILU_BACK:
{
ggml_compute_forward_silu_back(params, tensor);
} break;
case GGML_OP_NORM:
{
ggml_compute_forward_norm(params, tensor);
} break;
case GGML_OP_RMS_NORM:
{
ggml_compute_forward_rms_norm(params, tensor);
} break;
case GGML_OP_RMS_NORM_BACK:
{
ggml_compute_forward_rms_norm_back(params, tensor);
} break;
case GGML_OP_GROUP_NORM:
{
ggml_compute_forward_group_norm(params, tensor);
} break;
case GGML_OP_L2_NORM:
{
ggml_compute_forward_l2_norm(params, tensor);
} break;
case GGML_OP_MUL_MAT:
{
ggml_compute_forward_mul_mat(params, tensor);
} break;
case GGML_OP_MUL_MAT_ID:
{
ggml_compute_forward_mul_mat_id(params, tensor);
} break;
case GGML_OP_OUT_PROD:
{
ggml_compute_forward_out_prod(params, tensor);
} break;
case GGML_OP_SCALE:
{
ggml_compute_forward_scale(params, tensor);
} break;
case GGML_OP_SET:
{
ggml_compute_forward_set(params, tensor);
} break;
case GGML_OP_CPY:
{
ggml_compute_forward_cpy(params, tensor);
} break;
case GGML_OP_CONT:
{
ggml_compute_forward_cont(params, tensor);
} break;
case GGML_OP_RESHAPE:
{
ggml_compute_forward_reshape(params, tensor);
} break;
case GGML_OP_VIEW:
{
ggml_compute_forward_view(params, tensor);
} break;
case GGML_OP_PERMUTE:
{
ggml_compute_forward_permute(params, tensor);
} break;
case GGML_OP_TRANSPOSE:
{
ggml_compute_forward_transpose(params, tensor);
} break;
case GGML_OP_GET_ROWS:
{
ggml_compute_forward_get_rows(params, tensor);
} break;
case GGML_OP_GET_ROWS_BACK:
{
ggml_compute_forward_get_rows_back(params, tensor);
} break;
case GGML_OP_DIAG:
{
ggml_compute_forward_diag(params, tensor);
} break;
case GGML_OP_DIAG_MASK_INF:
{
ggml_compute_forward_diag_mask_inf(params, tensor);
} break;
case GGML_OP_DIAG_MASK_ZERO:
{
ggml_compute_forward_diag_mask_zero(params, tensor);
} break;
case GGML_OP_SOFT_MAX:
{
ggml_compute_forward_soft_max(params, tensor);
} break;
case GGML_OP_SOFT_MAX_BACK:
{
ggml_compute_forward_soft_max_ext_back(params, tensor);
} break;
case GGML_OP_ROPE:
{
ggml_compute_forward_rope(params, tensor);
} break;
case GGML_OP_ROPE_BACK:
{
ggml_compute_forward_rope_back(params, tensor);
} break;
case GGML_OP_CLAMP:
{
ggml_compute_forward_clamp(params, tensor);
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
ggml_compute_forward_conv_transpose_1d(params, tensor);
} break;
case GGML_OP_IM2COL:
{
ggml_compute_forward_im2col(params, tensor);
} break;
case GGML_OP_IM2COL_BACK:
{
ggml_compute_forward_im2col_back_f32(params, tensor);
} break;
case GGML_OP_CONV_2D_DW:
{
ggml_compute_forward_conv_2d_dw(params, tensor);
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
ggml_compute_forward_conv_transpose_2d(params, tensor);
} break;
case GGML_OP_POOL_1D:
{
ggml_compute_forward_pool_1d(params, tensor);
} break;
case GGML_OP_POOL_2D:
{
ggml_compute_forward_pool_2d(params, tensor);
} break;
case GGML_OP_POOL_2D_BACK:
{
ggml_compute_forward_pool_2d_back(params, tensor);
} break;
case GGML_OP_UPSCALE:
{
ggml_compute_forward_upscale(params, tensor);
} break;
case GGML_OP_PAD:
{
ggml_compute_forward_pad(params, tensor);
} break;
case GGML_OP_PAD_REFLECT_1D:
{
ggml_compute_forward_pad_reflect_1d(params, tensor);
} break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
ggml_compute_forward_timestep_embedding(params, tensor);
} break;
case GGML_OP_ARGSORT:
{
ggml_compute_forward_argsort(params, tensor);
} break;
case GGML_OP_LEAKY_RELU:
{
ggml_compute_forward_leaky_relu(params, tensor);
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
} break;
case GGML_OP_FLASH_ATTN_BACK:
{
int32_t t = ggml_get_op_params_i32(tensor, 0);
GGML_ASSERT(t == 0 || t == 1);
bool masked = t != 0;
ggml_compute_forward_flash_attn_back(params, masked, tensor);
} break;
case GGML_OP_SSM_CONV:
{
ggml_compute_forward_ssm_conv(params, tensor);
} break;
case GGML_OP_SSM_SCAN:
{
ggml_compute_forward_ssm_scan(params, tensor);
} break;
case GGML_OP_WIN_PART:
{
ggml_compute_forward_win_part(params, tensor);
} break;
case GGML_OP_WIN_UNPART:
{
ggml_compute_forward_win_unpart(params, tensor);
} break;
case GGML_OP_UNARY:
{
ggml_compute_forward_unary(params, tensor);
} break;
case GGML_OP_GET_REL_POS:
{
ggml_compute_forward_get_rel_pos(params, tensor);
} break;
case GGML_OP_ADD_REL_POS:
{
ggml_compute_forward_add_rel_pos(params, tensor);
} break;
case GGML_OP_RWKV_WKV6:
{
ggml_compute_forward_rwkv_wkv6(params, tensor);
} break;
case GGML_OP_GATED_LINEAR_ATTN:
{
ggml_compute_forward_gla(params, tensor);
} break;
case GGML_OP_RWKV_WKV7:
{
ggml_compute_forward_rwkv_wkv7(params, tensor);
} break;
case GGML_OP_MAP_CUSTOM1:
{
ggml_compute_forward_map_custom1(params, tensor);
}
break;
case GGML_OP_MAP_CUSTOM2:
{
ggml_compute_forward_map_custom2(params, tensor);
}
break;
case GGML_OP_MAP_CUSTOM3:
{
ggml_compute_forward_map_custom3(params, tensor);
}
break;
case GGML_OP_CUSTOM:
{
ggml_compute_forward_custom(params, tensor);
}
break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
ggml_compute_forward_cross_entropy_loss(params, tensor);
}
break;
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
{
ggml_compute_forward_cross_entropy_loss_back(params, tensor);
}
break;
case GGML_OP_OPT_STEP_ADAMW:
{
ggml_compute_forward_opt_step_adamw(params, tensor);
}
break;
case GGML_OP_NONE:
{
// nop
} break;
case GGML_OP_COUNT:
{
GGML_ABORT("fatal error");
}
}
}
// Android's libc implementation "bionic" does not support setting affinity
#if defined(__gnu_linux__)
static void set_numa_thread_affinity(int thread_n) {
if (!ggml_is_numa()) {
return;
}
int node_num;
int rv;
size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);
switch(g_state.numa.numa_strategy) {
case GGML_NUMA_STRATEGY_DISTRIBUTE:
// run thread on node_num thread_n / (threads per node)
node_num = thread_n % g_state.numa.n_nodes;
break;
case GGML_NUMA_STRATEGY_ISOLATE:
// run thread on current_node
node_num = g_state.numa.current_node;
break;
case GGML_NUMA_STRATEGY_NUMACTL:
// use the cpuset that numactl gave us
rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset);
if (rv) {
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv));
}
return;
default:
return;
}
struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
CPU_ZERO_S(setsize, cpus);
for (size_t i = 0; i < node->n_cpus; ++i) {
CPU_SET_S(node->cpus[i], setsize, cpus);
}
rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
if (rv) {
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
}
CPU_FREE(cpus);
}
static void clear_numa_thread_affinity(void) {
if (!ggml_is_numa()) {
return;
}
size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);
cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
CPU_ZERO_S(setsize, cpus);
for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) {
CPU_SET_S(i, setsize, cpus);
}
int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
if (rv) {
fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
}
CPU_FREE(cpus);
}
#else
// TODO: Windows etc.
// (the linux implementation may also work on BSD, someone should test)
static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); }
static void clear_numa_thread_affinity(void) {}
#endif
static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
int n_tasks = 0;
if (ggml_is_empty(node)) {
// no need to multi-thread a no-op
n_tasks = 1;
return n_tasks;
}
switch (node->op) {
case GGML_OP_CPY:
case GGML_OP_DUP:
case GGML_OP_CONT:
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_ACC:
{
n_tasks = n_threads;
} break;
case GGML_OP_SUB:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_LOG:
case GGML_OP_SIN:
case GGML_OP_COS:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_MEAN:
case GGML_OP_ARGMAX:
{
n_tasks = 1;
} break;
case GGML_OP_COUNT_EQUAL:
{
n_tasks = n_threads;
} break;
case GGML_OP_REPEAT:
case GGML_OP_REPEAT_BACK:
case GGML_OP_LEAKY_RELU:
{
n_tasks = 1;
} break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(node)) {
case GGML_UNARY_OP_ABS:
case GGML_UNARY_OP_SGN:
case GGML_UNARY_OP_NEG:
case GGML_UNARY_OP_STEP:
case GGML_UNARY_OP_TANH:
case GGML_UNARY_OP_ELU:
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_SIGMOID:
case GGML_UNARY_OP_HARDSWISH:
case GGML_UNARY_OP_HARDSIGMOID:
case GGML_UNARY_OP_EXP:
{
n_tasks = 1;
} break;
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_SILU:
{
n_tasks = n_threads;
} break;
default:
GGML_ABORT("fatal error");
}
break;
case GGML_OP_SILU_BACK:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_RMS_NORM_BACK:
case GGML_OP_L2_NORM:
case GGML_OP_GROUP_NORM:
case GGML_OP_CONCAT:
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_OUT_PROD:
{
n_tasks = n_threads;
} break;
case GGML_OP_GET_ROWS:
{
// FIXME: get_rows can use additional threads, but the cost of launching additional threads
// decreases performance with GPU offloading
//n_tasks = n_threads;
n_tasks = 1;
} break;
case GGML_OP_SCALE:
case GGML_OP_SET:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_GET_ROWS_BACK:
case GGML_OP_DIAG:
{
n_tasks = 1;
} break;
case GGML_OP_DIAG_MASK_ZERO:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX_BACK:
case GGML_OP_ROPE:
case GGML_OP_ROPE_BACK:
case GGML_OP_ADD_REL_POS:
{
n_tasks = n_threads;
} break;
case GGML_OP_CLAMP:
{
n_tasks = 1; //TODO
} break;
case GGML_OP_SOFT_MAX:
{
n_tasks = MIN(n_threads, ggml_nrows(node->src[0]));
} break;
case GGML_OP_IM2COL:
case GGML_OP_IM2COL_BACK:
case GGML_OP_CONV_2D_DW:
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_CONV_TRANSPOSE_2D:
{
n_tasks = n_threads;
} break;
case GGML_OP_POOL_1D:
case GGML_OP_POOL_2D:
case GGML_OP_POOL_2D_BACK:
{
n_tasks = 1;
} break;
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
case GGML_OP_FLASH_ATTN_EXT:
case GGML_OP_FLASH_ATTN_BACK:
case GGML_OP_SSM_CONV:
case GGML_OP_SSM_SCAN:
case GGML_OP_RWKV_WKV6:
case GGML_OP_GATED_LINEAR_ATTN:
case GGML_OP_RWKV_WKV7:
{
n_tasks = n_threads;
} break;
case GGML_OP_WIN_PART:
case GGML_OP_WIN_UNPART:
case GGML_OP_GET_REL_POS:
{
n_tasks = 1;
} break;
case GGML_OP_MAP_CUSTOM1:
{
struct ggml_map_custom1_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_MAP_CUSTOM2:
{
struct ggml_map_custom2_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_MAP_CUSTOM3:
{
struct ggml_map_custom3_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_CUSTOM:
{
struct ggml_custom_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
case GGML_OP_OPT_STEP_ADAMW:
{
n_tasks = n_threads;
} break;
case GGML_OP_NONE:
{
n_tasks = 1;
} break;
case GGML_OP_COUNT:
{
GGML_ABORT("fatal error");
}
default:
{
fprintf(stderr, "%s: op not implemented: ", __func__);
if (node->op < GGML_OP_COUNT) {
fprintf(stderr, "%s\n", ggml_op_name(node->op));
} else {
fprintf(stderr, "%d\n", node->op);
}
GGML_ABORT("fatal error");
}
}
assert(n_tasks > 0);
return n_tasks;
}
static thread_ret_t ggml_graph_compute_secondary_thread(void* data);
#if defined(_WIN32)
#include "windows.h"
// TODO: support > 64 CPUs
static bool ggml_thread_apply_affinity(bool * mask) {
HANDLE h = GetCurrentThread();
uint64_t bitmask = 0ULL;
assert(GGML_MAX_N_THREADS >= 64);
for (int32_t i = 0; i < 8; i++) {
int32_t idx = i * 8;
uint8_t val = 0;
val |= mask[idx + 0] << 0;
val |= mask[idx + 1] << 1;
val |= mask[idx + 2] << 2;
val |= mask[idx + 3] << 3;
val |= mask[idx + 4] << 4;
val |= mask[idx + 5] << 5;
val |= mask[idx + 6] << 6;
val |= mask[idx + 7] << 7;
bitmask |= (uint64_t)val << idx;
}
for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) {
if (mask[i]) {
fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n");
break;
}
}
DWORD_PTR m = (DWORD_PTR)bitmask;
m = SetThreadAffinityMask(h, m);
return m != 0;
}
static bool ggml_thread_apply_priority(int32_t prio) {
// Note that on Windows the Process Priority Class must be updated in order to set Thread priority.
// This is up to the applications.
DWORD p = THREAD_PRIORITY_NORMAL;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break;
case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break;
case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break;
case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break;
}
if (prio == GGML_SCHED_PRIO_NORMAL) {
// Keep inherited policy/priority
return true;
}
if (!SetThreadPriority(GetCurrentThread(), p)) {
fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError());
return false;
}
return true;
}
#elif defined(__APPLE__)
#include <sys/types.h>
#include <sys/resource.h>
static bool ggml_thread_apply_affinity(const bool * mask) {
// Not supported on Apple platforms
UNUSED(mask);
return true;
}
static bool ggml_thread_apply_priority(int32_t prio) {
struct sched_param p;
int32_t policy = SCHED_OTHER;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break;
case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break;
case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break;
case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break;
}
if (prio == GGML_SCHED_PRIO_NORMAL) {
// Keep inherited policy/priority
return true;
}
int32_t err = pthread_setschedparam(pthread_self(), policy, &p);
if (err != 0) {
fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err);
return false;
}
return true;
}
#elif defined(__gnu_linux__)
// TODO: this may not work on BSD, to be verified
static bool ggml_thread_apply_affinity(const bool * mask) {
cpu_set_t cpuset;
int err;
CPU_ZERO(&cpuset);
for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
if (mask[i]) {
GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i);
CPU_SET(i, &cpuset);
}
}
#ifdef __ANDROID__
err = sched_setaffinity(0, sizeof(cpuset), &cpuset);
if (err < 0) {
err = errno;
}
#else
err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset);
#endif
if (err != 0) {
fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err);
return false;
}
return true;
}
static bool ggml_thread_apply_priority(int32_t prio) {
struct sched_param p;
int32_t policy = SCHED_OTHER;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break;
case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break;
case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break;
case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break;
}
if (prio == GGML_SCHED_PRIO_NORMAL) {
// Keep inherited policy/priority
return true;
}
int32_t err = pthread_setschedparam(pthread_self(), policy, &p);
if (err != 0) {
fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err);
return false;
}
return true;
}
#else // unsupported platforms
static bool ggml_thread_apply_affinity(const bool * mask) {
UNUSED(mask);
return true;
}
static bool ggml_thread_apply_priority(int32_t prio) {
UNUSED(prio);
return true;
}
#endif
static bool ggml_thread_cpumask_is_valid(const bool * mask) {
for (int i = 0; i < GGML_MAX_N_THREADS; i++) {
if (mask[i]) { return true; }
}
return false;
}
static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) {
if (!strict) {
memcpy(local_mask, global_mask, GGML_MAX_N_THREADS);
return;
} else {
memset(local_mask, 0, GGML_MAX_N_THREADS);
int32_t base_idx = *iter;
for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
int32_t idx = base_idx + i;
if (idx >= GGML_MAX_N_THREADS) {
// Just a cheaper modulo
idx -= GGML_MAX_N_THREADS;
}
if (global_mask[idx]) {
local_mask[idx] = 1;
*iter = idx + 1;
return;
}
}
}
}
void ggml_threadpool_free(struct ggml_threadpool* threadpool) {
if (!threadpool) return;
const int n_threads = threadpool->n_threads_max;
#ifndef GGML_USE_OPENMP
struct ggml_compute_state* workers = threadpool->workers;
ggml_mutex_lock(&threadpool->mutex);
threadpool->stop = true;
threadpool->pause = false;
ggml_cond_broadcast(&threadpool->cond);
ggml_mutex_unlock(&threadpool->mutex);
for (int j = 1; j < n_threads; j++) {
int32_t rc = ggml_thread_join(workers[j].thrd, NULL);
GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED);
UNUSED(rc);
}
ggml_mutex_destroy(&threadpool->mutex);
ggml_cond_destroy(&threadpool->cond);
#endif // GGML_USE_OPENMP
const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads;
ggml_aligned_free(threadpool->workers, workers_size);
ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool));
}
#ifndef GGML_USE_OPENMP
// pause/resume must be called under mutex
static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) {
GGML_PRINT_DEBUG("Pausing threadpool\n");
threadpool->pause = true;
ggml_cond_broadcast(&threadpool->cond);
}
static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) {
GGML_PRINT_DEBUG("Resuming threadpool\n");
threadpool->pause = false;
ggml_cond_broadcast(&threadpool->cond);
}
#endif
void ggml_threadpool_pause(struct ggml_threadpool * threadpool) {
#ifndef GGML_USE_OPENMP
ggml_mutex_lock(&threadpool->mutex);
if (!threadpool->pause) {
ggml_threadpool_pause_locked(threadpool);
}
ggml_mutex_unlock(&threadpool->mutex);
#else
UNUSED(threadpool);
#endif
}
void ggml_threadpool_resume(struct ggml_threadpool * threadpool) {
#ifndef GGML_USE_OPENMP
ggml_mutex_lock(&threadpool->mutex);
if (threadpool->pause) {
ggml_threadpool_resume_locked(threadpool);
}
ggml_mutex_unlock(&threadpool->mutex);
#else
UNUSED(threadpool);
#endif
}
struct ggml_cplan ggml_graph_plan(
const struct ggml_cgraph * cgraph,
int n_threads,
struct ggml_threadpool * threadpool) {
if (threadpool == NULL) {
//GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads);
}
if (n_threads <= 0) {
n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS;
}
size_t work_size = 0;
struct ggml_cplan cplan;
memset(&cplan, 0, sizeof(struct ggml_cplan));
int max_tasks = 1;
// thread scheduling for the different operations + work buffer size estimation
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
const int n_tasks = ggml_get_n_tasks(node, n_threads);
max_tasks = MAX(max_tasks, n_tasks);
size_t cur = 0;
if (!ggml_cpu_extra_work_size(n_threads, node, &cur)) {
switch (node->op) {
case GGML_OP_CPY:
case GGML_OP_DUP:
{
if (ggml_is_quantized(node->type) ||
// F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
(node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
(node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
}
} break;
case GGML_OP_ADD:
case GGML_OP_ADD1:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_ACC:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
}
} break;
case GGML_OP_COUNT_EQUAL:
{
cur = ggml_type_size(node->type)*n_tasks;
} break;
case GGML_OP_MUL_MAT:
{
const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type;
if (node->src[1]->type != vec_dot_type) {
cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1]));
}
} break;
case GGML_OP_MUL_MAT_ID:
{
cur = 0;
const struct ggml_tensor * src0 = node->src[0];
const struct ggml_tensor * src1 = node->src[1];
const struct ggml_tensor * ids = node->src[2];
const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type;
const int n_as = src0->ne[2];
// src1
if (src1->type != vec_dot_type) {
cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)) + sizeof(int64_t);
}
// matrix_row_counts
cur += n_as * sizeof(int64_t) + sizeof(int64_t);
// matrix_rows
cur += n_as*ids->ne[0]*ids->ne[1]*sizeof(struct mmid_row_mapping) + sizeof(int64_t);
// atomic_current_chunk
cur += CACHE_LINE_SIZE*n_as + CACHE_LINE_SIZE;
} break;
case GGML_OP_OUT_PROD:
{
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_SOFT_MAX:
case GGML_OP_ROPE:
case GGML_OP_ROPE_BACK:
{
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
GGML_ASSERT(node->src[0]->ne[3] == 1);
GGML_ASSERT(node->src[1]->ne[2] == 1);
GGML_ASSERT(node->src[1]->ne[3] == 1);
const int64_t ne00 = node->src[0]->ne[0]; // K
const int64_t ne01 = node->src[0]->ne[1]; // Cout
const int64_t ne02 = node->src[0]->ne[2]; // Cin
const int64_t ne10 = node->src[1]->ne[0]; // L
const int64_t ne11 = node->src[1]->ne[1]; // Cin
if ((node->src[0]->type == GGML_TYPE_F16 ||
node->src[0]->type == GGML_TYPE_BF16) &&
node->src[1]->type == GGML_TYPE_F32) {
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02;
cur += sizeof(ggml_fp16_t)*ne10*ne11;
} else if (node->src[0]->type == GGML_TYPE_F32 &&
node->src[1]->type == GGML_TYPE_F32) {
cur += sizeof(float)*ne00*ne01*ne02;
cur += sizeof(float)*ne10*ne11;
} else {
GGML_ABORT("fatal error");
}
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
const int64_t ne00 = node->src[0]->ne[0]; // W
const int64_t ne01 = node->src[0]->ne[1]; // H
const int64_t ne02 = node->src[0]->ne[2]; // Channels Out
const int64_t ne03 = node->src[0]->ne[3]; // Channels In
const int64_t ne10 = node->src[1]->ne[0]; // W
const int64_t ne11 = node->src[1]->ne[1]; // H
const int64_t ne12 = node->src[1]->ne[2]; // Channels In
cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
const int64_t ne10 = node->src[1]->ne[0]; // DK
const int64_t ne20 = node->src[2]->ne[0]; // DV
cur = sizeof(float)*(1*ne10 + 2*ne20)*n_tasks; // 1x head size K + 2x head size V (per thread)
} break;
case GGML_OP_FLASH_ATTN_BACK:
{
const int64_t D = node->src[0]->ne[0];
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
if (node->src[1]->type == GGML_TYPE_F32) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
} else if (node->src[1]->type == GGML_TYPE_F16) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
} else if (node->src[1]->type == GGML_TYPE_BF16) {
cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
} break;
case GGML_OP_COUNT:
{
GGML_ABORT("fatal error");
}
default:
break;
}
}
work_size = MAX(work_size, cur);
}
if (work_size > 0) {
work_size += CACHE_LINE_SIZE*(n_threads);
}
cplan.threadpool = threadpool;
cplan.n_threads = MIN(max_tasks, n_threads);
cplan.work_size = work_size;
cplan.work_data = NULL;
return cplan;
}
static thread_ret_t ggml_graph_compute_thread(void * data) {
struct ggml_compute_state * state = (struct ggml_compute_state *) data;
struct ggml_threadpool * tp = state->threadpool;
const struct ggml_cgraph * cgraph = tp->cgraph;
const struct ggml_cplan * cplan = tp->cplan;
set_numa_thread_affinity(state->ith);
struct ggml_compute_params params = {
/*.ith =*/ state->ith,
/*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed),
/*.wsize =*/ cplan->work_size,
/*.wdata =*/ cplan->work_data,
/*.threadpool=*/ tp,
};
for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
struct ggml_tensor * node = cgraph->nodes[node_n];
ggml_compute_forward(&params, node);
if (state->ith == 0 && cplan->abort_callback &&
cplan->abort_callback(cplan->abort_callback_data)) {
atomic_store_explicit(&tp->abort, node_n + 1, memory_order_relaxed);
tp->ec = GGML_STATUS_ABORTED;
}
if (node_n + 1 < cgraph->n_nodes) {
ggml_barrier(state->threadpool);
}
}
ggml_barrier(state->threadpool);
return 0;
}
#ifndef GGML_USE_OPENMP
// check if thread is active
static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) {
struct ggml_threadpool * threadpool = state->threadpool;
int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed);
return (state->ith < n_threads);
}
// check if thread is ready to proceed (exit from polling or sleeping)
static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) {
struct ggml_threadpool * threadpool = state->threadpool;
if (state->pending || threadpool->stop || threadpool->pause) { return true; }
// check for new graph/work
int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed);
if (new_graph != state->last_graph) {
state->pending = ggml_graph_compute_thread_active(state);
state->last_graph = new_graph;
}
return state->pending;
}
// sync thread state after polling
static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) {
// TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead
#ifdef GGML_TSAN_ENABLED
atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst);
#else
atomic_thread_fence(memory_order_seq_cst);
#endif
UNUSED(state);
}
static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) {
struct ggml_threadpool * threadpool = state->threadpool;
// Skip polling for unused threads
if (!ggml_graph_compute_thread_active(state)) {
return state->pending;
}
// This seems to make 0 ... 100 a decent range for polling level across modern processors.
// Perhaps, we can adjust it dynamically based on load and things.
const uint64_t n_rounds = 1024UL * 128 * threadpool->poll;
for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) {
// No new work. Keep polling.
ggml_thread_cpu_relax();
}
return state->pending;
}
static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) {
struct ggml_threadpool * threadpool = state->threadpool;
if (ggml_graph_compute_poll_for_work(state)) {
ggml_graph_compute_thread_sync(state);
return state->pending;
}
ggml_mutex_lock_shared(&threadpool->mutex);
while (!ggml_graph_compute_thread_ready(state)) {
// No new work. Wait for the signal.
GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith);
ggml_cond_wait(&threadpool->cond, &threadpool->mutex);
}
ggml_mutex_unlock_shared(&threadpool->mutex);
return state->pending;
}
static thread_ret_t ggml_graph_compute_secondary_thread(void* data) {
struct ggml_compute_state * state = (struct ggml_compute_state *) data;
struct ggml_threadpool * threadpool = state->threadpool;
ggml_thread_apply_priority(threadpool->prio);
if (ggml_thread_cpumask_is_valid(state->cpumask)) {
ggml_thread_apply_affinity(state->cpumask);
}
while (true) {
// Check if we need to sleep
while (threadpool->pause) {
GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith);
ggml_mutex_lock_shared(&threadpool->mutex);
if (threadpool->pause) {
ggml_cond_wait(&threadpool->cond, &threadpool->mutex);
}
GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith);
ggml_mutex_unlock_shared(&threadpool->mutex);
}
// This needs to be checked for after the cond_wait
if (threadpool->stop) break;
// Check if there is new work
// The main thread is the only one that can dispatch new work
ggml_graph_compute_check_for_work(state);
if (state->pending) {
state->pending = false;
ggml_graph_compute_thread(state);
}
}
return (thread_ret_t) 0;
}
// Start processing new graph
static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads)
{
// Always take the mutex here because the worker threads are doing hybrid poll/wait
ggml_mutex_lock(&threadpool->mutex);
GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads);
// Update the number of active threads
atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
// Indicate the graph is ready to be processed
// We need the full seq-cst fence here because of the polling threads (used in thread_sync)
atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst);
if (threadpool->pause) {
// Update main thread prio and affinity to match the threadpool settings
ggml_thread_apply_priority(threadpool->prio);
if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) {
ggml_thread_apply_affinity(threadpool->workers[0].cpumask);
}
// resume does cond broadcast
ggml_threadpool_resume_locked(threadpool);
} else {
ggml_cond_broadcast(&threadpool->cond);
}
ggml_mutex_unlock(&threadpool->mutex);
}
#endif // GGML_USE_OPENMP
static struct ggml_threadpool * ggml_threadpool_new_impl(
struct ggml_threadpool_params * tpp,
struct ggml_cgraph * cgraph,
struct ggml_cplan * cplan) {
struct ggml_threadpool * threadpool =
ggml_aligned_malloc(sizeof(struct ggml_threadpool));
{
threadpool->cgraph = cgraph;
threadpool->cplan = cplan;
threadpool->n_graph = 0;
threadpool->n_barrier = 0;
threadpool->n_barrier_passed = 0;
threadpool->current_chunk = 0;
threadpool->stop = false;
threadpool->pause = tpp->paused;
threadpool->abort = -1;
threadpool->workers = NULL;
threadpool->n_threads_max = tpp->n_threads;
threadpool->n_threads_cur = tpp->n_threads;
threadpool->poll = tpp->poll;
threadpool->prio = tpp->prio;
threadpool->ec = GGML_STATUS_SUCCESS;
}
// Allocate and init workers state
const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads;
struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size);
memset(workers, 0, workers_size);
for (int j = 0; j < tpp->n_threads; j++) {
workers[j].threadpool = threadpool;
workers[j].ith = j;
}
threadpool->workers = workers;
#ifndef GGML_USE_OPENMP
ggml_mutex_init(&threadpool->mutex);
ggml_cond_init(&threadpool->cond);
// Spin the threads for all workers, and update CPU placements.
// Place the main thread last (towards the higher numbered CPU cores).
int32_t cpumask_iter = 0;
for (int j = 1; j < tpp->n_threads; j++) {
ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter);
int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]);
GGML_ASSERT(rc == 0);
}
ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter);
if (!threadpool->pause) {
// Update main thread prio and affinity at the start, otherwise we'll do it in resume
ggml_thread_apply_priority(threadpool->prio);
if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) {
ggml_thread_apply_affinity(threadpool->workers[0].cpumask);
}
}
#endif // GGML_USE_OPENMP
return threadpool;
}
struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) {
return ggml_threadpool_new_impl(tpp, NULL, NULL);
}
enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
ggml_cpu_init();
GGML_ASSERT(cplan);
GGML_ASSERT(cplan->n_threads > 0);
GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL);
int n_threads = cplan->n_threads;
struct ggml_threadpool * threadpool = cplan->threadpool;
bool disposable_threadpool = false;
if (threadpool == NULL) {
//GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads);
disposable_threadpool = true;
struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads);
threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan);
} else {
// Reset some of the parameters that need resetting
// No worker threads should be accessing the parameters below at this stage
threadpool->cgraph = cgraph;
threadpool->cplan = cplan;
threadpool->current_chunk = 0;
threadpool->abort = -1;
threadpool->ec = GGML_STATUS_SUCCESS;
}
#ifdef GGML_USE_OPENMP
if (n_threads > 1) {
#pragma omp parallel num_threads(n_threads)
{
#pragma omp single
{
// update the number of threads from the actual number of threads that we got from OpenMP
n_threads = omp_get_num_threads();
atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
}
ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]);
}
} else {
atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed);
ggml_graph_compute_thread(&threadpool->workers[0]);
}
#else
if (n_threads > threadpool->n_threads_max) {
GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max);
n_threads = threadpool->n_threads_max;
}
// Kick all threads to start the new graph
ggml_graph_compute_kickoff(threadpool, n_threads);
// This is a work thread too
ggml_graph_compute_thread(&threadpool->workers[0]);
#endif
// don't leave affinity set on the main thread
clear_numa_thread_affinity();
enum ggml_status ret = threadpool->ec;
if (disposable_threadpool) {
ggml_threadpool_free(threadpool);
}
return ret;
}
enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL);
cplan.work_data = (uint8_t *)ggml_new_buffer(ctx, cplan.work_size);
return ggml_graph_compute(cgraph, &cplan);
}
int ggml_cpu_has_avx(void) {
#if defined(__AVX__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_avx_vnni(void) {
#if defined(__AVXVNNI__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_avx2(void) {
#if defined(__AVX2__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_avx512(void) {
#if defined(__AVX512F__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_avx512_vbmi(void) {
#if defined(__AVX512VBMI__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_avx512_vnni(void) {
#if defined(__AVX512VNNI__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_avx512_bf16(void) {
#if defined(__AVX512BF16__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_amx_int8(void) {
#if defined(__AMX_INT8__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_bmi2(void) {
#if defined(__BMI2__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_fma(void) {
#if defined(__FMA__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_arm_fma(void) {
#if defined(__ARM_FEATURE_FMA)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_riscv_v(void) {
#if defined(__riscv_v_intrinsic)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_f16c(void) {
#if defined(__F16C__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_fp16_va(void) {
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_wasm_simd(void) {
#if defined(__wasm_simd128__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_llamafile(void) {
#if defined(GGML_USE_LLAMAFILE)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_sse3(void) {
#if defined(__SSE3__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_ssse3(void) {
#if defined(__SSSE3__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_vsx(void) {
#if defined(__POWER9_VECTOR__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_vxe(void) {
#if defined(__VXE__) || defined(__VXE2__)
return 1;
#else
return 0;
#endif
}
int ggml_cpu_has_neon(void) {
#if defined(__ARM_ARCH) && defined(__ARM_NEON)
return ggml_arm_arch_features.has_neon;
#else
return 0;
#endif
}
int ggml_cpu_has_dotprod(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_DOTPROD)
return ggml_arm_arch_features.has_dotprod;
#else
return 0;
#endif
}
int ggml_cpu_has_sve(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE)
return ggml_arm_arch_features.has_sve;
#else
return 0;
#endif
}
int ggml_cpu_has_matmul_int8(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_MATMUL_INT8)
return ggml_arm_arch_features.has_i8mm;
#else
return 0;
#endif
}
int ggml_cpu_get_sve_cnt(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE)
return ggml_arm_arch_features.sve_cnt;
#else
return 0;
#endif
}
int ggml_cpu_has_sme(void) {
#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SME)
return ggml_arm_arch_features.has_sme;
#else
return 0;
#endif
}
void ggml_cpu_init(void) {
// needed to initialize f16 tables
{
struct ggml_init_params params = { 0, NULL, false };
struct ggml_context * ctx = ggml_init(params);
ggml_free(ctx);
}
ggml_critical_section_start();
static bool is_first_call = true;
if (is_first_call) {
// initialize GELU, Quick GELU, SILU and EXP F32 tables
{
const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
for (int i = 0; i < (1 << 16); ++i) {
union {
uint16_t u16;
ggml_fp16_t fp16;
} u = {i};
float f = GGML_FP16_TO_FP32(u.fp16);
ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f));
}
const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
}
#if defined(__ARM_ARCH)
ggml_init_arm_arch_features();
#endif
is_first_call = false;
}
ggml_critical_section_end();
}
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