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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	.github/workflows/close-issue.yml
#	.github/workflows/nix-ci-aarch64.yml
#	.github/workflows/nix-ci.yml
#	README.md
#	ci/run.sh
#	examples/server/README.md
#	ggml/src/ggml-cuda.cu
#	ggml/src/ggml-metal.m
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo 2024-10-05 22:24:08 +08:00
commit da6cf261a8
28 changed files with 725 additions and 421 deletions
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2
common/arg.cpp
View file

@ -912,7 +912,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
).set_sparam());
add_opt(llama_arg(
{"-s", "--seed"}, "SEED",
format("RNG seed (default: %u, use random seed for %u)", params.sparams.seed, LLAMA_DEFAULT_SEED),
format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED),
[](gpt_params & params, const std::string & value) {
params.sparams.seed = std::stoul(value);
}

30
common/common.cpp
View file

@ -840,6 +840,31 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
return iparams;
}
if (params.reranking) {
bool ok = true;
if (llama_token_bos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have a BOS token, reranking will not work\n", __func__);
ok = false;
}
if (llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__);
ok = false;
}
if (llama_token_sep(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have a SEP token, reranking will not work\n", __func__);
ok = false;
}
if (!ok) {
llama_free_model(model);
return iparams;
}
}
auto cparams = llama_context_params_from_gpt_params(params);
llama_context * lctx = llama_new_context_with_model(model, cparams);
@ -857,6 +882,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
@ -869,6 +895,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
if (err) {
llama_free(lctx);
llama_free_model(model);
return iparams;
}
}
@ -891,7 +918,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.sparams.ignore_eos && llama_token_eos(model) == -1) {
if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sparams.ignore_eos = false;
}
@ -932,6 +959,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
iparams.model = model;
iparams.context = lctx;
return iparams;
}

12
examples/rpc/rpc-server.cpp
View file

@ -6,6 +6,10 @@
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#include "ggml-rpc.h"
#ifdef _WIN32
# include <windows.h>
@ -79,6 +83,12 @@ static ggml_backend_t create_backend() {
if (!backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
#elif GGML_USE_VULKAN
fprintf(stderr, "%s: using Vulkan backend\n", __func__);
backend = ggml_backend_vk_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__);
}
#endif
// if there aren't GPU Backends fallback to CPU backend
@ -92,6 +102,8 @@ static ggml_backend_t create_backend() {
static void get_backend_memory(size_t * free_mem, size_t * total_mem) {
#ifdef GGML_USE_CUDA
ggml_backend_cuda_get_device_memory(0, free_mem, total_mem);
#elif GGML_USE_VULKAN
ggml_backend_vk_get_device_memory(0, free_mem, total_mem);
#else
#ifdef _WIN32
MEMORYSTATUSEX status;

4
examples/server/server.cpp
View file

@ -2028,7 +2028,7 @@ struct server_context {
continue;
}
// prompt: <s>query</s><s>doc</s>
// prompt: [BOS]query[EOS][SEP]doc[EOS]
prompt_tokens.clear();
prompt_tokens.push_back(llama_token_bos(model));
{
@ -2036,7 +2036,7 @@ struct server_context {
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
}
prompt_tokens.push_back(llama_token_eos(model));
prompt_tokens.push_back(llama_token_bos(model));
prompt_tokens.push_back(llama_token_sep(model));
{
const auto part = tokenize(slot.prompt[1], false);
prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());

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

@ -24,7 +24,7 @@ GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, st
// Graph allocator
/*
Example usage:
ggml_gallocr_t galloc = ggml_gallocr_new(ggml_bacckend_cpu_buffer_type());
ggml_gallocr_t galloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
// optional: create a worst-case graph and reserve the buffers to avoid reallocations
ggml_gallocr_reserve(galloc, build_graph(max_batch));

9
ggml/include/ggml-backend.h
View file

@ -164,7 +164,7 @@ extern "C" {
GGML_API size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg);
GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index);
GGML_API void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name);
GGML_API void ggml_backend_reg_set_log_callback(ggml_backend_reg_t reg, ggml_log_callback log_callback, void * user_data);
// Functions that may be obtained using ggml_backend_reg_get_proc_address
typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(const float *);
@ -184,9 +184,6 @@ extern "C" {
GGML_API ggml_backend_dev_t ggml_backend_dev_by_name(const char * name);
GGML_API ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type);
// Set the log callback for all registered backends
GGML_API void ggml_backend_set_log_callback(ggml_log_callback log_callback, void * user_data);
// Direct backend (stream) initialization
// = ggml_backend_dev_init(ggml_backend_dev_by_name(name), params)
GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params);
@ -250,7 +247,7 @@ extern "C" {
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
GGML_API ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
@ -265,7 +262,7 @@ extern "C" {
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
// Allocate and compute graph on the backend scheduler
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);

11
ggml/include/ggml-cann.h
View file

@ -116,17 +116,6 @@ GGML_API void ggml_backend_cann_get_device_memory(int32_t device,
size_t* free,
size_t* total);
/**
* @brief Set the logging callback for GGML.
*
* This function sets the logging callback and user data for logging.
*
* @param log_callback The logging callback to set.
* @param user_data User data to pass to the logging callback.
*/
GGML_API void ggml_backend_cann_log_set_callback(ggml_log_callback log_callback,
void* user_data);
#ifdef __cplusplus
}
#endif

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

@ -41,8 +41,6 @@ GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, siz
GGML_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
GGML_API void ggml_backend_cuda_unregister_host_buffer(void * buffer);
GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data);
GGML_API ggml_backend_reg_t ggml_backend_cuda_reg(void);
#ifdef __cplusplus

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

@ -39,8 +39,6 @@ extern "C" {
// user-code should use only these functions
//
GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
GGML_API ggml_backend_t ggml_backend_metal_init(void);
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);

11
ggml/include/ggml.h
View file

@ -462,6 +462,7 @@ extern "C" {
GGML_OP_SUM_ROWS,
GGML_OP_MEAN,
GGML_OP_ARGMAX,
GGML_OP_COUNT_EQUAL,
GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK,
GGML_OP_CONCAT,
@ -1000,6 +1001,12 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// count number of equal elements in a and b
GGML_API struct ggml_tensor * ggml_count_equal(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// if a is the same shape as b, and a is not parameter, return a
// otherwise, return a new tensor: repeat(a) to fit in b
GGML_API struct ggml_tensor * ggml_repeat(
@ -2173,6 +2180,10 @@ extern "C" {
typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched, bool * cancel);
typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data);
// optimization parameters
//
// see ggml.c (ggml_opt_default_params) for default values

3
ggml/src/ggml-backend-impl.h
View file

@ -215,9 +215,6 @@ extern "C" {
// (optional) get a pointer to a function in the backend
// backends can add custom functions that are not part of the standard ggml-backend interface
void * (*get_proc_address)(ggml_backend_reg_t reg, const char * name);
// (optional) set the log callback for the backend
void (*set_log_callback)(ggml_backend_reg_t reg, ggml_log_callback log_callback, void * user_data);
};
struct ggml_backend_reg {

102
ggml/src/ggml-backend.cpp
View file

@ -1,5 +1,13 @@
// Note: porting this file to C++ is a work in progress
#ifdef _WIN32
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <windows.h>
#endif
#include "ggml-backend-impl.h"
#include "ggml-alloc.h"
#include "ggml-impl.h"
@ -10,9 +18,15 @@
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <string>
#include <vector>
#ifdef __APPLE__
#include <sys/types.h>
#include <sys/sysctl.h>
#endif
// backend buffer type
const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
@ -505,12 +519,6 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na
return reg->iface.get_proc_address(reg, name);
}
void ggml_backend_reg_set_log_callback(ggml_backend_reg_t reg, ggml_log_callback log_callback, void * user_data) {
if (reg->iface.set_log_callback) {
reg->iface.set_log_callback(reg, log_callback, user_data);
}
}
// Backend registry
#ifdef GGML_USE_CUDA
@ -614,13 +622,6 @@ ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) {
return NULL;
}
void ggml_backend_set_log_callback(ggml_log_callback log_callback, void * user_data) {
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
ggml_backend_reg_t reg = ggml_backend_reg_get(i);
ggml_backend_reg_set_log_callback(reg, log_callback, user_data);
}
}
// Convenience functions
ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) {
ggml_backend_dev_t dev = ggml_backend_dev_by_name(name);
@ -1021,6 +1022,70 @@ ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size)
////////////////////////
struct ggml_backend_cpu_device_context {
std::string description = "CPU";
ggml_backend_cpu_device_context() {
#ifdef __APPLE__
size_t len = 0;
if (!sysctlbyname("machdep.cpu.brand_string", NULL, &len, NULL, 0)) {
description.resize(len);
sysctlbyname("machdep.cpu.brand_string", &description[0], &len, NULL, 0); // NOLINT
}
#elif defined(__linux__)
FILE * f = fopen("/proc/cpuinfo", "r");
if (f) {
char buf[1024];
while (fgets(buf, sizeof(buf), f)) {
if (strncmp(buf, "model name", 10) == 0) {
char * p = strchr(buf, ':');
if (p) {
p++;
while (std::isspace(*p)) {
p++;
}
while (std::isspace(p[strlen(p) - 1])) {
p[strlen(p) - 1] = '\0';
}
description = p;
break;
}
}
}
fclose(f);
}
#elif defined(_WIN32)
HKEY hKey;
if (RegOpenKeyEx(HKEY_LOCAL_MACHINE,
TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"),
0,
KEY_READ,
&hKey) == ERROR_SUCCESS) {
DWORD cpu_brand_size = 0;
if (RegQueryValueExA(hKey,
TEXT("ProcessorNameString"),
NULL,
NULL,
NULL,
&cpu_brand_size) == ERROR_SUCCESS) {
description.resize(cpu_brand_size);
if (RegQueryValueExA(hKey,
TEXT("ProcessorNameString"),
NULL,
NULL,
(LPBYTE)&description[0], // NOLINT
&cpu_brand_size) == ERROR_SUCCESS) {
if (description.find('\0') != std::string::npos) {
description.resize(description.find('\0'));
}
}
}
RegCloseKey(hKey);
}
#endif
}
};
static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) {
return "CPU";
@ -1028,10 +1093,9 @@ static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) {
}
static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t dev) {
// TODO
return "CPU";
struct ggml_backend_cpu_device_context * ctx = (struct ggml_backend_cpu_device_context *)dev->context;
GGML_UNUSED(dev);
return ctx->description.c_str();
}
static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
@ -1144,10 +1208,11 @@ static size_t ggml_backend_cpu_reg_get_device_count(ggml_backend_reg_t reg) {
static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_cpu_device_context ctx;
static ggml_backend_device ggml_backend_cpu_device = {
/* .iface = */ ggml_backend_cpu_device_i,
/* .reg = */ reg,
/* .context = */ NULL,
/* .context = */ &ctx,
};
return &ggml_backend_cpu_device;
@ -1161,7 +1226,6 @@ static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = {
/* .get_device_count = */ ggml_backend_cpu_reg_get_device_count,
/* .get_device = */ ggml_backend_cpu_reg_get_device,
/* .get_proc_address = */ NULL,
/* .set_log_callback = */ NULL,
};
ggml_backend_reg_t ggml_backend_cpu_reg(void) {

89
ggml/src/ggml-cann.cpp
View file

@ -39,69 +39,6 @@
#include "ggml-common.h"
/**
* @brief Default logging callback for GGML.
*
* This function is the default logging callback that logs messages to stderr.
*
* @param level The log level.
* @param msg The log message.
* @param user_data User data passed to the callback.
*/
static void ggml_cann_default_log_callback(enum ggml_log_level level,
const char* msg, void* user_data) {
GGML_UNUSED(level);
GGML_UNUSED(user_data);
fprintf(stderr, "%s", msg);
}
ggml_log_callback ggml_cann_log_callback = ggml_cann_default_log_callback;
void* ggml_cann_log_user_data = NULL;
GGML_API void ggml_backend_cann_log_set_callback(ggml_log_callback log_callback,
void* user_data) {
ggml_cann_log_callback = log_callback;
ggml_cann_log_user_data = user_data;
}
#define GGML_CANN_LOG_INFO(...) ggml_cann_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
#define GGML_CANN_LOG_WARN(...) ggml_cann_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
#define GGML_CANN_LOG_ERROR(...) \
ggml_cann_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
GGML_ATTRIBUTE_FORMAT(2, 3)
/**
* @brief Log a message using the current logging callback.
*
* This function formats a log message and passes it to the current logging
* callback.
*
* @param level The log level.
* @param format The format string for the log message.
* @param ... The arguments for the format string.
*/
static void ggml_cann_log(enum ggml_log_level level, const char* format, ...) {
if (ggml_cann_log_callback != NULL) {
va_list args;
va_start(args, format);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
ggml_cann_log_callback(level, buffer, ggml_cann_log_user_data);
} else {
// vsnprintf adds a null terminator
std::vector<char> buffer2(len + 1);
va_end(args);
va_start(args, format);
vsnprintf(&buffer2[0], buffer2.size(), format, args);
ggml_cann_log_callback(level, buffer2.data(),
ggml_cann_log_user_data);
}
va_end(args);
}
}
/**
* @brief Handles CANN errors by printing an error message and aborting.
*
@ -116,10 +53,10 @@ static void ggml_cann_log(enum ggml_log_level level, const char* format, ...) {
int32_t id = -1;
aclrtGetDevice(&id);
GGML_CANN_LOG_ERROR("CANN error: %s\n", msg);
GGML_CANN_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func,
GGML_LOG_ERROR("CANN error: %s\n", msg);
GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func,
file, line);
GGML_CANN_LOG_ERROR(" %s\n", stmt);
GGML_LOG_ERROR(" %s\n", stmt);
// abort with GGML_ASSERT to get a stack trace
GGML_ABORT("CANN error");
}
@ -165,7 +102,7 @@ static ggml_cann_device_info ggml_cann_init() {
aclError err = aclrtGetDeviceCount((uint32_t*)&info.device_count);
if (err != ACL_SUCCESS) {
GGML_CANN_LOG_ERROR("%s: failed to initialize CANN: %s\n",
GGML_LOG_ERROR("%s: failed to initialize CANN: %s\n",
__func__, aclGetRecentErrMsg());
return info;
}
@ -315,7 +252,7 @@ struct ggml_cann_pool_leg : public ggml_cann_pool {
*actual_size = look_ahead_size;
pool_size += look_ahead_size;
#ifdef DEBUG_CANN_MALLOC
GGML_CANN_LOG_INFO(
GGML_LOG_INFO(
"%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, "
"requested %u MB\n",
__func__, device, nnz, (uint32_t)(max_size / 1024 / 1024),
@ -470,7 +407,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
// add to the pool
pool_size += reserve_size;
// GGML_CANN_LOG_INFO("cann pool[%d]: size increased to %llu MB (
// GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB (
// reserved %llu MB)\n",
// device, (unsigned long long) (pool_size/1024/1024),
// (unsigned long long) (reserve_size/1024/1024));
@ -483,7 +420,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
pool_used += size;
#ifdef DEBUG_CANN_MALLOC
GGML_CANN_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device,
GGML_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device,
(unsigned long long)size, (unsigned long long)ptr);
#endif
return ptr;
@ -497,7 +434,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
*/
void free(void* ptr, size_t size) override {
#ifdef DEBUG_CANN_MALLOC
GGML_CANN_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device,
GGML_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device,
(unsigned long long)size, (unsigned long long)ptr);
#endif
@ -1095,7 +1032,7 @@ ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
void* dev_ptr;
aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST);
if (err != ACL_SUCCESS) {
GGML_CANN_LOG_ERROR(
GGML_LOG_ERROR(
"%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n",
__func__, size / 1024.0 / 1024.0, buft_ctx->device,
aclGetRecentErrMsg());
@ -1280,7 +1217,7 @@ static void * ggml_cann_host_malloc(size_t size) {
aclError err = aclrtMallocHost((void **) &hostPtr, size);
if (err != ACL_SUCCESS) {
GGML_CANN_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
size / 1024.0 / 1024.0, aclGetRecentErrMsg());
return nullptr;
}
@ -1733,7 +1670,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(
bool ok = ggml_cann_compute_forward(*cann_ctx, node);
if (!ok) {
GGML_CANN_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
node->name, ggml_op_name(node->op));
}
GGML_ASSERT(ok);
@ -2043,13 +1980,13 @@ static ggml_guid_t ggml_backend_cann_guid() {
ggml_backend_t ggml_backend_cann_init(int32_t device) {
aclInit(nullptr);
if (device < 0 || device >= ggml_backend_cann_get_device_count()) {
GGML_CANN_LOG_ERROR("%s: error: invalid device %d\n", __func__, device);
GGML_LOG_ERROR("%s: error: invalid device %d\n", __func__, device);
return nullptr;
}
ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device);
if (ctx == nullptr) {
GGML_CANN_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
return nullptr;
}
ggml_cann_set_device(ctx->device);

131
ggml/src/ggml-cuda.cu
View file

@ -7,12 +7,14 @@ bool g_mul_mat_q = false;
#include "ggml-cuda/common.cuh"
#include "ggml-cuda/acc.cuh"
#include "ggml-cuda/arange.cuh"
#include "ggml-cuda/argmax.cuh"
#include "ggml-cuda/argsort.cuh"
#include "ggml-cuda/binbcast.cuh"
#include "ggml-cuda/clamp.cuh"
#include "ggml-cuda/concat.cuh"
#include "ggml-cuda/conv-transpose-1d.cuh"
#include "ggml-cuda/convert.cuh"
#include "ggml-cuda/count-equal.cuh"
#include "ggml-cuda/cpy.cuh"
#include "ggml-cuda/cross-entropy-loss.cuh"
#include "ggml-cuda/diagmask.cuh"
@ -58,52 +60,14 @@ bool g_mul_mat_q = false;
static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
static void ggml_cuda_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
GGML_UNUSED(level);
GGML_UNUSED(user_data);
fprintf(stderr, "%s", msg);
}
ggml_log_callback ggml_cuda_log_callback = ggml_cuda_default_log_callback;
void * ggml_cuda_log_user_data = NULL;
GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data) {
ggml_cuda_log_callback = log_callback;
ggml_cuda_log_user_data = user_data;
}
#define GGML_CUDA_LOG_INFO(...) ggml_cuda_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
#define GGML_CUDA_LOG_WARN(...) ggml_cuda_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
#define GGML_CUDA_LOG_ERROR(...) ggml_cuda_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
GGML_ATTRIBUTE_FORMAT(2, 3)
static void ggml_cuda_log(enum ggml_log_level level, const char * format, ...) {
if (ggml_cuda_log_callback != NULL) {
va_list args;
va_start(args, format);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
ggml_cuda_log_callback(level, buffer, ggml_cuda_log_user_data);
} else {
std::vector<char> buffer2(len + 1); // vsnprintf adds a null terminator
va_end(args);
va_start(args, format);
vsnprintf(&buffer2[0], buffer2.size(), format, args);
ggml_cuda_log_callback(level, buffer2.data(), ggml_cuda_log_user_data);
}
va_end(args);
}
}
[[noreturn]]
void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
int id = -1; // in case cudaGetDevice fails
cudaGetDevice(&id);
GGML_CUDA_LOG_ERROR(GGML_CUDA_NAME " error: %s\n", msg);
GGML_CUDA_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line);
GGML_CUDA_LOG_ERROR(" %s\n", stmt);
GGML_LOG_ERROR(GGML_CUDA_NAME " error: %s\n", msg);
GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line);
GGML_LOG_ERROR(" %s\n", stmt);
// abort with GGML_ABORT to get a stack trace
GGML_ABORT(GGML_CUDA_NAME " error");
}
@ -168,24 +132,24 @@ static ggml_cuda_device_info ggml_cuda_init() {
cudaError_t err = cudaGetDeviceCount(&info.device_count);
if (err != cudaSuccess) {
GGML_CUDA_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
GGML_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
return info;
}
GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES);
int64_t total_vram = 0;
// #ifdef GGML_CUDA_FORCE_MMQ
// GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
// #else
// GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
// #endif // GGML_CUDA_FORCE_MMQ
// #ifdef GGML_CUDA_FORCE_CUBLAS
// GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: yes\n", __func__);
// #else
// GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: no\n", __func__);
// #endif // GGML_CUDA_FORCE_CUBLAS
GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
//#ifdef GGML_CUDA_FORCE_MMQ
// GGML_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
//#else
// GGML_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
//#endif // GGML_CUDA_FORCE_MMQ
//#ifdef GGML_CUDA_FORCE_CUBLAS
// GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: yes\n", __func__);
//#else
// GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: no\n", __func__);
//#endif // GGML_CUDA_FORCE_CUBLAS
GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
for (int id = 0; id < info.device_count; ++id) {
int device_vmm = 0;
@ -206,7 +170,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
cudaDeviceProp prop;
CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
GGML_CUDA_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
info.default_tensor_split[id] = total_vram;
total_vram += prop.totalGlobalMem;
@ -327,7 +291,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
return;
}
}
GGML_CUDA_LOG_WARN(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
GGML_LOG_WARN(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
ggml_cuda_set_device(device);
CUDA_CHECK(cudaFree(ptr));
pool_size -= size;
@ -591,7 +555,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
if (err != cudaSuccess) {
// clear the error
cudaGetLastError();
GGML_CUDA_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err));
GGML_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err));
return nullptr;
}
@ -1016,7 +980,7 @@ static void * ggml_cuda_host_malloc(size_t size) {
if (err != cudaSuccess) {
// clear the error
cudaGetLastError();
GGML_CUDA_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
size / 1024.0 / 1024.0, cudaGetErrorString(err));
return nullptr;
}
@ -2185,6 +2149,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
}
switch (dst->op) {
case GGML_OP_ARGMAX:
ggml_cuda_argmax(ctx, dst);
break;
case GGML_OP_COUNT_EQUAL:
ggml_cuda_count_equal(ctx, dst);
break;
case GGML_OP_REPEAT:
ggml_cuda_op_repeat(ctx, dst);
break;
@ -2287,7 +2257,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
break;
case GGML_OP_MUL_MAT:
if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
GGML_CUDA_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
GGML_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
return false;
} else {
ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
@ -2371,7 +2341,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
GGML_CUDA_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst));
GGML_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst));
CUDA_CHECK(err);
}
@ -2440,7 +2410,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: backend and buffer devices do not match\n", __func__);
GGML_LOG_WARN("%s: backend and buffer devices do not match\n", __func__);
#endif
return false;
}
@ -2482,6 +2452,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
GGML_UNUSED(backend);
}
#ifdef USE_CUDA_GRAPH
static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
graph_node_properties->node_address = node->data;
graph_node_properties->node_op = node->op;
@ -2532,6 +2503,7 @@ static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_gra
return true;
}
#endif
static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
@ -2556,7 +2528,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
GGML_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
#endif
}
}
@ -2607,14 +2579,14 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
GGML_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
#endif
}
if (node->op == GGML_OP_MUL_MAT_ID) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
GGML_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
#endif
}
@ -2623,7 +2595,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
// Changes in batch size or context size can cause changes to the grid size of some kernels.
use_cuda_graph = false;
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
GGML_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
#endif
}
@ -2635,7 +2607,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (!ptr) {
use_cuda_graph = false;
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
GGML_LOG_WARN("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
#endif
} else {
if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
@ -2659,7 +2631,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
GGML_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
#endif
}
}
@ -2698,7 +2670,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
if (!ok) {
GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}
GGML_ASSERT(ok);
}
@ -2717,7 +2689,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
use_cuda_graph = false;
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
#ifndef NDEBUG
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
GGML_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
#endif
} else {
graph_evaluated_or_captured = true; // CUDA graph has been captured
@ -2784,7 +2756,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
if (stat == cudaErrorGraphExecUpdateFailure) {
#ifndef NDEBUG
GGML_CUDA_LOG_ERROR("%s: CUDA graph update failed\n", __func__);
GGML_LOG_ERROR("%s: CUDA graph update failed\n", __func__);
#endif
// The pre-existing graph exec cannot be updated due to violated constraints
// so instead clear error and re-instantiate
@ -2886,7 +2858,7 @@ bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
// clear the error
cudaGetLastError();
GGML_CUDA_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
GGML_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
size / 1024.0 / 1024.0, cudaGetErrorString(err));
return false;
}
@ -3115,6 +3087,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
return false;
} break;
case GGML_OP_DUP:
{
ggml_type src0_type = op->src[0]->type;
return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
} break;
case GGML_OP_ARGMAX:
case GGML_OP_COUNT_EQUAL:
{
return true;
} break;
case GGML_OP_REPEAT:
{
ggml_type src0_type = op->src[0]->type;
@ -3309,17 +3290,11 @@ static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, con
return nullptr;
}
static void ggml_backend_cuda_reg_set_log_callback(ggml_backend_reg_t reg, ggml_log_callback log_callback, void * user_data) {
GGML_UNUSED(reg);
ggml_backend_cuda_log_set_callback(log_callback, user_data);
}
static const ggml_backend_reg_i ggml_backend_cuda_reg_interface = {
/* .get_name = */ ggml_backend_cuda_reg_get_name,
/* .get_device_count = */ ggml_backend_cuda_reg_get_device_count,
/* .get_device_get = */ ggml_backend_cuda_reg_get_device,
/* .get_proc_address = */ ggml_backend_cuda_reg_get_proc_address,
/* .set_log_callback = */ ggml_backend_cuda_reg_set_log_callback,
};
// backend registry
@ -3365,13 +3340,13 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
ggml_backend_t ggml_backend_cuda_init(int device) {
if (device < 0 || device >= ggml_backend_cuda_get_device_count()) {
GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device);
GGML_LOG_ERROR("%s: invalid device %d\n", __func__, device);
return nullptr;
}
ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
if (ctx == nullptr) {
GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__);
GGML_LOG_ERROR("%s: failed to allocate context\n", __func__);
return nullptr;
}

79
ggml/src/ggml-cuda/argmax.cu Normal file
View file

@ -0,0 +1,79 @@
#include "common.cuh"
#include "argmax.cuh"
#include "sum.cuh"
#include <cstdint>
static __global__ void argmax_f32(
const float * x, int32_t * dst, const int64_t ncols, const int64_t nrows) {
int argmax_thread = 0;
const int64_t row0 = (int64_t)blockIdx.x*WARP_SIZE;
#pragma unroll
for (int64_t row1 = 0; row1 < WARP_SIZE; ++row1) {
const int64_t row = row0 + row1;
if (row >= nrows) {
break;
}
float maxval = -FLT_MAX;
int argmax = -1;
for (int32_t col = threadIdx.x; col < ncols; col += WARP_SIZE) {
const float val = x[row*ncols + col];
const int bigger = val > maxval;
const int not_bigger = bigger ^ 0x00000001;
maxval = maxval*not_bigger + val*bigger;
argmax = argmax*not_bigger + col*bigger;
}
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, mask, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, mask, WARP_SIZE);
const int bigger = val > maxval;
const int not_bigger = bigger ^ 0x00000001;
maxval = maxval*not_bigger + val*bigger;
argmax = argmax*not_bigger + col*bigger;
}
const int store = row1 == threadIdx.x;
argmax_thread += store*argmax;
}
const int row = row0 + threadIdx.x;
if (row >= nrows) {
return;
}
dst[row] = argmax_thread;
}
void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_is_contiguous(src0));
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
const float * src0_d = (const float *) src0->data;
int32_t * dst_d = (int32_t *) dst->data;
cudaStream_t stream = ctx.stream();
const int64_t num_blocks = (nrows + WARP_SIZE - 1) / WARP_SIZE;
const dim3 blocks_dim(WARP_SIZE, 1, 1);
const dim3 blocks_num(num_blocks, 1, 1);
argmax_f32<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00, nrows);
}

3
ggml/src/ggml-cuda/argmax.cuh Normal file
View file

@ -0,0 +1,3 @@
#include "common.cuh"
void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

12
ggml/src/ggml-cuda/common.cuh
View file

@ -175,6 +175,18 @@ static __device__ void no_device_code(
#define NO_DEVICE_CODE //GGML_ABORT("NO_DEVICE_CODE not valid in host code.")
#endif // __CUDA_ARCH__
static __device__ __forceinline__ int warp_reduce_sum(int x) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE
return __reduce_add_sync(0xffffffff, x);
#else
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
}
return x;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE
}
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {

64
ggml/src/ggml-cuda/count-equal.cu Normal file
View file

@ -0,0 +1,64 @@
#include "common.cuh"
#include "count-equal.cuh"
#include <cstdint>
template <typename T>
static __global__ void count_equal(const T * __restrict__ x, const T * __restrict__ y, int64_t * __restrict__ dst, const int64_t dk, const int64_t k) {
const int64_t i0 = (int64_t) blockIdx.x*dk;
const int64_t i1 = min(i0 + dk, k);
int nequal = 0;
for (int64_t i = i0 + threadIdx.x; i < i1; i += WARP_SIZE) {
const T xi = x[i];
const T yi = y[i];
nequal += xi == yi;
}
nequal = warp_reduce_sum(nequal);
if (threadIdx.x != 0) {
return;
}
atomicAdd((int *) dst, nequal);
}
void ggml_cuda_count_equal(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == src1->type);
GGML_ASSERT( dst->type == GGML_TYPE_I64);
GGML_ASSERT(ggml_are_same_shape(src0, src1));
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_contiguous(dst));
int64_t * dst_d = (int64_t *) dst->data;
cudaStream_t stream = ctx.stream();
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
const int64_t ne = ggml_nelements(src0);
GGML_ASSERT(ne < (1 << 30) && "atomicAdd implementation only supports int");
const int64_t dne = GGML_PAD(ne / (4*nsm), CUDA_COUNT_EQUAL_CHUNK_SIZE);
CUDA_CHECK(cudaMemsetAsync(dst_d, 0, ggml_nbytes(dst), stream));
const dim3 blocks_dim(WARP_SIZE, 1, 1);
const dim3 blocks_num(std::min((int64_t)4*nsm, (ne + CUDA_COUNT_EQUAL_CHUNK_SIZE - 1)/CUDA_COUNT_EQUAL_CHUNK_SIZE), 1, 1);
switch (src0->type) {
case GGML_TYPE_I32: {
const int * src0_d = (const int *) src0->data;
const int * src1_d = (const int *) src1->data;
count_equal<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, src1_d, dst_d, dne, ne);
} break;
default:
GGML_ASSERT(false);
break;
}
}

5
ggml/src/ggml-cuda/count-equal.cuh Normal file
View file

@ -0,0 +1,5 @@
#include "common.cuh"
#define CUDA_COUNT_EQUAL_CHUNK_SIZE 128
void ggml_cuda_count_equal(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

2
ggml/src/ggml-cuda/fattn-tile-f16.cu
View file

@ -259,7 +259,7 @@ static __global__ void flash_attn_tile_ext_f16(
}
half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]);
kqsum_j = warp_reduce_sum(kqsum_j);
kqsum_j = warp_reduce_sum((float)kqsum_j);
#pragma unroll
for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) {

6
ggml/src/ggml-cuda/fattn-vec-f16.cuh
View file

@ -196,7 +196,7 @@ static __global__ void flash_attn_vec_ext_f16(
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]);
sum = warp_reduce_sum(sum);
sum = warp_reduce_sum((float)sum);
if (use_logit_softcap) {
sum = logit_softcap*tanhf(sum);
@ -265,7 +265,7 @@ static __global__ void flash_attn_vec_ext_f16(
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqsum[j] = warp_reduce_sum(kqsum[j]);
kqsum[j] = warp_reduce_sum((float)kqsum[j]);
if (threadIdx.x == 0) {
kqsum_shared[j][threadIdx.y] = kqsum[j];
}
@ -280,7 +280,7 @@ static __global__ void flash_attn_vec_ext_f16(
}
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
kqsum[j_VKQ] = warp_reduce_sum((float)kqsum[j_VKQ]);
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
if (parallel_blocks == 1) {

15
ggml/src/ggml-impl.h
View file

@ -33,6 +33,21 @@ extern "C" {
#endif
#endif
//
// logging
//
GGML_ATTRIBUTE_FORMAT(2, 3)
void ggml_log_internal (enum ggml_log_level level, const char * format, ...);
void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data);
#define GGML_LOG(...) ggml_log_internal(GGML_LOG_LEVEL_NONE , __VA_ARGS__)
#define GGML_LOG_INFO(...) ggml_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define GGML_LOG_WARN(...) ggml_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define GGML_LOG_ERROR(...) ggml_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
#define GGML_LOG_CONT(...) ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
// bitset
typedef uint32_t ggml_bitset_t;

183
ggml/src/ggml-metal.m
View file

@ -18,19 +18,6 @@
// max number of MTLCommandBuffer used to submit a graph for processing
#define GGML_METAL_MAX_COMMAND_BUFFERS 8
#ifdef GGML_METAL_NDEBUG
#define GGML_METAL_LOG(...)
#define GGML_METAL_LOG_INFO(...)
#define GGML_METAL_LOG_WARN(...)
#define GGML_METAL_LOG_ERROR(...)
#else
#define GGML_METAL_LOG(...) ggml_metal_log(GGML_LOG_LEVEL_NONE, __VA_ARGS__)
#define GGML_METAL_LOG_INFO(...) ggml_metal_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
#define GGML_METAL_LOG_WARN(...) ggml_metal_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
#define GGML_METAL_LOG_ERROR(...) ggml_metal_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define GGML_METAL_LOG_DEBUG(...) ggml_metal_log(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
#endif
#define UNUSED(x) (void)(x)
struct ggml_metal_kernel {
@ -230,8 +217,6 @@ struct ggml_backend_metal_context {
id<MTLDevice> device;
id<MTLCommandQueue> queue;
MTLComputePassDescriptor * edesc;
dispatch_queue_t d_queue;
struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT];
@ -277,51 +262,19 @@ struct ggml_backend_metal_context {
@implementation GGMLMetalClass
@end
static void ggml_metal_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
fprintf(stderr, "%s", msg);
UNUSED(level);
UNUSED(user_data);
}
ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback;
void * ggml_metal_log_user_data = NULL;
GGML_ATTRIBUTE_FORMAT(2, 3)
static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
if (ggml_metal_log_callback != NULL) {
va_list args;
va_start(args, format);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
ggml_metal_log_callback(level, buffer, ggml_metal_log_user_data);
} else {
char* buffer2 = malloc(len+1);
va_end(args);
va_start(args, format);
vsnprintf(buffer2, len+1, format, args);
buffer2[len] = 0;
ggml_metal_log_callback(level, buffer2, ggml_metal_log_user_data);
free(buffer2);
}
va_end(args);
}
}
static void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
#if TARGET_OS_OSX
kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE);
if (err != KERN_SUCCESS) {
GGML_METAL_LOG_ERROR("%s: error: vm_allocate failed\n", __func__);
GGML_LOG_ERROR("%s: error: vm_allocate failed\n", __func__);
return NULL;
}
#else
const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
if (result != 0) {
GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
GGML_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
return NULL;
}
#endif
@ -330,27 +283,25 @@ static void * ggml_metal_host_malloc(size_t n) {
}
static struct ggml_backend_metal_context * ggml_metal_init(void) {
GGML_METAL_LOG_INFO("%s: allocating\n", __func__);
GGML_LOG_INFO("%s: allocating\n", __func__);
#if TARGET_OS_OSX && !GGML_METAL_NDEBUG
// Show all the Metal device instances in the system
NSArray * devices = MTLCopyAllDevices();
for (id<MTLDevice> device in devices) {
GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]);
GGML_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]);
}
[devices release]; // since it was created by a *Copy* C method
#endif
// Pick and show default Metal device
id<MTLDevice> device = MTLCreateSystemDefaultDevice();
GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
// Configure context
struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context));
ctx->device = device;
ctx->queue = [ctx->device newCommandQueue];
ctx->edesc = MTLComputePassDescriptor.computePassDescriptor;
ctx->edesc.dispatchType = MTLDispatchTypeSerial;
ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
id<MTLLibrary> metal_library;
@ -381,28 +332,28 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
if (try_metallib && path_lib != nil) {
// pre-compiled library found
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
metal_library = [ctx->device newLibraryWithURL:libURL error:&error];
if (error) {
GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
} else {
#if GGML_METAL_EMBED_LIBRARY
GGML_METAL_LOG_INFO("%s: using embedded metal library\n", __func__);
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
extern const char ggml_metallib_start[];
extern const char ggml_metallib_end[];
NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
#else
GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
NSString * path_source;
NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
if (path_resource) {
path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal-merged.metal"];
@ -411,15 +362,15 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
}
if (path_source == nil) {
GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal-merged.metal, falling back to trying cwd\n", __func__);
GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal-merged.metal, falling back to trying cwd\n", __func__);
path_source = @"ggml-metal.metal";
}
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
if (error) {
GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
#endif // GGML_METAL_EMBED_LIBRARY
@ -435,7 +386,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
metal_library = [ctx->device newLibraryWithSource:src options:options error:&error];
if (error) {
GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
}
@ -443,7 +394,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
}
// print MTL GPU family:
GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]);
GGML_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]);
const NSInteger MTLGPUFamilyMetal3 = 5001;
@ -453,21 +404,21 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
{
for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
if ([ctx->device supportsFamily:i]) {
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
break;
}
}
for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) {
if ([ctx->device supportsFamily:i]) {
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i);
GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i);
break;
}
}
for (int i = MTLGPUFamilyMetal3 + 5; i >= MTLGPUFamilyMetal3; --i) {
if ([ctx->device supportsFamily:i]) {
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3 + 3, i);
GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3 + 3, i);
break;
}
}
@ -478,9 +429,9 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
ctx->support_simdgroup_mm = [ctx->device supportsFamily:MTLGPUFamilyApple7];
GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false");
GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false");
GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
GGML_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false");
GGML_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false");
GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
ctx->capture_next_compute = false;
ctx->capture_started = false;
@ -494,13 +445,13 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
if (@available(macOS 10.12, iOS 16.0, *)) {
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
GGML_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
}
#elif TARGET_OS_OSX
if (ctx->device.maxTransferRate != 0) {
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6);
GGML_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6);
} else {
GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
GGML_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
}
#endif
@ -513,7 +464,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
}
/*
GGML_METAL_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
GGML_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
(int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
(int) kernel->pipeline.threadExecutionWidth); \
*/
@ -524,12 +475,12 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:metal_function error:&error]; \
[metal_function release]; \
if (error) { \
GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
[metal_library release]; \
return NULL; \
} \
} else { \
GGML_METAL_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
GGML_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
}
// simd_sum and simd_max requires MTLGPUFamilyApple7
@ -726,7 +677,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(void) {
}
static void ggml_metal_free(struct ggml_backend_metal_context * ctx) {
GGML_METAL_LOG_INFO("%s: deallocating\n", __func__);
GGML_LOG_INFO("%s: deallocating\n", __func__);
for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) {
[ctx->kernels[i].pipeline release];
@ -764,7 +715,7 @@ struct ggml_backend_metal_buffer_context {
// Metal buffer based on the host memory pointer
//
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs) {
//GGML_METAL_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
//GGML_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
const int64_t tsize = ggml_nbytes(t);
@ -776,17 +727,17 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs
for (int i = 0; i < buf_ctx->n_buffers; ++i) {
const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data;
//GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size);
//GGML_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size);
if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) {
*offs = (size_t) ioffs;
//GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs);
//GGML_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs);
return buf_ctx->buffers[i].metal;
}
}
GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name);
GGML_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name);
return nil;
}
@ -918,7 +869,7 @@ static void ggml_metal_encode_node(
struct ggml_tensor * node = ggml_graph_node(gf, idx);
//GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, idx, ggml_op_name(node->op));
//GGML_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, idx, ggml_op_name(node->op));
struct ggml_tensor * src0 = node->src[0];
struct ggml_tensor * src1 = node->src[1];
@ -944,7 +895,7 @@ static void ggml_metal_encode_node(
}
if (!ggml_metal_supports_op(ctx, dst)) {
GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
GGML_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
GGML_ABORT("unsupported op");
}
@ -1002,17 +953,17 @@ static void ggml_metal_encode_node(
id<MTLBuffer> id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil;
id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil;
//GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
//GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
//if (src0) {
// GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
// GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
// ggml_is_contiguous(src0), src0->name);
//}
//if (src1) {
// GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
// GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
// ggml_is_contiguous(src1), src1->name);
//}
//if (dst) {
// GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2,
// GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2,
// dst->name);
//}
@ -1404,7 +1355,7 @@ static void ggml_metal_encode_node(
} break;
default:
{
GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
GGML_ABORT("fatal error");
}
} break;
@ -1956,7 +1907,7 @@ static void ggml_metal_encode_node(
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
GGML_LOG_ERROR("Asserting on type %d\n", (int)src0t);
GGML_ABORT("not implemented");
}
};
@ -2252,7 +2203,7 @@ static void ggml_metal_encode_node(
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
GGML_LOG_ERROR("Asserting on type %d\n", (int)src2t);
GGML_ABORT("not implemented");
}
};
@ -2821,8 +2772,8 @@ static void ggml_metal_encode_node(
//case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
default:
{
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_METAL_LOG_ERROR("add template specialization for this size\n");
GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_LOG_ERROR("add template specialization for this size\n");
GGML_ABORT("add template specialization for this size");
}
}
@ -2834,8 +2785,8 @@ static void ggml_metal_encode_node(
//case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
default:
{
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_METAL_LOG_ERROR("add template specialization for this size\n");
GGML_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_LOG_ERROR("add template specialization for this size\n");
GGML_ABORT("add template specialization for this size");
}
}
@ -2996,7 +2947,7 @@ static void ggml_metal_encode_node(
} break;
default:
{
GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
GGML_ABORT("fatal error");
}
}
@ -3041,8 +2992,7 @@ static enum ggml_status ggml_metal_graph_compute(
NSError * error = nil;
if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) {
GGML_METAL_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]);
GGML_ABORT("capture failed");
GGML_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]);
} else {
[ctx->capture_scope beginScope];
ctx->capture_started = true;
@ -3061,7 +3011,7 @@ static enum ggml_status ggml_metal_graph_compute(
const int n_nodes_per_cb = ctx->n_nodes_per_cb;
id<MTLCommandBuffer> command_buffer = ctx->command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: ctx->edesc];
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoder];
int node_start = 0;
int node_end = n_nodes_0;
@ -3123,9 +3073,9 @@ static enum ggml_status ggml_metal_graph_compute(
MTLCommandBufferStatus status = [command_buffer status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status);
GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status);
if (status == MTLCommandBufferStatusError) {
GGML_METAL_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]);
GGML_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]);
}
return GGML_STATUS_FAILED;
@ -3138,9 +3088,9 @@ static enum ggml_status ggml_metal_graph_compute(
MTLCommandBufferStatus status = [command_buffer status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
if (status == MTLCommandBufferStatusError) {
GGML_METAL_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]);
GGML_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]);
}
return GGML_STATUS_FAILED;
@ -3157,7 +3107,7 @@ static enum ggml_status ggml_metal_graph_compute(
}
if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) {
GGML_METAL_LOG_INFO("%s: command buffer %d aborted", __func__, i);
GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i);
return GGML_STATUS_ABORTED;
}
@ -3286,17 +3236,17 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t s
#ifndef GGML_METAL_NDEBUG
#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
if (@available(macOS 10.12, iOS 16.0, *)) {
GGML_METAL_LOG_DEBUG("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)\n",
GGML_LOG_DEBUG("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)\n",
__func__,
size_aligned / 1024.0 / 1024.0,
device.currentAllocatedSize / 1024.0 / 1024.0,
device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
GGML_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
}
} else {
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
__func__,
size_aligned / 1024.0 / 1024.0,
device.currentAllocatedSize / 1024.0 / 1024.0);
@ -3308,7 +3258,7 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t s
}
static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));
const size_t size_page = sysconf(_SC_PAGESIZE);
@ -3338,7 +3288,7 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
}
if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
free(ctx);
ggml_backend_metal_free_device();
return NULL;
@ -3390,7 +3340,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
// buffer from ptr
ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));
ctx->all_data = data;
ctx->all_size = size;
@ -3423,7 +3373,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
return false;
}
}
@ -3449,7 +3399,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0);
GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0);
return false;
}
}
@ -3457,7 +3407,7 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
ggml_backend_metal_log_allocated_size(device, size_step_aligned);
if (i + size_step < size) {
GGML_METAL_LOG_INFO("\n");
GGML_LOG_INFO("\n");
}
++ctx->n_buffers;
@ -3514,7 +3464,7 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS);
if (ctx->n_cb > 2) {
GGML_METAL_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb);
GGML_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb);
}
}
@ -3544,11 +3494,6 @@ static struct ggml_backend_i ggml_backend_metal_i = {
/* .event_wait = */ NULL,
};
void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
ggml_metal_log_callback = log_callback;
ggml_metal_log_user_data = user_data;
}
static ggml_guid_t ggml_backend_metal_guid(void) {
static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 };
return &guid;
@ -3557,7 +3502,7 @@ static ggml_guid_t ggml_backend_metal_guid(void) {
ggml_backend_t ggml_backend_metal_init(void) {
struct ggml_backend_metal_context * ctx = ggml_metal_init();
if (ctx == NULL) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
return NULL;
}

16
ggml/src/ggml-sycl/dequantize.hpp
View file

@ -55,12 +55,12 @@ static __dpct_inline__ void dequantize_q4_1(const void *vx, const int64_t ib,
#ifdef GGML_SYCL_F16
// v = v * {d, d};
// v = v + {m, m};
v.s0() = (v.s0() * d) + m;
v.s1() = (v.s1() * d) + m;
v.s0() = sycl::fma(v.s0(), d, m);
v.s1() = sycl::fma(v.s1(), d, m);
#else
v.x() = (v.x() * d) + m;
v.y() = (v.y() * d) + m;
v.x() = sycl::fma(v.x(), d, m);
v.y() = sycl::fma(v.y(), d, m);
#endif // GGML_SYCL_F16
}
@ -110,11 +110,11 @@ static __dpct_inline__ void dequantize_q5_1(const void *vx, const int64_t ib,
#ifdef GGML_SYCL_F16
// v = v * {d, d};
// v = v + {m, m};
v.s0() = (v.s0() * d) + m;
v.s1() = (v.s1() * d) + m;
v.s0() = sycl::fma(v.s0(), d, m);
v.s1() = sycl::fma(v.s1(), d, m);
#else
v.x() = (v.x() * d) + m;
v.y() = (v.y() * d) + m;
v.x() = sycl::fma(v.x(), d, m);
v.y() = sycl::fma(v.y(), d, m);
#endif // GGML_SYCL_F16
}

279
ggml/src/ggml.c
View file

@ -319,26 +319,63 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) {
// logging
//
struct ggml_logger_state {
ggml_log_callback log_callback;
void * log_callback_user_data;
};
static struct ggml_logger_state g_logger_state = {ggml_log_callback_default, NULL};
static void ggml_log_internal_v(enum ggml_log_level level, const char * format, va_list args) {
if (format == NULL)
return;
va_list args_copy;
va_copy(args_copy, args);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data);
} else {
char * buffer2 = (char *) calloc(len + 1, sizeof(char));
vsnprintf(buffer2, len + 1, format, args_copy);
buffer2[len] = 0;
g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data);
free(buffer2);
}
va_end(args_copy);
}
void ggml_log_internal(enum ggml_log_level level, const char * format, ...) {
va_list args;
va_start(args, format);
ggml_log_internal_v(level, format, args);
va_end(args);
}
void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
fputs(text, stderr);
fflush(stderr);
}
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
#define GGML_PRINT(...) printf(__VA_ARGS__)
//
// end of logging block
//
@ -355,7 +392,7 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) {
#else
inline static void * ggml_aligned_malloc(size_t size) {
if (size == 0) {
GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
return NULL;
}
void * aligned_memory = NULL;
@ -377,7 +414,7 @@ inline static void * ggml_aligned_malloc(size_t size) {
error_desc = "insufficient memory";
break;
}
GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0));
GGML_LOG_ERROR("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0));
GGML_ABORT("fatal error");
return NULL;
}
@ -393,12 +430,12 @@ inline static void * ggml_aligned_malloc(size_t size) {
inline static void * ggml_malloc(size_t size) {
if (size == 0) {
GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_malloc!\n");
GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_malloc!\n");
return NULL;
}
void * result = malloc(size);
if (result == NULL) {
GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
GGML_LOG_ERROR("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
GGML_ABORT("fatal error");
}
return result;
@ -407,12 +444,12 @@ inline static void * ggml_malloc(size_t size) {
// calloc
inline static void * ggml_calloc(size_t num, size_t size) {
if (num == 0 || size == 0) {
GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_calloc!\n");
GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_calloc!\n");
return NULL;
}
void * result = calloc(num, size);
if (result == NULL) {
GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
GGML_LOG_ERROR("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
GGML_ABORT("fatal error");
}
return result;
@ -2974,6 +3011,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"SUM_ROWS",
"MEAN",
"ARGMAX",
"COUNT_EQUAL",
"REPEAT",
"REPEAT_BACK",
"CONCAT",
@ -3047,7 +3085,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"OPT_STEP_ADAMW",
};
static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -3068,6 +3106,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"Σx_k",
"Σx/n",
"argmax(x)",
"count_equal(x)",
"repeat(x)",
"repeat_back(x)",
"concat(x, y)",
@ -3141,7 +3180,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"adamw(x)",
};
static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -3367,7 +3406,7 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
if (fptr != NULL) {
char buf[42];
if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) {
GGML_PRINT("WARNING: /proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n");
GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n");
}
fclose(fptr);
}
@ -3385,21 +3424,21 @@ bool ggml_is_numa(void) {
////////////////////////////////////////////////////////////////////////////////
void ggml_print_object(const struct ggml_object * obj) {
GGML_PRINT(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n",
GGML_LOG_INFO(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n",
obj->type, obj->offs, obj->size, (const void *) obj->next);
}
void ggml_print_objects(const struct ggml_context * ctx) {
struct ggml_object * obj = ctx->objects_begin;
GGML_PRINT("%s: objects in context %p:\n", __func__, (const void *) ctx);
GGML_LOG_INFO("%s: objects in context %p:\n", __func__, (const void *) ctx);
while (obj != NULL) {
ggml_print_object(obj);
obj = obj->next;
}
GGML_PRINT("%s: --- end ---\n", __func__);
GGML_LOG_INFO("%s: --- end ---\n", __func__);
}
int64_t ggml_nelements(const struct ggml_tensor * tensor) {
@ -3987,7 +4026,7 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml
struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
GGML_LOG_WARN("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
__func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size);
assert(false);
return NULL;
@ -4051,7 +4090,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
if (ctx->scratch.data != NULL) {
// allocate tensor data in the scratch buffer
if (ctx->scratch.offs + data_size > ctx->scratch.size) {
GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
GGML_LOG_WARN("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
__func__, ctx->scratch.offs + data_size, ctx->scratch.size);
assert(false);
return NULL;
@ -4220,9 +4259,13 @@ static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, floa
}
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
if (ggml_is_empty(tensor)) {
return tensor;
}
if (tensor->buffer) {
ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor));
} else {
GGML_ASSERT(tensor->data);
memset(tensor->data, 0, ggml_nbytes(tensor));
}
return tensor;
@ -5206,6 +5249,23 @@ struct ggml_tensor * ggml_argmax(
return result;
}
// ggml_count_equal
struct ggml_tensor * ggml_count_equal(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
GGML_ASSERT(ggml_are_same_shape(a, b));
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, 1);
result->op = GGML_OP_COUNT_EQUAL;
result->src[0] = a;
result->src[1] = b;
return result;
}
// ggml_repeat
struct ggml_tensor * ggml_repeat(
@ -10815,6 +10875,86 @@ static void ggml_compute_forward_argmax(
}
}
// ggml_compute_forward_count_equal
static void ggml_compute_forward_count_equal_i32(
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;
GGML_ASSERT(src0->type == GGML_TYPE_I32);
GGML_ASSERT(src1->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_are_same_shape(src0, src1));
GGML_ASSERT(ggml_is_scalar(dst));
GGML_ASSERT(dst->type == GGML_TYPE_I64);
const int64_t nr = ggml_nrows(src0);
const int ith = params->ith;
const int nth = params->nth;
int64_t * sums = (int64_t *) params->wdata;
int64_t sum_thread = 0;
// rows per thread
const int64_t dr = (nr + nth - 1)/nth;
// row range for this thread
const int64_t ir0 = dr*ith;
const int64_t ir1 = MIN(ir0 + dr, nr);
for (int64_t ir = ir0; ir < ir1; ++ir) {
const int64_t i03 = ir / (ne02*ne01);
const int64_t i02 = (ir - i03*ne03) / ne01;
const int64_t i01 = ir - i03*ne03 - i02*ne02;
const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01;
const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11;
for (int64_t i00 = 0; i00 < ne00; ++i00) {
const int32_t val0 = *((const int32_t *) (data0 + i00*nb00));
const int32_t val1 = *((const int32_t *) (data1 + i00*nb10));
sum_thread += val0 == val1;
}
}
if (ith != 0) {
sums[ith] = sum_thread;
}
ggml_barrier(params->threadpool);
if (ith != 0) {
return;
}
for (int ith_other = 1; ith_other < nth; ++ith_other) {
sum_thread += sums[ith_other];
}
*((int64_t *) dst->data) = sum_thread;
}
static void ggml_compute_forward_count_equal(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_I32:
{
ggml_compute_forward_count_equal_i32(params, dst);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
// ggml_compute_forward_repeat
static void ggml_compute_forward_repeat_f32(
@ -16869,41 +17009,40 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_scalar(dst));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
GGML_ASSERT(ggml_are_same_shape(src0, src1));
GGML_ASSERT(ggml_is_scalar(dst));
GGML_ASSERT(dst->type == GGML_TYPE_F32);
// TODO: handle transposed/permuted matrices
const int64_t nc = src0->ne[0];
const int64_t nr = ggml_nrows(src0);
const int ith = params->ith;
const int nth = params->nth;
float * sums = (float *) params->wdata;
// TODO: handle transposed/permuted matrices
const int nc = src0->ne[0];
const int nr = ggml_nrows(src0);
float * sums = (float *) params->wdata;
float * st = ((float *) params->wdata) + nth + ith*nc;
float sum_thread = 0.0f;
GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
if (ith == 0) {
memset(sums, 0, sizeof(float) * (nth + nth * nc));
}
ggml_barrier(params->threadpool);
// rows per thread
const int dr = (nr + nth - 1)/nth;
const int64_t dr = (nr + nth - 1)/nth;
// row range for this thread
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
const int64_t ir0 = dr*ith;
const int64_t ir1 = MIN(ir0 + dr, nr);
for (int i1 = ir0; i1 < ir1; i1++) {
float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]);
float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
float * st = ((float *) params->wdata) + nth + ith*nc;
for (int64_t i1 = ir0; i1 < ir1; ++i1) {
const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]);
const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
//printf("p[%d] = %f\n", i, p[i]);
assert(!isnan(s0[i]));
assert(!isnan(s1[i]));
@ -16912,23 +17051,24 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
float max = -INFINITY;
ggml_vec_max_f32(nc, &max, s0);
ggml_float sum = ggml_vec_log_soft_max_f32(nc, st, s0, max);
assert(sum >= 0.0);
const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max);
assert(sum_softmax >= 0.0);
ggml_vec_add1_f32(nc, st, st, -sum);
ggml_vec_add1_f32(nc, st, st, -sum_softmax);
ggml_vec_mul_f32(nc, st, st, s1);
float st_sum = 0.0f;
ggml_vec_sum_f32(nc, &st_sum, st);
sums[ith] += st_sum;
float sum_st = 0.0f;
ggml_vec_sum_f32(nc, &sum_st, st);
sum_thread += sum_st;
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
assert(!isnan(st[i]));
assert(!isinf(st[i]));
}
#endif
}
sums[ith] = sum_thread;
ggml_barrier(params->threadpool);
if (ith == 0) {
@ -16994,7 +17134,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
//printf("p[%d] = %f\n", i, p[i]);
assert(!isnan(s0[i]));
assert(!isnan(s1[i]));
@ -17013,7 +17153,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
ggml_vec_scale_f32(nc, ds0, d_by_nr);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
assert(!isnan(ds0[i]));
assert(!isinf(ds0[i]));
}
@ -17201,6 +17341,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
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);
@ -17951,6 +18095,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
} break;
case GGML_OP_MEAN:
case GGML_OP_ARGMAX:
case GGML_OP_COUNT_EQUAL:
{
GGML_ABORT("fatal error"); // TODO: implement
}
@ -18724,6 +18869,10 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
for (int i = 0; i < gf->n_nodes; ++i) {
struct ggml_tensor * node = gf->nodes[i];
if (node->type == GGML_TYPE_I32) {
continue;
}
bool needs_grad = node->flags & GGML_TENSOR_FLAG_PARAM;
bool ignore_src[GGML_MAX_SRC] = {false};
switch (node->op) {
@ -19127,6 +19276,13 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
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:
@ -19625,6 +19781,10 @@ struct ggml_cplan ggml_graph_plan(
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[node->src[0]->type].vec_dot_type;
@ -20073,7 +20233,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
}
#else
if (n_threads > threadpool->n_threads_max) {
GGML_PRINT("WARNING: cplan requested more threads (%d) than available (%d)\n", 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;
}
@ -20612,30 +20772,30 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
}
void ggml_graph_print(const struct ggml_cgraph * cgraph) {
GGML_PRINT("=== GRAPH ===\n");
GGML_LOG_INFO("=== GRAPH ===\n");
GGML_PRINT("n_nodes = %d\n", cgraph->n_nodes);
GGML_LOG_INFO("n_nodes = %d\n", cgraph->n_nodes);
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s\n",
GGML_LOG_INFO(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s\n",
i,
node->ne[0], node->ne[1], node->ne[2],
ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " ");
}
GGML_PRINT("n_leafs = %d\n", cgraph->n_leafs);
GGML_LOG_INFO("n_leafs = %d\n", cgraph->n_leafs);
for (int i = 0; i < cgraph->n_leafs; i++) {
struct ggml_tensor * node = cgraph->leafs[i];
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n",
GGML_LOG_INFO(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n",
i,
node->ne[0], node->ne[1],
ggml_op_name(node->op),
ggml_get_name(node));
}
GGML_PRINT("========================================\n");
GGML_LOG_INFO("========================================\n");
}
// check if node is part of the graph
@ -20806,7 +20966,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
fclose(fp);
GGML_PRINT("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename);
GGML_LOG_INFO("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename);
}
////////////////////////////////////////////////////////////////////////////////
@ -23354,4 +23514,9 @@ int ggml_cpu_get_sve_cnt(void) {
return 0;
#endif
}
void ggml_log_set(ggml_log_callback log_callback, void * user_data) {
g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default;
g_logger_state.log_callback_user_data = user_data;
}
////////////////////////////////////////////////////////////////////////////////

26
gguf-py/gguf/vocab.py
View file

@ -122,8 +122,30 @@ class SpecialVocab:
tokenizer = json.load(f)
if self.load_merges:
merges = tokenizer.get('model', {}).get('merges')
if isinstance(merges, list) and merges and isinstance(merges[0], str):
self.merges = merges
if isinstance(merges, list) and merges:
if isinstance(merges[0], str):
self.merges = merges
elif isinstance(merges[0], list) and len(merges[0]) == 2 and isinstance(merges[0][0], str):
# New format since transformers 4.45 to support spaces in merges
# ref: https://github.com/ggerganov/llama.cpp/issues/9692
# TODO: internally store as the new format instead of converting to old
if any(' ' in s for pair in merges for s in pair):
logger.warning(f'Spaces in merges detected, encoding as {chr(ord(" ") + 256)!r}')
self.merges = [
' '.join(
[
# ensure the spaces are properly encoded
''.join(
chr(ord(c) + 256) if c == ' ' else c
for c in part
)
for part in pair
]
)
for pair in merges
]
else:
raise ValueError("Unknown tokenizer merges format")
added_tokens = tokenizer.get('added_tokens', {})
else:
added_tokens = {}

18
src/llama-vocab.h
View file

@ -40,17 +40,17 @@ struct llama_vocab {
id special_bos_id = 1;
id special_eos_id = 2;
id special_unk_id = 0;
id special_sep_id = -1;
id special_pad_id = -1;
id special_cls_id = -1;
id special_mask_id = -1;
id special_sep_id = LLAMA_TOKEN_NULL;
id special_pad_id = LLAMA_TOKEN_NULL;
id special_cls_id = LLAMA_TOKEN_NULL;
id special_mask_id = LLAMA_TOKEN_NULL;
id linefeed_id = 13;
id special_prefix_id = -1;
id special_suffix_id = -1;
id special_middle_id = -1;
id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token
id special_eom_id = -1;
id special_prefix_id = LLAMA_TOKEN_NULL;
id special_suffix_id = LLAMA_TOKEN_NULL;
id special_middle_id = LLAMA_TOKEN_NULL;
id special_eot_id = LLAMA_TOKEN_NULL; // TODO: move above after "eos_id", and here add "file separator" token
id special_eom_id = LLAMA_TOKEN_NULL;
// set of all tokens that cause "end of generation"
std::set<id> special_eog_ids;

28
src/llama.cpp
View file

@ -2279,17 +2279,12 @@ static std::string llama_token_to_piece(const struct llama_model * model, llama_
// globals
//
struct llama_state {
llama_state() {
llama_log_set(log_callback, log_callback_user_data);
}
// We save the log callback globally
struct llama_logger_state {
ggml_log_callback log_callback = llama_log_callback_default;
void * log_callback_user_data = nullptr;
};
static llama_state g_state;
static llama_logger_state g_logger_state;
// available llama models
enum e_model {
@ -2430,7 +2425,7 @@ struct llama_hparams {
// needed by encoder-decoder models (e.g. T5, FLAN-T5)
// ref: https://github.com/ggerganov/llama.cpp/pull/8141
llama_token dec_start_token_id = -1;
llama_token dec_start_token_id = LLAMA_TOKEN_NULL;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
@ -21928,16 +21923,9 @@ const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal
}
void llama_log_set(ggml_log_callback log_callback, void * user_data) {
g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
g_state.log_callback_user_data = user_data;
ggml_backend_set_log_callback(log_callback, user_data);
#ifdef GGML_USE_METAL
ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
#elif defined(GGML_USE_CANN)
ggml_backend_cann_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
#endif
ggml_log_set(log_callback, user_data);
g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
g_logger_state.log_callback_user_data = user_data;
}
static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
@ -21946,12 +21934,12 @@ static void llama_log_internal_v(ggml_log_level level, const char * format, va_l
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
g_state.log_callback(level, buffer, g_state.log_callback_user_data);
g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data);
} else {
char * buffer2 = new char[len + 1];
vsnprintf(buffer2, len + 1, format, args_copy);
buffer2[len] = 0;
g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data);
delete[] buffer2;
}
va_end(args_copy);