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sync : ggml (ggml-backend) (#3548)

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* sync : ggml (ggml-backend)

ggml-ci

* zig : add ggml-backend to the build
This commit is contained in:
Georgi Gerganov 2023-10-08 20:19:14 +03:00 committed by GitHub
parent eee42c670e
commit db3abcc114
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GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 1285 additions and 268 deletions
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2
CMakeLists.txt
View File

@ -663,6 +663,8 @@ add_library(ggml OBJECT
ggml.h
ggml-alloc.c
ggml-alloc.h
ggml-backend.c
ggml-backend.h
${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}

7
Makefile
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@ -512,9 +512,12 @@ ggml.o: ggml.c ggml.h ggml-cuda.h
ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
$(CC) $(CFLAGS) -c $< -o $@
OBJS += ggml-alloc.o
ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
$(CC) $(CFLAGS) -c $< -o $@
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h
OBJS += ggml-alloc.o ggml-backend.o
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common.o: common/common.cpp common/common.h build-info.h common/log.h

15
build.zig
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@ -124,20 +124,21 @@ pub fn build(b: *std.build.Builder) !void {
const ggml = make.obj("ggml", "ggml.c");
const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
const llama = make.obj("llama", "llama.cpp");
const common = make.obj("common", "common/common.cpp");
const console = make.obj("console", "common/console.cpp");
const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp");
const train = make.obj("train", "common/train.cpp");
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, llama, common, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, llama, common });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, llama, common });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, llama, common });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, llama, common, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, llama, common, train });
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, llama, common, grammar_parser });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, grammar_parser });
if (server.target.isWindows()) {
server.linkSystemLibrary("ws2_32");
}

169
ggml-alloc.c
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@ -1,4 +1,5 @@
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml.h"
#include <assert.h>
#include <stdarg.h>
@ -6,25 +7,6 @@
#include <stdlib.h>
#include <string.h>
#ifdef __has_include
#if __has_include(<unistd.h>)
#include <unistd.h>
#if defined(_POSIX_MAPPED_FILES)
#include <sys/types.h>
#include <sys/mman.h>
#endif
#endif
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <memoryapi.h>
#endif
#define UNUSED(x) (void)(x)
#define MAX(a, b) ((a) > (b) ? (a) : (b))
@ -80,8 +62,9 @@ struct free_block {
#define MAX_FREE_BLOCKS 256
struct ggml_allocr {
struct ggml_backend_buffer * buffer;
bool buffer_owned;
void * data;
size_t size;
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
@ -119,16 +102,9 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens
}
#endif
static size_t ggml_allocr_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
return ggml_nbytes(tensor);
UNUSED(alloc);
}
// check if a tensor is allocated by this buffer
static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) {
void * ptr = tensor->data;
return ptr >= alloc->data && (char *)ptr < (char *)alloc->data + alloc->max_size;
return tensor->buffer == alloc->buffer;
}
static bool ggml_is_view(struct ggml_tensor * t) {
@ -136,11 +112,10 @@ static bool ggml_is_view(struct ggml_tensor * t) {
}
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
#ifdef GGML_ALLOCATOR_DEBUG
GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources
GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
#endif
size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
@ -188,6 +163,8 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
tensor->data = addr;
AT_PRINTF("%s: allocated data at %p\n", __func__, tensor->data);
tensor->buffer = alloc->buffer;
ggml_backend_buffer_init_tensor(alloc->buffer, tensor);
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, tensor);
@ -208,19 +185,21 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
// this is a very naive implementation, but for our case the number of free blocks should be very small
static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
void * ptr = tensor->data;
if (ggml_allocr_is_own(alloc, tensor) == false) {
// the tensor was not allocated in this buffer
// this can happen because the graph allocator will try to free weights and other tensors from different buffers
// the easiest way to deal with this is just to ignore it
AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer);
return;
}
size_t size = ggml_allocr_get_alloc_size(alloc, tensor);
void * ptr = tensor->data;
size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
AT_PRINTF("%s: alloc->data = %p alloc->data+alloc->size = %p alloc->data+alloc->max_size = %p\n", __func__, alloc->data, (char*)alloc->data + alloc->size, (char*)alloc->data + alloc->max_size);
ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
@ -285,15 +264,18 @@ void ggml_allocr_reset(struct ggml_allocr * alloc) {
alloc->n_free_blocks = 1;
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
alloc->free_blocks[0].size = alloc->size - align_offset;
alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset;
}
struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr));
*alloc = (struct ggml_allocr){
/*.data = */ data,
/*.size = */ size,
/*.buffer = */ buffer,
/*.buffer_owned = */ true,
/*.base = */ ggml_backend_buffer_get_base(buffer),
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
@ -312,74 +294,26 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
return alloc;
}
// OS specific functions to allocate and free uncommitted virtual memory
static void * alloc_vmem(size_t size) {
#if defined(_WIN32)
return VirtualAlloc(NULL, size, MEM_RESERVE, PAGE_NOACCESS);
#elif defined(_POSIX_MAPPED_FILES)
void * ptr = mmap(NULL, size, PROT_NONE, MAP_PRIVATE | MAP_ANON, -1, 0);
if (ptr == MAP_FAILED) {
return NULL;
}
return ptr;
#else
// use a fixed address for other platforms
uintptr_t base_addr = (uintptr_t)-size - 0x100;
return (void *)base_addr;
#endif
}
static void free_vmem(void * base_addr, size_t size) {
#if defined(_WIN32)
VirtualFree(base_addr, 0, MEM_RELEASE);
UNUSED(size);
#elif defined(_POSIX_MAPPED_FILES)
munmap(base_addr, size);
#else
// nothing to do
UNUSED(base_addr);
UNUSED(size);
#endif
}
// allocate uncommitted virtual memory to measure the size of the graph
static void alloc_measure_vmem(void ** base_addr, size_t * size) {
// 128GB for 64-bit, 1GB for 32-bit
*size = sizeof(void *) == 4 ? 1ULL<<30 : 1ULL<<37;
do {
*base_addr = alloc_vmem(*size);
if (*base_addr != NULL) {
AT_PRINTF("allocated %.2f GB of virtual memory for measure buffer at %p\n", *size / 1024.0 / 1024.0 / 1024.0, *base_addr);
return;
}
// try again with half the size
*size /= 2;
} while (*size > 0);
GGML_ASSERT(!"failed to allocate virtual memory for measure buffer");
}
static void free_measure_vmem(void * base_addr, size_t size) {
free_vmem(base_addr, size);
}
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
struct ggml_allocr * alloc = ggml_allocr_new((void *)0x1000, (size_t)-0x1001, alignment);
alloc->measure = true;
void * base_addr;
size_t size;
return alloc;
}
alloc_measure_vmem(&base_addr, &size);
struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr));
*alloc = (struct ggml_allocr){
/*.data = */ base_addr,
/*.size = */ size,
/*.alignment = */ alignment,
/*.buffer = */ buffer,
/*.buffer_owned = */ false,
/*.base = */ ggml_backend_buffer_get_base(buffer),
/*.alignment = */ ggml_backend_buffer_get_alignment(buffer),
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ true,
/*.measure = */ false,
/*.parse_seq = */ {0},
/*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG
@ -393,8 +327,8 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
}
void ggml_allocr_free(struct ggml_allocr * alloc) {
if (alloc->measure) {
free_measure_vmem(alloc->data, alloc->size);
if (alloc->buffer_owned) {
ggml_backend_buffer_free(alloc->buffer);
}
free(alloc);
}
@ -437,7 +371,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
case GGML_OP_ROPE:
case GGML_OP_RMS_NORM:
case GGML_OP_SOFT_MAX:
case GGML_OP_CONT:
return true;
default:
@ -445,12 +378,23 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
}
}
static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view) {
assert(view->view_src != NULL && view->view_src->data != NULL);
view->backend = view->view_src->backend;
view->buffer = view->view_src->buffer;
view->data = (char *)view->view_src->data + view->view_offs;
// FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
// due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
assert(ggml_allocr_is_measure(alloc) || view->buffer->backend == alloc->buffer->backend);
ggml_backend_buffer_init_tensor(alloc->buffer, view);
}
static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
struct hash_node * ht = alloc->hash_table;
if (node->data == NULL) {
if (ggml_is_view(node)) {
assert(node->view_src->data != NULL);
node->data = (char *)node->view_src->data + node->view_offs;
init_view(alloc, node);
} else {
// see if we can reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) {
@ -478,13 +422,17 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
// adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
// for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
node->data = parent->data;
node->view_src = view_src;
view_src_hn->n_views += 1;
init_view(alloc, node);
return;
}
}
else {
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
node->data = parent->data;
node->view_src = parent;
p_hn->n_views += 1;
init_view(alloc, node);
return;
}
}
@ -495,7 +443,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
}
}
static size_t ggml_allocr_alloc_graph_tensors_n(
size_t ggml_allocr_alloc_graph_n(
struct ggml_allocr * alloc,
struct ggml_cgraph ** graphs, int n_graphs,
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
@ -513,6 +461,10 @@ static size_t ggml_allocr_alloc_graph_tensors_n(
if (ggml_is_view(node)) {
struct ggml_tensor * view_src = node->view_src;
hash_get(ht, view_src)->n_views += 1;
if (node->buffer == NULL && node->data != NULL) {
// view of a pre-allocated tensor, didn't call init_view() yet
init_view(alloc, node);
}
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
@ -521,6 +473,9 @@ static size_t ggml_allocr_alloc_graph_tensors_n(
break;
}
hash_get(ht, parent)->n_children += 1;
if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) {
init_view(alloc, parent);
}
}
}
}
@ -631,7 +586,7 @@ static size_t ggml_allocr_alloc_graph_tensors_n(
}
size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
return ggml_allocr_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
return ggml_allocr_alloc_graph_n(alloc, &graph, 1, NULL, NULL);
}
size_t ggml_allocr_max_size(struct ggml_allocr * alloc) {

16
ggml-alloc.h
View File

@ -6,21 +6,27 @@
extern "C" {
#endif
struct ggml_backend_buffer;
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer);
// tell the allocator to parse nodes following the order described in the list
// you should call this if your graph are optimized to execute out-of-order
GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
GGML_API void ggml_allocr_free (struct ggml_allocr * alloc);
GGML_API bool ggml_allocr_is_measure (struct ggml_allocr * alloc);
GGML_API void ggml_allocr_reset (struct ggml_allocr * alloc);
GGML_API void ggml_allocr_alloc (struct ggml_allocr * alloc, struct ggml_tensor * tensor);
GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
GGML_API size_t ggml_allocr_max_size(struct ggml_allocr * alloc);
GGML_API size_t ggml_allocr_max_size (struct ggml_allocr * alloc);
GGML_API size_t ggml_allocr_alloc_graph_n(
struct ggml_allocr * alloc,
struct ggml_cgraph ** graphs, int n_graphs,
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs);
#ifdef __cplusplus
}

385
ggml-backend.c Normal file
View File

@ -0,0 +1,385 @@
#include "ggml-backend.h"
#include "ggml-alloc.h"
#include <assert.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define UNUSED GGML_UNUSED
#define MAX(a, b) ((a) > (b) ? (a) : (b))
// backend buffer
ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size) {
ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));
GGML_ASSERT(iface.get_base != NULL);
(*buffer) = (struct ggml_backend_buffer) {
/* .interface = */ iface,
/* .backend = */ backend,
/* .context = */ context,
/* .size = */ size,
};
return buffer;
}
void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
if (buffer->iface.free_buffer != NULL) {
buffer->iface.free_buffer(buffer);
}
free(buffer);
}
size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) {
return ggml_backend_get_alignment(buffer->backend);
}
void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
return buffer->iface.get_base(buffer);
}
size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
return buffer->size;
}
size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
if (buffer->iface.get_alloc_size) {
return buffer->iface.get_alloc_size(buffer, tensor);
}
return ggml_nbytes(tensor);
}
void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
if (buffer->iface.init_tensor) {
buffer->iface.init_tensor(buffer, tensor);
}
}
void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
if (buffer->iface.free_tensor) {
buffer->iface.free_tensor(buffer, tensor);
}
}
// backend
ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) {
return tensor->buffer->backend;
}
const char * ggml_backend_name(ggml_backend_t backend) {
return backend->iface.get_name(backend);
}
void ggml_backend_free(ggml_backend_t backend) {
backend->iface.free(backend);
}
ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
return backend->iface.alloc_buffer(backend, size);
}
size_t ggml_backend_get_alignment(ggml_backend_t backend) {
return backend->iface.get_alignment(backend);
}
void ggml_backend_tensor_set_async(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
}
void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
}
void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
}
void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
}
void ggml_backend_synchronize(ggml_backend_t backend) {
backend->iface.synchronize(backend);
}
ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
return backend->iface.graph_plan_create(backend, cgraph);
}
void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
backend->iface.graph_plan_free(backend, plan);
}
void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
backend->iface.graph_plan_compute(backend, plan);
}
void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
backend->iface.graph_compute(backend, cgraph);
}
bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
return backend->iface.supports_op(backend, op);
}
// backend copy
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
if (a->type != b->type) {
return false;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (a->ne[i] != b->ne[i]) {
return false;
}
if (a->nb[i] != b->nb[i]) {
return false;
}
}
return true;
}
void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
//printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]);
//printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]);
GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
// printf("cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src));
if (src == dst) {
return;
}
// TODO: allow backends to support copy to/from same backend
if (ggml_get_backend(dst)->iface.cpy_tensor_from != NULL) {
ggml_get_backend(dst)->iface.cpy_tensor_from(ggml_get_backend(dst)->context, src, dst);
} else if (ggml_get_backend(src)->iface.cpy_tensor_to != NULL) {
ggml_get_backend(src)->iface.cpy_tensor_to(ggml_get_backend(src)->context, src, dst);
} else {
// shouldn't be hit when copying from/to CPU
#ifndef NDEBUG
fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to are implemented for backends %s and %s, falling back to get/set\n", ggml_backend_name(src->buffer->backend), ggml_backend_name(dst->buffer->backend));
#endif
size_t nbytes = ggml_nbytes(src);
void * data = malloc(nbytes);
ggml_backend_tensor_get(src, data, 0, nbytes);
ggml_backend_tensor_set(dst, data, 0, nbytes);
free(data);
}
}
// backend CPU
struct ggml_backend_cpu_context {
int n_threads;
void * work_data;
size_t work_size;
};
static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
return "CPU";
UNUSED(backend);
}
static void ggml_backend_cpu_free(ggml_backend_t backend) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
free(cpu_ctx->work_data);
free(cpu_ctx);
free(backend);
}
static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *)buffer->context;
}
static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
UNUSED(buffer);
}
static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
/* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer,
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .init_tensor = */ NULL, // no initialization required
/* .free_tensor = */ NULL, // no cleanup required
};
// for buffers from ptr, free is not called
static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
/* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .init_tensor = */ NULL,
/* .free_tensor = */ NULL,
};
static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backend, size_t size) {
size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned
void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size);
}
static size_t ggml_backend_cpu_get_alignment(ggml_backend_t backend) {
return TENSOR_ALIGNMENT;
UNUSED(backend);
}
static void ggml_backend_cpu_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy((char *)tensor->data + offset, data, size);
UNUSED(backend);
}
static void ggml_backend_cpu_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy(data, (const char *)tensor->data + offset, size);
UNUSED(backend);
}
static void ggml_backend_cpu_synchronize(ggml_backend_t backend) {
UNUSED(backend);
}
static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
UNUSED(backend);
}
static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
// for a backend such as CUDA that can queue async calls, it is ok to do this asynchronously, but it may not be the case for other backends
ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
UNUSED(backend);
}
struct ggml_backend_plan_cpu {
struct ggml_cplan cplan;
struct ggml_cgraph cgraph;
};
static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
cpu_plan->cgraph = *cgraph;
if (cpu_plan->cplan.work_size > 0) {
cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
}
return cpu_plan;
}
static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
free(cpu_plan->cplan.work_data);
free(cpu_plan);
UNUSED(backend);
}
static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
UNUSED(backend);
}
static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
if (cpu_ctx->work_size < cplan.work_size) {
// TODO: may be faster to free and use malloc to avoid the copy
cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
cpu_ctx->work_size = cplan.work_size;
}
cplan.work_data = cpu_ctx->work_data;
ggml_graph_compute(cgraph, &cplan);
}
static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
return true;
UNUSED(backend);
UNUSED(op);
}
static struct ggml_backend_i cpu_backend_i = {
/* .get_name = */ ggml_backend_cpu_name,
/* .free = */ ggml_backend_cpu_free,
/* .alloc_buffer = */ ggml_backend_cpu_alloc_buffer,
/* .get_alignment = */ ggml_backend_cpu_get_alignment,
/* .set_tensor_async = */ ggml_backend_cpu_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_cpu_get_tensor_async,
/* .synchronize = */ ggml_backend_cpu_synchronize,
/* .cpy_tensor_from = */ ggml_backend_cpu_cpy_tensor_from,
/* .cpy_tensor_to = */ ggml_backend_cpu_cpy_tensor_to,
/* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create,
/* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free,
/* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute,
/* .graph_compute = */ ggml_backend_cpu_graph_compute,
/* .supports_op = */ ggml_backend_cpu_supports_op,
};
ggml_backend_t ggml_backend_cpu_init(void) {
struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
ctx->n_threads = GGML_DEFAULT_N_THREADS;
ctx->work_data = NULL;
ctx->work_size = 0;
ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
*cpu_backend = (struct ggml_backend) {
/* .interface = */ cpu_backend_i,
/* .context = */ ctx
};
return cpu_backend;
}
bool ggml_backend_is_cpu(ggml_backend_t backend) {
return backend->iface.get_name == ggml_backend_cpu_name;
}
void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
ctx->n_threads = n_threads;
}
ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) {
return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size);
}

143
ggml-backend.h Normal file
View File

@ -0,0 +1,143 @@
#pragma once
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_backend;
struct ggml_backend_buffer;
// type-erased backend-specific types / wrappers
typedef void * ggml_backend_context_t;
typedef void * ggml_backend_graph_plan_t;
typedef void * ggml_backend_buffer_context_t;
// avoid accessing internals of these types
typedef struct ggml_backend * ggml_backend_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
//
// backend buffer
//
struct ggml_backend_buffer_i {
void (*free_buffer) (ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
};
// TODO: hide behind API
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_t backend;
ggml_backend_buffer_context_t context;
size_t size;
};
// backend buffer functions
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
//
// backend
//
struct ggml_backend_i {
const char * (*get_name)(ggml_backend_t backend);
void (*free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
// get buffer alignment
size_t (*get_alignment)(ggml_backend_t backend);
// tensor data access
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph without a plan
void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation
bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
};
// TODO: hide behind API
struct ggml_backend {
struct ggml_backend_i iface;
ggml_backend_context_t context;
};
// backend helper functions
GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
GGML_API const char * ggml_backend_name(ggml_backend_t backend);
GGML_API void ggml_backend_free(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API void ggml_backend_tensor_set_async( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op);
// tensor copy between different backends
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
//
// CPU backend
//
GGML_API ggml_backend_t ggml_backend_cpu_init(void);
GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
#ifdef __cplusplus
}
#endif

535
ggml-cuda.cu
View File

@ -62,6 +62,7 @@
#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
#define cudaMemcpyKind hipMemcpyKind
#define cudaMemset hipMemset
#define cudaMemsetAsync hipMemsetAsync
#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
#define cudaSetDevice hipSetDevice
#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
@ -419,6 +420,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
#define CUDA_QUANTIZE_BLOCK_SIZE 256
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
#define CUDA_GET_ROWS_BLOCK_SIZE 256
// dmmv = dequantize_mul_mat_vec
#ifndef GGML_CUDA_DMMV_X
@ -1574,6 +1576,34 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
reinterpret_cast<half&>(y[ib].ds.y) = sum;
}
template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void k_get_rows(const void * x, const int32_t * y, dst_t * dst, const int ncols) {
const int col = (blockIdx.x*blockDim.x + threadIdx.x)*2;
const int row = blockDim.y*blockIdx.y + threadIdx.y;
if (col >= ncols) {
return;
}
const int r = y[row];
// copy x[r*ncols + col] to dst[row*ncols + col]
const int xi = r*ncols + col;
const int di = row*ncols + col;
const int ib = xi/qk; // block index
const int iqs = (xi%qk)/qr; // quant index
const int iybs = di - di%qk; // y block start index
const int y_offset = qr == 1 ? 1 : qk/2;
// dequantize
dfloat2 v;
dequantize_kernel(x, ib, iqs, v);
dst[iybs + iqs + 0] = v.x;
dst[iybs + iqs + y_offset] = v.y;
}
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
@ -4555,6 +4585,15 @@ static __global__ void scale_f32(const float * x, float * dst, const float scale
dst[i] = scale * x[i];
}
template<int qk, int qr, dequantize_kernel_t dq>
static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
const int block_num_x = (ncols + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
const dim3 block_nums(block_num_x, nrows, 1);
k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(x, y, dst, ncols);
}
static void add_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
const int num_blocks = (kx + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
@ -5703,7 +5742,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
} else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
kind = cudaMemcpyDeviceToDevice;
struct ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
int id;
CUDA_CHECK(cudaGetDevice(&id));
src_ptr = (char *) extra->data_device[id];
@ -5739,6 +5778,107 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
}
}
static void ggml_cuda_op_repeat(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & stream) {
// guaranteed to be an integer due to the check in ggml_can_repeat
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t ne3 = dst->ne[3];
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const size_t nb0 = dst->nb[0];
const size_t nb1 = dst->nb[1];
const size_t nb2 = dst->nb[2];
const size_t nb3 = dst->nb[3];
const size_t nb00 = src0->nb[0];
const size_t nb01 = src0->nb[1];
const size_t nb02 = src0->nb[2];
const size_t nb03 = src0->nb[3];
const int nr0 = (int)(ne0/ne00);
const int nr1 = (int)(ne1/ne01);
const int nr2 = (int)(ne2/ne02);
const int nr3 = (int)(ne3/ne03);
// TODO: support for transposed / permuted tensors
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
// TODO: very inefficient, implement in a kernel, or fewer cudaMemcpyAsync calls for contiguous tensors
for (int i3 = 0; i3 < nr3; i3++) {
for (int k3 = 0; k3 < ne03; k3++) {
for (int i2 = 0; i2 < nr2; i2++) {
for (int k2 = 0; k2 < ne02; k2++) {
for (int i1 = 0; i1 < nr1; i1++) {
for (int k1 = 0; k1 < ne01; k1++) {
for (int i0 = 0; i0 < nr0; i0++) {
CUDA_CHECK(cudaMemcpyAsync(
(char *) dst_d + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0,
(const char *) src0_d + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01,
ne00*nb0, cudaMemcpyDeviceToDevice, stream));
}
}
}
}
}
}
}
(void) src1;
(void) src1_d;
}
static void ggml_cuda_op_get_rows(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & stream) {
GGML_ASSERT(src1->type == GGML_TYPE_I32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_contiguous(dst));
const int ncols = src0->ne[0];
const int nrows = ggml_nelements(src1);
const int32_t * src1_i32 = (const int32_t *) src1_d;
switch (src0->type) {
case GGML_TYPE_F16:
get_rows_cuda<1, 1, convert_f16>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
case GGML_TYPE_F32:
get_rows_cuda<1, 1, convert_f32>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
case GGML_TYPE_Q4_0:
get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
case GGML_TYPE_Q4_1:
get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
case GGML_TYPE_Q5_0:
get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
case GGML_TYPE_Q5_1:
get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
case GGML_TYPE_Q8_0:
get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
break;
default:
// TODO: k-quants
GGML_ASSERT(false);
break;
}
}
inline void ggml_cuda_op_add(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
@ -6343,7 +6483,14 @@ inline void ggml_cuda_op_scale(
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const float scale = ((float *) src1->data)[0];
float scale;
// HACK: support for ggml backend interface
if (src1->backend == GGML_BACKEND_CPU) {
scale = ((float *) src1->data)[0];
} else {
// TODO: pass pointer to kernel instead of copying to host
CUDA_CHECK(cudaMemcpy(&scale, src1->data, sizeof(float), cudaMemcpyDeviceToHost));
}
scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
CUDA_CHECK(cudaGetLastError());
@ -6362,9 +6509,9 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s
GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT);
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
struct ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
struct ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
@ -6505,9 +6652,9 @@ static void ggml_cuda_op_mul_mat(
const size_t q8_1_ts = sizeof(block_q8_1);
const size_t q8_1_bs = QK8_1;
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
struct ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool src0_is_contiguous = ggml_is_contiguous(src0);
@ -6585,7 +6732,7 @@ static void ggml_cuda_op_mul_mat(
if (convert_src1_to_q8_1) {
src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]);
if (split && src1_on_device && src1_is_contiguous) {
if (src1_on_device && src1_is_contiguous) {
quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());
}
@ -6667,7 +6814,7 @@ static void ggml_cuda_op_mul_mat(
GGML_ASSERT(false);
}
if (convert_src1_to_q8_1 && src1->backend == GGML_BACKEND_CPU) {
if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());
}
@ -6758,6 +6905,14 @@ static void ggml_cuda_op_mul_mat(
}
}
static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_repeat);
}
static void ggml_cuda_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_get_rows);
}
static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add);
}
@ -6812,13 +6967,13 @@ static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tens
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
void * src0_ddq = src0_extra->data_device[g_main_device];
struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
struct ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
@ -6843,13 +6998,13 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
void * src0_ddq = src0_extra->data_device[g_main_device];
struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
struct ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
const int64_t row_stride_x = nb01 / sizeof(half);
@ -6870,11 +7025,11 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
}
}
if (all_on_device && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
if (all_on_device && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
} else if (all_on_device && !ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->ne[1] == 1) {
ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
}else if (src0->type == GGML_TYPE_F32) {
} else if (src0->type == GGML_TYPE_F32) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
} else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
@ -6935,8 +7090,8 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
const struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
const struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
@ -6991,8 +7146,8 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
const size_t nb1 = tensor->nb[1];
ggml_backend backend = tensor->backend;
struct ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
ggml_backend_type backend = tensor->backend;
ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
memset(extra, 0, sizeof(*extra));
for (int64_t id = 0; id < g_device_count; ++id) {
@ -7046,7 +7201,6 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
}
CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice));
extra->data_device[id] = buf;
@ -7085,17 +7239,17 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) {
delete extra;
}
static struct ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
static size_t g_temp_tensor_extra_index = 0;
static struct ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
if (g_temp_tensor_extras == nullptr) {
g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES];
}
size_t alloc_index = g_temp_tensor_extra_index;
g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_MAX_NODES;
struct ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
memset(extra, 0, sizeof(*extra));
return extra;
@ -7123,7 +7277,7 @@ static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scra
return;
}
struct ggml_tensor_extra_gpu * extra;
ggml_tensor_extra_gpu * extra;
const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
tensor->op == GGML_OP_VIEW ||
@ -7132,7 +7286,7 @@ static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scra
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
size_t offset = 0;
if (tensor->op == GGML_OP_VIEW) {
@ -7141,7 +7295,7 @@ static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scra
extra = ggml_cuda_alloc_temp_tensor_extra();
extra->data_device[g_main_device] = src0_ddc + offset;
} else if (tensor->op == GGML_OP_CPY) {
struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
void * src1_ddv = src1_extra->data_device[g_main_device];
extra = ggml_cuda_alloc_temp_tensor_extra();
extra->data_device[g_main_device] = src1_ddv;
@ -7183,13 +7337,13 @@ void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset)
CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size));
}
struct ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
tensor->op == GGML_OP_VIEW;
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
size_t view_offset = 0;
if (tensor->op == GGML_OP_VIEW) {
@ -7207,7 +7361,7 @@ void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(ggml_is_contiguous(tensor));
struct ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice));
}
@ -7264,58 +7418,47 @@ void ggml_cuda_free_scratch() {
g_scratch_buffer = nullptr;
}
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor){
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
ggml_cuda_func_t func;
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
switch (tensor->op) {
case GGML_OP_DUP:
if (!any_on_device) {
if (!any_on_device && tensor->op != GGML_OP_MUL_MAT) {
return false;
}
switch (tensor->op) {
case GGML_OP_REPEAT:
func = ggml_cuda_repeat;
break;
case GGML_OP_GET_ROWS:
func = ggml_cuda_get_rows;
break;
case GGML_OP_DUP:
func = ggml_cuda_dup;
break;
case GGML_OP_ADD:
if (!any_on_device) {
return false;
}
func = ggml_cuda_add;
break;
case GGML_OP_MUL:
if (!any_on_device) {
return false;
}
func = ggml_cuda_mul;
break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(tensor)) {
case GGML_UNARY_OP_GELU:
if (!any_on_device) {
return false;
}
func = ggml_cuda_gelu;
break;
case GGML_UNARY_OP_SILU:
if (!any_on_device) {
return false;
}
func = ggml_cuda_silu;
break;
default:
return false;
} break;
case GGML_OP_NORM:
if (!any_on_device) {
return false;
}
func = ggml_cuda_norm;
break;
case GGML_OP_RMS_NORM:
if (!any_on_device) {
return false;
}
func = ggml_cuda_rms_norm;
break;
case GGML_OP_MUL_MAT:
@ -7325,54 +7468,30 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
func = ggml_cuda_mul_mat;
break;
case GGML_OP_SCALE:
if (!any_on_device) {
return false;
}
func = ggml_cuda_scale;
break;
case GGML_OP_CPY:
if (!any_on_device) {
return false;
}
func = ggml_cuda_cpy;
break;
case GGML_OP_CONT:
if (!any_on_device) {
return false;
}
func = ggml_cuda_dup;
break;
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
if (!any_on_device) {
return false;
}
func = ggml_cuda_nop;
break;
case GGML_OP_DIAG_MASK_INF:
if (!any_on_device) {
return false;
}
func = ggml_cuda_diag_mask_inf;
break;
case GGML_OP_SOFT_MAX:
if (!any_on_device) {
return false;
}
func = ggml_cuda_soft_max;
break;
case GGML_OP_ROPE:
if (!any_on_device) {
return false;
}
func = ggml_cuda_rope;
break;
case GGML_OP_ALIBI:
if (!any_on_device) {
return false;
}
func = ggml_cuda_alibi;
break;
default:
@ -7400,3 +7519,263 @@ void ggml_cuda_get_device_description(int device, char * description, size_t des
CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
snprintf(description, description_size, "%s", prop.name);
}
////////////////////////////////////////////////////////////////////////////////
// backend interface
#define UNUSED GGML_UNUSED
struct ggml_backend_context_cuda {
};
static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
return GGML_CUDA_NAME;
UNUSED(backend);
}
static void ggml_backend_cuda_free(ggml_backend_t backend) {
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
delete cuda_ctx;
delete backend;
}
struct ggml_backend_buffer_context_cuda {
void * device;
ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
size_t temp_tensor_extra_index = 0;
~ggml_backend_buffer_context_cuda() {
delete[] temp_tensor_extras;
}
ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
if (temp_tensor_extras == nullptr) {
temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES];
}
size_t alloc_index = temp_tensor_extra_index;
temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_MAX_NODES;
ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
memset(extra, 0, sizeof(*extra));
return extra;
}
};
static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
CUDA_CHECK(cudaFree(ctx->device));
delete ctx;
}
static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
return ctx->device;
}
static size_t ggml_backend_cuda_buffer_get_alloc_size(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
int64_t row_low = 0;
int64_t row_high = ggml_nrows(tensor);
int64_t nrows_split = row_high - row_low;
size_t size = ggml_nbytes_split(tensor, nrows_split);
int64_t ne0 = tensor->ne[0];
if (ggml_is_quantized(tensor->type)) {
if (ne0 % MATRIX_ROW_PADDING != 0) {
size += (MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING)
* ggml_type_size(tensor->type)/ggml_blck_size(tensor->type);
}
}
return size;
UNUSED(buffer);
}
static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
if (tensor->view_src != NULL && tensor->view_offs == 0) {
assert(tensor->view_src->buffer->backend == buffer->backend);
tensor->backend = tensor->view_src->backend;
tensor->extra = tensor->view_src->extra;
return;
}
ggml_tensor_extra_gpu * extra = ctx->ggml_cuda_alloc_temp_tensor_extra();
extra->data_device[g_main_device] = tensor->data;
tensor->backend = GGML_BACKEND_GPU;
tensor->extra = extra;
if (ggml_is_quantized(tensor->type)) {
// initialize padding to 0 to avoid possible NaN values
int64_t row_low = 0;
int64_t row_high = ggml_nrows(tensor);
int64_t nrows_split = row_high - row_low;
size_t original_size = ggml_nbytes_split(tensor, nrows_split);
size_t padded_size = ggml_backend_cuda_buffer_get_alloc_size(tensor->buffer, tensor);
if (padded_size > original_size && tensor->view_src == nullptr) {
CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[g_main_device][0]));
}
}
UNUSED(buffer);
}
static struct ggml_backend_buffer_i cuda_backend_buffer_interface = {
/* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer,
/* .get_base = */ ggml_backend_cuda_buffer_get_base,
/* .get_alloc_size = */ ggml_backend_cuda_buffer_get_alloc_size,
/* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor,
/* .free_tensor = */ NULL,
};
static ggml_backend_buffer_t ggml_backend_cuda_alloc_buffer(ggml_backend_t backend, size_t size) {
ggml_cuda_set_device(g_main_device);
ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda;
CUDA_CHECK(cudaMalloc(&ctx->device, size));
return ggml_backend_buffer_init(backend, cuda_backend_buffer_interface, ctx, size);
}
static size_t ggml_backend_cuda_get_alignment(ggml_backend_t backend) {
return 128;
UNUSED(backend);
}
static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[g_main_device][0]));
UNUSED(backend);
}
static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
UNUSED(backend);
}
static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
UNUSED(backend);
}
static ggml_backend_graph_plan_t ggml_backend_cuda_graph_plan_create(ggml_backend_t backend, ggml_cgraph * cgraph) {
GGML_ASSERT(!"not implemented");
return nullptr;
UNUSED(backend);
UNUSED(cgraph);
}
static void ggml_backend_cuda_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
GGML_ASSERT(!"not implemented");
UNUSED(backend);
UNUSED(plan);
}
static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
GGML_ASSERT(!"not implemented");
UNUSED(backend);
UNUSED(plan);
}
static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_cuda_set_device(g_main_device);
ggml_compute_params params = {};
params.type = GGML_TASK_COMPUTE;
params.ith = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
assert(node->backend == GGML_BACKEND_GPU);
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->backend == GGML_BACKEND_GPU);
}
}
bool ok = ggml_cuda_compute_forward(&params, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}
GGML_ASSERT(ok);
#if 0
if (node->type == GGML_TYPE_F32) {
cudaDeviceSynchronize();
std::vector<float> tmp(ggml_nelements(node), 0.0f);
cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost);
printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op),
ggml_type_name(node->src[0]->type),
node->src[1] ? ggml_type_name(node->src[1]->type) : "none",
node->src[0]->name,
node->src[1] ? node->src[1]->name : "none");
double sum = 0.0;
double sq_sum = 0.0;
for (int i = 0; i < ggml_nelements(node); i++) {
printf("%f ", tmp[i]);
sum += tmp[i];
sq_sum += tmp[i]*tmp[i];
}
printf("\n");
printf("sum: %f, ", sum);
printf("sq_sum: %f\n", sq_sum);
}
#endif
}
UNUSED(backend);
}
static ggml_backend_i cuda_backend_i = {
/* .get_name = */ ggml_backend_cuda_name,
/* .free = */ ggml_backend_cuda_free,
/* .alloc_buffer = */ ggml_backend_cuda_alloc_buffer,
/* .get_alignment = */ ggml_backend_cuda_get_alignment,
/* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async,
/* .synchronize = */ ggml_backend_cuda_synchronize,
/* .cpy_tensor_from = */ nullptr,
/* .cpy_tensor_to = */ nullptr,
/* .graph_plan_create = */ ggml_backend_cuda_graph_plan_create,
/* .graph_plan_free = */ ggml_backend_cuda_graph_plan_free,
/* .graph_plan_compute = */ ggml_backend_cuda_graph_plan_compute,
/* .graph_compute = */ ggml_backend_cuda_graph_compute,
/* .supports_op = */ nullptr,
};
ggml_backend_t ggml_backend_cuda_init() {
ggml_init_cublas(); // TODO: remove from ggml.c
ggml_backend_context_cuda * ctx = new ggml_backend_context_cuda;
ggml_backend_t cuda_backend = new ggml_backend {
/* .interface = */ cuda_backend_i,
/* .context = */ ctx
};
return cuda_backend;
}

4
ggml-cuda.h
View File

@ -1,6 +1,7 @@
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef GGML_USE_HIPBLAS
#define GGML_CUDA_NAME "ROCm"
@ -42,6 +43,9 @@ GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, s
GGML_API int ggml_cuda_get_device_count(void);
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
// backend API
GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
#ifdef __cplusplus
}
#endif

19
ggml-metal.h
View File

@ -20,6 +20,7 @@
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#include <stddef.h>
#include <stdbool.h>
@ -35,10 +36,15 @@ struct ggml_cgraph;
extern "C" {
#endif
void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
//
// internal API
// temporary exposed to user-code
//
struct ggml_metal_context;
void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
// number of command buffers to use
struct ggml_metal_context * ggml_metal_init(int n_cb);
void ggml_metal_free(struct ggml_metal_context * ctx);
@ -83,6 +89,17 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
// creates gf->n_threads command buffers in parallel
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
//
// backend API
// user-code should use only these functions
//
GGML_API ggml_backend_t ggml_backend_metal_init(void);
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
#ifdef __cplusplus
}
#endif

137
ggml-metal.m
View File

@ -1456,3 +1456,140 @@ void ggml_metal_graph_compute(
}
}
////////////////////////////////////////////////////////////////////////////////
// backend interface
static const char * ggml_backend_metal_name(ggml_backend_t backend) {
return "Metal";
UNUSED(backend);
}
static void ggml_backend_metal_free(ggml_backend_t backend) {
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
ggml_metal_free(ctx);
free(backend);
}
static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *)buffer->context;
}
static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
UNUSED(buffer);
}
static struct ggml_backend_buffer_i metal_backend_buffer_i = {
/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
/* .get_base = */ ggml_backend_metal_buffer_get_base,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .init_tensor = */ NULL, // no initialization required
/* .free_tensor = */ NULL, // no cleanup required
};
static ggml_backend_buffer_t ggml_backend_metal_alloc_buffer(ggml_backend_t backend, size_t size) {
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
void * data = ggml_metal_host_malloc(size);
// TODO: set proper name of the buffers
ggml_metal_add_buffer(ctx, "backend", data, size, 0);
return ggml_backend_buffer_init(backend, metal_backend_buffer_i, data, size);
}
static size_t ggml_backend_metal_get_alignment(ggml_backend_t backend) {
return 32;
UNUSED(backend);
}
static void ggml_backend_metal_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy((char *)tensor->data + offset, data, size);
UNUSED(backend);
}
static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy(data, (const char *)tensor->data + offset, size);
UNUSED(backend);
}
static void ggml_backend_metal_synchronize(ggml_backend_t backend) {
UNUSED(backend);
}
static void ggml_backend_metal_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
UNUSED(backend);
}
static void ggml_backend_metal_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
UNUSED(backend);
}
static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
ggml_metal_graph_compute(metal_ctx, cgraph);
}
static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
return true;
UNUSED(backend);
UNUSED(op);
}
static struct ggml_backend_i metal_backend_i = {
/* .get_name = */ ggml_backend_metal_name,
/* .free = */ ggml_backend_metal_free,
/* .alloc_buffer = */ ggml_backend_metal_alloc_buffer,
/* .get_alignment = */ ggml_backend_metal_get_alignment,
/* .set_tensor_async = */ ggml_backend_metal_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_metal_get_tensor_async,
/* .synchronize = */ ggml_backend_metal_synchronize,
/* .cpy_tensor_from = */ ggml_backend_metal_cpy_tensor_from,
/* .cpy_tensor_to = */ ggml_backend_metal_cpy_tensor_to,
/* .graph_plan_create = */ NULL, // the metal implementation does not require creating graph plans atm
/* .graph_plan_free = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_metal_graph_compute,
/* .supports_op = */ ggml_backend_metal_supports_op,
};
ggml_backend_t ggml_backend_metal_init(void) {
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend));
*metal_backend = (struct ggml_backend) {
/* .interface = */ metal_backend_i,
/* .context = */ ctx,
};
return metal_backend;
}
bool ggml_backend_is_metal(ggml_backend_t backend) {
return backend->iface.get_name == ggml_backend_metal_name;
}
void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
ggml_metal_set_n_cb(ctx, n_cb);
}

37
ggml.c
View File

@ -162,40 +162,16 @@ typedef void * thread_ret_t;
#define GGML_PRINT(...) printf(__VA_ARGS__)
//
// end of logging block
//
#ifdef GGML_USE_ACCELERATE
// uncomment to use vDSP for soft max computation
// note: not sure if it is actually faster
//#define GGML_SOFT_MAX_ACCELERATE
#endif
//
// logging
//
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
#define GGML_PRINT(...) printf(__VA_ARGS__)
//
// end of logging block
//
#if defined(_MSC_VER) || defined(__MINGW32__)
#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
@ -4951,6 +4927,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
*result = (struct ggml_tensor) {
/*.type =*/ type,
/*.backend =*/ GGML_BACKEND_CPU,
/*.buffer =*/ NULL,
/*.n_dims =*/ n_dims,
/*.ne =*/ { 1, 1, 1, 1 },
/*.nb =*/ { 0, 0, 0, 0 },
@ -20203,6 +20180,10 @@ static enum ggml_opt_result ggml_opt_lbfgs(
ggml_vec_cpy_f32(nx, xp, x);
ggml_vec_cpy_f32(nx, gp, g);
// TODO: instead of passing &cancel here, use the return code of the linesearch
// to determine if the optimization should be cancelled
// this is a simple change, but not doing this atm, since I don't have a nice
// way to test and don't want to break something with so many changes lined up
ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data);
if (cancel) {
return GGML_OPT_CANCEL;

14
ggml.h
View File

@ -326,7 +326,7 @@ extern "C" {
GGML_TYPE_COUNT,
};
enum ggml_backend {
enum ggml_backend_type {
GGML_BACKEND_CPU = 0,
GGML_BACKEND_GPU = 10,
GGML_BACKEND_GPU_SPLIT = 20,
@ -480,7 +480,9 @@ extern "C" {
// n-dimensional tensor
struct ggml_tensor {
enum ggml_type type;
enum ggml_backend backend;
enum ggml_backend_type backend;
struct ggml_backend_buffer * buffer;
int n_dims;
int64_t ne[GGML_MAX_DIMS]; // number of elements
@ -514,7 +516,7 @@ extern "C" {
void * extra; // extra things e.g. for ggml-cuda.cu
char padding[4];
char padding[12];
};
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
@ -1358,7 +1360,7 @@ extern "C" {
// alibi position embedding
// in-place, returns view(a)
struct ggml_tensor * ggml_alibi(
GGML_API struct ggml_tensor * ggml_alibi(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
@ -1367,7 +1369,7 @@ extern "C" {
// clamp
// in-place, returns view(a)
struct ggml_tensor * ggml_clamp(
GGML_API struct ggml_tensor * ggml_clamp(
struct ggml_context * ctx,
struct ggml_tensor * a,
float min,
@ -2102,7 +2104,7 @@ extern "C" {
enum ggml_type vec_dot_type;
} ggml_type_traits_t;
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
#ifdef __cplusplus
}

44
llama.cpp
View File

@ -1730,7 +1730,7 @@ struct llama_model_loader {
}
}
struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend backend) {
struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) {
if (backend != GGML_BACKEND_CPU) {
ggml_set_no_alloc(ctx, true);
}
@ -1748,7 +1748,7 @@ struct llama_model_loader {
return tensor;
}
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend backend) {
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
if (cur == NULL) {
@ -2299,8 +2299,8 @@ static void llm_load_tensors(
// output
{
ggml_backend backend_norm;
ggml_backend backend_output;
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
@ -2335,8 +2335,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
auto & layer = model.layers[i];
@ -2365,8 +2365,8 @@ static void llm_load_tensors(
{
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
{
ggml_backend backend_norm;
ggml_backend backend_output;
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
@ -2401,8 +2401,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
auto & layer = model.layers[i];
@ -2435,8 +2435,8 @@ static void llm_load_tensors(
// output
{
ggml_backend backend_norm;
ggml_backend backend_output;
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
@ -2473,8 +2473,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
auto & layer = model.layers[i];
@ -2512,8 +2512,8 @@ static void llm_load_tensors(
// output
{
ggml_backend backend_norm;
ggml_backend backend_output;
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
@ -2550,8 +2550,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
auto & layer = model.layers[i];
@ -2589,8 +2589,8 @@ static void llm_load_tensors(
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
{
ggml_backend backend_norm;
ggml_backend backend_output;
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
@ -2624,8 +2624,8 @@ static void llm_load_tensors(
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);

2
scripts/sync-ggml.sh
View File

@ -2,6 +2,7 @@
cp -rpv ../ggml/src/ggml.c ./ggml.c
cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c
cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c
cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
@ -11,6 +12,7 @@ cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h
cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp