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metal : remove old API (#4919)

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ggml-ci
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Georgi Gerganov 2024-01-13 20:45:45 +02:00 committed by GitHub
parent 0ea069b87b
commit 4be5ef556d
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7 changed files with 27 additions and 427 deletions
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9
Makefile
View File

@ -43,10 +43,6 @@ ifeq ($(UNAME_S),Darwin)
endif
endif
ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
BUILD_TARGETS += metal
endif
default: $(BUILD_TARGETS)
test: $(TEST_TARGETS)
@ -671,11 +667,6 @@ lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
endif
ifeq ($(UNAME_S),Darwin)
swift: examples/batched.swift
(cd examples/batched.swift; make build)

3
examples/CMakeLists.txt
View File

@ -37,9 +37,6 @@ else()
add_subdirectory(lookup)
add_subdirectory(train-text-from-scratch)
add_subdirectory(imatrix)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()
if (LLAMA_BUILD_SERVER)
add_subdirectory(server)
endif()

4
examples/metal/CMakeLists.txt
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@ -1,4 +0,0 @@
set(TEST_TARGET metal)
add_executable(${TEST_TARGET} metal.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TEST_TARGET} PRIVATE ggml)

103
examples/metal/metal.cpp
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@ -1,103 +0,0 @@
// Evaluate a statically exported ggml computation graph with Metal
//
// - First, export a LLaMA graph:
//
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export
//
// - Run this tool to evaluate the exported graph:
//
// $ ./bin/metal llama.ggml
//
// The purpose of this tool is mostly for debugging and demonstration purposes.
// The main limitation of exporting computation graphs is that their sizes are static which often
// can be a problem for real-world applications.
//
#include "ggml.h"
#include "ggml-metal.h"
#include <cstdio>
#include <cstring>
#include <cstdlib>
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 2) {
fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]);
return -1;
}
const char * fname_cgraph = argv[1];
// load the compute graph
struct ggml_context * ctx_data = NULL;
struct ggml_context * ctx_eval = NULL;
struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
// this allocates all Metal resources and memory buffers
auto * ctx_metal = ggml_metal_init(1);
const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);
const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);
ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data);
ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval);
// main
{
struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd");
*(int32_t *) input->data = 1; // BOS
ggml_metal_set_tensor(ctx_metal, input);
// warmup
ggml_metal_graph_compute(ctx_metal, gf);
const int n_iter = 16;
const int64_t t0 = ggml_time_us();
// the actual inference happens here
for (int i = 0; i < n_iter; ++i) {
ggml_metal_graph_compute(ctx_metal, gf);
}
const int64_t t1 = ggml_time_us();
printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);
}
// debug output
{
struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1];
ggml_metal_get_tensor(ctx_metal, logits);
float * ptr = (float *) ggml_get_data(logits);
printf("logits: ");
for (int i = 0; i < 10; i++) {
printf("%8.4f ", ptr[i]);
}
printf("\n");
int imax = 0;
double sum = 0.0;
double vmax = -1e9;
for (int i = 0; i < 32000; i++) {
sum += (double) ptr[i];
if (ptr[i] > vmax) {
vmax = ptr[i];
imax = i;
}
}
printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax);
}
ggml_metal_free(ctx_metal);
ggml_free(ctx_data);
ggml_free(ctx_eval);
return 0;
}

55
ggml-metal.h
View File

@ -36,64 +36,13 @@ struct ggml_cgraph;
extern "C" {
#endif
//
// 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);
void * ggml_metal_host_malloc(size_t n);
void ggml_metal_host_free (void * data);
// set the number of command buffers to use
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
// creates a mapping between a host memory buffer and a device memory buffer
// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
// - the mapping is used during computation to determine the arguments of the compute kernels
// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
// - max_size specifies the maximum size of a tensor and is used to create shared views such
// that it is guaranteed that the tensor will fit in at least one of the views
//
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size,
size_t max_size);
// set data from host memory into the device
void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// get data from the device into host memory
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// try to find operations that can be run concurrently in the graph
// you should run it again if the topology of your graph changes
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
// output the concur_list for ggml_alloc
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel
bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
//
// backend API
// 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);

276
ggml-metal.m
View File

@ -24,8 +24,6 @@
#define UNUSED(x) (void)(x)
#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE)
#define GGML_METAL_MAX_KERNELS 256
struct ggml_metal_buffer {
@ -182,9 +180,6 @@ struct ggml_metal_context {
struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS];
int concur_list[GGML_MAX_CONCUR];
int concur_list_len;
bool support_simdgroup_reduction;
bool support_simdgroup_mm;
};
@ -200,7 +195,6 @@ struct ggml_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);
@ -211,11 +205,6 @@ static void ggml_metal_default_log_callback(enum ggml_log_level level, const cha
ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback;
void * ggml_metal_log_user_data = NULL;
void ggml_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;
}
GGML_ATTRIBUTE_FORMAT(2, 3)
static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
if (ggml_metal_log_callback != NULL) {
@ -238,7 +227,18 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
}
}
struct ggml_metal_context * ggml_metal_init(int n_cb) {
static void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
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__);
return NULL;
}
return data;
}
static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_LOG_INFO("%s: allocating\n", __func__);
id<MTLDevice> device;
@ -264,7 +264,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0;
ctx->concur_list_len = 0;
ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
@ -531,7 +530,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
return ctx;
}
void ggml_metal_free(struct ggml_metal_context * ctx) {
static void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_LOG_INFO("%s: deallocating\n", __func__);
for (int i = 0; i < ctx->n_buffers; ++i) {
@ -557,33 +556,6 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
free(ctx);
}
void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
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__);
return NULL;
}
return data;
}
void ggml_metal_host_free(void * data) {
free(data);
}
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
}
int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
return ctx->concur_list_len;
}
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
return ctx->concur_list;
}
// temporarily defined here for compatibility between ggml-backend and the old API
struct ggml_backend_metal_buffer {
@ -656,209 +628,6 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
return nil;
}
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size,
size_t max_size) {
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
GGML_METAL_LOG_ERROR("%s: error: too many buffers\n", __func__);
return false;
}
if (data) {
// verify that the buffer does not overlap with any of the existing buffers
for (int i = 0; i < ctx->n_buffers; ++i) {
const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
GGML_METAL_LOG_ERROR("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
return false;
}
}
const size_t size_page = sysconf(_SC_PAGESIZE);
size_t size_aligned = size;
if ((size_aligned % size_page) != 0) {
size_aligned += (size_page - (size_aligned % size_page));
}
// the buffer fits into the max buffer size allowed by the device
if (size_aligned <= ctx->device.maxBufferLength) {
ctx->buffers[ctx->n_buffers].name = name;
ctx->buffers[ctx->n_buffers].data = data;
ctx->buffers[ctx->n_buffers].size = size;
ctx->buffers[ctx->n_buffers].metal = [ctx->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 '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
return false;
}
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0);
++ctx->n_buffers;
} else {
// this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
// one of the views
const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
const size_t size_step = ctx->device.maxBufferLength - size_ovlp;
const size_t size_view = ctx->device.maxBufferLength;
for (size_t i = 0; i < size; i += size_step) {
const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);
ctx->buffers[ctx->n_buffers].name = name;
ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i);
ctx->buffers[ctx->n_buffers].size = size_step_aligned;
ctx->buffers[ctx->n_buffers].metal = [ctx->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 '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
return false;
}
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
if (i + size_step < size) {
GGML_METAL_LOG_INFO("\n");
}
++ctx->n_buffers;
}
}
#if TARGET_OS_OSX
GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
} else {
GGML_METAL_LOG_INFO("\n");
}
#else
GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0);
#endif
}
return true;
}
void ggml_metal_set_tensor(
struct ggml_metal_context * ctx,
struct ggml_tensor * t) {
size_t offs;
id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t));
}
void ggml_metal_get_tensor(
struct ggml_metal_context * ctx,
struct ggml_tensor * t) {
size_t offs;
id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
}
void ggml_metal_graph_find_concurrency(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf, bool check_mem) {
int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
int nodes_unused[GGML_MAX_CONCUR];
for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; }
for (int i = 0; i < gf->n_nodes; i++) { nodes_unused[i] = 1; }
ctx->concur_list_len = 0;
int n_left = gf->n_nodes;
int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
while (n_left > 0) {
// number of nodes at a layer (that can be issued concurrently)
int concurrency = 0;
for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
if (nodes_unused[i]) {
// if the requirements for gf->nodes[i] are satisfied
int exe_flag = 1;
// scan all srcs
for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
if (src_cur) {
// if is leaf nodes it's satisfied.
// TODO: ggml_is_leaf()
if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {
continue;
}
// otherwise this src should be the output from previous nodes.
int is_found = 0;
// scan 2*search_depth back because we inserted barrier.
//for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) {
if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) {
is_found = 1;
break;
}
}
if (is_found == 0) {
exe_flag = 0;
break;
}
}
}
if (exe_flag && check_mem) {
// check if nodes[i]'s data will be overwritten by a node before nodes[i].
// if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
int64_t data_start = (int64_t) gf->nodes[i]->data;
int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]);
for (int j = n_start; j < i; j++) {
if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
&& gf->nodes[j]->op != GGML_OP_VIEW \
&& gf->nodes[j]->op != GGML_OP_TRANSPOSE \
&& gf->nodes[j]->op != GGML_OP_PERMUTE) {
if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
continue;
}
exe_flag = 0;
}
}
}
if (exe_flag) {
ctx->concur_list[level_pos + concurrency] = i;
nodes_unused[i] = 0;
concurrency++;
ctx->concur_list_len++;
}
}
}
n_left -= concurrency;
// adding a barrier different layer
ctx->concur_list[level_pos + concurrency] = -1;
ctx->concur_list_len++;
// jump all sorted nodes at nodes_bak
while (!nodes_unused[n_start]) {
n_start++;
}
level_pos += concurrency + 1;
}
if (ctx->concur_list_len > GGML_MAX_CONCUR) {
GGML_METAL_LOG_WARN("%s: too many elements for metal ctx->concur_list!\n", __func__);
}
}
static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) {
switch (op->op) {
case GGML_OP_UNARY:
@ -940,19 +709,15 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
}
}
bool ggml_metal_graph_compute(
static bool ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
@autoreleasepool {
// if there is ctx->concur_list, dispatch concurrently
// else fallback to serial dispatch
MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR;
const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes;
edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
const int n_nodes = gf->n_nodes;
edesc.dispatchType = MTLDispatchTypeSerial;
// create multiple command buffers and enqueue them
// then, we encode the graph into the command buffers in parallel
@ -983,7 +748,7 @@ bool ggml_metal_graph_compute(
const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
for (int ind = node_start; ind < node_end; ++ind) {
const int i = has_concur ? ctx->concur_list[ind] : ind;
const int i = ind;
if (i == -1) {
[encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
@ -2823,6 +2588,11 @@ static struct ggml_backend_i ggml_backend_metal_i = {
/* .supports_op = */ ggml_backend_metal_supports_op,
};
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;
}
ggml_backend_t ggml_backend_metal_init(void) {
struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
@ -2849,7 +2619,7 @@ 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);
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
}
bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {

4
llama.cpp
View File

@ -1266,7 +1266,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g
struct llama_state {
llama_state() {
#ifdef GGML_USE_METAL
ggml_metal_log_set_callback(log_callback, log_callback_user_data);
ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
#endif
}
@ -10470,7 +10470,7 @@ 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;
#ifdef GGML_USE_METAL
ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
#endif
}