mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 15:29:43 +00:00
metal : handle buffers larger than device's maxBufferLength (#1826)
* metal : handle buffers larger than device's maxBufferLength * metal : print more verbose device info + handle errors * metal : fix prints for overlapping views * metal : minimize view overlap to try to utilize device memory better
This commit is contained in:
Georgi Gerganov
GitHub
parent
57cd69460f
commit
ce2c7d72e2
6 changed files with 125 additions and 35 deletions
2
Makefile
2
Makefile
|
|
@ -252,7 +252,7 @@ $(info )
|
|||
ggml.o: ggml.c ggml.h ggml-cuda.h
|
||||
$(CC) $(CFLAGS) -c $< -o $@
|
||||
|
||||
llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
|
||||
llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $@
|
||||
|
||||
common.o: examples/common.cpp examples/common.h
|
||||
|
|
|
|||
|
|
@ -41,12 +41,15 @@ void ggml_metal_free(struct ggml_metal_context * ctx);
|
|||
// - 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 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);
|
||||
|
|
|
|||
98
ggml-metal.m
98
ggml-metal.m
|
|
@ -183,6 +183,14 @@ struct ggml_metal_context * ggml_metal_init(void) {
|
|||
#undef GGML_METAL_ADD_KERNEL
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
|
||||
fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
|
||||
if (ctx->device.maxTransferRate != 0) {
|
||||
fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
|
||||
} else {
|
||||
fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__);
|
||||
}
|
||||
|
||||
return ctx;
|
||||
}
|
||||
|
||||
|
|
@ -199,10 +207,13 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
|
|||
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
|
||||
//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
|
||||
|
||||
const int64_t tsize = ggml_nbytes(t);
|
||||
|
||||
// find the view that contains the tensor fully
|
||||
for (int i = 0; i < ctx->n_buffers; ++i) {
|
||||
const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
|
||||
|
||||
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
|
||||
if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
|
||||
*offs = (size_t) ioffs;
|
||||
|
||||
//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
|
||||
|
|
@ -220,7 +231,8 @@ bool ggml_metal_add_buffer(
|
|||
struct ggml_metal_context * ctx,
|
||||
const char * name,
|
||||
void * data,
|
||||
size_t size) {
|
||||
size_t size,
|
||||
size_t max_size) {
|
||||
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
|
||||
fprintf(stderr, "%s: too many buffers\n", __func__);
|
||||
return false;
|
||||
|
|
@ -237,30 +249,68 @@ bool ggml_metal_add_buffer(
|
|||
}
|
||||
}
|
||||
|
||||
size_t page_size = getpagesize();
|
||||
size_t aligned_size = size;
|
||||
if ((aligned_size % page_size) != 0) {
|
||||
aligned_size += (page_size - (aligned_size % page_size));
|
||||
const size_t size_page = getpagesize();
|
||||
|
||||
size_t size_aligned = size;
|
||||
if ((size_aligned % size_page) != 0) {
|
||||
size_aligned += (size_page - (size_aligned % size_page));
|
||||
}
|
||||
|
||||
ctx->buffers[ctx->n_buffers].name = name;
|
||||
ctx->buffers[ctx->n_buffers].data = data;
|
||||
ctx->buffers[ctx->n_buffers].size = size;
|
||||
// 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;
|
||||
|
||||
if (ctx->device.maxBufferLength < aligned_size) {
|
||||
fprintf(stderr, "%s: buffer '%s' size %zu is larger than buffer maximum of %zu\n", __func__, name, aligned_size, ctx->device.maxBufferLength);
|
||||
return false;
|
||||
}
|
||||
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:aligned_size options:MTLResourceStorageModeShared deallocator:nil];
|
||||
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) {
|
||||
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
|
||||
return false;
|
||||
if (ctx->buffers[ctx->n_buffers].metal == nil) {
|
||||
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
|
||||
return false;
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __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) {
|
||||
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
|
||||
return false;
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
|
||||
if (i + size_step < size) {
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
|
||||
++ctx->n_buffers;
|
||||
}
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
|
||||
fprintf(stderr, ", (%8.2f / %8.2f)",
|
||||
ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
|
||||
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
|
||||
|
||||
++ctx->n_buffers;
|
||||
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
|
||||
fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n");
|
||||
} else {
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
|
|
@ -909,4 +959,14 @@ void ggml_metal_graph_compute(
|
|||
dispatch_barrier_sync(queue, ^{});
|
||||
|
||||
[command_buffers[n_cb - 1] waitUntilCompleted];
|
||||
|
||||
// check status of command buffers
|
||||
// needed to detect if the device ran out-of-memory for example (#1881)
|
||||
for (int i = 0; i < n_cb; i++) {
|
||||
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status];
|
||||
if (status != MTLCommandBufferStatusCompleted) {
|
||||
fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status);
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
24
ggml.c
24
ggml.c
|
|
@ -4154,14 +4154,34 @@ void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) {
|
|||
ctx->no_alloc = no_alloc;
|
||||
}
|
||||
|
||||
void * ggml_get_mem_buffer(struct ggml_context * ctx) {
|
||||
void * ggml_get_mem_buffer(const struct ggml_context * ctx) {
|
||||
return ctx->mem_buffer;
|
||||
}
|
||||
|
||||
size_t ggml_get_mem_size(struct ggml_context * ctx) {
|
||||
size_t ggml_get_mem_size(const struct ggml_context * ctx) {
|
||||
return ctx->mem_size;
|
||||
}
|
||||
|
||||
size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
|
||||
size_t max_size = 0;
|
||||
|
||||
struct ggml_object * obj = ctx->objects_begin;
|
||||
|
||||
while (obj != NULL) {
|
||||
struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs);
|
||||
|
||||
const size_t size = ggml_nbytes(tensor);
|
||||
|
||||
if (max_size < size) {
|
||||
max_size = size;
|
||||
}
|
||||
|
||||
obj = obj->next;
|
||||
}
|
||||
|
||||
return max_size;
|
||||
}
|
||||
|
||||
// IMPORTANT:
|
||||
// when creating "opt" tensors, always save and load the scratch buffer
|
||||
// this is an error prone process, but it is necessary to support inplace
|
||||
|
|
|
|||
5
ggml.h
5
ggml.h
|
|
@ -500,8 +500,9 @@ extern "C" {
|
|||
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
|
||||
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
|
||||
|
||||
GGML_API void * ggml_get_mem_buffer(struct ggml_context * ctx);
|
||||
GGML_API size_t ggml_get_mem_size (struct ggml_context * ctx);
|
||||
GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
|
||||
GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx);
|
||||
GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_new_tensor(
|
||||
struct ggml_context * ctx,
|
||||
|
|
|
|||
26
llama.cpp
26
llama.cpp
|
|
@ -2696,16 +2696,21 @@ struct llama_context * llama_init_from_file(
|
|||
// this allocates all Metal resources and memory buffers
|
||||
ctx->ctx_metal = ggml_metal_init();
|
||||
|
||||
void *data_ptr = NULL;
|
||||
void * data_ptr = NULL;
|
||||
size_t data_size = 0;
|
||||
|
||||
if (params.use_mmap) {
|
||||
data_ptr = ctx->model.mapping->addr;
|
||||
data_size= ctx->model.mapping->size;
|
||||
data_ptr = ctx->model.mapping->addr;
|
||||
data_size = ctx->model.mapping->size;
|
||||
} else {
|
||||
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
||||
data_size= ggml_get_mem_size(ctx->model.ctx);
|
||||
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
||||
data_size = ggml_get_mem_size (ctx->model.ctx);
|
||||
}
|
||||
|
||||
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
|
||||
|
||||
printf("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
|
||||
|
||||
#define LLAMA_METAL_CHECK_BUF(result) \
|
||||
if (!(result)) { \
|
||||
fprintf(stderr, "%s: failed to add buffer\n", __func__); \
|
||||
|
|
@ -2713,12 +2718,13 @@ struct llama_context * llama_init_from_file(
|
|||
return NULL; \
|
||||
}
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size, 0));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0));
|
||||
#undef LLAMA_METAL_CHECK_BUF
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
Loading…
Reference in a new issue