llama : initial ggml-backend integration (#4520)
* llama : initial ggml-backend integration * add ggml-metal * cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST access all tensor data with ggml_backend_tensor_get/set * add ggml_backend_buffer_clear zero-init KV cache buffer * add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data * disable gpu backends with ngl 0 * more accurate mlock * unmap offloaded part of the model * use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap * update quantize and lora * update session copy/set to use ggml-backend ggml-ci * use posix_fadvise instead of posix_fadvise64 * ggml_backend_alloc_ctx_tensors_from_buft : remove old print * llama_mmap::align_offset : use pointers instead of references for out parameters * restore progress_callback behavior * move final progress_callback call to load_all_data * cuda : fix fprintf format string (minor) * do not offload scales * llama_mmap : avoid unmapping the same fragments again in the destructor * remove unnecessary unmap * metal : add default log function that prints to stderr, cleanup code ggml-ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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11 changed files with 926 additions and 752 deletions
228
ggml-metal.m
228
ggml-metal.m
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@ -180,7 +180,15 @@ struct ggml_metal_context {
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@implementation GGMLMetalClass
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@end
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ggml_log_callback ggml_metal_log_callback = NULL;
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static void ggml_metal_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
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fprintf(stderr, "%s", msg);
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UNUSED(level);
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UNUSED(user_data);
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}
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ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback;
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void * ggml_metal_log_user_data = NULL;
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void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
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@ -607,12 +615,24 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
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}
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// temporarily defined here for compatibility between ggml-backend and the old API
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struct ggml_backend_metal_buffer_context {
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void * data;
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struct ggml_backend_metal_buffer {
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void * data;
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size_t size;
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id<MTLBuffer> metal;
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};
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struct ggml_backend_metal_buffer_context {
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void * all_data;
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size_t all_size;
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bool owned;
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// multiple buffers are used only to avoid the maximum buffer size limitation when using mmap
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int n_buffers;
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struct ggml_backend_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
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};
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// finds the Metal buffer that contains the tensor data on the GPU device
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// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
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// Metal buffer based on the host memory pointer
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@ -622,17 +642,29 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
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const int64_t tsize = ggml_nbytes(t);
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ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer;
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// compatibility with ggml-backend
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if (t->buffer && t->buffer->buft == ggml_backend_metal_buffer_type()) {
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struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) t->buffer->context;
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if (buffer && buffer->buft == ggml_backend_metal_buffer_type()) {
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struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context;
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const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->data;
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// find the view that contains the tensor fully
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for (int i = 0; i < buf_ctx->n_buffers; ++i) {
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const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data;
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GGML_ASSERT(ioffs >= 0 && ioffs + tsize <= (int64_t) t->buffer->size);
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//GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size);
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if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) {
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*offs = (size_t) ioffs;
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*offs = (size_t) ioffs;
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//GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs);
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return buf_ctx->metal;
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return buf_ctx->buffers[i].metal;
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}
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}
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GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name);
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return nil;
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}
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// find the view that contains the tensor fully
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@ -2361,6 +2393,7 @@ void ggml_metal_graph_compute(
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// backend interface
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// default buffer
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static id<MTLDevice> g_backend_device = nil;
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static int g_backend_device_ref_count = 0;
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@ -2388,34 +2421,31 @@ static void ggml_backend_metal_free_device(void) {
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static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
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struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
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return ctx->data;
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return ctx->all_data;
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}
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static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
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struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
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[ctx->metal release];
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for (int i = 0; i < ctx->n_buffers; i++) {
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[ctx->buffers[i].metal release];
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}
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ggml_backend_metal_free_device();
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free(ctx->data);
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free(ctx);
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if (ctx->owned) {
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free(ctx->all_data);
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}
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UNUSED(buffer);
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free(ctx);
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}
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static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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memcpy((char *)tensor->data + offset, data, size);
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UNUSED(buffer);
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}
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static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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memcpy(data, (const char *)tensor->data + offset, size);
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UNUSED(buffer);
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@ -2433,7 +2463,13 @@ static void ggml_backend_metal_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer
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UNUSED(buffer);
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}
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static struct ggml_backend_buffer_i metal_backend_buffer_i = {
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static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
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struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
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memset(ctx->all_data, value, ctx->all_size);
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}
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static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
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/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
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/* .get_base = */ ggml_backend_metal_buffer_get_base,
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/* .init_tensor = */ NULL,
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@ -2441,8 +2477,11 @@ static struct ggml_backend_buffer_i metal_backend_buffer_i = {
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/* .get_tensor = */ ggml_backend_metal_buffer_get_tensor,
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/* .cpy_tensor_from = */ ggml_backend_metal_buffer_cpy_tensor_from,
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/* .cpy_tensor_to = */ ggml_backend_metal_buffer_cpy_tensor_to,
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/* .clear = */ ggml_backend_metal_buffer_clear,
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};
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// default buffer type
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static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
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struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
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@ -2453,13 +2492,46 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
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size_aligned += (size_page - (size_aligned % size_page));
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}
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ctx->data = ggml_metal_host_malloc(size);
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ctx->metal = [ggml_backend_metal_get_device() newBufferWithBytesNoCopy:ctx->data
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id<MTLDevice> device = ggml_backend_metal_get_device();
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ctx->all_data = ggml_metal_host_malloc(size_aligned);
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ctx->all_size = size_aligned;
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ctx->owned = true;
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ctx->n_buffers = 1;
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ctx->buffers[0].data = ctx->all_data;
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ctx->buffers[0].size = size;
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ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data
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length:size_aligned
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options:MTLResourceStorageModeShared
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deallocator:nil];
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return ggml_backend_buffer_init(buft, metal_backend_buffer_i, ctx, size);
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if (ctx->buffers[0].metal == nil) {
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GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
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free(ctx);
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ggml_backend_metal_free_device();
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return NULL;
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}
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GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
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#if TARGET_OS_OSX
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GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
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device.currentAllocatedSize / 1024.0 / 1024.0,
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device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
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if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
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GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
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} else {
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GGML_METAL_LOG_INFO("\n");
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}
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#else
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GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
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#endif
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return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
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}
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static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
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@ -2470,7 +2542,13 @@ static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_t
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static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
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return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend);
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GGML_UNUSED(buft);
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UNUSED(buft);
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}
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static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
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return true;
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UNUSED(buft);
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}
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ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
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@ -2480,6 +2558,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
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/* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment,
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/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
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/* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend,
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/* .is_host = */ ggml_backend_metal_buffer_type_is_host,
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},
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/* .context = */ NULL,
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};
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@ -2487,6 +2566,87 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
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return &ggml_backend_buffer_type_metal;
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}
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// buffer from ptr
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ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
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struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
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ctx->all_data = data;
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ctx->all_size = size;
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ctx->owned = false;
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ctx->n_buffers = 0;
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const size_t size_page = sysconf(_SC_PAGESIZE);
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size_t size_aligned = size;
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if ((size_aligned % size_page) != 0) {
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size_aligned += (size_page - (size_aligned % size_page));
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}
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id<MTLDevice> device = ggml_backend_metal_get_device();
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// the buffer fits into the max buffer size allowed by the device
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if (size_aligned <= device.maxBufferLength) {
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ctx->buffers[ctx->n_buffers].data = data;
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ctx->buffers[ctx->n_buffers].size = size;
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ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
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if (ctx->buffers[ctx->n_buffers].metal == nil) {
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GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
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return false;
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}
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GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
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++ctx->n_buffers;
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} else {
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// this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
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// one of the views
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const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
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const size_t size_step = device.maxBufferLength - size_ovlp;
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const size_t size_view = device.maxBufferLength;
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for (size_t i = 0; i < size; i += size_step) {
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const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);
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ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i);
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ctx->buffers[ctx->n_buffers].size = size_step_aligned;
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ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
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if (ctx->buffers[ctx->n_buffers].metal == nil) {
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GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0);
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return false;
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}
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GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i);
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if (i + size_step < size) {
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GGML_METAL_LOG_INFO("\n");
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}
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++ctx->n_buffers;
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}
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}
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#if TARGET_OS_OSX
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GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
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device.currentAllocatedSize / 1024.0 / 1024.0,
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device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
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if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
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GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
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} else {
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GGML_METAL_LOG_INFO("\n");
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}
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#else
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GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
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#endif
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return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
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}
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// backend
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static const char * ggml_backend_metal_name(ggml_backend_t backend) {
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return "Metal";
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@ -2499,10 +2659,6 @@ static void ggml_backend_metal_free(ggml_backend_t backend) {
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free(backend);
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}
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static void ggml_backend_metal_synchronize(ggml_backend_t backend) {
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UNUSED(backend);
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}
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static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) {
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return ggml_backend_metal_buffer_type();
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@ -2529,25 +2685,15 @@ static struct ggml_backend_i metal_backend_i = {
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/* .get_tensor_async = */ NULL,
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/* .cpy_tensor_from_async = */ NULL,
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/* .cpy_tensor_to_async = */ NULL,
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/* .synchronize = */ ggml_backend_metal_synchronize,
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/* .graph_plan_create = */ NULL, // the metal implementation does not require creating graph plans atm
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/* .synchronize = */ NULL,
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/* .graph_plan_create = */ NULL,
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/* .graph_plan_free = */ NULL,
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/* .graph_plan_compute = */ NULL,
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/* .graph_compute = */ ggml_backend_metal_graph_compute,
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/* .supports_op = */ ggml_backend_metal_supports_op,
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};
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// TODO: make a common log callback for all backends in ggml-backend
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static void ggml_backend_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
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fprintf(stderr, "%s", msg);
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UNUSED(level);
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UNUSED(user_data);
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}
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ggml_backend_t ggml_backend_metal_init(void) {
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ggml_metal_log_set_callback(ggml_backend_log_callback, NULL);
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struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
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if (ctx == NULL) {
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