diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index 5933b8e8f63ee..b9ea19d36a594 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -321,6 +321,12 @@ extern "C" { GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); #endif + // Utility to query whether cached GGML graph is in use + GGML_API bool ggml_use_cached_graph(ggml_backend_sched_t sched); + + // Set whether or not to use GGML graph caching + GGML_API void ggml_set_cached_graph(ggml_backend_sched_t sched, bool set_value); + #ifdef __cplusplus } #endif diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 4508da4fb3a41..434893608d093 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -574,6 +574,13 @@ extern "C" { GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up) }; + // Flag (used on GGML_OP_CPY nodes) on whether node is associated with K or V cache + enum ggml_kv_cache_flag { + GGML_KV_CACHE_FLAG_NONE = 0, + GGML_KV_CACHE_FLAG_K = 1, + GGML_KV_CACHE_FLAG_V = 2 + }; + // n-dimensional tensor struct ggml_tensor { enum ggml_type type; diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index fb1d3ead3be69..f5627a404790d 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -1382,6 +1382,13 @@ struct ggml_backend_sched_split { struct ggml_cgraph graph; }; +// Object to facilitate GML graph caching +struct ggml_cached_graph { + bool is_active; + ggml_backend_t input_backend; + struct ggml_tensor * input_cpy[GGML_SCHED_MAX_SPLIT_INPUTS]; +}; + struct ggml_backend_sched { bool is_reset; // true if the scheduler has been reset since the last graph split bool is_alloc; @@ -1427,6 +1434,8 @@ struct ggml_backend_sched { size_t context_buffer_size; bool debug; + + struct ggml_cached_graph cached_graph; }; #define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor) @@ -2113,6 +2122,14 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s struct ggml_tensor * input = split->inputs[j]; struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy); + if (!sched->cached_graph.is_active) { + sched->cached_graph.input_backend = input_backend; + sched->cached_graph.input_cpy[j] = input_cpy; + } + else { + input_backend = sched->cached_graph.input_backend; + input_cpy = sched->cached_graph.input_cpy[j]; + } if (input->flags & GGML_TENSOR_FLAG_INPUT) { // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done if (sched->events[split_backend_id][sched->cur_copy] != NULL) { @@ -2245,6 +2262,8 @@ ggml_backend_sched_t ggml_backend_sched_new( ggml_backend_sched_reset(sched); + sched->cached_graph.is_active = false; + return sched; } @@ -2321,6 +2340,9 @@ enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, st } enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + + if(!sched->cached_graph.is_active) + { if (!sched->is_reset && !sched->is_alloc) { ggml_backend_sched_reset(sched); } @@ -2330,7 +2352,7 @@ enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sch return GGML_STATUS_ALLOC_FAILED; } } - + } return ggml_backend_sched_compute_splits(sched); } @@ -2595,3 +2617,12 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t return true; } + +bool ggml_use_cached_graph(ggml_backend_sched_t sched) { + return sched->cached_graph.is_active; +} + +void ggml_set_cached_graph(ggml_backend_sched_t sched, bool set_value) { + sched->cached_graph.is_active = set_value; +} + diff --git a/src/llama.cpp b/src/llama.cpp index da7afb1ee5b46..14190ea56d45a 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -7,6 +7,7 @@ #include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" +#include "../ggml/src/ggml-impl.h" #if defined(GGML_USE_VULKAN) # include "ggml-vulkan.h" @@ -3254,6 +3255,17 @@ struct llama_sbatch { } }; +// Object used to allow caching of GGML graph between tokens where possible. +struct ggml_cached_graph { + bool is_active = false; + ggml_cgraph * gf; + size_t n; + ggml_backend_t backend_res; + ggml_backend_t backend_embd; + struct ggml_tensor * res; + struct ggml_tensor * embd; +}; + struct llama_context { llama_context(const llama_model & model) : model(model) @@ -3352,6 +3364,8 @@ struct llama_context { struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch] struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc] struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch] + + struct ggml_cached_graph cached_graph; }; struct llama_lora_weight { @@ -9146,7 +9160,6 @@ static void llm_build_kv_store( v_cur = ggml_transpose(ctx, v_cur); } cb(v_cache_view, "v_cache_view", il); - ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view)); } @@ -17181,11 +17194,44 @@ static int llama_decode_internal( ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); - ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); + ggml_cgraph * gf; + // the output is always the last tensor in the graph + struct ggml_tensor * res; + struct ggml_tensor * embd; + + bool n_has_changed_since_last_token = false; + if(lctx.cached_graph.n != kv_self.n) n_has_changed_since_last_token = true; + lctx.cached_graph.n = kv_self.n; + + // Re-build graph only if graph caching is not possible + if(!ggml_use_cached_graph(lctx.sched) || n_has_changed_since_last_token) { + + gf = llama_build_graph(lctx, ubatch, false); + + // Set whether GGML graph caching is in use within GGML module, based on + // whether caching was activated here during the previous token + ggml_set_cached_graph(lctx.sched,lctx.cached_graph.is_active); + + // Disable future graph caching in presence of env var, + // if there are multiple devices, if batch size is greater than 1, + // or if nsplits is not 2. + // TO DO enable graph caching for these cases + bool disable_cached_ggml_graph = (getenv("GGML_DISABLE_GRAPH_CACHING") != nullptr) + || (llama_get_device_count(model) > 1) + || (ggml_backend_sched_get_n_splits(lctx.sched) != 2); + for (int i = 0 ; i < ggml_graph_n_nodes(gf); i++) { + if (gf->nodes[i]->op == GGML_OP_ADD && gf->nodes[i]->src[1] && gf->nodes[i]->src[1]->ne[1] > 1) { + disable_cached_ggml_graph = true; + break; + } + } + + // Set whether graph caching should be used for future tokens + lctx.cached_graph.is_active=!disable_cached_ggml_graph; // the output is always the last tensor in the graph - struct ggml_tensor * res = ggml_graph_node(gf, -1); - struct ggml_tensor * embd = ggml_graph_node(gf, -2); + res = ggml_graph_node(gf, -1); + embd = ggml_graph_node(gf, -2); if (lctx.n_outputs == 0) { // no output @@ -17205,10 +17251,60 @@ static int llama_decode_internal( embd = nullptr; // do not extract embeddings when not needed GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor"); } + lctx.cached_graph.res = res; + lctx.cached_graph.embd = embd; // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); ggml_backend_sched_alloc_graph(lctx.sched, gf); + } + else { + gf = lctx.cached_graph.gf; + res = lctx.cached_graph.res; + embd = lctx.cached_graph.embd; + } + lctx.cached_graph.gf = gf; + + // Update K and V cache parameters in cached graph. + if(gf != nullptr && gf->nodes != nullptr && ggml_use_cached_graph(lctx.sched)) { + + const struct llama_hparams & hparams = model.hparams; + const int64_t kv_head = kv_self.head; + + for (int i = 0; i < ggml_graph_n_nodes(gf); i++) { + ggml_tensor * node = gf->nodes[i]; + if (node->op == GGML_OP_CPY) { + + // K cache + const char* k_prefix = "k_cache_view-"; + if (strncmp(node->src[1]->name, k_prefix, strlen(k_prefix)) == 0) { + int il = atoi(node->src[1]->name + strlen(k_prefix)); // Layer index from name + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + ggml_tensor * tmp_tensor = kv_self.k_l[il]; + size_t tmp_offset = (ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa))*kv_head; + node->src[1]->data = static_cast(tmp_tensor->data) + tmp_offset; + } + + // V cache + const char* v_prefix = "v_cache_view-"; + if (strncmp(node->src[1]->name, v_prefix, strlen(v_prefix)) == 0) { + int il = atoi(node->src[1]->name + strlen(v_prefix)); // Layer index from name + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); + ggml_tensor * tmp_tensor = kv_self.v_l[il]; + size_t tmp_offset; + if (cparams.flash_attn) { + tmp_offset = (kv_head)*ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa); + } else { + tmp_offset = (kv_head)*ggml_element_size(kv_self.v_l[il]); + } + node->src[1]->data = static_cast(tmp_tensor->data) + tmp_offset; + } + + } + } + + } + llama_set_inputs(lctx, ubatch); llama_graph_compute(lctx, gf, n_threads, threadpool); @@ -17231,11 +17327,15 @@ static int llama_decode_internal( // extract logits if (res) { ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res); - GGML_ASSERT(backend_res != nullptr); - GGML_ASSERT(lctx.logits != nullptr); - float * logits_out = lctx.logits + n_outputs_prev*n_vocab; const int32_t n_outputs_new = lctx.n_outputs; + if(!ggml_use_cached_graph(lctx.sched)) + lctx.cached_graph.backend_res = backend_res; + else + backend_res = lctx.cached_graph.backend_res; + + GGML_ASSERT(backend_res != nullptr); + GGML_ASSERT(lctx.logits != nullptr); if (n_outputs_new) { GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs); @@ -17247,6 +17347,12 @@ static int llama_decode_internal( // extract embeddings if (embd) { ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + + + if(!ggml_use_cached_graph(lctx.sched)) + lctx.cached_graph.backend_embd = backend_embd; + else + backend_embd = lctx.cached_graph.backend_embd; GGML_ASSERT(backend_embd != nullptr); switch (cparams.pooling_type) {