diff --git a/ggml-backend.c b/ggml-backend.c index 13c71c310c446..a7a61ac34ddcc 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1036,6 +1036,13 @@ 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; @@ -1087,6 +1094,8 @@ struct ggml_backend_sched { __attribute__((aligned(GGML_MEM_ALIGN))) #endif char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)]; + + struct ggml_cached_graph cached_graph; }; #define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor) @@ -1753,6 +1762,14 @@ 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 = sched->tensor_copies[hash_id(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) { @@ -1872,6 +1889,8 @@ ggml_backend_sched_t ggml_backend_sched_new( ggml_backend_sched_reset(sched); + sched->cached_graph.is_active = false; + return sched; } @@ -1947,6 +1966,9 @@ 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); } @@ -1956,7 +1978,7 @@ 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); } @@ -2223,3 +2245,12 @@ 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/ggml-backend.h b/ggml-backend.h index 4a38eeb5c23bd..1d406dc9d0ee6 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -230,6 +230,11 @@ extern "C" { GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor); + // 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 } diff --git a/llama.cpp b/llama.cpp index 25671e98b67b2..4b7e038fc2de9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2745,6 +2745,17 @@ struct llama_model { } }; +// 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), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {} ~llama_context() { @@ -2846,6 +2857,8 @@ struct llama_context { // control vectors struct llama_control_vector cvec; + + struct ggml_cached_graph cached_graph; }; static size_t llama_get_device_count(const llama_model & model) { @@ -14668,12 +14681,42 @@ 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, u_batch, false); - + ggml_cgraph * gf; // the output is always the last tensor in the graph - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2]; + 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, u_batch, 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, or if batch size is greater than 1 + // 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); + for (int i = 0 ; i < gf->n_nodes; 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 + res = gf->nodes[gf->n_nodes - 1]; + embd = gf->nodes[gf->n_nodes - 2]; if (lctx.n_outputs == 0) { // no output res = nullptr; @@ -14689,10 +14732,71 @@ 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; + + if(ggml_use_cached_graph(lctx.sched)) { + + // If using flash attention, find mask node so it can be skipped when updating + // KV cache paramaters in cached graph nodes below + void * flash_attn_mask_node = nullptr; + if(cparams.flash_attn) { + for (int i = 0; i < gf->n_nodes; i++) { + ggml_tensor * node = gf->nodes[i]; + if (node->op == GGML_OP_FLASH_ATTN_EXT) { + flash_attn_mask_node = node->src[3]; + break; + } + } + } + + // Temporarily store KV cache parameters that will need updated in cached graph. + const struct llama_hparams & hparams = model.hparams; + const int64_t n_layer = hparams.n_layer; + const int64_t kv_head = kv_self.head; + std::vector kv_cache_ptrs; + for (int il = 0; il < n_layer; ++il) { + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + 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; + kv_cache_ptrs.push_back(static_cast(tmp_tensor->data) + tmp_offset); + tmp_tensor = kv_self.v_l[il]; + 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]); + } + kv_cache_ptrs.push_back(static_cast(tmp_tensor->data) + tmp_offset); + } + + // Update KV cache parameters in cached graph. + int copy_op_count = 0; + if(gf != nullptr && gf->nodes != nullptr){ + for (int i = 0; i < gf->n_nodes; i++) { + ggml_tensor * node = gf->nodes[i]; + if (node->op == GGML_OP_CPY) { + if (node != flash_attn_mask_node) { + node->src[1]->data = kv_cache_ptrs[copy_op_count]; + copy_op_count++; + } + } + } + } + + } + llama_set_inputs(lctx, u_batch); llama_graph_compute(lctx, gf, n_threads); @@ -14715,11 +14819,15 @@ 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); @@ -14731,6 +14839,12 @@ 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) {