diff options
| -rw-r--r-- | ggml/include/ggml-backend.h | 6 | ||||
| -rw-r--r-- | ggml/include/ggml.h | 7 | ||||
| -rw-r--r-- | ggml/src/ggml-backend.c | 45 | ||||
| -rw-r--r-- | src/llama.cpp | 116 |
4 files changed, 161 insertions, 13 deletions
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index 5f3f1e28..621620bc 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -232,6 +232,12 @@ 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/ggml/include/ggml.h b/ggml/include/ggml.h index a467c297..a99dc6b5 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -597,6 +597,13 @@ extern "C" { GGML_TENSOR_FLAG_PARAM = 4, }; + // 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 + }; + // ggml object struct ggml_object { size_t offs; diff --git a/ggml/src/ggml-backend.c b/ggml/src/ggml-backend.c index e1651cc6..76d37f74 100644 --- a/ggml/src/ggml-backend.c +++ b/ggml/src/ggml-backend.c @@ -1040,6 +1040,13 @@ 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; @@ -1085,6 +1092,8 @@ 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) @@ -1762,6 +1771,14 @@ 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) { @@ -1893,6 +1910,8 @@ ggml_backend_sched_t ggml_backend_sched_new( ggml_backend_sched_reset(sched); + sched->cached_graph.is_active = false; + return sched; } @@ -1969,16 +1988,16 @@ 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->is_reset && !sched->is_alloc) { - ggml_backend_sched_reset(sched); - } - - if (!sched->is_alloc) { - if (!ggml_backend_sched_alloc_graph(sched, graph)) { - return GGML_STATUS_ALLOC_FAILED; + if(!sched->cached_graph.is_active) { + if (!sched->is_reset && !sched->is_alloc) { + ggml_backend_sched_reset(sched); + } + if (!sched->is_alloc) { + if (!ggml_backend_sched_alloc_graph(sched, graph)) { + return GGML_STATUS_ALLOC_FAILED; + } } } - return ggml_backend_sched_compute_splits(sched); } @@ -2243,3 +2262,13 @@ 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 c950a46d..c5df16e3 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -8,6 +8,7 @@ #include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" +#include "../ggml/src/ggml-impl.h" #ifdef GGML_USE_RPC # include "ggml-rpc.h" @@ -2659,6 +2660,17 @@ 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) @@ -2759,6 +2771,8 @@ 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 { @@ -14877,11 +14891,44 @@ 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, u_batch, 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, 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, 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 < 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 - struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; - struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2]; + res = gf->nodes[gf->n_nodes - 1]; + embd = gf->nodes[gf->n_nodes - 2]; if (lctx.n_outputs == 0) { // no output @@ -14901,9 +14948,58 @@ 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 < gf->n_nodes; 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<char*>(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<char*>(tmp_tensor->data) + tmp_offset; + } + + } + } + + } llama_set_inputs(lctx, u_batch); @@ -14927,12 +15023,18 @@ 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); GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size); @@ -14943,6 +15045,10 @@ 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) { |