Merge pull request #314 from agray3/ag_ggml_graph_caching

ggml: avoid rebuild of GGML graph for each token (ggerganov#7456)

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

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;

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;
+}
+

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<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, 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) {