model : avoid ggml_cont_3d for fused QKV weights (ggml-org#15662)

* model : avoid ggml_cont_3d for fused QKV weights

ggml-ci

* kv-cache : make cpy_k and cpy_v implementation more readable

ggml-ci

* cont : add comments

ggml-ci

* cont : minor fix [no ci]

* cont : one more fix

* cont : clarity

ggml-ci

* kv-cache : require contiguous heads of k_cur and v_cur

ggml-ci

Author: ggerganov

src/llama-kv-cache.cpp

@@ -1018,16 +1018,33 @@ ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggm
 
     const int32_t ikv = map_layer_ids.at(il);
 
-    auto * k = layers[ikv].k;
+    ggml_tensor * k = layers[ikv].k;
+
+    const int64_t n_embd_head = k_cur->ne[0];
+    const int64_t n_head      = k_cur->ne[1];
+    const int64_t n_tokens    = k_cur->ne[2];
+
+    const int64_t n_embd_gqa = n_embd_head*n_head;
 
-    const int64_t n_tokens = k_cur->ne[2];
+    // we can merge dims 0 and 1
+    // TODO: add ggml helper function for this?
+    GGML_ASSERT(ggml_row_size(k_cur->type, n_embd_head) == k_cur->nb[1]);
 
-    k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
+    k_cur = ggml_view_2d(ctx, k_cur, n_embd_gqa, n_tokens, k_cur->nb[2], 0);
 
-    if (k->ne[2] > 1) {
-        k = ggml_reshape_2d(ctx, k, k->ne[0], k->ne[1]*k->ne[2]);
+    const int64_t n_stream = k->ne[2];
+
+    if (n_stream > 1) {
+        const int64_t kv_size = get_size();
+
+        assert(n_embd_gqa == k->ne[0]);
+        assert(kv_size    == k->ne[1]);
+
+        // merge the buffer across all streams because the idxs are global
+        k = ggml_reshape_2d(ctx, k, n_embd_gqa, kv_size*n_stream);
     }
 
+    // store the current K values into the cache
     return ggml_set_rows(ctx, k, k_cur, k_idxs);
 }
 
@@ -1038,28 +1055,51 @@ ggml_tensor * llama_kv_cache::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggm
 
     auto * v = layers[ikv].v;
 
-    const int64_t n_embd_v_gqa = v_cur->ne[0]*v_cur->ne[1];
-    const int64_t n_tokens     = v_cur->ne[2];
+    const int64_t n_embd_head = v_cur->ne[0];
+    const int64_t n_head      = v_cur->ne[1];
+    const int64_t n_tokens    = v_cur->ne[2];
+
+    const int64_t n_embd_gqa = n_embd_head*n_head;
 
-    v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
+    // we can merge dims 0 and 1
+    GGML_ASSERT(ggml_row_size(v_cur->type, n_embd_head) == v_cur->nb[1]);
 
+    const int64_t n_stream = v->ne[2];
+
+    // take this branch when FA is enabled (the V cache is not transposed)
     if (!v_trans) {
-        if (v->ne[2] > 1) {
-            v = ggml_reshape_2d(ctx, v, v->ne[0], v->ne[1]*v->ne[2]);
+        v_cur = ggml_view_2d(ctx, v_cur, n_embd_gqa, n_tokens, v_cur->nb[2], 0);
+
+        if (n_stream > 1) {
+            const int64_t kv_size = get_size();
+
+            assert(n_embd_gqa == v->ne[0]);
+            assert(kv_size    == v->ne[1]);
+
+            // merge the buffer across all streams because the idxs are global
+            v = ggml_reshape_2d(ctx, v, n_embd_gqa, kv_size*n_stream);
         }
 
         return ggml_set_rows(ctx, v, v_cur, v_idxs);
     }
 
+    if (ggml_row_size(v_cur->type, n_embd_gqa) == v_cur->nb[2]) {
+        // we can merge dims 0, 1 and 2
+        v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens);
+    } else {
+        // otherwise -> make a copy to get contiguous data
+        v_cur = ggml_cont_2d   (ctx, v_cur, n_embd_gqa, n_tokens);
+    }
+
     // [TAG_V_CACHE_VARIABLE]
-    if (n_embd_v_gqa < v->ne[0]) {
-        v_cur = ggml_pad(ctx, v_cur, v->ne[0] - n_embd_v_gqa, 0, 0, 0);
+    if (n_embd_gqa < v->ne[0]) {
+        v_cur = ggml_pad(ctx, v_cur, v->ne[0] - n_embd_gqa, 0, 0, 0);
     }
 
-    // the row becomes a single element
-    ggml_tensor * v_view = ggml_reshape_2d(ctx, v, 1, v->ne[0]*v->ne[1]*v->ne[2]);
+    // in this branch the v_idxs are constructed in such a way that each row is a single head element
+    ggml_tensor * v_view = ggml_reshape_2d(ctx, v, 1, ggml_nelements(v));
 
-    v_cur = ggml_reshape_2d(ctx, v_cur, 1, v_cur->ne[0]*v_cur->ne[1]);
+    v_cur = ggml_reshape_2d(ctx, v_cur, 1, ggml_nelements(v_cur));
 
     return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
 }

src/llama-kv-cache.h

@@ -317,9 +317,17 @@ class llama_kv_cache_context : public llama_memory_context_i {
     ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
 
     // store k_cur and v_cur in the cache based on the provided head location
+    // note: the heads in k_cur and v_cur should be layed out contiguously in memory
+    //   - k_cur  [n_embd_head_k, n_head_k, n_tokens]
+    //   - k_idxs [n_tokens]
+    //   - v_cur  [n_embd_head_v, n_head_v, n_tokens]
+    //   - v_idxs [n_tokens] or [n_tokens*n_embd_v_gqa] depending if V cache is transposed
     ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;
     ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const;
 
+    // create destination indices for each head of the current batch for where it would be written in the KV cache
+    // the indices address the global KV cache (not per stream) - this is not relevant for the user of this API, but
+    //   helps understand the implementation logic of cpy_k and cpy_v
     ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
     ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;