feat: Support hybrid recurrent in llama-graph

NOTE: I intentionally did not add support for s_mask since it will be going
away soon

Branch: HybridRecurrentCache

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

src/llama-graph.cpp

@@ -7,6 +7,7 @@
 #include "llama-kv-cache-unified.h"
 #include "llama-kv-cache-unified-iswa.h"
 #include "llama-kv-cache-recurrent.h"
+#include "llama-kv-cache-hybrid-recurrent.h"
 
 #include <cassert>
 #include <cmath>
@@ -403,6 +404,13 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
     }
 }
 
+llm_graph_input_attn_kv_hybrid_recurrent::llm_graph_input_attn_kv_hybrid_recurrent(
+        const llama_hparams & hparams,
+        const llama_cparams & cparams,
+        const llama_kv_cache_hybrid_recurrent_state * kv_state) :
+    llm_graph_input_attn_kv_unified(hparams, cparams, kv_state->get_state_attn()) {
+}
+
 //
 // llm_graph_context
 //
@@ -961,8 +969,10 @@ ggml_tensor * llm_graph_context::build_inp_cls() const {
     return cur;
 }
 
-ggml_tensor * llm_graph_context::build_inp_s_copy() const {
+ggml_tensor * llm_graph_context::build_inp_s_copy(const llama_kv_cache_recurrent_state * kv_state) const {
-    const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
+    if (kv_state == nullptr) {
+        kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
+    }
 
     auto inp = std::make_unique<llm_graph_input_s_copy>(kv_state);
 
@@ -1291,6 +1301,44 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
+llm_graph_input_attn_kv_hybrid_recurrent * llm_graph_context::build_attn_inp_kv_hybrid_recurrent() const {
+    const auto * kv_state = static_cast<const llama_kv_cache_hybrid_recurrent_state *>(mstate);
+
+    auto inp = std::make_unique<llm_graph_input_attn_kv_hybrid_recurrent>(hparams, cparams, kv_state);
+
+    {
+        GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Hybrid recurrent is not supported with SWA attention layers");
+
+        const auto n_kv = kv_state->get_state_attn()->get_n_kv();
+
+        inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        //cb(inp->self_kq_mask, "KQ_mask", -1);
+        ggml_set_input(inp->self_kq_mask);
+
+        inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
+    }
+
+    return (llm_graph_input_attn_kv_hybrid_recurrent *) res->add_input(std::move(inp));
+}
+
+ggml_tensor * llm_graph_context::build_attn(
+        llm_graph_input_attn_kv_hybrid_recurrent * inp,
+        ggml_cgraph * gf,
+        ggml_tensor * wo,
+        ggml_tensor * wo_b,
+        ggml_tensor * q_cur,
+        ggml_tensor * k_cur,
+        ggml_tensor * v_cur,
+        ggml_tensor * kq_b,
+        ggml_tensor * v_mla,
+            float     kq_scale,
+            int       il) const {
+    return build_attn(
+        static_cast<llm_graph_input_attn_kv_unified *>(inp),
+        gf, wo, wo_b, q_cur, k_cur, v_cur, kq_b, v_mla, kq_scale, il
+    );
+}
+
 llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
     const auto * kv_state = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
 

src/llama-graph.h

@@ -22,6 +22,7 @@ struct llama_memory_state_i;
 class llama_kv_cache_unified_state;
 class llama_kv_cache_unified_iswa_state;
 class llama_kv_cache_recurrent_state;
+class llama_kv_cache_hybrid_recurrent_state;
 
 // certain models (typically multi-modal) can produce different types of graphs
 enum llm_graph_type {
@@ -242,7 +243,7 @@ public:
         cparams(cparams),
         kv_state(kv_state) {
     }
-    ~llm_graph_input_attn_kv_unified() = default;
+    virtual ~llm_graph_input_attn_kv_unified() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
@@ -285,6 +286,16 @@ public:
     const llama_kv_cache_unified_iswa_state * kv_state;
 };
 
+class llm_graph_input_attn_kv_hybrid_recurrent : public llm_graph_input_attn_kv_unified {
+public:
+    llm_graph_input_attn_kv_hybrid_recurrent(
+            const llama_hparams & hparams,
+            const llama_cparams & cparams,
+            const llama_kv_cache_hybrid_recurrent_state * kv_state);
+
+    virtual ~llm_graph_input_attn_kv_hybrid_recurrent() = default;
+};
+
 class llm_graph_input_attn_cross : public llm_graph_input_i {
 public:
     llm_graph_input_attn_cross(const llama_cross * cross) : cross(cross) {}
@@ -508,7 +519,7 @@ struct llm_graph_context {
     ggml_tensor * build_inp_out_ids() const;
     ggml_tensor * build_inp_mean() const;
     ggml_tensor * build_inp_cls() const;
-    ggml_tensor * build_inp_s_copy() const;
+    ggml_tensor * build_inp_s_copy(const llama_kv_cache_recurrent_state * kv_state = nullptr) const;
 
     ggml_tensor * build_inp_cross_embd() const;
     ggml_tensor * build_inp_pos_bucket_enc() const;
@@ -574,6 +585,21 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
+    llm_graph_input_attn_kv_hybrid_recurrent * build_attn_inp_kv_hybrid_recurrent() const;
+
+    ggml_tensor * build_attn(
+            llm_graph_input_attn_kv_hybrid_recurrent * inp,
+            ggml_cgraph * gf,
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
+            ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
+            ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
+            ggml_tensor * kq_b,
+            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
+                  float   kq_scale,
+                    int   il) const;
+
     llm_graph_input_attn_cross * build_attn_inp_cross() const;
 
     ggml_tensor * build_attn(