refactor: Remove layer index from n_embd_k/v_s

Now that it's not used at all in the unified cache, we don't need to use
the layer index to zero it out for attention layers.

Branch: HybridRecurrentCache

Signed-off-by: Gabe Goodhart <[email protected]>

Author: gabe-l-hart

src/llama-hparams.cpp

@@ -65,10 +65,7 @@ uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
     return n_embd_head_v * n_head_kv;
 }
 
-uint32_t llama_hparams::n_embd_k_s(uint32_t il) const {
-    if (!recurrent_layer(il)) {
-        return 0;
-    }
+uint32_t llama_hparams::n_embd_k_s() const {
     if (wkv_head_size != 0) {
         // for RWKV models
         return token_shift_count * n_embd;
@@ -79,10 +76,7 @@ uint32_t llama_hparams::n_embd_k_s(uint32_t il) const {
     return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
 }
 
-uint32_t llama_hparams::n_embd_v_s(uint32_t il) const {
-    if (!recurrent_layer(il)) {
-        return 0;
-    }
+uint32_t llama_hparams::n_embd_v_s() const {
     if (wkv_head_size != 0) {
         // corresponds to RWKV's wkv_states size
         return n_embd * wkv_head_size;

src/llama-hparams.h

@@ -184,10 +184,10 @@ struct llama_hparams {
 
     // dimension of the rolling state embeddings
     // corresponds to Mamba's conv_states size or RWKV's token_shift states size
-    uint32_t n_embd_k_s(uint32_t il = 0) const;
+    uint32_t n_embd_k_s() const;
 
     // dimension of the recurrent state embeddings
-    uint32_t n_embd_v_s(uint32_t il = 0) const;
+    uint32_t n_embd_v_s() const;
 
     // whether or not the given layer is recurrent (for hybrid models)
     bool recurrent_layer(uint32_t il) const;

src/llama-kv-cache-recurrent.cpp

@@ -69,8 +69,8 @@ llama_kv_cache_recurrent::llama_kv_cache_recurrent(
             continue;
         }
 
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(i);
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(i);
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
 
         const char * dev_name = "CPU";
 
@@ -756,7 +756,7 @@ void llama_kv_cache_recurrent::state_write_data(llama_io_write_i & io, const std
     // Iterate and write all the keys first, each row is a cell
     // Get whole range at a time
     for (uint32_t il = 0; il < n_layer; ++il) {
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(il);
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
 
         // Write key type
         const int32_t k_type_i = (int32_t)k_l[il]->type;
@@ -776,7 +776,7 @@ void llama_kv_cache_recurrent::state_write_data(llama_io_write_i & io, const std
 
     if (!v_trans) {
         for (uint32_t il = 0; il < n_layer; ++il) {
-            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(il);
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
             // Write value type
             const int32_t v_type_i = (int32_t)v_l[il]->type;
@@ -797,7 +797,7 @@ void llama_kv_cache_recurrent::state_write_data(llama_io_write_i & io, const std
         // When v is transposed, we also need the element size and get the element ranges from each row
         const uint32_t kv_size = size;
         for (uint32_t il = 0; il < n_layer; ++il) {
-            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(il);
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
             // Write value type
             const int32_t v_type_i = (int32_t)v_l[il]->type;
@@ -944,7 +944,7 @@ bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t ce
 
     // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
     for (uint32_t il = 0; il < n_layer; ++il) {
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(il);
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
 
         // Read type of key
         int32_t k_type_i_ref;
@@ -972,7 +972,7 @@ bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t ce
 
     if (!v_trans) {
         for (uint32_t il = 0; il < n_layer; ++il) {
-            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(il);
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
             // Read type of value
             int32_t v_type_i_ref;
@@ -1000,7 +1000,7 @@ bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t ce
     } else {
         // For each layer, read the values for each cell (transposed)
         for (uint32_t il = 0; il < n_layer; ++il) {
-            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(il);
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
 
             // Read type of value
             int32_t v_type_i_ref;

src/llama-model.cpp

@@ -8939,11 +8939,11 @@ struct llm_build_mamba : public llm_graph_context {
         // (ab)using the KV cache to store the states
         ggml_tensor * conv = build_recurrent_state(
                 gf, conv_states_all, state_copy,
-                hparams.n_embd_k_s(il), n_seqs);
+                hparams.n_embd_k_s(), n_seqs);
         conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs);
         ggml_tensor * ssm = build_recurrent_state(
                 gf, ssm_states_all, state_copy,
-                hparams.n_embd_v_s(il), n_seqs);
+                hparams.n_embd_v_s(), n_seqs);
         ssm = ggml_reshape_3d(ctx0, ssm, d_state, d_inner, n_seqs);
 
         // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}