memory : move the recurrent state into the memory context

Author: ggerganov

src/llama-graph.cpp

@@ -235,6 +235,12 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
     }
 }
 
+llm_graph_input_rs::llm_graph_input_rs(const llama_memory_recurrent_context * mctx) :
+    mctx(mctx),
+    head(mctx->get_head()),
+    rs_z(mctx->get_rs_z()) {
+}
+
 void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
     GGML_UNUSED(ubatch);
 
@@ -263,8 +269,8 @@ bool llm_graph_input_rs::can_reuse(const llm_graph_params & params) {
     res &= s_copy_main->ne[0]  == params.ubatch.n_seqs;
     res &= s_copy_extra->ne[0] == mctx->get_n_rs() - params.ubatch.n_seqs;
 
-    res &= head == mctx->get_head();
-    res &= rs_z == mctx->get_rs_z();
+    res &= this->head == mctx->get_head();
+    res &= this->rs_z == mctx->get_rs_z();
 
     return res;
 }
@@ -1899,9 +1905,6 @@ static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
     inp->s_copy_main  = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
     inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);
 
-    inp->head = mctx_cur->get_head();
-    inp->rs_z = mctx_cur->get_rs_z();
-
     return inp;
 }
 

src/llama-graph.h

@@ -219,7 +219,7 @@ class llm_graph_input_cls : public llm_graph_input_i {
 
 class llm_graph_input_rs : public llm_graph_input_i {
 public:
-    llm_graph_input_rs(const llama_memory_recurrent_context * mctx) : mctx(mctx) {}
+    llm_graph_input_rs(const llama_memory_recurrent_context * mctx);
     virtual ~llm_graph_input_rs() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
@@ -235,9 +235,9 @@ class llm_graph_input_rs : public llm_graph_input_i {
 
     const llama_memory_recurrent_context * mctx;
 
-    // used in view offsets, need to match for valid graph reuse
-    uint32_t head;
-    int32_t rs_z;
+    // need to match for valid graph reuse
+    const uint32_t head;
+    const  int32_t rs_z;
 };
 
 class llm_graph_input_cross_embd : public llm_graph_input_i {

src/llama-memory-recurrent.cpp

@@ -1092,12 +1092,15 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
 llama_memory_recurrent_context::llama_memory_recurrent_context(llama_memory_status status) : status(status) {}
 
 llama_memory_recurrent_context::llama_memory_recurrent_context(
-        llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), is_full(true) {
+        llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem),
+        n_rs(mem->size), head(0), rs_z(0), size(mem->size) {
 }
 
 llama_memory_recurrent_context::llama_memory_recurrent_context(
         llama_memory_recurrent * mem,
-        std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)) {}
+        std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)),
+        n_rs(mem->n), head(mem->head), rs_z(mem->rs_z), size(mem->size) {
+}
 
 llama_memory_recurrent_context::~llama_memory_recurrent_context() = default;
 
@@ -1138,19 +1141,19 @@ const llama_ubatch & llama_memory_recurrent_context::get_ubatch() const {
 }
 
 uint32_t llama_memory_recurrent_context::get_n_rs() const {
-    return is_full ? mem->size : mem->n;
+    return n_rs;
 }
 
 uint32_t llama_memory_recurrent_context::get_head() const {
-    return is_full ? 0 : mem->head;
+    return head;
 }
 
 int32_t llama_memory_recurrent_context::get_rs_z() const {
-    return is_full ? 0 : mem->rs_z;
+    return rs_z;
 }
 
 uint32_t llama_memory_recurrent_context::get_size() const {
-    return mem->size;
+    return size;
 }
 
 ggml_tensor * llama_memory_recurrent_context::get_r_l(int32_t il) const {
@@ -1162,5 +1165,5 @@ ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const {
 }
 
 int32_t llama_memory_recurrent_context::s_copy(int i) const {
-    return  mem->cells[i + mem->head].src0;
+    return  mem->cells[i + head].src0;
 }

src/llama-memory-recurrent.h

@@ -175,8 +175,10 @@ class llama_memory_recurrent_context : public llama_memory_context_i {
 
     //
     // data needed for building the compute graph for the current ubatch:
-    // TODO: extract all the state like `head` and `n` here
     //
 
-    const bool is_full = false;
+    const uint32_t n_rs = 0;
+    const uint32_t head = 0;
+    const int32_t  rs_z = -1;
+    const uint32_t size = 0;
 };