fix: Use llm_graph_context_mamba in llm_build_granite_hybrid

Branch: GraniteFourWithJamba

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

Author: gabe-l-hart

src/llama-model.cpp

@@ -14011,14 +14011,14 @@ struct llm_build_granite : public llm_graph_context {
     }
 };
 
-struct llm_build_granite_hybrid : public llm_graph_context {
+struct llm_build_granite_hybrid : public llm_graph_context_mamba {
 
     llm_build_granite_hybrid(
                  const llama_model & model,
             const llm_graph_params & params,
                        ggml_cgraph * gf,
                const bool use_rope = true) :
-        llm_graph_context(params) {
+        llm_graph_context_mamba(params) {
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -14095,135 +14095,6 @@ struct llm_build_granite_hybrid : public llm_graph_context {
         ggml_build_forward_expand(gf, cur);
     }
 
-    ggml_tensor * build_mamba2_layer(
-        llm_graph_input_rs * inp,
-               ggml_cgraph * gf,
-               ggml_tensor * cur,
-         const llama_model & model,
-        const llama_ubatch & ubatch,
-                     int   il) const {
-        const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
-
-        const auto kv_head = mctx_cur->get_head();
-
-        const int64_t d_conv  = hparams.ssm_d_conv;
-        const int64_t d_inner = hparams.ssm_d_inner;
-        const int64_t d_state = hparams.ssm_d_state;
-        const int64_t n_head  = hparams.ssm_dt_rank;
-        const int64_t head_dim = d_inner / n_head;
-        const int64_t n_group = hparams.ssm_n_group;
-        const int64_t n_seqs  = ubatch.n_seqs;
-
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-        GGML_ASSERT(n_seqs != 0);
-        GGML_ASSERT(ubatch.equal_seqs);
-        GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-        ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-        ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-        ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs);
-        conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs);
-
-        // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
-        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
-
-        // d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
-
-        // {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs}
-        ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur);
-
-        // split the above in three
-        ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0);
-        ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_inner + 2*n_group*d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_inner*ggml_element_size(zxBCdt));
-        ggml_tensor * dt = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2*d_inner + 2*n_group*d_state)*ggml_element_size(zxBCdt));
-
-        // conv
-        {
-            // => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs}
-            ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
-
-            // copy last (d_conv - 1) columns back into the state cache
-            ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
-
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0, last_conv,
-                    ggml_view_1d(ctx0, conv_states_all,
-                        (d_conv - 1)*(d_inner + 2*n_group*d_state)*(n_seqs),
-                        kv_head*(d_conv - 1)*(d_inner + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
-
-            // 1D convolution
-            // The equivalent is to make a self-overlapping view of conv_x
-            // over d_conv columns at each stride in the 3rd dimension,
-            // then element-wise multiply that with the conv1d weight,
-            // then sum the elements of each row,
-            // (the last two steps are a dot product over rows (also doable with mul_mat))
-            // then permute away the ne[0] dimension,
-            // and then you're left with the resulting x tensor.
-            // For simultaneous sequences, all sequences need to have the same length.
-            xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
-
-            // bias
-            xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
-
-            xBC = ggml_silu(ctx0, xBC);
-        }
-
-        // ssm
-        {
-            // These correspond to V K Q in SSM/attention duality
-            ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*xBC->nb[0], xBC->nb[1], xBC->nb[2], 0);
-            ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], d_inner*ggml_element_size(xBC));
-            ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], (d_inner + n_group*d_state)*ggml_element_size(xBC));
-
-            // {n_head, n_seq_tokens, n_seqs}
-            dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
-
-            ggml_tensor * A = model.layers[il].ssm_a;
-
-            // use the states and the indices provided by build_rs
-            // (this is necessary in order to properly use the states before they are overwritten,
-            //  while avoiding to make unnecessary copies of the states)
-            auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
-                ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size());
-
-                // TODO: use semistructured matrices to implement state-space duality
-                // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
-                return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
-            };
-
-            ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
-
-            // store last states
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0,
-                    ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, ggml_nelements(x)*x->nb[0]),
-                    ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
-
-            ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
-
-            // TODO: skip computing output earlier for unused tokens
-
-            y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
-            y = ggml_mul(ctx0, y, ggml_silu(ctx0, ggml_cont(ctx0, z)));
-
-            // grouped RMS norm
-            y = ggml_reshape_4d(ctx0, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs);
-            y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
-            y = ggml_reshape_3d(ctx0, y, d_inner, n_seq_tokens, n_seqs);
-
-            // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
-            cur = build_lora_mm(model.layers[il].ssm_out, y);
-        }
-
-        // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
-        cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
-        // cb(cur, "mamba_out", il);
-
-        return cur;
-    }
-
     ggml_tensor * build_granite_attention_layer(
               ggml_cgraph                     * gf,
               ggml_tensor                     * cur,