Add missing LFM2 code

src/models/llm_build_lfm2.cpp

@@ -1,37 +1,38 @@
-#include "../llama-model.h"
+#include "llm_build_lfm2.h"
+
 #include "../llama-graph.h"
+#include "../llama-model.h"
+#include "../llama-memory-hybrid.h"
 
-#include "llm_build_lfm2.h"
 #include <cmath>
 
-llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params),
+    model(model) {
     ggml_tensor * cur = build_inp_embd(model.tok_embd);
     cb(cur, "model.embed_tokens", -1);
 
     ggml_tensor * inp_pos     = build_inp_pos();
-    auto        * inp_hybrid  = build_inp_mem_hybrid();
+    auto *        inp_hybrid  = build_inp_mem_hybrid();
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
         auto * prev_cur = cur;
-        cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cur             = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
         cb(cur, "model.layers.{}.operator_norm", il);
 
-        // TODO: implement recurrent/attention logic inline
-        // cur = hparams.is_recurrent(il) ?
-        //     build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
-        //     build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il) ;
+        cur = hparams.is_recurrent(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
+                                         build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il);
 
         if (il == n_layer - 1 && inp_out_ids) {
-            cur      = ggml_get_rows(ctx0,      cur, inp_out_ids);
+            cur      = ggml_get_rows(ctx0, cur, inp_out_ids);
             prev_cur = ggml_get_rows(ctx0, prev_cur, inp_out_ids);
         }
-;
+
         cur = ggml_add(ctx0, prev_cur, cur);
-        // TODO: implement feed_forward inline
-        // cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
+        cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
     }
-;
+
     cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
     cb(cur, "model.embedding_norm", -1);
     res->t_embd = cur;
@@ -43,4 +44,117 @@ llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_params
 
     ggml_build_forward_expand(gf, cur);
 }
-;
+
+ggml_tensor * llm_build_lfm2::build_feed_forward(ggml_tensor * cur, int il) const {
+    cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+    cb(cur, "model.layers.{}.ffn_norm", il);
+
+    GGML_ASSERT(!model.layers[il].ffn_up_b);
+    GGML_ASSERT(!model.layers[il].ffn_gate_b);
+    GGML_ASSERT(!model.layers[il].ffn_down_b);
+    cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+    cb(cur, "model.layers.{}.feed_forward.w2", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_lfm2::build_attn_block(ggml_tensor *             cur,
+                                               ggml_tensor *             inp_pos,
+                                               llm_graph_input_attn_kv * inp_attn,
+                                               int                       il) const {
+    GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il));
+    const auto n_embd_head = hparams.n_embd_head_v;
+    const auto n_head_kv   = hparams.n_head_kv(il);
+
+    auto * q = build_lora_mm(model.layers[il].wq, cur);
+    cb(q, "model.layers.{}.self_attn.q_proj", il);
+    auto * k = build_lora_mm(model.layers[il].wk, cur);
+    cb(k, "model.layers.{}.self_attn.k_proj", il);
+    auto * v = build_lora_mm(model.layers[il].wv, cur);
+    cb(v, "model.layers.{}.self_attn.v_proj", il);
+
+    q = ggml_reshape_3d(ctx0, q, n_embd_head, n_head, n_tokens);
+    k = ggml_reshape_3d(ctx0, k, n_embd_head, n_head_kv, n_tokens);
+    v = ggml_reshape_3d(ctx0, v, n_embd_head, n_head_kv, n_tokens);
+
+    // qk norm
+    q = build_norm(q, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+    cb(q, "model.layers.{}.self_attn.q_layernorm", il);
+    k = build_norm(k, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+    cb(k, "model.layers.{}.self_attn.k_layernorm", il);
+
+    // RoPE
+    q = ggml_rope_ext(ctx0, q, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
+                      attn_factor, beta_fast, beta_slow);
+    k = ggml_rope_ext(ctx0, k, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
+                      attn_factor, beta_fast, beta_slow);
+
+    cur = build_attn(inp_attn, model.layers[il].wo, NULL, q, k, v, nullptr, nullptr, nullptr,
+                     1.0f / sqrtf(float(n_embd_head)), il);
+
+    cb(cur, "model.layers.{}.self_attn.out_proj", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_lfm2::build_shortconv_block(ggml_tensor * cur, llm_graph_input_rs * inp_recr, int il) {
+    const auto *   mctx_cur     = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
+    const uint32_t kv_head      = mctx_cur->get_head();
+    const int64_t  n_seq_tokens = ubatch.n_seq_tokens;
+    const int64_t  n_seqs       = ubatch.n_seqs;
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    GGML_ASSERT(hparams.n_shortconv_l_cache > 1);
+    const uint32_t d_conv = hparams.n_shortconv_l_cache - 1;
+
+    // {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);
+
+    auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur);
+    cb(bcx, "model.layers.{}.conv.in_proj", il);
+
+    constexpr auto n_chunks = 3;
+    GGML_ASSERT(bcx->ne[0] % n_chunks == 0);
+    const auto chunk_size = bcx->ne[0] / n_chunks;
+    auto *     b          = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2],
+                                         0 * chunk_size * ggml_element_size(bcx));
+    auto *     c          = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2],
+                                         1 * chunk_size * ggml_element_size(bcx));
+    auto *     x          = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2],
+                                         2 * chunk_size * ggml_element_size(bcx));
+
+    auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x));
+
+    // read conv state
+    auto * conv_state = mctx_cur->get_r_l(il);
+    auto * conv_rs    = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs);
+    auto * conv       = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);
+
+    bx = ggml_concat(ctx0, conv, bx, 0);
+    GGML_ASSERT(bx->ne[0] > conv->ne[0]);
+
+    // last d_conv columns is a new conv state
+    auto * new_conv = ggml_view_3d(ctx0, bx, conv->ne[0], bx->ne[1], bx->ne[2], bx->nb[1], bx->nb[2],
+                                   (bx->ne[0] - conv->ne[0]) * ggml_element_size(bx));
+    GGML_ASSERT(ggml_are_same_shape(conv, new_conv));
+
+    // write new conv conv state
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_conv,
+                                           ggml_view_1d(ctx0, conv_state, ggml_nelements(new_conv),
+                                                        kv_head * d_conv * n_embd * ggml_element_size(new_conv))));
+
+    auto * conv_kernel = model.layers[il].shortconv.conv;
+    auto * conv_out    = ggml_ssm_conv(ctx0, bx, conv_kernel);
+    cb(conv_out, "model.layers.{}.conv.conv", il);
+
+    auto * y = ggml_mul(ctx0, c, conv_out);
+    y        = build_lora_mm(model.layers[il].shortconv.out_proj, y);
+    cb(y, "model.layers.{}.conv.out_proj", il);
+    // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
+    y = ggml_reshape_2d(ctx0, y, y->ne[0], n_seq_tokens * n_seqs);
+
+    return y;
+}

src/models/llm_build_lfm2.h

@@ -6,5 +6,11 @@
 #include <cmath>
 
 struct llm_build_lfm2 : public llm_graph_context {
+    const llama_model & model;
+    
     llm_build_lfm2(const llama_model & model, const llm_graph_params & params);
+    ggml_tensor * build_feed_forward(ggml_tensor * cur, int il) const;
+    ggml_tensor * build_attn_block(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn, int il) const;
+    ggml_tensor * build_shortconv_block(ggml_tensor * cur, llm_graph_input_rs * inp_recr, int il);
+    
 };