feat: implemented sampling for MTP

common/sampling.cpp

@@ -582,3 +582,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
 
     return samplers;
 }
+
+void common_sampler_apply_chain(struct common_sampler * gsmpl, struct llama_token_data_array * cur_p) {
+    llama_sampler_apply(gsmpl->chain, cur_p);
+}

common/sampling.h

@@ -105,3 +105,5 @@ std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std:
 
 llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
                 const char * grammar_kind, const char * grammar_data);
+
+void common_sampler_apply_chain(struct common_sampler * gsmpl, struct llama_token_data_array * cur_p);

common/speculative.cpp

@@ -370,56 +370,35 @@ llama_token mtp_speculative_gen_draft(
     int32_t n_past,
     int32_t last_tok_idx) {
 
-    llama_token token_data[] = { id_last };
-    llama_pos pos_data[] = { n_past };
-    int32_t n_seq_id_data[] = { 1 };
-    llama_seq_id seq_id_data_internal[] = { 0 };
-    llama_seq_id* seq_id_data[] = {seq_id_data_internal};
-    int8_t logits_data[] = { (int8_t) (smpl != nullptr) };
-
-    llama_batch batch = {
-        /*.n_tokens = */    1,
-        /*.token = */       token_data,
-        /*.embd = */        nullptr,
-        /*.pos = */         pos_data,
-        /*.n_seq_id = */    n_seq_id_data,
-        /*.seq_id = */      seq_id_data,
-        /*.logits = */      logits_data
-    };
-
-    return llama_build_and_execute_mtp_graph(ctx, batch, id_last, n_past, last_tok_idx);
-    //LOG_INF("updating kv cache for n_past: %d\n", n_past);
-
-    /*
     if (!smpl) {
         return -1;
     }
-    else {
-        common_sampler_sample(smpl, ctx, last_tok_idx, true);
-        const auto* cur_p = common_sampler_get_candidates(smpl);
 
-        //for (int k = 0; k < std::min(3, (int)cur_p->size); ++k) {
-        //    LOG_INF(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
-        //        k, 0, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str());
-        //}
+    llama_batch batch = llama_batch_init(1, 0, 1);
+    common_batch_add(batch, id_last, n_past, {0}, true);
 
-        const llama_token id = cur_p->data[0].id;
-        return id;
+    llama_build_and_execute_mtp_graph(ctx, batch, id_last, n_past, last_tok_idx);
+
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+    const int n_vocab = llama_n_vocab(vocab);
+
+    llama_token_data_array * cur_p = common_sampler_get_candidates(smpl);
+
+    cur_p->size = n_vocab;
+    for (int i = 0; i < n_vocab; ++i) {
+        cur_p->data[i].id = i;
+        cur_p->data[i].logit = llama_get_logits_ith(ctx, last_tok_idx)[i];
     }
-    */
-    // LOG_INF("cur_p->size: %d\n", cur_p->size);
+    cur_p->sorted = false;
 
+    common_sampler_apply_chain(smpl, cur_p);
 
-    // add drafted token for each sequence
+    const llama_token id = cur_p->data[0].id;
 
-    // skip accepting draft token -- since we're only drafting one token this can't affect future outputs
-    // smpl will accept the token if it doesn't get rejected by main model later
-    // common_sampler_accept(smpl, id, true);
+    llama_batch_free(batch);
 
-    //llama_tokens result;
-    //result.reserve(1);
-    //result.push_back(id);
-    //return result;
+    return id;
 }
 
 
@@ -438,4 +417,4 @@ void mtp_update_kv_cache(struct llama_context * ctx, std::vector<mtp_kv_update_d
     }
 
     tokens.clear();
-}
+}

common/speculative.h

@@ -44,9 +44,9 @@ llama_token mtp_speculative_gen_draft(
 
 // sample up to n_draft tokens and add them to the batch using the draft model
 llama_tokens common_speculative_gen_draft(
-               struct common_speculative * spec,
-        struct common_speculative_params   params,
-                      const llama_tokens & prompt,
-                             llama_token   id_last);
+    struct common_speculative * spec,
+    struct common_speculative_params   params,
+    const llama_tokens & prompt,
+    llama_token   id_last);
 
 void mtp_update_kv_cache(struct llama_context * ctx, std::vector<mtp_kv_update_data>& tokens, size_t batch_start = 0, size_t n_tokens = -1);

include/llama.h

@@ -1454,8 +1454,8 @@ extern "C" {
             ggml_opt_epoch_callback   callback_train,
             ggml_opt_epoch_callback   callback_eval);
 
-    LLAMA_API llama_token llama_build_and_execute_mtp_graph(struct llama_context * ctx,
-        const llama_batch batch_inp, llama_token last_token_id, int32_t n_past, int32_t last_tok_idx);
+        LLAMA_API void llama_build_and_execute_mtp_graph(struct llama_context * ctx,
+                const llama_batch batch_inp, llama_token last_token_id, int32_t n_past, int32_t last_tok_idx);
 
 #ifdef __cplusplus
 }

src/llama-context.cpp

@@ -2995,7 +2995,7 @@ void llama_opt_epoch(
         callback_eval);
 }
 
-llama_token llama_build_and_execute_mtp_graph(struct llama_context * ctx,
+void llama_build_and_execute_mtp_graph(struct llama_context * ctx,
     const llama_batch batch_inp, llama_token last_token_id, int32_t n_past, int32_t last_tok_idx) {
 
     const auto * model = llama_get_model(ctx);
@@ -3033,6 +3033,12 @@ llama_token llama_build_and_execute_mtp_graph(struct llama_context * ctx,
 
     auto * gf = model->build_mtp_graph(*params_mtp, last_token_id, n_past);
 
+    if (!gf) {
+        LLAMA_LOG_ERROR("%s: ERROR - The construction of the MTP graph failed (returned null).", __func__);
+        if (sched) ggml_backend_sched_free(sched);
+        return;
+    }
+
     ggml_backend_sched_reset(sched); // clear the allocation of the previous graph
     ggml_backend_sched_alloc_graph(sched, gf); // explicitly allocate the new graph but do not execute it
 
@@ -3044,29 +3050,24 @@ llama_token llama_build_and_execute_mtp_graph(struct llama_context * ctx,
 
     ggml_backend_sched_graph_compute(sched, gf); // execute the graph
 
-    //struct ggml_tensor * logits_mtp = res_mtp->get_logits();
-
-    //LLAMA_LOG_INFO("logits_mtp pointer address: %p\n", (void*)logits_mtp);
-
-    //if (logits_mtp) {
-    //    ctx->set_logits_ith(logits_mtp, sched, last_tok_idx);
-    //}
-    struct ggml_tensor * token_id_tensor = ggml_get_tensor(res_mtp->get_ctx(), "mtp_argmax_result");
-
-
-    llama_token token_id = 0; // The C++ variable to hold the result.
-
-    // ggml_backend_tensor_get is the function for GPU->CPU copies.
-    // We are copying a single 32-bit integer.
-    ggml_backend_tensor_get(
-        token_id_tensor,
-        &token_id,                // Pointer to our C++ variable
-        0,                        // Starting offset in bytes
-        sizeof(llama_token)       // Number of bytes to copy
-    );
+    struct ggml_tensor * logits_mtp = res_mtp->get_logits();
+
+    if (logits_mtp) {
+        float * logits_dest = ctx->get_logits_ith(last_tok_idx);
+        ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched, logits_mtp);
+        if (backend_res) {
+            // ggml_backend_tensor_get is the function for GPU->CPU copies.
+            // We are copying a single 32-bit integer.
+            ggml_backend_tensor_get(logits_mtp, 
+                                    logits_dest, // Pointer to our C++ variable
+                                    0,          // Starting offset in bytes
+                                    ggml_nbytes(logits_mtp)); // Number of bytes to copy
+        } else {
+            LLAMA_LOG_ERROR("%s: ERROR - Could not obtain the backend for the logits tensor.", __func__);
+        }
+    } else {
+        LLAMA_LOG_WARN("%s: WARNING - The MTP graph did not produce a logit tensor.", __func__);
+    }
 
     ggml_backend_sched_free(sched);
-
-    return token_id;
-}
-
+}

src/llama-model.cpp

@@ -13950,6 +13950,7 @@ struct llm_build_glm4_moe_mtp : public llm_graph_context {
         // For v0, let's rebuild the computational graph for every step + this mimics the vLLM impl parameterization
         llama_token last_token_id, int n_past
     ) : llm_graph_context(params) {
+
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
@@ -13964,8 +13965,6 @@ struct llm_build_glm4_moe_mtp : public llm_graph_context {
         //llm_graph_input_attn_no_cache * inp_attn = build_attn_inp_no_cache();//nullptr;
         auto * inp_attn = build_attn_inp_kv_unified();
 
-        ggml_tensor * cur;
-
         // get MTP embedding for last (conventionally sampled) token
         // ggml_tensor * inp_token_id = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, 1);
         // LLAMA_LOG_INFO("step: '%d'\n", 5641);
@@ -13979,7 +13978,7 @@ struct llm_build_glm4_moe_mtp : public llm_graph_context {
 
         //ggml_tensor * inp_token_id = ggml_new_i32(ctx0, last_token_id);
         //ggml_set_no_alloc(ctx0, true);
-
+        
         ggml_tensor * token_emb = ggml_get_rows(ctx0, mtp_layer.nextn.embed_tokens, inp_token_id);
         ggml_tensor * token_emb_norm = build_norm(token_emb, mtp_layer.nextn.enorm, NULL, LLM_NORM_RMS, il);
 
@@ -13994,9 +13993,7 @@ struct llm_build_glm4_moe_mtp : public llm_graph_context {
 
         ggml_tensor * combined = ggml_concat(ctx0, token_emb_norm, hidden_state_norm, 0);  // torch.cat
 
-
-        cur = build_lora_mm(mtp_layer.nextn.eh_proj, combined);                            // eh_proj
-
+        ggml_tensor* cur = build_lora_mm(mtp_layer.nextn.eh_proj, combined);               // eh_proj
 
         // now proceed through last layer (skipped in main model)
         ggml_tensor * inpSA = cur;
@@ -14096,14 +14093,9 @@ struct llm_build_glm4_moe_mtp : public llm_graph_context {
 
         cur = build_norm(cur, mtp_layer.nextn.shared_head_norm, NULL, LLM_NORM_RMS, il);
         cur = build_lora_mm(mtp_layer.nextn.shared_head_head, cur);
-
+        
         res->t_logits = cur;
-
         ggml_build_forward_expand(gf, res->t_logits);
-
-        struct ggml_tensor * token_id_tensor = ggml_argmax(ctx0, cur);
-        ggml_set_name(token_id_tensor, "mtp_argmax_result");
-        ggml_build_forward_expand(gf, token_id_tensor);
     }
 };