#include "models.h" template <> llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const bool use_mrope = hparams.use_mrope(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); ggml_tensor * cur; ggml_tensor * inpL; inpL = build_inp_embd(model.tok_embd); if (ubatch.embd && !use_mrope) { // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results GGML_ABORT("This GGUF does not support multimodal. Please reconvert it."); } // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); auto * inp_attn = build_attn_inp_kv(); ggml_tensor * inp_out_ids = build_inp_out_ids(); // Only process up to last layer (skip final NextN layer) // Final layer tensors are loaded but not processed in forward pass const int n_transformer_layers = n_layer - hparams.nextn_predict_layers; for (int il = 0; il < n_transformer_layers; ++il) { // input for next layer bool is_output_layer = (il == n_transformer_layers - 1); inpL = build_layer(model, inp_attn, inpL, inp_pos, inp_out_ids, sections, is_output_layer, il); } cur = inpL; cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); cb(cur, "result_norm", -1); res->t_embd = cur; // Output projection cur = build_lora_mm(model.output, cur); cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); } // MTP model template <> llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : 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); int sections[4]; std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections); ggml_tensor * cur; ggml_tensor * inpL; // for now, we only support one single NextN layer for simplicity GGML_ASSERT(hparams.nextn_predict_layers == 1); const int il = n_layer - hparams.nextn_predict_layers; auto & mtp_layer = model.layers[il]; ggml_tensor * inp_token_embd = build_inp_embd(mtp_layer.nextn.embed_tokens // can be nullptr on GLM-4.6 ? mtp_layer.nextn.embed_tokens : model.tok_embd); ggml_tensor * inp_state_embd = build_inp_cross_mtp(); // check number of input tokens GGML_ASSERT(inp_state_embd->ne[1] == inp_token_embd->ne[1]); inp_token_embd = build_norm(inp_token_embd, mtp_layer.nextn.enorm, NULL, LLM_NORM_RMS, il); inp_state_embd = build_norm(inp_state_embd, mtp_layer.nextn.hnorm, NULL, LLM_NORM_RMS, il); inpL = ggml_concat(ctx0, inp_token_embd, inp_state_embd, 0); cb(inpL, "inp_mtp", il); inpL = build_lora_mm(mtp_layer.nextn.eh_proj, inpL); cb(inpL, "inp_mtp_projected", il); // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); auto * inp_attn = build_attn_inp_kv(); ggml_tensor * inp_out_ids = build_inp_out_ids(); { bool is_output_layer = true; // TODO: we only have one single nextn layer for now, may need to change in the future inpL = build_layer(model, inp_attn, inpL, inp_pos, inp_out_ids, sections, is_output_layer, il); } cur = inpL; cur = build_norm(cur, mtp_layer.nextn.shared_head_norm // can be nullptr on GLM-4.6 ? mtp_layer.nextn.shared_head_norm : model.output_norm, NULL, LLM_NORM_RMS, -1); cb(cur, "result_norm", -1); res->t_embd = cur; // lm_head cur = build_lora_mm(mtp_layer.nextn.shared_head_head // can be nullptr on GLM-4.6 ? mtp_layer.nextn.shared_head_head : model.output, cur); cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); } template ggml_tensor * llm_build_glm4::build_layer(const llama_model & model, llm_graph_input_attn_kv * inp_attn, ggml_tensor * inpL, ggml_tensor * inp_pos, ggml_tensor * inp_out_ids, int sections[4], bool is_output_layer, int il) { bool use_mrope = hparams.use_mrope(); const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); ggml_tensor * inpSA = inpL; // Pre-attention norm ggml_tensor * cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "attn_norm", il); // self-attention { ggml_tensor * Qcur = nullptr; ggml_tensor * Kcur = nullptr; ggml_tensor * Vcur = nullptr; if (model.layers[il].wqkv == nullptr) { Qcur = build_lora_mm(model.layers[il].wq, cur); if (model.layers[il].bq) { Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); } Kcur = build_lora_mm(model.layers[il].wk, cur); if (model.layers[il].bk) { Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); } Vcur = build_lora_mm(model.layers[il].wv, cur); if (model.layers[il].bv) { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); } Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); } else { cur = build_lora_mm(model.layers[il].wqkv, cur); cb(cur, "wqkv", il); if (model.layers[il].bqkv) { cur = ggml_add(ctx0, cur, model.layers[il].bqkv); cb(cur, "bqkv", il); } Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd)); Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd)); Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)); } if (use_mrope) { Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr, n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr, n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else { // Normal RoPE Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); cur = build_attn(inp_attn, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il); } if (is_output_layer && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } // Post-attention norm (new!) cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il); cb(cur, "post_attn_norm", il); // Add the input (residual connection after post-attention norm) ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); // FF { // Pre-MLP norm cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il); // MLP cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, NULL, NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il); cb(cur, "ffn_out", il); // Post-MLP norm cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il); cb(cur, "post_mlp_norm", il); } cur = ggml_add(ctx0, cur, ffn_inp); cur = build_cvec(cur, il); cb(cur, "l_out", il); return cur; }