model: support GLM 4.5 family of models (#14939)
* model: Add GLM 4.5 (#14921) Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Merge in PR suggestions Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: Add GLM 4.5 family of models (#14921) 1. Updated tensor_mapping.py with NextN tensor mappings - Added proper tensor mappings for all NextN/MTP tensors in /Users/samm/git/llama.cpp/gguf-py/gguf/tensor_mapping.py - Added mappings for: eh_proj, embed_tokens, enorm, hnorm, shared_head.head, shared_head.norm 2. Added num_nextn_predict_layers configuration - Added LLM_KV_NUM_NEXTN_PREDICT_LAYERS constant to llama-arch.h and llama-arch.cpp - Added num_nextn_predict_layers field to llama_hparams struct - Updated GLM4_MOE parameter loading in llama-model.cpp to read this parameter - Modified tensor loading logic to conditionally load NextN tensors based on num_nextn_predict_layers - Added GGUF writer support in gguf_writer.py with add_num_nextn_predict_layers() method - Updated conversion script to extract and write this parameter from HuggingFace config 3. Added FIM tokens for GLM4_MOE - Added GLM-4.5's FIM tokens to llama-vocab.cpp: - <|code_prefix|> for FIM_PRE - <|code_suffix|> for FIM_SUF - <|code_middle|> for FIM_MID 4. Removed manual NextN tensor handling - Removed the special-case handling in convert_hf_to_gguf.py that manually mapped NextN tensors - NextN tensors are now handled automatically through the proper tensor mapping system * glm 4.5 update tensors names * model: glm 4.5 apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: glm 4.5 apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: glm 4.5 apply suggestions from code review * Apply suggestions from code review * patch broken chat template * typings fix * add TENSOR_SKIP flag Co-authored-by: Diego Devesa <slarengh@gmail.com> * Update src/llama-model-loader.h Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Diego Devesa <slarengh@gmail.com>
This commit is contained in:
Sam
@@ -62,6 +62,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
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{ LLM_ARCH_CHATGLM, "chatglm" },
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{ LLM_ARCH_GLM4, "glm4" },
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{ LLM_ARCH_GLM4_MOE, "glm4moe" },
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{ LLM_ARCH_BITNET, "bitnet" },
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{ LLM_ARCH_T5, "t5" },
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{ LLM_ARCH_T5ENCODER, "t5encoder" },
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@@ -127,6 +128,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" },
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{ LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" },
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{ LLM_KV_MOE_EVERY_N_LAYERS, "%s.moe_every_n_layers" },
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{ LLM_KV_NEXTN_PREDICT_LAYERS, "%s.nextn_predict_layers" },
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{ LLM_KV_POOLING_TYPE, "%s.pooling_type" },
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{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
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{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
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@@ -1391,6 +1393,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
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},
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},
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{
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LLM_ARCH_GLM4_MOE,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
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{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
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{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
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{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
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// NextN/MTP tensors - preserved but unused (in final layer, dynamic layer number)
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{ LLM_TENSOR_NEXTN_EH_PROJ, "blk.%d.nextn.eh_proj" },
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{ LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.nextn.embed_tokens" },
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{ LLM_TENSOR_NEXTN_ENORM, "blk.%d.nextn.enorm" },
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{ LLM_TENSOR_NEXTN_HNORM, "blk.%d.nextn.hnorm" },
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{ LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.nextn.shared_head_head" },
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{ LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.nextn.shared_head_norm" },
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},
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},
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{
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LLM_ARCH_BITNET,
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{
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@@ -2181,6 +2217,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
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{LLM_TENSOR_SHORTCONV_CONV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
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{LLM_TENSOR_SHORTCONV_INPROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_SHORTCONV_OUTPROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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// NextN/MTP tensors are currently ignored (reserved for future MTP support)
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// These tensors only exist in the last layer(s) and are treated as output tensors
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{LLM_TENSOR_NEXTN_EH_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_NEXTN_EMBED_TOKENS, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
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{LLM_TENSOR_NEXTN_ENORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
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{LLM_TENSOR_NEXTN_HNORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
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{LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
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};
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LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
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@@ -66,6 +66,7 @@ enum llm_arch {
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LLM_ARCH_DEEPSEEK2,
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LLM_ARCH_CHATGLM,
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LLM_ARCH_GLM4,
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LLM_ARCH_GLM4_MOE,
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LLM_ARCH_BITNET,
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LLM_ARCH_T5,
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LLM_ARCH_T5ENCODER,
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@@ -131,6 +132,7 @@ enum llm_kv {
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LLM_KV_EXPERT_WEIGHTS_NORM,
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LLM_KV_EXPERT_GATING_FUNC,
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LLM_KV_MOE_EVERY_N_LAYERS,
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LLM_KV_NEXTN_PREDICT_LAYERS,
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LLM_KV_POOLING_TYPE,
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LLM_KV_LOGIT_SCALE,
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LLM_KV_DECODER_START_TOKEN_ID,
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@@ -409,6 +411,12 @@ enum llm_tensor {
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LLM_TENSOR_SHORTCONV_CONV,
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LLM_TENSOR_SHORTCONV_INPROJ,
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LLM_TENSOR_SHORTCONV_OUTPROJ,
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LLM_TENSOR_NEXTN_EH_PROJ,
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LLM_TENSOR_NEXTN_EMBED_TOKENS,
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LLM_TENSOR_NEXTN_ENORM,
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LLM_TENSOR_NEXTN_HNORM,
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LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
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LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
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};
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enum llm_tensor_layer {
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@@ -749,8 +749,8 @@ ggml_tensor * llm_graph_context::build_ffn(
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if (down) {
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cur = build_lora_mm(down, cur);
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if (arch == LLM_ARCH_GLM4) {
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// GLM4 seems to have numerical issues with half-precision accumulators
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if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
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// GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
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ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
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}
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}
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@@ -1391,8 +1391,8 @@ ggml_tensor * llm_graph_context::build_attn(
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if (wo) {
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cur = build_lora_mm(wo, cur);
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if (arch == LLM_ARCH_GLM4) {
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// GLM4 seems to have numerical issues with half-precision accumulators
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if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
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// GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
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ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
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}
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}
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@@ -73,6 +73,7 @@ struct llama_hparams {
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bool expert_weights_norm = false;
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uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
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uint32_t moe_every_n_layers = 0;
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uint32_t nextn_predict_layers = 0;
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float f_norm_eps;
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float f_norm_rms_eps;
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@@ -39,6 +39,10 @@ llama_kv_cache_unified::llama_kv_cache_unified(
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if (model.arch == LLM_ARCH_GEMMA3N) {
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n_layer_cache = 20;
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}
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if (model.arch == LLM_ARCH_GLM4_MOE) {
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// GLM-4.5: Only process up to last layer, skip final NextN layer
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n_layer_cache = hparams.n_layer - hparams.nextn_predict_layers;
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}
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// create a context for each buffer type
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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@@ -58,8 +58,9 @@ struct llama_model_loader {
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}
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};
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static const int TENSOR_NOT_REQUIRED = 1;
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static const int TENSOR_DUPLICATED = 2;
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static const int TENSOR_NOT_REQUIRED = 1 << 0;
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static const int TENSOR_DUPLICATED = 1 << 1;
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static const int TENSOR_SKIP = 1 << 2;
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int n_kv = 0;
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int n_tensors = 0;
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@@ -109,8 +109,10 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_A13B: return "A13B";
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case LLM_TYPE_21B_A3B: return "21B.A3B";
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case LLM_TYPE_30B_A3B: return "30B.A3B";
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case LLM_TYPE_106B_A12B: return "106B.A12B";
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case LLM_TYPE_235B_A22B: return "235B.A22B";
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case LLM_TYPE_300B_A47B: return "300B.A47B";
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case LLM_TYPE_355B_A32B: return "355B.A32B";
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case LLM_TYPE_E2B: return "E2B";
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case LLM_TYPE_E4B: return "E4B";
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default: return "?B";
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@@ -1434,6 +1436,34 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_GLM4_MOE:
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{
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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// MoE parameters
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ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
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ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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// Expert gating function (GLM-4.5 uses sigmoid)
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
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hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
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}
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// NextN/MTP parameters
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ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
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switch (hparams.n_layer) {
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case 47: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer)
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case 93: type = LLM_TYPE_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer)
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_BITNET:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@@ -1949,6 +1979,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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const auto TENSOR_DUPLICATED = llama_model_loader::TENSOR_DUPLICATED;
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const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
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const auto TENSOR_SKIP = llama_model_loader::TENSOR_SKIP;
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// create tensors for the weights
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{
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@@ -2004,7 +2035,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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}
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// skip unused tensors
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if (info.op == GGML_OP_NONE) {
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if (info.op == GGML_OP_NONE || flags & TENSOR_SKIP) {
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const size_t nbytes = ggml_nbytes(t_meta);
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LLAMA_LOG_WARN("model has unused tensor %s (size = %zu bytes) -- ignoring\n", tn.str().c_str(), nbytes);
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@@ -4427,6 +4458,105 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
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}
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} break;
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case LLM_ARCH_GLM4_MOE:
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{
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const int64_t n_expert = hparams.n_expert;
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const int64_t n_expert_used = hparams.n_expert_used;
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const int64_t n_expert_shared = hparams.n_expert_shared;
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GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
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GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
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}
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// Load ALL tensors including NextN layer to satisfy total tensor count
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// but only PROCESS up to last layer (skipping final NextN layer) in forward pass
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for (int i = 0; i < n_layer; ++i) {
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int flags = 0;
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if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
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// skip all tensors in the NextN layers
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flags |= TENSOR_SKIP;
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}
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
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// GLM-style attention with bias terms
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, flags);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, flags);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, flags);
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layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd_head_k * n_head }, flags);
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layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_k_gqa }, flags);
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layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_v_gqa }, flags);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
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// K/Q norm tensors (optional for GLM-4.5 355B variant)
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layer.attn_q_norm = create_tensor(
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tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
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layer.attn_k_norm = create_tensor(
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tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, flags);
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// Check if this layer uses MoE or dense FFN based on n_layer_dense_lead
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// GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE
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const bool use_moe = (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead);
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|
||||
if (use_moe) {
|
||||
// MoE layers
|
||||
layer.ffn_gate_inp =
|
||||
create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags);
|
||||
|
||||
// MoE branch
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
|
||||
|
||||
layer.ffn_gate_exps = create_tensor(
|
||||
tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
|
||||
layer.ffn_down_exps = create_tensor(
|
||||
tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
|
||||
layer.ffn_up_exps = create_tensor(
|
||||
tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
|
||||
|
||||
// Shared expert
|
||||
if (n_expert_shared > 0) {
|
||||
const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
|
||||
layer.ffn_gate_shexp = create_tensor(
|
||||
tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
|
||||
layer.ffn_down_shexp = create_tensor(
|
||||
tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
|
||||
layer.ffn_up_shexp = create_tensor(
|
||||
tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
|
||||
}
|
||||
} else {
|
||||
// Dense layers (first k layers) - GLM uses separate gate/up projections
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags);
|
||||
}
|
||||
|
||||
// NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
|
||||
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
|
||||
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
|
||||
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
|
||||
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags);
|
||||
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags);
|
||||
}
|
||||
}
|
||||
}
|
||||
break;
|
||||
case LLM_ARCH_NEMOTRON:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
@@ -13564,6 +13694,169 @@ struct llm_build_glm4 : public llm_graph_context {
|
||||
}
|
||||
};
|
||||
|
||||
struct llm_build_glm4_moe : public llm_graph_context {
|
||||
llm_build_glm4_moe(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);
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
auto * inp_attn = build_attn_inp_kv_unified();
|
||||
|
||||
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) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
// Pre-attention norm
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
if (model.layers[il].bq) {
|
||||
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
||||
}
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
if (model.layers[il].bk) {
|
||||
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
||||
}
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
if (model.layers[il].bv) {
|
||||
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
||||
}
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
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);
|
||||
|
||||
// Apply Q/K norm if available (GLM-4.5 355B variant)
|
||||
if (model.layers[il].attn_q_norm) {
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
}
|
||||
if (model.layers[il].attn_k_norm) {
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(Kcur, "Kcur_normed", il);
|
||||
}
|
||||
|
||||
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, 1.0f/sqrtf(float(n_embd_head)), il);
|
||||
}
|
||||
|
||||
if (il == n_transformer_layers - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// Post-attention norm
|
||||
cur = build_norm(ffn_inp, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "post_attn_norm", il);
|
||||
|
||||
// Check if this is a dense layer (n_layer_dense_lead=1, so layer 0 is dense)
|
||||
if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
|
||||
// Dense FFN layer
|
||||
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, "ffn_out", il);
|
||||
} else {
|
||||
// MoE layer with shared experts
|
||||
const int64_t n_expert = hparams.n_expert;
|
||||
const int64_t n_expert_used = hparams.n_expert_used;
|
||||
|
||||
// Process routed experts using existing MoE infrastructure
|
||||
ggml_tensor * routed_out = build_moe_ffn(cur,
|
||||
model.layers[il].ffn_gate_inp,
|
||||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
model.layers[il].ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
true, hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(routed_out, "ffn_moe_out", il);
|
||||
|
||||
// Process shared expert on original input
|
||||
ggml_tensor * shared_out = build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, NULL, NULL,
|
||||
model.layers[il].ffn_gate_shexp, NULL, NULL,
|
||||
model.layers[il].ffn_down_shexp, NULL, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(shared_out, "ffn_shexp_out", il);
|
||||
|
||||
// Final output: routed_output + shared_output
|
||||
cur = ggml_add(ctx0, routed_out, shared_out);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// lm_head
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
};
|
||||
|
||||
struct llm_build_nemotron : public llm_graph_context {
|
||||
llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
@@ -17877,6 +18170,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
||||
{
|
||||
llm = std::make_unique<llm_build_glm4>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_GLM4_MOE:
|
||||
{
|
||||
llm = std::make_unique<llm_build_glm4_moe>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_BITNET:
|
||||
{
|
||||
llm = std::make_unique<llm_build_bitnet>(*this, params);
|
||||
@@ -18208,6 +18505,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
case LLM_ARCH_LFM2:
|
||||
case LLM_ARCH_SMALLTHINKER:
|
||||
case LLM_ARCH_GLM4_MOE:
|
||||
return LLAMA_ROPE_TYPE_NEOX;
|
||||
|
||||
case LLM_ARCH_QWEN2VL:
|
||||
|
||||
@@ -101,8 +101,10 @@ enum llm_type {
|
||||
LLM_TYPE_A13B,
|
||||
LLM_TYPE_21B_A3B, // Ernie MoE small
|
||||
LLM_TYPE_30B_A3B,
|
||||
LLM_TYPE_106B_A12B, // GLM-4.5-Air
|
||||
LLM_TYPE_235B_A22B,
|
||||
LLM_TYPE_300B_A47B, // Ernie MoE big
|
||||
LLM_TYPE_355B_A32B, // GLM-4.5
|
||||
LLM_TYPE_E2B,
|
||||
LLM_TYPE_E4B,
|
||||
};
|
||||
@@ -166,6 +168,15 @@ struct llama_layer_shortconv {
|
||||
struct ggml_tensor * out_proj = nullptr;
|
||||
};
|
||||
|
||||
struct llama_layer_nextn {
|
||||
struct ggml_tensor * eh_proj = nullptr;
|
||||
struct ggml_tensor * embed_tokens = nullptr;
|
||||
struct ggml_tensor * enorm = nullptr;
|
||||
struct ggml_tensor * hnorm = nullptr;
|
||||
struct ggml_tensor * shared_head_head = nullptr;
|
||||
struct ggml_tensor * shared_head_norm = nullptr;
|
||||
};
|
||||
|
||||
struct llama_layer {
|
||||
// normalization
|
||||
struct ggml_tensor * attn_norm = nullptr;
|
||||
@@ -354,6 +365,8 @@ struct llama_layer {
|
||||
struct llama_layer_convnext convnext;
|
||||
|
||||
struct llama_layer_shortconv shortconv;
|
||||
|
||||
struct llama_layer_nextn nextn;
|
||||
};
|
||||
|
||||
struct llama_model {
|
||||
|
||||
@@ -2191,6 +2191,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|| t.first == "<｜fim▁begin｜>" // DeepSeek
|
||||
|| t.first == "<PRE>"
|
||||
|| t.first == "▁<PRE>" // CodeLlama
|
||||
|| t.first == "<|code_prefix|>" // GLM-4.5
|
||||
) {
|
||||
special_fim_pre_id = t.second;
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
@@ -2210,6 +2211,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|| t.first == "<｜fim▁hole｜>" // DeepSeek
|
||||
|| t.first == "<SUF>"
|
||||
|| t.first == "▁<SUF>" // CodeLlama
|
||||
|| t.first == "<|code_suffix|>" // GLM-4.5
|
||||
) {
|
||||
special_fim_suf_id = t.second;
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
@@ -2229,6 +2231,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
||||
|| t.first == "<｜fim▁end｜>" // DeepSeek
|
||||
|| t.first == "<MID>"
|
||||
|| t.first == "▁<MID>" // CodeLlama
|
||||
|| t.first == "<|code_middle|>" // GLM-4.5
|
||||
) {
|
||||
special_fim_mid_id = t.second;
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
|
||||
Reference in New Issue
Block a user