model : add AfmoeForCausalLM support (#16477)
* Add AFMOE model support * Update to vocab * Add model sizing * Undo Rope change for ARCEE model * Address review comments * Update modeling code is_sliding -> use_rope, replace hard-coded logic * Fix AFMOE tokenizer * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update AFMoE tokenizer class identification to be more unique --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Bartowski
GitHub
@@ -35,6 +35,7 @@ add_library(llama
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unicode-data.cpp
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unicode.cpp
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unicode.h
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models/afmoe.cpp
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models/apertus.cpp
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models/arcee.cpp
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models/arctic.cpp
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@@ -90,6 +90,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_BAILINGMOE2, "bailingmoe2" },
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{ LLM_ARCH_DOTS1, "dots1" },
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{ LLM_ARCH_ARCEE, "arcee" },
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{ LLM_ARCH_AFMOE, "afmoe" },
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{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
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{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
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{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
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@@ -333,6 +334,36 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_AFMOE,
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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_ATTN_GATE, "blk.%d.attn_gate" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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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_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_UP_SHEXP, "blk.%d.ffn_up_shexp" },
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{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
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{ LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
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},
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},
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{
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LLM_ARCH_LLAMA4,
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{
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@@ -2444,6 +2475,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
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{LLM_TENSOR_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_ATTN_QKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_ATTN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_FFN_GATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_FFN_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_FFN_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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@@ -94,6 +94,7 @@ enum llm_arch {
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LLM_ARCH_BAILINGMOE2,
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LLM_ARCH_DOTS1,
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LLM_ARCH_ARCEE,
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LLM_ARCH_AFMOE,
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LLM_ARCH_ERNIE4_5,
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LLM_ARCH_ERNIE4_5_MOE,
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LLM_ARCH_HUNYUAN_MOE,
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@@ -312,6 +313,7 @@ enum llm_tensor {
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LLM_TENSOR_ATTN_POST_NORM,
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LLM_TENSOR_ATTN_ROT_EMBD,
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LLM_TENSOR_ATTN_SINKS,
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LLM_TENSOR_ATTN_GATE,
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LLM_TENSOR_FFN_GATE_INP,
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LLM_TENSOR_FFN_GATE_INP_SHEXP,
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LLM_TENSOR_FFN_NORM,
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@@ -84,6 +84,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_15B: return "15B";
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case LLM_TYPE_16B: return "16B";
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case LLM_TYPE_20B: return "20B";
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case LLM_TYPE_26B: return "26B";
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case LLM_TYPE_27B: return "27B";
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case LLM_TYPE_30B: return "30B";
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case LLM_TYPE_32B: return "32B";
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@@ -695,6 +696,37 @@ 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_AFMOE:
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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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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
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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_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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// Set up interleaved sliding window attention (ISWA)
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// Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)
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if (hparams.n_swa > 0) {
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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hparams.set_swa_pattern(4);
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} else {
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hparams.swa_type = LLAMA_SWA_TYPE_NONE;
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}
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// Default to sigmoid if not set
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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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switch (hparams.n_layer) {
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case 56: type = LLM_TYPE_6B; break;
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case 32: type = LLM_TYPE_26B; break;
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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_DECI:
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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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@@ -5749,6 +5781,71 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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} break;
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case LLM_ARCH_AFMOE:
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{
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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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const int64_t n_ff_exp = hparams.n_ff_exp;
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const int64_t n_expert_shared = hparams.n_expert_shared;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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// dual attention normalization
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
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// attention projections
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
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// Q/K normalization
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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// attention gating
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
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// dual ffn normalization
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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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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if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) {
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// MoE layers
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
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// grouped expert weights
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
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// shared expert
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if (n_expert_shared > 0) {
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const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
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}
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} else {
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// Dense layers
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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}
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} break;
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case LLM_ARCH_ERNIE4_5:
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case LLM_ARCH_ERNIE4_5_MOE:
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{
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@@ -7243,6 +7340,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
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{
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llm = std::make_unique<llm_build_arcee>(*this, params);
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} break;
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case LLM_ARCH_AFMOE:
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{
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llm = std::make_unique<llm_build_afmoe>(*this, params);
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} break;
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case LLM_ARCH_ERNIE4_5:
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{
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llm = std::make_unique<llm_build_ernie4_5>(*this, params);
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@@ -7528,6 +7629,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_MINIMAX_M2:
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case LLM_ARCH_COGVLM:
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case LLM_ARCH_PANGU_EMBED:
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case LLM_ARCH_AFMOE:
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return LLAMA_ROPE_TYPE_NEOX;
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case LLM_ARCH_QWEN2VL:
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@@ -76,6 +76,7 @@ enum llm_type {
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LLM_TYPE_15B,
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LLM_TYPE_16B,
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LLM_TYPE_20B,
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LLM_TYPE_26B,
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LLM_TYPE_27B,
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LLM_TYPE_30B,
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LLM_TYPE_32B,
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@@ -234,6 +235,7 @@ struct llama_layer {
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struct ggml_tensor * wk_enc = nullptr;
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struct ggml_tensor * wv_enc = nullptr;
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struct ggml_tensor * wo_enc = nullptr;
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struct ggml_tensor * wqkv_gate = nullptr;
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// attention bias
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struct ggml_tensor * bq = nullptr;
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@@ -443,6 +443,17 @@ struct llm_tokenizer_bpe : llm_tokenizer {
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"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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case LLAMA_VOCAB_PRE_TYPE_AFMOE:
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regex_exprs = {
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// Digit handling - uses custom implementation in unicode.cpp
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// Groups digits with leading 1-2 based on total length modulo 3
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"\\p{AFMoE_digits}",
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// CJK and Asian scripts (using direct Unicode literals)
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"[一-鿿㐀-䶿豈--ゟ゠-ヿ･-ﾟ⼀-เ--ក-က-႟ꩠ-ꩿꧠ-가-ᄀ-ᇿ]+",
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// Main BPE pattern
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"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\\r\\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
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};
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break;
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default:
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// default regex for BPE tokenization pre-processing
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regex_exprs = {
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@@ -1993,6 +2004,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
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tokenizer_pre == "grok-2") {
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pre_type = LLAMA_VOCAB_PRE_TYPE_GROK_2;
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clean_spaces = false;
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} else if (
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tokenizer_pre == "afmoe") {
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pre_type = LLAMA_VOCAB_PRE_TYPE_AFMOE;
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clean_spaces = false;
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} else if (
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tokenizer_pre == "minimax-m2") {
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pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;
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@@ -50,6 +50,7 @@ enum llama_vocab_pre_type {
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LLAMA_VOCAB_PRE_TYPE_GROK_2 = 39,
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LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
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LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2 = 41,
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LLAMA_VOCAB_PRE_TYPE_AFMOE = 42,
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};
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struct LLM_KV;
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@@ -0,0 +1,187 @@
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#include "models.h"
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llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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// MuP scaling: embeddings * sqrt(hidden_size)
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// mup_enabled = true, hidden_size = 1024, scale = 32.0
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inpL = ggml_scale(ctx0, inpL, sqrtf(float(n_embd)));
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cb(inpL, "inp_embd_scaled", -1);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv_iswa();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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// dual attention normalization (pre)
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cur = build_norm(inpL,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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||||
ggml_tensor * attn_inp = cur; // save input for gate computation
|
||||
|
||||
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// compute gate from input
|
||||
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
|
||||
cb(gate, "attn_gate_proj", 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);
|
||||
|
||||
// Q/K normalization
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
cb(Kcur, "Kcur_normed", il);
|
||||
|
||||
// RoPE only for sliding_attention layers
|
||||
const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
|
||||
((il + 1) % hparams.n_no_rope_layer_step) != 0;
|
||||
if (use_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);
|
||||
cb(Qcur, "Qcur_rope", il);
|
||||
|
||||
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(Kcur, "Kcur_rope", il);
|
||||
}
|
||||
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
NULL, NULL, // wo will be applied after gating
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "attn_out", il);
|
||||
|
||||
// attention gating: attn_out * sigmoid(gate) BEFORE o_proj
|
||||
gate = ggml_sigmoid(ctx0, gate);
|
||||
cb(gate, "attn_gate_sig", il);
|
||||
cur = ggml_mul(ctx0, cur, gate);
|
||||
cb(cur, "attn_gated", il);
|
||||
|
||||
// now apply output projection
|
||||
cur = build_lora_mm(model.layers[il].wo, cur);
|
||||
cb(cur, "attn_o_proj", il);
|
||||
}
|
||||
|
||||
// dual attention normalization (post)
|
||||
cur = build_norm(cur,
|
||||
model.layers[il].attn_post_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_post_norm", il);
|
||||
|
||||
if (il == n_layer - 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);
|
||||
|
||||
// dual ffn normalization (pre)
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
// MoE or dense FFN
|
||||
if ((uint32_t)il >= hparams.n_layer_dense_lead) {
|
||||
// MoE layer with sigmoid routing, normalization, and scaling
|
||||
ggml_tensor * moe_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, // norm_w (route_norm=True)
|
||||
hparams.expert_weights_scale, // scale_w
|
||||
hparams.expert_weights_scale, // w_scale (route_scale=2.826)
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// shared expert
|
||||
if (hparams.n_expert_shared > 0) {
|
||||
ggml_tensor * ffn_shexp = 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(ffn_shexp, "ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
cur = moe_out;
|
||||
}
|
||||
} else {
|
||||
// dense 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);
|
||||
}
|
||||
|
||||
// dual ffn normalization (post)
|
||||
cur = build_norm(cur,
|
||||
model.layers[il].ffn_post_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_post_norm", 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);
|
||||
}
|
||||
@@ -57,6 +57,10 @@ struct llm_build_rwkv7_base : public llm_graph_context {
|
||||
int il) const;
|
||||
};
|
||||
|
||||
struct llm_build_afmoe : public llm_graph_context {
|
||||
llm_build_afmoe(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_apertus : public llm_graph_context {
|
||||
llm_build_apertus(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
@@ -729,6 +729,80 @@ static std::vector<size_t> unicode_regex_split_custom_kimi_k2(const std::string
|
||||
return bpe_offsets;
|
||||
}
|
||||
|
||||
// AFMOE digit handling: splits digits with leading 1-2 based on total length modulo 3
|
||||
static std::vector<size_t> unicode_regex_split_custom_afmoe(const std::string & text, const std::vector<size_t> & offsets) {
|
||||
std::vector<size_t> bpe_offsets;
|
||||
bpe_offsets.reserve(offsets.size());
|
||||
|
||||
const auto cpts = unicode_cpts_from_utf8(text);
|
||||
|
||||
size_t start = 0;
|
||||
for (auto offset : offsets) {
|
||||
const size_t offset_ini = start;
|
||||
const size_t offset_end = start + offset;
|
||||
assert(offset_end <= cpts.size());
|
||||
start = offset_end;
|
||||
|
||||
auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
|
||||
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
|
||||
};
|
||||
|
||||
size_t _prev_end = offset_ini;
|
||||
auto _add_token = [&] (const size_t end) -> size_t {
|
||||
assert(_prev_end <= end && end <= offset_end);
|
||||
size_t len = end - _prev_end;
|
||||
if (len > 0) {
|
||||
bpe_offsets.push_back(len);
|
||||
}
|
||||
_prev_end = end;
|
||||
return len;
|
||||
};
|
||||
|
||||
for (size_t pos = offset_ini; pos < offset_end; ) {
|
||||
const auto flags = _get_flags(pos);
|
||||
|
||||
// Handle digit sequences with special splitting logic
|
||||
if (flags.is_number) {
|
||||
size_t digit_start = pos;
|
||||
size_t digit_count = 0;
|
||||
|
||||
// Count consecutive digits
|
||||
while (_get_flags(pos).is_number && pos < offset_end) {
|
||||
digit_count++;
|
||||
pos++;
|
||||
}
|
||||
|
||||
// Split based on total length modulo 3
|
||||
size_t remainder = digit_count % 3;
|
||||
size_t current = digit_start;
|
||||
|
||||
// Emit leading 1-2 digits if needed
|
||||
if (remainder > 0) {
|
||||
_add_token(current + remainder);
|
||||
current += remainder;
|
||||
}
|
||||
|
||||
// Emit groups of 3
|
||||
while (current < digit_start + digit_count) {
|
||||
_add_token(current + 3);
|
||||
current += 3;
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
// For non-digits, just move forward
|
||||
pos++;
|
||||
}
|
||||
|
||||
// Add any remaining content
|
||||
if (_prev_end < offset_end) {
|
||||
_add_token(offset_end);
|
||||
}
|
||||
}
|
||||
|
||||
return bpe_offsets;
|
||||
}
|
||||
|
||||
static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
|
||||
std::vector<size_t> bpe_offsets;
|
||||
|
||||
@@ -742,6 +816,9 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
|
||||
} else if (regex_expr == "\\p{Han}+") {
|
||||
// K2's first pattern - handle all K2 patterns together
|
||||
bpe_offsets = unicode_regex_split_custom_kimi_k2(text, offsets);
|
||||
} else if (regex_expr == "\\p{AFMoE_digits}") {
|
||||
// AFMOE digit pattern - use custom implementation for proper splitting
|
||||
bpe_offsets = unicode_regex_split_custom_afmoe(text, offsets);
|
||||
}
|
||||
|
||||
return bpe_offsets;
|
||||
|
||||
Reference in New Issue
Block a user