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model : add hunyuan moe (#14425)
* model : add hunyuan moe * tokenizer ok * fix tensor name * cgraph init * chat template * wip * almost working * skip embed, fix bos * cleanup * yarn scaling * cleanup * correct rope type * failed token fix * ntk alpha freq_base * tokenization working * cleanup and pr changes * vocab_size sanity check * ntk alpha generic * Update convert_hf_to_gguf.py * Apply suggestions from code review * fix regression * fix style --------- Co-authored-by: kooshi <1934337+kooshi@users.noreply.github.com>
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Xuan-Son Nguyen
GitHub
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12 changed files with 449 additions and 0 deletions
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@ -102,6 +102,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_57B_A14B: return "57B.A14B";
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case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
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case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
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case LLM_TYPE_A13B: return "A13B";
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case LLM_TYPE_30B_A3B: return "30B.A3B";
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case LLM_TYPE_235B_A22B: return "235B.A22B";
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case LLM_TYPE_E2B: return "E2B";
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@ -1549,6 +1550,17 @@ 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_HUNYUAN_MOE:
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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_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
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switch (hparams.n_layer) {
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case 32: type = LLM_TYPE_A13B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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default: throw std::runtime_error("unsupported model architecture");
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}
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@ -4475,6 +4487,43 @@ 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_HUNYUAN_MOE:
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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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for (int i = 0; i < n_layer; ++i) {
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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}, 0);
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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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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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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.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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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_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, 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, n_expert}, 0);
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -14645,6 +14694,168 @@ struct llm_build_arcee : public llm_graph_context {
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}
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};
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struct llm_build_hunyuan_moe : public llm_graph_context {
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llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : 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_ASSERT(n_embd_head == hparams.n_rot);
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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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// 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_unified();
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const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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// norm
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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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// rope freq factors for llama3; may return nullptr for llama2 and other models
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ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
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// compute Q and K and RoPE them
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = build_norm(Kcur,
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model.layers[il].attn_k_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(Kcur, "Kcur_norm", il);
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Qcur = build_norm(Qcur,
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model.layers[il].attn_q_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_norm", il);
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cur = build_attn(inp_attn, gf,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
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cb(cur, "attn_out", il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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// feed-forward network (non-MoE)
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ggml_tensor * cur_mlp = build_ffn(cur,
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model.layers[il].ffn_up_shexp, NULL, NULL,
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model.layers[il].ffn_gate_shexp, NULL, NULL,
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model.layers[il].ffn_down_shexp, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur_mlp, "ffn_mlp", il);
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// MoE branch
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ggml_tensor * cur_moe = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU,
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true, // norm_topk_prob
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false,
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0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il);
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cb(cur_moe, "ffn_moe_out", il);
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ggml_tensor * ffn_out = ggml_add(ctx0, cur_moe, cur_mlp);
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cb(ffn_out, "ffn_out", il);
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cur = ggml_add(ctx0, ffn_out, ffn_inp);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = build_norm(cur,
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model.output_norm, NULL,
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LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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// lm_head
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cur = build_lora_mm(model.output, cur);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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};
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llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
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llama_memory_i * res;
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@ -15025,6 +15236,10 @@ llm_graph_result_ptr llama_model::build_graph(
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{
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llm = std::make_unique<llm_build_ernie4_5>(*this, params, gf);
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} break;
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case LLM_ARCH_HUNYUAN_MOE:
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{
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llm = std::make_unique<llm_build_hunyuan_moe>(*this, params, gf);
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} break;
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default:
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GGML_ABORT("fatal error");
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}
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@ -15213,6 +15428,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_EXAONE:
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case LLM_ARCH_MINICPM3:
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case LLM_ARCH_DOTS1:
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case LLM_ARCH_HUNYUAN_MOE:
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return LLAMA_ROPE_TYPE_NEOX;
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case LLM_ARCH_QWEN2VL:
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