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model : add EXAONE 4.0 support (#14630)
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lgai-exaone
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9 changed files with 333 additions and 0 deletions
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@ -1490,6 +1490,23 @@ 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_EXAONE4:
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{
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if (hparams.n_layer == 64) { // 32B
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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hparams.n_swa = 4096;
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hparams.set_swa_pattern(4);
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}
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 30: type = LLM_TYPE_1_2B; break;
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case 64: type = LLM_TYPE_32B; 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_RWKV6:
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case LLM_ARCH_RWKV6QWEN2:
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{
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@ -4355,6 +4372,39 @@ 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_EXAONE4:
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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.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, n_embd}, 0);
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layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 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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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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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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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_RWKV6:
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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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@ -13478,6 +13528,142 @@ struct llm_build_exaone : public llm_graph_context {
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}
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};
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template <bool iswa>
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struct llm_build_exaone4 : public llm_graph_context {
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llm_build_exaone4(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_k;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_v);
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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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using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_unified_iswa, llm_graph_input_attn_kv_unified>;
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inp_attn_type * inp_attn = nullptr;
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if constexpr (iswa) {
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inp_attn = build_attn_inp_kv_unified_iswa();
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} else {
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inp_attn = build_attn_inp_kv_unified();
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}
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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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// use RoPE for SWA layers or non-SWA models
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const bool use_rope = hparams.is_swa(il) || hparams.swa_type == LLAMA_SWA_TYPE_NONE;
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cur = inpL;
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// self-attention
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{
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ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
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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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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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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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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 = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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cb(Kcur, "Kcur_normed", il);
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if (use_rope) {
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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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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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}
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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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cur = build_attn(inp_attn, gf,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), 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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cur = build_norm(cur,
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model.layers[il].attn_post_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "attn_post_norm", il);
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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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// feed-forward network
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cur = build_ffn(ffn_inp,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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cur = build_norm(cur,
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model.layers[il].ffn_post_norm, NULL,
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LLM_NORM_RMS, -1);
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cb(cur, "ffn_post_norm", -1);
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cur = ggml_add(ctx0, cur, 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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struct llm_build_rwkv6_base : public llm_graph_context {
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const llama_model & model;
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@ -17163,6 +17349,14 @@ 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_exaone>(*this, params);
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} break;
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case LLM_ARCH_EXAONE4:
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{
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if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
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llm = std::make_unique<llm_build_exaone4<true>>(*this, params, gf);
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} else {
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llm = std::make_unique<llm_build_exaone4<false>>(*this, params, gf);
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}
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} break;
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case LLM_ARCH_RWKV6:
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{
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llm = std::make_unique<llm_build_rwkv6>(*this, params);
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@ -17430,6 +17624,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_ORION:
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case LLM_ARCH_NEMOTRON:
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case LLM_ARCH_EXAONE:
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case LLM_ARCH_EXAONE4:
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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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