mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-05-15 13:34:06 +00:00
First draft
This commit is contained in:
Piotr Wilkin
@@ -112,6 +112,7 @@ 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_80B_A3B: return "80B.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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@@ -1809,6 +1810,29 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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// For Granite MoE Shared
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
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} break;
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case LLM_ARCH_QWEN3NEXT:
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{
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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// Load linear attention (gated delta net) parameters
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ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
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ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
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ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
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ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
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ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
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// Mark recurrent layers (linear attention layers)
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for (uint32_t i = 0; i < hparams.n_layer; ++i) {
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hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0); // TODO: extract the magic 4 from "full_attention_interval"
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}
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switch (hparams.n_layer) {
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case 80: type = LLM_TYPE_80B_A3B; 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_CHAMELEON:
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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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@@ -2360,6 +2384,76 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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}
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}
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} break;
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case LLM_ARCH_QWEN3NEXT:
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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 ? hparams.n_ff_exp : n_ff / n_expert_used;
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// Calculate dimensions from hyperparameters
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const int64_t head_k_dim = hparams.ssm_d_state;
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const int64_t head_v_dim = hparams.ssm_d_state;
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const int64_t n_k_heads = hparams.ssm_n_group;
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const int64_t n_v_heads = hparams.ssm_dt_rank;
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const int64_t key_dim = head_k_dim * n_k_heads;
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const int64_t value_dim = head_v_dim * n_v_heads;
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const int64_t conv_dim = key_dim * 2 + value_dim;
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// Calculate projection sizes
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const int64_t qkvz_projection_size = key_dim * 2 + value_dim * 2;
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const int64_t ba_projection_size = n_v_heads * 2;
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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.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
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if ((i + 1) % 4 == 0) { // TODO: magic 4
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// Attention layers
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_ff }, 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 for attention layers
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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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} else {
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// Linear attention (gated delta net) specific tensors
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// Create tensors with calculated dimensions
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layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, qkvz_projection_size }, 0);
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layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
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layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0);
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layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), { hparams.ssm_dt_rank }, 0);
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layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_projection_size }, 0);
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layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0);
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layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { n_ff, n_embd }, 0);
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}
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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_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 experts
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layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 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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}
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break;
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case LLM_ARCH_LLADA:
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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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@@ -6075,7 +6169,8 @@ void llama_model::print_info() const {
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arch == LLM_ARCH_FALCON_H1 ||
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arch == LLM_ARCH_PLAMO2 ||
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arch == LLM_ARCH_GRANITE_HYBRID ||
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arch == LLM_ARCH_NEMOTRON_H) {
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arch == LLM_ARCH_NEMOTRON_H ||
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arch == LLM_ARCH_QWEN3NEXT) {
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LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
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LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
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LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
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@@ -18827,6 +18922,329 @@ struct llm_build_smallthinker : public llm_graph_context{
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}
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};
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struct llm_build_qwen3next : public llm_graph_context_mamba {
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llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(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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auto * inp = build_inp_mem_hybrid();
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ggml_tensor * inp_pos = build_inp_pos();
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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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struct ggml_tensor * inpSA = inpL;
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// Pre-norm for attention/linear attention
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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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// Determine layer type and build appropriate attention mechanism
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if (hparams.is_recurrent(il)) {
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// Linear attention layer (gated delta net)
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cur = build_qwen3next_linear_attn_layer(inp->get_recr(), cur, model, ubatch, il);
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} else {
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// Full attention layer
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cur = build_qwen3next_attention_layer(
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cur, inp_pos, inp->get_attn(), model,
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n_embd_head, il);
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}
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// Post-attention norm
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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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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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// Residual connection
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cur = ggml_add(ctx0, cur, inpSA);
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cb(cur, "attn_residual", il);
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// FFN layer (MoE or dense)
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cur = build_layer_ffn(cur, model, 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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// Final norm
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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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private:
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ggml_tensor * build_qwen3next_attention_layer(
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ggml_tensor * cur,
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ggml_tensor * inp_pos,
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llm_graph_input_attn_kv * inp_attn,
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const llama_model & model,
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const int64_t n_embd_head,
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const int il) {
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// QKV projection with gating
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ggml_tensor * qkv_g = build_lora_mm(model.layers[il].wq, cur);
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cb(qkv_g, "qkv_g", il);
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// Split into Q and gate
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const int64_t n_embd_q = hparams.n_head(il) * n_embd_head;
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ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv_g, n_embd_head, hparams.n_head(il), n_tokens,
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n_embd_head * sizeof(float), qkv_g->nb[1], 0);
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ggml_tensor * gate = ggml_view_3d(ctx0, qkv_g, n_embd_head, hparams.n_head(il), n_tokens,
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n_embd_head * sizeof(float), qkv_g->nb[1], n_embd_q * ggml_element_size(qkv_g));
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// K and V projections
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, hparams.n_head(il), n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
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// Apply Q/K normalization
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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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// Apply RoPE
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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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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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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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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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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// Attention computation
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const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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cur = build_attn(inp_attn,
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model.layers[il].wo, nullptr,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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// Apply gating
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gate = ggml_reshape_2d(ctx0, gate, n_embd_q, n_tokens);
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cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate));
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cb(cur, "attn_gated", il);
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return cur;
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}
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ggml_tensor * build_qwen3next_linear_attn_layer(llm_graph_input_rs * inp,
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ggml_tensor * cur,
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const llama_model & model,
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const llama_ubatch & ubatch,
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int il) {
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// Gated Delta Net implementation using the new ggml_delta_net function
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const auto * mctx_cur = inp->mctx;
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const auto kv_head = mctx_cur->get_head();
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const int64_t d_inner = hparams.ssm_d_inner;
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const int64_t d_state = hparams.ssm_d_state;
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const int64_t n_heads = hparams.ssm_dt_rank;
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const int64_t head_dim = d_inner / n_heads;
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const int64_t n_seqs = ubatch.n_seqs;
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const int64_t n_seq_tokens = ubatch.n_seq_tokens;
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GGML_ASSERT(n_seqs != 0);
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GGML_ASSERT(ubatch.equal_seqs());
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GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
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// Input projection for QKV and beta/alpha
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ggml_tensor * qkvz_ba = build_lora_mm(model.layers[il].ssm_in, cur);
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cb(qkvz_ba, "linear_attn_in_proj", il);
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// Split into QKV and beta/alpha components
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const int64_t qkv_size = d_inner * 2 + d_state * 2;
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ggml_tensor * qkv =
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ggml_view_3d(ctx0, qkvz_ba, qkv_size, n_tokens, 1, qkv_size * sizeof(float), qkvz_ba->nb[1], 0);
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ggml_tensor * ba = ggml_view_2d(ctx0, qkvz_ba, n_embd, n_tokens,
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qkvz_ba->nb[1], qkv_size * sizeof(float));
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// Reshape QKV for processing
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qkv = ggml_reshape_3d(ctx0, qkv, head_dim, n_heads * 2 + d_state * 2 / head_dim, n_tokens);
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// Split into individual components
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ggml_tensor * query =
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ggml_view_3d(ctx0, qkv, head_dim, n_heads, n_tokens, head_dim * sizeof(float), qkv->nb[1], 0);
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ggml_tensor * key = ggml_view_3d(ctx0, qkv, head_dim, n_heads, n_tokens, head_dim * sizeof(float), qkv->nb[1],
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n_heads * head_dim * sizeof(float));
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ggml_tensor * value = ggml_view_3d(ctx0, qkv, head_dim, n_heads, n_tokens, head_dim * sizeof(float), qkv->nb[1],
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n_heads * head_dim * 2 * sizeof(float));
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// Process beta and alpha parameters (corrected dimensions)
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ggml_tensor * beta_alpha = build_lora_mm(model.layers[il].ssm_beta_alpha, ba);
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ggml_tensor * beta =
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ggml_view_3d(ctx0, beta_alpha, n_heads, n_tokens, n_seqs, n_heads * sizeof(float), beta_alpha->nb[1], 0);
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ggml_tensor * alpha = ggml_view_3d(ctx0, beta_alpha, n_heads, n_tokens, n_seqs, n_heads * sizeof(float),
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beta_alpha->nb[1], n_heads * sizeof(float));
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// Apply sigmoid to beta (exactly like reference: beta = b.sigmoid())
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beta = ggml_sigmoid(ctx0, beta);
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ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt); // a + dt_bias
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ggml_tensor * alpha_exp = ggml_exp(ctx0, alpha_biased); // exp(a + dt_bias)
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ggml_tensor * one_tensor = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); // Create scalar tensor
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one_tensor = ggml_exp(ctx0, one_tensor); // e^0 = 1
|
||||
ggml_tensor * one_plus_exp = ggml_add1(ctx0, alpha_exp, one_tensor); // 1 + exp(a + dt_bias)
|
||||
ggml_tensor * alpha_softplus = ggml_log(ctx0, one_plus_exp); // log(1 + exp(...))
|
||||
ggml_tensor * A_log_exp = ggml_exp(ctx0, model.layers[il].ssm_a); // A_log.exp()
|
||||
ggml_tensor * gate_scaled = ggml_mul(ctx0, alpha_softplus, A_log_exp); // A_log.exp() * softplus
|
||||
ggml_tensor * gate = ggml_neg(ctx0, gate_scaled); // - (A_log.exp() * softplus)
|
||||
|
||||
// Get convolution weights and bias
|
||||
ggml_tensor * conv_weight = model.layers[il].ssm_conv1d;
|
||||
ggml_tensor * conv_bias = nullptr; // Add if your model has conv bias
|
||||
|
||||
// Get recurrent states (conv_states not needed as it's handled internally by ggml_delta_net)
|
||||
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
|
||||
|
||||
// Reshape tensors to match ggml_delta_net expectations
|
||||
// [S, H, n_tokens, n_seqs] format
|
||||
query = ggml_reshape_4d(ctx0, query, head_dim, n_heads, n_tokens, n_seqs);
|
||||
key = ggml_reshape_4d(ctx0, key, head_dim, n_heads, n_tokens, n_seqs);
|
||||
value = ggml_reshape_4d(ctx0, value, head_dim, n_heads, n_tokens, n_seqs);
|
||||
|
||||
// Beta tensor
|
||||
beta = ggml_reshape_3d(ctx0, beta, n_heads, n_tokens, n_seqs);
|
||||
|
||||
// Get current state slice
|
||||
ggml_tensor * state = ggml_view_4d(ctx0, ssm_states_all, head_dim, head_dim, n_heads, n_seqs,
|
||||
ssm_states_all->nb[0], ssm_states_all->nb[1], ssm_states_all->nb[2],
|
||||
kv_head * head_dim * head_dim * n_heads * ggml_element_size(ssm_states_all));
|
||||
state = ggml_cont(ctx0, state);
|
||||
gate = ggml_repeat(ctx0, gate, ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_heads, n_tokens, n_seqs));
|
||||
|
||||
// Call the new ggml_delta_net function
|
||||
ggml_tensor * output = ggml_delta_net(ctx0,
|
||||
key, // k tensor
|
||||
value, // v tensor
|
||||
query, // q tensor
|
||||
gate, // g tensor
|
||||
conv_weight, // conv_weight tensor
|
||||
conv_bias, // conv_bias tensor (can be nullptr)
|
||||
beta, // beta tensor
|
||||
state, // state tensor
|
||||
64, // chunk_size (adjust as needed)
|
||||
true, // use_qk_l2norm
|
||||
1.0f // scale (adjust based on your model)
|
||||
);
|
||||
cb(output, "delta_net_output", il);
|
||||
|
||||
// Extract the output part (first half of the concatenated result)
|
||||
ggml_tensor * attn_out = ggml_view_4d(ctx0, output, head_dim, n_heads, n_tokens, n_seqs, output->nb[0],
|
||||
output->nb[1], output->nb[2], 0);
|
||||
|
||||
// Extract the new state (second half of the concatenated result)
|
||||
ggml_tensor * new_state =
|
||||
ggml_view_4d(ctx0, output, head_dim, head_dim, n_heads, n_seqs, output->nb[0], output->nb[1], output->nb[2],
|
||||
n_tokens * head_dim * n_heads * sizeof(float));
|
||||
|
||||
// Update the recurrent states
|
||||
ggml_build_forward_expand(
|
||||
gf, ggml_cpy(ctx0, new_state,
|
||||
ggml_view_1d(
|
||||
ctx0, ssm_states_all, head_dim * head_dim * n_heads * n_seqs,
|
||||
kv_head * n_seqs * head_dim * head_dim * n_heads * ggml_element_size(ssm_states_all))));
|
||||
|
||||
// Apply normalization and gating
|
||||
attn_out = build_norm(attn_out, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
|
||||
|
||||
// Output projection
|
||||
cur = build_lora_mm(model.layers[il].wo, attn_out);
|
||||
cb(cur, "linear_attn_out", il);
|
||||
|
||||
// Reshape back to original dimensions
|
||||
cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
|
||||
return cur;
|
||||
}
|
||||
ggml_tensor * build_layer_ffn(ggml_tensor * cur, const llama_model & model, const int il) {
|
||||
// Check if this is an MoE layer
|
||||
if (model.layers[il].ffn_gate_inp != nullptr) {
|
||||
// MoE branch
|
||||
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,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, true,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// Add shared experts if present
|
||||
if (model.layers[il].ffn_up_shexp != nullptr) {
|
||||
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 FFN branch
|
||||
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);
|
||||
}
|
||||
|
||||
// Residual connection
|
||||
cur = ggml_add(ctx0, cur, cur); // This should be the residual from before FFN
|
||||
cb(cur, "ffn_residual", il);
|
||||
|
||||
return cur;
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
|
||||
llama_memory_i * res;
|
||||
|
||||
@@ -19349,6 +19767,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
||||
llm = std::make_unique<llm_build_smallthinker<false>>(*this, params);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_QWEN3NEXT:
|
||||
{
|
||||
llm = std::make_unique<llm_build_qwen3next>(*this, params);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
@@ -19524,6 +19946,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_QWEN2MOE:
|
||||
case LLM_ARCH_QWEN3:
|
||||
case LLM_ARCH_QWEN3MOE:
|
||||
case LLM_ARCH_QWEN3NEXT:
|
||||
case LLM_ARCH_LLADA_MOE:
|
||||
case LLM_ARCH_OLMO2:
|
||||
case LLM_ARCH_OLMOE:
|
||||
|
||||
Reference in New Issue
Block a user