#include "llm_build_qwen3next.h" #include "../../ggml/src/ggml-impl.h" #include // Implementation of depthwise 1D convolution using F32 to avoid F16 limitations static ggml_tensor* ggml_conv_1d_dw_f32( ggml_context * ctx, ggml_tensor * kernel, ggml_tensor * input, int stride, int padding, int dilation) { // Following the pattern from ggml_conv_1d_dw but using F32 // Reshape input from [length, channels, batch, dummy] to [length, 1, channels, batch] ggml_tensor* reshaped_input = ggml_reshape_4d(ctx, input, input->ne[0], 1, input->ne[1], input->ne[2]); // Apply im2col with F32 destination type to avoid F16 requirement ggml_tensor* im2col_result = ggml_im2col(ctx, kernel, reshaped_input, stride, 0, padding, 0, dilation, 0, false, GGML_TYPE_F32); // Now multiply: im2col_result * kernel (following the exact pattern from ggml_conv_1d_dw) // In ggml_conv_1d_dw: ggml_mul_mat(ctx, im2col, a) where a is the kernel ggml_tensor* mul_result = ggml_mul_mat(ctx, im2col_result, kernel); // Reshape the result following ggml_conv_1d_dw: [result->ne[0], result->ne[2], 1] ggml_tensor* output_3d = ggml_reshape_3d(ctx, mul_result, mul_result->ne[0], mul_result->ne[2], 1); return output_3d; } llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); ggml_tensor * cur; ggml_tensor * inpL; inpL = build_inp_embd(model.tok_embd); cb(inpL, "model.embed_tokens", -1); auto * inp = build_inp_mem_hybrid(); ggml_tensor * inp_pos = build_inp_pos(); ggml_tensor * inp_out_ids = build_inp_out_ids(); for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; cur = build_q3n_norm(inpL, model.layers[il].attn_norm, il); cb(cur, "attn_norm", il); // Determine layer type and build appropriate attention mechanism if (hparams.is_recurrent(il)) { // Linear attention layer (gated delta net) cur = build_qwen3next_linear_attn_layer(inp->get_recr(), cur, model, ubatch, il); } else { // Full attention layer cur = build_qwen3next_attention_layer(cur, inp_pos, inp->get_attn(), model, n_embd_head, 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); } // Residual connection cur = ggml_add(ctx0, cur, inpSA); cb(cur, "attn_residual", il); // Save the tensor before post-attention norm for residual connection ggml_tensor * ffn_residual = cur; // Post-attention norm ggml_tensor * attn_post_norm = build_q3n_norm(cur, model.layers[il].attn_post_norm, il); cb(attn_post_norm, "attn_post_norm", il); // FFN layer (MoE or dense) - without residual connection cur = build_layer_ffn(attn_post_norm, model, il, false); cb(cur, "ffn_out", il); // Residual connection for FFN - add to the tensor BEFORE post_attention_layernorm cur = ggml_add(ctx0, cur, ffn_residual); cb(cur, "post_moe", il); // Input for next layer inpL = cur; } cur = inpL; // Final norm cur = build_q3n_norm(cur, model.output_norm, -1); cb(cur, "result_norm", -1); res->t_embd = cur; // LM head cur = build_lora_mm(model.output, cur); cb(cur, "result_output", -1); ggml_set_output(cur); res->t_logits = cur; ggml_build_forward_expand(gf, cur); } struct ggml_tensor * llm_build_qwen3next::build_q3n_norm(struct ggml_tensor * input, struct ggml_tensor * weights, int layer) { ggml_tensor * input_norm = ggml_scale_bias(ctx0, weights, 1.0f, 1.0f); return build_norm(input, input_norm, nullptr, LLM_NORM_RMS, layer); } struct ggml_tensor * llm_build_qwen3next::build_q3n_gated_norm(struct ggml_tensor * input, struct ggml_tensor * weights, struct ggml_tensor * gate, int layer) { ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer); ggml_tensor * gated_silu = ggml_silu(ctx0, gate); return ggml_mul(ctx0, normalized, gated_silu); } struct ggml_tensor * llm_build_qwen3next::build_qwen3next_attention_layer(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn, const llama_model & model, const int64_t n_embd_head, const int il) { // compute Q and K and RoPE them // Qwen3Next uses a single Q projection that outputs query + gate struct ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); cb(Qcur_full, "Qcur_full", il); Qcur_full = ggml_reshape_4d(ctx0, Qcur_full, n_embd_head * 2, n_head, n_tokens, 1); // Split Q projection into query and gate // The split should be along dimension 0 (the feature dimension) struct ggml_tensor * Qcur = ggml_view_4d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, 1, Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], 0); struct ggml_tensor * gate = ggml_view_4d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens, 1, Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full)); cb(Qcur, "Qcur", il); cb(gate, "gate", il); // Now reshape Qcur to [n_embd_head, n_head, n_tokens] for multi-head attention Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); cb(Qcur, "Qcur_reshaped", il); // Apply Q normalization only to the query part Qcur = build_q3n_norm(Qcur, model.layers[il].attn_q_norm, il); cb(Qcur, "Qcur_normed", il); // Reshape gate to [n_embd, n_tokens] for the sigmoid gating (flatten the heads) gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens); cb(gate, "gate_reshaped", il); struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); cb(Kcur, "Kcur", il); struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); cb(Vcur, "Vcur", il); Qcur = ggml_cont_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); // Apply Q/K normalization Kcur = build_q3n_norm(Kcur, model.layers[il].attn_k_norm, il); cb(Kcur, "Kcur_normed", il); // Apply 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); Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); // Attention computation const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale; cur = build_attn(inp_attn, nullptr, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); // Apply gating directly using the original gate tensor cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate)); cb(cur, "attn_gated", il); cur = build_lora_mm(model.layers[il].wo, cur); cb(cur, "attn_output", il); return cur; } // delta_net // prepare all the tensor data for the operation so we only // do the absolutely necessary steps in the op itself struct ggml_tensor * llm_build_qwen3next::delta_net( struct ggml_context * ctx, struct ggml_tensor * q, struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * g, struct ggml_tensor * beta, struct ggml_tensor * state, bool use_qk_l2norm, float eps_norm, const int il ) { GGML_ASSERT(ggml_is_contiguous(q)); GGML_ASSERT(ggml_is_contiguous(k)); GGML_ASSERT(ggml_is_contiguous(v)); GGML_ASSERT(ggml_is_contiguous(g)); GGML_ASSERT(ggml_is_contiguous(beta)); GGML_ASSERT(ggml_is_contiguous(state)); const int64_t S_k = q->ne[0]; const int64_t H_k = q->ne[1]; const int64_t n_tokens = q->ne[2]; const int64_t n_seqs = q->ne[3]; const int64_t S_v = v->ne[0]; const int64_t H_v = v->ne[1]; GGML_ASSERT(v->ne[2] == n_tokens); GGML_ASSERT(k->ne[2] == n_tokens); GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs); GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs); GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == 1); GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs); GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs); GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case cb(q, "q_prenorm", il); cb(k, "k_prenorm", il); if (use_qk_l2norm) { q = ggml_l2_norm(ctx, q, eps_norm); k = ggml_l2_norm(ctx, k, eps_norm); } cb(k, "k_postnorm", il); cb(q, "q_prescale", il); int64_t pad_size = (GGML_DELTA_NET_CHUNK - n_tokens % GGML_DELTA_NET_CHUNK) % GGML_DELTA_NET_CHUNK; // yes, n_tokens, not H_k, the reference implementation has wrong naming int64_t num_chunks = (n_tokens + pad_size) / GGML_DELTA_NET_CHUNK; float scale = 1.0f / sqrtf(S_v); q = ggml_scale(ctx, q, scale); cb(beta, "beta_raw", il); beta = ggml_sigmoid(ctx, beta); cb(q, "q_postscale", il); cb(beta, "beta_sigmoid", il); // First, permute to chunked format: [S_k, n_tokens, H_k, n_seqs] q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); cb(q, "q_reshape", il); k = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3)); cb(k, "k_reshape", il); v = ggml_cont(ctx, ggml_permute(ctx, v, 0, 2, 1, 3)); cb(v, "v_reshape", il); beta = ggml_cont(ctx, ggml_permute(ctx, beta, 1, 2, 0, 3)); cb(beta, "beta_reshape", il); g = ggml_cont(ctx, ggml_permute(ctx, g, 2, 0, 3, 1)); cb(g, "g_permute", il); // Then, pad the second dimension (n_tokens) to chunk_size q = ggml_pad(ctx, q, 0, pad_size, 0, 0); k = ggml_pad(ctx, k, 0, pad_size, 0, 0); v = ggml_pad(ctx, v, 0, pad_size, 0, 0); // ... except for beta and g, where we pad the last dimension beta = ggml_pad(ctx, beta, pad_size, 0, 0, 0); g = ggml_pad(ctx, g, pad_size, 0, 0, 0); cb(q, "q_pad", il); cb(k, "k_pad", il); cb(v, "v_pad", il); cb(beta, "beta_pad", il); cb(g, "g_pad", il); GGML_ASSERT(q->ne[1] % GGML_DELTA_NET_CHUNK == 0 && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs); GGML_ASSERT(k->ne[1] % GGML_DELTA_NET_CHUNK == 0 && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs); GGML_ASSERT(v->ne[1] % GGML_DELTA_NET_CHUNK == 0 && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs); GGML_ASSERT(beta->ne[0] % GGML_DELTA_NET_CHUNK == 0 && beta->ne[1] == H_k && beta->ne[2] == 1 && beta->ne[3] == n_seqs); GGML_ASSERT(g->ne[0] % GGML_DELTA_NET_CHUNK == 0 && g->ne[2] == H_k && g->ne[1] == 1 && g->ne[3] == n_seqs); ggml_tensor * beta_unsq = ggml_cont_4d(ctx, beta, 1, GGML_DELTA_NET_CHUNK * num_chunks, H_k, n_seqs); ggml_tensor * beta_bcast = ggml_repeat_4d(ctx, beta_unsq, S_v, GGML_DELTA_NET_CHUNK * num_chunks, H_k, n_seqs); cb(beta_unsq, "beta_unsq", il); cb(beta_bcast, "beta_bcast", il); struct ggml_tensor * v_beta = ggml_mul(ctx, v, beta_bcast); v_beta = ggml_reshape_4d(ctx, v_beta, S_v, GGML_DELTA_NET_CHUNK, H_k * num_chunks, n_seqs); cb(v_beta, "v_beta", il); struct ggml_tensor * k_beta = ggml_mul(ctx, k, beta_bcast); k_beta = ggml_reshape_4d(ctx, k_beta, S_v, GGML_DELTA_NET_CHUNK, H_k * num_chunks, n_seqs); cb(k_beta, "k_beta", il); k = ggml_reshape_4d(ctx, k, S_v, GGML_DELTA_NET_CHUNK, H_k * num_chunks, n_seqs); cb(k_beta, "k_reshape", il); g = ggml_reshape_4d(ctx, g, GGML_DELTA_NET_CHUNK, 1, H_k * num_chunks, n_seqs); cb(g, "g_reshape", il); struct ggml_tensor * g_cumsum = ggml_cumsum(ctx, g); cb(g_cumsum, "g_cumsum", il); struct ggml_tensor * gcs_i = ggml_cont_4d(ctx, g_cumsum, GGML_DELTA_NET_CHUNK, 1, num_chunks * H_v, n_seqs); // [chunk_size, 1, n_tokens, n_seqs] struct ggml_tensor * gcs_j = ggml_cont_4d(ctx, g_cumsum, 1, GGML_DELTA_NET_CHUNK, num_chunks * H_v, n_seqs); // [1, chunk_size, n_tokens, n_seqs] // Broadcast both tensors to [chunk_size, chunk_size, H_v, n_seqs] struct ggml_tensor * gcs_i_broadcast = ggml_repeat_4d(ctx, gcs_i, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v, n_seqs); // [chunk_size, 1, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs] struct ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx, gcs_j, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v, n_seqs); // [1, chunk_size, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs] struct ggml_tensor * decay_mask = ggml_sub(ctx, gcs_j_broadcast, gcs_i_broadcast); cb(decay_mask, "sub", il); // Apply lower triangular mask to ensure attention is causal (only past tokens influence current) decay_mask = ggml_tri_keep(ctx, decay_mask, GGML_TRI_TYPE_LOWER_DIAG); cb(decay_mask, "sub_tri", il); // Apply exponential to get the decay mask values decay_mask = ggml_exp(ctx, decay_mask); cb(decay_mask, "sub_tri_exp", il); // Apply lower triangular mask again to ensure only lower triangular values remain decay_mask = ggml_tri_keep(ctx, decay_mask, GGML_TRI_TYPE_LOWER_DIAG); cb(decay_mask, "decay_mask", il); struct ggml_tensor * kmulkbeta = ggml_mul_mat(ctx, ggml_cont(ctx, k), ggml_cont(ctx, k_beta)); cb(kmulkbeta, "k_beta @ k_t ", il); struct ggml_tensor * k_decay = ggml_mul(ctx, kmulkbeta, decay_mask); cb(k_decay, "(k_beta @ k_t) * decay_mask", il); struct ggml_tensor * attn = ggml_neg(ctx, ggml_tri_keep(ctx, k_decay, GGML_TRI_TYPE_LOWER)); cb(attn, "attn_in", il); // We'll be returning the result as a 1D tensor due to the dimensions mismatch of the state and output tensors const int64_t ne[1] = { (S_v * H_v * n_tokens * n_seqs ) + (S_v * S_v * H_v * n_seqs) }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 1, ne); ggml_set_op_params_i32(result, 0, H_v); ggml_set_op_params_i32(result, 1, S_k); ggml_set_op_params_i32(result, 2, S_v); ggml_set_op_params_i32(result, 3, n_tokens); // Pass original n_tokens result->op = GGML_OP_DELTA_NET; result->src[0] = q; result->src[1] = k; result->src[2] = v; result->src[3] = g_cumsum; result->src[4] = state; result->src[5] = decay_mask; result->src[6] = v_beta; result->src[7] = k_beta; result->src[8] = attn; return result; } ggml_tensor * llm_build_qwen3next::build_qwen3next_linear_attn_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il) { // Gated Delta Net implementation using the new delta_net function const auto * mctx_cur = inp->mctx; const int64_t d_inner = hparams.ssm_d_inner; const int64_t n_heads = hparams.ssm_dt_rank; const int64_t head_dim = d_inner / n_heads; const int64_t n_seqs = ubatch.n_seqs; const int64_t head_k_dim = hparams.ssm_d_state; const int64_t head_v_dim = hparams.ssm_d_state; const int64_t num_k_heads = hparams.ssm_n_group; const int64_t num_v_heads = hparams.ssm_dt_rank; const int64_t n_seq_tokens = ubatch.n_seq_tokens; const int64_t n_tokens = ubatch.n_tokens; GGML_ASSERT(n_seqs != 0); GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); // Input projections ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, cur); cb(mixed_qkvz, "linear_attn_mixed_qkvz", il); ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur); cb(mixed_ba, "linear_attn_mixed_ba", il); int64_t qkvz_new_dim = 2 * head_k_dim + 2 * head_v_dim * num_v_heads / num_k_heads; ggml_tensor * mixed_qkvz_reshaped = ggml_cont_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_tokens, n_seqs); // Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads] int64_t ba_new_dim = 2 * num_v_heads / num_k_heads; ggml_tensor * mixed_ba_reshaped = ggml_cont_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_tokens, n_seqs); // Split mixed_ba into b and a (beta and alpha parameters) int64_t split_sizes_ba[2] = { num_v_heads / num_k_heads, // beta size num_v_heads / num_k_heads // alpha size }; ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_tokens, n_seqs, mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0); cb(b, "b", il); ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_tokens, n_seqs, mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped)); cb(a, "a", il); // Reshape b and a to merge head dimensions: [batch, seq_len, num_k_heads, num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads] ggml_tensor * beta = ggml_cont_3d(ctx0, b, num_v_heads, n_tokens, n_seqs); ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_tokens, n_seqs); GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba)); ggml_tensor * alpha_softplus = softplus(alpha, model.layers[il].ssm_dt); cb(alpha_softplus, "a_softplus", il); ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus cb(gate, "gate", il); // Get convolution states from cache ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); // Build the convolution states tensor ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs); cb(conv_states, "conv_states", il); // Split mixed_qkvz into query, key, value, z int64_t split_sizes_qkvz[4] = { head_k_dim, // query size head_k_dim, // key size head_v_dim * num_v_heads / num_k_heads, // value size head_v_dim * num_v_heads / num_k_heads // z size }; ggml_tensor * query = ggml_cont(ctx0, ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_tokens, n_seqs, mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0)); cb(query, "q", il); ggml_tensor * key = ggml_cont(ctx0, ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_tokens, n_seqs, mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], split_sizes_qkvz[0] * sizeof(float))); cb(key, "k", il); ggml_tensor * value = ggml_cont(ctx0, ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_tokens, n_seqs, mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], (split_sizes_qkvz[0] + split_sizes_qkvz[1]) * sizeof(float))); cb(value, "v", il); ggml_tensor * z = ggml_cont(ctx0, ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_tokens, n_seqs, mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float))); cb(z, "z", il); // Reshape value and z to merge head dimensions: [batch, seq_len, num_k_heads, head_v_dim*num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads, head_v_dim] ggml_tensor * value_reshaped = ggml_reshape_4d(ctx0, ggml_cont(ctx0, value), head_v_dim, num_v_heads, n_tokens, n_seqs); ggml_tensor * z_reshaped = ggml_reshape_4d(ctx0, ggml_cont(ctx0, z), head_v_dim, num_v_heads, n_tokens, n_seqs); GGML_ASSERT(ggml_nelements(query) + ggml_nelements(key) + ggml_nelements(value_reshaped) + ggml_nelements(z_reshaped) == ggml_nelements(mixed_qkvz)); // After creating query, key, and value_reshaped, reshape each to flatten the head dimensions // query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs] ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_tokens, n_seqs); cb(query_flat, "query_flat", il); // key: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs] ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_tokens, n_seqs); cb(key_flat, "key_flat", il); // value_reshaped: [head_v_dim, num_v_heads, n_tokens, n_seqs] -> [head_v_dim * num_v_heads, n_tokens, n_seqs] ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value_reshaped, head_v_dim * num_v_heads, n_tokens, n_seqs); cb(value_flat, "value_flat", il); // Now concatenate along the feature dimension (dim 0) to get [conv_dim, n_tokens, n_seqs] ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0); qkv_mixed = ggml_concat(ctx0, qkv_mixed, value_flat, 0); qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3); cb(qkv_mixed, "qkv_mixed_concatenated", il); // Calculate the total conv dimension int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads; // Calculate convolution kernel size ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d; const int64_t conv_kernel_size = conv_kernel->ne[0]; conv_kernel = ggml_permute(ctx0, conv_kernel, 0, 2, 1, 3); conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state, n_seqs); cb(conv_states, "conv_states_reshaped", il); ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0); cb(conv_input, "conv_input", il); // Apply convolution ggml_tensor * conv_output = ggml_conv_1d_dw_f32(ctx0, conv_kernel, conv_input, 1, conv_kernel_size - 1, n_seqs); cb(conv_output, "conv_output_raw", il); // Remove the padding ggml_tensor * conv_output_no_padding = ggml_view_4d(ctx0, conv_output, conv_output->ne[0] - (conv_kernel_size - 1), conv_output->ne[1], conv_output->ne[2], conv_output->ne[3], conv_output->nb[1], conv_output->nb[2], conv_output->nb[3], (conv_kernel_size - 1) * ggml_element_size(conv_output)); cb(conv_output_no_padding, "conv_output_no_padding", il); // Take only the first (n_tokens * n_seqs) values ggml_tensor * conv_output_proper = ggml_view_4d(ctx0, conv_output_no_padding, n_tokens * n_seqs, conv_output_no_padding->ne[1], conv_output_no_padding->ne[2], conv_output_no_padding->ne[3], conv_output_no_padding->nb[1], conv_output_no_padding->nb[2], conv_output_no_padding->nb[3], 0); cb(conv_output_proper, "conv_output_proper", il); conv_output_proper = ggml_permute(ctx0, conv_output_proper, 0, 1, 3, 2); conv_output_proper = ggml_cont_4d(ctx0, conv_output_proper, qkv_dim, 1, n_tokens, n_seqs); ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper); cb(conv_output_silu, "conv_output_silu", il); // Update convolution state cache // Extract the last (conv_kernel_size - 1) states from conv_input ggml_tensor * last_conv_states = ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, qkv_dim, n_seqs, conv_input->nb[1], conv_input->nb[2], n_seq_tokens * conv_input->nb[0]); ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * qkv_dim * n_seqs, mctx_cur->get_head() * (conv_kernel_size - 1) * qkv_dim * ggml_element_size(conv_states_all)))); cb(conv_states_all, "conv_states_updated", il); conv_output_proper = ggml_reshape_2d(ctx0, conv_output_silu, n_tokens * n_seqs, qkv_dim); cb(conv_output_proper, "conv_output_final", il); ggml_tensor * conv_transposed = ggml_transpose(ctx0, conv_output_proper); cb(conv_transposed, "conv_transposed", il); ggml_tensor * conv_qkv_mix = ggml_cont_2d(ctx0, conv_transposed, qkv_dim, n_tokens * n_seqs); cb(conv_qkv_mix, "conv_qkv_mix", il); // Extract the convolved Q, K, V from conv_output ggml_tensor * q_conv = ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_tokens * n_seqs, conv_qkv_mix->nb[1], 0); cb(q_conv, "q_conv", il); ggml_tensor * k_conv = ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_tokens * n_seqs, conv_qkv_mix->nb[1], head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); cb(k_conv, "k_conv", il); ggml_tensor * v_conv = ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_tokens * n_seqs, conv_qkv_mix->nb[1], 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); cb(v_conv, "v_conv", il); // Unsqueeze them q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_tokens, n_seqs); k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_tokens, n_seqs); v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_tokens, n_seqs); beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_tokens, n_seqs); ggml_tensor * state = ggml_reshape_4d(ctx0, ssm_states_all, head_dim, head_dim * n_heads, 1, 1); // if head keys and value keys are different, repeat to force tensors into matching shapes if (num_k_heads != num_v_heads) { GGML_ASSERT(num_v_heads % num_k_heads == 0); int64_t repeat_factor = num_v_heads / num_k_heads; q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_k_heads * repeat_factor, n_tokens, n_seqs); k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_k_heads * repeat_factor, n_tokens, n_seqs); } cb(q_conv, "q_conv_predelta", il); cb(k_conv, "k_conv_predelta", il); cb(v_conv, "v_conv_predelta", il); // Call the new delta_net function with the corrected flow ggml_tensor * attn_out = delta_net(ctx0, q_conv, k_conv, v_conv, gate, beta, state, true, hparams.f_norm_rms_eps, il); cb(attn_out, "attn_out", il); // The tensors were concatenated 1d, so we need to extract them 1d as well const int64_t output_flat_size = head_dim * n_heads * n_tokens * n_seqs; ggml_tensor * attn_out_1d = ggml_view_1d(ctx0, attn_out, output_flat_size, 0); cb(attn_out_1d, "attn_out_1d", il); ggml_tensor * attn_out_final = ggml_cont(ctx0, ggml_permute(ctx0, ggml_cont_4d(ctx0, attn_out_1d, head_dim, n_tokens, n_heads, n_seqs), 0, 2, 1, 3)); cb(attn_out_final, "attn_out_final", il); // Extract the state part (second part of the concatenated tensor) // State starts after n_tokens elements along dimension 1 const int64_t state_flat_size = head_dim * head_dim * n_heads * n_seqs; ggml_tensor * state_1d = ggml_view_1d(ctx0, attn_out, state_flat_size, output_flat_size * ggml_element_size(attn_out)); cb(state_1d, "state_1d", il); ggml_tensor * new_state = ggml_reshape_4d(ctx0, state_1d, head_dim, head_dim, n_heads, n_seqs); cb(new_state, "new_state", il); // Update the recurrent states - we use the new_state directly since it's already the last state ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_state, ssm_states_all)); // Reshape both attn_out_final and z to 2D tensors for normalization // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim] ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, ggml_cont(ctx0, attn_out_final), head_dim, n_heads * n_tokens * n_seqs); // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim] ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z_reshaped, head_dim, n_heads * n_tokens * n_seqs); // Apply gated normalization: self.norm(core_attn_out, z) ggml_tensor * attn_out_norm = build_q3n_gated_norm(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il); // Reshape back to original dimensions: [n_heads * n_tokens * n_seqs, head_dim] -> [head_dim, n_heads, n_tokens, n_seqs] ggml_tensor * gated_output_4d = ggml_reshape_4d(ctx0, attn_out_norm, head_dim, n_heads, n_tokens, n_seqs); // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim] ggml_tensor * final_output = ggml_reshape_3d(ctx0, gated_output_4d, n_heads * head_dim, n_tokens, n_seqs); cb(final_output, "final_output", il); // Output projection cur = build_lora_mm(model.layers[il].ssm_out, final_output); cb(cur, "linear_attn_out", il); // Reshape back to original dimensions cur = ggml_cont(ctx0, ggml_reshape_2d(ctx0, cur, n_embd, n_tokens)); return cur; } ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const llama_model & model, const int il, bool do_residual) { // 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 - following Qwen3Next reference implementation 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); // Apply shared expert gating as in the reference implementation // The shared expert has its own gate that is sigmoided // Note: ffn_gate_inp_shexp is the shared expert gate (outputs 1 value per token) ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur); cb(shared_gate, "shared_expert_gate", il); // Apply sigmoid to the gate shared_gate = ggml_sigmoid(ctx0, shared_gate); cb(shared_gate, "shared_expert_gate_sigmoid", il); // The gate needs to be broadcast to match the dimensions of ffn_shexp // ffn_shexp is [n_embd, n_tokens, 1, 1] and shared_gate is [1, n_tokens, 1, 1] // We need to repeat the gate along the feature dimension shared_gate = ggml_repeat(ctx0, shared_gate, ffn_shexp); cb(shared_gate, "shared_expert_gate_broadcast", il); // Apply the gate to the shared expert output ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate); cb(ffn_shexp, "ffn_shexp_gated", 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 (only if requested) if (do_residual) { cur = ggml_add(ctx0, cur, cur); cb(cur, "ffn_residual", il); } cur = build_cvec(cur, il); cb(cur, "l_out", il); return cur; }; ggml_tensor * llm_build_qwen3next::softplus(ggml_tensor * alpha, ggml_tensor * dt_bias) { ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, dt_bias); // a + dt_bias ggml_tensor * alpha_exp = ggml_exp(ctx0, alpha_biased); // exp(a + dt_bias) ggml_tensor * one_plus_exp = ggml_scale_bias(ctx0, alpha_exp, 1.0f, 1.0f); // 1 + exp(a + dt_bias) ggml_tensor * alpha_softplus = ggml_log(ctx0, one_plus_exp); // log(1 + exp(...)) return alpha_softplus; }