#include "ggml.h" #include "models.h" #define CHUNK_SIZE 64 llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params), model(model) { 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(); ggml_tensor * causal_mask = ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f), GGML_TRI_TYPE_LOWER); ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f)); ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity); ggml_build_forward_expand(gf, causal_mask); ggml_build_forward_expand(gf, identity); ggml_build_forward_expand(gf, diag_mask); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, 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_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il); } else { // Full attention layer cur = build_layer_attn(inp->get_attn(), cur, inp_pos, 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_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il); cb(attn_post_norm, "attn_post_norm", il); // FFN layer (MoE or dense) - without residual connection cur = build_layer_ffn(attn_post_norm, il); cb(cur, "ffn_out", il); // Residual connection for FFN - add to the tensor from 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_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1); cb(cur, "result_norm", -1); res->t_embd = cur; // LM head cur = build_lora_mm(model.output, cur); cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); } // utility to get one slice from the third dimension // input dim: [x, y, c, b] // output dim: [x, y, 1, b] static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) { return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3], t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c); } std::pair llm_build_qwen3next::build_delta_net_chunking( ggml_tensor * q, ggml_tensor * k, ggml_tensor * v, ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state, ggml_tensor * causal_mask, ggml_tensor * identity, ggml_tensor * diag_mask, int il) { 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] == n_seqs); 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 const float eps_norm = hparams.f_norm_rms_eps; q = ggml_l2_norm(ctx0, q, eps_norm); k = ggml_l2_norm(ctx0, k, eps_norm); const float scale = 1.0f / sqrtf(S_v); q = ggml_scale(ctx0, q, scale); beta = ggml_sigmoid(ctx0, beta); cb(q, "q_in", il); cb(k, "k_in", il); cb(v, "v_in", il); cb(beta, "beta_in", il); cb(g, "g_in", il); q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs); beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3)); state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs); cb(q, "q_perm", il); cb(k, "k_perm", il); cb(v, "v_perm", il); cb(beta, "beta_perm", il); cb(g, "g_perm", il); cb(state, "state_in", il); GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs); GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs); GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs); GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs); // Do padding const int64_t chunk_size = CHUNK_SIZE; const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size; const int64_t n_chunks = (n_tokens + pad) / chunk_size; q = ggml_pad(ctx0, q, 0, pad, 0, 0); k = ggml_pad(ctx0, k, 0, pad, 0, 0); v = ggml_pad(ctx0, v, 0, pad, 0, 0); g = ggml_pad(ctx0, g, pad, 0, 0, 0); beta = ggml_pad(ctx0, beta, 0, pad, 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_tensor * v_beta = ggml_mul(ctx0, v, beta); ggml_tensor * k_beta = ggml_mul(ctx0, k, beta); cb(v_beta, "v_beta", il); cb(k_beta, "k_beta", il); q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs); k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs); k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs); v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs); v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs); g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs); beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs); ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g); cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs); ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs); ggml_tensor * gcs_j_broadcast = ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs); ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i); cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) decay_mask = ggml_mul(ctx0, decay_mask, diag_mask); decay_mask = ggml_exp(ctx0, decay_mask); decay_mask = ggml_mul(ctx0, decay_mask, diag_mask); ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta); ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask); ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask)); cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask); ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower); ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false); attn = ggml_mul(ctx0, lin_solve, causal_mask); attn = ggml_add(ctx0, attn, identity); cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn); ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum)); ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t); ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp); cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs) ggml_tensor * k_cumdecay = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp))))); cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q); attn_kq = ggml_mul(ctx0, attn_kq, decay_mask); attn_kq = ggml_mul(ctx0, attn_kq, diag_mask); cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) // vectorized calculation of key_gdiff // improved from the chunked version: // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1) // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp() // key_gdiff = key * g_diff.unsqueeze(-1) // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew // get last element in g_cumsum along chunk_size dimension (ne0) // example: [[x, y, z, ..., last], ...] -> [[last], ...] ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3], g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3], (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum)); g_last = ggml_cont(ctx0, g_last); cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs) ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last); cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs) ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last)); cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff); ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp); cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs) // state to be updated per chunk ggml_tensor * new_state = state; // ggml_dup(ctx0, state); cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs) // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs) ggml_tensor * core_attn_out = nullptr; for (int64_t chunk = 0; chunk < n_chunks; chunk++) { // shape: (S_k, chunk_size, 1, H_k * n_seqs) ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul // shape: (S_v, chunk_size, 1, H_v * n_seqs) ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul // shape: (chunk_size, 1, H_v * n_seqs) ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0) // replaced by precomputed attn_kq ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk); cb(attn_chunk, "attn_chunk", il); ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs); // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk); cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs) // v_new = v_i - v_prime ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime); ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new)); cb(v_new, "v_new_chunk", il); // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk); ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp); cb(attn_inter, "attn_inter_chunk", il); // core_attn_out[:, :, i] = attn_inter + attn @ v_new ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk); cb(v_attn, "v_attn_chunk", il); ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn); cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs) core_attn_out = core_attn_out == nullptr ? core_attn_out_chunk : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2); // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk)); //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why? ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff))); // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk)); new_state = ggml_add(ctx0, ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)), ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs)); } // truncate padded tokens ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out, S_v, n_tokens, H_v, n_seqs, ggml_row_size(core_attn_out->type, S_v), ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks), ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0); output_tokens = ggml_cont(ctx0, output_tokens); cb(output_tokens, "output_tokens", il); // permute back to (S_v, H_v, n_tokens, n_seqs) output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3); output_tokens = ggml_cont(ctx0, output_tokens); return {output_tokens, new_state}; } std::pair llm_build_qwen3next::build_delta_net_autoregressive( ggml_tensor * q, ggml_tensor * k, ggml_tensor * v, ggml_tensor * g, ggml_tensor * beta, ggml_tensor * state, int il) { 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(n_tokens == 1); // This function is optimized for single token processing 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] == n_seqs); 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 const float eps_norm = hparams.f_norm_rms_eps; q = ggml_l2_norm(ctx0, q, eps_norm); k = ggml_l2_norm(ctx0, k, eps_norm); const float scale = 1.0f / sqrtf(S_v); q = ggml_scale(ctx0, q, scale); beta = ggml_sigmoid(ctx0, beta); cb(q, "q_in", il); cb(k, "k_in", il); cb(v, "v_in", il); cb(beta, "beta_in", il); cb(g, "g_in", il); state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs); ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs); ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs); // Apply exponential to g_t g_t = ggml_exp(ctx0, g_t); // Apply the gated delta rule for the single timestep // last_recurrent_state = last_recurrent_state * g_t state = ggml_mul(ctx0, state, g_t); // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2) ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs); ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed); // we need to sum over dim=-2, so we transpose, sum, then transpose again kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem)))); // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v) ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs); // delta = (v_t - kv_mem) * beta_t ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs] ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t); // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta); state = ggml_add(ctx0, state, k_t_delta); // Compute the attention output // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2) ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed); // again, since it's over dim = -2, transpose, sum, transpose back ggml_tensor * core_attn_out = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q)))); // core_attn_out should be [S_v, 1, H_v, n_seqs] after this cb(core_attn_out, "output_tokens", il); cb(state, "new_state", il); return {core_attn_out, state}; } ggml_tensor * llm_build_qwen3next::build_norm_gated( ggml_tensor * input, ggml_tensor * weights, 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); } ggml_tensor * llm_build_qwen3next::build_layer_attn( llm_graph_input_attn_kv * inp, ggml_tensor * cur, ggml_tensor * inp_pos, int il) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention // Qwen3Next uses a single Q projection that outputs query + gate 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) 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); 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 Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); cb(Qcur, "Qcur_normed", il); ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); cb(Kcur, "Kcur", il); ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); cb(Vcur, "Vcur", il); // Apply K normalization Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); cb(Kcur, "Kcur_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); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); // 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, nullptr, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); cb(cur, "attn_pregate", il); ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate); cb(gate_sigmoid, "gate_sigmoid", il); cur = ggml_mul(ctx0, cur, gate_sigmoid); cb(cur, "attn_gated", il); cur = build_lora_mm(model.layers[il].wo, cur); cb(cur, "attn_output", il); return cur; } std::pair llm_build_qwen3next::build_qkvz( ggml_tensor * input, int il) { const int64_t d_inner = hparams.ssm_d_inner; const int64_t n_seqs = ubatch.n_seqs; const int64_t head_k_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 head_v_dim = d_inner / num_v_heads; const int64_t n_seq_tokens = ubatch.n_seq_tokens; if (model.layers[il].wqkv) { // optimized path ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input); qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs); cb(qkv_mixed, "linear_attn_qkv_mixed", il); ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input); cb(z, "z", il); return { qkv_mixed, z }; } else { // legacy (slower) path ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input); cb(mixed_qkvz, "linear_attn_mixed_qkvz", 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_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs); // 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_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_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_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs, mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped)); cb(key, "k", il); ggml_tensor * value = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_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]) * ggml_element_size(mixed_qkvz_reshaped)); cb(value, "v", il); ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_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]) * ggml_element_size(mixed_qkvz_reshaped)); z = ggml_cont(ctx0, z); cb(z, "z", il); // 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_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_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_cont_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_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_cont_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_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); cb(qkv_mixed, "qkv_mixed", il); return { qkv_mixed, z }; } } ggml_tensor * llm_build_qwen3next::build_layer_attn_linear( llm_graph_input_rs * inp, ggml_tensor * cur, ggml_tensor * causal_mask, ggml_tensor * identity, ggml_tensor * diag_mask, int il) { const auto * mctx_cur = inp->mctx; const int64_t d_inner = hparams.ssm_d_inner; const int64_t n_seqs = ubatch.n_seqs; const int64_t head_k_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 head_v_dim = d_inner / num_v_heads; const int64_t n_seq_tokens = ubatch.n_seq_tokens; const auto kv_head = mctx_cur->get_head(); GGML_ASSERT(n_seqs != 0); GGML_ASSERT(ubatch.equal_seqs()); GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); // Input projections auto qkvz = build_qkvz(cur, il); ggml_tensor * qkv_mixed = qkvz.first; ggml_tensor * z = qkvz.second; ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur); cb(mixed_ba, "linear_attn_mixed_ba", il); // 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_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_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_seq_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_seq_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); ggml_tensor * beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs); // Reshape 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 * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs); ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt); ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased); 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); // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state(); // 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); // Calculate convolution kernel size ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d; const int64_t conv_kernel_size = conv_kernel->ne[0]; const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state; conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs); cb(conv_states, "conv_states_reshaped", il); qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3); cb(qkv_mixed, "qkv_mixed_permuted", il); ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0); cb(conv_input, "conv_input", 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, conv_channels, n_seqs, conv_input->nb[1], conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input)); cb(last_conv_states, "last_conv_states", il); ggml_tensor * state_update_target = ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs, kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all)); cb(state_update_target, "state_update_target", il); ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target)); cb(conv_states_all, "conv_states_updated", il); // Apply SSM convolution ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel); cb(conv_output_proper, "conv_output_raw", il); ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper); cb(conv_output_silu, "conv_output_silu", il); ggml_tensor * conv_qkv_mix = conv_output_silu; // Calculate the total conv dimension int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads; int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim); // 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_seq_tokens * n_seqs, nb1_qkv, 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_seq_tokens * n_seqs, nb1_qkv, 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_seq_tokens * n_seqs, nb1_qkv, 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_seq_tokens, n_seqs); k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs); cb(state, "state_predelta", il); // 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; // repeat interleave: reshape to (repeat part, 1, remaining part), do repeat, then reshape back ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs); ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs); // Repeat along the third dimension (the new dimension with size 1) ggml_tensor * q_repeated = ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1); ggml_tensor * k_repeated = ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1); // Reshape back to merge the head and repeat dimensions // From [head_dim, num_k_heads, repeat_factor, n_seq_tokens * n_seqs] // Back to [head_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs] q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs); k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs); } cb(q_conv, "q_conv_predelta", il); cb(k_conv, "k_conv_predelta", il); cb(v_conv, "v_conv_predelta", il); // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens std::pair attn_out; // pair of (output, new_state) if (n_seq_tokens == 1) { attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il); } else { attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il); } ggml_tensor * output = attn_out.first; ggml_tensor * new_state = attn_out.second; cb(output, "attn_output", il); cb(new_state, "new_state", il); // Update the recurrent states ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_state, ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, kv_head * hparams.n_embd_s() * ggml_element_size(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, output, head_v_dim, num_v_heads * n_seq_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, head_v_dim, num_v_heads * n_seq_tokens * n_seqs); // Apply gated normalization: self.norm(core_attn_out, z) ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il); // 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, attn_out_norm, head_v_dim * num_v_heads, n_seq_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_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); return cur; } ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, 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 - 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); // 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 (not currently used I believe) 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); } return cur; }