874 lines
39 KiB
C++
874 lines
39 KiB
C++
#include "ggml.h"
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#include "models.h"
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#define CHUNK_SIZE 64
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llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context_mamba(params), model(model) {
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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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cb(inpL, "model.embed_tokens", -1);
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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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ggml_tensor * causal_mask =
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ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
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GGML_TRI_TYPE_LOWER);
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ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
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ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
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ggml_build_forward_expand(gf, causal_mask);
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ggml_build_forward_expand(gf, identity);
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ggml_build_forward_expand(gf, diag_mask);
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, 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_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
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} else {
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// Full attention layer
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cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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// 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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// Save the tensor before post-attention norm for residual connection
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ggml_tensor * ffn_residual = cur;
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// Post-attention norm
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ggml_tensor * attn_post_norm = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
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cb(attn_post_norm, "attn_post_norm", il);
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// FFN layer (MoE or dense) - without residual connection
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cur = build_layer_ffn(attn_post_norm, il);
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cb(cur, "ffn_out", il);
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// Residual connection for FFN - add to the tensor from before post_attention_layernorm
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cur = ggml_add(ctx0, cur, ffn_residual);
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cb(cur, "post_moe", 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, model.output_norm, nullptr, 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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// utility to get one slice from the third dimension
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// input dim: [x, y, c, b]
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// output dim: [x, y, 1, b]
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static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) {
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return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3],
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t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c);
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}
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std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_chunking(
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ggml_tensor * q,
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ggml_tensor * k,
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ggml_tensor * v,
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ggml_tensor * g,
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ggml_tensor * beta,
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ggml_tensor * state,
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ggml_tensor * causal_mask,
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ggml_tensor * identity,
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ggml_tensor * diag_mask,
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int il) {
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const int64_t S_k = q->ne[0];
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const int64_t H_k = q->ne[1];
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const int64_t n_tokens = q->ne[2];
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const int64_t n_seqs = q->ne[3];
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const int64_t S_v = v->ne[0];
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const int64_t H_v = v->ne[1];
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GGML_ASSERT(v->ne[2] == n_tokens);
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GGML_ASSERT(k->ne[2] == n_tokens);
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GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
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GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
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GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs);
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GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs);
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GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs);
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GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case
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const float eps_norm = hparams.f_norm_rms_eps;
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q = ggml_l2_norm(ctx0, q, eps_norm);
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k = ggml_l2_norm(ctx0, k, eps_norm);
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const float scale = 1.0f / sqrtf(S_v);
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q = ggml_scale(ctx0, q, scale);
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beta = ggml_sigmoid(ctx0, beta);
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cb(q, "q_in", il);
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cb(k, "k_in", il);
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cb(v, "v_in", il);
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cb(beta, "beta_in", il);
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cb(g, "g_in", il);
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q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
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k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
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v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
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g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
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beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
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state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
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cb(q, "q_perm", il);
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cb(k, "k_perm", il);
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cb(v, "v_perm", il);
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cb(beta, "beta_perm", il);
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cb(g, "g_perm", il);
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cb(state, "state_in", il);
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GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
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GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
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GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
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GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
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// Do padding
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const int64_t chunk_size = CHUNK_SIZE;
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const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size;
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const int64_t n_chunks = (n_tokens + pad) / chunk_size;
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q = ggml_pad(ctx0, q, 0, pad, 0, 0);
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k = ggml_pad(ctx0, k, 0, pad, 0, 0);
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v = ggml_pad(ctx0, v, 0, pad, 0, 0);
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g = ggml_pad(ctx0, g, pad, 0, 0, 0);
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beta = ggml_pad(ctx0, beta, 0, pad, 0, 0);
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cb(q, "q_pad", il);
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cb(k, "k_pad", il);
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cb(v, "v_pad", il);
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cb(beta, "beta_pad", il);
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cb(g, "g_pad", il);
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ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
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ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
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cb(v_beta, "v_beta", il);
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cb(k_beta, "k_beta", il);
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q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
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k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
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k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
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v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
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v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
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g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
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beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
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ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
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cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
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ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
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ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
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ggml_tensor * gcs_j_broadcast =
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ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
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ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
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cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
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decay_mask = ggml_exp(ctx0, decay_mask);
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decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
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ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
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ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
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ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
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cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
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ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
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ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
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attn = ggml_mul(ctx0, lin_solve, causal_mask);
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attn = ggml_add(ctx0, attn, identity);
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cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
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ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
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ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
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ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
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cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
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ggml_tensor * k_cumdecay =
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ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
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cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q);
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attn_kq = ggml_mul(ctx0, attn_kq, decay_mask);
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attn_kq = ggml_mul(ctx0, attn_kq, diag_mask);
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cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs)
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// vectorized calculation of key_gdiff
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// improved from the chunked version:
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// g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
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// g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
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// key_gdiff = key * g_diff.unsqueeze(-1)
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// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
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// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
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// get last element in g_cumsum along chunk_size dimension (ne0)
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// example: [[x, y, z, ..., last], ...] -> [[last], ...]
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ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3],
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g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3],
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(g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum));
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g_last = ggml_cont(ctx0, g_last);
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cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
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ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last);
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cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs)
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ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last));
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cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
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ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
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ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp);
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cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs)
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// state to be updated per chunk
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ggml_tensor * new_state = state; // ggml_dup(ctx0, state);
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cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs)
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// shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs)
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ggml_tensor * core_attn_out = nullptr;
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for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
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// shape: (S_k, chunk_size, 1, H_k * n_seqs)
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ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul
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// shape: (S_v, chunk_size, 1, H_v * n_seqs)
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ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat
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// shape: (chunk_size, 1, n_chunks, H_v * n_seqs)
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ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul
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// shape: (chunk_size, 1, H_v * n_seqs)
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ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat
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// attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
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// replaced by precomputed attn_kq
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ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk);
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cb(attn_chunk, "attn_chunk", il);
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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);
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// v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
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ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk);
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cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs)
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// v_new = v_i - v_prime
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ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime);
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ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
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cb(v_new, "v_new_chunk", il);
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// attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
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ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
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ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
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cb(attn_inter, "attn_inter_chunk", il);
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// core_attn_out[:, :, i] = attn_inter + attn @ v_new
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ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk);
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cb(v_attn, "v_attn_chunk", il);
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ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
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cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs)
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core_attn_out = core_attn_out == nullptr
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? core_attn_out_chunk
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: ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2);
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// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
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ggml_tensor * k_gdiff = ggml_cont(ctx0, get_slice_2d(ctx0, key_gdiff, chunk));
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//ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why?
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ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, k_gdiff)));
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// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
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|
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<ggml_tensor *, ggml_tensor *> 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<ggml_tensor *, ggml_tensor *> 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);
|
|
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|
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);
|
|
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|
ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
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ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
|
|
cb(alpha_softplus, "a_softplus", il);
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|
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;
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const int64_t conv_kernel_size = conv_kernel->ne[0];
|
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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<ggml_tensor *, ggml_tensor *> 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;
|
|
}
|