844 lines
34 KiB
C++
844 lines
34 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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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, 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 * b,
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ggml_tensor * s,
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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(S_k == S_v);
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GGML_ASSERT(H_v % H_k == 0);
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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(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs);
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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(b->ne[0] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
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GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
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const float scale = 1.0f / sqrtf(S_k);
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q = ggml_scale(ctx0, q, scale);
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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(b, "b_in", il);
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cb(g, "g_in", il);
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q = ggml_permute(ctx0, q, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
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k = ggml_permute(ctx0, k, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
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v = ggml_permute(ctx0, v, 0, 2, 1, 3); // [S_v, n_tokens, H_v, n_seqs]
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g = ggml_permute(ctx0, g, 2, 1, 3, 0); // [ 1, n_tokens, H_v, n_seqs]
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b = ggml_permute(ctx0, b, 2, 0, 1, 3); // [ 1, n_tokens, H_v, n_seqs]
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const int CS = CHUNK_SIZE;
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const int pad = (CS - n_tokens % CS) % CS;
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const int n_chunks = (n_tokens + pad) / CS;
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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, 0, pad, 0, 0);
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b = ggml_pad(ctx0, b, 0, pad, 0, 0);
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ggml_tensor * v_b = ggml_mul(ctx0, v, b);
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ggml_tensor * k_b = ggml_mul(ctx0, k, b);
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cb(v_b, "v_b", il);
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cb(k_b, "k_b", il);
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q = ggml_reshape_4d(ctx0, q, S_k, CS, n_chunks, H_k * n_seqs);
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k = ggml_reshape_4d(ctx0, k, S_k, CS, n_chunks, H_k * n_seqs);
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k_b = ggml_reshape_4d(ctx0, k_b, S_k, CS, n_chunks, H_v * n_seqs);
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v = ggml_reshape_4d(ctx0, v, S_v, CS, n_chunks, H_v * n_seqs);
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v_b = ggml_reshape_4d(ctx0, v_b, S_v, CS, n_chunks, H_v * n_seqs);
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g = ggml_reshape_4d(ctx0, g, CS, 1, n_chunks, H_v * n_seqs);
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b = ggml_reshape_4d(ctx0, b, 1, CS, n_chunks, H_v * n_seqs);
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// [CS, 1, n_chunks, H_v * n_seqs]
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ggml_tensor * g_cs = ggml_cumsum(ctx0, g);
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cb(g_cs, "g_cs", il);
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ggml_tensor * g_cs_i = g_cs;
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ggml_tensor * g_cs_j = ggml_reshape_4d(ctx0, g_cs, 1, CS, n_chunks, H_v * n_seqs);
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g_cs_j = ggml_repeat_4d(ctx0, g_cs_j, CS, CS, n_chunks, H_v * n_seqs);
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// [CS, CS, n_chunks, H_v * n_seqs]
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ggml_tensor * decay_mask;
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decay_mask = ggml_sub(ctx0, g_cs_j, g_cs_i);
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decay_mask = ggml_tri(ctx0, decay_mask, GGML_TRI_TYPE_LOWER_DIAG);
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decay_mask = ggml_exp(ctx0, decay_mask);
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cb(decay_mask, "decay_mask", il);
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// [CS, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * kb;
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kb = ggml_mul_mat(ctx0, k, k_b);
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kb = ggml_mul (ctx0, kb, decay_mask);
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// [CS, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * attn;
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attn = ggml_tri(ctx0, kb, GGML_TRI_TYPE_LOWER);
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ggml_tensor * identity;
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identity = ggml_view_1d(ctx0, attn, CS, 0);
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identity = ggml_fill (ctx0, identity, 1.0f);
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identity = ggml_diag (ctx0, identity);
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ggml_tensor * lhs = ggml_add(ctx0, attn, identity);
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cb(lhs, "dnet_add_ch_lhs", il);
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attn = ggml_neg(ctx0, attn);
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ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
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attn = ggml_add(ctx0, lin_solve, identity);
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cb(attn, "dnet_add_ch_attn_solved", il); // [CS, CS, n_chunks, H_k * n_seqs]
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// [S_v, CS, n_chunks, H_v * n_seqs]
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v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_b)), attn);
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// [CS, 1, n_chunks, H_v * n_seqs]
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ggml_tensor * g_exp = ggml_exp(ctx0, g_cs);
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k_b = ggml_cont(ctx0, ggml_transpose(ctx0, k_b));
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// [CS, S_k, n_chunks, H_k * n_seqs]
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ggml_tensor * kbg = ggml_mul(ctx0, k_b, g_exp);
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cb(kbg, "k_beta_g_exp", il);
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// [S_k, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * k_cd = ggml_mul_mat(ctx0, kbg, attn);
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cb(k_cd, "k_cumdecay", il);
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// [S_k, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * g_exp_t = ggml_transpose(ctx0, g_exp);
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ggml_tensor * q_g_exp = ggml_mul(ctx0, q, g_exp_t);
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// [CS, CS, n_chunks, H_k * n_seqs]
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ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
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kq = ggml_mul(ctx0, kq, decay_mask);
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kq = ggml_tri(ctx0, kq, GGML_TRI_TYPE_LOWER_DIAG);
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cb(kq, "kq", il);
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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 CS dimension (ne0)
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// example: [[x, y, z, ..., last], ...] -> [[last], ...]
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// [1, 1, n_chunks, H_v * n_seqs]
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ggml_tensor * g_last = ggml_view_4d(ctx0, g_cs, 1, 1, g_cs->ne[2], g_cs->ne[3],
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g_cs->nb[1],
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g_cs->nb[2],
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g_cs->nb[3],
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ggml_row_size(g_cs->type, g_cs->ne[0] - 1));
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cb(g_last, "g_last", il);
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// TODO: remove this cont when CUDA supports non-cont unary ops
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g_last = ggml_cont(ctx0, g_last);
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// [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);
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// [CS, 1, n_chunks, H_v * n_seqs]
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ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cs, g_last));
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cb(g_diff, "g_diff", il);
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ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
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ggml_tensor * g_diff_exp_t = ggml_transpose(ctx0, g_diff_exp);
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// [S_k, CS, n_chunks, H_v * n_seqs]
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ggml_tensor * kg = ggml_mul(ctx0, k, g_diff_exp_t);
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cb(kg, "key_gdiff", il);
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// [CS, S_k, n_chunks, H_v * n_seqs]
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ggml_tensor * kg_t = ggml_cont(ctx0, ggml_transpose(ctx0, kg));
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cb(kg_t, "key_gdiff_t", il);
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ggml_tensor * s_t = ggml_transpose(ctx0, s);
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s_t = ggml_cont_4d(ctx0, s_t, S_v, S_v, 1, H_v * n_seqs);
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cb(s_t, "dnet_add_ch_state", il);
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// [CS, S_v, n_chunks, H_v * n_seqs]
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ggml_tensor * v_t = ggml_cont(ctx0, ggml_transpose(ctx0, v));
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for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
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ggml_tensor * ch_k_cd = get_slice_2d(ctx0, k_cd, chunk); // [S_k, CS, 1, H_k * n_seqs]
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ggml_tensor * ch_v_t = get_slice_2d(ctx0, v_t, chunk); // [ CS, S_v, 1, H_v * n_seqs]
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ggml_tensor * ch_kq = get_slice_2d(ctx0, kq, chunk); // [ CS, CS, 1, H_k * n_seqs]
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ggml_tensor * ch_q_g_exp = get_slice_2d(ctx0, q_g_exp, chunk); // [S_k, CS, 1, H_k * n_seqs]
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ggml_tensor * ch_kg_t = get_slice_2d(ctx0, kg_t, chunk); // [ CS, S_k, 1, H_v * n_seqs]
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// [CS, S_v, 1, H_v * n_seqs]
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ggml_tensor * v_t_p = ggml_mul_mat(ctx0, ch_k_cd, s_t);
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cb(v_t_p, "v_prime", il);
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// [CS, S_v, 1, H_v * n_seqs]
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ggml_tensor * v_t_new = ggml_sub(ctx0, ch_v_t, v_t_p);
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cb(v_t_new, "v_t_new", il);
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// [S_v, CS, 1, H_v * n_seqs]
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ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_t_new, ch_kq);
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cb(v_attn, "v_attn", il);
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// [S_v, CS, 1, H_v * n_seqs]
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ggml_tensor * attn_inter = ggml_mul_mat(ctx0, s_t, ch_q_g_exp);
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cb(attn_inter, "attn_inter", il);
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// [S_v, CS, 1, H_v * n_seqs]
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ggml_tensor * o_ch = ggml_add(ctx0, attn_inter, v_attn);
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cb(o_ch, "dnet_add_ch_attn_out", il);
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v = ggml_set_inplace(ctx0, v, o_ch, v->nb[1], v->nb[2], v->nb[3], chunk * v->nb[2]);
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// kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
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// TODO: head broadcast might not work here - probably will need a transpose
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ggml_tensor * kgv = ggml_mul_mat(ctx0, ch_kg_t, v_t_new); // [S_k, S_v, 1, H_k * n_seqs]
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// last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
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ggml_tensor * ch_g_last_exp = get_slice_2d(ctx0, g_last_exp, chunk);
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s_t = ggml_mul(ctx0, s_t, ch_g_last_exp);
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s_t = ggml_add(ctx0, s_t, kgv);
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cb(s_t, "dnet_add_ch_state", il);
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}
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s_t = ggml_reshape_4d(ctx0, s_t, S_v, S_v, H_v, n_seqs);
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// truncate padded tokens
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ggml_tensor * o = ggml_view_4d(ctx0, v,
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S_v, n_tokens, H_v, n_seqs,
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ggml_row_size(v->type, S_v),
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ggml_row_size(v->type, S_v * CS * n_chunks),
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ggml_row_size(v->type, S_v * CS * n_chunks * H_v), 0);
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o = ggml_permute (ctx0, o, 0, 2, 1, 3); // [S_v, H_v, n_tokens, n_seqs]
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s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs]
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return {o, s};
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}
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std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3next::build_delta_net_autoregressive(
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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 * b, // beta
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ggml_tensor * s, // state
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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(n_tokens == 1);
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GGML_ASSERT(S_k == S_v);
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GGML_ASSERT(H_v % H_k == 0);
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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(v->ne[0] == S_v && v->ne[1] == H_v && v->ne[2] == n_tokens && v->ne[3] == n_seqs);
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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(b->ne[0] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
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GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
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|
|
const float scale = 1.0f / sqrtf(S_k);
|
|
|
|
q = ggml_scale(ctx0, q, scale);
|
|
|
|
q = ggml_permute(ctx0, q, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
|
|
k = ggml_permute(ctx0, k, 0, 2, 1, 3); // [S_k, n_tokens, H_k, n_seqs]
|
|
v = ggml_permute(ctx0, v, 0, 2, 1, 3); // [S_v, n_tokens, H_v, n_seqs]
|
|
|
|
cb(q, "q_in", il);
|
|
cb(k, "k_in", il);
|
|
cb(v, "v_in", il);
|
|
cb(b, "b_in", il);
|
|
cb(g, "g_in", il);
|
|
|
|
g = ggml_reshape_4d(ctx0, g, 1, 1, H_v, n_seqs);
|
|
b = ggml_reshape_4d(ctx0, b, 1, 1, H_v, n_seqs);
|
|
|
|
// [S_v, S_v, H_v, n_seqs]
|
|
g = ggml_exp(ctx0, g);
|
|
s = ggml_mul(ctx0, s, g);
|
|
|
|
ggml_tensor * s_t = ggml_cont(ctx0, ggml_transpose(ctx0, s));
|
|
|
|
// [1, S_v, H_v, n_seqs]
|
|
ggml_tensor * sk;
|
|
sk = ggml_mul (ctx0, s_t, k);
|
|
sk = ggml_sum_rows(ctx0, sk);
|
|
|
|
// [S_v, 1, H_v, n_seqs]
|
|
ggml_tensor * d;
|
|
d = ggml_sub(ctx0, v, ggml_transpose(ctx0, sk));
|
|
d = ggml_mul(ctx0, d, b);
|
|
|
|
// [1, S_v, H_v, n_seqs]
|
|
ggml_tensor * d_t;
|
|
d_t = ggml_transpose(ctx0, d);
|
|
|
|
// [S_v, S_v, H_v, n_seqs]
|
|
ggml_tensor * kd;
|
|
k = ggml_repeat(ctx0, k, s);
|
|
kd = ggml_mul (ctx0, k, d_t);
|
|
|
|
s_t = ggml_add(ctx0, s_t, kd);
|
|
|
|
cb(s_t, "dnet_add_ar_state", il);
|
|
|
|
ggml_tensor * s_q = ggml_mul (ctx0, s_t, q);
|
|
ggml_tensor * o = ggml_sum_rows(ctx0, s_q);
|
|
|
|
o = ggml_permute (ctx0, o, 2, 0, 1, 3); // [S_v, H_v, n_tokens, n_seqs]
|
|
s = ggml_transpose(ctx0, s_t); // [S_v, S_v, H_v, n_seqs]
|
|
|
|
return {o, s};
|
|
}
|
|
|
|
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);
|
|
cb(Qcur, "Qcur_view", il);
|
|
|
|
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(gate, "gate", 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);
|
|
|
|
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);
|
|
|
|
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
|
cb(Qcur, "Qcur_normed", il);
|
|
|
|
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
|
cb(Kcur, "Kcur_normed", il);
|
|
|
|
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);
|
|
|
|
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);
|
|
|
|
// TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont
|
|
gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
|
|
|
|
gate = ggml_sigmoid(ctx0, gate);
|
|
cb(gate, "gate_sigmoid", il);
|
|
|
|
gate = ggml_reshape_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
|
|
|
|
cur = ggml_mul(ctx0, cur, gate);
|
|
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,
|
|
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);
|
|
|
|
// TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont
|
|
b = ggml_cont(ctx0, b);
|
|
|
|
ggml_tensor * beta = ggml_sigmoid(ctx0, b);
|
|
|
|
beta = ggml_reshape_4d(ctx0, beta, 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);
|
|
|
|
// 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_transpose(ctx0, qkv_mixed);
|
|
cb(qkv_mixed, "qkv_mixed_transposed", 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);
|
|
|
|
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, n_seqs);
|
|
cb(state, "state_predelta", il);
|
|
|
|
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_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
|
|
ggml_row_size(conv_qkv_mix->type, head_k_dim),
|
|
nb1_qkv,
|
|
nb1_qkv * n_seq_tokens,
|
|
0);
|
|
|
|
ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
|
|
ggml_row_size(conv_qkv_mix->type, head_k_dim),
|
|
nb1_qkv,
|
|
nb1_qkv * n_seq_tokens,
|
|
head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
|
|
|
|
ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,
|
|
ggml_row_size(conv_qkv_mix->type, head_v_dim),
|
|
nb1_qkv,
|
|
nb1_qkv * n_seq_tokens,
|
|
ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));
|
|
|
|
cb(q_conv, "q_conv", il);
|
|
cb(k_conv, "k_conv", il);
|
|
cb(v_conv, "v_conv", il);
|
|
|
|
const float eps_norm = hparams.f_norm_rms_eps;
|
|
|
|
q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);
|
|
k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);
|
|
|
|
//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);
|
|
|
|
// 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, 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))));
|
|
|
|
// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
|
|
ggml_tensor * z_2d = ggml_reshape_4d(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(output, 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_reshape_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);
|
|
|
|
shared_gate = ggml_sigmoid(ctx0, shared_gate);
|
|
cb(shared_gate, "shared_expert_gate_sigmoid", il);
|
|
|
|
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;
|
|
}
|