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@@ -0,0 +1,1042 @@
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+#include "ggml.h"
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+#include "models.h"
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+
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+#define CHUNK_SIZE 64
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+
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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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+
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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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+
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+ auto * inp = build_inp_mem_hybrid();
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+
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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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+
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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, ubatch.n_seq_tokens, ubatch.n_seq_tokens), 1.0f),
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+ GGML_TRI_TYPE_LOWER);
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+
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+ ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens), 1.0f));
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+
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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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+
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+ for (int il = 0; il < n_layer; ++il) {
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+ ggml_tensor * inpSA = inpL;
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+
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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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+
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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, 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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ cb(cur, "result_norm", -1);
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+ res->t_embd = cur;
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+
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+ // LM head
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+ cur = build_lora_mm(model.output, cur);
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+
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+ cb(cur, "result_output", -1);
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+ res->t_logits = cur;
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+
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+ ggml_build_forward_expand(gf, cur);
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+}
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+
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+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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+ int il) {
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+ GGML_ASSERT(ggml_is_contiguous(q));
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+ GGML_ASSERT(ggml_is_contiguous(k));
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+ GGML_ASSERT(ggml_is_contiguous(v));
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+ GGML_ASSERT(ggml_is_contiguous(g));
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+ GGML_ASSERT(ggml_is_contiguous(beta));
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+ GGML_ASSERT(ggml_is_contiguous(state));
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ // TODO: can this ever be false?
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+ const bool use_qk_l2norm = true;
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+
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+ if (use_qk_l2norm) {
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+ const float eps_norm = hparams.f_norm_rms_eps;
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+
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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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+ }
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+
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+ const float scale = 1.0f / sqrtf(S_v);
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+
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+ q = ggml_scale(ctx0, q, scale);
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+
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+ beta = ggml_sigmoid(ctx0, beta);
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+
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+ ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ // Do padding
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+ const int64_t chunk_size = CHUNK_SIZE;
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+
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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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+
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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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+
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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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+
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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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+
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+ cb(v_beta, "v_beta", il);
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+ cb(k_beta, "k_beta", il);
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+
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+ ggml_tensor * chunked_mask =
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+ ggml_view_4d(ctx0, causal_mask, chunk_size,
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+ chunk_size, causal_mask->ne[2], causal_mask->ne[3],
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+ causal_mask->nb[1], causal_mask->nb[2], causal_mask->nb[3], 0);
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+
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+ ggml_tensor * chunked_diag_mask =
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+ ggml_view_4d(ctx0, causal_diag_mask, chunk_size,
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+ chunk_size, causal_diag_mask->ne[2], causal_diag_mask->ne[3],
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+ causal_diag_mask->nb[1], causal_diag_mask->nb[2], causal_diag_mask->nb[3], 0);
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+
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+ ggml_tensor * chunked_identity =
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+ ggml_view_4d(ctx0, identity, chunk_size,
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+ chunk_size, identity->ne[2], identity->ne[3],
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+ identity->nb[1], identity->nb[2], identity->nb[3], 0);
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+
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+ q = ggml_cont_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs);
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+ k = ggml_cont_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs);
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+ k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
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+ v = ggml_cont_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs);
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+ v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
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+
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+ g = ggml_cont_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
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+ beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
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+
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+ ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
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+
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+ cb(g_cumsum, "g_cumsum", il);
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+
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+ ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
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+ ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
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+
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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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+
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+ ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
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+
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+ cb(decay_mask, "decay_mask", il);
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+
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+ decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
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+ decay_mask = ggml_exp(ctx0, decay_mask);
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+ decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
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+
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+ ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
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+
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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, chunked_mask));
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+
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+ cb(attn, "attn_pre_solve", il);
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+
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+ ggml_tensor * attn_lower = ggml_mul(ctx0, attn, chunked_mask);
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+ ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, chunked_identity, attn_lower), attn_lower);
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+
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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, chunked_mask);
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+ attn = ggml_add(ctx0, attn, chunked_identity);
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+
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+ cb(attn, "attn_solved", il);
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+
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+ v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
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+
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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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+
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+ ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
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+
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+ cb(kbeta_gexp, "kbeta_gexp", il);
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+
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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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+
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+ cb(k_cumdecay, "k_cumdecay", il);
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+
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+ ggml_tensor * core_attn_out = nullptr;
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+ ggml_tensor * new_state = ggml_dup(ctx0, state);
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+
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+ cb(new_state, "new_state", il);
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+
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+ for (int64_t chunk = 0; chunk < n_chunks; chunk++) {
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+ auto chunkify = [=](ggml_tensor * t) {
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+ return ggml_cont(ctx0, ggml_view_4d(ctx0, t, t->ne[0], chunk_size, 1, t->ne[3],
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+ t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
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+ };
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+
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+ auto chunkify_g = [=](ggml_tensor * t) {
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+ return ggml_cont(ctx0, ggml_view_4d(ctx0, t, chunk_size, t->ne[1], 1, t->ne[3],
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+ t->nb[1], t->nb[2], t->nb[3], t->nb[2] * chunk));
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+ };
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+
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+ ggml_tensor * k_chunk = chunkify(k);
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+ ggml_tensor * q_chunk = chunkify(q);
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+ ggml_tensor * v_chunk = chunkify(v);
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+
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+ ggml_tensor * g_cs_chunk = chunkify_g(g_cumsum);
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+ ggml_tensor * g_cs_chunk_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cs_chunk));
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+
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+ ggml_tensor * decay_mask_chunk = chunkify(decay_mask);
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+ ggml_tensor * k_cumdecay_chunk = chunkify(k_cumdecay);
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+
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+ ggml_tensor * gexp_chunk = ggml_exp(ctx0, g_cs_chunk_t);
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+
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+ // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
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+ attn = ggml_mul_mat(ctx0, k_chunk, q_chunk);
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+ attn = ggml_mul(ctx0, attn, decay_mask_chunk);
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+ attn = ggml_mul(ctx0, attn, ggml_add(ctx0, chunked_identity, chunked_mask));
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+
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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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+
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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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+
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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);
|
|
|
+ ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
|
|
|
+
|
|
|
+ // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
|
|
|
+ ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk);
|
|
|
+ ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
|
|
|
+
|
|
|
+ // core_attn_out[:, :, i] = attn_inter + attn @ v_new
|
|
|
+ ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
|
|
|
+
|
|
|
+ ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn);
|
|
|
+
|
|
|
+ core_attn_out = core_attn_out == nullptr ? core_attn_out_chunk : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 1);
|
|
|
+
|
|
|
+ // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
|
|
|
+ // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
|
|
|
+ // key_gdiff = key * g_diff.unsqueeze(-1)
|
|
|
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
|
|
|
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
|
|
|
+
|
|
|
+ ggml_tensor * g_cum_last =
|
|
|
+ ggml_cont(ctx0, ggml_view_4d(ctx0, g_cs_chunk_t, g_cs_chunk_t->ne[0], 1, g_cs_chunk_t->ne[2], g_cs_chunk_t->ne[3],
|
|
|
+ g_cs_chunk_t->nb[1], g_cs_chunk_t->nb[2], g_cs_chunk_t->nb[3],
|
|
|
+ g_cs_chunk_t->nb[0] * (g_cs_chunk_t->ne[1] - 1)));
|
|
|
+
|
|
|
+ ggml_tensor * gexp_last =
|
|
|
+ ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
|
|
|
+
|
|
|
+ ggml_tensor * g_cum_last_3d =
|
|
|
+ ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
|
|
|
+
|
|
|
+ ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cs_chunk, g_cs_chunk->ne[0], g_cs_chunk->ne[2], g_cs_chunk->ne[3]);
|
|
|
+
|
|
|
+ ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
|
|
|
+
|
|
|
+ ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
|
|
|
+
|
|
|
+ ggml_tensor * key_gdiff = ggml_mul(ctx0, k_chunk,
|
|
|
+ ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
|
|
|
+ g_diff_exp->ne[2] * g_diff_exp->ne[3]));
|
|
|
+
|
|
|
+ ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
|
|
|
+
|
|
|
+ new_state = ggml_add(ctx0,
|
|
|
+ ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last, gexp_last->ne[0], gexp_last->ne[1], H_v, n_seqs)),
|
|
|
+ ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs));
|
|
|
+ }
|
|
|
+
|
|
|
+ core_attn_out = ggml_cont_4d(ctx0, core_attn_out, S_v, chunk_size * n_chunks, H_v, n_seqs);
|
|
|
+
|
|
|
+ ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out, S_v, n_tokens, H_v, n_seqs, core_attn_out->nb[1], core_attn_out->nb[2], core_attn_out->nb[3], 0);
|
|
|
+ cb(output_tokens, "output_tokens", il);
|
|
|
+
|
|
|
+ // flatten output
|
|
|
+ ggml_tensor * flat_output =
|
|
|
+ ggml_cont_1d(ctx0, ggml_permute(ctx0, output_tokens, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
|
|
|
+
|
|
|
+ ggml_tensor * flat_state = ggml_cont_1d(ctx0, new_state, S_v * S_v * H_v * n_seqs);
|
|
|
+
|
|
|
+ return ggml_concat(ctx0, flat_output, flat_state, 0);
|
|
|
+}
|
|
|
+
|
|
|
+ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
|
|
|
+ ggml_tensor * q,
|
|
|
+ ggml_tensor * k,
|
|
|
+ ggml_tensor * v,
|
|
|
+ ggml_tensor * g,
|
|
|
+ ggml_tensor * beta,
|
|
|
+ ggml_tensor * state,
|
|
|
+ ggml_tensor * causal_mask,
|
|
|
+ ggml_tensor * identity,
|
|
|
+ int il) {
|
|
|
+ GGML_ASSERT(ggml_is_contiguous(q));
|
|
|
+ GGML_ASSERT(ggml_is_contiguous(k));
|
|
|
+ GGML_ASSERT(ggml_is_contiguous(v));
|
|
|
+ GGML_ASSERT(ggml_is_contiguous(g));
|
|
|
+ GGML_ASSERT(ggml_is_contiguous(beta));
|
|
|
+ GGML_ASSERT(ggml_is_contiguous(state));
|
|
|
+
|
|
|
+ const int64_t S_k = q->ne[0];
|
|
|
+ const int64_t H_k = q->ne[1];
|
|
|
+ const int64_t n_tokens = q->ne[2];
|
|
|
+ const int64_t n_seqs = q->ne[3];
|
|
|
+
|
|
|
+ const int64_t S_v = v->ne[0];
|
|
|
+ const int64_t H_v = v->ne[1];
|
|
|
+
|
|
|
+ GGML_ASSERT(v->ne[2] == n_tokens);
|
|
|
+ GGML_ASSERT(k->ne[2] == n_tokens);
|
|
|
+ GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
|
|
|
+ GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs);
|
|
|
+ GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == 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
|
|
|
+
|
|
|
+ // TODO: can this ever be false?
|
|
|
+ const bool use_qk_l2norm = true;
|
|
|
+
|
|
|
+ if (use_qk_l2norm) {
|
|
|
+ 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);
|
|
|
+
|
|
|
+ ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
|
|
|
+
|
|
|
+ cb(q, "q_in", il);
|
|
|
+ cb(k, "k_in", il);
|
|
|
+ cb(v, "v_in", il);
|
|
|
+ cb(beta, "beta_in", il);
|
|
|
+ cb(g, "g_in", il);
|
|
|
+
|
|
|
+ q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
|
|
|
+ k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
|
|
|
+ v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
|
|
|
+ g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
|
|
|
+
|
|
|
+ beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
|
|
|
+ state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
|
|
|
+
|
|
|
+ cb(q, "q_perm", il);
|
|
|
+ cb(k, "k_perm", il);
|
|
|
+ cb(v, "v_perm", il);
|
|
|
+ cb(beta, "beta_perm", il);
|
|
|
+ cb(g, "g_perm", il);
|
|
|
+ cb(state, "state_in", il);
|
|
|
+
|
|
|
+ GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
|
|
|
+ GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
|
|
|
+ GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
|
|
|
+ GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
|
|
|
+
|
|
|
+ ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
|
|
|
+ ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
|
|
|
+
|
|
|
+ ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
|
|
|
+
|
|
|
+ cb(k_beta, "k_beta", il);
|
|
|
+ cb(v_beta, "v_beta", il);
|
|
|
+ cb(g_cumsum, "g_cumsum", il);
|
|
|
+
|
|
|
+ ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, n_tokens, 1, H_v, n_seqs); // [chunk_size, 1, n_tokens, n_seqs]
|
|
|
+ ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, n_tokens, H_v, n_seqs); // [1, chunk_size, n_tokens, n_seqs]
|
|
|
+
|
|
|
+ // Broadcast both tensors to [chunk_size, chunk_size, H_v, n_seqs]
|
|
|
+ // ggml_tensor * gcs_i_broadcast =
|
|
|
+ // ggml_repeat_4d(ctx0, gcs_i, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v,
|
|
|
+ // n_seqs); // [chunk_size, 1, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
|
|
|
+ // Don't need this, this one will get auto-broadcast
|
|
|
+ ggml_tensor * gcs_j_broadcast =
|
|
|
+ ggml_repeat_4d(ctx0, gcs_j, n_tokens, n_tokens, H_v, n_seqs); // [1, chunk_size, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
|
|
|
+
|
|
|
+ ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
|
|
|
+
|
|
|
+ // Apply lower triangular mask to ensure attention is causal (only past tokens influence current)
|
|
|
+ decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
|
|
|
+ // Apply exponential to get the decay mask values
|
|
|
+ decay_mask = ggml_exp(ctx0, decay_mask);
|
|
|
+ // Apply lower triangular mask again to ensure only lower triangular values remain
|
|
|
+ decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
|
|
|
+
|
|
|
+ cb(decay_mask, "decay_mask", il);
|
|
|
+
|
|
|
+ // attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
|
|
|
+ ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
|
|
|
+
|
|
|
+ cb(kmulkbeta, "kmulkbeta", il);
|
|
|
+
|
|
|
+ ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
|
|
|
+ ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
|
|
|
+
|
|
|
+ cb(attn, "attn_pre_rec", il);
|
|
|
+
|
|
|
+ // for i in range(1, chunk_size):
|
|
|
+ // row = attn[..., i, :i].clone()
|
|
|
+ // sub = attn[..., :i, :i].clone()
|
|
|
+ // attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
|
|
|
+ // attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
|
|
|
+ //
|
|
|
+ // We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
|
|
|
+ ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
|
|
|
+ ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
|
|
|
+
|
|
|
+ ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
|
|
|
+ attn = ggml_mul(ctx0, lin_solve, causal_mask);
|
|
|
+ attn = ggml_add(ctx0, attn, identity);
|
|
|
+
|
|
|
+ // value = attn @ v_beta
|
|
|
+ v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
|
|
|
+
|
|
|
+ cb(v, "value_beta", il);
|
|
|
+
|
|
|
+ // k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
|
|
|
+ ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
|
|
|
+ ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t);
|
|
|
+
|
|
|
+ cb(gexp, "g_cum_exp", il);
|
|
|
+
|
|
|
+ ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
|
|
|
+
|
|
|
+ cb(kbeta_gexp, "kbeta_gexp", il);
|
|
|
+
|
|
|
+ ggml_tensor * k_cumdecay =
|
|
|
+ ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
|
|
|
+
|
|
|
+ cb(k_cumdecay, "k_cumdecay", il);
|
|
|
+
|
|
|
+ // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
|
|
|
+ attn = ggml_mul_mat(ctx0, k, q);
|
|
|
+ attn = ggml_mul(ctx0, attn, decay_mask);
|
|
|
+ attn = ggml_mul(ctx0, attn, ggml_add(ctx0, identity, causal_mask));
|
|
|
+
|
|
|
+ cb(attn, "attn_decay_key", il);
|
|
|
+
|
|
|
+ ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
|
|
|
+
|
|
|
+ // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
|
|
|
+ ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay);
|
|
|
+
|
|
|
+ cb(v_prime, "v_prime", il);
|
|
|
+
|
|
|
+ // v_new = v_i - v_prime
|
|
|
+ ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v, v_prime), v_prime);
|
|
|
+
|
|
|
+ ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
|
|
|
+
|
|
|
+ cb(v_new, "v_new", il);
|
|
|
+
|
|
|
+ // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
|
|
|
+ ggml_tensor * q_g_exp = ggml_mul(ctx0, q, gexp);
|
|
|
+ ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
|
|
|
+
|
|
|
+ cb(attn_inter, "attn_inter", il);
|
|
|
+
|
|
|
+ // core_attn_out[:, :, i] = attn_inter + attn @ v_new
|
|
|
+ ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
|
|
|
+
|
|
|
+ cb(v_attn, "v_attn", il);
|
|
|
+
|
|
|
+ ggml_tensor * core_attn_out = ggml_add(ctx0, attn_inter, v_attn);
|
|
|
+
|
|
|
+ cb(core_attn_out, "core_attn_out", il);
|
|
|
+
|
|
|
+ // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
|
|
|
+ // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
|
|
|
+ // key_gdiff = key * g_diff.unsqueeze(-1)
|
|
|
+ // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
|
|
|
+ // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
|
|
|
+
|
|
|
+ ggml_tensor * g_cum_last =
|
|
|
+ ggml_cont(ctx0, ggml_view_4d(ctx0, g_cumsum_t, g_cumsum_t->ne[0], 1, g_cumsum_t->ne[2], g_cumsum_t->ne[3],
|
|
|
+ g_cumsum_t->nb[1], g_cumsum_t->nb[2], g_cumsum_t->nb[3],
|
|
|
+ g_cumsum_t->nb[0] * (g_cumsum_t->ne[1] - 1)));
|
|
|
+
|
|
|
+ cb(g_cum_last, "g_cum_last", il);
|
|
|
+
|
|
|
+ ggml_tensor * gexp_last =
|
|
|
+ ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
|
|
|
+
|
|
|
+ cb(gexp_last, "gexp_last", il);
|
|
|
+
|
|
|
+ ggml_tensor * g_cum_last_3d =
|
|
|
+ ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
|
|
|
+
|
|
|
+ cb(g_cum_last_3d, "g_cum_last_3d", il);
|
|
|
+
|
|
|
+ ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cumsum, g_cumsum->ne[0], g_cumsum->ne[2], g_cumsum->ne[3]);
|
|
|
+
|
|
|
+ cb(g_cumsum_3d, "g_cumsum_3d", il);
|
|
|
+
|
|
|
+ ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
|
|
|
+
|
|
|
+ cb(g_diff, "g_diff", il);
|
|
|
+
|
|
|
+ ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
|
|
|
+
|
|
|
+ cb(g_diff_exp, "g_diff_exp", il);
|
|
|
+
|
|
|
+ ggml_tensor * key_gdiff = ggml_mul(ctx0, k,
|
|
|
+ ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
|
|
|
+ g_diff_exp->ne[2] * g_diff_exp->ne[3]));
|
|
|
+
|
|
|
+ cb(key_gdiff, "key_gdiff", il);
|
|
|
+
|
|
|
+ ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
|
|
|
+
|
|
|
+ cb(kgdmulvnew, "kgdmulvnew", il);
|
|
|
+
|
|
|
+ state = ggml_add(ctx0, ggml_mul(ctx0, state, gexp_last), kgdmulvnew);
|
|
|
+
|
|
|
+ cb(state, "new_state", il);
|
|
|
+
|
|
|
+ // flatten output
|
|
|
+ ggml_tensor * flat_output =
|
|
|
+ ggml_cont_1d(ctx0, ggml_permute(ctx0, core_attn_out, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
|
|
|
+
|
|
|
+ ggml_tensor * flat_state = ggml_cont_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
|
|
|
+
|
|
|
+ return ggml_concat(ctx0, flat_output, flat_state, 0);
|
|
|
+}
|
|
|
+
|
|
|
+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;
|
|
|
+}
|
|
|
+
|
|
|
+ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
|
|
|
+ llm_graph_input_rs * inp,
|
|
|
+ ggml_tensor * cur,
|
|
|
+ ggml_tensor * causal_mask,
|
|
|
+ ggml_tensor * identity,
|
|
|
+ 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
|
|
|
+ ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, cur);
|
|
|
+ cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
|
|
|
+
|
|
|
+ ggml_tensor * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
|
|
|
+ cb(mixed_ba, "linear_attn_mixed_ba", il);
|
|
|
+
|
|
|
+ int64_t qkvz_new_dim = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
|
|
|
+ ggml_tensor * mixed_qkvz_reshaped = ggml_cont_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
|
|
|
+
|
|
|
+ // Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads]
|
|
|
+ int64_t ba_new_dim = 2 * num_v_heads / num_k_heads;
|
|
|
+ ggml_tensor * mixed_ba_reshaped = ggml_cont_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_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);
|
|
|
+
|
|
|
+ // Reshape b and a to merge head dimensions: [batch, seq_len, num_k_heads, num_v_heads/num_k_heads] -> [batch, seq_len, num_v_heads]
|
|
|
+ ggml_tensor * beta = ggml_cont_3d(ctx0, b, num_v_heads, n_seq_tokens, n_seqs);
|
|
|
+ ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
|
|
|
+
|
|
|
+ GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba));
|
|
|
+
|
|
|
+ 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);
|
|
|
+
|
|
|
+ // 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] * sizeof(float));
|
|
|
+ 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]) * sizeof(float));
|
|
|
+ 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]) * sizeof(float));
|
|
|
+ cb(z, "z", il);
|
|
|
+
|
|
|
+ GGML_ASSERT(ggml_nelements(query) + ggml_nelements(key) + ggml_nelements(value) + ggml_nelements(z) ==
|
|
|
+ ggml_nelements(mixed_qkvz));
|
|
|
+
|
|
|
+ // After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
|
|
|
+ // query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
|
|
|
+ ggml_tensor * query_flat = ggml_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);
|
|
|
+
|
|
|
+ // 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);
|
|
|
+
|
|
|
+ // 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);
|
|
|
+
|
|
|
+ qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
|
|
|
+ cb(qkv_mixed, "qkv_mixed_permuted", il);
|
|
|
+
|
|
|
+ // Calculate the total conv dimension
|
|
|
+ int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
|
|
|
+
|
|
|
+ // Calculate convolution kernel size
|
|
|
+ ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
|
|
|
+ const int64_t conv_kernel_size = conv_kernel->ne[0];
|
|
|
+ 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);
|
|
|
+
|
|
|
+ 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);
|
|
|
+
|
|
|
+ conv_output_proper = ggml_cont(ctx0, ggml_transpose(ctx0, conv_output_proper));
|
|
|
+ cb(conv_output_proper, "conv_output_pre_silu", 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 =
|
|
|
+ ggml_cont_2d(ctx0, ggml_transpose(ctx0, conv_output_silu), qkv_dim, n_seq_tokens * n_seqs);
|
|
|
+ cb(conv_qkv_mix, "conv_qkv_mix", il);
|
|
|
+
|
|
|
+ // Extract the convolved Q, K, V from conv_output
|
|
|
+ ggml_tensor * q_conv =
|
|
|
+ ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, conv_qkv_mix->nb[1], 0);
|
|
|
+ cb(q_conv, "q_conv", il);
|
|
|
+ ggml_tensor * k_conv =
|
|
|
+ ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, conv_qkv_mix->nb[1],
|
|
|
+ head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
|
|
|
+ cb(k_conv, "k_conv", il);
|
|
|
+ ggml_tensor * v_conv =
|
|
|
+ ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, conv_qkv_mix->nb[1],
|
|
|
+ 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
|
|
|
+ cb(v_conv, "v_conv", il);
|
|
|
+
|
|
|
+ // Unsqueeze them
|
|
|
+ q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_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);
|
|
|
+
|
|
|
+ beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, 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 and build_delta_net_recurrent based on n_tokens
|
|
|
+ ggml_tensor * attn_out = n_seq_tokens > CHUNK_SIZE ?
|
|
|
+ build_delta_net_chunking (q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il) :
|
|
|
+ build_delta_net_recurrent(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il);
|
|
|
+ cb(attn_out, "attn_out", il);
|
|
|
+
|
|
|
+ // The tensors were concatenated 1d, so we need to extract them 1d as well
|
|
|
+ const int64_t output_flat_size = head_v_dim * num_v_heads * n_seq_tokens * n_seqs;
|
|
|
+ ggml_tensor * attn_out_1d = ggml_view_1d(ctx0, attn_out, output_flat_size, 0);
|
|
|
+ cb(attn_out_1d, "attn_out_1d", il);
|
|
|
+
|
|
|
+ ggml_tensor * attn_out_final = ggml_cont_4d(ctx0, attn_out_1d, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
|
|
|
+ cb(attn_out_final, "attn_out_reshaped", il);
|
|
|
+
|
|
|
+ // Extract the state part (second part of the concatenated tensor)
|
|
|
+ // State starts after n_tokens elements along dimension 1
|
|
|
+ const int64_t state_flat_size = head_v_dim * head_v_dim * num_v_heads * n_seqs;
|
|
|
+
|
|
|
+ ggml_tensor * state_1d =
|
|
|
+ ggml_view_1d(ctx0, attn_out, state_flat_size, output_flat_size * ggml_element_size(attn_out));
|
|
|
+ cb(state_1d, "state_1d", il);
|
|
|
+
|
|
|
+ // Update the recurrent states
|
|
|
+ ggml_build_forward_expand(gf,
|
|
|
+ ggml_cpy(ctx0, state_1d,
|
|
|
+ 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))));
|
|
|
+
|
|
|
+ GGML_ASSERT(ggml_nelements(attn_out_1d) + ggml_nelements(state_1d) == ggml_nelements(attn_out));
|
|
|
+
|
|
|
+ // 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_cont_2d(ctx0, attn_out_final, 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_cont_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);
|
|
|
+
|
|
|
+ // The gate needs to be broadcast to match the dimensions of ffn_shexp
|
|
|
+ // ffn_shexp is [n_embd, n_tokens, 1, 1] and shared_gate is [1, n_tokens, 1, 1]
|
|
|
+ // We need to repeat the gate along the feature dimension
|
|
|
+ shared_gate = ggml_repeat(ctx0, shared_gate, ffn_shexp);
|
|
|
+ cb(shared_gate, "shared_expert_gate_broadcast", il);
|
|
|
+
|
|
|
+ // Apply the gate to the shared expert output
|
|
|
+ ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
|
|
|
+ cb(ffn_shexp, "ffn_shexp_gated", il);
|
|
|
+
|
|
|
+ cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
|
|
+ cb(cur, "ffn_out", il);
|
|
|
+ } else {
|
|
|
+ cur = moe_out;
|
|
|
+ }
|
|
|
+ } else {
|
|
|
+ // Dense FFN branch (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;
|
|
|
+}
|
Piotr Wilkin (ilintar)