424 lines
16 KiB
C++
424 lines
16 KiB
C++
#include "models.h"
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#include "llama-memory-recurrent.h"
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llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) :
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llm_build_delta_net_base(params), model(model) {
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const int64_t n_embd_head = hparams.n_embd_head_v();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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int sections[4];
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std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
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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.input_embed", -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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ggml_build_forward_expand(gf, cur);
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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, sections, 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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// MOE FFN layer
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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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std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen35moe::build_qkvz(
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ggml_tensor * input,
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int il) {
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const int64_t n_seqs = ubatch.n_seqs;
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const int64_t n_seq_tokens = ubatch.n_seq_tokens;
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ggml_tensor * qkv_mixed = build_lora_mm(model.layers[il].wqkv, input);
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qkv_mixed = ggml_reshape_3d(ctx0, qkv_mixed, qkv_mixed->ne[0], n_seq_tokens, n_seqs);
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cb(qkv_mixed, "linear_attn_qkv_mixed", il);
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ggml_tensor * z = build_lora_mm(model.layers[il].wqkv_gate, input);
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cb(z, "z", il);
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return { qkv_mixed, z };
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}
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ggml_tensor * llm_build_qwen35moe::build_norm_gated(
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ggml_tensor * input,
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ggml_tensor * weights,
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ggml_tensor * gate,
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int layer) {
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ggml_tensor * normalized = build_norm(input, weights, nullptr, LLM_NORM_RMS, layer);
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ggml_tensor * gated_silu = ggml_silu(ctx0, gate);
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return ggml_mul(ctx0, normalized, gated_silu);
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}
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ggml_tensor * llm_build_qwen35moe ::build_layer_attn(
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llm_graph_input_attn_kv * inp,
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ggml_tensor * cur,
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ggml_tensor * inp_pos,
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int * sections,
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int il) {
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const int64_t n_embd_head = hparams.n_embd_head_v();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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// Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention
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// Qwen3Next uses a single Q projection that outputs query + gate
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ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); // [ (n_embd_head * 2) * n_head, n_tokens ]
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cb(Qcur_full, "Qcur_full", il);
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ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
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ggml_element_size(Qcur_full) * n_embd_head * 2,
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ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, 0);
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cb(Qcur, "Qcur_reshaped", il);
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// Apply Q normalization
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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// Apply K normalization
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
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cb(Kcur, "Kcur_normed", il);
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ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, n_embd_head, n_head, n_tokens,
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ggml_element_size(Qcur_full) * n_embd_head * 2,
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ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head,
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ggml_element_size(Qcur_full) * n_embd_head);
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gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens);
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cb(gate, "gate_reshaped", il);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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// Apply IMRoPE
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Qcur = ggml_rope_multi(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = ggml_rope_multi(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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// Attention computation
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const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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cur = build_attn(inp,
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nullptr, nullptr,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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cb(cur, "attn_pregate", il);
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ggml_tensor * gate_sigmoid = ggml_sigmoid(ctx0, gate);
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cb(gate_sigmoid, "gate_sigmoid", il);
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cur = ggml_mul(ctx0, cur, gate_sigmoid);
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cb(cur, "attn_gated", il);
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cur = build_lora_mm(model.layers[il].wo, cur);
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cb(cur, "attn_output", il);
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return cur;
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}
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ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear(
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llm_graph_input_rs * inp,
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ggml_tensor * cur,
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int il) {
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const auto * mctx_cur = inp->mctx;
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const int64_t d_inner = hparams.ssm_d_inner;
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const int64_t n_seqs = ubatch.n_seqs;
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const int64_t head_k_dim = hparams.ssm_d_state;
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const int64_t num_k_heads = hparams.ssm_n_group;
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const int64_t num_v_heads = hparams.ssm_dt_rank;
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const int64_t head_v_dim = d_inner / num_v_heads;
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const int64_t n_seq_tokens = ubatch.n_seq_tokens;
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const auto kv_head = mctx_cur->get_head();
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GGML_ASSERT(n_seqs != 0);
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GGML_ASSERT(ubatch.equal_seqs());
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GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
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// Input projections
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auto qkvz = build_qkvz(cur, il);
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ggml_tensor * qkv_mixed = qkvz.first;
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ggml_tensor * z = qkvz.second;
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ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur);
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beta = ggml_reshape_4d(ctx0, beta, 1, num_v_heads, n_seq_tokens, n_seqs);
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cb(beta, "beta", il);
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beta = ggml_sigmoid(ctx0, beta);
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ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur);
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alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs);
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cb(alpha, "alpha", il);
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ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
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ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
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cb(alpha_softplus, "a_softplus", il);
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ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus
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cb(gate, "gate", il);
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gate = ggml_reshape_4d(ctx0, gate, 1, num_v_heads, n_seq_tokens, n_seqs);
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// Get convolution states from cache
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ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
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ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
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// Build the convolution states tensor
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ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
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cb(conv_states, "conv_states", il);
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// Calculate convolution kernel size
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ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d;
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const int64_t conv_kernel_size = conv_kernel->ne[0];
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const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state;
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conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs);
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cb(conv_states, "conv_states_reshaped", il);
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qkv_mixed = ggml_transpose(ctx0, qkv_mixed);
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cb(qkv_mixed, "qkv_mixed_transposed", il);
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ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0);
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cb(conv_input, "conv_input", il);
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// Update convolution state cache
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// Extract the last (conv_kernel_size - 1) states from conv_input
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ggml_tensor * last_conv_states =
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ggml_view_3d(ctx0, conv_input, conv_kernel_size - 1, conv_channels, n_seqs, conv_input->nb[1],
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conv_input->nb[2], (conv_input->ne[0] - conv_states->ne[0]) * ggml_element_size(conv_input));
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cb(last_conv_states, "last_conv_states", il);
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ggml_tensor * state_update_target =
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ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
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kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
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cb(state_update_target, "state_update_target", il);
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target));
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ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs);
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state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs);
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cb(state, "state_predelta", il);
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ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel);
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cb(conv_output_proper, "conv_output_raw", il);
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ggml_tensor * conv_output_silu = ggml_silu(ctx0, conv_output_proper);
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cb(conv_output_silu, "conv_output_silu", il);
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ggml_tensor * conv_qkv_mix = conv_output_silu;
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// Calculate the total conv dimension
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int64_t qkv_dim = head_k_dim * num_k_heads * 2 + head_v_dim * num_v_heads;
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int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim);
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// Extract the convolved Q, K, V from conv_output
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ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
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ggml_row_size(conv_qkv_mix->type, head_k_dim),
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nb1_qkv,
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nb1_qkv * n_seq_tokens,
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0);
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ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs,
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ggml_row_size(conv_qkv_mix->type, head_k_dim),
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nb1_qkv,
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nb1_qkv * n_seq_tokens,
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head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
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ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs,
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ggml_row_size(conv_qkv_mix->type, head_v_dim),
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nb1_qkv,
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nb1_qkv * n_seq_tokens,
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ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads));
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cb(q_conv, "q_conv", il);
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cb(k_conv, "k_conv", il);
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cb(v_conv, "v_conv", il);
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const float eps_norm = hparams.f_norm_rms_eps;
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q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm);
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k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm);
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//q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
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//k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs);
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//v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
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// if head keys and value keys are different, repeat to force tensors into matching shapes
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// note: need explicit repeat only if we are not using the fused GDN
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if (num_k_heads != num_v_heads && (!cparams.fused_gdn_ar || !cparams.fused_gdn_ch)) {
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GGML_ASSERT(num_v_heads % num_k_heads == 0);
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q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
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k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs);
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}
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cb(q_conv, "q_conv_predelta", il);
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cb(k_conv, "k_conv_predelta", il);
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cb(v_conv, "v_conv_predelta", il);
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std::pair<ggml_tensor *, ggml_tensor *> attn_out;
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if (n_seq_tokens == 1) {
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attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
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} else {
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attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il);
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}
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ggml_tensor * output = attn_out.first;
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ggml_tensor * new_state = attn_out.second;
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cb(output, "attn_output", il);
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cb(new_state, "new_state", il);
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// Update the recurrent states
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ggml_build_forward_expand(gf,
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ggml_cpy(ctx0, new_state,
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ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
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kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
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// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]
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ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
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// Apply gated normalization: self.norm(core_attn_out, z)
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ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
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// Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
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ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
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cb(final_output, "final_output", il);
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// Output projection
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cur = build_lora_mm(model.layers[il].ssm_out, final_output);
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cb(cur, "linear_attn_out", il);
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// Reshape back to original dimensions
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cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
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return cur;
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}
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ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int il) {
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// Check if this is an MoE layer
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GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);
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ggml_tensor * moe_out =
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build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
|
|
nullptr,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU, true,
|
|
hparams.expert_weights_scale,
|
|
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
|
|
nullptr, model.layers[il].ffn_gate_up_exps);
|
|
cb(moe_out, "ffn_moe_out", il);
|
|
|
|
// Add shared experts if present - following Qwen3Next reference implementation
|
|
if (model.layers[il].ffn_up_shexp != nullptr) {
|
|
ggml_tensor * ffn_shexp =
|
|
build_ffn(cur,
|
|
model.layers[il].ffn_up_shexp, NULL, NULL,
|
|
model.layers[il].ffn_gate_shexp, NULL, NULL,
|
|
model.layers[il].ffn_down_shexp, NULL, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
|
cb(ffn_shexp, "ffn_shexp", il);
|
|
|
|
// Apply shared expert gating as in the reference implementation
|
|
// The shared expert has its own gate that is sigmoided
|
|
// Note: ffn_gate_inp_shexp is the shared expert gate (outputs 1 value per token)
|
|
ggml_tensor * shared_gate = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
|
|
cb(shared_gate, "shared_expert_gate", il);
|
|
|
|
// Apply sigmoid to the gate
|
|
shared_gate = ggml_sigmoid(ctx0, shared_gate);
|
|
cb(shared_gate, "shared_expert_gate_sigmoid", il);
|
|
|
|
|
|
// Apply the gate to the shared expert output
|
|
ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate);
|
|
cb(ffn_shexp, "ffn_shexp_gated", il);
|
|
|
|
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
|
cb(cur, "ffn_out", il);
|
|
} else {
|
|
cur = moe_out;
|
|
}
|
|
|
|
return cur;
|
|
}
|