mirror of
https://github.com/LostRuins/koboldcpp.git
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39bf692af1
* Unified delta net handling * Remove old methods. * Refactor and optimize * Adapt autoregressive version from @ymcki * Change to decay mask approach * Fix bad permute * Qwen 3.5 support * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Further fixes * Use inheritance, remove unneeded conts * Not like this! * Remove ggml.h explicit import * Remove transformers, fix the views * ACTUALLY fix views, make super calls explicit in conversion. * Fix conversion again * Remove extra ggml.h imports --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
421 lines
17 KiB
C++
421 lines
17 KiB
C++
#include "models.h"
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#define CHUNK_SIZE 64
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llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context_delta(params), model(model) {
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build_graph();
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}
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// virtual call in constructor fix
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llm_build_qwen3_5::llm_build_qwen3_5(const llama_model & model, const llm_graph_params & params, defer_graph_build_t /*tag*/) :
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llm_graph_context_delta(params), model(model) {
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}
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void llm_build_qwen3_5::build_graph() {
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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cb(inpL, "model.embed_tokens", -1);
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auto * inp = build_inp_mem_hybrid();
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ggml_tensor * inp_pos = build_inp_pos();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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ggml_tensor * causal_mask =
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ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
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GGML_TRI_TYPE_LOWER);
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ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
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ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
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ggml_build_forward_expand(gf, causal_mask);
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ggml_build_forward_expand(gf, identity);
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ggml_build_forward_expand(gf, diag_mask);
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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if (hparams.is_recurrent(il)) {
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cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
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} else {
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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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cur = ggml_add(ctx0, cur, inpSA);
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cb(cur, "attn_residual", il);
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ggml_tensor * ffn_residual = cur;
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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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cur = build_layer_ffn(attn_post_norm, il);
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cb(cur, "ffn_out", il);
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cur = ggml_add(ctx0, cur, ffn_residual);
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cb(cur, "post_ffn", il);
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inpL = cur;
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}
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cur = inpL;
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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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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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ggml_tensor * llm_build_qwen3_5::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_qwen3_5::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 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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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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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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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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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, 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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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base,
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freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
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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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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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std::pair<ggml_tensor *, ggml_tensor *> llm_build_qwen3_5::build_qkvz(
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ggml_tensor * input,
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int il) {
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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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if (model.layers[il].wqkv) {
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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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// legacy path for combined in_proj_qkvz
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ggml_tensor * mixed_qkvz = build_lora_mm(model.layers[il].ssm_in, input);
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cb(mixed_qkvz, "linear_attn_mixed_qkvz", il);
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int64_t qkvz_new_dim = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
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ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
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int64_t split_sizes_qkvz[4] = {
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head_k_dim,
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head_k_dim,
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head_v_dim * num_v_heads / num_k_heads,
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head_v_dim * num_v_heads / num_k_heads
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};
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ggml_tensor * query =
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ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[0], num_k_heads, n_seq_tokens, n_seqs,
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mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3], 0);
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cb(query, "q", il);
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ggml_tensor * key = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[1], num_k_heads, n_seq_tokens, n_seqs,
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mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
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split_sizes_qkvz[0] * ggml_element_size(mixed_qkvz_reshaped));
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cb(key, "k", il);
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ggml_tensor * value =
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ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[2], num_k_heads, n_seq_tokens, n_seqs,
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mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
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(split_sizes_qkvz[0] + split_sizes_qkvz[1]) * ggml_element_size(mixed_qkvz_reshaped));
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cb(value, "v", il);
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ggml_tensor * z = ggml_view_4d(ctx0, mixed_qkvz_reshaped, split_sizes_qkvz[3], num_k_heads, n_seq_tokens, n_seqs,
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mixed_qkvz_reshaped->nb[1], mixed_qkvz_reshaped->nb[2], mixed_qkvz_reshaped->nb[3],
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(split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * ggml_element_size(mixed_qkvz_reshaped));
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z = ggml_cont(ctx0, z);
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cb(z, "z", il);
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ggml_tensor * query_flat = ggml_reshape_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
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cb(query_flat, "query_flat", il);
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ggml_tensor * key_flat = ggml_reshape_3d(ctx0, key, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
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cb(key_flat, "key_flat", il);
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ggml_tensor * value_flat = ggml_reshape_3d(ctx0, value, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
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cb(value_flat, "value_flat", il);
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ggml_tensor * qkv_mixed = ggml_concat(ctx0, query_flat, key_flat, 0);
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qkv_mixed = ggml_concat(ctx0, qkv_mixed, value_flat, 0);
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cb(qkv_mixed, "qkv_mixed", il);
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return { qkv_mixed, z };
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}
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ggml_tensor * llm_build_qwen3_5::build_layer_attn_linear(
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llm_graph_input_rs * inp,
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ggml_tensor * cur,
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ggml_tensor * causal_mask,
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ggml_tensor * identity,
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ggml_tensor * diag_mask,
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int il) {
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const 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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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 * mixed_ba = build_lora_mm(model.layers[il].ssm_beta_alpha, cur);
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cb(mixed_ba, "linear_attn_mixed_ba", il);
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int64_t ba_new_dim = 2 * num_v_heads / num_k_heads;
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ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
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int64_t split_sizes_ba[2] = {
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num_v_heads / num_k_heads,
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num_v_heads / num_k_heads
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};
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ggml_tensor * b = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[0], num_k_heads, n_seq_tokens, n_seqs,
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mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3], 0);
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cb(b, "b", il);
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ggml_tensor * a = ggml_view_4d(ctx0, mixed_ba_reshaped, split_sizes_ba[1], num_k_heads, n_seq_tokens, n_seqs,
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mixed_ba_reshaped->nb[1], mixed_ba_reshaped->nb[2], mixed_ba_reshaped->nb[3],
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split_sizes_ba[0] * ggml_element_size(mixed_ba_reshaped));
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cb(a, "a", il);
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ggml_tensor * beta = ggml_cont_4d(ctx0, b, num_v_heads, 1, n_seq_tokens, n_seqs);
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ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
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ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
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ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
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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);
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cb(gate, "gate", il);
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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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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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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_permute(ctx0, qkv_mixed, 1, 0, 2, 3);
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cb(qkv_mixed, "qkv_mixed_permuted", 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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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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cb(conv_states_all, "conv_states_updated", 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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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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ggml_tensor * q_conv =
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ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0);
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cb(q_conv, "q_conv", il);
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ggml_tensor * k_conv =
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ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv,
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head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
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cb(k_conv, "k_conv", il);
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ggml_tensor * v_conv =
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ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv,
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2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix));
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cb(v_conv, "v_conv", il);
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|
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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);
|
|
|
|
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);
|
|
|
|
if (num_k_heads != num_v_heads) {
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|
GGML_ASSERT(num_v_heads % num_k_heads == 0);
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|
int64_t repeat_factor = num_v_heads / num_k_heads;
|
|
|
|
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);
|
|
|
|
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);
|
|
|
|
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);
|
|
|
|
std::pair<ggml_tensor *, ggml_tensor *> attn_out = build_delta_net_unified(ctx0, q_conv, k_conv, v_conv,
|
|
gate, beta, state, causal_mask, identity, diag_mask,
|
|
il, CHUNK_SIZE, hparams.f_norm_rms_eps);
|
|
|
|
ggml_tensor * output = attn_out.first;
|
|
ggml_tensor * new_state = attn_out.second;
|
|
cb(output, "attn_output", il);
|
|
cb(new_state, "new_state", il);
|
|
|
|
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))));
|
|
|
|
ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
|
|
|
|
ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs);
|
|
|
|
ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il);
|
|
|
|
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);
|
|
|
|
cur = build_lora_mm(model.layers[il].ssm_out, final_output);
|
|
cb(cur, "linear_attn_out", il);
|
|
|
|
cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
|
|
return cur;
|
|
}
|
|
|
|
ggml_tensor * llm_build_qwen3_5::build_layer_ffn(ggml_tensor * cur, const int il) {
|
|
// Qwen3.5 Dense always uses dense FFN
|
|
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;
|
|
}
|