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https://github.com/ggml-org/llama.cpp.git
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bea04522ff
* Sqashed: llama-model.cpp refactoring * Fix formatting of attn / ffn / ffn_moe calls * Fix import regression / unify spacing in models.h * totally DID NOT miss those! * Add missing qwen3vl(moe) models * Add missing new .cpp files to build * Remove extra semicolons * Editor checker * Update src/models/models.h Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
237 lines
10 KiB
C++
237 lines
10 KiB
C++
#include "models.h"
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llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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bool is_lite = (hparams.n_layer == 27);
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const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0);
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// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
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const int64_t n_embd_head_k = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k;
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const int64_t n_embd_head_v = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v;
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const int64_t n_embd_head_qk_rope = hparams.n_rot;
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const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
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const uint32_t kv_lora_rank = hparams.n_lora_kv;
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// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
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// See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
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const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
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const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
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const float attn_factor = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
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ggml_tensor * cur;
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ggml_tensor * inpL;
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// {n_embd, n_tokens}
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inpL = build_inp_embd(model.tok_embd);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv();
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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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// norm
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self_attention
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{
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ggml_tensor * q = NULL;
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if (!is_lite) {
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q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
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cb(q, "q", il);
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q = build_norm(q, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
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cb(q, "q", il);
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q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
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cb(q, "q", il);
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} else {
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q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(q, "q", il);
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}
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// split into {n_embd_head_qk_nope, n_head, n_tokens}
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ggml_tensor * q_nope =
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ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
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ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
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cb(q_nope, "q_nope", il);
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// and {n_embd_head_qk_rope, n_head, n_tokens}
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ggml_tensor * q_pe = ggml_view_3d(
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ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
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ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
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cb(q_pe, "q_pe", il);
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ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
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cb(kv_cmpr_pe, "kv_cmpr_pe", il);
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// split into {kv_lora_rank, n_tokens}
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ggml_tensor * kv_cmpr =
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ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
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ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
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cb(kv_cmpr, "kv_cmpr", il);
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// and {n_embd_head_qk_rope, 1, n_tokens}
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ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
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ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
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ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
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ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
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cb(k_pe, "k_pe", il);
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q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, 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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cb(q_pe, "q_pe", il);
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k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, 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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cb(k_pe, "k_pe", il);
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kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
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cb(kv_cmpr, "kv_cmpr", il);
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if (is_mla) {
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// {n_embd_head_qk_nope, n_tokens, n_head}
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q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
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cb(q_nope, "q_nope_perm", il);
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// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
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ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
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cb(q_nope_absorbed, "q_nope_absorbed", il);
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// {kv_lora_rank, n_head, n_tokens}
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q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
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cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
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// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
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// note: rope must go first for in-place context shifting in build_rope_shift()
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ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope_absorbed, 0);
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cb(Qcur, "Qcur", il);
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kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
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cb(kv_cmpr, "kv_cmpr_reshape", il);
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// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
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ggml_tensor * Kcur = ggml_concat(ctx0, k_pe, kv_cmpr, 0);
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cb(Kcur, "Kcur", il);
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// {kv_lora_rank, 1, n_tokens}
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ggml_tensor * Vcur = kv_cmpr;
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cb(Vcur, "Vcur", il);
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// note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group)
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cur = build_attn(inp_attn,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
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} else {
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ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr);
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cb(kv, "kv", il);
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// split into {n_embd_head_qk_nope, n_head, n_tokens}
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ggml_tensor * k_nope =
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ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
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ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
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ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head, 0);
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cb(k_nope, "k_nope_view", il);
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// and {n_embd_head_v, n_head, n_tokens}
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ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v, n_head, n_tokens,
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ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
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ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head,
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ggml_row_size(kv->type, n_embd_head_qk_nope));
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cb(Vcur, "Vcur_view", il);
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Vcur = ggml_cont(ctx0, Vcur);
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cb(Vcur, "Vcur_cont", il);
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// note: rope must go first for in-place context shifting in build_rope_shift()
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ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope, 0);
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cb(Qcur, "Qcur", il);
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ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
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cb(Kcur, "Kcur", il);
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// note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
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cur = build_attn(inp_attn,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, 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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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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if ((uint32_t) il < hparams.n_layer_dense_lead) {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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// MoE branch
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ggml_tensor * moe_out = 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,
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model.layers[il].ffn_exp_probs_b,
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n_expert, n_expert_used,
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LLM_FFN_SILU, hparams.expert_weights_norm,
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true, hparams.expert_weights_scale,
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(llama_expert_gating_func_type) hparams.expert_gating_func,
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il);
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cb(moe_out, "ffn_moe_out", il);
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// FFN shared expert
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{
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ggml_tensor * ffn_shexp =
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build_ffn(cur,
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model.layers[il].ffn_up_shexp, NULL, NULL,
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model.layers[il].ffn_gate_shexp, NULL, NULL,
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model.layers[il].ffn_down_shexp, NULL, NULL,
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NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(ffn_shexp, "ffn_shexp", il);
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cur = ggml_add(ctx0, moe_out, ffn_shexp);
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cb(cur, "ffn_out", il);
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}
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", 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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cur = build_norm(cur, model.output_norm, NULL, 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 = ggml_mul_mat(ctx0, 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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