188 lines
6.9 KiB
C++
188 lines
6.9 KiB
C++
#include "models.h"
|
|
|
|
llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
ggml_tensor * cur;
|
|
ggml_tensor * inpL;
|
|
|
|
inpL = build_inp_embd(model.tok_embd);
|
|
|
|
// MuP scaling: embeddings * sqrt(hidden_size)
|
|
// mup_enabled = true, hidden_size = 1024, scale = 32.0
|
|
inpL = ggml_scale(ctx0, inpL, sqrtf(float(n_embd)));
|
|
cb(inpL, "inp_embd_scaled", -1);
|
|
|
|
// inp_pos - contains the positions
|
|
ggml_tensor * inp_pos = build_inp_pos();
|
|
auto * inp_attn = build_attn_inp_kv_iswa();
|
|
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
|
|
const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_tensor * inpSA = inpL;
|
|
|
|
// dual attention normalization (pre)
|
|
cur = build_norm(inpL,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
ggml_tensor * attn_inp = cur; // save input for gate computation
|
|
|
|
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", 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);
|
|
|
|
// compute gate from input
|
|
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
|
|
cb(gate, "attn_gate_proj", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
// Q/K normalization
|
|
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
|
|
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
|
|
cb(Qcur, "Qcur_normed", il);
|
|
cb(Kcur, "Kcur_normed", il);
|
|
|
|
// RoPE only for sliding_attention layers
|
|
const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
|
|
((il + 1) % hparams.n_no_rope_layer_step) != 0;
|
|
if (use_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);
|
|
cb(Qcur, "Qcur_rope", il);
|
|
|
|
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(Kcur, "Kcur_rope", il);
|
|
}
|
|
|
|
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
cur = build_attn(inp_attn,
|
|
NULL, NULL, // wo will be applied after gating
|
|
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
|
cb(cur, "attn_out", il);
|
|
|
|
// attention gating: attn_out * sigmoid(gate) BEFORE o_proj
|
|
gate = ggml_sigmoid(ctx0, gate);
|
|
cb(gate, "attn_gate_sig", il);
|
|
cur = ggml_mul(ctx0, cur, gate);
|
|
cb(cur, "attn_gated", il);
|
|
|
|
// now apply output projection
|
|
cur = build_lora_mm(model.layers[il].wo, cur);
|
|
cb(cur, "attn_o_proj", il);
|
|
}
|
|
|
|
// dual attention normalization (post)
|
|
cur = build_norm(cur,
|
|
model.layers[il].attn_post_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "attn_post_norm", il);
|
|
|
|
if (il == n_layer - 1 && inp_out_ids) {
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// dual ffn normalization (pre)
|
|
cur = build_norm(ffn_inp,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
// MoE or dense FFN
|
|
if ((uint32_t)il >= hparams.n_layer_dense_lead) {
|
|
// MoE layer with sigmoid routing, normalization, and scaling
|
|
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,
|
|
model.layers[il].ffn_exp_probs_b,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU,
|
|
hparams.expert_weights_norm, // norm_w (route_norm=True)
|
|
hparams.expert_weights_scale, // scale_w
|
|
hparams.expert_weights_scale, // w_scale (route_scale=2.826)
|
|
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
|
il);
|
|
cb(moe_out, "ffn_moe_out", il);
|
|
|
|
// shared expert
|
|
if (hparams.n_expert_shared > 0) {
|
|
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);
|
|
|
|
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
|
cb(cur, "ffn_out", il);
|
|
} else {
|
|
cur = moe_out;
|
|
}
|
|
} else {
|
|
// dense layer
|
|
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);
|
|
}
|
|
|
|
// dual ffn normalization (post)
|
|
cur = build_norm(cur,
|
|
model.layers[il].ffn_post_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "ffn_post_norm", il);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cur = build_cvec(cur, il);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = build_norm(cur,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
res->t_embd = cur;
|
|
|
|
// lm_head
|
|
cur = build_lora_mm(model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
res->t_logits = cur;
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
}
|