#include "models.h"

void llama_model_plm::load_arch_hparams(llama_model_loader & ml) {

ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);

switch (hparams.n_layer) {

case 32: type = LLM_TYPE_1_8B; break;

default: type = LLM_TYPE_UNKNOWN;

}

}

void llama_model_plm::load_arch_tensors(llama_model_loader &) {

LLAMA_LOAD_LOCALS;

const int64_t n_embd_head_qk_rope = hparams.n_rot();

const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();

const int64_t kv_lora_rank = hparams.n_lora_kv;

tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

// output

output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

// output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);

output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);

for (int i = 0; i < n_layer; ++i) {

auto & layer = layers[i];

layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);

layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);

layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);

layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);

layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0);

layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);

layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);

}

}

std::unique_ptr<llm_graph_context> llama_model_plm::build_arch_graph(const llm_graph_params & params) const {

return std::make_unique<graph>(*this, params);

}

llama_model_plm::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {

const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k()));

const uint32_t n_embd_head_qk_rope = hparams.n_rot();

const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();

const uint32_t kv_lora_rank = hparams.n_lora_kv;

ggml_tensor * cur;

ggml_tensor * inpL;

// {n_embd, n_tokens}

inpL = build_inp_embd(model.tok_embd);

// inp_pos - contains the positions

ggml_tensor * inp_pos = build_inp_pos();

auto * inp_attn = build_attn_inp_kv();

ggml_tensor * inp_out_ids = build_inp_out_ids();

for (int il = 0; il < n_layer; ++il) {

ggml_tensor * inpSA = inpL;

// norm

cur = build_norm(inpL,

model.layers[il].attn_norm, NULL,

LLM_NORM_RMS, il);

cb(cur, "attn_norm", il);

// self_attention

{

ggml_tensor * q = NULL;

q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);

cb(q, "q", il);

// split into {n_head * n_embd_head_qk_nope, n_tokens}

ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,

ggml_row_size(q->type, hparams.n_embd_head_k()),

ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),

0);

cb(q_nope, "q_nope", il);

// and {n_head * n_embd_head_qk_rope, n_tokens}

ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,

ggml_row_size(q->type, hparams.n_embd_head_k()),

ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),

ggml_row_size(q->type, n_embd_head_qk_nope));

cb(q_pe, "q_pe", il);

// {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}

ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);

cb(kv_pe_compresseed, "kv_pe_compresseed", il);

// split into {kv_lora_rank, n_tokens}

ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,

kv_pe_compresseed->nb[1],

0);

cb(kv_compressed, "kv_compressed", il);

// and {n_embd_head_qk_rope, n_tokens}

ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,

kv_pe_compresseed->nb[1],

kv_pe_compresseed->nb[1],

ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));

cb(k_pe, "k_pe", il);

kv_compressed = build_norm(kv_compressed,

model.layers[il].attn_kv_a_norm, NULL,

LLM_NORM_RMS, il);

cb(kv_compressed, "kv_compressed", il);

// {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}

ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);

cb(kv, "kv", il);

// split into {n_head * n_embd_head_qk_nope, n_tokens}

ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,

ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()),

ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())),

0);

cb(k_nope, "k_nope", il);

// and {n_head * n_embd_head_v, n_tokens}

ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens,

ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())),

ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head),

ggml_row_size(kv->type, (n_embd_head_qk_nope)));

cb(v_states, "v_states", il);

v_states = ggml_cont(ctx0, v_states);

cb(v_states, "v_states", il);

v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v() * n_head, n_tokens,

ggml_row_size(kv->type, hparams.n_embd_head_v() * n_head),

0);

cb(v_states, "v_states", il);

q_pe = ggml_rope_ext(

ctx0, q_pe, inp_pos, nullptr,

n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

ext_factor, attn_factor, beta_fast, beta_slow

);

cb(q_pe, "q_pe", il);

// shared RoPE key

k_pe = ggml_rope_ext(

ctx0, k_pe, inp_pos, nullptr,

n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

ext_factor, attn_factor, beta_fast, beta_slow

);

cb(k_pe, "k_pe", il);

ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);

cb(q_states, "q_states", il);

ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);

cb(k_states, "k_states", il);

cur = build_attn(inp_attn,

model.layers[il].wo, NULL, model.layers[il].wo_s,

q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, 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);

cur = build_norm(ffn_inp,

model.layers[il].ffn_norm, NULL,

LLM_NORM_RMS, il);

cb(cur, "ffn_norm", il);

cur = build_ffn(cur,

model.layers[il].ffn_up, NULL, NULL,

NULL, NULL, NULL,

model.layers[il].ffn_down, NULL, NULL,

NULL,

LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);

cb(cur, "ffn_out", 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;

cur = build_lora_mm(model.output, cur, model.output_s);

cb(cur, "result_output", -1);

res->t_logits = cur;

ggml_build_forward_expand(gf, cur);

}