126 lines
4.9 KiB
C++
126 lines
4.9 KiB
C++
#include "../llama-build-context.h"
|
|
#include "../llama-model.h"
|
|
#include "../llama-context.h"
|
|
|
|
ggml_cgraph * llm_build_context::build_phi3() {
|
|
ggml_cgraph * gf = new_graph_custom();
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v(0);
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask;
|
|
if (hparams.n_swa == 0) {
|
|
// Phi-4 does not use SWA
|
|
KQ_mask = build_inp_KQ_mask();
|
|
}
|
|
else {
|
|
KQ_mask = build_inp_KQ_mask_swa();
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
auto residual = inpL;
|
|
|
|
// self-attention
|
|
{
|
|
// rope freq factors for 128k context
|
|
struct ggml_tensor * rope_factors = build_rope_factors(il);
|
|
|
|
struct ggml_tensor * attn_norm_output = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
|
|
cb(attn_norm_output, "attn_norm", il);
|
|
|
|
struct ggml_tensor * Qcur = nullptr;
|
|
struct ggml_tensor * Kcur = nullptr;
|
|
struct ggml_tensor * Vcur = nullptr;
|
|
|
|
if (model.layers[il].wqkv) {
|
|
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
|
|
cb(cur, "wqkv", il);
|
|
|
|
Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd));
|
|
Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd));
|
|
Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
|
|
}
|
|
else {
|
|
Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
|
|
Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
|
|
Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
|
|
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);
|
|
}
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_ext(
|
|
ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_ext(
|
|
ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, residual);
|
|
residual = cur;
|
|
|
|
// FF
|
|
// special-case: the up and gate tensors are merged into a single tensor
|
|
// TOOD: support into llm_build_ffn
|
|
{
|
|
cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
|
|
model.layers[il].ffn_up, NULL, NULL,
|
|
NULL, NULL, NULL,
|
|
model.layers[il].ffn_down, NULL, NULL,
|
|
NULL,
|
|
LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, residual, cur);
|
|
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|