#include "models.h"
void llama_model_t5::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_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
uint32_t dec_start_token_id;
if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
hparams.dec_start_token_id = dec_start_token_id;
}
hparams.dec_n_layer = hparams.n_layer;
ml.get_key(LLM_KV_DECODER_BLOCK_COUNT, hparams.dec_n_layer, false);
switch (hparams.n_layer) {
case 6: type = LLM_TYPE_60M; break; // t5-small
case 8: type = LLM_TYPE_80M; break; // flan-t5-small
case 12:
switch (hparams.n_ff()) {
case 3072: type = LLM_TYPE_220M; break; // t5-base
case 2048: type = LLM_TYPE_250M; break; // flan-t5-base
default: type = LLM_TYPE_UNKNOWN;
} break;
case 24:
switch (hparams.n_ff()) {
case 4096: type = LLM_TYPE_770M; break; // t5-large
case 2816: type = LLM_TYPE_780M; break; // flan-t5-large
case 16384: type = LLM_TYPE_3B; break; // t5-3b
case 5120: type = LLM_TYPE_3B; break; // flan-t5-xl
case 65536: type = LLM_TYPE_11B; break; // t5-11b
case 10240: type = LLM_TYPE_11B; break; // flan-t5-xxl
default: type = LLM_TYPE_UNKNOWN;
} break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_t5::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
// n_layer: number of encoder_layers
// dec_n_layer: number of decoder_layers
const int dec_n_layer = hparams.dec_n_layer;
if (dec_n_layer > n_layer) {
layers.resize(dec_n_layer);
}
// load encoder layers
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
// load decoder layers
for (int i = 0; i < dec_n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
layer.attn_norm_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}, 0);
// this tensor seems to be unused in HF transformers implementation
layer.attn_rel_b_cross = create_tensor(
tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_t5::build_arch_graph(const llm_graph_params & params) const {
switch (params.gtype) {
case LLM_GRAPH_TYPE_ENCODER:
return std::make_unique<graph<true>>(*this, params);
case LLM_GRAPH_TYPE_DEFAULT:
case LLM_GRAPH_TYPE_DECODER:
return std::make_unique<graph<false>>(*this, params);
default:
GGML_ABORT("invalid graph type");
};
}
template <>
llama_model_t5::graph<false>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
//const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
ggml_tensor * embd_enc = build_inp_cross_embd();
ggml_tensor * pos_bucket_dec = build_inp_pos_bucket_dec();
const int64_t n_outputs_enc = embd_enc->ne[1];
auto * inp_attn_self = build_attn_inp_kv();
auto * inp_attn_cross = build_attn_inp_cross();
ggml_tensor * inp_out_ids = build_inp_out_ids();
const int64_t dec_n_layer = hparams.dec_n_layer;
for (int il = 0; il < dec_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
{
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);
ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b);
cur = build_attn(inp_attn_self,
model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
cb(cur, "kqv_out", il);
}
cur = ggml_add(ctx0, cur, inpSA);
cb(cur, "cross_inp", il);
ggml_tensor * inpCA = cur;
// norm
cur = build_norm(cur,
model.layers[il].attn_norm_cross, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm_cross", il);
// cross-attention
{
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_cross, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_cross, embd_enc);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_cross, embd_enc);
cb(Vcur, "Vcur", 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_outputs_enc);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_outputs_enc);
cur = build_attn(inp_attn_cross,
model.layers[il].wo_cross, nullptr, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
cb(cur, "kqv_out", il);
//ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
//ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
//ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
//cb(kq, "kq", il);
//kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
//cb(kq, "kq_soft_max_ext", il);
//ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
//cb(v, "v", il);
//ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
//cb(kqv, "kqv", il);
//ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
//cb(kqv_merged, "kqv_merged", il);
//cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
//cb(cur, "kqv_merged_cont", il);
//ggml_build_forward_expand(gf, cur);
//cur = build_lora_mm(model.layers[il].wo_cross, cur);
//cb(cur, "kqv_out", il);
}
if (il == dec_n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
// T5 uses relu, flan-T5 uses gelu-gated
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,
model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cb(cur, "result_embd", -1);
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);
}
template <>
llama_model_t5::graph<true>::graph(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);
ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();
auto * inp_attn = build_attn_inp_no_cache();
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_enc, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_enc, cur);
cb(Vcur, "Vcur", 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);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);
cur = build_attn(inp_attn,
model.layers[il].wo_enc, nullptr, nullptr,
Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
cb(cur, "kqv_out", 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);
// feed-forward network
{
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm_enc, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
// T5 uses relu, flan-T5 uses gelu-gated
cur = build_ffn(cur,
model.layers[il].ffn_up_enc, NULL, NULL,
model.layers[il].ffn_gate_enc, NULL, NULL,
model.layers[il].ffn_down_enc, NULL, NULL,
NULL,
model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cb(cur, "result_embd", -1);
cur = build_norm(cur,
model.output_norm_enc, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
ggml_build_forward_expand(gf, cur);
}
