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