122 lines
4.0 KiB
C++
122 lines
4.0 KiB
C++
#include "models.h"
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llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context_mamba(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_build_forward_expand(gf, inpL);
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auto * inp = build_inp_mem_hybrid();
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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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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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if (hparams.is_recurrent(il)) {
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// ssm layer //
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cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
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} else if (hparams.n_ff(il) == 0) {
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// attention layer //
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cur = build_attention_layer(cur, inp->get_attn(), model, n_embd_head, il);
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} else {
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cur = build_ffn_layer(cur, model, 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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// add residual
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cur = ggml_add(ctx0, cur, inpSA);
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cb(cur, "nemotron_h_block_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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cur = build_norm(cur, model.output_norm, NULL, 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);
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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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ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor * cur,
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llm_graph_input_attn_kv * inp_attn,
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const llama_model & model,
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const int64_t n_embd_head,
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const int il) {
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// compute Q and K and (optionally) RoPE them
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, hparams.n_head(il), n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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const float kq_scale =
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hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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cur = build_attn(inp_attn,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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cb(cur, "attn_out", il);
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return cur;
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}
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ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, const int il) {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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NULL, NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL, LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
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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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return cur;
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}
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