212 lines
6.6 KiB
C++
212 lines
6.6 KiB
C++
#include "models.h"
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llm_build_granite::llm_build_granite(
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const llama_model & model,
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const llm_graph_params & params)
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: 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_ASSERT(n_embd_head == hparams.n_rot);
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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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// inp_pos - built only if rope enabled
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ggml_tensor * inp_pos = nullptr;
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if (hparams.rope_finetuned) {
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inp_pos = build_inp_pos();
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}
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auto * inp_attn = build_attn_inp_kv();
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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, 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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cur = build_attention_layer(
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cur, inp_pos, inp_attn,
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model, n_embd_head, il);
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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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// ffn
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cur = build_layer_ffn(cur, inpSA, model, 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,
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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);
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// For Granite architectures - scale logits
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cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
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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_granite::build_attention_layer(
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ggml_tensor * cur,
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ggml_tensor * inp_pos,
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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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const bool use_rope = hparams.rope_finetuned;
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if (use_rope) {
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ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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}
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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 = 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_granite::build_layer_ffn(
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ggml_tensor * cur,
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ggml_tensor * inpSA,
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const llama_model & model,
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const int il) {
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// For Granite architectures - scale residual
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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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 (non-MoE)
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if (model.layers[il].ffn_gate_inp == nullptr) {
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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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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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// MoE branch
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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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ggml_tensor * moe_out = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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false, 0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il);
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cb(moe_out, "ffn_moe_out", il);
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// For Granite MoE Shared
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if (hparams.n_ff_shexp > 0) {
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ggml_tensor * ffn_shexp = build_ffn(cur,
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model.layers[il].ffn_up_shexp, NULL, NULL,
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model.layers[il].ffn_gate_shexp, NULL, NULL,
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model.layers[il].ffn_down_shexp, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(ffn_shexp, "ffn_shexp", il);
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cur = ggml_add(ctx0, moe_out, ffn_shexp);
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cb(cur, "ffn_out", il);
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} else {
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cur = moe_out;
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}
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}
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// For Granite architectures - scale residual
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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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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return cur;
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}
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