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https://github.com/ggerganov/llama.cpp.git
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update kqv code
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okada
parent
ca8f698638
commit
febc63598b
185
llama.cpp
185
llama.cpp
@ -5520,6 +5520,10 @@ struct llm_build_context {
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inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
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cb(inpL, "inp_embd", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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cb(inp_pos, "inp_pos", -1);
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// KQ_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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cb(KQ_scale, "KQ_scale", -1);
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@ -5528,10 +5532,6 @@ struct llm_build_context {
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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cb(KQ_mask, "KQ_mask", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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cb(inp_pos, "inp_pos", -1);
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
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@ -5544,137 +5544,104 @@ struct llm_build_context {
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attention_norm_0", il);
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cb(cur, "attention_norm", il);
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struct ggml_tensor * attention_norm = cur;
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(tmpk, "tmpk", il);
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(tmpq, "tmpq", il);
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struct ggml_tensor * Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
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n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos,
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(Qcur, "Qcur", il);
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// store key and value to memory
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{
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// compute the transposed [n_tokens, n_embd] V matrix
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Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(tmpv, "tmpv", il);
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llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
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cb(Vcur, "Vcur", il);
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auto plamo_llm_build_kqv = [](
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struct ggml_context * ctx,
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const llama_hparams & hparams,
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const llama_kv_cache & kv,
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struct ggml_tensor * wo,
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struct ggml_tensor * q_cur,
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struct ggml_tensor * kq_mask,
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int64_t n_ctx,
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int32_t n_tokens,
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int32_t n_kv,
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const llm_build_cb & cb,
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int il) {
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_head_kv = hparams.n_head_kv;
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const int64_t n_embd_head = hparams.n_embd_head();
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const int64_t n_embd_gqa = hparams.n_embd_gqa();
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//struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k_l[il], n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k_l[il])*n_embd_gqa)*(il*n_ctx + kv_head));
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struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k_l[il], n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k_l[il])*n_embd_gqa)*kv_head);
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struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
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cb(q, "q", il);
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struct ggml_tensor * k =
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ggml_view_3d(ctx, kv.k_l[il],
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n_embd_head, n_kv, n_head_kv,
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ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
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ggml_row_size(kv.k_l[il]->type, n_embd_head),
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0);
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cb(k, "k", il);
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/*
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struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
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( n_ctx)*ggml_element_size(kv_self.v),
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(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
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*/
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struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v_l[il], n_tokens, n_embd_gqa,
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n_ctx*ggml_element_size(kv_self.v_l[il]),
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kv_head*ggml_element_size(kv_self.v_l[il]));
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// we should avoid to repeat K but current ggml_mul_mat generates wrong values for grouped query att
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struct ggml_tensor * k_repeated = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, k->ne[0], k->ne[1], q->ne[2]);
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cb(k_repeated, "k_repeated", il);
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struct ggml_tensor * kq = ggml_mul_mat(ctx, ggml_repeat(ctx, k, k_repeated), q);
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cb(kq, "kq", il);
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kq = ggml_soft_max_ext(ctx, kq, kq_mask, 1.0f/sqrtf(float(n_embd_head)));
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cb(kq, "kq_soft_max_ext", il);
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// split cached v into n_head heads
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struct ggml_tensor * v =
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ggml_view_3d(ctx, kv.v_l[il],
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n_kv, n_embd_head, n_head_kv,
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ggml_element_size(kv.v_l[il])*n_ctx,
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ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head,
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0);
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cb(v, "v", il);
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// important: storing RoPE-ed version of K in the KV cache!
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
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}
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// we should avoid to repeat V but current ggml_mul_mat generates wrong values for grouped query att
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struct ggml_tensor * v_repeated = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, v->ne[0], v->ne[1], q->ne[2]);
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cb(k_repeated, "v_repeated", il);
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struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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cb(Q, "Q", il);
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struct ggml_tensor * kqv = ggml_mul_mat(ctx, ggml_repeat(ctx, v, v_repeated), kq);
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cb(kqv, "kqv", il);
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/*
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struct ggml_tensor * K =
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ggml_view_3d(ctx0, kv_self.k,
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n_embd_head, n_kv, n_head_kv,
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ggml_element_size(kv_self.k)*n_embd_gqa,
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ggml_element_size(kv_self.k)*n_embd_head,
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ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
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*/
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struct ggml_tensor * K =
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ggml_view_3d(ctx0, kv_self.k_l[il],
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n_embd_head, n_kv, n_head_kv,
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ggml_element_size(kv_self.k_l[il])*n_embd_gqa,
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ggml_element_size(kv_self.k_l[il])*n_embd_head,
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0);
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cb(K, "K", il);
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struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
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cb(kqv_merged, "kqv_merged", il);
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// K * Q
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//struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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// we should avoid to repeat K but current ggml_mul_mat generates wrong values for grouped query att
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struct ggml_tensor * K_repeated = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, K->ne[0], K->ne[1], Q->ne[2]);
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cb(K_repeated, "K_repeated", il);
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, ggml_repeat(ctx0, K, K_repeated), Q);
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cb(KQ, "KQ", il);
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struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens);
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cb(cur, "kqv_merged_cont", il);
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// KQ_scaled = KQ / sqrt(n_embd_head)
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// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
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struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
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cb(KQ_scaled, "KQ_scaled", il);
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cur = ggml_mul_mat(ctx, wo, cur);
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return cur;
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};
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
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cb(KQ_masked, "KQ_masked", il);
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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cb(KQ_soft_max, "KQ_soft_max", il);
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// split cached V into n_head heads
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/*
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v,
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n_kv, n_embd_head, n_head_kv,
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ggml_element_size(kv_self.v)*n_ctx,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
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*/
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v_l[il],
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n_kv, n_embd_head, n_head_kv,
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ggml_element_size(kv_self.v_l[il])*n_ctx,
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ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head,
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0);
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cb(V, "V", il);
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//struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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// we should avoid to repeat V but current ggml_mul_mat generates wrong values for grouped query att
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struct ggml_tensor * V_repeated = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, V->ne[0], V->ne[1], Q->ne[2]);
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cb(V_repeated, "V_repeated", il);
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, ggml_repeat(ctx0, V, V_repeated), KQ_soft_max);
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cb(KQV, "KQV", il);
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// KQV_merged = KQV.permute(0, 2, 1, 3)
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struct 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 = KQV_merged.contiguous().view(n_embd, n_tokens)
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cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
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cb(cur, "KQV_merged_contiguous", il);
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// projection (no bias)
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cur = ggml_mul_mat(ctx0,
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cur = plamo_llm_build_kqv(ctx0, hparams, kv_self,
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model.layers[il].wo,
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cur);
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cb(cur, "result_wo", il);
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Qcur, KQ_mask, n_ctx, n_tokens, n_kv, cb, il);
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cb(cur, "kqv_out", il);
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}
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struct ggml_tensor * sa_out = cur;
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