llama : fix rwkv inference (#11618)
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
parent
74b0807245
commit
1eca8916b5
3 changed files with 409 additions and 349 deletions
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@ -1970,6 +1970,228 @@ ggml_tensor * llama_context::build_mamba_layer(
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}
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ggml_tensor * llama_context::build_rwkv_token_shift_load(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) {
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const auto & hparams = model.hparams;
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const auto token_shift_count = hparams.token_shift_count;
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const auto & n_tokens = ubatch.n_tokens;
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const int64_t n_seqs = ubatch.n_seqs;
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struct ggml_tensor * token_shift_all = kv_self.k_l[il];
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struct ggml_tensor * token_shift = build_copy_mask_state(
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ctx0, graph, token_shift_all, state_copy, state_mask,
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n_tokens, hparams.n_embd_k_s(), n_seqs, worst_case);
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token_shift = ggml_reshape_3d(ctx0, token_shift, hparams.n_embd, token_shift_count, n_seqs);
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return token_shift;
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}
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ggml_tensor * llama_context::build_rwkv_token_shift_store(
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ggml_context * ctx0,
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ggml_tensor * token_shift,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) {
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const auto & hparams = model.hparams;
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const auto token_shift_count = hparams.token_shift_count;
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const auto n_embd = hparams.n_embd;
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const auto & n_tokens = ubatch.n_tokens;
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const int64_t n_seqs = ubatch.n_seqs;
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const auto kv_head = worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head;
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return ggml_cpy(
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ctx0,
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ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * token_shift_count, 0),
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ggml_view_1d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self.k_l[il]))
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);
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}
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ggml_tensor * llama_context::build_rwkv6_time_mix(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case) {
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const auto & hparams = model.hparams;
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const auto n_tokens = ubatch.n_tokens;
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const auto n_seqs = ubatch.n_seqs;
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const auto n_embd = hparams.n_embd;
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const auto head_size = hparams.wkv_head_size;
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const auto n_head = n_embd / head_size;
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const auto n_head_kv = hparams.n_head_kv(il);
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const auto kv_head = worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head;
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const auto layer = &model.layers[il];
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bool is_qrwkv = layer->time_mix_first == nullptr;
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struct ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
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struct ggml_tensor * xxx = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->time_mix_lerp_x), cur);
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xxx = ggml_reshape_4d(
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ctx0,
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ggml_tanh(
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ctx0,
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ggml_mul_mat(ctx0, layer->time_mix_w1, xxx)
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),
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layer->time_mix_w1->ne[1] / 5, 1, 5, n_tokens
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);
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xxx = ggml_cont(ctx0, ggml_permute(ctx0, xxx, 0, 1, 3, 2));
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xxx = ggml_mul_mat(
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ctx0,
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ggml_reshape_4d(
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ctx0,
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layer->time_mix_w2,
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layer->time_mix_w2->ne[0], layer->time_mix_w2->ne[1], 1, 5
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),
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xxx
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);
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struct ggml_tensor *xw, *xk, *xv, *xr, *xg;
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if (layer->time_mix_lerp_fused) {
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// fusing these weights makes some performance improvement
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sx = ggml_reshape_3d(ctx0, sx, n_embd, 1, n_tokens);
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cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
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xxx = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xxx, layer->time_mix_lerp_fused), sx), cur);
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xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
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xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
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xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
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xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
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xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
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} else {
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// for backward compatibility
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xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
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xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
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xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
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xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
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xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
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xw = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xw, layer->time_mix_lerp_w), sx), cur);
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xk = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xk, layer->time_mix_lerp_k), sx), cur);
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xv = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xv, layer->time_mix_lerp_v), sx), cur);
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xr = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xr, layer->time_mix_lerp_r), sx), cur);
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xg = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xg, layer->time_mix_lerp_g), sx), cur);
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}
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struct ggml_tensor * r = build_lora_mm(ctx0, layer->time_mix_receptance, xr);
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struct ggml_tensor * k = build_lora_mm(ctx0, layer->time_mix_key, xk);
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struct ggml_tensor * v = build_lora_mm(ctx0, layer->time_mix_value, xv);
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if (layer->time_mix_receptance_b) {
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r = ggml_add(ctx0, r, layer->time_mix_receptance_b);
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}
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if (layer->time_mix_key_b) {
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k = ggml_add(ctx0, k, layer->time_mix_key_b);
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}
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if (layer->time_mix_value_b) {
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v = ggml_add(ctx0, v, layer->time_mix_value_b);
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}
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struct ggml_tensor * g = build_lora_mm(ctx0, layer->time_mix_gate, xg);
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if (is_qrwkv) {
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g = ggml_sigmoid(ctx0, g);
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} else {
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g = ggml_silu(ctx0, g);
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}
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if (n_head_kv != 0 && n_head_kv != n_head) {
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GGML_ASSERT(n_head % n_head_kv == 0);
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k = ggml_reshape_4d(ctx0, k, head_size, 1, n_head_kv, n_tokens);
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v = ggml_reshape_4d(ctx0, v, head_size, 1, n_head_kv, n_tokens);
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struct ggml_tensor * tmp = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, head_size, n_head / n_head_kv, n_head_kv, n_tokens);
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k = ggml_repeat(ctx0, k, tmp);
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v = ggml_repeat(ctx0, v, tmp);
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}
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k = ggml_reshape_3d(ctx0, k, head_size, n_head, n_tokens);
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v = ggml_reshape_3d(ctx0, v, head_size, n_head, n_tokens);
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r = ggml_reshape_3d(ctx0, r, head_size, n_head, n_tokens);
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struct ggml_tensor * w = ggml_mul_mat(
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ctx0,
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layer->time_mix_decay_w2,
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ggml_tanh(
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ctx0,
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ggml_mul_mat(ctx0, layer->time_mix_decay_w1, xw)
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)
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);
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w = ggml_add(ctx0, w, layer->time_mix_decay);
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w = ggml_exp(ctx0, ggml_neg(ctx0, ggml_exp(ctx0, w)));
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w = ggml_reshape_3d(ctx0, w, head_size, n_head, n_tokens);
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if (is_qrwkv) {
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// k = k * (1 - w)
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k = ggml_sub(ctx0, k, ggml_mul(ctx0, k, w));
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}
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struct ggml_tensor * wkv_state = build_copy_mask_state(
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ctx0, graph, kv_self.v_l[il], state_copy, state_mask,
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n_tokens, hparams.n_embd_v_s(), n_seqs, worst_case);
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struct ggml_tensor * wkv_output;
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if (is_qrwkv) {
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wkv_output = ggml_gated_linear_attn(ctx0, k, v, r, w, wkv_state, pow(head_size, -0.5f));
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} else {
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wkv_output = ggml_rwkv_wkv6(ctx0, k, v, r, layer->time_mix_first, w, wkv_state);
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}
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cur = ggml_view_1d(ctx0, wkv_output, n_embd * n_tokens, 0);
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wkv_state = ggml_view_1d(ctx0, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
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ggml_build_forward_expand(
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graph,
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ggml_cpy(
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ctx0,
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wkv_state,
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ggml_view_1d(
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ctx0,
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kv_self.v_l[il],
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hparams.n_embd_v_s() * n_seqs,
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hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
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)
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)
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);
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if (!is_qrwkv) {
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// group norm with head_count groups
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cur = ggml_reshape_3d(ctx0, cur, n_embd / n_head, n_head, n_tokens);
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cur = ggml_norm(ctx0, cur, 64e-5f);
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// Convert back to regular vectors.
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cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
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cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer->time_mix_ln), layer->time_mix_ln_b);
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} else {
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cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
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}
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cur = ggml_mul(ctx0, cur, g);
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cur = build_lora_mm(ctx0, layer->time_mix_output, cur);
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return cur;
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}
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// llama output
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size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
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@ -248,6 +248,33 @@ struct llama_context {
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int il,
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bool worst_case);
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ggml_tensor * build_rwkv_token_shift_load(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case);
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ggml_tensor * build_rwkv_token_shift_store(
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ggml_context * ctx0,
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ggml_tensor * token_shift,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case);
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ggml_tensor * build_rwkv6_time_mix(
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ggml_context * ctx0,
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ggml_cgraph * graph,
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ggml_tensor * cur,
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ggml_tensor * x_prev,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il,
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bool worst_case);
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struct ggml_tensor * inp_s_copy; // I32 [kv_size]
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struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
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509
src/llama.cpp
509
src/llama.cpp
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@ -574,175 +574,34 @@ struct llm_build_context {
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return cur;
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}
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//struct ggml_tensor * build_rwkv6_time_mix(
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// const struct llama_layer * layer,
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// struct ggml_tensor * cur,
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// struct ggml_tensor * x_prev,
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// struct ggml_tensor ** wkv_state,
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// size_t wkv_head_size,
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// size_t head_count_kv) {
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// size_t n_embd = cur->ne[0];
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// size_t n_seq_tokens = cur->ne[1];
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// size_t n_seqs = cur->ne[2];
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struct ggml_tensor * build_rwkv_channel_mix(
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const struct llama_layer * layer,
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struct ggml_tensor * cur,
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struct ggml_tensor * x_prev,
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const llm_arch arch) {
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struct ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
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switch (arch) {
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case LLM_ARCH_RWKV6:
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{
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struct ggml_tensor * xk = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_k), cur);
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struct ggml_tensor * xr = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_r), cur);
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// size_t head_size = wkv_head_size;
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// size_t head_count = n_embd / head_size;
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struct ggml_tensor * r = ggml_sigmoid(ctx0, build_lora_mm(layer->channel_mix_receptance, xr));
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struct ggml_tensor * k = ggml_sqr(
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ctx0,
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ggml_relu(
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ctx0,
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build_lora_mm(layer->channel_mix_key, xk)
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)
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);
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cur = ggml_mul(ctx0, r, build_lora_mm(layer->channel_mix_value, k));
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} break;
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default:
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GGML_ABORT("fatal error");
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}
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// size_t n_tokens = n_seqs * n_seq_tokens;
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// bool is_qrwkv = layer->time_mix_first == nullptr;
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// struct ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
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// sx = ggml_reshape_2d(ctx0, sx, n_embd, n_tokens);
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// cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
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// struct ggml_tensor * xxx = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->time_mix_lerp_x), cur);
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// xxx = ggml_reshape_4d(
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// ctx0,
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// ggml_tanh(
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// ctx0,
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// ggml_mul_mat(ctx0, layer->time_mix_w1, xxx)
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// ),
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// layer->time_mix_w1->ne[1] / 5, 1, 5, n_tokens
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// );
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// xxx = ggml_cont(ctx0, ggml_permute(ctx0, xxx, 0, 1, 3, 2));
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// xxx = ggml_mul_mat(
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// ctx0,
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// ggml_reshape_4d(
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// ctx0,
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// layer->time_mix_w2,
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// layer->time_mix_w2->ne[0], layer->time_mix_w2->ne[1], 1, 5
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// ),
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// xxx
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// );
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// struct ggml_tensor *xw, *xk, *xv, *xr, *xg;
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// if (layer->time_mix_lerp_fused) {
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// // fusing these weights makes some performance improvement
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// sx = ggml_reshape_3d(ctx0, sx, n_embd, 1, n_tokens);
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// cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
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// xxx = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xxx, layer->time_mix_lerp_fused), sx), cur);
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// xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
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// xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
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// xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
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// xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
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// xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
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// } else {
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// // for backward compatibility
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// xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
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// xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
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// xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
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// xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
|
||||
// xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
|
||||
|
||||
// xw = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xw, layer->time_mix_lerp_w), sx), cur);
|
||||
// xk = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xk, layer->time_mix_lerp_k), sx), cur);
|
||||
// xv = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xv, layer->time_mix_lerp_v), sx), cur);
|
||||
// xr = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xr, layer->time_mix_lerp_r), sx), cur);
|
||||
// xg = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xg, layer->time_mix_lerp_g), sx), cur);
|
||||
// }
|
||||
|
||||
// struct ggml_tensor * r = build_lora_mm(layer->time_mix_receptance, xr);
|
||||
// struct ggml_tensor * k = build_lora_mm(layer->time_mix_key, xk);
|
||||
// struct ggml_tensor * v = build_lora_mm(layer->time_mix_value, xv);
|
||||
// if (layer->time_mix_receptance_b) {
|
||||
// r = ggml_add(ctx0, r, layer->time_mix_receptance_b);
|
||||
// }
|
||||
// if (layer->time_mix_key_b) {
|
||||
// k = ggml_add(ctx0, k, layer->time_mix_key_b);
|
||||
// }
|
||||
// if (layer->time_mix_value_b) {
|
||||
// v = ggml_add(ctx0, v, layer->time_mix_value_b);
|
||||
// }
|
||||
|
||||
// struct ggml_tensor * g = build_lora_mm(layer->time_mix_gate, xg);
|
||||
// if (is_qrwkv) {
|
||||
// g = ggml_sigmoid(ctx0, g);
|
||||
// } else {
|
||||
// g = ggml_silu(ctx0, g);
|
||||
// }
|
||||
|
||||
// if (head_count_kv != head_count) {
|
||||
// GGML_ASSERT(head_count % head_count_kv == 0);
|
||||
// k = ggml_reshape_4d(ctx0, k, head_size, 1, head_count_kv, n_tokens);
|
||||
// v = ggml_reshape_4d(ctx0, v, head_size, 1, head_count_kv, n_tokens);
|
||||
// struct ggml_tensor * tmp = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, head_size, head_count / head_count_kv, head_count_kv, n_tokens);
|
||||
// k = ggml_repeat(ctx0, k, tmp);
|
||||
// v = ggml_repeat(ctx0, v, tmp);
|
||||
// }
|
||||
|
||||
// k = ggml_reshape_3d(ctx0, k, head_size, head_count, n_tokens);
|
||||
// v = ggml_reshape_3d(ctx0, v, head_size, head_count, n_tokens);
|
||||
// r = ggml_reshape_3d(ctx0, r, head_size, head_count, n_tokens);
|
||||
|
||||
// struct ggml_tensor * w = ggml_mul_mat(
|
||||
// ctx0,
|
||||
// layer->time_mix_decay_w2,
|
||||
// ggml_tanh(
|
||||
// ctx0,
|
||||
// ggml_mul_mat(ctx0, layer->time_mix_decay_w1, xw)
|
||||
// )
|
||||
// );
|
||||
|
||||
// w = ggml_add(ctx0, w, layer->time_mix_decay);
|
||||
// w = ggml_exp(ctx0, ggml_neg(ctx0, ggml_exp(ctx0, w)));
|
||||
// w = ggml_reshape_3d(ctx0, w, head_size, head_count, n_tokens);
|
||||
|
||||
// if (is_qrwkv) {
|
||||
// // k = k * (1 - w)
|
||||
// k = ggml_sub(ctx0, k, ggml_mul(ctx0, k, w));
|
||||
// }
|
||||
|
||||
// struct ggml_tensor * wkv_output;
|
||||
// if (!layer->time_mix_first) {
|
||||
// wkv_output = ggml_gated_linear_attn(ctx0, k, v, r, w, *wkv_state, pow(head_size, -0.5f));
|
||||
// } else {
|
||||
// wkv_output = ggml_rwkv_wkv6(ctx0, k, v, r, layer->time_mix_first, w, *wkv_state);
|
||||
// }
|
||||
// cur = ggml_view_1d(ctx0, wkv_output, n_embd * n_tokens, 0);
|
||||
// *wkv_state = ggml_view_1d(ctx0, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
|
||||
|
||||
// if (!is_qrwkv) {
|
||||
// // group norm with head_count groups
|
||||
// cur = ggml_reshape_3d(ctx0, cur, n_embd / head_count, head_count, n_tokens);
|
||||
// cur = ggml_norm(ctx0, cur, 64e-5f);
|
||||
|
||||
// // Convert back to regular vectors.
|
||||
// cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
// cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer->time_mix_ln), layer->time_mix_ln_b);
|
||||
// } else {
|
||||
// cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
// }
|
||||
|
||||
// cur = ggml_mul(ctx0, cur, g);
|
||||
// cur = build_lora_mm(layer->time_mix_output, cur);
|
||||
|
||||
// return ggml_reshape_3d(ctx0, cur, n_embd, n_seq_tokens, n_seqs);
|
||||
//}
|
||||
|
||||
//struct ggml_tensor * build_rwkv6_channel_mix(
|
||||
// const struct llama_layer * layer,
|
||||
// struct ggml_tensor * cur,
|
||||
// struct ggml_tensor * x_prev) {
|
||||
// struct ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
|
||||
// struct ggml_tensor * xk = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_k), cur);
|
||||
// struct ggml_tensor * xr = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_r), cur);
|
||||
|
||||
// struct ggml_tensor * r = ggml_sigmoid(ctx0, build_lora_mm(layer->channel_mix_receptance, xr));
|
||||
// struct ggml_tensor * k = ggml_sqr(
|
||||
// ctx0,
|
||||
// ggml_relu(
|
||||
// ctx0,
|
||||
// build_lora_mm(layer->channel_mix_key, xk)
|
||||
// )
|
||||
// );
|
||||
|
||||
// return ggml_mul(ctx0, r, build_lora_mm(layer->channel_mix_value, k));
|
||||
//}
|
||||
return cur;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_k_shift() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
|
||||
|
@ -6935,226 +6794,178 @@ struct llm_build_context {
|
|||
return gf;
|
||||
}
|
||||
|
||||
//ggml_cgraph * build_rwkv6() {
|
||||
// struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
|
||||
ggml_cgraph * build_rwkv6() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
|
||||
|
||||
// // Token shift state dimensions should be 2 * n_emb
|
||||
// GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2);
|
||||
GGML_ASSERT(hparams.token_shift_count == 2);
|
||||
|
||||
// const int64_t n_seqs = ubatch.n_seqs;
|
||||
// const int64_t n_seq_tokens = ubatch.n_seq_tokens;
|
||||
// const int64_t n_tokens = ubatch.n_tokens;
|
||||
// GGML_ASSERT(n_seqs != 0);
|
||||
// GGML_ASSERT(ubatch.equal_seqs);
|
||||
// GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs);
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
// struct ggml_tensor * cur;
|
||||
// struct ggml_tensor * inpL;
|
||||
// struct ggml_tensor * state_copy = build_inp_s_copy();
|
||||
// struct ggml_tensor * state_mask = build_inp_s_mask();
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
|
||||
|
||||
// inpL = build_inp_embd(model.tok_embd);
|
||||
// inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
|
||||
struct ggml_tensor * state_copy = lctx.build_inp_s_copy(ctx0, worst_case);
|
||||
struct ggml_tensor * state_mask = lctx.build_inp_s_mask(ctx0, worst_case);
|
||||
|
||||
// for (int il = 0; il < n_layer; ++il) {
|
||||
// const llama_layer * layer = &model.layers[il];
|
||||
const auto n_embd = hparams.n_embd;
|
||||
const auto n_seq_tokens = ubatch.n_seq_tokens;
|
||||
const auto n_seqs = ubatch.n_seqs;
|
||||
|
||||
// // (ab)using the KV cache to store the states
|
||||
// struct ggml_tensor * token_shift = build_copy_mask_state(
|
||||
// gf, kv_self.k_l[il], state_copy, state_mask,
|
||||
// hparams.n_embd_k_s(), n_seqs);
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
const llama_layer * layer = &model.layers[il];
|
||||
|
||||
// struct ggml_tensor * wkv_states = build_copy_mask_state(
|
||||
// gf, kv_self.v_l[il], state_copy, state_mask,
|
||||
// hparams.n_embd_v_s(), n_seqs);
|
||||
struct ggml_tensor * token_shift = lctx.build_rwkv_token_shift_load(
|
||||
ctx0, gf, state_copy, state_mask, ubatch, il, worst_case
|
||||
);
|
||||
|
||||
// cur = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
|
||||
// token_shift = ggml_reshape_3d(ctx0, token_shift, n_embd, 2, n_seqs);
|
||||
struct ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
|
||||
struct ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift));
|
||||
|
||||
// struct ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
|
||||
// struct ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift));
|
||||
struct ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
|
||||
cb(att_norm, "attn_norm", il);
|
||||
|
||||
// struct ggml_tensor * x_norm_att = build_norm(cur, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
|
||||
// struct ggml_tensor * x_prev = ggml_concat(
|
||||
// ctx0,
|
||||
// att_shift,
|
||||
// ggml_view_3d(ctx0, x_norm_att, n_embd, n_seq_tokens - 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], 0),
|
||||
// 1
|
||||
// );
|
||||
struct ggml_tensor * x_prev = ggml_concat(
|
||||
ctx0,
|
||||
att_shift,
|
||||
ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
|
||||
1
|
||||
);
|
||||
|
||||
// cur = ggml_add(ctx0, cur, build_rwkv6_time_mix(layer, x_norm_att, x_prev, &wkv_states, hparams.wkv_head_size, n_embd / hparams.wkv_head_size));
|
||||
// ggml_build_forward_expand(gf, cur);
|
||||
// ggml_build_forward_expand(
|
||||
// gf,
|
||||
// ggml_cpy(
|
||||
// ctx0,
|
||||
// wkv_states,
|
||||
// ggml_view_1d(
|
||||
// ctx0,
|
||||
// kv_self.v_l[il],
|
||||
// hparams.n_embd_v_s() * n_seqs,
|
||||
// hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
|
||||
// )
|
||||
// )
|
||||
// );
|
||||
cur = lctx.build_rwkv6_time_mix(ctx0, gf, att_norm, x_prev, state_copy, state_mask, ubatch, il, worst_case);
|
||||
|
||||
// struct ggml_tensor * x_norm_ffn = build_norm(cur, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
|
||||
// x_prev = ggml_concat(
|
||||
// ctx0,
|
||||
// ffn_shift,
|
||||
// ggml_view_3d(ctx0, x_norm_ffn, n_embd, n_seq_tokens - 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], 0),
|
||||
// 1
|
||||
// );
|
||||
// cur = ggml_add(ctx0, cur, build_rwkv6_channel_mix(layer, x_norm_ffn, x_prev));
|
||||
// ggml_build_forward_expand(gf, cur);
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
|
||||
// struct ggml_tensor * last_norm_ffn = ggml_view_3d(ctx0, x_norm_ffn, n_embd, 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_ffn));
|
||||
struct ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
|
||||
cb(ffn_norm, "ffn_norm", il);
|
||||
|
||||
// token_shift = ggml_concat(ctx0, last_norm_att, last_norm_ffn, 1);
|
||||
x_prev = ggml_concat(
|
||||
ctx0,
|
||||
ffn_shift,
|
||||
ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0),
|
||||
1
|
||||
);
|
||||
|
||||
// ggml_build_forward_expand(
|
||||
// gf,
|
||||
// ggml_cpy(
|
||||
// ctx0,
|
||||
// ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * 2, 0),
|
||||
// ggml_view_1d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self.k_l[il]))
|
||||
// )
|
||||
// );
|
||||
cur = build_rwkv_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV6);
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
// if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
|
||||
// cur = ggml_scale(ctx0, cur, 0.5F);
|
||||
// }
|
||||
token_shift = ggml_concat(ctx0,
|
||||
ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm)),
|
||||
ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(ffn_norm)),
|
||||
1
|
||||
);
|
||||
ggml_build_forward_expand(gf, lctx.build_rwkv_token_shift_store(ctx0, token_shift, ubatch, il, worst_case));
|
||||
|
||||
// cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
// cb(cur, "l_out", il);
|
||||
if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
|
||||
cur = ggml_scale(ctx0, cur, 0.5F);
|
||||
}
|
||||
|
||||
// // input for next layer
|
||||
// inpL = cur;
|
||||
// }
|
||||
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// cur = inpL;
|
||||
// struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
// cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
// cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
// cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
|
||||
// cb(cur, "result_norm", -1);
|
||||
cur = inpL;
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
|
||||
// cur = build_lora_mm(model.output, cur);
|
||||
// cb(cur, "result_output", -1);
|
||||
cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// ggml_build_forward_expand(gf, cur);
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
// return gf;
|
||||
//}
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
// ref: https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1/blob/main/modeling_rwkv6qwen2.py
|
||||
//ggml_cgraph * build_rwkv6qwen2() {
|
||||
// struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
|
||||
ggml_cgraph * build_rwkv6qwen2() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(), false);
|
||||
|
||||
// GGML_ASSERT(n_embd == hparams.n_embd_k_s());
|
||||
GGML_ASSERT(n_embd == hparams.n_embd_k_s());
|
||||
|
||||
// const int64_t n_seqs = ubatch.n_seqs;
|
||||
// const int64_t n_seq_tokens = ubatch.n_seq_tokens;
|
||||
// const int64_t n_tokens = ubatch.n_tokens;
|
||||
// GGML_ASSERT(n_seqs != 0);
|
||||
// GGML_ASSERT(ubatch.equal_seqs);
|
||||
// GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs);
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
// struct ggml_tensor * cur;
|
||||
// struct ggml_tensor * inpL;
|
||||
// struct ggml_tensor * state_copy = build_inp_s_copy();
|
||||
// struct ggml_tensor * state_mask = build_inp_s_mask();
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inpL = build_inp_embd(model.tok_embd);
|
||||
struct ggml_tensor * state_copy = lctx.build_inp_s_copy(ctx0, worst_case);
|
||||
struct ggml_tensor * state_mask = lctx.build_inp_s_mask(ctx0, worst_case);
|
||||
|
||||
// for (int il = 0; il < n_layer; ++il) {
|
||||
// const llama_layer * layer = &model.layers[il];
|
||||
const auto n_embd = hparams.n_embd;
|
||||
const auto n_seq_tokens = ubatch.n_seq_tokens;
|
||||
const auto n_seqs = ubatch.n_seqs;
|
||||
|
||||
// // (ab)using the KV cache to store the states
|
||||
// struct ggml_tensor * token_shift = build_copy_mask_state(
|
||||
// gf, kv_self.k_l[il], state_copy, state_mask,
|
||||
// hparams.n_embd_k_s(), n_seqs);
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// struct ggml_tensor * wkv_states = build_copy_mask_state(
|
||||
// gf, kv_self.v_l[il], state_copy, state_mask,
|
||||
// hparams.n_embd_v_s(), n_seqs);
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
const llama_layer * layer = &model.layers[il];
|
||||
|
||||
// cur = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
|
||||
// token_shift = ggml_reshape_3d(ctx0, token_shift, n_embd, 1, n_seqs);
|
||||
struct ggml_tensor * token_shift = lctx.build_rwkv_token_shift_load(
|
||||
ctx0, gf, state_copy, state_mask, ubatch, il, worst_case
|
||||
);
|
||||
|
||||
// struct ggml_tensor * x_norm_att = build_norm(cur, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
|
||||
// struct ggml_tensor * x_prev = ggml_concat(
|
||||
// ctx0,
|
||||
// token_shift,
|
||||
// ggml_view_3d(ctx0, x_norm_att, n_embd, n_seq_tokens - 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], 0),
|
||||
// 1
|
||||
// );
|
||||
struct ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
|
||||
cb(att_norm, "attn_norm", il);
|
||||
|
||||
// struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
|
||||
// ggml_build_forward_expand(
|
||||
// gf,
|
||||
// ggml_cpy(
|
||||
// ctx0,
|
||||
// ggml_view_1d(ctx0, last_norm_att, n_embd * n_seqs, 0),
|
||||
// ggml_view_1d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self.k_l[il]))
|
||||
struct ggml_tensor * x_prev = ggml_concat(
|
||||
ctx0,
|
||||
token_shift,
|
||||
ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
|
||||
1
|
||||
);
|
||||
|
||||
// struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, build_rwkv6_time_mix(layer, x_norm_att, x_prev, &wkv_states, hparams.wkv_head_size, hparams.n_head_kv()));
|
||||
// ggml_build_forward_expand(gf, ffn_inp);
|
||||
// ggml_build_forward_expand(
|
||||
// gf,
|
||||
// ggml_cpy(
|
||||
// ctx0,
|
||||
// wkv_states,
|
||||
// ggml_view_1d(
|
||||
// ctx0,
|
||||
// kv_self.v_l[il],
|
||||
// hparams.n_embd_v_s() * n_seqs,
|
||||
// hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
|
||||
// )
|
||||
// )
|
||||
// );
|
||||
cur = lctx.build_rwkv6_time_mix(ctx0, gf, att_norm, x_prev, state_copy, state_mask, ubatch, il, worst_case);
|
||||
|
||||
// cb(ffn_inp, "ffn_inp", il);
|
||||
token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
|
||||
ggml_build_forward_expand(gf, lctx.build_rwkv_token_shift_store(ctx0, token_shift, ubatch, il, worst_case));
|
||||
|
||||
// // feed-forward network
|
||||
// cur = build_norm(ffn_inp,
|
||||
// model.layers[il].ffn_norm, NULL,
|
||||
// LLM_NORM_RMS, il);
|
||||
// cb(cur, "ffn_norm", il);
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// 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,
|
||||
// LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
// cb(cur, "ffn_out", il);
|
||||
// feed-forward network
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
// cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
// cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
// cb(cur, "l_out", il);
|
||||
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,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
// // input for next layer
|
||||
// inpL = cur;
|
||||
// }
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// cur = inpL;
|
||||
// struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
// cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
// cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
// cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
|
||||
// cb(cur, "result_norm", -1);
|
||||
cur = inpL;
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
|
||||
// cur = build_lora_mm(model.output, cur);
|
||||
// cb(cur, "result_output", -1);
|
||||
cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// ggml_build_forward_expand(gf, cur);
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
// return gf;
|
||||
//}
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
// ref: https://github.com/facebookresearch/chameleon
|
||||
// based on the original build_llama() function, changes:
|
||||
|
@ -7726,14 +7537,14 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_exaone();
|
||||
} break;
|
||||
//case LLM_ARCH_RWKV6:
|
||||
// {
|
||||
// result = llm.build_rwkv6();
|
||||
// } break;
|
||||
//case LLM_ARCH_RWKV6QWEN2:
|
||||
// {
|
||||
// result = llm.build_rwkv6qwen2();
|
||||
// } break;
|
||||
case LLM_ARCH_RWKV6:
|
||||
{
|
||||
result = llm.build_rwkv6();
|
||||
} break;
|
||||
case LLM_ARCH_RWKV6QWEN2:
|
||||
{
|
||||
result = llm.build_rwkv6qwen2();
|
||||
} break;
|
||||
case LLM_ARCH_CHAMELEON:
|
||||
{
|
||||
result = llm.build_chameleon();
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue