223 lines
10 KiB
C++
223 lines
10 KiB
C++
#include "models.h"
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ggml_cgraph * clip_graph_conformer::build() {
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const int n_frames = img.nx;
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const int n_pos = n_frames / 2;
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const int n_pos_embd = (((((n_frames + 1) / 2) + 1) / 2 + 1) / 2) * 2 - 1;
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GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos);
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ggml_tensor * pos_emb = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 512, n_pos_embd);
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ggml_set_name(pos_emb, "pos_emb");
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ggml_set_input(pos_emb);
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ggml_build_forward_expand(gf, pos_emb);
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ggml_tensor * inp = build_inp_raw(1);
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cb(inp, "input", -1);
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auto * cur = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
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// pre encode, conv subsampling
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{
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// layer.0 - conv2d
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cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[0], cur, 2, 2, 1, 1, 1, 1);
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cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[0]);
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cb(cur, "conformer.pre_encode.conv.{}", 0);
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// layer.1 - relu
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cur = ggml_relu_inplace(ctx0, cur);
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// layer.2 conv2d dw
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cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[2], cur, 2, 2, 1, 1, 1, 1);
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cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[2]);
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cb(cur, "conformer.pre_encode.conv.{}", 2);
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// layer.3 conv2d
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cur = ggml_conv_2d_direct(ctx0, model.pre_encode_conv_X_w[3], cur, 1, 1, 0, 0, 1, 1);
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cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[3]);
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cb(cur, "conformer.pre_encode.conv.{}", 3);
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// layer.4 - relu
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cur = ggml_relu_inplace(ctx0, cur);
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// layer.5 conv2d dw
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cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[5], cur, 2, 2, 1, 1, 1, 1);
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cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[5]);
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cb(cur, "conformer.pre_encode.conv.{}", 5);
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// layer.6 conv2d
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cur = ggml_conv_2d_direct(ctx0, model.pre_encode_conv_X_w[6], cur, 1, 1, 0, 0, 1, 1);
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cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[6]);
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cb(cur, "conformer.pre_encode.conv.{}", 6);
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// layer.7 - relu
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cur = ggml_relu_inplace(ctx0, cur);
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// flatten channel and frequency axis
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cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
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cur = ggml_reshape_2d(ctx0, cur, cur->ne[0] * cur->ne[1], cur->ne[2]);
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// calculate out
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cur = ggml_mul_mat(ctx0, model.pre_encode_out_w, cur);
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cur = ggml_add(ctx0, cur, model.pre_encode_out_b);
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cb(cur, "conformer.pre_encode.out", -1);
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}
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// pos_emb
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cb(pos_emb, "pos_emb", -1);
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for (int il = 0; il < hparams.n_layer; il++) {
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const auto & layer = model.layers[il];
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auto * residual = cur;
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cb(cur, "layer.in", il);
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// feed_forward1
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cur = build_norm(cur, layer.ff_norm_w, layer.ff_norm_b, NORM_TYPE_NORMAL, 1e-5, il);
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cb(cur, "conformer.layers.{}.norm_feed_forward1", il);
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cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, nullptr, nullptr, layer.ff_down_w, layer.ff_down_b, FFN_SILU,
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il);
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cb(cur, "conformer.layers.{}.feed_forward1.linear2", il);
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const auto fc_factor = 0.5f;
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residual = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, fc_factor));
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// self-attention
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{
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cur = build_norm(residual, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, 1e-5, il);
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cb(cur, "conformer.layers.{}.norm_self_att", il);
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ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
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Qcur = ggml_add(ctx0, Qcur, layer.q_b);
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Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, Qcur->ne[1]);
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ggml_tensor * Q_bias_u = ggml_add(ctx0, Qcur, layer.pos_bias_u);
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Q_bias_u = ggml_permute(ctx0, Q_bias_u, 0, 2, 1, 3);
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ggml_tensor * Q_bias_v = ggml_add(ctx0, Qcur, layer.pos_bias_v);
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Q_bias_v = ggml_permute(ctx0, Q_bias_v, 0, 2, 1, 3);
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// TODO @ngxson : some cont can/should be removed when ggml_mul_mat support these cases
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ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
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Kcur = ggml_add(ctx0, Kcur, layer.k_b);
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Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, Kcur->ne[1]);
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Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
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ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
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Vcur = ggml_add(ctx0, Vcur, layer.v_b);
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Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, Vcur->ne[1]);
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Vcur = ggml_cont(ctx0, ggml_permute(ctx0, Vcur, 1, 2, 0, 3));
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// build_attn won't fit due to matrix_ac and matrix_bd separation
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ggml_tensor * matrix_ac = ggml_mul_mat(ctx0, Q_bias_u, Kcur);
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matrix_ac = ggml_cont(ctx0, ggml_permute(ctx0, matrix_ac, 1, 0, 2, 3));
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cb(matrix_ac, "conformer.layers.{}.self_attn.id3", il);
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auto * p = ggml_mul_mat(ctx0, layer.linear_pos_w, pos_emb);
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cb(p, "conformer.layers.{}.self_attn.linear_pos", il);
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p = ggml_reshape_3d(ctx0, p, d_head, n_head, p->ne[1]);
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p = ggml_permute(ctx0, p, 0, 2, 1, 3);
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auto * matrix_bd = ggml_mul_mat(ctx0, Q_bias_v, p);
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matrix_bd = ggml_cont(ctx0, ggml_permute(ctx0, matrix_bd, 1, 0, 2, 3));
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// rel shift
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{
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const auto pos_len = matrix_bd->ne[0];
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const auto q_len = matrix_bd->ne[1];
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const auto h = matrix_bd->ne[2];
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matrix_bd = ggml_pad(ctx0, matrix_bd, 1, 0, 0, 0);
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matrix_bd = ggml_roll(ctx0, matrix_bd, 1, 0, 0, 0);
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matrix_bd = ggml_reshape_3d(ctx0, matrix_bd, q_len, pos_len + 1, h);
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matrix_bd = ggml_view_3d(ctx0, matrix_bd, q_len, pos_len, h, matrix_bd->nb[1],
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matrix_bd->nb[2], matrix_bd->nb[0] * q_len);
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matrix_bd = ggml_cont_3d(ctx0, matrix_bd, pos_len, q_len, h);
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}
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matrix_bd = ggml_view_3d(ctx0, matrix_bd, matrix_ac->ne[0], matrix_bd->ne[1],
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matrix_bd->ne[2], matrix_bd->nb[1], matrix_bd->nb[2], 0);
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auto * scores = ggml_add(ctx0, matrix_ac, matrix_bd);
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scores = ggml_scale(ctx0, scores, 1.0f / std::sqrt(d_head));
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cb(scores, "conformer.layers.{}.self_attn.id0", il);
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ggml_tensor * attn = ggml_soft_max(ctx0, scores);
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ggml_tensor * x = ggml_mul_mat(ctx0, attn, Vcur);
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x = ggml_permute(ctx0, x, 2, 0, 1, 3);
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x = ggml_cont_2d(ctx0, x, x->ne[0] * x->ne[1], x->ne[2]);
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ggml_tensor * out = ggml_mul_mat(ctx0, layer.o_w, x);
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out = ggml_add(ctx0, out, layer.o_b);
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cb(out, "conformer.layers.{}.self_attn.linear_out", il);
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cur = out;
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}
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residual = ggml_add(ctx0, residual, cur);
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cur = build_norm(residual, layer.norm_conv_w, layer.norm_conv_b, NORM_TYPE_NORMAL, 1e-5, il);
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cb(cur, "conformer.layers.{}.norm_conv", il);
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// conv
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{
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auto * x = cur;
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auto * conv_pw1_w =
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ggml_reshape_2d(ctx0, layer.conv_pw1_w, layer.conv_pw1_w->ne[1], layer.conv_pw1_w->ne[2]);
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x = ggml_mul_mat(ctx0, conv_pw1_w, x);
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x = ggml_add(ctx0, x, layer.conv_pw1_b);
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cb(x, "conformer.layers.{}.conv.pointwise_conv1", il);
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// ggml_glu doesn't support sigmoid
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// TODO @ngxson : support this ops in ggml
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{
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int64_t d = x->ne[0] / 2;
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ggml_tensor * gate = ggml_sigmoid(ctx0, ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], d * x->nb[0]));
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x = ggml_mul(ctx0, ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], 0), gate);
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x = ggml_cont(ctx0, ggml_transpose(ctx0, x));
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}
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// use ggml_ssm_conv for f32 precision
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x = ggml_pad(ctx0, x, 4, 0, 0, 0);
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x = ggml_roll(ctx0, x, 4, 0, 0, 0);
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x = ggml_pad(ctx0, x, 4, 0, 0, 0);
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auto * conv_dw_w = ggml_reshape_2d(ctx0, layer.conv_dw_w, layer.conv_dw_w->ne[0], layer.conv_dw_w->ne[2]);
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x = ggml_ssm_conv(ctx0, x, conv_dw_w);
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x = ggml_add(ctx0, x, ggml_reshape_1d(ctx0, layer.conv_dw_b, layer.conv_dw_b->ne[0]));
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x = ggml_add(ctx0, ggml_mul(ctx0, x, layer.conv_norm_w), layer.conv_norm_b);
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x = ggml_silu(ctx0, x);
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// pointwise_conv2
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auto * conv_pw2_w =
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ggml_reshape_2d(ctx0, layer.conv_pw2_w, layer.conv_pw2_w->ne[1], layer.conv_pw2_w->ne[2]);
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x = ggml_mul_mat(ctx0, conv_pw2_w, x);
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x = ggml_add(ctx0, x, layer.conv_pw2_b);
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cur = x;
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}
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residual = ggml_add(ctx0, residual, cur);
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cur = build_norm(residual, layer.ff_norm_1_w, layer.ff_norm_1_b, NORM_TYPE_NORMAL, 1e-5, il);
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cb(cur, "conformer.layers.{}.norm_feed_forward2", il);
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cur = build_ffn(cur, layer.ff_up_1_w, layer.ff_up_1_b, nullptr, nullptr, layer.ff_down_1_w, layer.ff_down_1_b,
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FFN_SILU, il); // TODO(tarek): read activation for ffn from hparams
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cb(cur, "conformer.layers.{}.feed_forward2.linear2", il);
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residual = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, fc_factor));
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cb(residual, "conformer.layers.{}.conv.id", il);
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cur = build_norm(residual, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, 1e-5, il);
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cb(cur, "conformer.layers.{}.norm_out", il);
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}
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// audio adapter
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cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1);
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cb(cur, "audio_adapter.model.{}", 0);
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cur = build_ffn(cur, model.mm_1_w, model.mm_1_b, nullptr, nullptr, model.mm_3_w, model.mm_3_b, FFN_GELU_ERF, -1);
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cb(cur, "projected", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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