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llama.cpp
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remove some redundant ggml_cont

This commit is contained in:
Xuan Son Nguyen 2025-12-16 17:20:13 +01:00
parent cea578bc8c
commit a3ebc93d71
1 changed files with 14 additions and 13 deletions
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27
tools/mtmd/models/conformer.cpp
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@ -92,10 +92,11 @@ ggml_cgraph * clip_graph_conformer::build() {
Qcur = ggml_add(ctx0, Qcur, layer.q_b);
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, Qcur->ne[1]);
ggml_tensor * Q_bias_u = ggml_add(ctx0, Qcur, layer.pos_bias_u);
Q_bias_u = ggml_cont(ctx0, ggml_permute(ctx0, Q_bias_u, 0, 2, 1, 3));
Q_bias_u = ggml_permute(ctx0, Q_bias_u, 0, 2, 1, 3);
ggml_tensor * Q_bias_v = ggml_add(ctx0, Qcur, layer.pos_bias_v);
Q_bias_v = ggml_cont(ctx0, ggml_permute(ctx0, Q_bias_v, 0, 2, 1, 3));
Q_bias_v = ggml_permute(ctx0, Q_bias_v, 0, 2, 1, 3);
// TODO @ngxson : some cont can/should be removed when ggml_mul_mat support these cases
ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
Kcur = ggml_add(ctx0, Kcur, layer.k_b);
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, Kcur->ne[1]);
@ -114,7 +115,7 @@ ggml_cgraph * clip_graph_conformer::build() {
auto * p = ggml_mul_mat(ctx0, layer.linear_pos_w, pos_emb);
cb(p, "conformer.layers.{}.self_attn.linear_pos", il);
p = ggml_reshape_3d(ctx0, p, d_head, n_head, p->ne[1]);
p = ggml_cont(ctx0, ggml_permute(ctx0, p, 0, 2, 1, 3));
p = ggml_permute(ctx0, p, 0, 2, 1, 3);
auto * matrix_bd = ggml_mul_mat(ctx0, Q_bias_v, p);
matrix_bd = ggml_cont(ctx0, ggml_permute(ctx0, matrix_bd, 1, 0, 2, 3));
@ -127,24 +128,24 @@ ggml_cgraph * clip_graph_conformer::build() {
matrix_bd = ggml_pad(ctx0, matrix_bd, 1, 0, 0, 0);
matrix_bd = ggml_roll(ctx0, matrix_bd, 1, 0, 0, 0);
matrix_bd = ggml_reshape_3d(ctx0, matrix_bd, q_len, pos_len + 1, h);
matrix_bd = ggml_cont(ctx0, ggml_view_3d(ctx0, matrix_bd, q_len, pos_len, h, matrix_bd->nb[1],
matrix_bd->nb[2], matrix_bd->nb[0] * q_len));
matrix_bd = ggml_reshape_3d(ctx0, matrix_bd, pos_len, q_len, h);
matrix_bd = ggml_view_3d(ctx0, matrix_bd, q_len, pos_len, h, matrix_bd->nb[1],
matrix_bd->nb[2], matrix_bd->nb[0] * q_len);
matrix_bd = ggml_cont_3d(ctx0, matrix_bd, pos_len, q_len, h);
}
matrix_bd = ggml_cont(ctx0, ggml_view_3d(ctx0, matrix_bd, matrix_ac->ne[0], matrix_bd->ne[1],
matrix_bd->ne[2], matrix_bd->nb[1], matrix_bd->nb[2], 0));
matrix_bd = ggml_view_3d(ctx0, matrix_bd, matrix_ac->ne[0], matrix_bd->ne[1],
matrix_bd->ne[2], matrix_bd->nb[1], matrix_bd->nb[2], 0);
auto * scores = ggml_add(ctx0, matrix_ac, matrix_bd);
scores = ggml_scale(ctx0, scores, 1.0f / std::sqrt(d_head));
cb(scores, "conformer.layers.{}.self_attn.id0", il);
ggml_tensor * attn = ggml_soft_max(ctx0, scores);
ggml_tensor * x = ggml_mul_mat(ctx0, attn, Vcur);
x = ggml_cont(ctx0, ggml_permute(ctx0, x, 2, 0, 1, 3));
x = ggml_reshape_2d(ctx0, x, x->ne[0] * x->ne[1], x->ne[2]);
x = ggml_permute(ctx0, x, 2, 0, 1, 3);
x = ggml_cont_2d(ctx0, x, x->ne[0] * x->ne[1], x->ne[2]);
x = ggml_mul_mat(ctx0, layer.o_w, x);
ggml_tensor * out = ggml_add(ctx0, x, layer.o_b);
ggml_tensor * out = ggml_mul_mat(ctx0, layer.o_w, x);
out = ggml_add(ctx0, out, layer.o_b);
cb(out, "conformer.layers.{}.self_attn.linear_out", il);
cur = out;
@ -164,6 +165,7 @@ ggml_cgraph * clip_graph_conformer::build() {
cb(x, "conformer.layers.{}.conv.pointwise_conv1", il);
// ggml_glu doesn't support sigmoid
// TODO @ngxson : support this ops in ggml
{
int64_t d = x->ne[0] / 2;
ggml_tensor * gate = ggml_sigmoid(ctx0, ggml_view_2d(ctx0, x, d, x->ne[1], x->nb[1], d * x->nb[0]));
@ -175,7 +177,6 @@ ggml_cgraph * clip_graph_conformer::build() {
x = ggml_pad(ctx0, x, 4, 0, 0, 0);
x = ggml_roll(ctx0, x, 4, 0, 0, 0);
x = ggml_pad(ctx0, x, 4, 0, 0, 0);
x = ggml_cont(ctx0, x);
auto * conv_dw_w = ggml_reshape_2d(ctx0, layer.conv_dw_w, layer.conv_dw_w->ne[0], layer.conv_dw_w->ne[2]);
x = ggml_ssm_conv(ctx0, x, conv_dw_w);
x = ggml_add(ctx0, x, ggml_reshape_1d(ctx0, layer.conv_dw_b, layer.conv_dw_b->ne[0]));