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llama.cpp
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removed unnecessary ggml_cont before ggml_reshape

This commit is contained in:
Yee Man Chan 2026-01-14 03:22:53 +08:00
parent fe9d248ae6
commit 18ae7f4684
1 changed files with 10 additions and 23 deletions
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33
src/models/kimi-linear.cpp
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@ -134,12 +134,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_tensor * conv_weight = nullptr;
if (layer.ssm_q_conv) {
// Reshape conv weight from [d_conv, 1, d_inner, 1] to [d_conv, d_inner] for ggml_ssm_conv
// Cast to F32 if quantized (ggml_ssm_conv requires float weights)
ggml_tensor * q_conv_f32 = layer.ssm_q_conv;
if (q_conv_f32->type != GGML_TYPE_F32) {
q_conv_f32 = ggml_cast(ctx0, q_conv_f32, GGML_TYPE_F32);
}
conv_weight = ggml_reshape_2d(ctx0, q_conv_f32, d_conv, d_inner);
conv_weight = ggml_reshape_2d(ctx0, layer.ssm_q_conv, d_conv, d_inner);
}
// Apply conv1d
@ -166,7 +161,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_tensor * Kcur;
if (layer.ssm_k_conv) {
ggml_tensor * k_3d = ggml_reshape_3d(ctx0, k_proj, d_inner, n_seq_tokens, n_seqs);
ggml_tensor * conv_k = ggml_cont(ctx0, ggml_concat(ctx0, conv_state_k, ggml_transpose(ctx0, k_3d), 0));
ggml_tensor * conv_k = ggml_concat(ctx0, conv_state_k, ggml_transpose(ctx0, k_3d), 0);
// Save K conv state
ggml_tensor * last_conv_k = ggml_view_3d(ctx0, conv_k, d_conv - 1, d_inner, n_seqs,
@ -176,11 +171,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
(kv_head * n_embd_r_total + conv_state_size) * ggml_element_size(conv_states_all))));
ggml_tensor * k_conv_f32 = layer.ssm_k_conv;
if (k_conv_f32->type != GGML_TYPE_F32) {
k_conv_f32 = ggml_cast(ctx0, k_conv_f32, GGML_TYPE_F32);
}
ggml_tensor * k_conv_weight = ggml_reshape_2d(ctx0, k_conv_f32, d_conv, d_inner);
ggml_tensor * k_conv_weight = ggml_reshape_2d(ctx0, layer.ssm_k_conv, d_conv, d_inner);
Kcur = ggml_ssm_conv(ctx0, conv_k, k_conv_weight);
cb(Kcur, "K conv1d", il);
Kcur = ggml_reshape_2d(ctx0, Kcur, d_inner, n_tokens);
@ -197,7 +188,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_tensor * Vcur;
if (layer.ssm_v_conv) {
ggml_tensor * v_3d = ggml_reshape_3d(ctx0, v_proj, d_inner, n_seq_tokens, n_seqs);
ggml_tensor * conv_v = ggml_cont(ctx0, ggml_concat(ctx0, conv_state_v, ggml_transpose(ctx0, v_3d), 0));
ggml_tensor * conv_v = ggml_concat(ctx0, conv_state_v, ggml_transpose(ctx0, v_3d), 0);
// Save V conv state
ggml_tensor * last_conv_v = ggml_view_3d(ctx0, conv_v, d_conv - 1, d_inner, n_seqs,
@ -207,11 +198,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
(kv_head * n_embd_r_total + 2 * conv_state_size) * ggml_element_size(conv_states_all))));
ggml_tensor * v_conv_f32 = layer.ssm_v_conv;
if (v_conv_f32->type != GGML_TYPE_F32) {
v_conv_f32 = ggml_cast(ctx0, v_conv_f32, GGML_TYPE_F32);
}
ggml_tensor * v_conv_weight = ggml_reshape_2d(ctx0, v_conv_f32, d_conv, d_inner);
ggml_tensor * v_conv_weight = ggml_reshape_2d(ctx0, layer.ssm_v_conv, d_conv, d_inner);
Vcur = ggml_ssm_conv(ctx0, conv_v, v_conv_weight);
cb(Vcur, "V conv1d", il);
Vcur = ggml_reshape_2d(ctx0, Vcur, d_inner, n_tokens);
@ -243,17 +230,17 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// Step 4: Compute beta (mixing coefficient)
ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur);
beta = ggml_cont_4d(ctx0, beta, n_head, 1, n_seq_tokens, n_seqs);
beta = ggml_reshape_4d(ctx0, beta, n_head, 1, n_seq_tokens, n_seqs);
cb(beta, "kda_beta", il);
// Step 5: Reshape for KDA recurrence
// {n_embd, n_tokens} -> {n_embd, n_seq_tokens, n_seqs}
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
Qcur = ggml_cont(ctx0, ggml_reshape_4d(ctx0, Qcur, head_dim, n_head, n_seq_tokens, n_seqs));
Kcur = ggml_cont(ctx0, ggml_reshape_4d(ctx0, Kcur, head_dim, n_head, n_seq_tokens, n_seqs));
Vcur = ggml_cont(ctx0, ggml_reshape_4d(ctx0, Vcur, head_dim, n_head, n_seq_tokens, n_seqs));
g1 = ggml_cont(ctx0, ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs));
Qcur = ggml_reshape_4d(ctx0, Qcur, head_dim, n_head, n_seq_tokens, n_seqs);
Kcur = ggml_reshape_4d(ctx0, Kcur, head_dim, n_head, n_seq_tokens, n_seqs);
Vcur = ggml_reshape_4d(ctx0, Vcur, head_dim, n_head, n_seq_tokens, n_seqs);
g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs);
cb(Qcur, "kda_Q", il);
cb(Kcur, "kda_K", il);
cb(Vcur, "kda_V", il);