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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov
2026-05-02 15:02:42 +03:00
parent 18162bcf61
commit 4bf733672b
144 changed files with 3675 additions and 5535 deletions
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examples/talk-llama/models/qwen3next.cpp
+14 -11
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@@ -56,6 +56,9 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
cur = ggml_add(ctx0, cur, ffn_residual);
cb(cur, "post_moe", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// Input for next layer
inpL = cur;
}
@@ -154,7 +157,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
cur = build_attn(inp,
nullptr, nullptr,
nullptr, nullptr, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_pregate", il);
@@ -169,7 +172,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn(
cur = ggml_mul(ctx0, cur, gate);
cb(cur, "attn_gated", il);
cur = build_lora_mm(model.layers[il].wo, cur);
cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
cb(cur, "attn_output", il);
return cur;
@@ -351,7 +354,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
cb(last_conv_states, "last_conv_states", il);
ggml_tensor * state_update_target =
ggml_view_1d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels * n_seqs,
ggml_view_2d(ctx0, conv_states_all, (conv_kernel_size - 1) * conv_channels, n_seqs, conv_states_all->nb[1],
kv_head * (conv_kernel_size - 1) * conv_channels * ggml_element_size(conv_states_all));
cb(state_update_target, "state_update_target", il);
@@ -411,19 +414,19 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
GGML_ASSERT(num_v_heads % num_k_heads == 0);
int64_t repeat_factor = num_v_heads / num_k_heads;
// repeat interleave: reshape to (repeat part, 1, remaining part), do repeat, then reshape back
ggml_tensor * q_reshaped = ggml_reshape_3d(ctx0, q_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
ggml_tensor * k_reshaped = ggml_reshape_3d(ctx0, k_conv, head_k_dim, 1, num_k_heads * n_seq_tokens * n_seqs);
// repeat interleave: reshape to (repeat part, 1, remaining part...), do repeat, then reshape back
ggml_tensor * q_reshaped = ggml_reshape_4d(ctx0, q_conv, head_k_dim, 1, num_k_heads, n_seq_tokens * n_seqs);
ggml_tensor * k_reshaped = ggml_reshape_4d(ctx0, k_conv, head_k_dim, 1, num_k_heads, n_seq_tokens * n_seqs);
// Repeat along the third dimension (the new dimension with size 1)
ggml_tensor * q_repeated =
ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
ggml_repeat_4d(ctx0, q_reshaped, head_k_dim, repeat_factor, num_k_heads, n_seq_tokens * n_seqs);
ggml_tensor * k_repeated =
ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads * n_seq_tokens * n_seqs, 1);
ggml_repeat_4d(ctx0, k_reshaped, head_k_dim, repeat_factor, num_k_heads, n_seq_tokens * n_seqs);
// Reshape back to merge the head and repeat dimensions
// From [head_dim, num_k_heads, repeat_factor, n_seq_tokens * n_seqs]
// Back to [head_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs]
// From [head_dim, repeat_factor, num_k_heads, n_seq_tokens * n_seqs]
// Back to [head_dim, repeat_factor * num_k_heads, n_seq_tokens, n_seqs]
q_conv = ggml_reshape_4d(ctx0, q_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
k_conv = ggml_reshape_4d(ctx0, k_repeated, head_k_dim, num_k_heads * repeat_factor, n_seq_tokens, n_seqs);
}
@@ -442,7 +445,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
// Update the recurrent states
ggml_build_forward_expand(gf,
ggml_cpy(ctx0, new_state,
ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs,
ggml_view_2d(ctx0, ssm_states_all, hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all))));
// z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim]