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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov
2026-03-16 13:10:15 +02:00
parent ab1252c19e
commit 2bc630f197
131 changed files with 2374 additions and 1519 deletions
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examples/talk-llama/models/kimi-linear.cpp
+5 -16
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@@ -1,5 +1,4 @@
#include "models.h"
#include "ggml.h"
#include "llama-memory-recurrent.h"
@@ -103,7 +102,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
const int64_t kv_lora_rank = hparams.n_lora_kv;
// qk_rope_head_dim = 64 (from Kimi config) which is hparams.n_rot
// Confirmed from tensor shape: wkv_a_mqa [2304, 576] = [n_embd, kv_lora_rank + qk_rope_head_dim]
const int64_t n_embd_head_qk_rope = hparams.n_rot; // config.qk_rope_head_dim
const int64_t n_embd_head_qk_rope = hparams.n_rot(); // config.qk_rope_head_dim
const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; // 192 - 64 = 128
// Attention scale for MLA
const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla);
@@ -118,12 +117,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_build_forward_expand(gf, cur);
// Check layer type by checking which tensors exist
// KDA layers have ssm_a_log tensor, MLA layers have wkv_a_mqa tensor
bool is_kda = (layer.ssm_a != nullptr);
bool is_mla = (layer.wkv_a_mqa != nullptr);
if (is_kda) {
if (hparams.is_recurrent(il)) {
// === KDA Layer (Kimi Delta Attention) with Recurrent State ===
// Reference: vLLM kda.py
const auto * mctx_cur = inp_rs->mctx;
@@ -175,9 +169,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
Kcur = ggml_l2_norm(ctx0, Kcur, eps_norm);
// Choose between build_delta_net_chunking and build_delta_net_recurrent based on n_tokens
std::pair<ggml_tensor *, ggml_tensor *> attn_out = n_seq_tokens == 1 ?
build_delta_net_autoregressive(Qcur, Kcur, Vcur, g1, beta, state, il) :
build_delta_net_chunking(Qcur, Kcur, Vcur, g1, beta, state, il);
auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il);
ggml_tensor * output = ggml_cont(ctx0, attn_out.first);
ggml_tensor * new_state = attn_out.second;
@@ -211,7 +203,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
cur = ggml_mul_mat(ctx0, layer.wo, gated);
cb(cur, "kda_out", il);
} else if (is_mla) {
} else {
// === MLA Layer (Multi-head Latent Attention) without KV Cache ===
// Reference: vLLM mla.py
// Step 1: Q projection and reshape
@@ -310,9 +302,6 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
cur = build_attn(inp_attn_kv, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il);
cb(cur, "mla_out", il);
}
} else {
// Unknown layer type - this should not happen
GGML_ABORT("Kimi layer is neither KDA nor MLA - missing required tensors");
}
// On last layer, select only the output tokens
@@ -349,7 +338,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
hparams.n_expert,
hparams.n_expert_used,
LLM_FFN_SILU, true,
true, hparams.expert_weights_scale,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "ffn_moe_out", il);