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llama.cpp
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fix trailing spaces

This commit is contained in:
Yee Man Chan 2026-01-11 21:31:35 +08:00
parent 10be797c12
commit 6ae66fc40d
3 changed files with 42 additions and 48 deletions
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4
gguf-py/gguf/tensor_mapping.py
View File

@ -819,13 +819,13 @@ class TensorNameMap:
# Kimi Linear KDA (using SSM_ prefix for consistency)
MODEL_TENSOR.SSM_CONV1D_Q: (
"model.layers.{bid}.self_attn.q_conv1d",
),
),
MODEL_TENSOR.SSM_CONV1D_K: (
"model.layers.{bid}.self_attn.k_conv1d",
),
MODEL_TENSOR.SSM_CONV1D_V: (
"model.layers.{bid}.self_attn.v_conv1d",
),
),
MODEL_TENSOR.SSM_F_A: (
"model.layers.{bid}.self_attn.f_a_proj",
),

4
src/llama-vocab.cpp
View File

@ -1747,7 +1747,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
// Kimi-K2 uses custom tokenization without traditional BPE merges
const bool is_kimi_k2 = (tokenizer_pre == "kimi-k2");
if (merges_keyidx == -1) {
if (!is_kimi_k2) {
throw std::runtime_error("cannot find tokenizer merges in model file\n");
@ -1768,7 +1768,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
if (pos != std::string::npos) {
first = word.substr(0, pos);
second = word.substr(pos + 1);
}
}
bpe_ranks.emplace(std::make_pair(first, second), i);
}

82
src/models/kimi-linear.cpp
View File

@ -12,7 +12,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM)
// So we don't need inp_pos
auto * inp = build_inp_mem_hybrid();
auto * inp_rs = inp->get_recr();
auto * inp_attn = inp->get_attn();
@ -38,12 +38,12 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
const int64_t d_inner = n_head * head_dim; // 32 * 128 = 4096
const int64_t n_seqs = ubatch.n_seqs;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
// Verify batch consistency for recurrent layers
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(ubatch.equal_seqs());
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
// MLA params
const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla;
const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla;
@ -67,14 +67,13 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// KDA layers have ssm_a_log tensor, MLA layers have wkv_a_mqa tensor
bool is_kda = (layer.ssm_a_log != nullptr);
bool is_mla = (layer.wkv_a_mqa != nullptr);
if (is_kda) {
// === KDA Layer (Kimi Delta Attention) with Recurrent State ===
// Reference: vLLM kda.py
const auto * mctx_cur = inp_rs->mctx;
const auto kv_head = mctx_cur->get_head();
// Get conv states from r_l tensor (Q, K, V each have separate state)
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
cb(conv_states_all, "conv_states_all", il);
@ -85,7 +84,6 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// Each conv state is [(d_conv-1) * d_inner] per sequence, need to reshape to [d_conv-1, d_inner, n_seqs]
// Memory layout: for each seq, Q state is first conv_state_size elements, then K, then V
// conv_state_all has stride: nb[0] = element_size, nb[1] = n_embd_r_total * element_size
// View Q conv state: offset 0, size conv_state_size per seq
// conv_state_all is [n_embd_r_total, n_seqs] with memory layout:
// state[i + seq * n_embd_r_total] where i = conv_step + channel * (d_conv-1) + {0, conv_state_size, 2*conv_state_size} for Q/K/V
@ -104,7 +102,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
(d_conv - 1) * ggml_element_size(conv_state_all),
n_embd_r_total * ggml_element_size(conv_state_all),
2 * conv_state_size * ggml_element_size(conv_state_all)); // offset for V
// Step 1: Q, K, V projections -> [d_inner, n_tokens]
ggml_tensor * q_proj = ggml_mul_mat(ctx0, layer.wq, cur);
ggml_tensor * k_proj = ggml_mul_mat(ctx0, layer.wk, cur);
@ -112,14 +110,14 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
cb(q_proj, "kda_q_proj", il);
cb(k_proj, "kda_k_proj", il);
cb(v_proj, "kda_v_proj", il);
// Step 2: Causal Conv1d for Q
// Reshape input: {d_inner, n_tokens} -> {d_inner, n_seq_tokens, n_seqs}
ggml_tensor * q_3d = ggml_reshape_3d(ctx0, q_proj, d_inner, n_seq_tokens, n_seqs);
// Concat Q conv state and current input: {d_conv-1 + n_seq_tokens, d_inner, n_seqs}
ggml_tensor * conv_q = ggml_concat(ctx0, conv_state_q, ggml_transpose(ctx0, q_3d), 0);
// Save last (d_conv-1) columns back to Q conv state
ggml_tensor * last_conv_q = ggml_view_3d(ctx0, conv_q, d_conv - 1, d_inner, n_seqs,
conv_q->nb[1], conv_q->nb[2], n_seq_tokens * conv_q->nb[0]);
@ -127,7 +125,6 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_cpy(ctx0, last_conv_q,
ggml_view_1d(ctx0, conv_states_all, conv_state_size * n_seqs,
kv_head * n_embd_r_total * ggml_element_size(conv_states_all))));
// Reshape conv weight: GGUF [d_conv, 1, d_inner, 1] -> ggml_ssm_conv expects [d_conv, d_inner]
// GGUF stores as [d_conv, 1, d_inner, 1] with memory layout w[conv_step + channel * d_conv]
// vLLM stores as [d_inner, d_conv] with memory layout w[channel * d_conv + conv_step]
@ -143,13 +140,13 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
}
conv_weight = ggml_reshape_2d(ctx0, q_conv_f32, d_conv, d_inner);
}
// Apply conv1d
ggml_tensor * Qcur;
if (conv_weight) {
// Make conv_q contiguous for ggml_ssm_conv
conv_q = ggml_cont(ctx0, conv_q);
// ggml_ssm_conv output: {d_inner, n_seq_tokens, n_seqs}
Qcur = ggml_ssm_conv(ctx0, conv_q, conv_weight);
cb(Qcur, "Q conv1d", il);
@ -163,13 +160,13 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
} else {
GGML_ABORT("KDA layer missing Q conv weight");
}
// K conv1d (with separate K conv state)
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));
// Save K conv state
ggml_tensor * last_conv_k = ggml_view_3d(ctx0, conv_k, d_conv - 1, d_inner, n_seqs,
conv_k->nb[1], conv_k->nb[2], n_seq_tokens * conv_k->nb[0]);
@ -177,7 +174,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_cpy(ctx0, last_conv_k,
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);
@ -194,13 +191,13 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
} else {
GGML_ABORT("KDA layer missing K conv weight");
}
// V conv1d (with separate V conv state)
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));
// Save V conv state
ggml_tensor * last_conv_v = ggml_view_3d(ctx0, conv_v, d_conv - 1, d_inner, n_seqs,
conv_v->nb[1], conv_v->nb[2], n_seq_tokens * conv_v->nb[0]);
@ -208,7 +205,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_cpy(ctx0, last_conv_v,
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);
@ -225,7 +222,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
} else {
GGML_ABORT("KDA layer missing V conv weight");
}
// Step 3: Compute g1 (forget gate)
// g1 = -exp(A_log) * softplus(f_b(f_a(x)) + dt_bias)
ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur);
@ -234,7 +231,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
g1 = ggml_add(ctx0, g1, layer.ssm_dt_b);
g1 = ggml_softplus(ctx0, g1);
g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens);
// A_log shape is [1, n_head] or [1, n_head, 1, 1], need to broadcast to [head_dim, n_head, n_tokens]
// First compute -exp(A_log), then reshape for broadcasting
ggml_tensor * A_neg_exp = ggml_neg(ctx0, ggml_exp(ctx0, layer.ssm_a_log));
@ -242,16 +239,16 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
A_neg_exp = ggml_reshape_3d(ctx0, A_neg_exp, 1, n_head, 1);
g1 = ggml_mul(ctx0, g1, A_neg_exp);
cb(g1, "kda_g1", il);
// 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);
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));
@ -274,7 +271,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
const int64_t output_flat_size = head_dim * n_head * n_seq_tokens * n_seqs;
ggml_tensor * attn_out_1d = ggml_view_1d(ctx0, attn_out, output_flat_size, 0);
cb(attn_out_1d, "attn_out_1d", il);
ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, attn_out_1d, head_dim, n_head, n_seq_tokens * n_seqs);
cb(attn_out_final, "attn_out_reshaped", il);
// Extract the state part (second part of the concatenated tensor)
@ -299,7 +296,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.ssm_g_b, g_a);
cb(g2, "g2 g_b(g_a(cur_2d))", il);
g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_seq_tokens * n_seqs);
// Step 8: Apply o_norm with sigmoid gating
// Note: Kimi model uses sigmoid gating, not SiLU (despite FusedRMSNormGated default being swish)
// Formula: output = RMSNorm(x) * sigmoid(g)
@ -307,7 +304,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
cb(normed, "kda_normed", il);
ggml_tensor * gate = ggml_sigmoid(ctx0, g2);
ggml_tensor * gated = ggml_mul(ctx0, normed, gate);
// Step 9: Output projection
gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens);
cur = ggml_mul_mat(ctx0, layer.wo, gated);
@ -316,7 +313,6 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
} else if (is_mla) {
// === MLA Layer (Multi-head Latent Attention) without KV Cache ===
// Reference: vLLM mla.py
// Step 1: Q projection and reshape
// vLLM Kimi: q = q_proj(hidden_states), then view as [n_tokens, n_head, qk_head_dim]
// Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM)
@ -325,7 +321,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// Step 2: KV compression
// kv_cmpr_pe = kv_a_proj_with_mqa(hidden_states) -> [kv_lora_rank + qk_rope_head_dim, n_tokens]
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
// Split: kv_cmpr = kv_lora[:kv_lora_rank], k_pe = kv_lora[kv_lora_rank:]
ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
@ -333,10 +329,8 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
// Note: Kimi MLA does NOT apply RoPE (rotary_emb=None in vLLM)
// k_pe is used directly without RoPE
// Normalize kv_c
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
@ -346,7 +340,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla),
ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0);
cb(q_nope, "q_nope", il);
// and {n_embd_head_qk_rope, n_head, n_tokens}
ggml_tensor * q_pe = ggml_view_3d(
ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla),
@ -389,7 +383,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// KV decompression: kv = kv_b_proj(kv_c_normed)
ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr);
const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla;
// Split kv into k_nope and v
ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
ggml_row_size(kv->type, kv_per_head),
@ -401,7 +395,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
k_nope = ggml_cont(ctx0, k_nope);
Vcur = ggml_cont(ctx0, Vcur);
cb(Vcur, "mla_V", il);
// Concatenate k_nope + k_pe (broadcast k_pe to all heads)
// K = [k_nope, k_pe] where k_nope is [qk_nope_head_dim, n_head, n_tokens]
// and k_pe is [qk_rope_head_dim, 1, n_tokens] broadcast to all heads
@ -410,7 +404,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target);
ggml_tensor * Kcur = ggml_concat(ctx0, k_nope, k_pe_repeated, 0);
cb(Kcur, "mla_K", il);
// Direct softmax attention (with MHA KV cache)
// Use build_attn with inp_attn for proper mask handling
cur = build_attn(inp_attn, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il);
@ -420,13 +414,13 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
// 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
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
// Residual
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
@ -459,7 +453,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
cb(moe_out, "ffn_moe_out", il);
// Shared expert
{
ggml_tensor * ffn_shexp = build_ffn(cur,
@ -468,7 +462,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
layer.ffn_down_shexp, NULL, NULL,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
@ -663,7 +657,7 @@ ggml_tensor * llm_build_kimi_linear::build_kda_chunking(
Aqk = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_4d(ctx0, Aqk, chunk_size, chunk_size, n_chunks, HB)));
cb(Akk, "Akk", il);
cb(Aqk, "Aqk", il);
Akk = ggml_mul(ctx0, Akk, beta);
Akk = ggml_neg(ctx0, ggml_mul(ctx0, Akk, causal_mask));
cb(Akk, "attn_pre_solve", il);
@ -798,15 +792,15 @@ ggml_tensor * llm_build_kimi_linear::build_kda_autoregressive(
ggml_tensor * v,
ggml_tensor * gk,
ggml_tensor * beta,
ggml_tensor * state,
ggml_tensor * state,
int il) {
GGML_ASSERT(ggml_is_contiguous(q));
GGML_ASSERT(ggml_is_contiguous(k));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(v));
GGML_ASSERT(ggml_is_contiguous(gk));
GGML_ASSERT(ggml_is_contiguous(beta));
GGML_ASSERT(ggml_is_contiguous(state));
const int64_t S_k = q->ne[0];
const int64_t H_k = q->ne[1];
const int64_t n_tokens = q->ne[2];