ikawrakow/ik_llama.cpp
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Kawrakow
2026-01-10 13:17:22 +00:00
parent c7348f6f55
commit 1ee36144a8
3 changed files with 114 additions and 79 deletions
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158
src/llama-build-context.cpp
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@@ -8452,89 +8452,107 @@ ggml_cgraph * llm_build_context::build_bailingmoe2() {
const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
auto rope_cache = cparams.rope_cache && (rope_type == LLAMA_ROPE_TYPE_NEOX || rope_type == LLAMA_ROPE_TYPE_NORM) ?
ggml_rope_cache(ctx0, inp_pos, nullptr, n_embd_head, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow) : nullptr;
for (int il = 0; il < n_transformer_layers; ++il) {
ggml_tensor * inpSA = inpL;
//ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
//auto wqkv = model.split_mode == LLAMA_SPLIT_MODE_GRAPH ? nullptr :
cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, nullptr, KQ_mask,
nullptr, nullptr, kq_scale, 0.0f, 0, il, true, false, true);
// self_attention
{
auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, model.layers[il].wqkv, model.layers[il].bqkv,
nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr,
model.layers[il].attn_q_norm, model.layers[il].attn_k_norm, 0.0f, il);
//// norm
//cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
//cb(cur, "attn_norm", il);
if (rope_cache) {
Qcur = ggml_rope_fast(ctx0, Qcur, rope_cache);
Kcur = ggml_rope_fast(ctx0, Kcur, rope_cache);
} else {
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
//// self_attention
//{
// //auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, model.layers[il].wqkv, model.layers[il].bqkv,
// // nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr,
// // model.layers[il].attn_q_norm, model.layers[il].attn_k_norm, 0.0f, il);
// auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, nullptr, nullptr, nullptr, nullptr,
// model.layers[il].wq, nullptr, model.layers[il].wk, nullptr, model.layers[il].wv, nullptr,
// model.layers[il].attn_q_norm, model.layers[il].attn_k_norm, 0.0f, il);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
// if (rope_cache) {
// Qcur = ggml_rope_fast(ctx0, Qcur, rope_cache);
// Kcur = ggml_rope_fast(ctx0, Kcur, rope_cache);
// } else {
// Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
// ext_factor, attn_factor, beta_fast, beta_slow);
// Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
// ext_factor, attn_factor, beta_fast, beta_slow);
// }
cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
}
// cb(Qcur, "Qcur", il);
// cb(Kcur, "Kcur", il);
// cb(Vcur, "Vcur", il);
// cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo,
// Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
//}
if (il == n_transformer_layers - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
//inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
cb(sa_out, "sa_out", il);
//ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
//cb(sa_out, "sa_out", il);
// MoE branch
cur = llm_build_norm(ctx0, sa_out, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
//cur = llm_build_norm(ctx0, sa_out, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
//cb(cur, "ffn_norm", il);
if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
cur = llm_build_ffn(ctx0, lctx, nullptr, cur,
cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * moe_out =
llm_build_moe_ffn(ctx0, lctx, cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm,
true, hparams.expert_weights_scale,
(llm_expert_gating_func_type) hparams.expert_gating_func,
cb, il, gf);
cb(moe_out, "ffn_moe_out", il);
cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
model.layers[il].ffn_gate_inp, nullptr,
model.layers[il].ffn_up_exps, nullptr,
model.layers[il].ffn_gate_exps, nullptr,
model.layers[il].ffn_down_exps, nullptr,
model.layers[il].ffn_exp_probs_b,
model.layers[il].ffn_up_shexp, nullptr,
model.layers[il].ffn_gate_shexp, nullptr,
model.layers[il].ffn_down_shexp, nullptr,
n_expert, n_expert_used,
LLM_FFN_SILU, hparams.expert_weights_norm, true, hparams.expert_weights_scale,
(llm_expert_gating_func_type) hparams.expert_gating_func,
LLM_FFN_SILU,
cb, il, gf, true);
ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, nullptr, cur,
model.layers[il].ffn_up_shexp, NULL, NULL,
model.layers[il].ffn_gate_shexp, NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(ffn_shexp, "ffn_shexp", il);
//ggml_tensor * moe_out =
// llm_build_moe_ffn(ctx0, lctx, cur,
// model.layers[il].ffn_gate_inp,
// model.layers[il].ffn_up_exps,
// model.layers[il].ffn_gate_exps,
// model.layers[il].ffn_down_exps,
// model.layers[il].ffn_exp_probs_b,
// n_expert, n_expert_used,
// LLM_FFN_SILU, hparams.expert_weights_norm,
// true, hparams.expert_weights_scale,
// (llm_expert_gating_func_type) hparams.expert_gating_func,
// cb, il, gf);
//cb(moe_out, "ffn_moe_out", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
//ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, nullptr, cur,
// model.layers[il].ffn_up_shexp, NULL, NULL,
// model.layers[il].ffn_gate_shexp, NULL, NULL,
// model.layers[il].ffn_down_shexp, NULL, NULL,
// NULL,
// LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
//cb(ffn_shexp, "ffn_shexp", il);
//cur = ggml_add(ctx0, moe_out, ffn_shexp);
//cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, sa_out);
//cur = ggml_add(ctx0, cur, sa_out);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
@@ -8542,17 +8560,21 @@ ggml_cgraph * llm_build_context::build_bailingmoe2() {
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
cb(cur, "result_output", -1);
//cur = inpL;
//cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
//cb(cur, "result_norm", -1);
//// lm_head
//cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
//cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
@@ -9160,7 +9182,9 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
float freq_base_l = n_swa > 0 ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
float freq_scale_l = n_swa > 0 ? hparams.rope_freq_scale_train_swa : hparams.rope_freq_scale_train;
if (!model.layers[il].wqkv && !model.layers[il].wqk && cparams.flash_attn &&
auto wqkv = lctx.model.split_mode == LLAMA_SPLIT_MODE_GRAPH ? nullptr : model.layers[il].wqkv;
if (!wqkv && !model.layers[il].wqk && cparams.flash_attn &&
model.layers[il].wq->extra && model.layers[il].wk->extra && model.layers[il].wv->extra && model.layers[il].wo->extra) {
if (kv_self.k_l[il]->extra && kv_self.v_l[il]->extra) {
ggml_split_tensor_t * attn_norm = the_attn_norm ? (ggml_split_tensor_t *)the_attn_norm->extra : nullptr;

34
src/llama-load-tensors.cpp
View File

@@ -2428,14 +2428,13 @@ bool create_tensors_helper::create_bailingmoe2_tensors(const LLM_TN & tn) {
// output
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for bailingmoe2");
GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for bailingmoe2");
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(i);
int flags = 0;
@@ -2444,21 +2443,32 @@ bool create_tensors_helper::create_bailingmoe2_tensors(const LLM_TN & tn) {
flags |= llama_model_loader::TENSOR_SKIP;
}
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, flags);
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
if (model.split_mode == LLAMA_SPLIT_MODE_GRAPH) {
layer.wq = ggml_view_2d(ctx_split, layer.wqkv, layer.wqkv->ne[0], n_embd_head_k*n_head, layer.wqkv->nb[1], 0);
ggml_set_name(layer.wq, tn(LLM_TENSOR_ATTN_Q, "weight", i).c_str());
layer.wk = ggml_view_2d(ctx_split, layer.wqkv, layer.wqkv->ne[0], n_embd_head_k*n_head_kv, layer.wqkv->nb[1],
n_embd_head_k*n_head*layer.wqkv->nb[1]);
ggml_set_name(layer.wk, tn(LLM_TENSOR_ATTN_K, "weight", i).c_str());
layer.wv = ggml_view_2d(ctx_split, layer.wqkv, layer.wqkv->ne[0], n_embd_head_k*n_head_kv, layer.wqkv->nb[1],
n_embd_head_k*(n_head + n_head_kv)*layer.wqkv->nb[1]);
ggml_set_name(layer.wv, tn(LLM_TENSOR_ATTN_V, "weight", i).c_str());
}
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
layer.attn_q_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
layer.attn_k_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
layer.ffn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
const int64_t n_ff_shexp = (hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp) * n_expert_shared;
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert},
layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
layer.ffn_exp_probs_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert},
llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
@@ -2479,11 +2489,11 @@ bool create_tensors_helper::create_bailingmoe2_tensors(const LLM_TN & tn) {
layer.nextn.eh_proj = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
layer.nextn.embed_tokens = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab },
llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.nextn.enorm = create_tensor(ctx_layer, tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
layer.nextn.hnorm = create_tensor(ctx_layer, tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
layer.nextn.enorm = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
layer.nextn.hnorm = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
layer.nextn.shared_head_head = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.nextn.shared_head_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.layer_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, flags);
layer.nextn.shared_head_norm = create_tensor(ctx_split, tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.layer_out_norm = create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, flags);
}
}
return use_mmap_buffer;

1
src/llama.cpp
View File

@@ -1736,6 +1736,7 @@ static bool is_model_split_supported(const llama_model & model) {
LLM_ARCH_HUNYUAN_MOE,
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_ERNIE4_5_MOE,
LLM_ARCH_BAILINGMOE2,
};
auto it = k_supported.find(model.arch);
return it != k_supported.end();