mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
Split mode "graph" for Hunyuan-MoE (#1116)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
@@ -8316,7 +8316,6 @@ ggml_cgraph * llm_build_context::build_hunyuan_moe() {
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
@@ -8326,114 +8325,42 @@ ggml_cgraph * llm_build_context::build_hunyuan_moe() {
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
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);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// rope freq factors for llama3; may return nullptr for llama2 and other models
|
||||
struct ggml_tensor * rope_factors = build_rope_factors(il);
|
||||
|
||||
// compute Q and K and RoPE them
|
||||
auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, model.layers[il].wq, model.layers[il].bq,
|
||||
model.layers[il].wk, model.layers[il].bk,
|
||||
model.layers[il].wv, model.layers[il].bv, 0.f, il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, cb, il);
|
||||
cb(Kcur, "Kcur_norm", il);
|
||||
|
||||
Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, cb, il);
|
||||
cb(Qcur, "Qcur_norm", 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);
|
||||
cb(cur, "attn_out", il);
|
||||
}
|
||||
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);
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
cur = llm_build_norm(ctx0,ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
// feed-forward network (non-MoE)
|
||||
ggml_tensor * cur_mlp = 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(cur_mlp, "ffn_mlp", il);
|
||||
|
||||
// MoE branch
|
||||
ggml_tensor * cur_moe = 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,
|
||||
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,
|
||||
nullptr,
|
||||
model.layers[il].ffn_up_shexp, nullptr, // we don't have shared expert biases?
|
||||
model.layers[il].ffn_gate_shexp, nullptr,
|
||||
model.layers[il].ffn_down_shexp, nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU,
|
||||
true, // norm_topk_prob
|
||||
false,
|
||||
0.0,
|
||||
LLM_FFN_SILU, true, false, 0.0f,
|
||||
LLM_EXPERT_GATING_FUNC_SOFTMAX,
|
||||
cb,
|
||||
il, gf);
|
||||
cb(cur_moe, "ffn_moe_out", il);
|
||||
|
||||
ggml_tensor * ffn_out = ggml_add(ctx0, cur_moe, cur_mlp);
|
||||
cb(ffn_out, "ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, ffn_out, ffn_inp);
|
||||
LLM_FFN_SILU, cb, il, gf, true);
|
||||
|
||||
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// 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);
|
||||
//res->t_embd = cur;
|
||||
|
||||
// lm_head
|
||||
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
|
||||
|
||||
cur = build_output(lctx, ctx0, cur, model.output, model.output_norm, cb);
|
||||
cb(cur, "result_output", -1);
|
||||
//res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
|
||||
}
|
||||
|
||||
ggml_cgraph * llm_build_context::build_mimo2() {
|
||||
|
||||
@@ -2537,24 +2537,23 @@ bool create_tensors_helper::create_hunyuan_tensors(const LLM_TN & tn) {
|
||||
create_embd_output(tn, n_embd, n_vocab);
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
ggml_context * ctx_layer = ctx_for_layer(i);
|
||||
ggml_context * ctx_split = ctx_for_layer_split(i);
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
||||
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
||||
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
||||
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
||||
|
||||
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
layer.attn_k_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
layer.attn_q_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
|
||||
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
||||
layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
||||
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
||||
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
|
||||
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
||||
|
||||
@@ -1733,6 +1733,7 @@ static bool is_model_split_supported(const llama_model & model) {
|
||||
LLM_ARCH_MIMO2,
|
||||
LLM_ARCH_QWEN3,
|
||||
LLM_ARCH_QWEN3VL,
|
||||
LLM_ARCH_HUNYUAN_MOE,
|
||||
};
|
||||
auto it = k_supported.find(model.arch);
|
||||
return it != k_supported.end();
|
||||
|
||||
Reference in New Issue
Block a user