model: add Hy3 (hy_v3) support with MTP speculative decoding (#25395)
* model: add Hy3 (hy_v3) architecture support Adds Tencent Hunyuan 3 (HF architecture HYV3ForCausalLM, GGUF arch hy_v3): a MoE decoder stack with per-head Q/K RMSNorm, a sigmoid router with expert selection bias, an always-active ungated shared expert, and leading dense block(s) (first_k_dense_replace). The base implementation is ported from charlie12345's fork (https://github.com/charlie12345/ROCmFPX, src/models/hyv3.cpp), adapted to current mainline APIs (hparams.n_layer(), build_qkv, build_moe_ffn with fused gate_up + scale tensors, output_s). Note: blk.N.exp_probs_b is stored without a .bias suffix for compatibility with existing hy_v3 GGUFs produced by that fork. Co-Authored-By: charlie12345 <charlie12345@users.noreply.github.com> Co-authored-by: Piotr Wilkin <ilintar@gmail.com> Assisted-by: Claude Fable 5
This commit is contained in:
Satinder Grewal
co-authored by
charlie12345
Piotr Wilkin
charlie12345
Piotr Wilkin
parent
6eddde06a4
commit
2969d6d15d
@@ -113,6 +113,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
||||
{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
|
||||
{ LLM_ARCH_HUNYUAN_DENSE, "hunyuan-dense" },
|
||||
{ LLM_ARCH_HUNYUAN_VL, "hunyuan_vl" },
|
||||
{ LLM_ARCH_HY_V3, "hy_v3" },
|
||||
{ LLM_ARCH_SMOLLM3, "smollm3" },
|
||||
{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
|
||||
{ LLM_ARCH_LFM2, "lfm2" },
|
||||
|
||||
@@ -118,6 +118,7 @@ enum llm_arch {
|
||||
LLM_ARCH_HUNYUAN_MOE,
|
||||
LLM_ARCH_HUNYUAN_DENSE,
|
||||
LLM_ARCH_HUNYUAN_VL,
|
||||
LLM_ARCH_HY_V3,
|
||||
LLM_ARCH_SMOLLM3,
|
||||
LLM_ARCH_OPENAI_MOE,
|
||||
LLM_ARCH_LFM2,
|
||||
|
||||
+4
-1
@@ -262,6 +262,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
|
||||
return new llama_model_hunyuan_vl(params);
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
return new llama_model_hunyuan_dense(params);
|
||||
case LLM_ARCH_HY_V3:
|
||||
return new llama_model_hy_v3(params);
|
||||
case LLM_ARCH_SMOLLM3:
|
||||
return new llama_model_smollm3(params);
|
||||
case LLM_ARCH_OPENAI_MOE:
|
||||
@@ -2169,7 +2171,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
||||
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
}
|
||||
|
||||
if (arch == LLM_ARCH_STEP35 && hparams.n_layer_nextn > 0) {
|
||||
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3) && hparams.n_layer_nextn > 0) {
|
||||
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
|
||||
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
} else {
|
||||
@@ -2525,6 +2527,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
||||
case LLM_ARCH_JAIS2:
|
||||
case LLM_ARCH_OPENAI_MOE:
|
||||
case LLM_ARCH_HUNYUAN_DENSE:
|
||||
case LLM_ARCH_HY_V3:
|
||||
case LLM_ARCH_LFM2:
|
||||
case LLM_ARCH_LFM2MOE:
|
||||
case LLM_ARCH_SMALLTHINKER:
|
||||
|
||||
@@ -0,0 +1,390 @@
|
||||
#include "models.h"
|
||||
|
||||
void llama_model_hy_v3::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
||||
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
||||
|
||||
// HY V3 uses a sigmoid router with expert selection bias by default
|
||||
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
|
||||
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
|
||||
}
|
||||
|
||||
// NextN/MTP (HY V3): extra decoder block(s) appended beyond the main stack
|
||||
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
|
||||
GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
|
||||
|
||||
switch (hparams.n_layer()) {
|
||||
case 48: type = LLM_TYPE_30B_A3B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_hy_v3::load_arch_tensors(llama_model_loader & ml) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
|
||||
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
|
||||
// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
|
||||
// tensors live in a separate file (e.g. user split target/draft). Mark
|
||||
// MTP tensors NOT_REQUIRED so the trunk loads cleanly.
|
||||
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
|
||||
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
|
||||
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
if (output == NULL) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
auto load_block = [&](int i, int flags) {
|
||||
auto & layer = layers[i];
|
||||
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / (n_expert_used > 0 ? n_expert_used : 1);
|
||||
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp;
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
|
||||
|
||||
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);
|
||||
|
||||
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
|
||||
|
||||
// dense FFN (leading dense blocks, first_k_dense_replace)
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
// MoE routed experts (sigmoid router + expert selection bias)
|
||||
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, i), {n_expert}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED);
|
||||
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, TENSOR_NOT_REQUIRED);
|
||||
|
||||
// shared expert (always active, no gate)
|
||||
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
|
||||
};
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
load_block(i, trunk_flags);
|
||||
}
|
||||
|
||||
// NextN/MTP block(s): a full hy_v3 decoder block plus the NextN projections.
|
||||
for (int i = n_layer; i < n_layer_all; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
load_block(i, mtp_flags);
|
||||
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, mtp_flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, mtp_flags);
|
||||
layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, mtp_flags);
|
||||
layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
|
||||
layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
|
||||
// hy_v3 stores the MTP block's trailing final_layernorm here (applied
|
||||
// after the decoder block, before the shared LM head).
|
||||
layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_hy_v3::build_arch_graph(const llm_graph_params & params) const {
|
||||
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
|
||||
return std::make_unique<graph_mtp>(*this, params);
|
||||
}
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
llama_model_hy_v3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v();
|
||||
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
|
||||
GGML_ASSERT(n_embd_head == n_rot);
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
auto * inp_attn = build_attn_inp_kv();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
|
||||
|
||||
// MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
|
||||
|
||||
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il);
|
||||
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
|
||||
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);
|
||||
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);
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "attn_out", il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
|
||||
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 = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
if (model.layers[il].ffn_gate_inp == nullptr) {
|
||||
// dense FFN (leading dense blocks)
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
|
||||
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_dense_out", il);
|
||||
} else {
|
||||
// MoE routed experts (sigmoid gating + expert selection bias)
|
||||
ggml_tensor * moe_out = build_moe_ffn(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,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr, model.layers[il].ffn_gate_up_exps,
|
||||
model.layers[il].ffn_up_exps_s,
|
||||
model.layers[il].ffn_gate_exps_s,
|
||||
model.layers[il].ffn_down_exps_s);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
// shared expert (always active, no gate)
|
||||
ggml_tensor * sh_out = build_ffn(cur,
|
||||
model.layers[il].ffn_up_shexp, nullptr, model.layers[il].ffn_up_shexp_s,
|
||||
model.layers[il].ffn_gate_shexp, nullptr, model.layers[il].ffn_gate_shexp_s,
|
||||
model.layers[il].ffn_down_shexp, nullptr, model.layers[il].ffn_down_shexp_s,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(sh_out, "ffn_shared_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, sh_out);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1);
|
||||
|
||||
// Post-final-norm hidden state: what the MTP draft head's hnorm consumes.
|
||||
// vLLM feeds the target model's normed output states, and the MTP layer
|
||||
// itself returns final_layernorm(h), so the chained state is post-norm.
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
cur = build_lora_mm(model.output, cur, model.output_s);
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// LLM_GRAPH_TYPE_DECODER_MTP draft head for HY V3 (MoE).
|
||||
// Semantics mirror vLLM's HYV3MultiTokenPredictorLayer (hy_v3_mtp.py):
|
||||
// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->
|
||||
// hy_v3 decoder block -> final_layernorm (stored as nextn.shared_head_norm) ->
|
||||
// shared LM head (the main model's lm_head; the checkpoint has no separate
|
||||
// MTP head or MTP embeddings).
|
||||
llama_model_hy_v3::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
|
||||
: llm_graph_context(params) {
|
||||
GGML_ASSERT(hparams.n_layer_nextn > 0 && "HY_V3 MTP requires n_layer_nextn > 0");
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
|
||||
GGML_ASSERT(n_embd_head == n_rot);
|
||||
|
||||
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
|
||||
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
|
||||
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
|
||||
"nextn_layer_offset out of range [0, n_layer_nextn)");
|
||||
const auto & layer = model.layers[il];
|
||||
|
||||
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
|
||||
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
|
||||
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
|
||||
|
||||
auto inp = std::make_unique<llm_graph_input_embd>(hparams.n_embd);
|
||||
|
||||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
ggml_set_input(inp->tokens);
|
||||
|
||||
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
|
||||
ggml_set_input(inp->embd);
|
||||
ggml_set_name(inp->embd, "mtp_h_input");
|
||||
|
||||
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
|
||||
|
||||
ggml_tensor * h_input = inp->embd;
|
||||
ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
|
||||
cb(tok_embd, "mtp_tok_embd", il);
|
||||
|
||||
res->add_input(std::move(inp));
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
auto * inp_attn = build_attn_inp_kv();
|
||||
|
||||
ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(h_norm, "mtp_hnorm", il);
|
||||
|
||||
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(e_norm, "mtp_enorm", il);
|
||||
|
||||
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
|
||||
cb(concat, "mtp_concat", il);
|
||||
|
||||
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat);
|
||||
cb(cur, "mtp_eh_proj", il);
|
||||
|
||||
ggml_tensor * inpSA = cur;
|
||||
|
||||
// mtp_block: a full hy_v3 decoder layer (mirrors the trunk graph)
|
||||
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_attn_norm", il);
|
||||
|
||||
{
|
||||
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
|
||||
|
||||
auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il);
|
||||
|
||||
Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
|
||||
Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
||||
|
||||
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);
|
||||
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);
|
||||
|
||||
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
layer.wo, layer.wo_b, layer.wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "mtp_attn_out", il);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "mtp_ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_ffn_norm", il);
|
||||
|
||||
if (layer.ffn_gate_inp == nullptr) {
|
||||
cur = build_ffn(cur,
|
||||
layer.ffn_up, layer.ffn_up_b, layer.ffn_up_s,
|
||||
layer.ffn_gate, layer.ffn_gate_b, layer.ffn_gate_s,
|
||||
layer.ffn_down, layer.ffn_down_b, layer.ffn_down_s,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "mtp_ffn_dense_out", il);
|
||||
} else {
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
layer.ffn_gate_inp,
|
||||
layer.ffn_up_exps,
|
||||
layer.ffn_gate_exps,
|
||||
layer.ffn_down_exps,
|
||||
layer.ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU,
|
||||
hparams.expert_weights_norm,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr, layer.ffn_gate_up_exps,
|
||||
layer.ffn_up_exps_s,
|
||||
layer.ffn_gate_exps_s,
|
||||
layer.ffn_down_exps_s);
|
||||
cb(moe_out, "mtp_ffn_moe_out", il);
|
||||
|
||||
ggml_tensor * sh_out = build_ffn(cur,
|
||||
layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s,
|
||||
layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
|
||||
layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
|
||||
nullptr,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(sh_out, "mtp_ffn_shared_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, sh_out);
|
||||
cb(cur, "mtp_ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "mtp_post_ffn", il);
|
||||
|
||||
// final_layernorm applied after the decoder block, before the shared head.
|
||||
// The post-norm hidden state seeds the next MTP step (matches vLLM, where
|
||||
// HYV3MultiTokenPredictorLayer returns final_layernorm(h)).
|
||||
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
|
||||
? layer.nextn.shared_head_norm
|
||||
: model.output_norm;
|
||||
GGML_ASSERT(head_norm_w && "HY_V3 MTP: missing both nextn.shared_head_norm and output_norm");
|
||||
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
cb(cur, "mtp_shared_head_norm", -1);
|
||||
|
||||
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
|
||||
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
|
||||
GGML_ASSERT(head_w && "HY_V3 MTP: missing LM head (nextn.shared_head_head or model.output)");
|
||||
cur = build_lora_mm(head_w, cur, head_s);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
res->t_logits = cur;
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
@@ -1729,6 +1729,22 @@ struct llama_model_hunyuan_moe : public llama_model_base {
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
struct llama_model_hy_v3 : public llama_model_base {
|
||||
llama_model_hy_v3(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
void load_arch_tensors(llama_model_loader & ml) override;
|
||||
|
||||
struct graph : public llm_graph_context {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct graph_mtp : public llm_graph_context {
|
||||
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
|
||||
struct llama_model_hunyuan_vl : public llama_model_base {
|
||||
llama_model_hunyuan_vl(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
|
||||
Reference in New Issue
Block a user