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Merged
ngxson merged 5 commits into from
Apr 16, 2026
Merged

model: using single llm_build per arch #21970

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be46a50
model: using single llm_build per arch
ngxson Apr 15, 2026
a2295b0
Merge branch 'master' into xsn/one_llm_build_per_arch
ngxson Apr 16, 2026
8c39fc8
fix merge
ngxson Apr 16, 2026
13a4d7a
Merge branch 'master' into xsn/one_llm_build_per_arch
ngxson Apr 16, 2026
e6125f8
nits
ngxson Apr 16, 2026
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6 changes: 3 additions & 3 deletions src/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -92,8 +92,8 @@ add_library(llama
models/lfm2.cpp
models/llada-moe.cpp
models/llada.cpp
models/llama-iswa.cpp
models/llama.cpp
models/llama4.cpp
models/maincoder.cpp
models/mamba-base.cpp
models/mamba.cpp
Expand Down Expand Up @@ -145,8 +145,8 @@ add_library(llama
models/starcoder.cpp
models/starcoder2.cpp
models/step35-iswa.cpp
models/t5-dec.cpp
models/t5-enc.cpp
models/t5.cpp
models/t5encoder.cpp
models/wavtokenizer-dec.cpp
models/xverse.cpp
)
Expand Down
31 changes: 12 additions & 19 deletions src/llama-model.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1271,8 +1271,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
// Set non-causal attention for diffusion models
hparams.causal_attn = false;
}
break;
} break;
case LLM_ARCH_LLADA:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
Expand All @@ -1286,8 +1285,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
// Set non-causal attention for diffusion models
hparams.causal_attn = false;
}
break;
} break;
case LLM_ARCH_LLADA_MOE:
{
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
Expand Down Expand Up @@ -8553,9 +8551,9 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
case LLM_ARCH_LLAMA4:
{
if (hparams.swa_type == LLAMA_SWA_TYPE_NONE) {
llm = std::make_unique<llm_build_llama<false>>(*this, params);
llm = std::make_unique<llm_build_llama4<false>>(*this, params);
} else {
llm = std::make_unique<llm_build_llama_iswa>(*this, params);
llm = std::make_unique<llm_build_llama4<true>>(*this, params);
}
} break;
case LLM_ARCH_LLAMA_EMBED:
Expand Down Expand Up @@ -8633,23 +8631,19 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
case LLM_ARCH_DREAM:
{
llm = std::make_unique<llm_build_dream>(*this, params);
}
break;
} break;
case LLM_ARCH_LLADA:
{
llm = std::make_unique<llm_build_llada>(*this, params);
}
break;
} break;
case LLM_ARCH_LLADA_MOE:
{
llm = std::make_unique<llm_build_llada_moe>(*this, params);
}
break;
} break;
case LLM_ARCH_RND1:
{
llm = std::make_unique<llm_build_rnd1>(*this, params);
}
break;
} break;
case LLM_ARCH_QWEN2VL:
{
llm = std::make_unique<llm_build_qwen2vl>(*this, params);
Expand Down Expand Up @@ -8839,21 +8833,20 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
switch (params.gtype) {
case LLM_GRAPH_TYPE_ENCODER:
llm = std::make_unique<llm_build_t5_enc>(*this, params);
llm = std::make_unique<llm_build_t5<true>>(*this, params);
break;
case LLM_GRAPH_TYPE_DEFAULT:
case LLM_GRAPH_TYPE_DECODER:
llm = std::make_unique<llm_build_t5_dec>(*this, params);
llm = std::make_unique<llm_build_t5<false>>(*this, params);
break;
default:
GGML_ABORT("invalid graph type");
};
} break;
case LLM_ARCH_T5ENCODER:
{
llm = std::make_unique<llm_build_t5_enc>(*this, params);
}
break;
llm = std::make_unique<llm_build_t5encoder>(*this, params);
} break;
case LLM_ARCH_JAIS:
{
llm = std::make_unique<llm_build_jais>(*this, params);
Expand Down
16 changes: 14 additions & 2 deletions src/models/llama-iswa.cpp → src/models/llama4.cpp
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#include "models.h"

llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
template <bool iswa>
llm_build_llama4<iswa>::llm_build_llama4(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());
Expand All @@ -18,7 +19,14 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_
ggml_tensor * inp_attn_scale = nullptr;
inp_attn_scale = build_inp_attn_scale();

auto * inp_attn = build_attn_inp_kv_iswa();
using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
inp_attn_type * inp_attn = nullptr;

if constexpr (iswa) {
inp_attn = build_attn_inp_kv_iswa();
} else {
inp_attn = build_attn_inp_kv();
}

const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;

Expand Down Expand Up @@ -157,3 +165,7 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_

ggml_build_forward_expand(gf, cur);
}

// Explicit template instantiations
template struct llm_build_llama4<false>;
template struct llm_build_llama4<true>;
16 changes: 9 additions & 7 deletions src/models/models.h
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Original file line number Diff line number Diff line change
Expand Up @@ -407,8 +407,9 @@ struct llm_build_llama : public llm_graph_context {
llm_build_llama(const llama_model & model, const llm_graph_params & params);
};

struct llm_build_llama_iswa : public llm_graph_context {
llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params);
template <bool iswa>
struct llm_build_llama4 : public llm_graph_context {
llm_build_llama4(const llama_model & model, const llm_graph_params & params);
};

struct llm_build_maincoder : public llm_graph_context {
Expand Down Expand Up @@ -495,7 +496,7 @@ struct llm_build_phi2 : public llm_graph_context {
llm_build_phi2(const llama_model & model, const llm_graph_params & params);
};

template<bool iswa>
template <bool iswa>
struct llm_build_phi3 : public llm_graph_context {
llm_build_phi3(const llama_model & model, const llm_graph_params & params);
};
Expand Down Expand Up @@ -701,12 +702,13 @@ struct llm_build_step35_iswa : public llm_graph_context {
llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params);
};

struct llm_build_t5_dec : public llm_graph_context {
llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
template <bool is_enc>
struct llm_build_t5 : public llm_graph_context {
llm_build_t5(const llama_model & model, const llm_graph_params & params);
};

struct llm_build_t5_enc : public llm_graph_context {
llm_build_t5_enc(const llama_model & model, const llm_graph_params & params);
struct llm_build_t5encoder : public llm_build_t5<true> {
llm_build_t5encoder(const llama_model & model, const llm_graph_params & params);
Comment on lines +710 to +711

@ggerganov ggerganov Apr 16, 2026

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Do we need to keep this, instead of using llm_build_t5<true>?

@CISC CISC Apr 16, 2026

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I think it's there to match the arch name (for the migration script)?

@ngxson ngxson Apr 16, 2026

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@ggerganov the t5encoder has its own tensor loader and hparams loader, so I think it make sense to have a dedicated graph builder. The dedicated class will then expanded into llama_model_t5encoder during the migration (via a script), and its hparams/tensors loader will be moved there

};

struct llm_build_wavtokenizer_dec : public llm_graph_context {
Expand Down
96 changes: 0 additions & 96 deletions src/models/t5-enc.cpp
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This file was deleted.

99 changes: 98 additions & 1 deletion src/models/t5-dec.cpp → src/models/t5.cpp
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#include "models.h"

llm_build_t5_dec::llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
template <>
llm_build_t5<false>::llm_build_t5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
//const int64_t n_embd_gqa = hparams.n_embd_v_gqa();

Expand Down Expand Up @@ -153,3 +154,99 @@ llm_build_t5_dec::llm_build_t5_dec(const llama_model & model, const llm_graph_pa

ggml_build_forward_expand(gf, cur);
}

template <>
llm_build_t5<true>::llm_build_t5(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_tensor * cur;
ggml_tensor * inpL;

inpL = build_inp_embd(model.tok_embd);

ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();

auto * inp_attn = build_attn_inp_no_cache();

ggml_tensor * inp_out_ids = build_inp_out_ids();

for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;

// norm
cur = build_norm(inpL,
model.layers[il].attn_norm_enc, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);

// self-attention
{
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);
cb(Qcur, "Qcur", il);

ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);
cb(Kcur, "Kcur", il);

ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_enc, cur);
cb(Vcur, "Vcur", 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);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);

cur = build_attn(inp_attn,
model.layers[il].wo_enc, nullptr, nullptr,
Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
cb(cur, "kqv_out", il);
}
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);

// feed-forward network
{
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm_enc, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);

// T5 uses relu, flan-T5 uses gelu-gated
cur = build_ffn(cur,
model.layers[il].ffn_up_enc, NULL, NULL,
model.layers[il].ffn_gate_enc, NULL, NULL,
model.layers[il].ffn_down_enc, NULL, NULL,
NULL,
model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);

cur = build_cvec(cur, il);
cb(cur, "l_out", il);

// input for next layer
inpL = cur;
}
cur = inpL;
cb(cur, "result_embd", -1);

cur = build_norm(cur,
model.output_norm_enc, NULL,
LLM_NORM_RMS, -1);

cb(cur, "result_norm", -1);
res->t_embd = cur;

ggml_build_forward_expand(gf, cur);
}
3 changes: 3 additions & 0 deletions src/models/t5encoder.cpp
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Original file line number Diff line number Diff line change
@@ -0,0 +1,3 @@
#include "models.h"

llm_build_t5encoder::llm_build_t5encoder(const llama_model & model, const llm_graph_params & params) : llm_build_t5<true>(model, params) {}
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