EngineX-Ascend/enginex-ascend-910-llama.cpp
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llama : support LiquidAI LFM2-MoE hybrid model (#16464)

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* llama : support LiquidAI LFM2-MoE hybrid model

Add support for [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B) model.
For more information about models, please read [the blog post](https://www.liquid.ai/company/news).

[HF PR](https://github.com/huggingface/transformers/pull/41401)
[GGUFs](https://huggingface.co/LiquidAI/LFM2-8B-A1B-GGUF)

* Do not use defaultdict

* Address PR feedback
This commit is contained in:
Tarek Dakhran
2025-10-07 20:03:35 +02:00
committed by GitHub
parent df1b612e29
commit aeaf8a36f0
7 changed files with 192 additions and 15 deletions
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81
src/llama-model.cpp
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@@ -114,6 +114,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
case LLM_TYPE_A13B: return "A13B";
case LLM_TYPE_8B_A1B: return "8B.A1B";
case LLM_TYPE_21B_A3B: return "21B.A3B";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_106B_A12B: return "106B.A12B";
@@ -1995,14 +1996,29 @@ void llama_model::load_hparams(llama_model_loader & ml) {
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
}
hparams.n_layer_dense_lead = hparams.n_layer;
switch (hparams.n_ff()) {
case 4608: type = LLM_TYPE_350M; break;
case 6912: type = LLM_TYPE_700M; break;
case 8192: type = LLM_TYPE_1_2B; break;
case 10752: type = LLM_TYPE_2_6B; break;
default: type = LLM_TYPE_UNKNOWN;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_LFM2MOE:
{
ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
}
type = LLM_TYPE_8B_A1B;
} break;
case LLM_ARCH_SMALLTHINKER:
{
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
@@ -5814,6 +5830,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
} break;
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
@@ -5825,11 +5842,23 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
// ffn is same for transformer and conv layers
const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
// ffn/moe is same for transformer and conv layers
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
if (is_moe_layer) {
GGML_ASSERT(n_expert && n_expert_used);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
} else { // dense
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
// for operator_norm
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
@@ -6310,7 +6339,7 @@ void llama_model::print_info() const {
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
}
if (arch == LLM_ARCH_SMALLTHINKER) {
if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
}
@@ -18602,6 +18631,8 @@ struct llm_build_lfm2 : public llm_graph_context {
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const bool is_moe_layer = il >= static_cast<int>(hparams.n_layer_dense_lead);
auto * prev_cur = cur;
cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "model.layers.{}.operator_norm", il);
@@ -18616,7 +18647,16 @@ struct llm_build_lfm2 : public llm_graph_context {
}
cur = ggml_add(ctx0, prev_cur, cur);
cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(ffn_norm_out, "model.layers.{}.ffn_norm", il);
ggml_tensor * ffn_out = is_moe_layer ?
build_moe_feed_forward(ffn_norm_out, il) :
build_dense_feed_forward(ffn_norm_out, il);
cb(ffn_norm_out, "model.layers.{}.ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_out);
}
cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
@@ -18631,23 +18671,32 @@ struct llm_build_lfm2 : public llm_graph_context {
ggml_build_forward_expand(gf, cur);
}
ggml_tensor * build_feed_forward(ggml_tensor * cur,
int il) const {
cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "model.layers.{}.ffn_norm", il);
ggml_tensor * build_moe_feed_forward(ggml_tensor * cur,
int il) const {
return 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, true,
false, 0.0,
static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
il);
}
ggml_tensor * build_dense_feed_forward(ggml_tensor * cur,
int il) const {
GGML_ASSERT(!model.layers[il].ffn_up_b);
GGML_ASSERT(!model.layers[il].ffn_gate_b);
GGML_ASSERT(!model.layers[il].ffn_down_b);
cur = build_ffn(cur,
return build_ffn(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, il);
cb(cur, "model.layers.{}.feed_forward.w2", il);
return cur;
}
ggml_tensor * build_attn_block(ggml_tensor * cur,
@@ -19817,6 +19866,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
llm = std::make_unique<llm_build_falcon_h1>(*this, params);
} break;
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
{
llm = std::make_unique<llm_build_lfm2>(*this, params);
} break;
@@ -20039,6 +20089,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_OPENAI_MOE:
case LLM_ARCH_HUNYUAN_DENSE:
case LLM_ARCH_LFM2:
case LLM_ARCH_LFM2MOE:
case LLM_ARCH_SMALLTHINKER:
case LLM_ARCH_GLM4_MOE:
case LLM_ARCH_SEED_OSS: