#include "models.h"
void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
// A layer is recurrent IFF the n_head_kv value is set to 0 and
// the n_ff value is set to 0
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
hparams.recurrent_layer_arr[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
}
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, false);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_MOE_LATENT_SIZE, hparams.moe_latent_size, false);
switch (hparams.n_layer) {
case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
case 56: type = LLM_TYPE_9B; break;
case 88: type = LLM_TYPE_120B_A12B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_nemotron_h::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
// mamba2 Mixer SSM params
// NOTE: int64_t for tensor dimensions
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
const int64_t d_state = hparams.ssm_d_state;
const int64_t n_ssm_head = hparams.ssm_dt_rank;
const int64_t n_group = hparams.ssm_n_group;
const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_ssm_head;
const int64_t moe_n_embd = hparams.moe_latent_size > 0 ? hparams.moe_latent_size : n_embd;
// embeddings
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
{
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 is NULL, init from the input tok embed, duplicated to allow offloading
if (output == NULL) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
// all blocks use the attn norm
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
if (hparams.is_recurrent(i)) {
// ssm layers
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
// no "weight" suffix for these
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
// out_proj
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
} else if (hparams.n_ff(i) == 0) {
// attention layers (with optional bias)
const int64_t n_head_i = hparams.n_head(i);
const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
} else {
if (n_expert != 0) {
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_shexp = hparams.n_ff_shexp;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert }, 0);
// MoE branch
layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, moe_n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0);
// Shared expert branch
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
} else {
// mlp layers
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { hparams.n_ff(i), n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, hparams.n_ff(i)}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
}
}
}
}
std::unique_ptr<llm_graph_context> llama_model_nemotron_h::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_build_mamba_base(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_build_forward_expand(gf, inpL);
auto * inp = build_inp_mem_hybrid();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
if (hparams.is_recurrent(il)) {
// ssm layer //
cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
} else if (hparams.n_ff(il) == 0) {
// attention layer //
cur = build_attention_layer(cur, inp->get_attn(), model, n_embd_head, il);
} else {
cur = build_ffn_layer(cur, model, 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);
}
// add residual
cur = ggml_add(ctx0, cur, inpSA);
cb(cur, "nemotron_h_block_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
ggml_tensor * llama_model_nemotron_h::graph::build_attention_layer(ggml_tensor * cur,
llm_graph_input_attn_kv * inp_attn,
const llama_model & model,
int64_t n_embd_head,
int il) {
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);
const float kq_scale =
hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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);
return cur;
}
ggml_tensor * llama_model_nemotron_h::graph::build_ffn_layer(ggml_tensor * cur, const llama_model & model, int il) {
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
NULL, NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
ggml_tensor * inp_emb = cur;
ggml_tensor * inp_latent = cur;
if (model.layers[il].ffn_latent_down) {
inp_latent = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_down, cur);
}
ggml_tensor * router_logits = build_lora_mm(model.layers[il].ffn_gate_inp, cur);
cb(router_logits, "ffn_moe_logits", il);
ggml_tensor * moe_out =
build_moe_ffn(inp_latent,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
nullptr, // no gate
model.layers[il].ffn_down_exps,
model.layers[il].ffn_exp_probs_b,
n_expert, n_expert_used,
LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
il,
router_logits, nullptr,
model.layers[il].ffn_up_exps_s,
nullptr, // no gate
model.layers[il].ffn_down_exps_s);
cb(moe_out, "ffn_moe_out", il);
if (model.layers[il].ffn_latent_up) {
moe_out = ggml_mul_mat(ctx0, model.layers[il].ffn_latent_up, moe_out);
}
ggml_tensor * ffn_shexp = build_ffn(inp_emb,
model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
NULL /* no gate */ , NULL, NULL,
model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
NULL,
LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
cb(ffn_shexp, "ffn_shexp", il);
cur = ggml_add(ctx0, moe_out, ffn_shexp);
cb(cur, "ffn_out", il);
}
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
return cur;
}
