// Add additional layer/vocab/etc checks here for other model sizes
default: type = LLM_TYPE_UNKNOWN;
}
+
+ // For Granite MoE Shared
+ ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
} break;
case LLM_ARCH_CHAMELEON:
{
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
+
+ // For Granite MoE Shared
+ if (hparams.n_ff_shexp > 0) {
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
+ }
}
}
} break;
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
}
- if (arch == LLM_ARCH_MINICPM || arch == LLM_ARCH_GRANITE || arch == LLM_ARCH_GRANITE_MOE) {
+ if (arch == LLM_ARCH_MINICPM ||
+ arch == LLM_ARCH_GRANITE ||
+ arch == LLM_ARCH_GRANITE_MOE) {
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
+ LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
}
if (arch == LLM_ARCH_BAILINGMOE) {
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
- // For Granite architecture
- if (hparams.f_residual_scale) {
- cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
- }
-
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cb(cur, "ffn_moe_out", il);
}
- // For Granite architecture
- if (hparams.f_residual_scale) {
- cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
- }
-
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
// lm_head
cur = build_lora_mm(model.output, cur);
- // For Granite architecture
- if (hparams.f_logit_scale) {
- cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
- }
-
cb(cur, "result_output", -1);
res->t_logits = cur;
continue;
}
- // For Granite architecture
- if (hparams.f_residual_scale) {
- cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
- }
-
// modified to support attention-free layer of Llama-3_1-Nemotron-51B
ggml_tensor * ffn_inp = cur;
if (n_head > 0) {
cb(cur, "ffn_out", il);
}
- // For Granite architecture
- if (hparams.f_residual_scale) {
- cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
- }
-
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
// lm_head
cur = build_lora_mm(model.output, cur);
- // For Granite architecture
- if (hparams.f_logit_scale) {
- cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
- }
-
cb(cur, "result_output", -1);
res->t_logits = cur;
}
};
+
+struct llm_build_granite : public llm_graph_context {
+ llm_build_granite(
+ const llama_model & model,
+ const llm_graph_params & params,
+ ggml_cgraph * gf,
+ const bool use_rope = true)
+ : 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 == hparams.n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - built only if rope enabled
+ ggml_tensor * inp_pos = nullptr;
+
+ auto * inp_attn = build_attn_inp_kv_unified();
+
+ const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and (optionally) RoPE them
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ 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);
+
+ if (use_rope) {
+
+ if (!inp_pos) {
+ inp_pos = build_inp_pos();
+ }
+ ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, 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
+ );
+ }
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(inp_attn, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ // For Granite architectures - scale residual
+ cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network (non-MoE)
+ if (model.layers[il].ffn_gate_inp == nullptr) {
+
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+
+ } else {
+ // MoE branch
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ 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,
+ nullptr,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, true,
+ false, 0.0,
+ LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+ il);
+ cb(moe_out, "ffn_moe_out", il);
+
+ // For Granite MoE Shared
+ if (hparams.n_ff_shexp > 0) {
+ ggml_tensor * ffn_shexp = build_ffn(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, il);
+ cb(ffn_shexp, "ffn_shexp", il);
+
+ cur = ggml_add(ctx0, moe_out, ffn_shexp);
+ cb(cur, "ffn_out", il);
+ } else {
+ cur = moe_out;
+ }
+ }
+
+ // For Granite architectures - scale residual
+ cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+ 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;
+
+ 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);
+
+ // For Granite architectures - scale logits
+ cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+};
+
// ref: https://github.com/facebookresearch/chameleon
// based on the original build_llama() function, changes:
// * qk-norm
case LLM_ARCH_LLAMA:
case LLM_ARCH_LLAMA4:
case LLM_ARCH_MINICPM:
- case LLM_ARCH_GRANITE:
- case LLM_ARCH_GRANITE_MOE:
{
llm = std::make_unique<llm_build_llama>(*this, params, gf);
} break;
{
llm = std::make_unique<llm_build_arwkv7>(*this, params, gf);
} break;
+ case LLM_ARCH_GRANITE:
+ case LLM_ARCH_GRANITE_MOE:
+ {
+ llm = std::make_unique<llm_build_granite>(*this, params, gf);
+ } break;
case LLM_ARCH_CHAMELEON:
{
llm = std::make_unique<llm_build_chameleon>(*this, params, gf);
overview / pkg / ggml / sources / llama.cpp / commitdiff