ggml_tensor * w_s) const {
ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
+ if (w_s) {
+ res = ggml_mul(ctx0, res, w_s);
+ }
+
for (const auto & lora : *loras) {
llama_adapter_lora_weight * lw = lora.first->get_weight(w);
if (lw == nullptr) {
res = ggml_add(ctx0, res, ab_cur);
}
- if (w_s) {
- res = ggml_mul(ctx0, res, w_s);
- }
-
return res;
}
ggml_tensor * llm_graph_context::build_lora_mm_id(
ggml_tensor * w, // ggml_tensor * as
ggml_tensor * cur, // ggml_tensor * b
- ggml_tensor * ids) const {
+ ggml_tensor * ids,
+ ggml_tensor * w_s) const {
ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
+
+ if (w_s) {
+ const int64_t n_expert = w_s->ne[0];
+ const int64_t n_tokens = cur->ne[2];
+ ggml_tensor * s = ggml_reshape_3d(ctx0, w_s, 1, n_expert, 1);
+ s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
+ s = ggml_get_rows(ctx0, s, ids);
+ res = ggml_mul(ctx0, res, s);
+ }
for (const auto & lora : *loras) {
llama_adapter_lora_weight * lw = lora.first->get_weight(w);
if (lw == nullptr) {
llm_ffn_op_type type_op,
llm_ffn_gate_type type_gate,
int il) const {
+ // NVFP4 support is currently restricted to
+ // 1) LORA absence (*_s would be applied after LORA residual, which is incorrect)
+ // 2) bias absense (*_s would be applied after bias addition, which is incorrect)
+ // TODO: disambiguate LLM-architectural scales (which use *_s) from NVFP4 scale_2 (which also uses *_s currently)
+ auto has_lora = [this](ggml_tensor * w) {
+ if (!w) {
+ return false;
+ }
+ for (const auto & lora : *loras) {
+ if (lora.first->get_weight(w) != nullptr) {
+ return true;
+ }
+ }
+ return false;
+ };
+
+ GGML_ASSERT(!up_s || !up_b || !up || up->type != GGML_TYPE_NVFP4);
+ GGML_ASSERT(!gate_s || !gate_b || !gate || gate->type != GGML_TYPE_NVFP4);
+ GGML_ASSERT(!down_s || !down_b || !down || down->type != GGML_TYPE_NVFP4);
+ GGML_ASSERT(!up_s || !up || up->type != GGML_TYPE_NVFP4 || !has_lora(up));
+ GGML_ASSERT(!gate_s || !gate || gate->type != GGML_TYPE_NVFP4 || !has_lora(gate));
+ GGML_ASSERT(!down_s || !down || down->type != GGML_TYPE_NVFP4 || !has_lora(down));
+
ggml_tensor * tmp = up ? build_lora_mm(up, cur) : cur;
cb(tmp, "ffn_up", il);
if (gate_up_exps) {
// merged gate_up path: one mul_mat_id, then split into gate and up views
- ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts); // [n_ff*2, n_expert_used, n_tokens]
+ ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts, up_exps_s); // [n_ff*2, n_expert_used, n_tokens]
cb(gate_up, "ffn_moe_gate_up", il);
+ if (up_exps_s) {
+ cb(gate_up, "ffn_moe_gate_up_scaled", il);
+ }
+
if (gate_up_exps_b) {
gate_up = ggml_add_id(ctx0, gate_up, gate_up_exps_b, selected_experts);
cb(gate_up, "ffn_moe_gate_up_biased", il);
}
- // apply per-expert scale2 to merged gate_up (use up_exps_s since gate and up are fused)
- if (up_exps_s) {
- ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
- s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
- s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
- gate_up = ggml_mul(ctx0, gate_up, s);
- cb(gate_up, "ffn_moe_gate_up_scaled", il);
- }
-
const int64_t n_ff = gate_up->ne[0] / 2;
cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0);
cb(cur, "ffn_moe_gate", il);
cb(up, "ffn_moe_up", il);
} else {
// separate gate and up path
- up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+ up = build_lora_mm_id(up_exps, cur, selected_experts, up_exps_s); // [n_ff, n_expert_used, n_tokens]
cb(up, "ffn_moe_up", il);
+ if (up_exps_s) {
+ cb(up, "ffn_moe_up_scaled", il);
+ }
+
if (up_exps_b) {
up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
cb(up, "ffn_moe_up_biased", il);
}
- // apply per-expert scale2 to up
- if (up_exps_s) {
- ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
- s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
- s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
- up = ggml_mul(ctx0, up, s);
- cb(up, "ffn_moe_up_scaled", il);
- }
-
if (gate_exps) {
- cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+ cur = build_lora_mm_id(gate_exps, cur, selected_experts, gate_exps_s); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate", il);
} else {
cur = up;
}
+ if (gate_exps_s) {
+ cb(cur, "ffn_moe_gate_scaled", il);
+ }
+
if (gate_exps_b) {
cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
cb(cur, "ffn_moe_gate_biased", il);
}
-
- // apply per-expert scale2 to gate
- if (gate_exps_s) {
- ggml_tensor * s = ggml_reshape_3d(ctx0, gate_exps_s, 1, n_expert, 1);
- s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
- s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
- cur = ggml_mul(ctx0, cur, s);
- cb(cur, "ffn_moe_gate_scaled", il);
- }
}
const bool has_gate = gate_exps || gate_up_exps;
GGML_ABORT("fatal error");
}
- experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
+ experts = build_lora_mm_id(down_exps, cur, selected_experts, down_exps_s); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);
+ if (down_exps_s) {
+ cb(experts, "ffn_moe_down_scaled", il);
+ }
+
if (down_exps_b) {
experts = ggml_add_id(ctx0, experts, down_exps_b, selected_experts);
cb(experts, "ffn_moe_down_biased", il);
}
- // apply per-expert scale2 to down
- if (down_exps_s) {
- ggml_tensor * s = ggml_reshape_3d(ctx0, down_exps_s, 1, n_expert, 1);
- s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
- s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
- experts = ggml_mul(ctx0, experts, s);
- cb(experts, "ffn_moe_down_scaled", il);
- }
-
if (!weight_before_ffn) {
experts = ggml_mul(ctx0, experts, weights);
cb(experts, "ffn_moe_weighted", il);
--- /dev/null
+#include "models.h"
+
+void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
+ const bool found_norm = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
+ const bool found_norm_rms = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
+ if (!found_norm && !found_norm_rms) {
+ throw std::runtime_error("missing Cohere2 MoE norm epsilon");
+ }
+ if (!found_norm_rms) {
+ hparams.f_norm_rms_eps = 0.0f;
+ }
+
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+ ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+ 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_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_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
+
+ 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");
+
+ if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+ hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+ }
+
+ hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+ uint32_t swa_period = 4;
+ if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
+ hparams.set_swa_pattern(swa_period, true);
+ } else {
+ ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+ }
+
+ hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
+ hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+ ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+
+ switch (hparams.n_layer()) {
+ case 49: type = LLM_TYPE_30B_A3B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+}
+
+void llama_model_cohere2moe::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. 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
+ 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
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+ }
+
+ if (n_expert == 0) {
+ throw std::runtime_error("n_expert must be > 0 for Cohere2Moe");
+ }
+ if (n_expert_used == 0) {
+ throw std::runtime_error("n_expert_used must be > 0 for Cohere2Moe");
+ }
+
+ auto load_block_trunk = [&](int i, int flags) {
+ auto & layer = layers[i];
+
+ 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_gqa, n_embd_gqa, flags);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
+
+ if (static_cast<uint32_t>(i) < hparams.n_layer_dense_lead) {
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags);
+ } else {
+ const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
+
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
+ create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
+
+ if (hparams.n_expert_shared > 0) {
+ const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+ }
+ }
+ };
+
+ auto load_block_mtp = [&](int i, int flags) {
+ auto & layer = layers[i];
+
+ // MTP block looks like a full-attention Cohere2 MoE decoder block.
+ 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_gqa, n_embd_gqa, flags);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
+
+ const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
+
+ // Routed experts
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
+ create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
+
+ if (hparams.n_expert_shared > 0) {
+ const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
+
+ // Shared experts
+ layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+ layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
+ layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
+ }
+
+ // NextN-specific tensors that define the MTP block.
+ layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
+ layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
+ layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, 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);
+ layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED);
+ };
+
+ for (int i = 0; i < n_layer; ++i) {
+ load_block_trunk(i, trunk_flags);
+ }
+ // MTP/NextN layers are loaded as extra decoder blocks.
+ for (int i = n_layer; i < n_layer_all; ++i) {
+ load_block_mtp(i, mtp_flags);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_cohere2moe::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_cohere2moe::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);
+
+ const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
+ const float f_logit_scale = hparams.f_logit_scale;
+ ggml_tensor * cur;
+ ggml_tensor * inpL = build_inp_embd(model.tok_embd);
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv_iswa();
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
+ for (int il = 0; il < n_layer; ++il) {
+ const bool is_swa = hparams.is_swa(il);
+ // Dense-prefix full-attention layers use RoPE; later layers follow the SWA pattern.
+ const bool force_rope = static_cast<uint32_t>(il) < hparams.n_layer_dense_lead;
+
+ cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, cohere2moe_norm_type, il);
+ cb(cur, "attn_norm", il);
+
+ ggml_tensor * ffn_inp = cur;
+
+ {
+ const auto & layer = model.layers[il];
+
+ auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
+ n_embd_head, n_head, n_head_kv, il);
+
+ if (is_swa || force_rope) {
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, 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,
+ layer.wo, layer.wo_b, layer.wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
+ 1.0f / sqrtf(float(n_embd_head)), il);
+ }
+
+ if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+ }
+
+ ggml_tensor * attn_out = cur;
+
+ const auto & layer = model.layers[il];
+
+ if (layer.ffn_gate_inp == nullptr) {
+ cur = build_ffn(ffn_inp,
+ layer.ffn_up, nullptr, layer.ffn_up_s,
+ layer.ffn_gate, nullptr, layer.ffn_gate_s,
+ layer.ffn_down, nullptr, layer.ffn_down_s,
+ nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+ } else {
+ cur = build_moe_ffn(ffn_inp,
+ layer.ffn_gate_inp,
+ layer.ffn_up_exps,
+ layer.ffn_gate_exps,
+ layer.ffn_down_exps,
+ nullptr,
+ 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(cur, "ffn_moe_out", il);
+
+ if (layer.ffn_up_shexp) {
+ ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+ 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(ffn_shexp, "ffn_shexp", il);
+
+ cur = ggml_add(ctx0, cur, ffn_shexp);
+ cur = ggml_scale(ctx0, cur, 0.5f);
+ cb(cur, "ffn_out", il);
+ }
+ }
+
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = ggml_add(ctx0, cur, attn_out);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ inpL = cur;
+ }
+
+ cur = inpL;
+ cur = build_norm(cur, model.output_norm, nullptr, cohere2moe_norm_type, -1);
+
+ 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);
+
+ if (f_logit_scale) {
+ cur = ggml_scale(ctx0, cur, f_logit_scale);
+ }
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
+
+llama_model_cohere2moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ GGML_ASSERT(hparams.n_layer_nextn > 0 && "COHERE2MOE MTP requires n_layer_nextn > 0");
+ GGML_ASSERT(hparams.n_layer_nextn == 1 && "COHERE2MOE MTP currently only supports a single MTP block");
+
+ 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();
+ 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");
+ GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
+
+ const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
+
+ // TODO: extract in a common llm_graph_context::build_inp_embd_h()
+ auto inp = std::make_unique<llm_graph_input_embd_h>(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_inp(), n_tokens);
+ ggml_set_input(inp->embd);
+
+ // TODO: make static using `ggml_build_forward_select()`
+ // see llm_graph_context::build_inp_embd() for reference
+ ggml_tensor * tok_embd;
+ if (ubatch.token) {
+ ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+ tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+ } else {
+ tok_embd = inp->embd;
+ }
+ cb(tok_embd, "mtp_tok_embd", il);
+
+ inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+ ggml_set_input(inp->h);
+ ggml_set_name(inp->h, "mtp_h_input");
+
+ ggml_tensor * h_embd = inp->h;
+
+ 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_iswa();
+
+ ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, cohere2moe_norm_type, il);
+ cb(h_norm, "mtp_hnorm", il);
+
+ ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, cohere2moe_norm_type, 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, layer.nextn.eh_proj_s);
+ cb(cur, "mtp_eh_proj", il);
+
+ ggml_tensor * inpL = cur;
+
+ cur = build_norm(cur, layer.attn_norm, nullptr, cohere2moe_norm_type, il);
+ cb(cur, "mtp_attn_norm", il);
+ ggml_tensor * ffn_inp = cur;
+
+ auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il);
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, 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, "mtp_Qcur", il);
+ cb(Kcur, "mtp_Kcur", il);
+ cb(Vcur, "mtp_Vcur", il);
+
+ cur = build_attn(inp_attn,
+ layer.wo, layer.wo_b, layer.wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
+ 1.0f / sqrtf(float(n_embd_head)), il);
+ cb(cur, "mtp_attn_out", il);
+
+ ggml_tensor * attn_out = cur;
+
+ cur = build_moe_ffn(ffn_inp,
+ layer.ffn_gate_inp,
+ layer.ffn_up_exps,
+ layer.ffn_gate_exps,
+ layer.ffn_down_exps,
+ nullptr,
+ 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(cur, "mtp_ffn_moe_out", il);
+
+ if (layer.ffn_up_shexp) {
+ ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+ 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(ffn_shexp, "mtp_ffn_shexp", il);
+
+ cur = ggml_add(ctx0, cur, ffn_shexp);
+ cur = ggml_scale(ctx0, cur, 0.5f);
+ cb(cur, "mtp_ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = ggml_add(ctx0, cur, attn_out);
+ cb(cur, "mtp_post_ffn", il);
+
+ ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
+ ? layer.nextn.shared_head_norm
+ : model.output_norm;
+ GGML_ASSERT(head_norm_w && "COHERE2MOE MTP: missing both nextn.shared_head_norm and output_norm");
+ cur = build_norm(cur, head_norm_w, nullptr, cohere2moe_norm_type, -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_ASSERT(head_w && "COHERE2MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
+ cur = build_lora_mm(head_w, cur, layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : nullptr);
+
+ if (hparams.f_logit_scale) {
+ cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
+ }
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
overview / pkg / ggml / sources / whisper.cpp / commitdiff