444 lines
19 KiB
C++
444 lines
19 KiB
C++
#include "models.h"
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void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
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const bool found_norm = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
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const bool found_norm_rms = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
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if (!found_norm && !found_norm_rms) {
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throw std::runtime_error("missing Cohere2 MoE norm epsilon");
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}
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if (!found_norm_rms) {
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hparams.f_norm_rms_eps = 0.0f;
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}
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
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GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
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if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
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hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
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}
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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uint32_t swa_period = 4;
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if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
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hparams.set_swa_pattern(swa_period, true);
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} else {
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ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
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}
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hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
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hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
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switch (hparams.n_layer()) {
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case 49: type = LLM_TYPE_30B_A3B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
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LLAMA_LOAD_LOCALS;
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const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
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// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
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// tensors live in a separate file. Mark MTP tensors NOT_REQUIRED so the
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// trunk loads cleanly.
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const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
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const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
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const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
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const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
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}
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if (n_expert == 0) {
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throw std::runtime_error("n_expert must be > 0 for Cohere2Moe");
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}
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if (n_expert_used == 0) {
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throw std::runtime_error("n_expert_used must be > 0 for Cohere2Moe");
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}
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auto load_block_trunk = [&](int i, int flags) {
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
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create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
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if (static_cast<uint32_t>(i) < hparams.n_layer_dense_lead) {
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags);
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} else {
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const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
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create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
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if (hparams.n_expert_shared > 0) {
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const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
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}
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}
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};
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auto load_block_mtp = [&](int i, int flags) {
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auto & layer = layers[i];
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// MTP block looks like a full-attention Cohere2 MoE decoder block.
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
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create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
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const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
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// Routed experts
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
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create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags);
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if (hparams.n_expert_shared > 0) {
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const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared;
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// Shared experts
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
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}
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// NextN-specific tensors that define the MTP block.
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layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
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layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
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layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
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layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
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layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED);
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};
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for (int i = 0; i < n_layer; ++i) {
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load_block_trunk(i, trunk_flags);
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}
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// MTP/NextN layers are loaded as extra decoder blocks.
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for (int i = n_layer; i < n_layer_all; ++i) {
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load_block_mtp(i, mtp_flags);
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_cohere2moe::build_arch_graph(const llm_graph_params & params) const {
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if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
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return std::make_unique<graph_mtp>(*this, params);
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}
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return std::make_unique<graph>(*this, params);
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}
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llama_model_cohere2moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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GGML_ASSERT(n_embd_head == n_rot);
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const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
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const float f_logit_scale = hparams.f_logit_scale;
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ggml_tensor * cur;
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ggml_tensor * inpL = build_inp_embd(model.tok_embd);
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv_iswa();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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// MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
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for (int il = 0; il < n_layer; ++il) {
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const bool is_swa = hparams.is_swa(il);
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// Dense-prefix full-attention layers use RoPE; later layers follow the SWA pattern.
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const bool force_rope = static_cast<uint32_t>(il) < hparams.n_layer_dense_lead;
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cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, cohere2moe_norm_type, il);
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cb(cur, "attn_norm", il);
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ggml_tensor * ffn_inp = cur;
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{
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const auto & layer = model.layers[il];
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auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
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n_embd_head, n_head, n_head_kv, il);
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if (is_swa || force_rope) {
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ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, rope_factors,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn,
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layer.wo, layer.wo_b, layer.wo_s,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
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1.0f / sqrtf(float(n_embd_head)), il);
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}
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if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
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}
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ggml_tensor * attn_out = cur;
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const auto & layer = model.layers[il];
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if (layer.ffn_gate_inp == nullptr) {
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cur = build_ffn(ffn_inp,
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layer.ffn_up, nullptr, layer.ffn_up_s,
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layer.ffn_gate, nullptr, layer.ffn_gate_s,
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layer.ffn_down, nullptr, layer.ffn_down_s,
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nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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cur = build_moe_ffn(ffn_inp,
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layer.ffn_gate_inp,
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layer.ffn_up_exps,
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layer.ffn_gate_exps,
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layer.ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, hparams.expert_weights_norm,
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hparams.expert_weights_scale,
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(llama_expert_gating_func_type) hparams.expert_gating_func,
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il,
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nullptr, layer.ffn_gate_up_exps,
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layer.ffn_up_exps_s,
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layer.ffn_gate_exps_s,
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layer.ffn_down_exps_s);
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cb(cur, "ffn_moe_out", il);
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if (layer.ffn_up_shexp) {
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ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
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layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s,
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layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s,
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layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s,
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nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(ffn_shexp, "ffn_shexp", il);
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cur = ggml_add(ctx0, cur, ffn_shexp);
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cur = ggml_scale(ctx0, cur, 0.5f);
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cb(cur, "ffn_out", il);
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}
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}
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cur = ggml_add(ctx0, cur, inpL);
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cur = ggml_add(ctx0, cur, attn_out);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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inpL = cur;
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}
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cur = inpL;
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cur = build_norm(cur, model.output_norm, nullptr, cohere2moe_norm_type, -1);
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cb(cur, "h_nextn", -1);
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res->t_h_nextn = cur;
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if (!cparams.embeddings_nextn_masked && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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}
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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cur = build_lora_mm(model.output, cur);
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if (f_logit_scale) {
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cur = ggml_scale(ctx0, cur, f_logit_scale);
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}
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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llama_model_cohere2moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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GGML_ASSERT(hparams.n_layer_nextn > 0 && "COHERE2MOE MTP requires n_layer_nextn > 0");
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GGML_ASSERT(hparams.n_layer_nextn == 1 && "COHERE2MOE MTP currently only supports a single MTP block");
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const int64_t n_embd_head = hparams.n_embd_head_v();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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GGML_ASSERT(n_embd_head == n_rot);
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const int il = hparams.n_layer();
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const auto & layer = model.layers[il];
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GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
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GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
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GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
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GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
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const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS;
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// TODO: extract in a common llm_graph_context::build_inp_embd_h()
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auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
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inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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ggml_set_input(inp->tokens);
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inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
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ggml_set_input(inp->embd);
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// TODO: make static using `ggml_build_forward_select()`
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// see llm_graph_context::build_inp_embd() for reference
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ggml_tensor * tok_embd;
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if (ubatch.token) {
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ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
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tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
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} else {
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tok_embd = inp->embd;
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}
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cb(tok_embd, "mtp_tok_embd", il);
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inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
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ggml_set_input(inp->h);
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ggml_set_name(inp->h, "mtp_h_input");
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ggml_tensor * h_embd = inp->h;
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res->add_input(std::move(inp));
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ggml_tensor * inp_pos = build_inp_pos();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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auto * inp_attn = build_attn_inp_kv_iswa();
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|
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ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, cohere2moe_norm_type, il);
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cb(h_norm, "mtp_hnorm", il);
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|
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ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, cohere2moe_norm_type, il);
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cb(e_norm, "mtp_enorm", il);
|
|
|
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ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
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cb(concat, "mtp_concat", il);
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|
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ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
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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,
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|
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);
|
|
}
|