diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 51 |
1 files changed, 32 insertions, 19 deletions
@@ -286,6 +286,7 @@ enum llm_kv { LLM_KV_LEADING_DENSE_BLOCK_COUNT, LLM_KV_FEED_FORWARD_LENGTH, LLM_KV_EXPERT_FEED_FORWARD_LENGTH, + LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, LLM_KV_USE_PARALLEL_RESIDUAL, LLM_KV_TENSOR_DATA_LAYOUT, LLM_KV_EXPERT_COUNT, @@ -364,21 +365,22 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = { { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, - { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, - { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, - { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, - { LLM_KV_BLOCK_COUNT, "%s.block_count" }, - { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" }, - { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, - { LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" }, - { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, - { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, - { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, - { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, - { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" }, - { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, - { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, - { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, + { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, + { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, + { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, + { LLM_KV_BLOCK_COUNT, "%s.block_count" }, + { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" }, + { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, + { LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" }, + { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" }, + { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, + { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, + { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, + { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, + { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" }, + { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, + { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, + { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -1970,6 +1972,7 @@ struct llama_hparams { uint32_t n_lora_q = 0; uint32_t n_lora_kv = 0; uint32_t n_ff_exp = 0; + uint32_t n_ff_shexp = 0; uint32_t n_expert_shared = 0; float expert_weights_scale = 0.0; @@ -2018,6 +2021,7 @@ struct llama_hparams { if (this->n_lora_q != other.n_lora_q) return true; if (this->n_lora_kv != other.n_lora_kv) return true; if (this->n_ff_exp != other.n_ff_exp) return true; + if (this->n_ff_shexp != other.n_ff_shexp) return true; if (this->n_expert_shared != other.n_expert_shared) return true; if (this->rope_finetuned != other.rope_finetuned) return true; @@ -4455,6 +4459,9 @@ static void llm_load_hparams( } break; case LLM_ARCH_QWEN2MOE: { + 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_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_A2_7B; break; @@ -5240,6 +5247,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale); LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); } + + if (model.arch == LLM_ARCH_QWEN2MOE) { + LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); + LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp); + } } // Returns false if cancelled by progress_callback @@ -6026,16 +6038,17 @@ static bool llm_load_tensors( GGML_ASSERT(hparams.n_expert_used > 0); // MoE branch - auto n_ff_exp = n_ff / hparams.n_expert_used; + auto n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / hparams.n_expert_used; layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); // Shared expert branch + auto n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}); - layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff}); - layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff, n_embd}); - layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}); } } break; case LLM_ARCH_PHI2: |