diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 62 |
1 files changed, 50 insertions, 12 deletions
@@ -716,6 +716,8 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, { @@ -1744,6 +1746,7 @@ enum e_model { MODEL_4B, MODEL_7B, MODEL_8B, + MODEL_12B, MODEL_13B, MODEL_14B, MODEL_15B, @@ -3607,6 +3610,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_3B: return "3B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; + case MODEL_12B: return "12B"; case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; @@ -3898,6 +3902,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; + case 40: model.type = e_model::MODEL_12B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -5128,8 +5133,13 @@ static bool llm_load_tensors( layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + // optional q and k layernorms, present in StableLM 2 12B + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false); + + // optional FFN norm, not present in StableLM 2 12B which uses parallel residual + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); @@ -8197,7 +8207,7 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; + // norm cur = llm_build_norm(ctx0, inpL, hparams, @@ -8206,6 +8216,8 @@ struct llm_build_context { LLM_NORM, cb, il); cb(cur, "attn_norm", il); + struct ggml_tensor * inpSA = cur; + // self-attention { // compute Q and K and RoPE them @@ -8230,15 +8242,36 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cb(Qcur, "Qcur", il); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + cb(Kcur, "Kcur", il); + + if (model.layers[il].attn_q_norm) { + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + NULL, + LLM_NORM, cb, il); + cb(Qcur, "Qcur", il); + } + if (model.layers[il].attn_k_norm) { + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + NULL, + LLM_NORM, cb, il); + cb(Kcur, "Kcur", il); + } + + Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8253,20 +8286,25 @@ struct llm_build_context { // skip computing output for unused tokens struct ggml_tensor * inp_out_ids = build_inp_out_ids(); cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); cb(ffn_inp, "ffn_inp", il); // feed-forward network { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - + if (model.layers[il].ffn_norm) { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + } else { + // parallel residual + cur = inpSA; + } cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, |