diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 48 |
1 files changed, 45 insertions, 3 deletions
@@ -594,6 +594,9 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, { LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" }, + { LLM_TENSOR_POS_EMBD, "position_embd" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"}, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"}, }, }, { @@ -4867,6 +4870,7 @@ static bool llm_load_tensors( case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false); // output { @@ -4905,6 +4909,12 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false); + + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false); + // AWQ ScaleActivation layer layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); } @@ -7721,6 +7731,7 @@ struct llm_build_context { GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); struct ggml_tensor * cur; + struct ggml_tensor * pos; struct ggml_tensor * inpL; inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); @@ -7731,6 +7742,16 @@ struct llm_build_context { // positions of the tokens in the KV cache struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); + if (model.pos_embd) { + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); + cb(pos, "pos_embd", -1); + + inpL = ggml_add(ctx0, inpL, pos); + cb(inpL, "inpL", -1); + } + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; @@ -7765,11 +7786,32 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + // Q/K Layernorm + if (model.layers[il].attn_q_norm) { + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, cb, il); + cb(Qcur, "Qcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, cb, il); + cb(Kcur, "Kcur", 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); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } else { + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } } if (il == n_layer - 1) { |