diff --git a/src/llama.cpp b/src/llama.cpp index 9d3720d2d..e8eba4e39 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1462,6 +1462,22 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "blk.%d.channel_mix_receptance" }, }, }, + { + LLM_ARCH_GRANITE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { 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_ARCH_UNKNOWN, { @@ -6915,6 +6931,7 @@ static bool llm_load_tensors( case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: case LLM_ARCH_MINICPM: + case LLM_ARCH_GRANITE: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -8898,6 +8915,11 @@ static struct ggml_tensor * llm_build_inp_embd( ggml_set_input(lctx.inp_embd); } + // For Granite architecture + if (hparams.f_embedding_multiplier != 0.0f) { + inpL = ggml_scale(ctx, inpL, hparams.f_embedding_multiplier); + } + cb(inpL, "inp_embd", -1); return inpL; @@ -10176,6 +10198,7 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + const float kq_scale = hparams.f_attention_multiplier == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_multiplier; for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -10228,7 +10251,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il); } if (il == n_layer - 1) { @@ -10239,6 +10262,11 @@ struct llm_build_context { inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } + // For Granite architecture + if (hparams.f_residual_multiplier) { + cur = ggml_scale(ctx0, cur, hparams.f_residual_multiplier); + } + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cb(ffn_inp, "ffn_inp", il); @@ -10275,6 +10303,11 @@ struct llm_build_context { cb(cur, "ffn_moe_out", il); } + // For Granite architecture + if (hparams.f_residual_multiplier) { + cur = ggml_scale(ctx0, cur, hparams.f_residual_multiplier); + } + cur = ggml_add(ctx0, cur, ffn_inp); cb(cur, "ffn_out", il); @@ -10294,6 +10327,12 @@ struct llm_build_context { // lm_head cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + + // For Granite architecture + if (hparams.f_logit_scale) { + cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale); + } + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -15819,6 +15858,7 @@ static struct ggml_cgraph * llama_build_graph( switch (model.arch) { case LLM_ARCH_LLAMA: + case LLM_ARCH_GRANITE: { result = llm.build_llama(); } break; @@ -19115,6 +19155,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_ARCTIC: case LLM_ARCH_DEEPSEEK2: case LLM_ARCH_CHATGLM: + case LLM_ARCH_GRANITE: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2