llama: Add support for Gemma2ForCausalLM (#8156)
* Inference support for Gemma 2 model family * Update convert-hf-to-gguf.py, constants, and tensor mappings * cleanup * format fix * Fix special token vocab bug * Don't add space prefix * fix deleted lines * Update src/llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Add model type names * Add control vector * Fix model type identification --------- Co-authored-by: Andrei Betlen <abetlen@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
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4 changed files with 274 additions and 1 deletions
198
src/llama.cpp
198
src/llama.cpp
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@ -217,6 +217,7 @@ enum llm_arch {
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LLM_ARCH_INTERNLM2,
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LLM_ARCH_MINICPM,
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LLM_ARCH_GEMMA,
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LLM_ARCH_GEMMA2,
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LLM_ARCH_STARCODER2,
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LLM_ARCH_MAMBA,
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LLM_ARCH_XVERSE,
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@ -257,6 +258,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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{ LLM_ARCH_MINICPM, "minicpm" },
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_GEMMA2, "gemma2" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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@ -478,10 +480,12 @@ enum llm_tensor {
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LLM_TENSOR_ATTN_NORM,
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LLM_TENSOR_ATTN_NORM_2,
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LLM_TENSOR_ATTN_OUT_NORM,
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LLM_TENSOR_ATTN_POST_NORM,
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LLM_TENSOR_ATTN_ROT_EMBD,
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LLM_TENSOR_FFN_GATE_INP,
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LLM_TENSOR_FFN_GATE_INP_SHEXP,
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LLM_TENSOR_FFN_NORM,
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LLM_TENSOR_FFN_POST_NORM,
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LLM_TENSOR_FFN_GATE,
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LLM_TENSOR_FFN_DOWN,
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LLM_TENSOR_FFN_UP,
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@ -1004,6 +1008,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_GEMMA2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
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},
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},
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{
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LLM_ARCH_STARCODER2,
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{
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@ -2039,6 +2061,8 @@ enum e_model {
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MODEL_16x12B,
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MODEL_10B_128x3_66B,
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MODEL_57B_A14B,
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MODEL_9B,
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MODEL_27B,
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};
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static const size_t kiB = 1024;
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@ -2215,6 +2239,7 @@ struct llama_layer {
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struct ggml_tensor * attn_q_a_norm;
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struct ggml_tensor * attn_kv_a_norm;
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struct ggml_tensor * attn_sub_norm;
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struct ggml_tensor * attn_post_norm;
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struct ggml_tensor * ffn_sub_norm;
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// attention
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@ -2238,6 +2263,7 @@ struct llama_layer {
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// normalization
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struct ggml_tensor * ffn_norm;
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struct ggml_tensor * ffn_norm_b;
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struct ggml_tensor * ffn_post_norm;
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struct ggml_tensor * layer_out_norm;
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struct ggml_tensor * layer_out_norm_b;
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struct ggml_tensor * ffn_norm_exps;
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@ -4269,6 +4295,8 @@ static const char * llama_model_type_name(e_model type) {
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case MODEL_16x12B: return "16x12B";
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case MODEL_10B_128x3_66B: return "10B+128x3.66B";
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case MODEL_57B_A14B: return "57B.A14B";
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case MODEL_9B: return "9B";
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case MODEL_27B: return "27B";
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default: return "?B";
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}
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}
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@ -4671,6 +4699,16 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_GEMMA2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 42: model.type = e_model::MODEL_9B; break;
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case 46: model.type = e_model::MODEL_27B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_STARCODER2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -6512,6 +6550,40 @@ static bool llm_load_tensors(
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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}
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} break;
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case LLM_ARCH_GEMMA2:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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// output
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
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const int64_t n_ff = hparams.n_ff;
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const int64_t n_embd_head_k = hparams.n_embd_head_k;
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const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
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const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
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for (uint32_t i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
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layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
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}
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} break;
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case LLM_ARCH_STARCODER2:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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@ -10923,6 +10995,125 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_gemma2() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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const int64_t n_embd_head_k = hparams.n_embd_head_k;
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
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cb(inpL, "inp_scaled", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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for (int il = 0; il < n_layer; ++il) {
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_rope_ext(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr,
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n_embd_head_k, 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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cb(Qcur, "Qcur", il);
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Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
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cb(Qcur, "Qcur_scaled", il);
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Kcur = ggml_rope_ext(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
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n_embd_head_k, 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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cb(Kcur, "Kcur", il);
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cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
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model.layers[il].wo, NULL,
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Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
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}
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].attn_post_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_post_norm", il);
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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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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}
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struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
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cb(sa_out, "sa_out", il);
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cur = llm_build_norm(ctx0, sa_out, hparams,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm", il);
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// feed-forward network
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{
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].ffn_post_norm, NULL,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "ffn_post_norm", -1);
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cur = ggml_add(ctx0, cur, sa_out);
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams,
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model.output_norm, NULL,
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LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = ggml_mul_mat(ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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struct ggml_cgraph * build_starcoder2() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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@ -12303,6 +12494,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_gemma();
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} break;
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case LLM_ARCH_GEMMA2:
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{
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result = llm.build_gemma2();
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} break;
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case LLM_ARCH_STARCODER2:
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{
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result = llm.build_starcoder2();
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@ -17597,6 +17792,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_PHI2:
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case LLM_ARCH_PHI3:
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case LLM_ARCH_GEMMA:
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case LLM_ARCH_GEMMA2:
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case LLM_ARCH_STARCODER2:
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case LLM_ARCH_GPTNEOX:
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return LLAMA_ROPE_TYPE_NEOX;
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@ -19486,7 +19682,7 @@ static int32_t llama_chat_apply_template_internal(
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if (add_ass) {
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ss << "<s>assistant\n";
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}
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} else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
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} else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl.find("<start_of_turn>") != std::string::npos) {
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// google/gemma-7b-it
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std::string system_prompt = "";
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for (auto message : chat) {
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