llama : add MiniCPM support (#5346)
* support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
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3 changed files with 259 additions and 1 deletions
190
llama.cpp
190
llama.cpp
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@ -205,6 +205,7 @@ enum llm_arch {
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LLM_ARCH_CODESHELL,
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LLM_ARCH_ORION,
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LLM_ARCH_INTERNLM2,
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LLM_ARCH_MINICPM,
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LLM_ARCH_UNKNOWN,
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};
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@ -228,6 +229,7 @@ static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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{ LLM_ARCH_MINICPM, "minicpm" },
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};
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enum llm_kv {
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@ -690,6 +692,29 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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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_MINICPM,
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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_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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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_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
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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_GATE_EXP, "blk.%d.ffn_gate.%d" },
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{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
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{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -1390,6 +1415,7 @@ enum e_model {
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MODEL_UNKNOWN,
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MODEL_0_5B,
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MODEL_1B,
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MODEL_2B,
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MODEL_3B,
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MODEL_4B,
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MODEL_7B,
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@ -2748,6 +2774,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
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static const char * llama_model_type_name(e_model type) {
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switch (type) {
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case MODEL_1B: return "1B";
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case MODEL_2B: return "2B";
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case MODEL_3B: return "3B";
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case MODEL_7B: return "7B";
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case MODEL_8B: return "8B";
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@ -2887,6 +2914,13 @@ 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_MINICPM:
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{
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switch (hparams.n_layer) {
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case 40: model.type = e_model::MODEL_2B; 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_FALCON:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -3524,13 +3558,16 @@ static bool llm_load_tensors(
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switch (model.arch) {
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case LLM_ARCH_LLAMA:
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case LLM_ARCH_REFACT:
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case LLM_ARCH_MINICPM:
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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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{
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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_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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if (model.arch != LLM_ARCH_MINICPM){
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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}
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}
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for (int i = 0; i < n_layer; ++i) {
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@ -6781,6 +6818,153 @@ struct llm_build_context {
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return gf;
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}
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// ref: https://arxiv.org/abs/2203.03466
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// https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
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// based on the original build_llama() function
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struct ggml_cgraph * build_minicpm() {
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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 = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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const int64_t n_embd = hparams.n_embd;
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//TODO: if the model varies, these parameters need to be read from the model
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const int64_t n_embd_base = 256;
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const float scale_embd = 12.0f;
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const float scale_depth = 1.4f;
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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, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
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cb(inpL, "inp_embd", -1);
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// scale the input embeddings
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inpL = ggml_scale(ctx0, inpL, scale_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 = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
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cb(inp_pos, "inp_pos", -1);
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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 = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
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cb(KQ_mask, "KQ_mask", -1);
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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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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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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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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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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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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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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cb(cur, "kqv_out", il);
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}
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// scale_res - scale the hidden states for residual connection
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const float scale_res = scale_depth/sqrtf(float(n_layer));
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cur = ggml_scale(ctx0, cur, scale_res);
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cb(cur, "hidden_scaled", -1);
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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{
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cur = llm_build_norm(ctx0, ffn_inp, 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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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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// scale the hidden states for residual connection
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cur = ggml_scale(ctx0, cur, scale_res);
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cb(cur, "hidden_scaled_ffn", -1);
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cur = ggml_add(ctx0, cur, ffn_inp);
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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 scaling
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const float scale_lmhead = float(n_embd_base)/float(n_embd);
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cur = ggml_scale(ctx0, cur, scale_lmhead);
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cb(cur, "lmhead_scaling", -1);
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// lm_head
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cur = ggml_mul_mat(ctx0, model.tok_embd, 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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};
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static struct ggml_cgraph * llama_build_graph(
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@ -6943,6 +7127,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_internlm2();
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} break;
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case LLM_ARCH_MINICPM:
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{
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result = llm.build_minicpm();
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} break;
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default:
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GGML_ASSERT(false);
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}
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