llama : add StarCoder2 support (#5795)
* Add support for starcoder2 * handle rope type * skip rope freq and rotary embeddings from being serialized * resolve comments * Update llama.cpp * remove redundant changes * handle `rope-theta` * llama : change starcoder2 rope type * address comment --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Sourab Mangrulkar
GitHub
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38d16b1426
commit
c29af7e225
4 changed files with 229 additions and 1 deletions
199
llama.cpp
199
llama.cpp
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@ -211,6 +211,7 @@ 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_STARCODER2,
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LLM_ARCH_UNKNOWN,
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};
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@ -238,6 +239,7 @@ static 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_STARCODER2, "starcoder2" },
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};
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enum llm_kv {
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@ -779,6 +781,24 @@ 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_STARCODER2,
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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_NORM, "blk.%d.ffn_norm" },
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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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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -3320,6 +3340,16 @@ 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_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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switch (hparams.n_layer) {
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case 30: model.type = e_model::MODEL_3B; break;
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case 32: model.type = e_model::MODEL_7B; break;
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case 40: model.type = e_model::MODEL_15B; 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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default: (void)0;
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}
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@ -4490,6 +4520,56 @@ 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_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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// 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_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
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// if output is NULL, init from the input tok embed
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if (model.output == NULL) {
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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ml.n_created--; // artificial tensor
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ml.size_data += ggml_nbytes(model.output);
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}
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}
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for (int 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.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", 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});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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// optional bias tensors
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layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
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layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
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layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", 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_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
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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_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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// optional bias tensors
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layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff});
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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -7559,6 +7639,120 @@ struct llm_build_context {
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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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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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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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// 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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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, model.layers[il].attn_norm_b,
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LLM_NORM, 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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n_rot, rope_type, 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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n_rot, rope_type, 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, nullptr, n_ctx, n_tokens, kv_head, n_kv, 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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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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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
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LLM_NORM, 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, model.layers[il].ffn_up_b,
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NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
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cb(cur, "ffn_out", il);
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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, model.output_norm_b,
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LLM_NORM, 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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};
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static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
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@ -7705,6 +7899,10 @@ 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_STARCODER2:
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{
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result = llm.build_starcoder2();
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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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@ -12084,6 +12282,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_QWEN2:
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case LLM_ARCH_PHI2:
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case LLM_ARCH_GEMMA:
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case LLM_ARCH_STARCODER2:
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return LLAMA_ROPE_TYPE_NEOX;
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// all model arches should be listed explicitly here
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