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https://github.com/ggerganov/whisper.cpp.git
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talk-llama : sync llama.cpp
ggml-ci
This commit is contained in:
Georgi Gerganov
@ -40,14 +40,17 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_335M: return "335M";
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case LLM_TYPE_410M: return "410M";
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case LLM_TYPE_450M: return "450M";
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case LLM_TYPE_475M: return "475M";
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case LLM_TYPE_770M: return "770M";
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case LLM_TYPE_780M: return "780M";
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case LLM_TYPE_0_5B: return "0.5B";
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case LLM_TYPE_0_6B: return "0.6B";
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case LLM_TYPE_1B: return "1B";
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case LLM_TYPE_1_3B: return "1.3B";
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case LLM_TYPE_1_4B: return "1.4B";
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case LLM_TYPE_1_5B: return "1.5B";
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case LLM_TYPE_1_6B: return "1.6B";
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case LLM_TYPE_1_7B: return "1.7B";
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case LLM_TYPE_1_8B: return "1.8B";
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case LLM_TYPE_2B: return "2B";
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case LLM_TYPE_2_8B: return "2.8B";
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@ -66,6 +69,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_15B: return "15B";
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case LLM_TYPE_16B: return "16B";
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case LLM_TYPE_20B: return "20B";
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case LLM_TYPE_27B: return "27B";
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case LLM_TYPE_30B: return "30B";
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case LLM_TYPE_32B: return "32B";
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case LLM_TYPE_34B: return "34B";
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@ -74,6 +78,7 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_65B: return "65B";
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case LLM_TYPE_70B: return "70B";
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case LLM_TYPE_236B: return "236B";
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case LLM_TYPE_290B: return "290B";
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case LLM_TYPE_314B: return "314B";
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case LLM_TYPE_671B: return "671B";
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case LLM_TYPE_SMALL: return "0.1B";
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@ -88,10 +93,10 @@ const char * llm_type_name(llm_type type) {
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case LLM_TYPE_16x3_8B: return "16x3.8B";
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case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
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case LLM_TYPE_57B_A14B: return "57B.A14B";
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case LLM_TYPE_27B: return "27B";
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case LLM_TYPE_290B: return "290B";
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case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
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case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
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case LLM_TYPE_30B_A3B: return "30B.A3B";
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case LLM_TYPE_235B_A22B: return "235B.A22B";
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default: return "?B";
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}
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}
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@ -695,13 +700,19 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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}
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} break;
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case LLM_ARCH_NOMIC_BERT:
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case LLM_ARCH_NOMIC_BERT_MOE:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
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ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
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ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers, 0);
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if (hparams.n_layer == 12 && hparams.n_embd == 768) {
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type = LLM_TYPE_137M;
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if (arch == LLM_ARCH_NOMIC_BERT) {
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type = LLM_TYPE_137M;
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} else if (arch == LLM_ARCH_NOMIC_BERT_MOE && hparams.moe_every_n_layers == 2) {
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type = LLM_TYPE_475M;
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}
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}
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} break;
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case LLM_ARCH_BLOOM:
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@ -791,6 +802,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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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 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
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case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;
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case 40: type = LLM_TYPE_14B; break;
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case 64: type = LLM_TYPE_32B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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@ -800,6 +815,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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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 48: type = LLM_TYPE_30B_A3B; break;
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case 94: type = LLM_TYPE_235B_A22B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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@ -2057,6 +2074,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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} break;
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case LLM_ARCH_BERT:
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case LLM_ARCH_NOMIC_BERT:
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case LLM_ARCH_NOMIC_BERT_MOE:
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{
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0);
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@ -2090,20 +2108,31 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
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}
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if (arch == LLM_ARCH_NOMIC_BERT_MOE) {
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layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
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}
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
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layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
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layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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if (arch == LLM_ARCH_BERT) {
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if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
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layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
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layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
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layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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} else {
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE) {
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layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
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layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
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layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
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} else {
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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}
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}
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layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
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@ -5730,6 +5759,11 @@ struct llm_build_bert : public llm_graph_context {
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cur = build_lora_mm(model.layers[il].wqkv, cur);
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cb(cur, "wqkv", il);
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if (model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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cb(cur, "bqkv", il);
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}
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Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
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Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
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Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
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@ -5782,13 +5816,29 @@ struct llm_build_bert : public llm_graph_context {
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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if (model.arch == LLM_ARCH_BERT) {
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if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
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// MoE branch
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cur = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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nullptr,
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model.layers[il].ffn_down_exps,
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nullptr,
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hparams.n_expert,
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hparams.n_expert_used,
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LLM_FFN_GELU,
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false, false,
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0.0f,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
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cb(cur, "ffn_moe_out", il);
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} else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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NULL, NULL, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_GELU, LLM_FFN_SEQ, il);
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cb(cur, "ffn_out", il);
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} else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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@ -5796,6 +5846,7 @@ struct llm_build_bert : public llm_graph_context {
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_GELU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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@ -5803,8 +5854,8 @@ struct llm_build_bert : public llm_graph_context {
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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}
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cb(cur, "ffn_out", il);
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// attentions bypass the intermediate layer
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cur = ggml_add(ctx0, cur, ffn_inp);
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@ -12842,6 +12893,7 @@ llm_graph_result_ptr llama_model::build_graph(
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case LLM_ARCH_BERT:
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case LLM_ARCH_JINA_BERT_V2:
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case LLM_ARCH_NOMIC_BERT:
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case LLM_ARCH_NOMIC_BERT_MOE:
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{
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llm = std::make_unique<llm_build_bert>(*this, params, gf);
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} break;
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@ -13200,6 +13252,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_DBRX:
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case LLM_ARCH_BERT:
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case LLM_ARCH_NOMIC_BERT:
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case LLM_ARCH_NOMIC_BERT_MOE:
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case LLM_ARCH_STABLELM:
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case LLM_ARCH_BITNET:
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case LLM_ARCH_QWEN:
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