Add support for ArcticForCausalLM (#7020)
* common : increase max number of experts to 128 * common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn * gguf-py : add architecture-specific block mappings that override selected general block mappings * convert-hf : add model conversion support for ArcticForCausalLM * convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM) * llama : add inference support for LLM_ARCH_ARCTIC --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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fairydreaming
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4 changed files with 456 additions and 43 deletions
304
llama.cpp
304
llama.cpp
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@ -103,7 +103,7 @@
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#endif
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#define LLAMA_MAX_NODES 8192
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#define LLAMA_MAX_EXPERTS 60
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#define LLAMA_MAX_EXPERTS 128
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//
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// logging
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@ -221,6 +221,7 @@ enum llm_arch {
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_DBRX,
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LLM_ARCH_OLMO,
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LLM_ARCH_ARCTIC,
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LLM_ARCH_UNKNOWN,
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};
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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_COMMAND_R, "command-r" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_ARCTIC, "arctic" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -455,6 +457,7 @@ enum llm_tensor {
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LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
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LLM_TENSOR_FFN_GATE_EXP,
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LLM_TENSOR_FFN_UP_EXP,
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LLM_TENSOR_FFN_NORM_EXPS,
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LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
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LLM_TENSOR_FFN_GATE_EXPS,
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LLM_TENSOR_FFN_UP_EXPS,
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@ -1032,6 +1035,28 @@ 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_ARCTIC,
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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_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_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_NORM_EXPS, "blk.%d.ffn_norm_exps" },
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{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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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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@ -1732,6 +1757,7 @@ enum e_model {
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MODEL_8x7B,
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MODEL_8x22B,
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MODEL_16x12B,
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MODEL_10B_128x3_66B,
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};
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static const size_t kiB = 1024;
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@ -1907,6 +1933,7 @@ struct llama_layer {
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struct ggml_tensor * ffn_norm_b;
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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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// ff
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struct ggml_tensor * ffn_gate; // w1
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@ -3781,47 +3808,48 @@ 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_14M: return "14M";
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case MODEL_17M: return "17M";
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case MODEL_22M: return "22M";
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case MODEL_33M: return "33M";
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case MODEL_70M: return "70M";
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case MODEL_109M: return "109M";
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case MODEL_137M: return "137M";
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case MODEL_160M: return "160M";
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case MODEL_335M: return "335M";
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case MODEL_410M: return "410M";
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case MODEL_0_5B: return "0.5B";
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case MODEL_1B: return "1B";
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case MODEL_1_4B: return "1.4B";
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case MODEL_2B: return "2B";
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case MODEL_2_8B: return "2.8B";
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case MODEL_3B: return "3B";
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case MODEL_4B: return "4B";
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case MODEL_6_9B: return "6.9B";
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case MODEL_7B: return "7B";
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case MODEL_8B: return "8B";
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case MODEL_12B: return "12B";
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case MODEL_13B: return "13B";
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case MODEL_14B: return "14B";
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case MODEL_15B: return "15B";
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case MODEL_20B: return "20B";
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case MODEL_30B: return "30B";
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case MODEL_34B: return "34B";
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case MODEL_35B: return "35B";
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case MODEL_40B: return "40B";
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case MODEL_65B: return "65B";
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case MODEL_70B: return "70B";
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case MODEL_314B: return "314B";
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case MODEL_SMALL: return "0.1B";
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case MODEL_MEDIUM: return "0.4B";
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case MODEL_LARGE: return "0.8B";
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case MODEL_XL: return "1.5B";
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case MODEL_A2_7B: return "A2.7B";
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case MODEL_8x7B: return "8x7B";
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case MODEL_8x22B: return "8x22B";
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case MODEL_16x12B: return "16x12B";
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default: return "?B";
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case MODEL_14M: return "14M";
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case MODEL_17M: return "17M";
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case MODEL_22M: return "22M";
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case MODEL_33M: return "33M";
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case MODEL_70M: return "70M";
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case MODEL_109M: return "109M";
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case MODEL_137M: return "137M";
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case MODEL_160M: return "160M";
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case MODEL_335M: return "335M";
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case MODEL_410M: return "410M";
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case MODEL_0_5B: return "0.5B";
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case MODEL_1B: return "1B";
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case MODEL_1_4B: return "1.4B";
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case MODEL_2B: return "2B";
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case MODEL_2_8B: return "2.8B";
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case MODEL_3B: return "3B";
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case MODEL_4B: return "4B";
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case MODEL_6_9B: return "6.9B";
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case MODEL_7B: return "7B";
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case MODEL_8B: return "8B";
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case MODEL_12B: return "12B";
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case MODEL_13B: return "13B";
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case MODEL_14B: return "14B";
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case MODEL_15B: return "15B";
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case MODEL_20B: return "20B";
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case MODEL_30B: return "30B";
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case MODEL_34B: return "34B";
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case MODEL_35B: return "35B";
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case MODEL_40B: return "40B";
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case MODEL_65B: return "65B";
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case MODEL_70B: return "70B";
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case MODEL_314B: return "314B";
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case MODEL_SMALL: return "0.1B";
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case MODEL_MEDIUM: return "0.4B";
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case MODEL_LARGE: return "0.8B";
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case MODEL_XL: return "1.5B";
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case MODEL_A2_7B: return "A2.7B";
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case MODEL_8x7B: return "8x7B";
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case MODEL_8x22B: return "8x22B";
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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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default: return "?B";
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}
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}
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@ -4343,6 +4371,19 @@ 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_ARCTIC:
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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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if (hparams.n_expert == 128) {
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switch (hparams.n_layer) {
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case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} else {
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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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@ -6129,6 +6170,46 @@ static bool llm_load_tensors(
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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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case LLM_ARCH_ARCTIC:
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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}, llama_model_loader::TENSOR_NOT_REQUIRED);
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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}, llama_model_loader::TENSOR_DUPLICATED);
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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.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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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_embd});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd});
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layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
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layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd});
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layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
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layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
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layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
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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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@ -10790,6 +10871,140 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_arctic() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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// mutable variable, needed during the last layer of the computation to skip unused tokens
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int32_t n_tokens = this->n_tokens;
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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, lctx, hparams, batch, model.tok_embd, cb);
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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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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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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, n_head, n_tokens), inp_pos, nullptr,
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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_ext(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
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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, 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/sqrtf(float(n_embd_head)), cb, il);
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}
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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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n_tokens = n_outputs;
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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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, 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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struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
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cb(ffn_out, "ffn_out", il);
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// MoE
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cur = llm_build_norm(ctx0, inpSA, hparams,
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model.layers[il].ffn_norm_exps, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "ffn_norm_exps", il);
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cur = llm_build_moe_ffn(ctx0, 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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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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cb, il);
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cb(cur, "ffn_moe_out", il);
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cur = ggml_add(ctx0, cur, ffn_out);
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cb(cur, "ffn_out", il);
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ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx0, cur, layer_dir);
|
||||
}
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
};
|
||||
|
||||
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
||||
|
|
@ -11004,6 +11219,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_gptneox();
|
||||
} break;
|
||||
case LLM_ARCH_ARCTIC:
|
||||
{
|
||||
result = llm.build_arctic();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -16015,6 +16234,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||
case LLM_ARCH_XVERSE:
|
||||
case LLM_ARCH_COMMAND_R:
|
||||
case LLM_ARCH_OLMO:
|
||||
case LLM_ARCH_ARCTIC:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue