if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21":
# ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small
res = "gpt3-finnish"
+ if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae":
+ # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
+ res = "exaone"
if res is None:
logger.warning("\n")
return [(self.map_tensor_name(name), data_torch)]
+
+@Model.register("ExaoneForCausalLM")
+class ExaoneModel(Model):
+ model_arch = gguf.MODEL_ARCH.EXAONE
+
+ def set_gguf_parameters(self):
+ hparams = self.hparams
+
+ assert(hparams["activation_function"] == "silu")
+
+ max_position_embeddings = hparams["max_position_embeddings"]
+ embed_dim = hparams["hidden_size"]
+ num_heads = hparams["num_attention_heads"]
+ num_kv_heads = hparams.get("num_key_value_heads", num_heads)
+ layer_norm_eps = hparams["layer_norm_epsilon"]
+ intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim
+ num_layers = hparams["num_layers"]
+ # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0
+ # attention_dropout_rate = hparams["attention_dropout"]
+ # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0
+ # embed_dropout_rate = hparams["embed_dropout"]
+ self.gguf_writer.add_embedding_length(embed_dim)
+ self.gguf_writer.add_head_count(num_heads)
+ self.gguf_writer.add_head_count_kv(num_kv_heads)
+ self.gguf_writer.add_context_length(max_position_embeddings)
+ self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps)
+ self.gguf_writer.add_feed_forward_length(intermediate_size)
+ self.gguf_writer.add_block_count(num_layers)
+ self.gguf_writer.add_file_type(self.ftype)
+
+ if (rope_theta := self.hparams.get("rope_theta")) is not None:
+ self.gguf_writer.add_rope_freq_base(rope_theta)
+ rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
+ rotary_factor = rotary_factor if rotary_factor is not None else 1.0
+ self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
+ if hparams.get("rope_scaling") is not None and "factor" in hparams["rope_scaling"]:
+ if hparams["rope_scaling"].get("type") == "linear":
+ self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+ self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
+
+ def prepare_tensors(self):
+ if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
+ if rope_scaling.get("rope_type", '').lower() == "llama3":
+ base = self.hparams.get("rope_theta", 10000.0)
+ dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+ freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+ factor = rope_scaling.get("factor", 8.0)
+ low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
+ high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+ old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+
+ low_freq_wavelen = old_context_len / low_freq_factor
+ high_freq_wavelen = old_context_len / high_freq_factor
+ assert low_freq_wavelen != high_freq_wavelen
+
+ rope_factors = []
+ for freq in freqs:
+ wavelen = 2 * math.pi / freq
+ if wavelen < high_freq_wavelen:
+ rope_factors.append(1)
+ elif wavelen > low_freq_wavelen:
+ rope_factors.append(factor)
+ else:
+ smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+ rope_factors.append(1 / ((1 - smooth) / factor + smooth))
+
+ self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
+
+ super().prepare_tensors()
+
###### CONVERSION LOGIC ######
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
- "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
+ "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf nemotron
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
- "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron
+ "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone
"output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
# Output norm
MODEL_TENSOR.OUTPUT_NORM: (
"gpt_neox.final_layer_norm", # gptneox
- "transformer.ln_f", # gpt2 gpt-j falcon jais
+ "transformer.ln_f", # gpt2 gpt-j falcon jais exaone
"model.norm", # llama-hf baichuan internlm2
"norm", # llama-pth
"transformer.norm_f", # mpt dbrx
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
- "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais
+ "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais exaone
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom
"model.layers.layers.{bid}.self_attn.q_proj", # plamo
"model.layers.{bid}.attention.wq", # internlm2
"transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok
+ "transformer.h.{bid}.attn.attention.q_proj", # exaone
),
# Attention key
"model.layers.layers.{bid}.self_attn.k_proj", # plamo
"model.layers.{bid}.attention.wk", # internlm2
"transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok
+ "transformer.h.{bid}.attn.attention.k_proj", # exaone
),
# Attention value
"transformer.h.{bid}.attn.v", # refact
"model.layers.layers.{bid}.self_attn.v_proj", # plamo
"model.layers.{bid}.attention.wv", # internlm2
- "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
+ "transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok
+ "transformer.h.{bid}.attn.attention.v_proj", # exaone
),
# Attention output
"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
"encoder.layers.{bid}.self_attention.dense", # chatglm
"transformer.layers.{bid}.attn.out_proj", # openelm
+ "transformer.h.{bid}.attn.attention.out_proj", # exaone
),
# Attention output norm
# Feed-forward norm
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
- "transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
+ "transformer.h.{bid}.ln_2", # gpt2 refact qwen jais exaone
"h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron
"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
"model.layers.{bid}.residual_mlp.w3", # arctic
"encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm
+ "transformer.h.{bid}.mlp.c_fc_1", # exaone
),
MODEL_TENSOR.FFN_UP_EXP: (
"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
"transformer.h.{bid}.mlp.linear_1", # refact
"model.layers.{bid}.residual_mlp.w1", # arctic
+ "transformer.h.{bid}.mlp.c_fc_0", # exaone
),
MODEL_TENSOR.FFN_GATE_EXP: (
"model.layers.{bid}.residual_mlp.w2", # arctic
"encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2
"encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm
+ "model.layers.h.{bid}.mlp.c_proj", # exaone
),
MODEL_TENSOR.FFN_DOWN_EXP: (
LLM_ARCH_T5ENCODER,
LLM_ARCH_JAIS,
LLM_ARCH_NEMOTRON,
+ LLM_ARCH_EXAONE,
LLM_ARCH_UNKNOWN,
};
{ LLM_ARCH_T5ENCODER, "t5encoder" },
{ LLM_ARCH_JAIS, "jais" },
{ LLM_ARCH_NEMOTRON, "nemotron" },
+ { LLM_ARCH_EXAONE, "exaone" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
+ {
+ LLM_ARCH_EXAONE,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_EXAONE:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_8B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
}
} else if (
tokenizer_pre == "gpt3-finnish") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
+ } else if (
+ tokenizer_pre == "exaone") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
} else {
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
}
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
} break;
+ case LLM_ARCH_EXAONE:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.rope_freqs = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_embd/n_head/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ }
+ } break;
default:
throw std::runtime_error("unknown architecture");
}
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-
}
if (il == n_layer - 1) {
return gf;
}
+
+ struct ggml_cgraph * build_exaone() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // rope freq factors for llama3; may return nullptr for llama2 and other models
+ struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ 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 = llm_build_lora_mm(lctx, 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) {
{
result = llm.build_nemotron();
} break;
+ case LLM_ARCH_EXAONE:
+ {
+ result = llm.build_exaone();
+ } break;
default:
GGML_ABORT("fatal error");
}
case LLM_ARCH_GPTNEOX:
case LLM_ARCH_CODESHELL:
case LLM_ARCH_NEMOTRON:
+ case LLM_ARCH_EXAONE:
return LLAMA_ROPE_TYPE_NEOX;
// all model arches should be listed explicitly here
if (add_ass) {
ss << "Assistant:";
}
+ } else if (tmpl == "exaone3" || (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]"))) {
+ // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
+ // EXAONE-3.0-7.8B-Instruct
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ ss << "[|system|]" << trim(message->content) << "[|endofturn|]\n";
+ } else if (role == "user") {
+ ss << "[|user|]" << trim(message->content) << "\n";
+ } else if (role == "assistant") {
+ ss << "[|assistant|]" << trim(message->content) << "[|endofturn|]\n";
+ }
+ }
+ if (add_ass) {
+ ss << "[|assistant|]";
+ }
} else {
// template not supported
return -1;
overview / pkg / ggml / sources / llama.cpp / commitdiff