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llama : add EXAONE model support (#9025)
* add exaone model support * add chat template * fix whitespace Co-authored-by: Georgi Gerganov <[email protected]> * add ftype * add exaone pre-tokenizer in `llama-vocab.cpp` Co-Authored-By: compilade <[email protected]> * fix lint Co-Authored-By: compilade <[email protected]> * add `EXAONE` to supported models in `README.md` * fix space Co-authored-by: compilade <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]> Co-authored-by: compilade <[email protected]> Co-authored-by: compilade <[email protected]>
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‎README.md

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@@ -105,6 +105,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
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- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
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- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
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- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
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(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
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‎convert_hf_to_gguf.py

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@@ -596,6 +596,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
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if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21":
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# ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small
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res = "gpt3-finnish"
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if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae":
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# ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
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res = "exaone"
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if res is None:
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logger.warning("\n")
@@ -3781,6 +3784,77 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("ExaoneForCausalLM")
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class ExaoneModel(Model):
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model_arch = gguf.MODEL_ARCH.EXAONE
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def set_gguf_parameters(self):
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hparams = self.hparams
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assert(hparams["activation_function"] == "silu")
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max_position_embeddings = hparams["max_position_embeddings"]
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embed_dim = hparams["hidden_size"]
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num_heads = hparams["num_attention_heads"]
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num_kv_heads = hparams.get("num_key_value_heads", num_heads)
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layer_norm_eps = hparams["layer_norm_epsilon"]
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intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim
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num_layers = hparams["num_layers"]
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# ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0
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# attention_dropout_rate = hparams["attention_dropout"]
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# ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0
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# embed_dropout_rate = hparams["embed_dropout"]
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self.gguf_writer.add_embedding_length(embed_dim)
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self.gguf_writer.add_head_count(num_heads)
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self.gguf_writer.add_head_count_kv(num_kv_heads)
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self.gguf_writer.add_context_length(max_position_embeddings)
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self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps)
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self.gguf_writer.add_feed_forward_length(intermediate_size)
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self.gguf_writer.add_block_count(num_layers)
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self.gguf_writer.add_file_type(self.ftype)
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if (rope_theta := self.hparams.get("rope_theta")) is not None:
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self.gguf_writer.add_rope_freq_base(rope_theta)
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rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
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rotary_factor = rotary_factor if rotary_factor is not None else 1.0
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self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
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if hparams.get("rope_scaling") is not None and "factor" in hparams["rope_scaling"]:
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if hparams["rope_scaling"].get("type") == "linear":
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
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def prepare_tensors(self):
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if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
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if rope_scaling.get("rope_type", '').lower() == "llama3":
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base = self.hparams.get("rope_theta", 10000.0)
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dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
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freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
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factor = rope_scaling.get("factor", 8.0)
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low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
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high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
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old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
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low_freq_wavelen = old_context_len / low_freq_factor
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high_freq_wavelen = old_context_len / high_freq_factor
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assert low_freq_wavelen != high_freq_wavelen
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rope_factors = []
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for freq in freqs:
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wavelen = 2 * math.pi / freq
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if wavelen < high_freq_wavelen:
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rope_factors.append(1)
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elif wavelen > low_freq_wavelen:
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rope_factors.append(factor)
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else:
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smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
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rope_factors.append(1 / ((1 - smooth) / factor + smooth))
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self.gguf_writer.add_tensor(self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), np.array(rope_factors, dtype=np.float32))
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super().prepare_tensors()
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37843858
###### CONVERSION LOGIC ######
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‎convert_hf_to_gguf_update.py

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@@ -96,6 +96,7 @@ class TOKENIZER_TYPE(IntEnum):
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{"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
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{'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
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{'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
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{"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
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]
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‎gguf-py/gguf/constants.py

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@@ -220,6 +220,7 @@ class MODEL_ARCH(IntEnum):
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T5ENCODER = auto()
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JAIS = auto()
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NEMOTRON = auto()
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EXAONE = auto()
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class MODEL_TENSOR(IntEnum):
@@ -349,6 +350,7 @@ class MODEL_TENSOR(IntEnum):
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MODEL_ARCH.T5ENCODER: "t5encoder",
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MODEL_ARCH.JAIS: "jais",
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MODEL_ARCH.NEMOTRON: "nemotron",
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MODEL_ARCH.EXAONE: "exaone",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -1082,6 +1084,22 @@ class MODEL_TENSOR(IntEnum):
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.EXAONE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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# TODO
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}
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‎gguf-py/gguf/tensor_mapping.py

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@@ -10,7 +10,7 @@ class TensorNameMap:
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# Token embeddings
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MODEL_TENSOR.TOKEN_EMBD: (
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"gpt_neox.embed_in", # gptneox
13-
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
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"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone
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"transformer.word_embeddings", # falcon
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"word_embeddings", # bloom
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"model.embed_tokens", # llama-hf nemotron
@@ -52,7 +52,7 @@ class TensorNameMap:
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# Output
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MODEL_TENSOR.OUTPUT: (
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"embed_out", # gptneox
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone
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"output", # llama-pth bloom internlm2
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"word_embeddings_for_head", # persimmon
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"lm_head.linear", # phi2
@@ -62,7 +62,7 @@ class TensorNameMap:
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# Output norm
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MODEL_TENSOR.OUTPUT_NORM: (
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"gpt_neox.final_layer_norm", # gptneox
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"transformer.ln_f", # gpt2 gpt-j falcon jais
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"transformer.ln_f", # gpt2 gpt-j falcon jais exaone
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"model.norm", # llama-hf baichuan internlm2
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"norm", # llama-pth
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"transformer.norm_f", # mpt dbrx
@@ -89,7 +89,7 @@ class TensorNameMap:
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# Attention norm
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MODEL_TENSOR.ATTN_NORM: (
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"gpt_neox.layers.{bid}.input_layernorm", # gptneox
92-
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais
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"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais exaone
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"transformer.blocks.{bid}.norm_1", # mpt
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"transformer.h.{bid}.input_layernorm", # falcon7b
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"h.{bid}.input_layernorm", # bloom
@@ -143,6 +143,7 @@ class TensorNameMap:
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"model.layers.layers.{bid}.self_attn.q_proj", # plamo
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"model.layers.{bid}.attention.wq", # internlm2
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"transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok
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"transformer.h.{bid}.attn.attention.q_proj", # exaone
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),
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# Attention key
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"model.layers.layers.{bid}.self_attn.k_proj", # plamo
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"model.layers.{bid}.attention.wk", # internlm2
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"transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok
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"transformer.h.{bid}.attn.attention.k_proj", # exaone
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),
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# Attention value
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166168
"transformer.h.{bid}.attn.v", # refact
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"model.layers.layers.{bid}.self_attn.v_proj", # plamo
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"model.layers.{bid}.attention.wv", # internlm2
169-
"transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
171+
"transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok
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"transformer.h.{bid}.attn.attention.v_proj", # exaone
170173
),
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172175
# Attention output
@@ -191,6 +194,7 @@ class TensorNameMap:
191194
"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
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"encoder.layers.{bid}.self_attention.dense", # chatglm
193196
"transformer.layers.{bid}.attn.out_proj", # openelm
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"transformer.h.{bid}.attn.attention.out_proj", # exaone
194198
),
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# Attention output norm
@@ -216,7 +220,7 @@ class TensorNameMap:
216220
# Feed-forward norm
217221
MODEL_TENSOR.FFN_NORM: (
218222
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
219-
"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
223+
"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais exaone
220224
"h.{bid}.post_attention_layernorm", # bloom
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"transformer.blocks.{bid}.norm_2", # mpt
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"model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron
@@ -278,6 +282,7 @@ class TensorNameMap:
278282
"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
279283
"model.layers.{bid}.residual_mlp.w3", # arctic
280284
"encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm
285+
"transformer.h.{bid}.mlp.c_fc_1", # exaone
281286
),
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283288
MODEL_TENSOR.FFN_UP_EXP: (
@@ -309,6 +314,7 @@ class TensorNameMap:
309314
"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
310315
"transformer.h.{bid}.mlp.linear_1", # refact
311316
"model.layers.{bid}.residual_mlp.w1", # arctic
317+
"transformer.h.{bid}.mlp.c_fc_0", # exaone
312318
),
313319

314320
MODEL_TENSOR.FFN_GATE_EXP: (
@@ -348,6 +354,7 @@ class TensorNameMap:
348354
"model.layers.{bid}.residual_mlp.w2", # arctic
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"encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2
350356
"encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm
357+
"model.layers.h.{bid}.mlp.c_proj", # exaone
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (

‎include/llama.h

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@@ -95,6 +95,7 @@ extern "C" {
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LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22,
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LLAMA_VOCAB_PRE_TYPE_BLOOM = 23,
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LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24,
98+
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
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};
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enum llama_rope_type {

‎src/llama-vocab.cpp

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@@ -388,6 +388,7 @@ struct llm_tokenizer_bpe {
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case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
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case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
390390
case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
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case LLAMA_VOCAB_PRE_TYPE_EXAONE:
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regex_exprs = {
392393
"\\p{N}",
393394
"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",

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