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llama : add EXAONE model support (#9025)
* add exaone model support * add chat template * fix whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add ftype * add exaone pre-tokenizer in `llama-vocab.cpp` Co-Authored-By: compilade <113953597+compilade@users.noreply.github.com> * fix lint Co-Authored-By: compilade <113953597+compilade@users.noreply.github.com> * add `EXAONE` to supported models in `README.md` * fix space Co-authored-by: compilade <git@compilade.net> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
Minsoo Cheong
GitHub
parent
fb487bb567
commit
c679e0cb5c
@ -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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@ -596,6 +596,9 @@ class Model:
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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")
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@ -3781,6 +3784,77 @@ class NemotronModel(Model):
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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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###### CONVERSION LOGIC ######
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@ -96,6 +96,7 @@ models = [
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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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@ -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):
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@ -349,6 +350,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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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] = {
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@ -1082,6 +1084,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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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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@ -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
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"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
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@ -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
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@ -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
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@ -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
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"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
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@ -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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@ -155,6 +156,7 @@ class TensorNameMap:
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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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@ -166,7 +168,8 @@ class TensorNameMap:
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"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
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"transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
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"transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok
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"transformer.h.{bid}.attn.attention.v_proj", # exaone
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),
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# Attention output
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@ -191,6 +194,7 @@ class TensorNameMap:
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"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
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"encoder.layers.{bid}.self_attention.dense", # chatglm
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"transformer.layers.{bid}.attn.out_proj", # openelm
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"transformer.h.{bid}.attn.attention.out_proj", # exaone
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),
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# Attention output norm
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@ -216,7 +220,7 @@ class TensorNameMap:
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# Feed-forward norm
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MODEL_TENSOR.FFN_NORM: (
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"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais exaone
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"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
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@ -278,6 +282,7 @@ class TensorNameMap:
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"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
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"model.layers.{bid}.residual_mlp.w3", # arctic
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"encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm
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"transformer.h.{bid}.mlp.c_fc_1", # exaone
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),
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MODEL_TENSOR.FFN_UP_EXP: (
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@ -309,6 +314,7 @@ class TensorNameMap:
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"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
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"transformer.h.{bid}.mlp.linear_1", # refact
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"model.layers.{bid}.residual_mlp.w1", # arctic
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"transformer.h.{bid}.mlp.c_fc_0", # exaone
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),
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MODEL_TENSOR.FFN_GATE_EXP: (
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@ -348,6 +354,7 @@ class TensorNameMap:
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"model.layers.{bid}.residual_mlp.w2", # arctic
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"encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2
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"encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm
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"model.layers.h.{bid}.mlp.c_proj", # exaone
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (
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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,
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LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
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};
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enum llama_rope_type {
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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:
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case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
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case LLAMA_VOCAB_PRE_TYPE_EXAONE:
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regex_exprs = {
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"\\p{N}",
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"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
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212
src/llama.cpp
212
src/llama.cpp
@ -211,6 +211,7 @@ enum llm_arch {
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LLM_ARCH_T5ENCODER,
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LLM_ARCH_JAIS,
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LLM_ARCH_NEMOTRON,
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LLM_ARCH_EXAONE,
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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_T5ENCODER, "t5encoder" },
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{ LLM_ARCH_JAIS, "jais" },
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{ LLM_ARCH_NEMOTRON, "nemotron" },
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{ LLM_ARCH_EXAONE, "exaone" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -1316,6 +1318,25 @@ 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_EXAONE,
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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_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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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -5263,6 +5284,15 @@ 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_EXAONE:
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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 32: model.type = e_model::MODEL_8B; 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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@ -5501,6 +5531,9 @@ static void llm_load_vocab(
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} else if (
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tokenizer_pre == "gpt3-finnish") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH;
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} else if (
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tokenizer_pre == "exaone") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE;
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} else {
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throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
|
||||
}
|
||||
@ -7638,6 +7671,36 @@ static bool llm_load_tensors(
|
||||
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");
|
||||
}
|
||||
@ -13895,7 +13958,6 @@ struct llm_build_context {
|
||||
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) {
|
||||
@ -13947,6 +14009,133 @@ struct llm_build_context {
|
||||
|
||||
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) {
|
||||
@ -14206,6 +14395,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_nemotron();
|
||||
} break;
|
||||
case LLM_ARCH_EXAONE:
|
||||
{
|
||||
result = llm.build_exaone();
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
@ -17277,6 +17470,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
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
|
||||
@ -19207,6 +19401,22 @@ static int32_t llama_chat_apply_template_internal(
|
||||
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;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user