From b7f834101476209767b7c8a52f17aa86cad79f44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B9=80=EC=A2=85=EA=B3=A4?= <149566442+Deepfocused@users.noreply.github.com> Date: Fri, 30 Aug 2024 17:08:28 +0900 Subject: [PATCH] EXAONE 3.0 Model Support (#1258) Co-authored-by: Yineng Zhang --- python/sglang/srt/configs/__init__.py | 5 + python/sglang/srt/configs/exaone.py | 195 ++++++++++ python/sglang/srt/hf_transformers_utils.py | 12 +- python/sglang/srt/models/exaone.py | 399 +++++++++++++++++++++ 4 files changed, 609 insertions(+), 2 deletions(-) create mode 100644 python/sglang/srt/configs/__init__.py create mode 100644 python/sglang/srt/configs/exaone.py create mode 100644 python/sglang/srt/models/exaone.py diff --git a/python/sglang/srt/configs/__init__.py b/python/sglang/srt/configs/__init__.py new file mode 100644 index 0000000000..9e74366709 --- /dev/null +++ b/python/sglang/srt/configs/__init__.py @@ -0,0 +1,5 @@ +from sglang.srt.configs.exaone import ExaoneConfig + +__all__ = [ + "ExaoneConfig", +] diff --git a/python/sglang/srt/configs/exaone.py b/python/sglang/srt/configs/exaone.py new file mode 100644 index 0000000000..7b0a2d290d --- /dev/null +++ b/python/sglang/srt/configs/exaone.py @@ -0,0 +1,195 @@ +# coding=utf-8 +# Copyright 2024 The LG AI Research EXAONE Lab. All rights reserved. +# Copyright 2024 The LG CNS AI Engineering Team. +# Copyright 2023-2024 SGLang Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" EXAONE model configuration """ +from typing import Any, Dict + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP: Dict[str, Any] = {} + + +# ruff: noqa: E501 +class ExaoneConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a :class:`~transformers.ExaoneModel`. It is used to + instantiate a EXAONE model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the Exaone + + Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model + outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. + + + Args: + vocab_size (:obj:`int`, `optional`, defaults to 102400): + Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the + :obj:`inputs_ids` passed when calling :class:`~transformers.ExaoneModel`. Vocabulary size of the model. + Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of + :class:`~transformers.EXAONEModel`. + max_position_embeddings (:obj:`int`, `optional`, defaults to 2048): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + hidden_size (:obj:`int`, `optional`, defaults to 2048): + Dimensionality of the encoder layers and the pooler layer. + num_layers (:obj:`int`, `optional`, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (:obj:`int`, `optional`, defaults to 32): + Number of attention heads for each attention layer in the Transformer decoder. + num_key_value_heads (:obj:`int`, `optional`): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + intermediate_size (:obj:`int`, `optional`, defaults to `hidden_size * 4`): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"silu"`): + The non-linear activation function (function or string) in the decoder. + rope_theta (:obj:`float`, `optional`, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (:obj:`Dict`, `optional`): + Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type + and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value + accordingly. + Expected contents: + `rope_type` (:obj:`str`): + The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', + 'llama3'], with 'default' being the original RoPE implementation. + `factor` (:obj:`float`, `optional`): + Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In + most scaling types, a `factor` of x will enable the model to handle sequences of length x * + original maximum pre-trained length. + `original_max_position_embeddings` (:obj:`int`, `optional`): + Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during + pretraining. + `attention_factor` (:obj:`float`, `optional`): + Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention + computation. If unspecified, it defaults to value recommended by the implementation, using the + `factor` field to infer the suggested value. + `beta_fast` (:obj:`float`, `optional`): + Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear + ramp function. If unspecified, it defaults to 32. + `beta_slow` (:obj:`float`, `optional`): + Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear + ramp function. If unspecified, it defaults to 1. + `short_factor` (:obj:`List[float]`, `optional`): + Only used with 'longrope'. The scaling factor to be applied to short contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `long_factor` (:obj:`List[float]`, `optional`): + Only used with 'longrope'. The scaling factor to be applied to long contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `low_freq_factor` (:obj:`float`, `optional`): + Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE + `high_freq_factor` (:obj:`float`, `optional`): + Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE + embed_dropout (:obj:`float`, `optional`, defaults to 0.0): + The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (:obj:`float`, `optional`, defaults to 0.0): + The dropout ratio for the attention probabilities. + layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5): + The epsilon used by the layer normalization layers. + initializer_range (:obj:`float`, `optional`, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if ``configs.is_decoder=True``. + bos_token_id (:obj:`int`, `optional`, defaults to 0): + Beginning of stream token id. + eos_token_id (:obj:`int`, `optional`, defaults to 2): + End of stream token id. + tie_word_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`): + Whether to tie weight embeddings + gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`): + If True, use gradient checkpointing to save memory at the expense of slower backward pass. + + Example:: + + >>> from transformers import EXAONEModel, ExaoneConfig + + >>> # Initializing a EXAONE configuration + >>> configuration = ExaoneConfig() + + >>> # Initializing a model from configuration + >>> model = EXAONEModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.configs + """ + + model_type = "exaone" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_hidden_layers": "num_layers"} + + def __init__( + self, + vocab_size=102400, + max_position_embeddings=2048, + hidden_size=2048, + num_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + intermediate_size=None, + activation_function="silu", + rope_theta=10000.0, + rope_scaling=None, + embed_dropout=0.0, + attention_dropout=0.0, + layer_norm_epsilon=1e-5, + initializer_range=0.02, + use_cache=True, + bos_token_id=0, + eos_token_id=2, + tie_word_embeddings=True, + **kwargs + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.num_layers = num_layers + self.num_attention_heads = num_attention_heads + self.num_hidden_layers = num_layers + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + if intermediate_size: + self.intermediate_size = intermediate_size + else: + self.intermediate_size = hidden_size * 4 + self.activation_function = activation_function + self.embed_dropout = embed_dropout + self.attention_dropout = attention_dropout + self.layer_norm_epsilon = layer_norm_epsilon + self.initializer_range = initializer_range + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + + super().__init__( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs + ) diff --git a/python/sglang/srt/hf_transformers_utils.py b/python/sglang/srt/hf_transformers_utils.py index 2be4169140..7fce3b2401 100644 --- a/python/sglang/srt/hf_transformers_utils.py +++ b/python/sglang/srt/hf_transformers_utils.py @@ -15,6 +15,7 @@ """Utilities for Huggingface Transformers.""" +import contextlib import functools import json import os @@ -34,14 +35,21 @@ try: from vllm.transformers_utils.configs import ChatGLMConfig, DbrxConfig + from sglang.srt.configs import ExaoneConfig + _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = { ChatGLMConfig.model_type: ChatGLMConfig, DbrxConfig.model_type: DbrxConfig, + ExaoneConfig.model_type: ExaoneConfig, } except ImportError: # We want this file to run without vllm dependency _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {} +for name, cls in _CONFIG_REGISTRY.items(): + with contextlib.suppress(ValueError): + AutoConfig.register(name, cls) + from sglang.srt.utils import is_multimodal_model @@ -53,7 +61,7 @@ def download_from_hf(model_path: str): def get_config_json(model_path: str): - with open(os.path.join(model_path, "config.json")) as f: + with open(os.path.join(model_path, "configs.json")) as f: config = json.load(f) return config @@ -89,7 +97,7 @@ def get_config( def get_context_length(config): - """Get the context length of a model from a huggingface model config.""" + """Get the context length of a model from a huggingface model configs.""" rope_scaling = getattr(config, "rope_scaling", None) if rope_scaling: rope_scaling_factor = config.rope_scaling["factor"] diff --git a/python/sglang/srt/models/exaone.py b/python/sglang/srt/models/exaone.py new file mode 100644 index 0000000000..4dcafed7ce --- /dev/null +++ b/python/sglang/srt/models/exaone.py @@ -0,0 +1,399 @@ +""" +Copyright 2024 The LGcns AI Engineering Team +Copyright 2023-2024 SGLang Team +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +# Adapted from llama2.py +"""Inference-only Exaone model compatible with HuggingFace weights.""" + +from typing import Any, Dict, Iterable, Optional, Tuple + +import torch +from torch import nn +from vllm.config import CacheConfig +from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.model_executor.layers.linear import ( + MergedColumnParallelLinear, + QKVParallelLinear, + RowParallelLinear, +) +from vllm.model_executor.layers.quantization.base_config import QuantizationConfig +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) +from vllm.model_executor.model_loader.weight_utils import default_weight_loader + +from sglang.srt.layers.activation import SiluAndMul +from sglang.srt.layers.layernorm import RMSNorm +from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput +from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.sampler import Sampler +from sglang.srt.model_executor.forward_batch_info import InputMetadata + + +class ExaoneGatedMLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.gate_up_proj = MergedColumnParallelLinear( + hidden_size, + [intermediate_size] * 2, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.gate_up_proj", + ) + self.c_proj = RowParallelLinear( + intermediate_size, + hidden_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.c_proj", + ) + if hidden_act != "silu": + raise ValueError( + f"Unsupported activation: {hidden_act}. " + "Only silu is supported for now." + ) + self.act_fn = SiluAndMul() + + def forward(self, x): + gate_up, _ = self.gate_up_proj(x) + x = self.act_fn(gate_up) + x, _ = self.c_proj(x) + return x + + +class ExaoneAttention(nn.Module): + def __init__( + self, + config, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + layer_id: int = 0, + rope_theta: float = 500000, + rope_scaling: Optional[Dict[str, Any]] = None, + rope_is_neox_style: bool = True, + max_position_embeddings: int = 4096, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = hidden_size + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = num_heads + assert self.total_num_heads % tp_size == 0 + self.num_heads = self.total_num_heads // tp_size + self.total_num_kv_heads = num_kv_heads + if self.total_num_kv_heads >= tp_size: + # Number of KV heads is greater than TP size, so we partition + # the KV heads across multiple tensor parallel GPUs. + assert self.total_num_kv_heads % tp_size == 0 + else: + # Number of KV heads is less than TP size, so we replicate + # the KV heads across multiple tensor parallel GPUs. + assert tp_size % self.total_num_kv_heads == 0 + self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) + # MistralConfig has an optional head_dim introduced by Mistral-Nemo + self.head_dim = getattr( + config, "head_dim", self.hidden_size // self.total_num_heads + ) + self.rotary_dim = int( + self.head_dim * getattr(config, "partial_rotary_factor", 1) + ) + self.q_size = self.num_heads * self.head_dim + self.kv_size = self.num_kv_heads * self.head_dim + self.scaling = self.head_dim**-0.5 + self.rope_theta = rope_theta + self.max_position_embeddings = max_position_embeddings + + self.qkv_proj = QKVParallelLinear( + hidden_size, + self.head_dim, + self.total_num_heads, + self.total_num_kv_heads, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.qkv_proj", + ) + self.out_proj = RowParallelLinear( + self.total_num_heads * self.head_dim, + hidden_size, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.out_proj", + ) + + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.rotary_dim, + max_position=max_position_embeddings, + base=rope_theta, + rope_scaling=rope_scaling, + is_neox_style=rope_is_neox_style, + ) + self.attn = RadixAttention( + self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads, + layer_id=layer_id, + ) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + input_metadata: InputMetadata, + ) -> torch.Tensor: + qkv, _ = self.qkv_proj(hidden_states) + q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v, input_metadata) + output, _ = self.out_proj(attn_output) + return output + + +class ExaoneDecoderLayer(nn.Module): + def __init__( + self, + config, + layer_id: int = 0, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + rope_theta = getattr(config, "rope_theta", 500000) + rope_scaling = getattr(config, "rope_scaling", None) + if rope_scaling is not None and getattr( + config, "original_max_position_embeddings", None + ): + rope_scaling["original_max_position_embeddings"] = ( + config.original_max_position_embeddings + ) + rope_is_neox_style = getattr(config, "rope_is_neox_style", True) + max_position_embeddings = getattr(config, "max_position_embeddings", 4096) + self.self_attn = ExaoneAttention( + config=config, + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + num_kv_heads=config.num_key_value_heads, + layer_id=layer_id, + rope_theta=rope_theta, + rope_scaling=rope_scaling, + rope_is_neox_style=rope_is_neox_style, + max_position_embeddings=max_position_embeddings, + quant_config=quant_config, + prefix=f"{prefix}.self_attn", + ) + self.mlp = ExaoneGatedMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.activation_function, + quant_config=quant_config, + prefix=f"{prefix}.mlp", + ) + rms_norm_eps = config.layer_norm_epsilon + self.ln_1 = RMSNorm(config.hidden_size, eps=rms_norm_eps) + self.ln_2 = RMSNorm(config.hidden_size, eps=rms_norm_eps) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + input_metadata: InputMetadata, + residual: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.ln_1(hidden_states) + else: + hidden_states, residual = self.ln_1(hidden_states, residual) + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + input_metadata=input_metadata, + ) + + # Fully Connected + hidden_states, residual = self.ln_2(hidden_states, residual) + hidden_states = self.mlp(hidden_states) + return hidden_states, residual + + +class ExaoneModel(nn.Module): + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + ) -> None: + super().__init__() + self.config = config + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.wte = VocabParallelEmbedding( + config.vocab_size, + config.hidden_size, + ) + self.h = nn.ModuleList( + [ + ExaoneDecoderLayer( + config, i, quant_config=quant_config, prefix=f"model.h.{i}" + ) + for i in range(config.num_hidden_layers) + ] + ) + rms_norm_eps = config.layer_norm_epsilon + self.ln_f = RMSNorm(config.hidden_size, eps=rms_norm_eps) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + input_metadata: InputMetadata, + input_embeds: torch.Tensor = None, + ) -> torch.Tensor: + if input_embeds is None: + hidden_states = self.wte(input_ids) + else: + hidden_states = input_embeds + residual = None + for i in range(len(self.h)): + layer = self.h[i] + hidden_states, residual = layer( + positions, + hidden_states, + input_metadata, + residual, + ) + hidden_states, _ = self.ln_f(hidden_states, residual) + return hidden_states + + +class ExaoneForCausalLM(nn.Module): + def __init__( + self, + config, + quant_config: Optional[QuantizationConfig] = None, + cache_config: Optional[CacheConfig] = None, + efficient_weight_load=False, + ) -> None: + super().__init__() + self.config = config + self.quant_config = quant_config + self.transformer = ExaoneModel(config, quant_config=quant_config) + self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size) + self.logits_processor = LogitsProcessor(config) + self.sampler = Sampler() + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + input_metadata: InputMetadata, + input_embeds: torch.Tensor = None, + ) -> LogitsProcessorOutput: + hidden_states = self.transformer( + input_ids, positions, input_metadata, input_embeds + ) + logits_output = self.logits_processor( + input_ids, hidden_states, self.lm_head.weight, input_metadata + ) + sample_output = self.sampler(logits_output, input_metadata.sampling_info) + return sample_output, logits_output + + def get_module_name(self, name): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id, num_shard) + ("qkv_proj", "q_proj", "q", 3), + ("qkv_proj", "k_proj", "k", 3), + ("qkv_proj", "v_proj", "v", 3), + ("gate_up_proj", "c_fc_0", 0, 2), + ("gate_up_proj", "c_fc_1", 1, 2), + ] + for param_name, weight_name, shard_id, num_shard in stacked_params_mapping: + if weight_name in name: + return ( + name.replace(weight_name, param_name)[: -len(".weight")], + num_shard, + ) + return name[: -len(".weight")], 1 + + def get_num_params(self): + params_dict = dict(self.named_parameters()) + return len(params_dict) + + def load_weights( + self, weights: Iterable[Tuple[str, torch.Tensor]], name=None, loaded_weight=None + ): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + ("qkv_proj", "q_proj", "q"), + ("qkv_proj", "k_proj", "k"), + ("qkv_proj", "v_proj", "v"), + ("gate_up_proj", "c_fc_0", 0), + ("gate_up_proj", "c_fc_1", 1), + ] + params_dict = dict(self.named_parameters()) + + def load_weights_per_param(name, loaded_weight): + if "rotary_emb.inv_freq" in name or "projector" in name: + return + if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: + # Models trained using ColossalAI may include these tensors in + # the checkpoint. Skip them. + return + if name.startswith("model.vision_tower") and name not in params_dict: + return + + for param_name, weight_name, shard_id in stacked_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + return + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + weight_loader(param, loaded_weight) + + if name is None or loaded_weight is None: + for name, loaded_weight in weights: + name = name.replace("attn.attention", "self_attn") + load_weights_per_param(name, loaded_weight) + else: + name = name.replace("attn.attention", "self_attn") + load_weights_per_param(name, loaded_weight) + + +EntryClass = ExaoneForCausalLM