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""" Arctic model configuration""" |
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from dataclasses import asdict, dataclass |
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from typing import Any, Dict |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json", |
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} |
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@dataclass |
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class ArcticLoraConfig: |
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lora_r: int = 64 |
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lora_alpha: float = 16 |
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shard_base_weights: bool = False |
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@dataclass |
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class ArcticQuantizationConfig: |
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q_bits: int = 8 |
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rounding: str = "nearest" |
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mantissa_bits: int = 3 |
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group_size: int = 512 |
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class ArcticConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an |
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Arctic model according to the specified arguments, defining the model architecture. Instantiating a configuration |
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with the defaults will yield a similar configuration to that of the #TODO(rsamdani): add what model has the default config.. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 32000): |
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Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`ArcticModel`] |
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hidden_size (`int`, *optional*, defaults to 4096): |
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Dimension of the hidden representations. |
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intermediate_size (`int`, *optional*, defaults to 14336): |
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Dimension of the MLP representations. |
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num_hidden_layers (`int`, *optional*, defaults to 32): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (`int`, *optional*, defaults to 32): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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num_key_value_heads (`int`, *optional*, defaults to 8): |
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If |
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if |
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When |
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed |
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by meanpooling all the original heads within that group. For more details checkout [this |
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`. |
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): |
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The non-linear activation function (function or string) in the decoder. |
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max_position_embeddings (`int`, *optional*, defaults to `4096*32`): |
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The maximum sequence length that this model might ever be used with. Arctic's sliding window attention |
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allows sequence of up to 4096*32 tokens. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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rms_norm_eps (`float`, *optional*, defaults to 1e-05): |
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The epsilon used by the rms normalization layers. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). Only |
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relevant if `config.is_decoder=True`. |
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pad_token_id (`int`, *optional*): |
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The id of the padding token. |
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bos_token_id (`int`, *optional*, defaults to 1): |
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The id of the "beginning-of-sequence" token. |
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eos_token_id (`int`, *optional*, defaults to 2): |
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The id of the "end-of-sequence" token. |
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tie_word_embeddings (`bool`, *optional*, defaults to `False`): |
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Whether the model's input and output word embeddings should be tied. |
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rope_theta (`float`, *optional*, defaults to 1000000.0): |
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The base period of the RoPE embeddings. |
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sliding_window (`int`, *optional*): |
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Sliding window attention window size. If not specified, will default to `4096`. |
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attention_dropout (`float`, *optional*, defaults to 0.0): |
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The dropout ratio for the attention probabilities. |
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num_experts_per_tok (`int`, *optional*, defaults to 2): |
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The number of experts to root per-token, can be also interpreted as the `top-p` routing |
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parameter |
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num_local_experts (`int`, *optional*, defaults to 8): |
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Number of experts per Sparse MLP layer. |
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001): |
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The aux loss factor for the total loss. |
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```python |
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>>> from transformers import ArcticModel, ArcticConfig |
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>>> # Initializing a Arctic 7B style configuration TODO(rsamdani): verify which model does the default configuration correspond to. |
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>>> configuration = ArcticConfig() |
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>>> # Initializing a model from the Arctic 7B style configuration |
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>>> model = ArcticModel(configuration) |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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model_type = "arctic" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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vocab_size=32000, |
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hidden_size=4096, |
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intermediate_size=14336, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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num_key_value_heads=None, |
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hidden_act="silu", |
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max_position_embeddings=4096, |
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initializer_range=0.02, |
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rms_norm_eps=1e-5, |
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use_cache=True, |
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pad_token_id=None, |
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bos_token_id=1, |
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eos_token_id=2, |
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tie_word_embeddings=False, |
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rope_theta=1e6, |
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sliding_window=None, |
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attention_dropout=0.0, |
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num_experts_per_tok=1, |
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num_local_experts=8, |
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router_aux_loss_coef=0.001, |
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moe_layer_frequency=2, |
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parallel_attn_mlp_res=False, |
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moe_train_capacity_factor=1, |
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moe_eval_capacity_factor=1, |
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enable_expert_tensor_parallelism=False, |
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moe_min_capacity=0, |
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moe_token_dropping=True, |
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quantization=None, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.max_position_embeddings = max_position_embeddings |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.sliding_window = sliding_window |
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if num_key_value_heads is None: |
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num_key_value_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.hidden_act = hidden_act |
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self.initializer_range = initializer_range |
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self.rms_norm_eps = rms_norm_eps |
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self.use_cache = use_cache |
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self.rope_theta = rope_theta |
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self.attention_dropout = attention_dropout |
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self.num_experts_per_tok = num_experts_per_tok |
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self.num_local_experts = num_local_experts |
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self.router_aux_loss_coef = router_aux_loss_coef |
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self.moe_layer_frequency = moe_layer_frequency |
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self.moe_train_capacity_factor = moe_train_capacity_factor |
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self.moe_eval_capacity_factor = moe_eval_capacity_factor |
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self.enable_expert_tensor_parallelism = enable_expert_tensor_parallelism |
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self.moe_min_capacity = moe_min_capacity |
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self.moe_token_dropping = moe_token_dropping |
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self.parallel_attn_mlp_res = parallel_attn_mlp_res |
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if isinstance(quantization, dict): |
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self.quantization = ArcticQuantizationConfig(**quantization) |
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else: |
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self.quantization = quantization |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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@classmethod |
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def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "ArcticConfig": |
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result = super().from_dict(config_dict, **kwargs) |
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if isinstance(result, tuple): |
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config = result[0] |
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else: |
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config = result |
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if isinstance(config.quantization, dict): |
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config.quantization = ArcticQuantizationConfig(**config.quantization) |
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return result |
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def to_dict(self) -> Dict[str, Any]: |
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ret = super().to_dict() |
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if isinstance(ret["quantization"], ArcticQuantizationConfig): |
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ret["quantization"] = asdict(ret["quantization"]) |
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return ret |
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