# Copyright 2023 Snowflake AI and the HuggingFace Inc. team. All rights reserved. # # 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. """ Arctic model configuration""" from dataclasses import asdict, dataclass from typing import Any, Dict from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = { "arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json", } @dataclass class ArcticLoraConfig: lora_r: int = 64 lora_alpha: float = 16 shard_base_weights: bool = False @dataclass class ArcticQuantizationConfig: q_bits: int = 8 rounding: str = "nearest" mantissa_bits: int = 3 group_size: int = 512 class ArcticConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an Arctic 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 #TODO(rsamdani): add what model has the default config.. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ArcticModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): 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 `8`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to `4096*32`): The maximum sequence length that this model might ever be used with. Arctic's sliding window attention allows sequence of up to 4096*32 tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. sliding_window (`int`, *optional*): Sliding window attention window size. If not specified, will default to `4096`. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_experts_per_tok (`int`, *optional*, defaults to 2): The number of experts to root per-token, can be also interpreted as the `top-p` routing parameter num_local_experts (`int`, *optional*, defaults to 8): Number of experts per Sparse MLP layer. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. ```python >>> from transformers import ArcticModel, ArcticConfig >>> # Initializing a Arctic 7B style configuration TODO(rsamdani): verify which model does the default configuration correspond to. >>> configuration = ArcticConfig() >>> # Initializing a model from the Arctic 7B style configuration >>> model = ArcticModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "arctic" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=32000, hidden_size=4096, intermediate_size=14336, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=4096, initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, pad_token_id=None, bos_token_id=1, eos_token_id=2, tie_word_embeddings=False, rope_theta=1e6, sliding_window=None, attention_dropout=0.0, num_experts_per_tok=1, num_local_experts=8, router_aux_loss_coef=0.001, moe_layer_frequency=2, parallel_attn_mlp_res=False, moe_train_capacity_factor=1, moe_eval_capacity_factor=1, enable_expert_tensor_parallelism=False, moe_min_capacity=0, moe_token_dropping=True, quantization=None, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.sliding_window = sliding_window # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.num_experts_per_tok = num_experts_per_tok self.num_local_experts = num_local_experts self.router_aux_loss_coef = router_aux_loss_coef self.moe_layer_frequency = moe_layer_frequency self.moe_train_capacity_factor = moe_train_capacity_factor self.moe_eval_capacity_factor = moe_eval_capacity_factor self.enable_expert_tensor_parallelism = enable_expert_tensor_parallelism self.moe_min_capacity = moe_min_capacity self.moe_token_dropping = moe_token_dropping self.parallel_attn_mlp_res = parallel_attn_mlp_res if isinstance(quantization, dict): self.quantization = ArcticQuantizationConfig(**quantization) else: self.quantization = quantization super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) @classmethod def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "ArcticConfig": result = super().from_dict(config_dict, **kwargs) if isinstance(result, tuple): config = result[0] else: config = result if isinstance(config.quantization, dict): config.quantization = ArcticQuantizationConfig(**config.quantization) return result def to_dict(self) -> Dict[str, Any]: ret = super().to_dict() if isinstance(ret["quantization"], ArcticQuantizationConfig): ret["quantization"] = asdict(ret["quantization"]) return ret