280 lines
11 KiB
Python
280 lines
11 KiB
Python
# Adapted from
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# https://huggingface.co/databricks/dbrx-base/blob/main/configuration_dbrx.py
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# https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/configs/dbrx.py
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"""Dbrx configuration."""
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from typing import Any, Optional
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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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DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP = {} # type: ignore
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class DbrxAttentionConfig(PretrainedConfig):
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"""Configuration class for Dbrx Attention.
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[`DbrxAttention`] class. It is used to instantiate attention layers
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according to the specified arguments, defining the layers architecture.
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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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attn_pdrop (`float`, *optional*, defaults to 0.0):
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The dropout probability for the attention layers.
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clip_qkv (`float`, *optional*, defaults to None):
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If not `None`, clip the queries, keys, and values in the attention layer to this value.
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kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
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rope_theta (float): The base frequency for rope.
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"""
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def __init__(
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self,
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attn_pdrop: float = 0,
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clip_qkv: Optional[float] = None,
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kv_n_heads: int = 1,
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rope_theta: float = 10000.0,
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**kwargs: Any,
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):
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super().__init__(**kwargs)
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self.attn_pdrop = attn_pdrop
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self.clip_qkv = clip_qkv
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self.kv_n_heads = kv_n_heads
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self.rope_theta = rope_theta
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for k in ["model_type"]:
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if k in kwargs:
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kwargs.pop(k)
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if len(kwargs) != 0:
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raise ValueError(f"Found unknown {kwargs=}")
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@classmethod
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def from_pretrained(
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cls, pretrained_model_name_or_path: str, **kwargs: Any
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) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(
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pretrained_model_name_or_path, **kwargs
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)
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if config_dict.get("model_type") == "dbrx":
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config_dict = config_dict["attn_config"]
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if (
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"model_type" in config_dict
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and hasattr(cls, "model_type")
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and config_dict["model_type"] != cls.model_type
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):
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logger.warning(
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"You are using a model of type %s to instantiate a model of "
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"type %s. This is not supported for all configurations of "
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"models and can yield errors.",
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config_dict["model_type"],
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cls.model_type,
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)
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return cls.from_dict(config_dict, **kwargs)
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class DbrxFFNConfig(PretrainedConfig):
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"""Configuration class for Dbrx FFN.
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[`DbrxFFN`] class. It is used to instantiate feedforward layers according to
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the specified arguments, defining the layers architecture.
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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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ffn_act_fn (dict, optional): A dict specifying activation function for the FFN.
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The dict should have a key 'name' with the value being the name of
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the activation function along with any additional keyword arguments.
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ffn_hidden_size (int, optional): The hidden size of the feedforward network.
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moe_num_experts (int, optional): The number of experts in the mixture of experts layer.
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moe_top_k (int, optional): The number of experts to use in the mixture of experts layer.
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moe_jitter_eps (float, optional): The jitter epsilon for the mixture of experts layer.
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moe_loss_weight (float, optional): The loss weight for the mixture of experts layer.
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moe_normalize_expert_weights (float, optional): The normalization factor for the expert weights.
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uniform_expert_assignment (bool, optional): Whether to use uniform expert assignment.
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This should only be used for benchmarking purposes.
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"""
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def __init__(
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self,
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ffn_act_fn: Optional[dict] = None,
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ffn_hidden_size: int = 3584,
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moe_num_experts: int = 4,
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moe_top_k: int = 1,
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moe_jitter_eps: Optional[float] = None,
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moe_loss_weight: float = 0.01,
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moe_normalize_expert_weights: Optional[float] = 1,
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uniform_expert_assignment: bool = False,
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**kwargs: Any,
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):
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super().__init__()
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if ffn_act_fn is None:
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ffn_act_fn = {"name": "silu"}
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self.ffn_act_fn = ffn_act_fn
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self.ffn_hidden_size = ffn_hidden_size
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self.moe_num_experts = moe_num_experts
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self.moe_top_k = moe_top_k
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self.moe_jitter_eps = moe_jitter_eps
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self.moe_loss_weight = moe_loss_weight
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self.moe_normalize_expert_weights = moe_normalize_expert_weights
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self.uniform_expert_assignment = uniform_expert_assignment
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for k in ["model_type"]:
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if k in kwargs:
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kwargs.pop(k)
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if len(kwargs) != 0:
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raise ValueError(f"Found unknown {kwargs=}")
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@classmethod
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def from_pretrained(
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cls, pretrained_model_name_or_path: str, **kwargs: Any
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) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(
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pretrained_model_name_or_path, **kwargs
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)
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if config_dict.get("model_type") == "dbrx":
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config_dict = config_dict["ffn_config"]
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if (
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"model_type" in config_dict
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and hasattr(cls, "model_type")
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and config_dict["model_type"] != cls.model_type
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):
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logger.warning(
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"You are using a model of type %s to instantiate a model of "
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"type %s. This is not supported for all "
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"configurations of models and can yield errors.",
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config_dict["model_type"],
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cls.model_type,
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)
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return cls.from_dict(config_dict, **kwargs)
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class DbrxConfig(PretrainedConfig):
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"""Configuration class for Dbrx.
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[`DbrxModel`]. It is used to instantiate a Dbrx model according to the
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specified arguments, defining the model architecture.
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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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d_model (`int`, *optional*, defaults to 6144):
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Dimensionality of the embeddings and hidden states.
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n_heads (`int`, *optional*, defaults to 48):
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Number of attention heads for each attention layer in the Transformer encoder.
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n_layers (`int`, *optional*, defaults to 40):
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Number of hidden layers in the Transformer encoder.
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max_seq_len (`int`, *optional*, defaults to 32768):
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The maximum sequence length of the model.
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vocab_size (`int`, *optional*, defaults to 100352):
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Vocabulary size of the Dbrx model. Defines the maximum number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`DbrxModel`].
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resid_pdrop (`float`, *optional*, defaults to 0.0):
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The dropout probability applied to the attention output before combining with residual.
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emb_pdrop (`float`, *optional*, defaults to 0.0):
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The dropout probability for the embedding layer.
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attn_config (`dict`, *optional*):
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A dictionary used to configure the model's attention module.
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ffn_config (`dict`, *optional*):
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A dictionary used to configure the model's FFN module.
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use_cache (`bool`, *optional*, defaults to `False`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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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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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not the router logits should be returned by the model. Enabling this will also
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allow the model to output the auxiliary loss. See [here]() for more details
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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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Example:
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```python
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>>> from transformers import DbrxConfig, DbrxModel
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>>> # Initializing a Dbrx configuration
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>>> configuration = DbrxConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = DbrxModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "dbrx"
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attribute_map = {
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"num_attention_heads": "n_heads",
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"hidden_size": "d_model",
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"num_hidden_layers": "n_layers",
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"max_position_embeddings": "max_seq_len",
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}
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def __init__(
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self,
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d_model: int = 2048,
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n_heads: int = 16,
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n_layers: int = 24,
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max_seq_len: int = 2048,
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vocab_size: int = 32000,
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resid_pdrop: float = 0.0,
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emb_pdrop: float = 0.0,
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attn_config: Optional[DbrxAttentionConfig] = None,
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ffn_config: Optional[DbrxFFNConfig] = None,
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use_cache: bool = True,
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initializer_range: float = 0.02,
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output_router_logits: bool = False,
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router_aux_loss_coef: float = 0.05,
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**kwargs: Any,
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):
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if attn_config is None:
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self.attn_config = DbrxAttentionConfig()
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elif isinstance(attn_config, dict):
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self.attn_config = DbrxAttentionConfig(**attn_config)
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else:
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self.attn_config = attn_config
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if ffn_config is None:
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self.ffn_config = DbrxFFNConfig()
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elif isinstance(ffn_config, dict):
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self.ffn_config = DbrxFFNConfig(**ffn_config)
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else:
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self.ffn_config = ffn_config
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self.d_model = d_model
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.max_seq_len = max_seq_len
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self.vocab_size = vocab_size
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self.resid_pdrop = resid_pdrop
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self.emb_pdrop = emb_pdrop
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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tie_word_embeddings = kwargs.pop("tie_word_embeddings", False)
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if tie_word_embeddings:
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raise ValueError("tie_word_embeddings is not supported for Dbrx models.")
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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