258 lines
11 KiB
Python
258 lines
11 KiB
Python
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# coding=utf-8
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# Copyright 2024 Databricks Mosaic Research and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" DBRX model configuration """
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from typing import Any, Optional
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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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*):
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If set, clip the queries, keys, and values in the attention layer to this value.
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kv_n_heads (`Optional[int]`, defaults to 1): For grouped_query_attention only, allow user to specify number of kv heads.
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rope_theta (`float`, defaults to 10000.0): 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 = 0.0,
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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(cls, pretrained_model_name_or_path: str, **kwargs: Any) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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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 "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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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*, defaults to `None`): 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 the activation function along with
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any additional keyword arguments. If `None`, then set to `{"name": "silu"}`.
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ffn_hidden_size (`int`, defaults to 3584): The hidden size of the feedforward network.
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moe_num_experts (`int`, defaults to 4): The number of experts in the mixture of experts layer.
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moe_top_k (`int`, defaults to 1): The number of experts to use in the mixture of experts layer.
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moe_jitter_eps (`float`, *optional*, defaults to `None`): If not `None`, the jitter epsilon for the mixture of experts layer.
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moe_loss_weight (`float`, defaults to 0.01): The loss weight for the mixture of experts layer.
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moe_normalize_expert_weights (`float`, *optional*, defaults to 1.0): The normalization factor for the expert weights.
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"""
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def __init__(
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self,
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ffn_act_fn: 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] = 1.0,
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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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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(cls, pretrained_model_name_or_path: str, **kwargs: Any) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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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 "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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+ f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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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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r"""
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This is the configuration class to store the configuration of a [`DbrxModel`]. It is used to instantiate a Dbrx model according to the
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specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a different configuration to that of the [databricks/dbrx-instruct](https://huggingface.co/databricks/dbrx-instruct) 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 2048):
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Dimensionality of the embeddings and hidden states.
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n_heads (`int`, *optional*, defaults to 16):
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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 24):
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Number of hidden layers in the Transformer encoder.
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max_seq_len (`int`, *optional*, defaults to 2048):
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The maximum sequence length of the model.
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vocab_size (`int`, *optional*, defaults to 32000):
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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 `True`):
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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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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(n_layers=2, d_model=256, n_heads=8, vocab_size=128)
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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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**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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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__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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