211 lines
9.8 KiB
Python
211 lines
9.8 KiB
Python
# coding=utf-8
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# Copyright 2025 The rednote-hilab team 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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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class Dots1Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Dots1Model`]. It is used to instantiate a
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`dots.llm1` model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of
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[rednote-hilab/dots.llm1.base](https://huggingface.co/rednote-hilab/dots.llm1.base).
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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 152064):
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Vocabulary size of the model. Defines the number of different tokens that can be represented by the
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`input_ids` passed when calling [`Dots1Model`].
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hidden_size (`int`, *optional*, defaults to 4608):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 10944):
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Dimension of the MLP representations.
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moe_intermediate_size (`int`, *optional*, defaults to 1408):
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Dimension of the MoE representations.
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num_hidden_layers (`int`, *optional*, defaults to 62):
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Number of hidden layers in the Transformer decoder.
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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 decoder.
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num_key_value_heads (`int`, *optional*, defaults to 32):
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Number of key/value heads for Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, Multi
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Head Attention (MHA) is used. If `num_key_value_heads=1`, Multi Query Attention (MQA) is used. Otherwise,
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Grouped Query Attention (GQA) is used. If not specified, defaults to `num_attention_heads`.
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n_shared_experts (`int`, *optional*, default=None):
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Number of shared experts. None means dense model.
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n_routed_experts (`int`, *optional*, default=None):
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Number of routed experts. None means dense model.
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n_group (`int`, *optional*, defaults to 1):
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Number of groups for routed experts.
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topk_group (`int`, *optional*, defaults to 1):
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Number of selected groups for each token (selected experts only within `topk_group` groups).
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num_experts_per_tok (`int`, *optional*, default=None):
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Number of selected experts. None means dense model.
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first_k_dense_replace (`int`, *optional*, defaults to 0):
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Number of dense layers at the beginning of the model before the first MoE layer.
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norm_topk_prob (`bool`, *optional*, defaults to `False`):
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Whether to normalize the weights of the routed experts.
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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).
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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Maximum sequence length the model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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Standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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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. Only relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie the input and output word embeddings.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`dict`, *optional*):
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Dictionary for scaling RoPE embeddings. Supports `{"type": strategy name, "factor": scaling factor}`.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the self-attention projections.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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Dropout ratio for the attention probabilities.
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routed_scaling_factor (`float`, *optional*, defaults to 1.0):
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Scaling factor for routed experts.
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sliding_window (`int`, *optional*, defaults to 4096):
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Size of the sliding window for attention. If not specified, defaults to `4096`.
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max_window_layers (`int`, *optional*, defaults to 62):
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The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
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additional layer afterwards will use SWA (Sliding Window Attention).
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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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Examples:
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```python
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>>> from transformers import Dots1Model, Dots1Config
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>>> # Initializing a Dots1 style configuration
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>>> configuration = Dots1Config()
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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 = "dots1"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = { # TODO: only replicate attention layers when > first_k_dense_replace
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.experts.*.gate_proj": "local_colwise",
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"layers.*.mlp.experts.*.up_proj": "local_colwise",
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"layers.*.mlp.experts.*.down_proj": "local_rowwise",
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"layers.*.mlp.experts.*": "local", # each expert is wrapped in a module list
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"layers.*.mlp.shared_experts.gate_proj": "local_colwise",
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"layers.*.mlp.shared_experts.up_proj": "local_colwise",
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"layers.*.mlp.shared_experts.down_proj": "local_rowwise",
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"layers.*.mlp.shared_experts": "local",
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"layers.*.mlp.gate_proj": "local_colwise",
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"layers.*.mlp.up_proj": "local_colwise",
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"layers.*.mlp.down_proj": "local_rowwise",
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"layers.*.mlp": "gather", # This is the only moment where results are gathered
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=152064,
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hidden_size=4608,
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intermediate_size=10944,
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moe_intermediate_size=1408,
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num_hidden_layers=62,
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num_attention_heads=32,
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num_key_value_heads=32,
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n_shared_experts=None,
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n_routed_experts=None,
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n_group=1,
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topk_group=1,
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num_experts_per_tok=None,
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first_k_dense_replace=0,
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norm_topk_prob=False,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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routed_scaling_factor=1.0,
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sliding_window=4096,
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max_window_layers=62,
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layer_types=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.moe_intermediate_size = moe_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.n_shared_experts = n_shared_experts
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self.n_routed_experts = n_routed_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.first_k_dense_replace = first_k_dense_replace
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self.norm_topk_prob = norm_topk_prob
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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.n_group = n_group
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self.topk_group = topk_group
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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.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.routed_scaling_factor = routed_scaling_factor
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self.sliding_window = sliding_window
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self.max_window_layers = max_window_layers
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self.layer_types = layer_types
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention"
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if self.sliding_window is not None and i >= self.max_window_layers
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else "full_attention"
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for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types)
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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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__all__ = ["Dots1Config"]
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