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Update configuration_gemmoe.py
Browse files- configuration_gemmoe.py +119 -110
configuration_gemmoe.py
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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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GEMMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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class GemmoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the
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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
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Vocabulary size of the
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`inputs_ids` passed when calling [`
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hidden_size (`int`, *optional*, defaults to
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to
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Dimension of the MLP representations.
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Dimension of the MoE representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to
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Number of attention heads for each attention layer in the Transformer decoder.
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Number of shared experts, None means dense model.
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n_routed_experts (`int`, *optional*, defaults to None):
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Number of routed experts, None means dense model.
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num_experts_per_tok (`int`, *optional*, defaults to None):
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Number of selected experts, None means dense model.
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moe_layer_freq (`int`, *optional*, defaults to 1):
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The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
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first_k_dense_replace (`int`, *optional*, defaults to 0):
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Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
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\--k dense layers--/
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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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scoring_func (`str`, *optional*, defaults to 'softmax'):
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Method of computing expert weights.
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aux_loss_alpha (`float`, *optional*, defaults to 0.001):
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Auxiliary loss weight coefficient.
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seq_aux = (`bool`, *optional*, defaults to True):
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Whether to compute the auxiliary loss for each individual sample.
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num_key_value_heads (`int`, *optional*):
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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
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`num_attention_heads`.
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The
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The maximum sequence length that this model might ever be used with.
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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-
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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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Padding token id.
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Beginning of stream token id.
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight 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 containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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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```python
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>>> from transformers import
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>>> # Initializing a
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>>> configuration =
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>>> #
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>>> configuration = model.config
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```"""
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@@ -109,92 +138,72 @@ class GemmoeConfig(PretrainedConfig):
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vocab_size=256000,
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hidden_size=3072,
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intermediate_size=24576,
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moe_intermediate_size = 1407,
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num_hidden_layers=28,
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num_attention_heads=16,
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num_key_value_heads=16,
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n_routed_experts = None,
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num_experts_per_tok = None,
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moe_layer_freq = 1,
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first_k_dense_replace = 0,
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norm_topk_prob = False,
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scoring_func = 'softmax',
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aux_loss_alpha = 0.001,
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seq_aux = True,
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hidden_act="gelu",
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max_position_embeddings=
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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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pad_token_id=0,
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bos_token_id=2,
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eos_token_id=1,
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tie_word_embeddings=True,
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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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**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.moe_layer_freq = moe_layer_freq
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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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self.scoring_func = scoring_func
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self.aux_loss_alpha = aux_loss_alpha
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self.seq_aux = seq_aux
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# for backward compatibility
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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.pretraining_tp = pretraining_tp
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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.
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self.
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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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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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# coding=utf-8
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# Copyright 2024 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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""" Gemmoe model configuration"""
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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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GEMMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"Crystalcareai/GemMoE-Beta-1": "https://huggingface.co/Crystalcareai/GemMoE-Beta-1/resolve/main/config.json",
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}
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class GemmoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`GemmoeModel`]. It is used to instantiate a Gemmoe
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Gemmoe-7B.
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e.g. [mhenrichsen/gemmoe-7b](https://huggingface.co/mhenrichsen/gemmoe-7b)
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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 256000):
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Vocabulary size of the Gemmoe model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`GemmoeModel`]
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hidden_size (`int`, *optional*, defaults to 3072):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 24576):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 28):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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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 16):
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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
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`num_attention_heads`.
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head_dim (`int`, *optional*, defaults to 256):
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The attention head dimension.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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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 8192):
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The maximum sequence length that this model might ever be used with.
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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-6):
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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*, defaults to 0):
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Padding token id.
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eos_token_id (`int`, *optional*, defaults to 1):
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End of stream token id.
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bos_token_id (`int`, *optional*, defaults to 2):
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Beginning of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight 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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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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 used in the sparse mixture of experts layer.
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num_local_experts (`int`, *optional*, defaults to 8):
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The number of local experts used in the sparse mixture of experts layer.
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router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
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The coefficient for the auxiliary loss of the router.
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output_router_logits (`bool`, *optional*, defaults to `False`):
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Whether or not to output the logits of the routers. They are useful for computing the router loss, and
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should not be returned during inference.
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n_shared_experts (`int`, *optional*, defaults to `None`):
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The number of shared experts used in the sparse mixture of experts layer. If set to `None`, no shared
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experts are used.
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n_routed_experts (`int`, *optional*, defaults to `None`):
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The number of routed experts used in the sparse mixture of experts layer. If set to `None`, all experts are
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routed experts.
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moe_layer_freq (`int`, *optional*, defaults to 1):
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The frequency of MoE layers in the model. A value of 1 means MoE layers are used in every layer, a value of
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2 means MoE layers are used in every other layer, and so on.
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first_k_dense_replace (`int`, *optional*, defaults to 0):
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The number of initial dense layers to replace with MoE layers. If set to 0 (default), no dense layers are
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replaced.
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norm_topk_prob (`bool`, *optional*, defaults to `False`):
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Whether to normalize the top-k probabilities of the router during training.
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scoring_func (`str`, *optional*, defaults to `'softmax'`):
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The scoring function used by the router. Can be 'softmax' or 'remap'.
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aux_loss_alpha (`float`, *optional*, defaults to 0.001):
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The weight of the auxiliary loss used for training the router.
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seq_aux (`bool`, *optional*, defaults to `True`):
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Whether to use sequence-level auxiliary loss for training the router.
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pretraining_tp (`int`, *optional*, defaults to 1):
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The tensor parallelism used for pretraining.
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rope_scaling (`float`, *optional*, defaults to `None`):
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The scaling factor for the Rotary Position Embedding (RoPE). If set to `None`, no scaling is applied.
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```python
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>>> from transformers import GemmoeModel, GemmoeConfig
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>>> # Initializing a Gemmoe gemmoe-7b style configuration
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>>> configuration = GemmoeConfig()
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>>> # Initializing a model from the gemmoe-7b style configuration
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>>> model = GemmoeModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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vocab_size=256000,
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hidden_size=3072,
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intermediate_size=24576,
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num_hidden_layers=28,
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num_attention_heads=16,
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num_key_value_heads=16,
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144 |
+
head_dim=256,
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145 |
hidden_act="gelu",
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146 |
+
max_position_embeddings=8192,
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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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pad_token_id=0,
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151 |
eos_token_id=1,
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+
bos_token_id=2,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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155 |
attention_bias=False,
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attention_dropout=0.0,
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157 |
+
num_experts_per_tok=2,
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158 |
+
num_local_experts=8,
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159 |
+
n_shared_experts=None,
|
160 |
+
n_routed_experts=None,
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161 |
+
moe_layer_freq=1,
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162 |
+
first_k_dense_replace=0,
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163 |
+
norm_topk_prob=False,
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164 |
+
scoring_func='softmax',
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165 |
+
aux_loss_alpha=0.001,
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166 |
+
seq_aux=True,
|
167 |
+
pretraining_tp=1,
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168 |
+
rope_scaling=None,
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169 |
+
router_aux_loss_coef=0.02,
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170 |
+
output_router_logits=False,
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**kwargs,
|
172 |
):
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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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176 |
self.intermediate_size = intermediate_size
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177 |
self.num_hidden_layers = num_hidden_layers
|
178 |
self.num_attention_heads = num_attention_heads
|
179 |
+
self.head_dim = head_dim
|
180 |
+
self.num_key_value_heads = num_key_value_heads
|
181 |
+
self.hidden_act = hidden_act
|
182 |
+
self.initializer_range = initializer_range
|
183 |
+
self.rms_norm_eps = rms_norm_eps
|
184 |
+
self.use_cache = use_cache
|
185 |
+
self.rope_theta = rope_theta
|
186 |
+
self.attention_bias = attention_bias
|
187 |
+
self.attention_dropout = attention_dropout
|
188 |
+
self.num_experts_per_tok = num_experts_per_tok
|
189 |
+
self.num_local_experts = num_local_experts
|
190 |
self.n_shared_experts = n_shared_experts
|
191 |
self.n_routed_experts = n_routed_experts
|
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|
192 |
self.moe_layer_freq = moe_layer_freq
|
193 |
self.first_k_dense_replace = first_k_dense_replace
|
194 |
self.norm_topk_prob = norm_topk_prob
|
195 |
self.scoring_func = scoring_func
|
196 |
self.aux_loss_alpha = aux_loss_alpha
|
197 |
self.seq_aux = seq_aux
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|
198 |
self.pretraining_tp = pretraining_tp
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|
199 |
self.rope_scaling = rope_scaling
|
200 |
+
self.router_aux_loss_coef = router_aux_loss_coef
|
201 |
+
self.output_router_logits = output_router_logits
|
202 |
+
|
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|
203 |
super().__init__(
|
204 |
pad_token_id=pad_token_id,
|
205 |
bos_token_id=bos_token_id,
|
206 |
eos_token_id=eos_token_id,
|
207 |
tie_word_embeddings=tie_word_embeddings,
|
208 |
**kwargs,
|
209 |
+
)
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