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+ # coding=utf-8
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+ # Copyright 2024 Microsoft 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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+
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+ """ PyTorch Phi-MoE model."""
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+
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ PHIMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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+ "microsoft/Phi-3.5-MoE-instruct": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/config.json",
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+ }
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+
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+ class PhiMoEConfig(PretrainedConfig):
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+ r"""
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+ This is the configuration class to store the configuration of a [`PhiMoEModel`]. It is used to instantiate a Phi-MoE
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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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+ [microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct).
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+
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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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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 32064):
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+ Vocabulary size of the PhiMoE model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`PhiMoEModel`]
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+ hidden_size (`int`, *optional*, defaults to 4096):
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+ Dimension of the hidden representations.
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+ intermediate_size (`int`, *optional*, defaults to 6400):
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+ Dimension of the MLP representations.
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+ num_hidden_layers (`int`, *optional*, defaults to 32):
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+ Number of hidden layers in the Transformer encoder.
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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 encoder.
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+ num_key_value_heads (`int`, *optional*, defaults to 8):
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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 `8`.
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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) in the decoder.
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+ max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
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+ The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
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+ allows sequence of up to 4096*32 tokens.
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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-05):
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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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+ The id of the padding token.
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+ bos_token_id (`int`, *optional*, defaults to 1):
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+ The id of the "beginning-of-sequence" token.
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+ eos_token_id (`int`, *optional*, defaults to 2):
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+ The id of the "end-of-sequence" token.
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+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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+ Whether the model's input and output word embeddings should be tied.
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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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+ The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
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+ contain the following keys: `type`, `short_factor`, `long_factor`, `short_mscale`, `long_mscale` and
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+ `original_max_position_embeddings`. The `type` must be `longrope`, the `short_mscale` and `long_scale` must
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+ be numbers, the `short_factor` and `long_factor` must be lists of numbers with the same length as half of
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+ the attention head size and the `original_max_position_embeddings` must be an integer.
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+ sliding_window (`int`, *optional*):
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+ Sliding window attention window size. If not specified, will default to `262144`.
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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 to root per-token, can be also interpreted as the `top-p` routing
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+ parameter
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+ num_local_experts (`int`, *optional*, defaults to 16):
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+ Number of experts per Sparse MLP layer.
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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. Enabeling 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 0.0):
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+ The aux loss factor for the total loss.
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+ router_jitter_noise (`float`, *optional*, defaults to 0.01):
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+ Amount of noise to add to the router.
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+
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+ ```python
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+ >>> from transformers import PhiMoEModel, PhiMoEConfig
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+
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+ >>> # Initializing a Phi-3 style configuration
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+ >>> configuration = PhiMoEConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct")
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+
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+ >>> # Initializing a model from the configuration
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+ >>> model = PhiMoEModel(configuration)
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+
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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 = "phimoe"
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+
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+ def __init__(
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+ self,
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+ vocab_size=32064,
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+ hidden_size=4096,
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+ intermediate_size=6400,
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+ num_hidden_layers=32,
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+ num_attention_heads=32,
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+ num_key_value_heads=8,
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+ hidden_act="silu",
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+ max_position_embeddings=4096 * 32,
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+ initializer_range=0.02,
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+ rms_norm_eps=1e-5,
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+ use_cache=True,
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+ pad_token_id=None,
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+ bos_token_id=1,
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+ eos_token_id=2,
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+ tie_word_embeddings=False,
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+ rope_theta=1e6,
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+ rope_scaling=None,
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+ sliding_window=None,
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+ attention_dropout=0.0,
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+ num_experts_per_tok=2,
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+ num_local_experts=16,
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+ output_router_logits=False,
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+ router_aux_loss_coef=0.001,
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+ router_jitter_noise=0.01,
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+ input_jitter_noise=0.0,
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+ attention_bias = False,
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+ lm_head_bias = False,
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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.num_hidden_layers = num_hidden_layers
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+ self.num_attention_heads = num_attention_heads
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+ self.sliding_window = sliding_window
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+ self.attention_bias = attention_bias
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+ self.lm_head_bias = lm_head_bias
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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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+
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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.attention_dropout = attention_dropout
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+
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+ self.num_experts_per_tok = num_experts_per_tok
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+ self.num_local_experts = num_local_experts
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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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+ self.router_jitter_noise = router_jitter_noise
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+ self.input_jitter_noise = input_jitter_noise
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+
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+ self.rope_scaling = rope_scaling
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+ self._rope_scaling_validation()
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+
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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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+
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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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+
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+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6:
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+ raise ValueError(
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+ "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, "
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+ f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, 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_short_factor = self.rope_scaling.get("short_factor", None)
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+ rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
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+ rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
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+ rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
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+ original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
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+ if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
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+ raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
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+ if not (
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+ isinstance(rope_scaling_short_factor, list)
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+ and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
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+ ):
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+ raise ValueError(
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+ f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
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+ )
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+ if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
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+ raise ValueError(
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+ f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
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+ )
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+ if not (
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+ isinstance(rope_scaling_long_factor, list)
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+ and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
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+ ):
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+ raise ValueError(
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+ f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
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+ )
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+ if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
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+ raise ValueError(
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+ f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
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+ )
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+ if not isinstance(rope_scaling_short_mscale, (int, float)):
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+ raise ValueError(
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+ f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
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+ )
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+ if not isinstance(rope_scaling_long_mscale, (int, float)):
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+ raise ValueError(
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+ f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
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+ )
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+ if not isinstance(original_max_position_embeddings, int):
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+ raise ValueError(
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+ f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}"
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+ )