152 lines
6.6 KiB
Python
152 lines
6.6 KiB
Python
# coding=utf-8
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# Copyright 2024 JetMoe AI 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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"""JetMoe model configuration"""
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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 JetMoeConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
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JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a configuration of the JetMoe-4B.
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[jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)
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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 32000):
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Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`JetMoeModel`]
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each key and value in the Transformer encoder.
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kv_channels (`int`, *optional*, defaults to 128):
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Defines the number of channels for the key and value tensors.
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intermediate_size (`int`, *optional*, defaults to 5632):
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Dimension of the MLP representations.
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
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up to 4096 tokens.
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activation_function (`string`, *optional*, defaults to `"silu"`):
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Defines the activation function for MLP experts.
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num_local_experts (`int`, *optional*, defaults to 8):
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Defines the number of experts in the MoE and MoA.
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num_experts_per_tok (`int, *optional*, defaults to 2):
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The number of experts to route per-token and for MoE and MoA.
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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.
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aux_loss_coef (`float`, *optional*, defaults to 0.01):
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The coefficient for the auxiliary loss.
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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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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 `True`):
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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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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.01):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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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 JetMoeModel, JetMoeConfig
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>>> # Initializing a JetMoe 4B style configuration
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>>> configuration = JetMoeConfig()
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>>> # Initializing a model from the JetMoe 4B style configuration
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>>> model = JetMoeModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "jetmoe"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=2048,
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num_hidden_layers=12,
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num_key_value_heads=16,
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kv_channels=128,
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intermediate_size=5632,
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max_position_embeddings=4096,
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activation_function="silu",
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num_local_experts=8,
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num_experts_per_tok=2,
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output_router_logits=False,
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aux_loss_coef=0.01,
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use_cache=True,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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rms_norm_eps=1e-6,
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initializer_range=0.01,
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attention_dropout=0.0,
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**kwargs,
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):
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if num_experts_per_tok > num_local_experts:
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raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_key_value_heads * num_experts_per_tok
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self.num_key_value_heads = num_key_value_heads
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self.kv_channels = kv_channels
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self.intermediate_size = intermediate_size
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self.max_position_embeddings = max_position_embeddings
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self.activation_function = activation_function
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self.num_local_experts = num_local_experts
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self.num_experts_per_tok = num_experts_per_tok
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self.output_router_logits = output_router_logits
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self.aux_loss_coef = aux_loss_coef
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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self.attention_dropout = attention_dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.rope_theta = rope_theta
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self.rms_norm_eps = rms_norm_eps
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super().__init__(
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bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
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)
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__all__ = ["JetMoeConfig"]
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