282 lines
13 KiB
Python
282 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2024 Meta Inc. 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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"""chameleon model configuration"""
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from typing import 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 ChameleonVQVAEConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ChameleonVQModel`]. It is used to instantiate a
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`ChameleonVQModel` according to the specified arguments, defining the model 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. Instantiating a
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configuration with the defaults will yield a similar configuration to the VQModel of the
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[meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B).
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Args:
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embed_dim (`int`, *optional*, defaults to 256):
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Dimensionality of each embedding vector.
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num_embeddings (`int`, *optional*, defaults to 8192):
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Number of codebook embeddings.
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double_latent (`bool`, *optional*, defaults to `False`):
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Whether to use double z channels.
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latent_channels (`int`, *optional*, defaults to 256):
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Number of channels for the latent space.
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resolution (`int`, *optional*, defaults to 512):
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Resolution of the input images.
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in_channels (`int`, *optional*, defaults to 3):
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Number of input channels.
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base_channels (`int`, *optional*, defaults to 128):
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Base channel count.
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channel_multiplier (`list[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`):
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Channel multipliers for each resolution.
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num_res_blocks (`int`, *optional*, defaults to 2):
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Number of residual blocks.
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attn_resolutions (`list[int]`, *optional*):
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Resolutions to apply attention.
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dropout (`float`, *optional*, defaults to 0.0):
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Dropout rate.
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attn_type (`str`, *optional*, defaults to `"vanilla"`):
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Attention type used in VQ-GAN encoder. Can be "vanilla" or None.
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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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"""
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model_type = "chameleon_vqgan"
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base_config_key = "vq_config"
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def __init__(
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self,
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embed_dim: int = 256,
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num_embeddings: int = 8192,
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double_latent: bool = False,
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latent_channels: int = 256,
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resolution: int = 512,
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in_channels: int = 3,
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base_channels: int = 128,
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channel_multiplier: list[int] = [1, 1, 2, 2, 4],
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num_res_blocks: int = 2,
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attn_resolutions: Optional[list[int]] = None,
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dropout: float = 0.0,
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attn_type: str = "vanilla",
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initializer_range=0.02,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.embed_dim = embed_dim
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self.num_embeddings = num_embeddings
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self.double_latent = double_latent
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self.latent_channels = latent_channels
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self.resolution = resolution
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self.in_channels = in_channels
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self.base_channels = base_channels
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self.channel_multiplier = channel_multiplier
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self.num_res_blocks = num_res_blocks
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self.attn_resolutions = attn_resolutions
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self.dropout = dropout
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self.attn_type = attn_type
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self.initializer_range = initializer_range
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class ChameleonConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ChameleonModel`]. It is used to instantiate a
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chameleon 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 the
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[meta/chameleon-7B](https://huggingface.co/meta/chameleon-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 65536):
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Vocabulary size of the chameleon model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ChameleonModel`]; this includes text and image tokens.
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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 11008):
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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 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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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, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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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):
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The maximum sequence length that this model might ever be used with. Chameleon supports up to 4096 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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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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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. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/Localchameleon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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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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model_parallel_size (`int`, *optional*, defaults to 1):
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Number of shards used when training the model. This will be used in qk layernorm because the original Chameleon inference
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doesn't do reduction in those layers and each rank has its own biases.
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swin_norm (`bool`, *optional*, defaults to `False`):
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Use Swin Transformer normalization.
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vq_config (`dict`, *optional*):
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ChameleonVQConfig instance containing the configuration for the VQ-VAE model.
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vocabulary_map (`dict`, *optional*):
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A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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```python
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>>> from transformers import ChameleonModel, ChameleonConfig
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>>> # Initializing a chameleon chameleon-7b style configuration
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>>> configuration = ChameleonConfig()
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>>> # Initializing a model from the chameleon-7b style configuration
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>>> model = ChameleonModel(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 = "chameleon"
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sub_configs = {"vq_config": ChameleonVQVAEConfig}
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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=65536,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-05,
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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=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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model_parallel_size=1,
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swin_norm=False,
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vq_config=None,
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vocabulary_map=None,
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mlp_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.mlp_bias = mlp_bias
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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._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.model_parallel_size = model_parallel_size
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self.swin_norm = swin_norm
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if vq_config is None:
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vq_config = {}
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logger.info("vq_config is None. initializing the ChameleonVQConfig with default values.")
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self.vq_config = ChameleonVQVAEConfig(**vq_config)
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self.vocabulary_map = vocabulary_map
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self.image_token_id = vocabulary_map.get("<image>") if vocabulary_map is not None else None
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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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__all__ = ["ChameleonConfig", "ChameleonVQVAEConfig"]
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