328 lines
16 KiB
Python
328 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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from ...configuration_utils import PretrainedConfig
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from ...modeling_rope_utils import rope_config_validation
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class Emu3VQVAEConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Emu3VQVAE`]. It is used to instantiate an VQ-VAE
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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 configuration to the VQ model presented in Emu3 paper.
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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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codebook_size (`int`, *optional*, defaults to 32768):
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Codebook size of the VQ model.
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embed_dim (`int`, *optional*, defaults to 4):
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Dimension of the quantized vector in codebook.
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latent_channels (`int`, *optional*, defaults to 4):
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Dimension of the output channel of encoder and the input channel of decoder
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double_latent (`bool`, *optional*, defaults to `False`):
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Whether double the output dim of the encoder.
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in_channels (`int`, *optional*, defaults to 3):
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Input channel of encoder.
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out_channels (`int`, *optional*, defaults to 3):
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Output channel of decoder.
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temporal_downsample_factor (`int`, *optional*, defaults to 4):
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Temporal downsample factor.
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base_channels (`int`, *optional*, defaults to 256):
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Basic channel number of the intermediate blocks.
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channel_multiplier (`list[int]`, *optional*, defaults to `[1, 2, 2, 4]`):
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Channel scaling factor of the intermediate blocks.
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num_res_blocks (`int`, *optional*, defaults to 2):
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Residual block number in each stage.
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attn_resolutions (`list[int]`, *optional*, defaults to `[3]`):
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Stage indices to apply attention.
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimension of the hidden representations in the attention layer.
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num_attention_heads (`int`, *optional*, defaults to 1):
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Number of attention heads for each attention layer.
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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 Emu3VQVAE, Emu3VQVAEConfig
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>>> # Initializing a video VQ model of Emu3 configuration
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>>> configuration = Emu3VQVAEConfig()
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>>> # Initializing a model from the Emu3 VQ model style configuration
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>>> model = Emu3VQVAE(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 = "emu3_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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codebook_size: int = 32768,
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embed_dim: int = 4,
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latent_channels: int = 4,
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double_latent: bool = False,
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in_channels: int = 3,
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out_channels: int = 3,
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temporal_downsample_factor: int = 4,
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base_channels: int = 256,
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channel_multiplier: list[int] = [1, 2, 2, 4],
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num_res_blocks: int = 2,
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attn_resolutions: list[int] = [3],
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hidden_size: int = 1024,
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num_attention_heads: int = 1,
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attention_dropout: float = 0.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.codebook_size = codebook_size
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self.embed_dim = embed_dim
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self.latent_channels = latent_channels
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self.double_latent = double_latent
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.temporal_downsample_factor = temporal_downsample_factor
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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.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.attention_dropout = attention_dropout
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class Emu3TextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Emu3TextModel`]. It is used to instantiate a
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emu3 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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[Emu3-community/Emu3-Chat-hf](https://huggingface.co/Emu3-community/Emu3-Chat-hf).
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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 184622):
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Vocabulary size of the Emu3 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Emu3Model`]
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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 14336):
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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 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, 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 9216):
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The maximum sequence length that this model might ever be used with. Emu supports up to 9216 tokens,
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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*, defaults to 151643):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 151849):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 151850):
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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 1000000.0):
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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. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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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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attention_bias (`bool`, *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 0.1):
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The dropout ratio for the attention probabilities.
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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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```python
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>>> from transformers import Emu3Model, Emu3Config
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>>> # Initializing a Emu3-community/Emu3-Chat-hf style configuration
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>>> configuration = Emu3Config()
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>>> # Initializing a model from the Emu3-community/Emu3-Chat-hf style configuration
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>>> model = Emu3Model(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 = "emu3_text_model"
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base_config_key = "text_config"
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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: int = 184622,
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hidden_size: int = 4096,
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intermediate_size: int = 14336,
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num_hidden_layers: int = 32,
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num_attention_heads: int = 32,
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num_key_value_heads: Optional[int] = 8,
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hidden_act: str = "silu",
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max_position_embeddings: int = 9216,
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rms_norm_eps: float = 1e-5,
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use_cache: bool = True,
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pad_token_id: int = 151643,
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bos_token_id: int = 151849,
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eos_token_id: int = 151850,
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tie_word_embeddings: bool = False,
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rope_theta: float = 1000000.0,
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rope_scaling: Optional = None,
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mlp_bias=False,
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attention_bias=False,
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attention_dropout: float = 0.1,
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initializer_range: float = 0.02,
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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.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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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.mlp_bias = mlp_bias
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self.attention_bias = attention_bias
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self.initializer_range = initializer_range
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rope_config_validation(self)
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self.attention_dropout = attention_dropout
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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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class Emu3Config(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate a
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emu3 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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[Emu3-community/Emu3-Chat-hf](https://huggingface.co/Emu3-community/Emu3-Chat-hf).
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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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vq_config (`Union[Dict, Emu3VQVAEConfig]`, *optional*):
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Emu3VQVAEConfig instance containing the configuration for the VQ-VAE model.
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text_config (`Union[Dict, Emu3TextConfig]``, *optional*):
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Emu3TextConfig instance containing the configuration for the language 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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"""
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model_type = "emu3"
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keys_to_ignore_at_inference = ["past_key_values"]
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sub_configs = {"text_config": Emu3TextConfig, "vq_config": Emu3VQVAEConfig}
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def __init__(
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self,
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vq_config: Union[dict, Emu3VQVAEConfig] = None,
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text_config: Union[dict, Emu3TextConfig] = None,
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vocabulary_map: Optional[dict[int, int]] = None,
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**kwargs,
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):
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if vq_config is None:
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vq_config = Emu3VQVAEConfig()
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elif isinstance(vq_config, dict):
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vq_config = Emu3VQVAEConfig(**vq_config)
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if text_config is None:
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text_config = Emu3TextConfig()
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elif isinstance(text_config, dict):
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text_config = Emu3TextConfig(**text_config)
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self.vq_config = vq_config
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self.text_config = text_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__(**kwargs)
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__all__ = ["Emu3Config", "Emu3TextConfig", "Emu3VQVAEConfig"]
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