Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/chameleon
/configuration_chameleon.py
# coding=utf-8 | |
# Copyright 2024 Meta Inc. and The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""chameleon model configuration""" | |
from typing import List | |
from ...configuration_utils import PretrainedConfig | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
class ChameleonVQVAEConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`ChameleonVQModel`]. It is used to instantiate a | |
`ChameleonVQModel` according to the specified arguments, defining the model architecture. | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. Instantiating a | |
configuration with the defaults will yield a similar configuration to the VQModel of the | |
[meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B). | |
Args: | |
embed_dim (`int`, *optional*, defaults to 256): | |
Dimensionality of each embedding vector. | |
num_embeddings (`int`, *optional*, defaults to 8192): | |
Number of codebook embeddings. | |
double_latent (`bool`, *optional*, defaults to `False`): | |
Whether to use double z channels. | |
latent_channels (`int`, *optional*, defaults to 256): | |
Number of channels for the latent space. | |
resolution (`int`, *optional*, defaults to 512): | |
Resolution of the input images. | |
in_channels (`int`, *optional*, defaults to 3): | |
Number of input channels. | |
base_channels (`int`, *optional*, defaults to 128): | |
Base channel count. | |
channel_multiplier (`List[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`): | |
Channel multipliers for each resolution. | |
num_res_blocks (`int`, *optional*, defaults to 2): | |
Number of residual blocks. | |
attn_resolutions (`List[int]`, *optional*): | |
Resolutions to apply attention. | |
dropout (`float`, *optional*, defaults to 0.0): | |
Dropout rate. | |
attn_type (`str`, *optional*, defaults to `"vanilla"`): | |
Attention type used in VQ-GAN encoder. Can be "vanilla" or None. | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
""" | |
model_type = "chameleon_vqgan" | |
def __init__( | |
self, | |
embed_dim: int = 256, | |
num_embeddings: int = 8192, | |
double_latent: bool = False, | |
latent_channels: int = 256, | |
resolution: int = 512, | |
in_channels: int = 3, | |
base_channels: int = 128, | |
channel_multiplier: List[int] = [1, 1, 2, 2, 4], | |
num_res_blocks: int = 2, | |
attn_resolutions: List[int] = None, | |
dropout: float = 0.0, | |
attn_type: str = "vanilla", | |
initializer_range=0.02, | |
**kwargs, | |
): | |
super().__init__(**kwargs) | |
self.embed_dim = embed_dim | |
self.num_embeddings = num_embeddings | |
self.double_latent = double_latent | |
self.latent_channels = latent_channels | |
self.resolution = resolution | |
self.in_channels = in_channels | |
self.base_channels = base_channels | |
self.channel_multiplier = channel_multiplier | |
self.num_res_blocks = num_res_blocks | |
self.attn_resolutions = attn_resolutions | |
self.dropout = dropout | |
self.attn_type = attn_type | |
self.initializer_range = initializer_range | |
class ChameleonConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`ChameleonModel`]. It is used to instantiate a | |
chameleon model according to the specified arguments, defining the model architecture. Instantiating a | |
configuration with the defaults will yield a similar configuration to that of the | |
[meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B). | |
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
documentation from [`PretrainedConfig`] for more information. | |
Args: | |
vocab_size (`int`, *optional*, defaults to 65536): | |
Vocabulary size of the chameleon model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`ChameleonModel`]; this includes text and image tokens. | |
hidden_size (`int`, *optional*, defaults to 4096): | |
Dimension of the hidden representations. | |
intermediate_size (`int`, *optional*, defaults to 11008): | |
Dimension of the MLP representations. | |
num_hidden_layers (`int`, *optional*, defaults to 32): | |
Number of hidden layers in the Transformer decoder. | |
num_attention_heads (`int`, *optional*, defaults to 32): | |
Number of attention heads for each attention layer in the Transformer decoder. | |
num_key_value_heads (`int`, *optional*, defaults to 32): | |
This is the number of key_value heads that should be used to implement Grouped Query Attention. If | |
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if | |
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When | |
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed | |
by meanpooling all the original heads within that group. For more details checkout [this | |
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to | |
`num_attention_heads`. | |
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): | |
The non-linear activation function (function or string) in the decoder. | |
max_position_embeddings (`int`, *optional*, defaults to 4096): | |
The maximum sequence length that this model might ever be used with. Chameleon supports up to 4096 tokens. | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
rms_norm_eps (`float`, *optional*, defaults to 1e-05): | |
The epsilon used by the rms normalization layers. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
Whether or not the model should return the last key/values attentions (not used by all models). Only | |
relevant if `config.is_decoder=True`. | |
pad_token_id (`int`, *optional*): | |
Padding token id. | |
bos_token_id (`int`, *optional*, defaults to 1): | |
Beginning of stream token id. | |
eos_token_id (`int`, *optional*, defaults to 2): | |
End of stream token id. | |
tie_word_embeddings (`bool`, *optional*, defaults to `False`): | |
Whether to tie weight embeddings | |
rope_theta (`float`, *optional*, defaults to 10000.0): | |
The base period of the RoPE embeddings. | |
rope_scaling (`Dict`, *optional*): | |
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling | |
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is | |
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update | |
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how | |
these scaling strategies behave: | |
https://www.reddit.com/r/Localchameleon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an | |
experimental feature, subject to breaking API changes in future versions. | |
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): | |
Whether to use a bias in the query, key, value and output projection layers during self-attention. | |
attention_dropout (`float`, *optional*, defaults to 0.0): | |
The dropout ratio for the attention probabilities. | |
model_parallel_size (`int`, *optional*, defaults to 1): | |
Number of shards used when training the model. This will be used in qk layernorm because the original Chameleon inference | |
doesn't do reduction in those layers and each rank has its own biases. | |
swin_norm (`bool`, *optional*, defaults to `False`): | |
Use Swin Transformer normalization. | |
vq_config (`dict`, *optional*): | |
ChameleonVQConfig instance containing the configuration for the VQ-VAE model. | |
vocabulary_map (`dict`, *optional*): | |
A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs. | |
mlp_bias (`bool`, *optional*, defaults to `False`): | |
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. | |
```python | |
>>> from transformers import ChameleonModel, ChameleonConfig | |
>>> # Initializing a chameleon chameleon-7b style configuration | |
>>> configuration = ChameleonConfig() | |
>>> # Initializing a model from the chameleon-7b style configuration | |
>>> model = ChameleonModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "chameleon" | |
keys_to_ignore_at_inference = ["past_key_values"] | |
def __init__( | |
self, | |
vocab_size=65536, | |
hidden_size=4096, | |
intermediate_size=11008, | |
num_hidden_layers=32, | |
num_attention_heads=32, | |
num_key_value_heads=32, | |
hidden_act="silu", | |
max_position_embeddings=4096, | |
initializer_range=0.02, | |
rms_norm_eps=1e-05, | |
use_cache=True, | |
pad_token_id=None, | |
bos_token_id=1, | |
eos_token_id=2, | |
tie_word_embeddings=False, | |
rope_theta=10000.0, | |
rope_scaling=None, | |
attention_bias=False, | |
attention_dropout=0.0, | |
model_parallel_size=1, | |
swin_norm=False, | |
vq_config=None, | |
vocabulary_map=None, | |
mlp_bias=False, | |
**kwargs, | |
): | |
self.vocab_size = vocab_size | |
self.max_position_embeddings = max_position_embeddings | |
self.hidden_size = hidden_size | |
self.intermediate_size = intermediate_size | |
self.num_hidden_layers = num_hidden_layers | |
self.num_attention_heads = num_attention_heads | |
self.mlp_bias = mlp_bias | |
self.num_key_value_heads = num_key_value_heads | |
self.hidden_act = hidden_act | |
self.initializer_range = initializer_range | |
self.rms_norm_eps = rms_norm_eps | |
self.use_cache = use_cache | |
self.rope_theta = rope_theta | |
self.rope_scaling = rope_scaling | |
self._rope_scaling_validation() | |
self.attention_bias = attention_bias | |
self.attention_dropout = attention_dropout | |
self.model_parallel_size = model_parallel_size | |
self.swin_norm = swin_norm | |
if vq_config is None: | |
vq_config = {} | |
logger.info("vq_config is None. initializing the ChameleonVQConfig with default values.") | |
self.vq_config = ChameleonVQVAEConfig(**vq_config) | |
self.vocabulary_map = vocabulary_map | |
super().__init__( | |
pad_token_id=pad_token_id, | |
bos_token_id=bos_token_id, | |
eos_token_id=eos_token_id, | |
tie_word_embeddings=tie_word_embeddings, | |
**kwargs, | |
) | |
def _rope_scaling_validation(self): | |
""" | |
Validate the `rope_scaling` configuration. | |
""" | |
if self.rope_scaling is None: | |
return | |
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: | |
raise ValueError( | |
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " | |
f"got {self.rope_scaling}" | |
) | |
rope_scaling_type = self.rope_scaling.get("type", None) | |
rope_scaling_factor = self.rope_scaling.get("factor", None) | |
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: | |
raise ValueError( | |
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" | |
) | |
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: | |
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") | |