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# coding=utf-8 | |
# Copyright 2024 The Emu team, BAAI and The HuggingFace Inc. team. All rights reserved. | |
# | |
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX | |
# and OPT implementations in this library. It has been modified from its | |
# original forms to accommodate minor architectural differences compared | |
# to GPT-NeoX and OPT used by the Meta AI team that trained the model. | |
# | |
# 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. | |
""" Emu3 model configuration""" | |
from typing import Optional | |
from transformers.configuration_utils import PretrainedConfig | |
from transformers.utils import logging | |
logger = logging.get_logger(__name__) | |
EMU3_PRETRAINED_CONFIG_ARCHIVE_MAP = {} | |
class Emu3Config(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate an Emu3 | |
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 Emu3-8B. | |
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 184622): | |
Vocabulary size of the Emu3 model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`Emu3Model`] | |
hidden_size (`int`, *optional*, defaults to 4096): | |
Dimension of the hidden representations. | |
intermediate_size (`int`, *optional*, defaults to 14336): | |
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 8): | |
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 9216): | |
The maximum sequence length that this model might ever be used with. Emu supports up to 9216 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*, 151643): | |
Padding token id. | |
bos_token_id (`int`, *optional*, defaults to 151849): | |
Beginning of stream token id. | |
eos_token_id (`int`, *optional*, defaults to 151850): | |
End of stream token id. | |
img_token_id (`int`, *optional*, defaults to 151851): | |
image token id. | |
boi_token_id (`int`, *optional*, defaults to 151852): | |
Beginning of image token id. | |
eoi_token_id (`int`, *optional*, defaults to 151853): | |
End of image token id. | |
eol_token_id (`int`, *optional*, defaults to 151846): | |
End of line token id. | |
eof_token_id (`int`, *optional*, defaults to 151847): | |
End of line token id. | |
image_area (`int`, *optional*, defaults to 720 * 720) | |
generated image area (image area used in training) | |
pretraining_tp (`int`, *optional*, defaults to 1): | |
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this | |
document](https://huggingface.co./docs/transformers/parallelism) to understand more about it. This value is | |
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this | |
issue](https://github.com/pytorch/pytorch/issues/76232). | |
tie_word_embeddings (`bool`, *optional*, defaults to `False`): | |
Whether to tie weight embeddings | |
rope_theta (`float`, *optional*, defaults to 1_000_000.0): | |
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/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an | |
experimental feature, subject to breaking API changes in future versions. | |
attention_dropout (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for the attention probabilities. | |
```python | |
>>> from transformers import Emu3Model, Emu3Config | |
>>> # Initializing a Emu3-8b style configuration | |
>>> configuration = Emu3Config() | |
>>> # Initializing a model from the Emu3-8b style configuration | |
>>> model = Emu3Model(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "Emu3" | |
keys_to_ignore_at_inference = ["past_key_values"] | |
def __init__( | |
self, | |
vocab_size: int = 184622, | |
hidden_size: int = 4096, | |
intermediate_size: int = 14336, | |
num_hidden_layers: int = 32, | |
num_attention_heads: int = 32, | |
num_key_value_heads: Optional[int] = 8, | |
hidden_act: str = "silu", | |
max_position_embeddings: int = 9216, | |
initializer_range: float = 0.02, | |
rms_norm_eps: float = 1e-5, | |
use_cache: bool = True, | |
pad_token_id: int = 151643, | |
bos_token_id: int = 151849, | |
eos_token_id: int = 151850, | |
img_token_id: int = 151851, | |
boi_token_id: int = 151852, | |
eoi_token_id: int = 151853, | |
eol_token_id: int = 151846, | |
eof_token_id: int = 151847, | |
image_area: int = 720 * 720, | |
pretraining_tp: int = 1, | |
tie_word_embeddings: bool = False, | |
rope_theta: float = 1000000.0, | |
rope_scaling: Optional = None, | |
attention_dropout: float = 0.1, | |
**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 | |
# for backward compatibility | |
if num_key_value_heads is None: | |
num_key_value_heads = num_attention_heads | |
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.pretraining_tp = pretraining_tp | |
self.use_cache = use_cache | |
self.rope_theta = rope_theta | |
self.rope_scaling = rope_scaling | |
self._rope_scaling_validation() | |
self.attention_dropout = attention_dropout | |
self.img_token_id = img_token_id | |
self.boi_token_id = boi_token_id | |
self.eoi_token_id = eoi_token_id | |
self.eol_token_id = eol_token_id | |
self.eof_token_id = eof_token_id | |
self.image_area = image_area | |
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}") | |