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Emu3 / emu3 /mllm /configuration_emu3.py
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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}")