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# coding=utf-8
# Copyright 2024 Microsoft 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.
import warnings
""" Florence-2 configuration"""
from typing import Optional
from transformers import AutoConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Florence2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Florence2VisionModel architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
drop_path_rate (`float`, *optional*, defaults to 0.1):
The dropout rate of the drop path layer.
patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]):
The patch size of the image.
patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]):
The patch stride of the image.
patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]):
The patch padding of the image.
patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]):
Whether to apply layer normalization before the patch embedding layer.
enable_checkpoint (`bool`, *optional*, defaults to False):
Whether to enable checkpointing.
dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]):
The dimension of the embedding layer.
num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):
The number of attention heads.
num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):
The number of groups.
depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]):
The depth of the model.
window_size (`int`, *optional*, defaults to 12):
The window size of the model.
projection_dim (`int`, *optional*, defaults to 1024):
The dimension of the projection layer.
visual_temporal_embedding (`dict`, *optional*):
The configuration of the visual temporal embedding.
image_pos_embed (`dict`, *optional*):
The configuration of the image position embedding.
image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]):
The source of the image feature.
Example:
```python
>>> from transformers import Florence2VisionConfig, Florence2VisionModel
>>> # Initializing a Florence2 Vision style configuration
>>> configuration = Florence2VisionConfig()
>>> # Initializing a model (with random weights)
>>> model = Florence2VisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "florence2_vision"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
drop_path_rate=0.1,
patch_size=[7, 3, 3, 3],
patch_stride=[4, 2, 2, 2],
patch_padding=[3, 1, 1, 1],
patch_prenorm=[False, True, True, True],
enable_checkpoint=False,
dim_embed=[256, 512, 1024, 2048],
num_heads=[8, 16, 32, 64],
num_groups=[8, 16, 32, 64],
depths=[1, 1, 9, 1],
window_size=12,
projection_dim=1024,
visual_temporal_embedding=None,
image_pos_embed=None,
image_feature_source=["spatial_avg_pool", "temporal_avg_pool"],
**kwargs,
):
self.drop_path_rate = drop_path_rate
self.patch_size = patch_size
self.patch_stride = patch_stride
self.patch_padding = patch_padding
self.patch_prenorm = patch_prenorm
self.enable_checkpoint = enable_checkpoint
self.dim_embed = dim_embed
self.num_heads = num_heads
self.num_groups = num_groups
self.depths = depths
self.window_size = window_size
self.projection_dim = projection_dim
self.visual_temporal_embedding = visual_temporal_embedding
self.image_pos_embed = image_pos_embed
self.image_feature_source = image_feature_source
super().__init__(**kwargs)
class Florence2LanguageConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Florence2LanguagePreTrainedModel`]. It is used to instantiate a BART
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 BART
[facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.
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 51289):
Vocabulary size of the Florence2Language model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Florence2LanguageModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
num_labels (`int`, *optional*, defaults to 3):
The number of labels to use in [`Florence2LanguageForSequenceClassification`].
forced_eos_token_id (`int`, *optional*, defaults to 2):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
Example:
```python
>>> from transformers import Florence2LanguageConfig, Florence2LanguageModel
>>> # Initializing a Florence2 Language style configuration
>>> configuration = Florence2LanguageConfig()
>>> # Initializing a model (with random weights)
>>> model = Florence2LangaugeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "florence2_language"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__(
self,
vocab_size=51289,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
use_cache=True,
num_labels=3,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
is_encoder_decoder=True,
decoder_start_token_id=2,
forced_eos_token_id=2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.classifier_dropout = classifier_dropout
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=num_labels,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
)
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
self.forced_bos_token_id = self.bos_token_id
warnings.warn(
f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"The config can simply be saved and uploaded again to be fixed."
)
class Florence2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Florence2ForConditionalGeneration`]. It is used to instantiate an
Florence-2 model 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.
Args:
vision_config (`Florence2VisionConfig`, *optional*):
Custom vision config or dict
text_config (`Union[AutoConfig, dict]`, *optional*):
The config object of the text backbone.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
vocab_size (`int`, *optional*, defaults to 51289):
Vocabulary size of the Florence2model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~Florence2ForConditionalGeneration`]
projection_dim (`int`, *optional*, defaults to 1024):
Dimension of the multimodal projection space.
Example:
```python
>>> from transformers import Florence2ForConditionalGeneration, Florence2Config, CLIPVisionConfig, BartConfig
>>> # Initializing a clip-like vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Bart config
>>> text_config = BartConfig()
>>> # Initializing a Florence-2 configuration
>>> configuration = Florence2Config(vision_config, text_config)
>>> # Initializing a model from the florence-2 configuration
>>> model = Florence2ForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "florence2"
is_composition = False
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
vocab_size=51289,
projection_dim=1024,
**kwargs,
):
self.ignore_index = ignore_index
self.vocab_size = vocab_size
self.projection_dim = projection_dim
if vision_config is not None:
vision_config = PretrainedConfig(**vision_config)
self.vision_config = vision_config
self.vocab_size = self.vocab_size
self.text_config = text_config
if text_config is not None:
self.text_config = Florence2LanguageConfig(**text_config)
super().__init__(**kwargs)