ai-content-maker/.venv/Lib/site-packages/transformers/models/clap/configuration_clap.py

428 lines
20 KiB
Python

# coding=utf-8
# Copyright 2023 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.
""" CLAP model configuration"""
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class ClapTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP
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 CLAP
[calp-hsat-fused](https://huggingface.co/laion/clap-hsat-fused) 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 30522):
Vocabulary size of the CLAP model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ClapTextModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"relu"`,
`"relu"`, `"silu"` and `"relu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
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).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`ClapTextModel`].
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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`.
projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
projection_dim (`int`, *optional*, defaults to 512)
Dimension of the projection head of the `ClapTextModelWithProjection`.
Examples:
```python
>>> from transformers import ClapTextConfig, ClapTextModel
>>> # Initializing a CLAP text configuration
>>> configuration = ClapTextConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = ClapTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "clap_text_model"
def __init__(
self,
vocab_size=50265,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=514,
type_vocab_size=1,
initializer_factor=1.0,
layer_norm_eps=1e-12,
projection_dim=512,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
position_embedding_type="absolute",
use_cache=True,
projection_hidden_act="relu",
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_factor = initializer_factor
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.projection_hidden_act = projection_hidden_act
self.projection_dim = projection_dim
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the text config dict if we are loading from ClapConfig
if config_dict.get("model_type") == "clap":
config_dict = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class ClapAudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP
[laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
window_size (`int`, *optional*, defaults to 8):
Image size of the spectrogram
num_mel_bins (`int`, *optional*, defaults to 64):
Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class.
spec_size (`int`, *optional*, defaults to 256):
Desired input size of the spectrogram that the model supports. It can be different from the output of the
`ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size`
of the audio models.
hidden_act (`str`, *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.
patch_size (`int`, *optional*, defaults to 4):
Patch size for the audio spectrogram
patch_stride (`list`, *optional*, defaults to `[4, 4]`):
Patch stride for the audio spectrogram
num_classes (`int`, *optional*, defaults to 527):
Number of classes used for the head training
hidden_size (`int`, *optional*, defaults to 768):
Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's
output,which is sent to the projection MLP layer.
projection_dim (`int`, *optional*, defaults to 512):
Hidden size of the projection layer.
depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`):
Depths used for the Swin Layers of the audio model
num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
Number of attention heads used for the Swin Layers of the audio model
enable_fusion (`bool`, *optional*, defaults to `False`):
Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the
best results.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the encoder.
fusion_type (`[type]`, *optional*):
Fusion type used for the patch fusion.
patch_embed_input_channels (`int`, *optional*, defaults to 1):
Number of channels used for the input spectrogram
flatten_patch_embeds (`bool`, *optional*, defaults to `True`):
Whether or not to flatten the patch embeddings
patch_embeds_hidden_size (`int`, *optional*, defaults to 96):
Hidden size of the patch embeddings. It is used as the number of output channels.
enable_patch_layer_norm (`bool`, *optional*, defaults to `True`):
Whether or not to enable layer normalization for the patch embeddings
drop_path_rate (`float`, *optional*, defaults to 0.0):
Drop path rate for the patch fusion
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not to add a bias to the query, key, value projections.
mlp_ratio (`float`, *optional*, defaults to 4.0):
Ratio of the mlp hidden dim to embedding dim.
aff_block_r (`int`, *optional*, defaults to 4):
downsize_ratio used in the AudioFF block
num_hidden_layers (`int`, *optional*, defaults to 4):
Number of hidden layers in the Transformer encoder.
projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
layer_norm_eps (`[type]`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import ClapAudioConfig, ClapAudioModel
>>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration
>>> configuration = ClapAudioConfig()
>>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration
>>> model = ClapAudioModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "clap_audio_model"
def __init__(
self,
window_size=8,
num_mel_bins=64,
spec_size=256,
hidden_act="gelu",
patch_size=4,
patch_stride=[4, 4],
num_classes=527,
hidden_size=768,
projection_dim=512,
depths=[2, 2, 6, 2],
num_attention_heads=[4, 8, 16, 32],
enable_fusion=False,
hidden_dropout_prob=0.1,
fusion_type=None,
patch_embed_input_channels=1,
flatten_patch_embeds=True,
patch_embeds_hidden_size=96,
enable_patch_layer_norm=True,
drop_path_rate=0.0,
attention_probs_dropout_prob=0.0,
qkv_bias=True,
mlp_ratio=4.0,
aff_block_r=4,
num_hidden_layers=4,
projection_hidden_act="relu",
layer_norm_eps=1e-5,
initializer_factor=1.0,
**kwargs,
):
super().__init__(**kwargs)
self.window_size = window_size
self.num_mel_bins = num_mel_bins
self.spec_size = spec_size
self.patch_size = patch_size
self.patch_stride = patch_stride
self.num_classes = num_classes
self.hidden_size = hidden_size
self.depths = depths
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.window_size = window_size
self.enable_fusion = enable_fusion
self.fusion_type = fusion_type
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.projection_dim = projection_dim
self.flatten_patch_embeds = flatten_patch_embeds
self.patch_embeds_hidden_size = patch_embeds_hidden_size
self.enable_patch_layer_norm = enable_patch_layer_norm
self.drop_path_rate = drop_path_rate
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.qkv_bias = qkv_bias
self.mlp_ratio = mlp_ratio
self.patch_embed_input_channels = patch_embed_input_channels
self.aff_block_r = aff_block_r
self.layer_norm_eps = layer_norm_eps
self.initializer_factor = initializer_factor
self.projection_hidden_act = projection_hidden_act
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the audio config dict if we are loading from ClapConfig
if config_dict.get("model_type") == "clap":
config_dict = config_dict["audio_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class ClapConfig(PretrainedConfig):
r"""
[`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate
a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a
configuration with the defaults will yield a similar configuration to that of the CLAP
[laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`ClapTextConfig`].
audio_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`ClapAudioConfig`].
logit_scale_init_value (`float`, *optional*, defaults to 14.29):
The inital value of the *logit_scale* paramter. Default is used as per the original CLAP implementation.
projection_dim (`int`, *optional*, defaults to 512):
Dimentionality of text and audio projection layers.
projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
Activation function for the projection layers.
initializer_factor (`float`, *optional*, defaults to 1.0):
Factor to scale the initialization of the model weights.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import ClapConfig, ClapModel
>>> # Initializing a ClapConfig with laion-ai/base style configuration
>>> configuration = ClapConfig()
>>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration
>>> model = ClapModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig
>>> from transformers import ClapTextConfig, ClapAudioConfig
>>> # Initializing a ClapText and ClapAudioConfig configuration
>>> config_text = ClapTextConfig()
>>> config_audio = ClapAudioConfig()
>>> config = ClapConfig.from_text_audio_configs(config_text, config_audio)
```"""
model_type = "clap"
def __init__(
self,
text_config=None,
audio_config=None,
logit_scale_init_value=(1 / 0.07),
projection_dim=512,
projection_hidden_act="relu",
initializer_factor=1.0,
**kwargs,
):
super().__init__(**kwargs)
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the ClapTextConfig with default values.")
if audio_config is None:
audio_config = {}
logger.info("audio_config is None. initializing the ClapAudioConfig with default values.")
self.text_config = ClapTextConfig(**text_config)
self.audio_config = ClapAudioConfig(**audio_config)
self.text_config.projection_dim = projection_dim
self.audio_config.projection_dim = projection_dim
self.text_config.projection_hidden_act = projection_hidden_act
self.audio_config.projection_hidden_act = projection_hidden_act
self.projection_dim = projection_dim
self.projection_hidden_act = projection_hidden_act
self.hidden_size = self.text_config.hidden_size
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor = initializer_factor
self.num_hidden_layers = self.text_config.num_hidden_layers + len(self.audio_config.depths)
@classmethod
def from_text_audio_configs(cls, text_config: ClapTextConfig, audio_config: ClapAudioConfig, **kwargs):
r"""
Instantiate a [`ClapConfig`] (or a derived class) from clap text model configuration and clap audio model
configuration.
Returns:
[`ClapConfig`]: An instance of a configuration object
"""
return cls(text_config=text_config.to_dict(), audio_config=audio_config.to_dict(), **kwargs)