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# Copyright 2024 The YourMT3 Authors.
#
# 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
#
# Please see the details in the LICENSE file.
import math
from typing import Optional, Union
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
class ConformerYMT3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConformerYMT3Encoder`]. It is used to
instantiate an ConformerYMT3Encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the Wav2Vec2Conformer
[facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-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:
d_model (`int`, *optional*, defaults to 512):
Dimensionality of the encoder layers and the pooler layer.
num_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
dropout_rate (`float`, *optional*, defaults to 0.05):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
output_hidden_size (`int`, *optional*):
Dimensionality of the encoder output layer. If not defined, this defaults to *hidden-size*. Only relevant
if `add_adapter is True`.
position_encoding_type (`str`, *optional*, defaults to `"relative"`):
Can be specified to `relative` or `rotary` for relative or rotary position embeddings respectively. If left
`None` no relative position embedding is applied.
rotary_embedding_base (`int`, *optional*, defaults to 10000):
If `"rotary"` position embeddings are used, defines the size of the embedding base.
num_max_positions (`int`, *optional*, defaults to 5000):
if `"relative"` position embeddings are used, defines the maximum source input positions.
conv_depthwise_kernel_size (`int`, defaults to 31):
Kernel size of convolutional depthwise 1D layer in Conformer blocks.
Example:
```python
>>> from transformers import ConformerYMT3Config, ConformerYMT3Encoder
>>> # Initializing a ConformerYMT3Encoder configuration
>>> configuration = ConformerYMT3Config()
>>> # Initializing a model (with random weights) from the facebook/wav2vec2-conformer-rel-pos-large style configuration
>>> model = ConformerYMT3Encoder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "conformer-ymt3"
def __init__(
self,
d_model=512, # 768
num_layers=8, # ConformerYMT3Encoder
num_heads=8, # ConformerYMT3SelfAttention
intermediate_size=2048, # 3072,# used in intermediate_dense of ConformerYMT3FeedForward
hidden_act="gelu", # used in intermediate_act_fn of ConformerYMT3FeedForward
dropout_rate=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 3, 3),
conv_bias=False,
position_encoding_type="rotary",
rotary_embedding_base=10000,
num_max_positions=1024,
conv_depthwise_kernel_size=31,
**kwargs,
):
super().__init__(**kwargs)
self.d_model = d_model
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_layers = num_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_heads = num_heads
self.dropout_rate = dropout_rate
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_max_positions = num_max_positions
self.position_encoding_type = position_encoding_type
self.rotary_embedding_base = rotary_embedding_base
# Conformer-block related
self.conv_depthwise_kernel_size = conv_depthwise_kernel_size
class ConformerYMT3PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConformerYMT3Config
base_model_prefix = "wav2vec2_conformer"
main_input_name = "input_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if module.__class__.__name__ == "ConformerYMT3SelfAttention":
if hasattr(module, "pos_bias_u"):
nn.init.xavier_uniform_(module.pos_bias_u)
if hasattr(module, "pos_bias_v"):
nn.init.xavier_uniform_(module.pos_bias_v)
elif isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
nn.init.uniform_(module.bias, a=-k, b=k)
def _set_gradient_checkpointing(self, module, value=False):
if module.__class__.__name__ == "ConformerYMT3Encoder":
module.gradient_checkpointing = value
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