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245 lines
12 KiB
245 lines
12 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/lasr/modular_lasr.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_lasr.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 The HuggingFace Inc. team and Google LLC. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...configuration_utils import PreTrainedConfig
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class LasrEncoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LasrEncoder`]. It is used to instantiate a
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`LasrEncoder` model according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 512):
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Dimension of the layers and the hidden states.
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num_hidden_layers (`int`, *optional*, defaults to 17):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 2048):
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Dimension of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in the attention layers.
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convolution_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in convolutions of the conformer's convolution module.
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conv_kernel_size (`int`, *optional*, defaults to 32):
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The kernel size of the convolution layers in the Conformer block.
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subsampling_conv_channels (`int`, *optional*, defaults to 256):
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The number of channels in the subsampling convolution layers.
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subsampling_conv_kernel_size (`int`, *optional*, defaults to 5):
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The kernel size of the subsampling convolution layers.
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subsampling_conv_stride (`int`, *optional*, defaults to 2):
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The stride of the subsampling convolution layers.
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num_mel_bins (`int`, *optional*, defaults to 128):
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Number of mel features.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for all fully connected layers in the embeddings, encoder, and pooler.
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dropout_positions (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the positions in the input sequence.
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layerdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the layers in the encoder.
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activation_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for activations inside the fully connected layer.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention layers.
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max_position_embeddings (`int`, *optional*, defaults to 10000):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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feed_forward_residual_weights (`tuple[float, float]`, *optional*, defaults to `[1.5, 0.5]`):
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The residual weights for the feed forward layers.
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conv_residual_weights (`tuple[float, float]`, *optional*, defaults to `[2.0, 1.0]`):
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The residual weights for the convolution layers.
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batch_norm_momentum (`float`, *optional*, defaults to 0.01):
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The momentum for the batch normalization layers.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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Example:
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```python
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>>> from transformers import LasrEncoderModel, LasrEncoderConfig
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>>> # Initializing a `LasrEncoder` configuration
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>>> configuration = LasrEncoderConfig()
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>>> # Initializing a model from the configuration
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>>> model = LasrEncoderModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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This configuration class is based on the LasrEncoder architecture from Google Health AI. You can find more details
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and pre-trained models at [TODO/TODO](https://huggingface.co/TODO/TODO).
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"""
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model_type = "lasr_encoder"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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hidden_size=512,
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num_hidden_layers=17,
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num_attention_heads=8,
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intermediate_size=2048,
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hidden_act="silu",
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attention_bias=False,
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convolution_bias=False,
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conv_kernel_size=32,
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subsampling_conv_channels=256,
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subsampling_conv_kernel_size=5,
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subsampling_conv_stride=2,
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num_mel_bins=128,
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dropout=0.1,
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dropout_positions=0.0,
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layerdrop=0.1,
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activation_dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=10000,
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initializer_range=0.02,
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layer_norm_eps=1e-6,
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feed_forward_residual_weights=[1.5, 0.5],
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conv_residual_weights=[2.0, 1.0],
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batch_norm_momentum=0.01,
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rope_parameters=None,
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**kwargs,
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):
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self.rope_parameters = rope_parameters
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self.layer_norm_eps = layer_norm_eps
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self.feed_forward_residual_weights = feed_forward_residual_weights
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self.conv_residual_weights = conv_residual_weights
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self.batch_norm_momentum = batch_norm_momentum
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_attention_heads # LlamaAttention compatibility
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.attention_bias = attention_bias
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self.convolution_bias = convolution_bias
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self.conv_kernel_size = conv_kernel_size
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self.subsampling_conv_kernel_size = subsampling_conv_kernel_size
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self.subsampling_conv_stride = subsampling_conv_stride
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self.subsampling_conv_channels = subsampling_conv_channels
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self.num_mel_bins = num_mel_bins
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self.dropout = dropout
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self.dropout_positions = dropout_positions
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self.layerdrop = layerdrop
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self.activation_dropout = activation_dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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super().__init__(
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**kwargs,
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)
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class LasrCTCConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LasrForCTC`]. It is used to instantiate a
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Lasr CTC model according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 512):
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Vocabulary size of the model.
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ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`):
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Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
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instance of [`LasrForCTC`].
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ctc_zero_infinity (`bool`, *optional*, defaults to `True`):
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Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
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occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
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of [`LasrForCTC`].
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encoder_config (`Union[dict, LasrEncoderConfig]`, *optional*):
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The config object or dictionary of the encoder.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id. Also used as blank token id.
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Example:
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```python
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>>> from transformers import LasrForCTC, LasrCTCConfig
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>>> # Initializing a Lasr configuration
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>>> configuration = LasrCTCConfig()
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>>> # Initializing a model from the configuration
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>>> model = LasrForCTC(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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This configuration class is based on the Lasr CTC architecture from Google Health AI. You can find more details
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and pre-trained models at [TODO/TODO](https://huggingface.co/TODO/TODO).
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"""
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model_type = "lasr_ctc"
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sub_configs = {"encoder_config": LasrEncoderConfig}
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def __init__(
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self,
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vocab_size=512,
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ctc_loss_reduction="mean",
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ctc_zero_infinity=True,
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encoder_config: dict | LasrEncoderConfig = None,
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pad_token_id=0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.ctc_loss_reduction = ctc_loss_reduction
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self.ctc_zero_infinity = ctc_zero_infinity
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if isinstance(encoder_config, dict):
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self.encoder_config = LasrEncoderConfig(**encoder_config)
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elif encoder_config is None:
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self.encoder_config = LasrEncoderConfig()
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self.encoder_config = self.encoder_config
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self.initializer_range = self.encoder_config.initializer_range
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self.pad_token_id = pad_token_id
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super().__init__(**kwargs)
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@classmethod
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def from_encoder_config(cls, encoder_config: LasrEncoderConfig, **kwargs):
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r"""
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Instantiate a [`LasrCTCConfig`] (or a derived class) from lasr encoder model configuration.
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Returns:
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[`LasrCTCConfig`]: An instance of a configuration object
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"""
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return cls(encoder_config=encoder_config.to_dict(), **kwargs)
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@property
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def inputs_to_logits_ratio(self):
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return self.encoder_config.subsampling_conv_stride**2
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__all__ = ["LasrEncoderConfig", "LasrCTCConfig"]
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