457 lines
20 KiB
Python
457 lines
20 KiB
Python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. 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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""" CLVP model configuration"""
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import os
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from typing import TYPE_CHECKING, Union
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if TYPE_CHECKING:
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pass
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import CLVP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class ClvpEncoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP
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text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults
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will yield a similar configuration to that of the encoder of the CLVP
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[susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) 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 256):
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Vocabulary size of the CLVP Encoder model.
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 1536):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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projection_dim (`int`, *optional*, defaults to 768):
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Dimensionality of the projection vector.
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num_hidden_layers (`int`, *optional*, defaults to 20):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the feed-forward layers in [`ClvpEncoderMLP`].
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use_rotary_embedding (`bool`, *optional*, defaults to `True`):
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Whether to use rotary_embedding or not.
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use_attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias in Query, Key and Value layers during self attention.
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summary_type (`str`, *optional*, defaults to `"mean"`):
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What strategy to use to get pooler_output from the last_hidden_state. `"last"`, `"first"`, `"mean"` and
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`"cls_index"` are supported.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
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testing).
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bos_token_id (`int`, *optional*, defaults to 255):
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Beginning of sequence token id.
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eos_token_id (`int`, *optional*, defaults to 0):
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End of sequence token id.
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Example:
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```python
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>>> from transformers import ClvpEncoderConfig, ClvpEncoder
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>>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration
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>>> encoder_configuration = ClvpEncoderConfig()
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>>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style configuration
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>>> model = ClvpEncoder(encoder_configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "clvp_encoder"
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def __init__(
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self,
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vocab_size=256,
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hidden_size=768,
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intermediate_size=1536,
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projection_dim=768,
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num_hidden_layers=20,
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num_attention_heads=12,
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hidden_act="gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.1,
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dropout=0.1,
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use_rotary_embedding=True,
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use_attention_bias=False,
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summary_type="mean",
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initializer_factor=1.0,
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bos_token_id=255,
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eos_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.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.projection_dim = projection_dim
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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.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.dropout = dropout
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self.use_rotary_embedding = use_rotary_embedding
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self.use_attention_bias = use_attention_bias
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self.summary_type = summary_type
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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@classmethod
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def from_pretrained(
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cls, pretrained_model_name_or_path: Union[str, os.PathLike], config_type: str = "text_config", **kwargs
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) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# make sure to have the config_type be either "text_config" or "speech_config"
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# this is to make sure that we can load only text or speech configs from the nested ClvpConfig.
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if config_type not in ["text_config", "speech_config"]:
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raise ValueError(
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f"We can only load either 'text_config' or 'speech_config' but you are trying to load" f"{config_type}"
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)
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# get the text config dict if we are loading from ClvpConfig
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if config_dict.get("model_type") == "clvp":
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config_dict = config_dict[config_type]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class ClvpDecoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP
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Decoder Model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Decoder part of the CLVP
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[susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) 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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The architecture is similar to GPT2.
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Args:
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vocab_size (`int`, *optional*, defaults to 8194):
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Vocabulary size of the model.
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max_position_embeddings (`int`, *optional*, defaults to 608):
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The maximum sequence length of mel tokens that this model might ever be used with. Similar to `n_positions`
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in `GPT2Config`.
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max_text_tokens (`int`, *optional*, defaults to 404):
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The maximum sequence length of text tokens that this model might ever be used with. Similar to
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`n_positions` in `GPT2Config`.
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the embeddings and hidden states.
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num_hidden_layers (`int`, *optional*, defaults to 30):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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n_inner (`int`, *optional*):
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Dimensionality of the inner feed-forward layers. `None` will set it to 4 times `hidden_size`.
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num_mel_attn_blocks (`int`, *optional*, defaults to 6):
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Denotes the number of self attention layers in [`ClvpConditioningEncoder`].
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activation_function (`str`, *optional*, defaults to `"gelu_new"`):
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Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
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resid_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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embd_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the embeddings.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon to use in the layer normalization layers.
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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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summary_type (`string`, *optional*, defaults to `"cls_index"`):
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Argument used when doing sequence summary.
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Has to be one of the following options:
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- `"last"`: Take the last token hidden state (like XLNet).
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- `"first"`: Take the first token hidden state (like BERT).
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- `"mean"`: Take the mean of all tokens hidden states.
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- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
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- `"attn"`: Not implemented now, use multi-head attention.
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summary_use_proj (`bool`, *optional*, defaults to `True`):
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Whether or not to add a projection after the vector extraction.
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summary_activation (`str`, *optional*):
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Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
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summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
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Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
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summary_first_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio to be used after the projection and activation.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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bos_token_id (`int`, *optional*, defaults to 8192):
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Beginning of sequence token id, used at the start of the generation.
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eos_token_id (`int`, *optional*, defaults to 8193):
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End of sequence token id, used in the method
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[`ClvpModelForConditionalGeneration.fix_speech_decoder_output()`] to correct decoder outputs.
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feature_size (`int`, *optional*, defaults to 80):
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The feature dimension of the extracted mel features. This value is used in [`ClvpConditioningEncoder`].
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use_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether to use bias in Query, Key and Value layers during self attention.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
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testing).
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decoder_fixing_codes (`list`, *optional*, defaults to `[83, 45, 45, 248]`):
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These values are used in the method `fix_speech_decoder_output` to fix decoder generated outputs.
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Example:
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```python
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>>> from transformers import ClvpDecoderConfig, ClvpDecoder
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>>> # Initializing a ClvpDecoderConfig with susnato/clvp_dev style configuration
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>>> decoder_configuration = ClvpDecoderConfig()
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>>> # Initializing a ClvpDecoder (with random weights) from the susnato/clvp_dev style configuration
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>>> model = ClvpDecoder(decoder_configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "clvp_decoder"
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def __init__(
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self,
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vocab_size=8194,
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max_position_embeddings=608,
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max_text_tokens=404,
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hidden_size=1024,
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num_hidden_layers=30,
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num_attention_heads=16,
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n_inner=None,
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num_mel_attn_blocks=6,
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activation_function="gelu_new",
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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attention_dropout=0.1,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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summary_type="cls_index",
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summary_use_proj=True,
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summary_activation=None,
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summary_proj_to_labels=True,
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summary_first_dropout=0.1,
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use_cache=True,
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bos_token_id=8192,
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eos_token_id=8193,
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feature_size=80,
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use_attention_bias=True,
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initializer_factor=1.0,
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decoder_fixing_codes=[83, 45, 45, 248],
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.max_text_tokens = max_text_tokens
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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.n_inner = n_inner
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self.num_mel_attn_blocks = num_mel_attn_blocks
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self.activation_function = activation_function
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_first_dropout = summary_first_dropout
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self.summary_proj_to_labels = summary_proj_to_labels
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self.use_cache = use_cache
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self.feature_size = feature_size
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self.use_attention_bias = use_attention_bias
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self.initializer_factor = initializer_factor
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self.decoder_fixing_codes = decoder_fixing_codes
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the speech config dict if we are loading from ClvpConfig
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if config_dict.get("model_type") == "clvp":
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config_dict = config_dict["decoder_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 ClvpConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
[`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It
|
||
|
|
is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and
|
||
|
|
decoder model configs. Instantiating a configuration with the defaults will yield a similar configuration to that
|
||
|
|
of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) 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 the CLVP text encoder.
|
||
|
|
speech_config (`dict`, *optional*):
|
||
|
|
Dictionary of configuration options used to initialize CLVP speech encoder.
|
||
|
|
decoder_config (`dict`, *optional*):
|
||
|
|
Dictionary of configuration options used to initialize [`ClvpDecoderConfig`].
|
||
|
|
projection_dim (`int`, *optional*, defaults to 768):
|
||
|
|
Dimentionality of text and speech projection layers.
|
||
|
|
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
|
||
|
|
The inital value of the *logit_scale* paramter. Default is used as per the original CLVP implementation.
|
||
|
|
initializer_factor (`float`, *optional*, defaults to 1.0):
|
||
|
|
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
|
||
|
|
testing).
|
||
|
|
kwargs (*optional*):
|
||
|
|
Dictionary of keyword arguments.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration
|
||
|
|
|
||
|
|
>>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration
|
||
|
|
>>> configuration = ClvpConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style configuration
|
||
|
|
>>> model = ClvpModelForConditionalGeneration(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
|
||
|
|
>>> # We can also initialize a CLVPConfig from a CLVPTextConfig, CLVPSpeechConfig and a CLVPAutoRegressiveConfig
|
||
|
|
>>> from transformers import ClvpEncoderConfig, ClvpDecoderConfig
|
||
|
|
|
||
|
|
>>> # Initializing a CLVP text, CLVP speech and CLVP decoder configuration
|
||
|
|
>>> config_text = ClvpEncoderConfig()
|
||
|
|
>>> config_speech = ClvpEncoderConfig()
|
||
|
|
>>> decoder_config = ClvpDecoderConfig()
|
||
|
|
|
||
|
|
>>> config = ClvpConfig.from_sub_model_configs(config_text, config_speech, decoder_config)
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "clvp"
|
||
|
|
is_composition = True
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
text_config=None,
|
||
|
|
speech_config=None,
|
||
|
|
decoder_config=None,
|
||
|
|
projection_dim=768,
|
||
|
|
logit_scale_init_value=2.6592,
|
||
|
|
initializer_factor=1.0,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
|
||
|
|
if text_config is None:
|
||
|
|
text_config = {}
|
||
|
|
logger.info("`text_config` is `None`. Initializing the `ClvpEncoderConfig` with default values.")
|
||
|
|
|
||
|
|
if speech_config is None:
|
||
|
|
speech_config = {}
|
||
|
|
logger.info("`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.")
|
||
|
|
|
||
|
|
if decoder_config is None:
|
||
|
|
decoder_config = {}
|
||
|
|
logger.info("`decoder_config` is `None`. initializing the `ClvpDecoderConfig` with default values.")
|
||
|
|
|
||
|
|
self.text_config = ClvpEncoderConfig(**text_config)
|
||
|
|
self.speech_config = ClvpEncoderConfig(**speech_config)
|
||
|
|
self.decoder_config = ClvpDecoderConfig(**decoder_config)
|
||
|
|
|
||
|
|
self.projection_dim = projection_dim
|
||
|
|
self.logit_scale_init_value = logit_scale_init_value
|
||
|
|
self.initializer_factor = initializer_factor
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_sub_model_configs(
|
||
|
|
cls,
|
||
|
|
text_config: ClvpEncoderConfig,
|
||
|
|
speech_config: ClvpEncoderConfig,
|
||
|
|
decoder_config: ClvpDecoderConfig,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
r"""
|
||
|
|
Instantiate a [`ClvpConfig`] (or a derived class) from CLVP text model configuration, CLVP speech model
|
||
|
|
configuration and CLVP decoder model configuration.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
text_config (`ClvpEncoderConfig`):
|
||
|
|
Text model configuration of type [`ClvpEncoderConfig`].
|
||
|
|
speech_config (`ClvpEncoderConfig`):
|
||
|
|
Speech model configuration of type [`ClvpEncoderConfig`].
|
||
|
|
decoder_config (`ClvpDecoderConfig`):
|
||
|
|
Decoder model configuration of type [`ClvpDecoderConfig`].
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`ClvpConfig`]: An instance of a configuration object
|
||
|
|
"""
|
||
|
|
|
||
|
|
return cls(
|
||
|
|
text_config=text_config.to_dict(),
|
||
|
|
speech_config=speech_config.to_dict(),
|
||
|
|
decoder_config=decoder_config.to_dict(),
|
||
|
|
**kwargs,
|
||
|
|
)
|