Upload 5 files
Browse files- ultravox_config.py +13 -6
ultravox_config.py
CHANGED
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@@ -32,6 +32,8 @@ class LossFunction(str, Enum):
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class LossConfig:
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loss_function: LossFunction = LossFunction.CrossEntropy
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kl_temperature: float = 2.0
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@property
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def requires_alt_fields(self):
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@@ -47,7 +49,7 @@ class UltravoxConfig(transformers.PretrainedConfig):
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documentation from [`PretrainedConfig`] for more information.
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Args:
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audio_config (`
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Custom audio config or dict
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text_config (`Union[AutoConfig, dict]`, *optional*):
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The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
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@@ -72,10 +74,10 @@ class UltravoxConfig(transformers.PretrainedConfig):
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Example:
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```python
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>>> from transformers import
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>>> # Initializing an audio encoder config
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>>> audio_config =
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>>> # Initializing a Llama config
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>>> text_config = LlamaConfig()
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@@ -84,13 +86,13 @@ class UltravoxConfig(transformers.PretrainedConfig):
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>>> configuration = UltravoxConfig(audio_config, text_config)
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>>> # Initializing a completely untrained model from the configuration
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>>> model =
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # Initialize a model from pretrained checkpoints and random projector weights
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>>> config = UltravoxConfig(audio_model_id="
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```"""
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model_type = "ultravox"
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@@ -140,7 +142,7 @@ class UltravoxConfig(transformers.PretrainedConfig):
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else:
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audio_config = audio_config or {}
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self.audio_config = transformers.CONFIG_MAPPING[
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audio_config.get("model_type", "
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](**audio_config)
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self.text_model_lora_config = (
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@@ -167,7 +169,12 @@ class UltravoxConfig(transformers.PretrainedConfig):
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# remove text_config and audio_config if text_model_id and audio_model_id are present
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if self.text_model_id is not None:
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diff_dict.pop("text_config", None)
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if self.audio_model_id is not None:
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diff_dict.pop("audio_config", None)
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return diff_dict
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class LossConfig:
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loss_function: LossFunction = LossFunction.CrossEntropy
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kl_temperature: float = 2.0
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# Number of tokens to ignore from the beginning of the sequence. Only used in LSM
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initial_tokens_to_ignore: int = 0
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@property
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def requires_alt_fields(self):
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documentation from [`PretrainedConfig`] for more information.
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Args:
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audio_config (`WhisperConfig`, *optional*):
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Custom audio config or dict
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text_config (`Union[AutoConfig, dict]`, *optional*):
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The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
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Example:
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```python
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>>> from transformers import UltravoxModel, WhisperConfig, UltravoxConfig, LlamaConfig
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>>> # Initializing an audio encoder config
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>>> audio_config = WhisperConfig()
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>>> # Initializing a Llama config
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>>> text_config = LlamaConfig()
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>>> configuration = UltravoxConfig(audio_config, text_config)
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>>> # Initializing a completely untrained model from the configuration
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>>> model = UltravoxModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # Initialize a model from pretrained checkpoints and random projector weights
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>>> config = UltravoxConfig(audio_model_id="openai/whisper-tiny", text_model_id="meta-llama/Llama-2-7b-chat-hf")
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```"""
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model_type = "ultravox"
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else:
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audio_config = audio_config or {}
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self.audio_config = transformers.CONFIG_MAPPING[
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audio_config.get("model_type", "whisper")
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](**audio_config)
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self.text_model_lora_config = (
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# remove text_config and audio_config if text_model_id and audio_model_id are present
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if self.text_model_id is not None:
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diff_dict.pop("text_config", None)
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elif "text_config" in diff_dict:
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diff_dict["text_config"].pop("_attn_implementation_autoset", None)
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if self.audio_model_id is not None:
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diff_dict.pop("audio_config", None)
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elif "audio_config" in diff_dict:
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diff_dict["audio_config"].pop("_attn_implementation_autoset", None)
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return diff_dict
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