203 lines
8.4 KiB
Python
203 lines
8.4 KiB
Python
# coding=utf-8
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# Copyright 2025 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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from ...configuration_utils import PretrainedConfig
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from ..auto import CONFIG_MAPPING, AutoConfig
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class VoxtralEncoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VoxtralEncoder`]. It is used to instantiate a
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Voxtral audio encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the audio encoder of the Voxtral
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architecture.
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e.g. [mistralai/Voxtral-Mini-3B-2507](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507)
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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 51866):
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Vocabulary size of the model.
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hidden_size (`int`, *optional*, defaults to 1280):
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Dimensionality of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 5120):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 20):
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Number of attention heads for each attention layer in the Transformer encoder.
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scale_embedding (`bool`, *optional*, defaults to `False`):
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Scale embeddings by dividing by sqrt(hidden_size) if True.
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activation_function (`str`, *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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num_mel_bins (`int`, *optional*, defaults to 128):
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Number of mel features used per input features. Should correspond to the value used in the
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`VoxtralProcessor` class.
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max_source_positions (`int`, *optional*, defaults to 1500):
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The maximum sequence length of log-mel filter-bank features 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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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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```python
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>>> from transformers import VoxtralEncoderConfig, VoxtralEncoder
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>>> # Initializing a VoxtralEncoderConfig
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>>> configuration = VoxtralEncoderConfig()
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>>> # Initializing a VoxtralEncoder (with random weights)
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>>> model = VoxtralEncoder(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 = "voxtral_encoder"
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attribute_map = {
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"d_model": "hidden_size",
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"encoder_layers": "num_hidden_layers",
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"encoder_attention_heads": "num_attention_heads",
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"encoder_ffn_dim": "intermediate_size",
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"encoder_layerdrop": "layerdrop",
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}
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def __init__(
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self,
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vocab_size=51866,
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hidden_size=1280,
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intermediate_size=5120,
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num_hidden_layers=32,
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num_attention_heads=20,
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scale_embedding=False,
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activation_function="gelu",
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num_mel_bins=128,
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max_source_positions=1500,
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initializer_range=0.02,
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attention_dropout=0.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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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.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.scale_embedding = scale_embedding # scale factor will be sqrt(hidden_size) if True
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self.activation_function = activation_function
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self.num_mel_bins = num_mel_bins
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self.max_source_positions = max_source_positions
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self.initializer_range = initializer_range
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# TODO: @eustlb, we do not use dropout and layerdrop, yet we need to hardcode them
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# to be able to use Whisper with modular (here actually from Qwen2-Audio and copied from).
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# After a future Whisper refactor, we should remove this.
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self.dropout = 0.0
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self.layerdrop = 0.0
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self.activation_dropout = 0.0
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self.attention_dropout = attention_dropout
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class VoxtralConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VoxtralForConditionalGeneration`]. It is used to instantiate an
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Voxtral 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 Voxtral-Mini-3B.
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e.g. [mistralai/Voxtral-Mini-3B-2507](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507)
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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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audio_config (`Union[AutoConfig, dict]`, *optional*):
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The config object or dictionary of the audio encoder.
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text_config (`Union[AutoConfig, dict]`, *optional*):
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The config object or dictionary of the text model.
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audio_token_id (`int`, *optional*):
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The image token index to encode the image prompt.
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projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The activation function (function or string) in the multi-modal projector.
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```python
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>>> from transformers import VoxtralForConditionalGeneration, VoxtralConfig
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>>> # Initializing a Voxtral configuration
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>>> configuration = VoxtralConfig(audio_token_id=24, projector_hidden_act="gelu")
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>>> # Initializing a 3B model with random weights
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>>> model = VoxtralForConditionalGeneration(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 = "voxtral"
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sub_configs = {"text_config": AutoConfig, "audio_config": AutoConfig}
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_default_text_config_kwargs = {
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"vocab_size": 131072,
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"hidden_size": 3072,
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"intermediate_size": 8192,
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"num_hidden_layers": 30,
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"num_key_value_heads": 8,
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"max_position_embeddings": 131072,
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"rms_norm_eps": 1e-05,
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"use_cache": True,
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"rope_theta": 100000000.0,
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"head_dim": 128,
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}
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def __init__(
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self,
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audio_config=None,
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text_config=None,
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audio_token_id=None,
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projector_hidden_act="gelu",
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**kwargs,
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):
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if isinstance(audio_config, dict):
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audio_config["model_type"] = (
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audio_config["model_type"] if "model_type" in audio_config else "voxtral_encoder"
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)
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audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config)
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elif audio_config is None:
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audio_config = CONFIG_MAPPING["voxtral_encoder"]()
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self.audio_config = audio_config
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if isinstance(text_config, dict):
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text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
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text_config = CONFIG_MAPPING[text_config["model_type"]](
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**{**self._default_text_config_kwargs, **text_config}
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)
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elif text_config is None:
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text_config = CONFIG_MAPPING["llama"](**self._default_text_config_kwargs)
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self.text_config = text_config
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self.vocab_size = text_config.vocab_size
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self.hidden_size = text_config.hidden_size
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self.audio_token_id = audio_token_id
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self.projector_hidden_act = projector_hidden_act
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super().__init__(**kwargs)
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__all__ = ["VoxtralEncoderConfig", "VoxtralConfig"]
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