333 lines
16 KiB
Python
333 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2024 Meta AI and 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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"""Moshi model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto.configuration_auto import AutoConfig
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logger = logging.get_logger(__name__)
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class MoshiDepthConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MoshiDepthDecoder`]. It is used to instantiate a
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Moshi depth decoder model according to the specified arguments, defining the Moshi depth decoder config.
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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 32000):
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Vocabulary size of the MoshiDepthDecoder model. Defines the number of different tokens that can be
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represented by the `inputs_ids` passed when calling [`MoshiDepthDecoder`].
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the layers and the pooler layer of the depth decoder.
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input_size (`int`, *optional*, defaults to 4096):
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Dimensionality of the input hidden states. Used to connect the main decoder to the depth decoder.
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num_hidden_layers (`int`, *optional*, defaults to 6):
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Number of depth decoder layers.
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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 depth decoder block.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`.
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audio_vocab_size (`int`, *optional*, defaults to 2048):
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Vocabulary size of the audio part of model. Defines the number of different tokens that can be
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represented by the `audio_codes` passed when calling the Moshi models.
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max_position_embeddings (`int`, *optional*, defaults to 9):
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The maximum sequence length that this model might ever be used with. Typically, set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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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 depth decoder.
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head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
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The attention head dimension.
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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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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). Only
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relevant if `config.is_decoder=True`.
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sliding_window (`int`, *optional*, defaults to 8):
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Sliding window attention window size. If not specified, will default to `8`.
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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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ffn_dim (`int`, *optional*, defaults to 5632):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the depth decoder block. Must be even.
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rms_norm_eps (`float`, *optional*, defaults to 1e-08):
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The epsilon used by the rms normalization layers.
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num_codebooks (`int`, *optional*, defaults to 8):
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The number of audio codebooks for each audio channels.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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kwargs (*optional*):
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Dictionary of keyword arguments. Notably:
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- **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
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defines the audio encoder config.
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Example:
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```python
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>>> from transformers import (
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... MoshiDepthConfig,
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... MoshiDepthDecoder,
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... )
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>>> configuration = MoshiDepthConfig()
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>>> # Initializing a MoshiDepthDecoder (with random weights) from the kmhf/hf-moshiko style configuration
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>>> model = MoshiDepthDecoder(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 = "moshi_depth"
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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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vocab_size=32000,
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hidden_size=1024,
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input_size=4096,
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num_hidden_layers=6,
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num_attention_heads=16,
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num_key_value_heads=None,
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audio_vocab_size=2048,
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max_position_embeddings=9,
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hidden_act="silu",
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head_dim=None,
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initializer_range=0.02,
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use_cache=True,
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sliding_window=8,
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attention_dropout=0.0,
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ffn_dim=5632,
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rms_norm_eps=1e-8,
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num_codebooks=8,
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tie_word_embeddings=False,
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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.input_size = input_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_key_value_heads if num_key_value_heads is not None else num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.hidden_act = hidden_act
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self.head_dim = head_dim or hidden_size // num_attention_heads
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.sliding_window = sliding_window
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self.attention_dropout = attention_dropout
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if ffn_dim % 2 == 1:
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raise ValueError(f"`ffn_dim={ffn_dim}` must be even.")
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self.ffn_dim = ffn_dim
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self.rms_norm_eps = rms_norm_eps
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self.num_codebooks = num_codebooks
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self.audio_vocab_size = audio_vocab_size
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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class MoshiConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MoshiModel`]. It is used to instantiate a
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Moshi model according to the specified arguments, defining the audio encoder, Moshi depth decoder and Moshi decoder
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configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Moshiko model,
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e.g. [kmhf/hf-moshiko](https://huggingface.co/kmhf/hf-moshiko)
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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 32000):
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Vocabulary size of the MoshiDecoder model. Defines the number of different tokens that can be
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represented by the `inputs_ids` passed when calling [`MoshiDecoder`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimensionality of the layers and the pooler layer of the main decoder.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of decoder layers.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the main decoder block.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`.
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audio_vocab_size (`int`, *optional*):
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Vocabulary size of the audio part of model. Defines the number of different tokens that can be
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represented by the `audio_codes` passed when calling the Moshi models.
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max_position_embeddings (`int`, *optional*, defaults to 3000):
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The maximum sequence length that this model might ever be used with. Typically, set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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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 decoder.
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head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
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The attention head dimension.
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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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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). Only
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relevant if `config.is_decoder=True`.
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sliding_window (`int`, *optional*, defaults to 3000):
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Sliding window attention window size. If not specified, will default to `3000`.
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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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ffn_dim (`int`, *optional*, defaults to 22528):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the main decoder block. Must be even.
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rms_norm_eps (`float`, *optional*, defaults to 1e-08):
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The epsilon used by the rms normalization layers.
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num_codebooks (`int`, *optional*, defaults to 8):
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The number of audio codebooks for each audio channels.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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kwargs (*optional*):
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Dictionary of keyword arguments. Notably:
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- **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
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defines the audio encoder config.
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- **depth__config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
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defines the depth decoder config.
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Example:
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```python
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>>> from transformers import (
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... MoshiConfig,
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... MoshiForConditionalGeneration,
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... )
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>>> configuration = MoshiConfig()
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>>> # Initializing a MoshiForConditionalGeneration (with random weights) from the kmhf/hf-moshiko style configuration
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>>> model = MoshiForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # Saving the model, including its configuration
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>>> model.save_pretrained("kmhf/hf-moshiko")
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>>> # loading model and config from pretrained folder
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>>> moshi_config = MoshiConfig.from_pretrained("kmhf/hf-moshiko")
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>>> model = MoshiForConditionalGeneration.from_pretrained("kmhf/hf-moshiko", config=moshi_config)
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```"""
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model_type = "moshi"
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keys_to_ignore_at_inference = ["past_key_values"]
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sub_configs = {"audio_encoder_config": AutoConfig, "depth_decoder_config": MoshiDepthConfig}
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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audio_vocab_size=None,
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max_position_embeddings=3000,
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rope_theta=10000.0,
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hidden_act="silu",
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head_dim=None,
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initializer_range=0.02,
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use_cache=True,
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sliding_window=3000,
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attention_dropout=0.0,
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ffn_dim=22528,
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rms_norm_eps=1e-8,
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num_codebooks=8,
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tie_word_embeddings=False,
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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.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_key_value_heads if num_key_value_heads is not None else num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.rope_theta = rope_theta
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self.hidden_act = hidden_act
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self.head_dim = head_dim or hidden_size // num_attention_heads
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.sliding_window = sliding_window
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self.attention_dropout = attention_dropout
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if ffn_dim % 2 == 1:
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raise ValueError(f"`ffn_dim={ffn_dim}` must be even.")
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self.ffn_dim = ffn_dim
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self.rms_norm_eps = rms_norm_eps
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self.num_codebooks = num_codebooks
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audio_encoder_config = kwargs.pop("audio_encoder_config", {})
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audio_encoder_model_type = audio_encoder_config.pop("model_type", "mimi")
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self.audio_encoder_config = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
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if self.num_codebooks > self.audio_encoder_config.num_codebooks:
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raise ValueError(
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f"`num_codebooks={num_codebooks}` is greater than the maximum number of codebooks that the audio encoder can deal with ({self.audio_encoder_config.num_codebooks}). Please lower it."
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)
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self.audio_vocab_size = (
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self.audio_encoder_config.codebook_size if audio_vocab_size is None else audio_vocab_size
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)
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depth_decoder_config = kwargs.pop("depth_decoder_config", {})
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depth_decoder_config.update(
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{
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"audio_vocab_size": self.audio_vocab_size,
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"input_size": hidden_size,
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"vocab_size": vocab_size,
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"num_codebooks": num_codebooks,
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}
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)
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self.depth_decoder_config = MoshiDepthConfig(**depth_decoder_config)
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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@property
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def sampling_rate(self):
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return self.audio_encoder_config.sampling_rate
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@classmethod
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def from_audio_encoder_config(
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cls,
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audio_encoder_config: PretrainedConfig,
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**kwargs,
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):
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r"""
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Instantiate a [`MoshiConfig`] (or a derived class) from an audio encoder configuration.
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Returns:
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[`MoshiConfig`]: An instance of a configuration object
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"""
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return cls(
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audio_encoder_config=audio_encoder_config.to_dict(),
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**kwargs,
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)
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__all__ = ["MoshiConfig", "MoshiDepthConfig"]
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