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354 lines
18 KiB
354 lines
18 KiB
# Copyright 2025 Sesame 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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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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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 CsmDepthDecoderConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CsmDepthDecoderModel`]. It is used to instantiate an CSM depth decoder
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield
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a similar configuration to that of the csm-1b.
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e.g. [sesame/csm-1b](https://huggingface.co/sesame/csm-1b)
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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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num_codebooks (`int`, *optional*, defaults to 32):
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Number of codebooks used in the underlying codec model responsible for tokenizing the audio.
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backbone_hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations of the backbone model used with this depth decoder.
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vocab_size (`int`, *optional*, defaults to 2051):
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Vocabulary size of the CsmDepthDecoder model. Defines the number of different audio tokens that can be represented by each codebook.
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 4):
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Number of hidden layers in the Transformer decoder.
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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 decoder.
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num_key_value_heads (`int`, *optional*, defaults to 2):
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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
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`num_attention_heads`.
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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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max_position_embeddings (`int`, *optional*, defaults to 33):
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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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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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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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pad_token_id (`int`, *optional*, defaults to 2050):
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Padding token id.
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bos_token_id (`int`, *optional*):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*):
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End of stream token id.
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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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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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head_dim (`int`, *optional*):
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The attention head dimension. If None, it will default to hidden_size // num_attention_heads
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```python
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>>> from transformers import CsmDepthDecoder, CsmDepthDecoderConfig
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>>> # Initializing a CsmDepthDecoder
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>>> configuration = CsmDepthDecoderConfig()
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>>> model = CsmDepthDecoderModel(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 = "csm_depth_decoder_model"
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base_config_key = "depth_decoder_config"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 500000.0
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def __init__(
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self,
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num_codebooks: int | None = 32,
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backbone_hidden_size: int | None = 2048,
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vocab_size: int | None = 2051,
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hidden_size: int | None = 1024,
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intermediate_size: int | None = 8192,
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num_hidden_layers: int | None = 4,
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num_attention_heads: int | None = 8,
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num_key_value_heads: int | None = 2,
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hidden_act: int | None = "silu",
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max_position_embeddings: int | None = 33,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = None,
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bos_token_id: int | None = None,
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eos_token_id: int | None = None,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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mlp_bias: bool | None = False,
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head_dim: int | None = None,
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**kwargs,
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):
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if kwargs.pop("tie_word_embeddings", False):
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raise ValueError("`tie_word_embeddings=True` is not supported for CsmDepthDecoderConfig")
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self.pad_token_id = pad_token_id
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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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self.num_codebooks = num_codebooks
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self.vocab_size = vocab_size
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self.backbone_hidden_size = backbone_hidden_size
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self.max_position_embeddings = max_position_embeddings
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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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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
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self.rope_parameters = rope_parameters
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super().__init__(**kwargs)
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class CsmConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CsmForConditionalGeneration`]. It is used to instantiate an CSM
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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 csm-1b.
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e.g. [sesame/csm-1b](https://huggingface.co/sesame/csm-1b)
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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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num_codebooks (`int`, *optional*, defaults to 32):
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Number of codebooks used in the underlying codec model responsible for tokenizing the audio.
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vocab_size (`int`, *optional*, defaults to 2051):
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Vocabulary size of the Csm model. Defines the number of different audio tokens that can be represented by each codebook.
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text_vocab_size (`int`, *optional*, defaults to 128256):
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Vocabulary size of the text input for the Csm model. Defines the number of different text tokens that can be represented.
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations of the backbone model.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations of the backbone model.
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num_hidden_layers (`int`, *optional*, defaults to 16):
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Number of hidden layers in the backbone model Transformer decoder.
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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 backbone model Transformer decoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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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).
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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 backbone model Transformer decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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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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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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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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pad_token_id (`int`, *optional*, defaults to 128002):
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Padding token id.
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codebook_pad_token_id (`int`, *optional*, defaults to 2050):
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Padding token id for codebook tokens.
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codebook_eos_token_id (`int`, *optional*, defaults to 0):
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End of stream token id for codebook tokens.
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bos_token_id (`int`, *optional*, defaults to 128000):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*):
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End of stream token id.
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audio_token_id (`int`, *optional*, defaults to 128002):
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Audio token id in the text input.
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audio_eos_token_id (`int`, *optional*, defaults to 128003):
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End of stream token id for audio in the text input.
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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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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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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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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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head_dim (`int`, *optional*):
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The attention head dimension. If None, it will default to hidden_size // num_attention_heads
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tie_codebooks_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie the codebook tokens embeddings of the backbone model to the codebook tokens embeddings of the depth decoder.
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depth_decoder_config (`CsmDepthDecoderConfig`, *optional*):
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Configuration for the depth decoder.
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codec_config (`PreTrainedConfig`, *optional*):
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Configuration for the codec.
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```python
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>>> from transformers import CsmForConditionalGeneration, CsmConfig
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>>> # Initializing a CsmConfig
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>>> configuration = CsmConfig()
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>>> # Initializing a model
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>>> model = CsmForConditionalGeneration(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 = "csm"
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base_config_key = "csm_config"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 500000.0
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sub_configs = {
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"codec_config": AutoConfig,
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"depth_decoder_config": CsmDepthDecoderConfig,
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}
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def __init__(
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self,
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num_codebooks: int | None = 32,
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vocab_size: int | None = 2051,
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text_vocab_size: int | None = 128256,
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hidden_size: int | None = 2048,
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intermediate_size: int | None = 8192,
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num_hidden_layers: int | None = 16,
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num_attention_heads: int | None = 32,
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num_key_value_heads: int | None = 8,
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hidden_act: str | None = "silu",
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max_position_embeddings: int | None = 2048,
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initializer_range: float | None = 0.02,
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rms_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = 128002,
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codebook_pad_token_id: int | None = 2050,
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codebook_eos_token_id: int | None = 0,
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bos_token_id: int | None = 128000,
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eos_token_id: int | None = None,
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audio_token_id: int | None = 128002,
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audio_eos_token_id: int | None = 128003,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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attention_bias: bool | None = False,
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attention_dropout: float | None = 0.0,
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mlp_bias: bool | None = False,
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head_dim: int | None = None,
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tie_codebooks_embeddings: bool | None = True,
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depth_decoder_config: dict | None = None,
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codec_config: dict | None = None,
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**kwargs,
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):
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if kwargs.pop("tie_word_embeddings", False):
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raise ValueError("`tie_word_embeddings=True` is not supported for CsmConfig")
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if depth_decoder_config is None:
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self.depth_decoder_config = CsmDepthDecoderConfig()
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logger.info("depth_decoder_config is None, using default depth decoder config.")
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elif isinstance(depth_decoder_config, dict):
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self.depth_decoder_config = CsmDepthDecoderConfig(**depth_decoder_config)
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elif isinstance(depth_decoder_config, CsmDepthDecoderConfig):
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self.depth_decoder_config = depth_decoder_config
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if codec_config is None:
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self.codec_config = AutoConfig.for_model("mimi")
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logger.info("codec_config is None, using default audio encoder config.")
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elif isinstance(codec_config, dict):
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self.codec_config = AutoConfig.for_model(**codec_config)
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elif isinstance(codec_config, PreTrainedConfig):
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self.codec_config = codec_config
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self.text_vocab_size = text_vocab_size
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self.num_codebooks = num_codebooks
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self.audio_token_id = audio_token_id
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self.audio_eos_token_id = audio_eos_token_id
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self.codebook_pad_token_id = codebook_pad_token_id
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self.codebook_eos_token_id = codebook_eos_token_id
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self.tie_codebooks_embeddings = tie_codebooks_embeddings
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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.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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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
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self.rope_parameters = rope_parameters
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self.pad_token_id = pad_token_id
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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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self.tie_word_embeddings = False
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super().__init__(**kwargs)
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__all__ = [
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"CsmDepthDecoderConfig",
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"CsmConfig",
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]
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