440 lines
23 KiB
Python
440 lines
23 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 Sesame and The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...modeling_rope_utils import rope_config_validation
|
|
from ...utils import logging
|
|
from ..auto.configuration_auto import AutoConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class CsmDepthDecoderConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`CsmDepthDecoderModel`]. It is used to instantiate an CSM depth decoder
|
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield
|
|
a similar configuration to that of the csm-1b.
|
|
|
|
e.g. [sesame/csm-1b](https://huggingface.co/sesame/csm-1b)
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
|
|
Args:
|
|
num_codebooks (`int`, *optional*, defaults to 32):
|
|
Number of codebooks used in the underlying codec model responsible for tokenizing the audio.
|
|
backbone_hidden_size (`int`, *optional*, defaults to 2048):
|
|
Dimension of the hidden representations of the backbone model used with this depth decoder.
|
|
vocab_size (`int`, *optional*, defaults to 2051):
|
|
Vocabulary size of the CsmDepthDecoder model. Defines the number of different audio tokens that can be represented by each codebook.
|
|
hidden_size (`int`, *optional*, defaults to 1024):
|
|
Dimension of the hidden representations.
|
|
intermediate_size (`int`, *optional*, defaults to 8192):
|
|
Dimension of the MLP representations.
|
|
num_hidden_layers (`int`, *optional*, defaults to 4):
|
|
Number of hidden layers in the Transformer decoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 8):
|
|
Number of attention heads for each attention layer in the Transformer decoder.
|
|
num_key_value_heads (`int`, *optional*, defaults to 2):
|
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
|
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
|
by meanpooling all the original heads within that group. For more details, check out [this
|
|
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
|
|
`num_attention_heads`.
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
|
The non-linear activation function (function or string) in the decoder.
|
|
max_position_embeddings (`int`, *optional*, defaults to 33):
|
|
The maximum sequence length that this model might ever be used with.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon used by the rms normalization layers.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
|
relevant if `config.is_decoder=True`.
|
|
pad_token_id (`int`, *optional*, defaults to 2050):
|
|
Padding token id.
|
|
bos_token_id (`int`, *optional*):
|
|
Beginning of stream token id.
|
|
eos_token_id (`int`, *optional*):
|
|
End of stream token id.
|
|
rope_theta (`float`, *optional*, defaults to 500000):
|
|
The base period of the RoPE embeddings.
|
|
rope_scaling (`Dict`, *optional*):
|
|
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
|
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
|
accordingly.
|
|
Expected contents:
|
|
`rope_type` (`str`):
|
|
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
|
'llama3'], with 'default' being the original RoPE implementation.
|
|
`factor` (`float`, *optional*):
|
|
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
|
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
|
original maximum pre-trained length.
|
|
`original_max_position_embeddings` (`int`, *optional*):
|
|
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
|
pretraining.
|
|
`attention_factor` (`float`, *optional*):
|
|
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
|
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
|
`factor` field to infer the suggested value.
|
|
`beta_fast` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 32.
|
|
`beta_slow` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 1.
|
|
`short_factor` (`list[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`long_factor` (`list[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`low_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
|
`high_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
|
attention_bias (`bool`, *optional*, defaults to `False`):
|
|
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
mlp_bias (`bool`, *optional*, defaults to `False`):
|
|
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
|
|
head_dim (`int`, *optional*):
|
|
The attention head dimension. If None, it will default to hidden_size // num_attention_heads
|
|
|
|
```python
|
|
>>> from transformers import CsmDepthDecoder, CsmDepthDecoderConfig
|
|
|
|
>>> # Initializing a CsmDepthDecoder
|
|
>>> configuration = CsmDepthDecoderConfig()
|
|
>>> model = CsmDepthDecoderModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "csm_depth_decoder_model"
|
|
base_config_key = "depth_decoder_config"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
def __init__(
|
|
self,
|
|
num_codebooks=32,
|
|
backbone_hidden_size=2048,
|
|
vocab_size=2051,
|
|
hidden_size=1024,
|
|
intermediate_size=8192,
|
|
num_hidden_layers=4,
|
|
num_attention_heads=8,
|
|
num_key_value_heads=2,
|
|
hidden_act="silu",
|
|
max_position_embeddings=33,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-5,
|
|
use_cache=True,
|
|
pad_token_id=None,
|
|
bos_token_id=None,
|
|
eos_token_id=None,
|
|
rope_theta=500000,
|
|
rope_scaling=None,
|
|
attention_bias=False,
|
|
attention_dropout=0.0,
|
|
mlp_bias=False,
|
|
head_dim=None,
|
|
**kwargs,
|
|
):
|
|
if kwargs.pop("tie_word_embeddings", False):
|
|
raise ValueError("`tie_word_embeddings=True` is not supported for CsmDepthDecoderConfig")
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=False,
|
|
**kwargs,
|
|
)
|
|
self.num_codebooks = num_codebooks
|
|
self.vocab_size = vocab_size
|
|
self.backbone_hidden_size = backbone_hidden_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.rope_scaling = rope_scaling
|
|
self.attention_bias = attention_bias
|
|
self.attention_dropout = attention_dropout
|
|
self.mlp_bias = mlp_bias
|
|
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
|
|
# Validate the correctness of rotary position embeddings parameters
|
|
# BC: if there is a 'type' field, copy it it to 'rope_type'.
|
|
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
|
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
|
rope_config_validation(self)
|
|
|
|
|
|
class CsmConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`CsmForConditionalGeneration`]. It is used to instantiate an CSM
|
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
|
with the defaults will yield a similar configuration to that of the csm-1b.
|
|
|
|
e.g. [sesame/csm-1b](https://huggingface.co/sesame/csm-1b)
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
Args:
|
|
num_codebooks (`int`, *optional*, defaults to 32):
|
|
Number of codebooks used in the underlying codec model responsible for tokenizing the audio.
|
|
vocab_size (`int`, *optional*, defaults to 2051):
|
|
Vocabulary size of the Csm model. Defines the number of different audio tokens that can be represented by each codebook.
|
|
text_vocab_size (`int`, *optional*, defaults to 128256):
|
|
Vocabulary size of the text input for the Csm model. Defines the number of different text tokens that can be represented.
|
|
hidden_size (`int`, *optional*, defaults to 2048):
|
|
Dimension of the hidden representations of the backbone model.
|
|
intermediate_size (`int`, *optional*, defaults to 8192):
|
|
Dimension of the MLP representations of the backbone model.
|
|
num_hidden_layers (`int`, *optional*, defaults to 16):
|
|
Number of hidden layers in the backbone model Transformer decoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
|
Number of attention heads for each attention layer in the backbone model Transformer decoder.
|
|
num_key_value_heads (`int`, *optional*, defaults to 8):
|
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
|
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
|
by meanpooling all the original heads within that group. For more details, check out [this
|
|
paper](https://huggingface.co/papers/2305.13245).
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
|
The non-linear activation function (function or string) in the backbone model Transformer decoder.
|
|
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
|
The maximum sequence length that this model might ever be used with.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon used by the rms normalization layers.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
|
relevant if `config.is_decoder=True`.
|
|
pad_token_id (`int`, *optional*, defaults to 128002):
|
|
Padding token id.
|
|
codebook_pad_token_id (`int`, *optional*, defaults to 2050):
|
|
Padding token id for codebook tokens.
|
|
codebook_eos_token_id (`int`, *optional*, defaults to 0):
|
|
End of stream token id for codebook tokens.
|
|
bos_token_id (`int`, *optional*, defaults to 128000):
|
|
Beginning of stream token id.
|
|
eos_token_id (`int`, *optional*):
|
|
End of stream token id.
|
|
audio_token_id (`int`, *optional*, defaults to 128002):
|
|
Audio token id in the text input.
|
|
audio_eos_token_id (`int`, *optional*, defaults to 128003):
|
|
End of stream token id for audio in the text input.
|
|
rope_theta (`float`, *optional*, defaults to 500000):
|
|
The base period of the RoPE embeddings.
|
|
rope_scaling (`Dict`, *optional*, defaults to `{'factor': 32.0, 'high_freq_factor': 0.5, 'low_freq_factor': 0.125, 'original_max_position_embeddings': 1024, 'rope_type': 'llama3'}`):
|
|
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
|
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
|
accordingly.
|
|
Expected contents:
|
|
`rope_type` (`str`):
|
|
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
|
'llama3'], with 'default' being the original RoPE implementation.
|
|
`factor` (`float`, *optional*):
|
|
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
|
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
|
original maximum pre-trained length.
|
|
`original_max_position_embeddings` (`int`, *optional*):
|
|
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
|
pretraining.
|
|
`attention_factor` (`float`, *optional*):
|
|
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
|
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
|
`factor` field to infer the suggested value.
|
|
`beta_fast` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 32.
|
|
`beta_slow` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 1.
|
|
`short_factor` (`list[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`long_factor` (`list[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`low_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
|
`high_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
|
attention_bias (`bool`, *optional*, defaults to `False`):
|
|
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
mlp_bias (`bool`, *optional*, defaults to `False`):
|
|
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
|
|
head_dim (`int`, *optional*):
|
|
The attention head dimension. If None, it will default to hidden_size // num_attention_heads
|
|
tie_codebooks_embeddings (`bool`, *optional*, defaults to `True`):
|
|
Whether to tie the codebook tokens embeddings of the backbone model to the codebook tokens embeddings of the depth decoder.
|
|
depth_decoder_config (`CsmDepthDecoderConfig`, *optional*):
|
|
Configuration for the depth decoder.
|
|
codec_config (`PretrainedConfig`, *optional*):
|
|
Configuration for the codec.
|
|
|
|
```python
|
|
>>> from transformers import CsmForConditionalGeneration, CsmConfig
|
|
|
|
>>> # Initializing a CsmConfig
|
|
>>> configuration = CsmConfig()
|
|
|
|
>>> # Initializing a model
|
|
>>> model = CsmForConditionalGeneration(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "csm"
|
|
base_config_key = "csm_config"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
sub_configs = {
|
|
"codec_config": AutoConfig,
|
|
"depth_decoder_config": CsmDepthDecoderConfig,
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
num_codebooks=32,
|
|
vocab_size=2051,
|
|
text_vocab_size=128256,
|
|
hidden_size=2048,
|
|
intermediate_size=8192,
|
|
num_hidden_layers=16,
|
|
num_attention_heads=32,
|
|
num_key_value_heads=8,
|
|
hidden_act="silu",
|
|
max_position_embeddings=2048,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-5,
|
|
use_cache=True,
|
|
pad_token_id=128002,
|
|
codebook_pad_token_id=2050,
|
|
codebook_eos_token_id=0,
|
|
bos_token_id=128000,
|
|
eos_token_id=None,
|
|
audio_token_id=128002,
|
|
audio_eos_token_id=128003,
|
|
rope_theta=500000,
|
|
rope_scaling=None,
|
|
attention_bias=False,
|
|
attention_dropout=0.0,
|
|
mlp_bias=False,
|
|
head_dim=None,
|
|
tie_codebooks_embeddings=True,
|
|
depth_decoder_config=None,
|
|
codec_config=None,
|
|
**kwargs,
|
|
):
|
|
if kwargs.pop("tie_word_embeddings", False):
|
|
raise ValueError("`tie_word_embeddings=True` is not supported for CsmConfig")
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
bos_token_id=bos_token_id,
|
|
eos_token_id=eos_token_id,
|
|
tie_word_embeddings=False,
|
|
**kwargs,
|
|
)
|
|
|
|
if depth_decoder_config is None:
|
|
self.depth_decoder_config = CsmDepthDecoderConfig()
|
|
logger.info("depth_decoder_config is None, using default depth decoder config.")
|
|
elif isinstance(depth_decoder_config, dict):
|
|
self.depth_decoder_config = CsmDepthDecoderConfig(**depth_decoder_config)
|
|
elif isinstance(depth_decoder_config, CsmDepthDecoderConfig):
|
|
self.depth_decoder_config = depth_decoder_config
|
|
|
|
if codec_config is None:
|
|
self.codec_config = AutoConfig.for_model("mimi")
|
|
logger.info("codec_config is None, using default audio encoder config.")
|
|
elif isinstance(codec_config, dict):
|
|
self.codec_config = AutoConfig.for_model(**codec_config)
|
|
elif isinstance(codec_config, PretrainedConfig):
|
|
self.codec_config = codec_config
|
|
|
|
self.text_vocab_size = text_vocab_size
|
|
self.num_codebooks = num_codebooks
|
|
self.audio_token_id = audio_token_id
|
|
self.audio_eos_token_id = audio_eos_token_id
|
|
self.codebook_pad_token_id = codebook_pad_token_id
|
|
self.codebook_eos_token_id = codebook_eos_token_id
|
|
self.tie_codebooks_embeddings = tie_codebooks_embeddings
|
|
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.rope_scaling = rope_scaling
|
|
self.attention_bias = attention_bias
|
|
self.attention_dropout = attention_dropout
|
|
self.mlp_bias = mlp_bias
|
|
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
|
|
# Validate the correctness of rotary position embeddings parameters
|
|
# BC: if there is a 'type' field, copy it it to 'rope_type'.
|
|
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
|
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
|
rope_config_validation(self)
|
|
|
|
|
|
__all__ = [
|
|
"CsmDepthDecoderConfig",
|
|
"CsmConfig",
|
|
]
|