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| """PyTorch Doge model configuration"""
|
|
|
| from transformers.configuration_utils import PretrainedConfig
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| from transformers.modeling_rope_utils import rope_config_validation
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|
|
|
|
| class DogeConfig(PretrainedConfig):
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| r"""
|
| This is the configuration class to store the configuration of a [`DogeModel`]. It is used to instantiate an Doge
|
| model according to the specified arguments, defining the model architecture like [LoserCheems/doge-tiny-test](https://huggingface.co/LoserCheems/doge-tiny-test)
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|
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| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| documentation from [`PretrainedConfig`] for more information.
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|
|
| Args:
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| vocab_size (`int`, *optional*, defaults to 32768):
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| Vocabulary size of the Doge model. Defines the number of different tokens that can be represented by the
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| `inputs_ids` passed when calling [`DogeModel`]
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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 4096):
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| Dimension of the CDMoE representations.
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| num_hidden_layers (`int`, *optional*, defaults to 16):
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| Number of hidden layers in the Transformer decoder.
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| hidden_bias (`bool`, *optional*, defaults to `False`):
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| Whether to use bias in the hidden layers.
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| hidden_dropout (`float`, *optional*, defaults to 0.0):
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| Dropout probability for each sequence transformation and state transformation module.
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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 2048):
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| The maximum sequence length that this model might ever be used with.
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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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| rope_scaling (`Dict`, *optional*):
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| Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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| and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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| accordingly.
|
| Expected contents:
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| `rope_type` (`str`):
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| 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*):
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| Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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| most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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| original maximum pre-trained length.
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| `original_max_position_embeddings` (`int`, *optional*):
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| Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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| pretraining.
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| `attention_factor` (`float`, *optional*):
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| Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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| computation. If unspecified, it defaults to value recommended by the implementation, using the
|
| `factor` field to infer the suggested value.
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| `beta_fast` (`float`, *optional*):
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| Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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| ramp function. If unspecified, it defaults to 32.
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| `beta_slow` (`float`, *optional*):
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| Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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| ramp function. If unspecified, it defaults to 1.
|
| `short_factor` (`List[float]`, *optional*):
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| Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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| `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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| size divided by the number of attention heads divided by 2
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| `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*):
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| Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
| 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-06):
|
| 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 0):
|
| Padding token id.
|
| bos_token_id (`int`, *optional*, defaults to 1):
|
| Beginning of stream token id.
|
| eos_token_id (`int`, *optional*, defaults to 2):
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| End of stream token id.
|
| tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| Whether to tie weight embeddings
|
| num_attention_heads (`int`, *optional*, defaults to 8):
|
| Number of attention heads for each attention layer in the Transformer decoder.
|
| attention_dropout (`float`, *optional*, defaults to 0.0):
|
| The dropout ratio for the attention probabilities.
|
| is_moe (`bool`, *optional*, defaults to `False`):
|
| Whether to use the Cross Domain Mixture of Experts, if `True`, the MoE will inherit the MLP to initialize
|
| num_cdmmoe_experts (`int`, *optional*, defaults to 4096):
|
| Number of Private Experts for the Cross Domain Mixture of Experts.
|
| num_cdmmoe_heads (`int`, *optional*, defaults to 4):
|
| Number of heads of Private Experts for the Cross Domain Mixture of Experts.
|
| num_cdmmoe_experts_per_head (`int`, *optional*, defaults to 8):
|
| Number of Private Experts per head for the Cross Domain Mixture of Experts.
|
| expert_retrieval_size (`int`, *optional*, defaults to 256):
|
| Dimension of the Expert retrieval states for the Cross Domain Mixture of Experts.
|
| """
|
|
|
| model_type = "doge"
|
| keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
| def __init__(
|
| self,
|
| vocab_size=32768,
|
| hidden_size=1024,
|
| intermediate_size=4096,
|
| num_hidden_layers=16,
|
| hidden_bias=False,
|
| hidden_dropout=0.0,
|
| hidden_act="silu",
|
| max_position_embeddings=2048,
|
| rope_theta=10000.0,
|
| rope_scaling=None,
|
| initializer_range=0.02,
|
| rms_norm_eps=1e-06,
|
| use_cache=True,
|
| pad_token_id=0,
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| bos_token_id=1,
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| eos_token_id=2,
|
| tie_word_embeddings=False,
|
| num_attention_heads=8,
|
| attention_dropout=0.0,
|
| is_moe=False,
|
| num_cdmmoe_experts=4096,
|
| num_cdmmoe_heads=4,
|
| num_cdmmoe_experts_per_head=8,
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| expert_retrieval_size=256,
|
| **kwargs,
|
| ):
|
| self.vocab_size = vocab_size
|
| self.hidden_size = hidden_size
|
| self.intermediate_size = intermediate_size
|
| self.num_hidden_layers = num_hidden_layers
|
| self.hidden_bias = hidden_bias
|
| self.hidden_dropout = hidden_dropout
|
| self.hidden_act = hidden_act
|
| self.max_position_embeddings = max_position_embeddings
|
| self.rope_theta = rope_theta
|
| self.rope_scaling = rope_scaling
|
| self.initializer_range = initializer_range
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| self.rms_norm_eps = rms_norm_eps
|
| self.use_cache = use_cache
|
| self.pad_token_id = pad_token_id
|
| self.bos_token_id = bos_token_id
|
| self.eos_token_id = eos_token_id
|
| self.tie_word_embeddings = tie_word_embeddings
|
| self.num_attention_heads = num_attention_heads
|
| self.attention_dropout = attention_dropout
|
| self.is_moe = is_moe
|
| self.num_cdmmoe_experts = num_cdmmoe_experts
|
| self.num_cdmmoe_heads = num_cdmmoe_heads
|
| self.num_cdmmoe_experts_per_head = num_cdmmoe_experts_per_head
|
| self.expert_retrieval_size = expert_retrieval_size
|
|
|
|
|
|
|
| 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)
|
|
|
| super().__init__(
|
| pad_token_id=pad_token_id,
|
| bos_token_id=bos_token_id,
|
| eos_token_id=eos_token_id,
|
| tie_word_embeddings=tie_word_embeddings,
|
| **kwargs,
|
| )
|
|
|
