75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
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# -*- coding: utf-8 -*-
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from typing import Optional
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from transformers.configuration_utils import PretrainedConfig
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class ABCConfig(PretrainedConfig):
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model_type = 'abc'
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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: int = 32000,
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hidden_size: int = 2048,
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gate_low_rank_dim: int = 16,
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clamp_min: float = -32,
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clamp_max: float = 32,
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hidden_ratio: Optional[int] = 4,
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intermediate_size: Optional[int] = None,
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num_hidden_layers: int = 24,
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num_heads: int = 4,
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num_slots: Optional[int] = 64,
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use_short_conv: bool = True,
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conv_size: int = 4,
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share_conv_kernel: bool = True,
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exapnd_k: float = 0.5,
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exapnd_v: float = 1,
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hidden_act: str = "swish",
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max_position_embeddings: int = 2048,
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elementwise_affine: Optional[bool] = True,
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norm_eps: float = 1e-6,
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use_cache: bool = True,
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pad_token_id: int = None,
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bos_token_id: int = 1,
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eos_token_id: int = 2,
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initializer_range: float = 0.02,
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tie_word_embeddings: bool = False,
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fuse_norm: bool = True,
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fuse_cross_entropy: bool = True,
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**kwargs
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):
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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.gate_low_rank_dim = gate_low_rank_dim
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self.clamp_min = clamp_min
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self.clamp_max = clamp_max
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self.hidden_ratio = hidden_ratio
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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_heads = num_heads
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self.num_slots = num_slots
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self.use_short_conv = use_short_conv
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self.conv_size = conv_size
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self.share_conv_kernel = share_conv_kernel
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self.expand_k = exapnd_k
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self.expand_v = exapnd_v
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self.hidden_act = hidden_act
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self.elementwise_affine = elementwise_affine
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self.norm_eps = norm_eps
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self.use_cache = use_cache
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self.initializer_range = initializer_range
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self.fuse_cross_entropy = fuse_cross_entropy
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self.fuse_norm = fuse_norm
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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