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from transformers import PretrainedConfig
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import json
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class StripedHyenaConfig(PretrainedConfig):
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model_type = "stripedhyena"
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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_filters=4096,
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inner_mlp_size=14336,
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attn_layer_idxs=[],
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hyena_layer_idxs=[],
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num_layers=32,
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tie_embeddings=False,
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short_filter_length=3,
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num_attention_heads=32,
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proj_groups=4,
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hyena_filter_groups=1,
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split_k0=True,
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column_split_hyena=True,
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column_split=False,
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model_parallel_size=1,
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pipe_parallel_size=1,
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short_filter_bias=True,
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mha_out_proj_bias=False,
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qkv_proj_bias=False,
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final_norm=True,
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use_cache=True,
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use_flash_attention_2=True,
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use_flash_rmsnorm=True,
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use_flash_depthwise=False,
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use_flashfft=False,
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inference_mode=False,
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prefill_style="fft",
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max_seqlen=32768,
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eps=1e-5,
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state_size=2,
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rotary_emb_base=500000,
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smeared_gqa=False,
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make_vocab_size_divisible_by=8,
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log_intermediate_values=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_filters = num_filters
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self.inner_mlp_size = inner_mlp_size
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self.attn_layer_idxs = attn_layer_idxs
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self.hyena_layer_idxs = hyena_layer_idxs
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self.num_layers = num_layers
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self.tie_embeddings = tie_embeddings
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self.short_filter_length = short_filter_length
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self.num_attention_heads = num_attention_heads
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self.proj_groups = proj_groups
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self.hyena_filter_groups = hyena_filter_groups
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self.split_k0 = split_k0
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self.column_split_hyena = column_split_hyena
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self.column_split = column_split
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self.model_parallel_size = model_parallel_size
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self.pipe_parallel_size = pipe_parallel_size
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self.short_filter_bias = short_filter_bias
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self.mha_out_proj_bias = mha_out_proj_bias
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self.qkv_proj_bias = qkv_proj_bias
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self.final_norm = final_norm
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self.use_cache = use_cache
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self.use_flash_attention_2 = use_flash_attention_2
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self.use_flash_rmsnorm = use_flash_rmsnorm
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self.use_flash_depthwise = use_flash_depthwise
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self.use_flashfft = use_flashfft
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self.inference_mode = inference_mode
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self.prefill_style = prefill_style
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self.max_seqlen = max_seqlen
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self.eps = eps
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self.state_size = state_size
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self.rotary_emb_base = rotary_emb_base
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self.smeared_gqa = smeared_gqa
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self.make_vocab_size_divisible_by = make_vocab_size_divisible_by
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self.log_intermediate_values = log_intermediate_values
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super().__init__(**kwargs)
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def to_dict(self):
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return {attr: getattr(self, attr) for attr in self.__dict__}
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@classmethod
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def from_original_config(cls, config_path, **kwargs):
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with open(config_path, "r") as f:
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config = json.load(f)
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return cls(**config, **kwargs)
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