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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class OrionConfig(PretrainedConfig): |
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""" |
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Args: |
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vocab_size (`int`, *optional*, defaults to 113664): |
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Vocabulary size of the Orion model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`OrionModel`] |
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hidden_size (`int`, *optional*, defaults to 4096): |
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Dimension of the hidden representations. |
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intermediate_size (`int`, *optional*, defaults to 14592): |
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Dimension of the MLP representations. |
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num_hidden_layers (`int`, *optional*, defaults to 32): |
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Number of hidden layers in the Transformer encoder. |
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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 Transformer encoder. |
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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 checkout [this |
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`. |
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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 `8192`): |
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The maximum sequence length that this model might ever be used with. Orion's sliding window attention |
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allows sequence of up to 4096*32 tokens. |
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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*): |
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The id of the padding token. |
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bos_token_id (`int`, *optional*, defaults to 1): |
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The id of the "beginning-of-sequence" token. |
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eos_token_id (`int`, *optional*, defaults to 2): |
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The id of the "end-of-sequence" token. |
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tie_word_embeddings (`bool`, *optional*, defaults to `False`): |
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Whether the model's input and output word embeddings should be tied. |
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rope_theta (`float`, *optional*, defaults to 1000000.0): |
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The base period of the RoPE embeddings. |
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sliding_window (`int`, *optional*): |
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Sliding window attention window size. If not specified, will default to `4096`. |
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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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num_experts_per_tok (`int`, *optional*, defaults to 2): |
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The number of experts to root per-token, can be also interpreted as the `top-p` routing |
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parameter |
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num_local_experts (`int`, *optional*, defaults to 8): |
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Number of experts per Sparse MLP layer. |
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output_router_logits (`bool`, *optional*, defaults to `False`): |
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Whether or not the router logits should be returned by the model. Enabeling this will also |
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allow the model to output the auxiliary loss. See [here]() for more details |
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router_aux_loss_coef (`float`, *optional*, defaults to 0.001): |
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The aux loss factor for the total loss. |
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""" |
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model_type = "orion_moe" |
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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=113664, |
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hidden_size=4096, |
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intermediate_size=14592, |
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num_hidden_layers=32, |
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num_attention_heads=32, |
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num_key_value_heads=8, |
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hidden_act="silu", |
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max_position_embeddings=8192, |
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initializer_range=0.02, |
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rms_norm_eps=1e-5, |
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use_cache=True, |
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pad_token_id=None, |
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bos_token_id=1, |
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eos_token_id=2, |
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tie_word_embeddings=False, |
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rope_theta=1e6, |
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sliding_window=None, |
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attention_dropout=0.0, |
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num_experts_per_tok=2, |
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num_local_experts=8, |
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output_router_logits=False, |
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router_aux_loss_coef=0.01, |
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router_jitter_noise=0.0, |
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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.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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self.sliding_window = sliding_window |
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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.rope_theta = rope_theta |
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self.attention_dropout = attention_dropout |
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self.num_experts_per_tok = num_experts_per_tok |
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self.num_local_experts = num_local_experts |
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self.output_router_logits = output_router_logits |
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self.router_aux_loss_coef = router_aux_loss_coef |
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self.router_jitter_noise = router_jitter_noise |
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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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