coffee
96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
from transformers import PretrainedConfig
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from transformers import logging
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from transformers.modeling_rope_utils import rope_config_validation
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logger = logging.get_logger(__name__)
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class DollyMoEConfig(PretrainedConfig):
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model_type = "dolly_moe"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.experts.*.gate_proj": "colwise",
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"layers.*.mlp.experts.*.up_proj": "colwise",
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"layers.*.mlp.experts.*.down_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=151936,
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hidden_size=1024,
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intermediate_size=3072,
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num_hidden_layers=28,
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num_attention_heads=16,
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num_key_value_heads=8,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=1000000.0,
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rope_scaling=None,
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attention_bias=False,
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use_sliding_window=False,
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sliding_window=4096,
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max_window_layers=28,
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attention_dropout=0.0,
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decoder_sparse_step=1,
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moe_intermediate_size=768,
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num_experts_per_tok=4,
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num_experts=8,
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norm_topk_prob=False,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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mlp_only_layers=None,
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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.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window if use_sliding_window else None
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self.max_window_layers = max_window_layers
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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.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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self.decoder_sparse_step = decoder_sparse_step
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self.moe_intermediate_size = moe_intermediate_size
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self.num_experts_per_tok = num_experts_per_tok
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self.num_experts = num_experts
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self.norm_topk_prob = norm_topk_prob
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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.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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__all__ = ["DollyMoEConfig"]
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