54600709b6
Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com> Co-authored-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
from typing import List, Optional
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from transformers import PretrainedConfig
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class MLPSpeculatorConfig(PretrainedConfig):
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model_type = "mlp_speculator"
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attribute_map = {
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"hidden_size": "emb_dim",
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}
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def __init__(self,
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vocab_size: int = 32000,
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emb_dim: int = 4096,
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inner_dim: int = 0,
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n_predict: int = 3,
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top_k_tokens_per_head: Optional[List[int]] = None,
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n_candidates: int = 5,
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tie_weights: bool = False,
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scale_input: bool = False,
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**kwargs):
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"""
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Initialize an MLPSpeculatorConfig
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Args:
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vocab_size: int
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the model vocab size
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emb_dim: int
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the model embedding dimension
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inner_dim: int
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the inner dimension of the model. If 0, will be the emb_dim.
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n_predict: int
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the number of lookaheads for the speculator
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top_k_tokens_per_head: List[int]
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Number of tokens to consider from each head when forming the
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candidate tree.
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For each candidate branch in the tree, head n produces topk[n]
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additional sub-branches.
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NOTE: This parameter is currently unused.
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n_candidates: int
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number of child candidates to create per sequence
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tie_weights: bool
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If true, use a single set of weights for every model
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head/stage after the first. The initial projection
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from the base model may have a different size, so that
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stays separate.
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scale_input: bool
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if True, will scale the initial hidden states from
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the base model.
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"""
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if top_k_tokens_per_head is None:
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top_k_tokens_per_head = [5, 4, 3]
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assert len(top_k_tokens_per_head) == n_predict
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self.vocab_size = vocab_size
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self.emb_dim = emb_dim
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self.inner_dim = inner_dim
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self.n_predict = n_predict
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self.top_k_tokens_per_head = top_k_tokens_per_head
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self.n_candidates = n_candidates
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self.num_lookahead_tokens = n_predict
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self.tie_weights = tie_weights
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self.scale_input = scale_input
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super().__init__(**kwargs)
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