46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import torch
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from torch.nn import Embedding
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from custom_nodes.KepPromptLang.lib.action.base import (
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Action,
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SingleArgAction,
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)
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from custom_nodes.KepPromptLang.lib.parser.registration import register_action
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class NormAction(SingleArgAction):
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grammar = 'norm(" arg+ ")"'
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chars = None
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display_name = "Normalize"
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action_name = "norm"
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description = "Normalizes the provided segments or actions."
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usage_examples = [
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"norm(cat)",
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"sum(cat|norm(sum(tiger|fish)))",
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]
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def token_length(self) -> int:
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"""
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Norm normalizes the embeddings of the base segment, so the length is the length of the base segment
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:return:
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"""
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total_length = 0
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for seg_or_action in self.arg:
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total_length += seg_or_action.token_length()
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return total_length
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def get_result(self, embedding_module: Embedding) -> torch.Tensor:
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all_embeddings = []
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for seg_or_action in self.arg:
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if isinstance(seg_or_action, Action):
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target_embeddings = seg_or_action.get_result(embedding_module)
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else:
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target_embeddings = seg_or_action.get_embeddings(embedding_module)
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all_embeddings.append(target_embeddings)
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target_embeddings = torch.cat(all_embeddings, dim=1)
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return torch.div(target_embeddings, torch.norm(target_embeddings, dim=-1, keepdim=True))
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