hopwise.model.logits_processor

Common logits processor in recommender system

Classes

LogitsProcessor	Fallback LogitsProcessor if transformers is not available.
ConstrainedLogitsProcessorWordLevel	Force the last token to be one of the force_tokens if the total length is reached, in the path generation stage
PrefixConstrainedLogitsProcessorWordLevel	Force the last token to be one of the force_tokens if the total length is reached, in the path generation stage
PLMLogitsProcessorWordLevel	https://dl.acm.org/doi/pdf/10.1145/3485447.3511937

Module Contents

class hopwise.model.logits_processor.LogitsProcessor

Fallback LogitsProcessor if transformers is not available.

__call__(input_ids, scores)

class hopwise.model.logits_processor.ConstrainedLogitsProcessorWordLevel(tokenized_ckg, tokenized_used_ids, max_sequence_length, tokenizer, mask_cache_size=3 * 10**4, pos_candidates_cache_size=1 * 10**5, task=KnowledgeEvaluationType.REC, **kwargs)

Bases: transformers.LogitsProcessor

Force the last token to be one of the force_tokens if the total length is reached, in the path generation stage this means to limit the hop size. This is a word-level constraint, does not work with piece tokenizers. If task is link prediction (LP) logit processor forces last token to reachable ones

tokenized_ckg

tokenized_used_ids

max_sequence_length

tokenizer

bos_token_id

pos_candidates_cache

mask_cache

task

is_bos_token_in_input(input_ids)

Check if the input contains a BOS token. Checking the first sequence is enough.

__call__(input_ids, scores)

process_scores_rec(input_ids, idx)

Process each score based on input length and update mask list.

process_scores_lp(input_ids, idx)

Process each score based on input length or skip.

is_next_token_entity(input_ids)

get_current_key(input_ids, idx)

get_candidates_rec(key1, key2=None)

Parameters:

• key1

• key2 – if key2 is not None, it returns entity candidates, otherwise relation candidates

get_candidates_lp(key)

get_banned_mask(key, candidate_tokens)

Retrieve or cache the banned token mask for a specific key.

class hopwise.model.logits_processor.PrefixConstrainedLogitsProcessorWordLevel(tokenized_ckg, tokenized_used_ids, max_sequence_length, tokenizer, **kwargs)

Bases: ConstrainedLogitsProcessorWordLevel

Force the last token to be one of the force_tokens if the total length is reached, in the path generation stage this means to limit the hop size. This is a word-level constraint, does not work with piece tokenizers. If task is link prediction (LP) logit processor forces last token to reachable ones

mask_cache = None

__call__(input_ids, scores)

class hopwise.model.logits_processor.PLMLogitsProcessorWordLevel(tokenized_ckg, tokenized_used_ids, max_sequence_length, tokenizer, pos_candidates_cache_size=1 * 10**5, task=KnowledgeEvaluationType.REC, **kwargs)

Bases: transformers.LogitsProcessor

https://dl.acm.org/doi/pdf/10.1145/3485447.3511937 Constraint decoding strategy for PLM, it forces the model to generate alternatively entities and relations

tokenized_ckg

tokenized_used_ids

max_sequence_length

tokenizer

bos_token_id

pos_candidates_cache

task

entity_token_ids

relation_token_ids

__call__(input_ids, scores)

is_bos_token_in_input(input_ids)

Check if the input contains a BOS token. Checking the first sequence is enough.

is_next_token_entity(input_ids)

process_scores(input_ids, idx)

Process each score based on input length and update mask to allow only entities or only relations.