421e7cee80
* initially add text_generation_webui support * add env requirements install * add necessary dependencies * update for starting webui * update shared and noted to place models * update heading of part3 * meet comments * add copyright license * remove extensions * convert tutorial to windows side * add warm-up to optimize performance
113 lines
5.5 KiB
Python
113 lines
5.5 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is adapted from
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# https://github.com/huggingface/transformers/pull/27557
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import math
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import torch
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from transformers.generation.logits_process import LogitsProcessor
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from transformers.utils import add_start_docstrings
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LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
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scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
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Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
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search or log softmax for each vocabulary token when using beam search
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Return:
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`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
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"""
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class GrammarConstrainedLogitsProcessor(LogitsProcessor):
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def __init__(self, grammar_constraint):
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self.last_size = None
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self.grammar_constraint = grammar_constraint
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self.batch_stacks = None
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def filter_logits(self, logits, device):
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# resolve each stack to a tensor of True/False for each token
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# indicating acceptance
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# acceptance = self.grammar_acceptor.filter_vocab(self.stacks, device)
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acceptance = self.grammar_constraint.batch_filter_vocab(self.batch_stacks, device)
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# logger.debug(acceptance)
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# Logits to -inf where False
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logits[~acceptance] = -math.inf
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# TODO: batching
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def process_logits(self, input_ids, scores, parse_start_index=None):
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"""
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:param input_ids:
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:param scores:
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:param parse_start_index: default None, which means generate from scratch. Set to 0 to parse all input_ids
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:return:
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"""
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# we dynamically create stacks at the first call, so that we know the batch size and beam size
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if self.batch_stacks is None:
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self.batch_stacks = [self.grammar_constraint.init_stacks() for _ in range(len(input_ids))]
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# if self.last_size is not set (which would be the case when processing the first token).
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# In this case, do nothing.
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if self.last_size is None:
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prefix_to_parse = [
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single_input_ids[parse_start_index:] if parse_start_index is not None else []
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for single_input_ids in input_ids
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]
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# self.grammar_acceptor.accept_token_ids(prefix_to_parse, self.stacks)
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self.batch_stacks = [
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self.grammar_constraint.accept_token_ids(prefix, stack)
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for prefix, stack in zip(prefix_to_parse, self.batch_stacks)
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]
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# if the length of the current input IDs (input_ids[0]) is exactly one more than self.last_size.
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# This is expected in a scenario where inputs are processed incrementally, one token at a time.
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elif len(input_ids[0]) == self.last_size + 1:
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# self.stacks = self.grammar_acceptor.accept_token_id(input_ids[0][-1], self.stacks)
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self.batch_stacks = [
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self.grammar_constraint.accept_token_id(single_input_ids[-1], stack)
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for single_input_ids, stack in zip(input_ids, self.batch_stacks)
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]
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# ensure that the input size is consistent with the expected incremental processing
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# (i.e., one token at a time).
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else:
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# here we check if the input_ids are one token longer than the last time we processed
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# but we don't check if input_ids are actually valid.
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# Imagine a scenario where we generate 10 tokens, then we replace the 10 generated tokens with 10 new tokens.
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# In this case, the input_ids will be consistent with the last_size, but the input_ids are not valid.
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# However, should we really check if the input_ids are valid here?
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# If we do, then we need to reparse the whole input_ids at each call, which is not efficient.
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# Maybe we should just trust the user to provide valid input_ids?
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# The conclusion is that, we assume the input_ids are valid, and our generation will be correct.
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# If the input_ids are not valid, then the generation result will be wrong and we don't take responsibility for that.
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raise RuntimeError(
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"Input ID's length is inconsistent with the current state of "
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"the GrammarConstrainedLogitsProcessor. If you want to process "
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"another input sequence, please instantiate a new "
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"GrammarConstrainedLogitsProcessor."
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)
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self.filter_logits(scores, scores.device)
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self.last_size = len(input_ids[0])
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return scores
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@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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return self.process_logits(input_ids, scores)
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