114 lines
4.7 KiB
Python
114 lines
4.7 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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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/main/src/transformers/generation/streamers.py
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#
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from typing import Optional, List
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import torch
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from transformers import TextIteratorStreamer
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class BatchTextIteratorStreamer(TextIteratorStreamer):
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"""
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A specialized version of TextIteratorStreamer that handles text streams in batches, providing
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an efficient way to process large volumes of text data asynchronously. This class is designed
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to aggregate multiple texts into batches, making it ideal for applications that need to
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perform batch operations on streamed text data, such as bulk text processing or machine
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learning model inference in an interactive environment.
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Parameters:
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tokenizer (`AutoTokenizer`):
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The tokenized used to decode the tokens.
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skip_prompt (`bool`, *optional*, defaults to `False`):
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Whether to skip the prompt to `.generate()` or not.
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timeout (`float`, *optional*):
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The timeout for the text queue. If `None`, the queue will
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block indefinitely. Useful to handle exceptions
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in `.generate()`, when it is called in a separate thread.
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decode_kwargs (`dict`, *optional*):
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Additional keyword arguments to pass to the tokenizer's `decode` method.
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batch_size(`int`)
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The size of the batches to process. This parameter must be specified and
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determines how many texts are processed together as a single batch.
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"""
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def __init__(
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self,
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batch_size: int,
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tokenizer: "AutoTokenizer",
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skip_prompt: bool = False,
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timeout: Optional[float] = None,
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**decode_kwargs
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):
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super().__init__(tokenizer, skip_prompt, timeout, **decode_kwargs)
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self.batch_size = batch_size
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self.token_cache = [[] for _ in range(batch_size)]
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self.print_len = [0 for _ in range(batch_size)]
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self.generate_exception = None
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def put(self, value):
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if len(value.shape) != 2:
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value = torch.reshape(
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value, (self.batch_size, value.shape[0] // self.batch_size)
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)
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if self.skip_prompt and self.next_tokens_are_prompt:
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self.next_tokens_are_prompt = False
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return
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printable_texts = list()
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for idx in range(self.batch_size):
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self.token_cache[idx].extend(value[idx].tolist())
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text = self.tokenizer.decode(self.token_cache[idx], **self.decode_kwargs)
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if text.endswith("\n"):
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printable_text = text[self.print_len[idx]:]
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self.token_cache[idx] = []
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self.print_len[idx] = 0
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# If the last token is a CJK character, we print the characters.
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elif len(text) > 0 and self._is_chinese_char(ord(text[-1])):
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printable_text = text[self.print_len[idx]:]
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self.print_len[idx] += len(printable_text)
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else:
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printable_text = text[self.print_len[idx]:text.rfind(" ") + 1]
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self.print_len[idx] += len(printable_text)
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printable_texts.append(printable_text)
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self.on_finalized_text(printable_texts)
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def end(self):
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printable_texts = list()
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for idx in range(self.batch_size):
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if len(self.token_cache[idx]) > 0:
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text = self.tokenizer.decode(
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self.token_cache[idx], **self.decode_kwargs
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)
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printable_text = text[self.print_len[idx]:]
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self.token_cache[idx] = []
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self.print_len[idx] = 0
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else:
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printable_text = ""
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printable_texts.append(printable_text)
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self.next_tokens_are_prompt = True
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self.on_finalized_text(printable_texts, stream_end=True)
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def on_finalized_text(self, texts: List[str], stream_end: bool = False):
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self.text_queue.put(texts, timeout=self.timeout)
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if stream_end:
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self.text_queue.put(self.stop_signal, timeout=self.timeout)
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