212 lines
8.9 KiB
Python
212 lines
8.9 KiB
Python
import logging
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import re
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from typing import List
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import numpy as np
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from transformers import Pipeline, PreTrainedTokenizer
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from transformers.utils import is_tf_available
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if is_tf_available():
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import tensorflow as tf
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logger = logging.getLogger(__name__)
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INSTRUCTION_KEY = "### Instruction:"
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RESPONSE_KEY = "### Response:"
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END_KEY = "### End"
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INTRO_BLURB = (
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"Below is an instruction that describes a task. Write a response that appropriately completes the request."
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)
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# This is the prompt that is used for generating responses using an already trained model. It ends with the response
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# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
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PROMPT_FOR_GENERATION_FORMAT = """{intro}
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{instruction_key}
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{instruction}
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{response_key}
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""".format(
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intro=INTRO_BLURB,
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instruction_key=INSTRUCTION_KEY,
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instruction="{instruction}",
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response_key=RESPONSE_KEY,
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)
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def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
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"""Gets the token ID for a given string that has been added to the tokenizer as a special token.
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When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
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treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to.
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Args:
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tokenizer (PreTrainedTokenizer): the tokenizer
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key (str): the key to convert to a single token
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Raises:
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RuntimeError: if more than one ID was generated
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Returns:
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int: the token ID for the given key
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"""
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token_ids = tokenizer.encode(key)
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if len(token_ids) > 1:
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raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
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return token_ids[0]
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class InstructionTextGenerationPipeline(Pipeline):
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def __init__(
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self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
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):
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"""Initialize the pipeline
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Args:
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do_sample (bool, optional): Whether or not to use sampling. Defaults to True.
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max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
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top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with
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probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92.
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top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering.
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Defaults to 0.
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"""
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super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k,
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**kwargs)
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def _sanitize_parameters(self,
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return_full_text: bool = None,
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**generate_kwargs):
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preprocess_params = {}
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# newer versions of the tokenizer configure the response key as a special token. newer versions still may
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# append a newline to yield a single token. find whatever token is configured for the response key.
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tokenizer_response_key = next(
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(token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
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)
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response_key_token_id = None
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end_key_token_id = None
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if tokenizer_response_key:
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try:
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response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
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end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)
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# Ensure generation stops once it generates "### End"
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generate_kwargs["eos_token_id"] = end_key_token_id
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except ValueError:
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pass
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forward_params = generate_kwargs
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postprocess_params = {
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"response_key_token_id": response_key_token_id,
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"end_key_token_id": end_key_token_id
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}
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if return_full_text is not None:
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postprocess_params["return_full_text"] = return_full_text
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return preprocess_params, forward_params, postprocess_params
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def preprocess(self, instruction_text, **generate_kwargs):
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prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
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inputs = self.tokenizer(
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prompt_text,
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return_tensors="pt",
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)
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inputs["prompt_text"] = prompt_text
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inputs["instruction_text"] = instruction_text
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return inputs
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def _forward(self, model_inputs, **generate_kwargs):
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input_ids = model_inputs["input_ids"]
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attention_mask = model_inputs.get("attention_mask", None)
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if input_ids.shape[1] == 0:
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input_ids = None
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attention_mask = None
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in_b = 1
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else:
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in_b = input_ids.shape[0]
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generated_sequence = self.model.generate(
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input_ids=input_ids.to(self.model.device),
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attention_mask=attention_mask.to(self.model.device) if attention_mask is not None else None,
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pad_token_id=self.tokenizer.pad_token_id,
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**generate_kwargs,
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)
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out_b = generated_sequence.shape[0]
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if self.framework == "pt":
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generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
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elif self.framework == "tf":
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generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
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instruction_text = model_inputs.pop("instruction_text")
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return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
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def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_full_text: bool = False):
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generated_sequence = model_outputs["generated_sequence"][0]
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instruction_text = model_outputs["instruction_text"]
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generated_sequence: List[List[int]] = generated_sequence.numpy().tolist()
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records = []
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for sequence in generated_sequence:
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# The response will be set to this variable if we can identify it.
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decoded = None
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# If we have token IDs for the response and end, then we can find the tokens and only decode between them.
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if response_key_token_id and end_key_token_id:
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# Find where "### Response:" is first found in the generated tokens. Considering this is part of the
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# prompt, we should definitely find it. We will return the tokens found after this token.
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try:
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response_pos = sequence.index(response_key_token_id)
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except ValueError:
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logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}")
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response_pos = None
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if response_pos:
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# Next find where "### End" is located. The model has been trained to end its responses with this
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# sequence (or actually, the token ID it maps to, since it is a special token). We may not find
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# this token, as the response could be truncated. If we don't find it then just return everything
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# to the end. Note that even though we set eos_token_id, we still see the this token at the end.
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try:
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end_pos = sequence.index(end_key_token_id)
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except ValueError:
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end_pos = None
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decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
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if not decoded:
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# Otherwise we'll decode everything and use a regex to find the response and end.
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fully_decoded = self.tokenizer.decode(sequence)
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# The response appears after "### Response:". The model has been trained to append "### End" at the
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# end.
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m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
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if m:
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decoded = m.group(1).strip()
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else:
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# The model might not generate the "### End" sequence before reaching the max tokens. In this case,
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# return everything after "### Response:".
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m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
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if m:
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decoded = m.group(1).strip()
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else:
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logger.warn(f"Failed to find response in:\n{fully_decoded}")
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# If the full text is requested, then append the decoded text to the original instruction.
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# This technically isn't the full text, as we format the instruction in the prompt the model has been
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# trained on, but to the client it will appear to be the full text.
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if return_full_text:
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decoded = f"{instruction_text}\n{decoded}"
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rec = {"generated_text": decoded}
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records.append(rec)
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return records |