dolly-v2-12b /
matthayes's picture
import logging
import re
from typing import List
import numpy as np
from transformers import Pipeline, PreTrainedTokenizer
from transformers.utils import is_tf_available
if is_tf_available():
import tensorflow as tf
logger = logging.getLogger(__name__)
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
END_KEY = "### End"
"Below is an instruction that describes a task. Write a response that appropriately completes the request."
# This is the prompt that is used for generating responses using an already trained model. It ends with the response
# key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
"""Gets the token ID for a given string that has been added to the tokenizer as a special token.
When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to.
tokenizer (PreTrainedTokenizer): the tokenizer
key (str): the key to convert to a single token
RuntimeError: if more than one ID was generated
int: the token ID for the given key
token_ids = tokenizer.encode(key)
if len(token_ids) > 1:
raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
return token_ids[0]
class InstructionTextGenerationPipeline(Pipeline):
def __init__(
self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
"""Initialize the pipeline
do_sample (bool, optional): Whether or not to use sampling. Defaults to True.
max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with
probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92.
top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering.
Defaults to 0.
super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k,
def _sanitize_parameters(self,
return_full_text: bool = None,
preprocess_params = {}
# newer versions of the tokenizer configure the response key as a special token. newer versions still may
# append a newline to yield a single token. find whatever token is configured for the response key.
tokenizer_response_key = next(
(token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None
response_key_token_id = None
end_key_token_id = None
if tokenizer_response_key:
response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key)
end_key_token_id = get_special_token_id(self.tokenizer, END_KEY)
# Ensure generation stops once it generates "### End"
generate_kwargs["eos_token_id"] = end_key_token_id
except ValueError:
forward_params = generate_kwargs
postprocess_params = {
"response_key_token_id": response_key_token_id,
"end_key_token_id": end_key_token_id
if return_full_text is not None:
postprocess_params["return_full_text"] = return_full_text
return preprocess_params, forward_params, postprocess_params
def preprocess(self, instruction_text, **generate_kwargs):
prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text)
inputs = self.tokenizer(
inputs["prompt_text"] = prompt_text
inputs["instruction_text"] = instruction_text
return inputs
def _forward(self, model_inputs, **generate_kwargs):
input_ids = model_inputs["input_ids"]
attention_mask = model_inputs.get("attention_mask", None)
if input_ids.shape[1] == 0:
input_ids = None
attention_mask = None
in_b = 1
in_b = input_ids.shape[0]
generated_sequence = self.model.generate(, if attention_mask is not None else None,
out_b = generated_sequence.shape[0]
if self.framework == "pt":
generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
elif self.framework == "tf":
generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
instruction_text = model_inputs.pop("instruction_text")
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_full_text: bool = False):
generated_sequence = model_outputs["generated_sequence"][0]
instruction_text = model_outputs["instruction_text"]
generated_sequence: List[List[int]] = generated_sequence.numpy().tolist()
records = []
for sequence in generated_sequence:
# The response will be set to this variable if we can identify it.
decoded = None
# If we have token IDs for the response and end, then we can find the tokens and only decode between them.
if response_key_token_id and end_key_token_id:
# Find where "### Response:" is first found in the generated tokens. Considering this is part of the
# prompt, we should definitely find it. We will return the tokens found after this token.
response_pos = sequence.index(response_key_token_id)
except ValueError:
logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}")
response_pos = None
if response_pos:
# Next find where "### End" is located. The model has been trained to end its responses with this
# sequence (or actually, the token ID it maps to, since it is a special token). We may not find
# this token, as the response could be truncated. If we don't find it then just return everything
# to the end. Note that even though we set eos_token_id, we still see the this token at the end.
end_pos = sequence.index(end_key_token_id)
except ValueError:
end_pos = None
decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
if not decoded:
# Otherwise we'll decode everything and use a regex to find the response and end.
fully_decoded = self.tokenizer.decode(sequence)
# The response appears after "### Response:". The model has been trained to append "### End" at the
# end.
m ="#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
if m:
decoded =
# The model might not generate the "### End" sequence before reaching the max tokens. In this case,
# return everything after "### Response:".
m ="#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
if m:
decoded =
logger.warn(f"Failed to find response in:\n{fully_decoded}")
# If the full text is requested, then append the decoded text to the original instruction.
# This technically isn't the full text, as we format the instruction in the prompt the model has been
# trained on, but to the client it will appear to be the full text.
if return_full_text:
decoded = f"{instruction_text}\n{decoded}"
rec = {"generated_text": decoded}
return records