File size: 7,154 Bytes
fb9c3bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
import logging
import re
import numpy as np
from transformers import Pipeline, PreTrainedTokenizer
logger = logging.getLogger(__name__)
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
END_KEY = "### End"
INTRO_BLURB = (
"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).
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
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.
Args:
tokenizer (PreTrainedTokenizer): the tokenizer
key (str): the key to convert to a single token
Raises:
RuntimeError: if more than one ID was generated
Returns:
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
):
super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)
def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs):
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:
try:
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:
pass
forward_params = generate_kwargs
postprocess_params = {
"response_key_token_id": response_key_token_id,
"end_key_token_id": end_key_token_id,
"return_instruction_text": return_instruction_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(
prompt_text,
return_tensors="pt",
)
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)
generated_sequence = self.model.generate(
input_ids=input_ids.to(self.model.device),
attention_mask=attention_mask,
pad_token_id=self.tokenizer.pad_token_id,
**generate_kwargs,
)[0].cpu()
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_instruction_text):
sequence = model_outputs["generated_sequence"]
instruction_text = model_outputs["instruction_text"]
# 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 = None
response_positions = np.where(sequence == response_key_token_id)[0]
if len(response_positions) == 0:
logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}")
else:
response_pos = response_positions[0]
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 = None
end_positions = np.where(sequence == end_key_token_id)[0]
if len(end_positions) > 0:
end_pos = end_positions[0]
decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
else:
# 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 = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
if m:
decoded = m.group(1).strip()
else:
# The model might not generate the "### End" sequence before reaching the max tokens. In this case,
# return everything after "### Response:".
m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
if m:
decoded = m.group(1).strip()
else:
logger.warn(f"Failed to find response in:\n{fully_decoded}")
if return_instruction_text:
return {"instruction_text": instruction_text, "generated_text": decoded}
return decoded
|