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"""Module containing PromptTokenizingStrategy and Prompter classes"""
import abc
import copy
import functools
import logging
from typing import Dict, List, Tuple, Union
from transformers import PreTrainedTokenizer
from axolotl.prompters import IGNORE_TOKEN_ID
IGNORE_INDEX = -100
LLAMA_DEFAULT_PAD_TOKEN = "[PAD]" # nosec
LLAMA_DEFAULT_EOS_TOKEN = "</s>" # nosec
LLAMA_DEFAULT_BOS_TOKEN = "<s>" # nosec
LLAMA_DEFAULT_UNK_TOKEN = "<unk>" # nosec
class InvalidDataException(Exception):
"""
Exception raised when the data is invalid
"""
class PromptTokenizingStrategy(abc.ABC):
"""
Abstract class for tokenizing strategies
"""
def __init__(
self,
prompter,
tokenizer,
train_on_inputs: bool = False,
sequence_len: int = 2048,
):
self.prompter = prompter
self.tokenizer: PreTrainedTokenizer = tokenizer
self.train_on_inputs = train_on_inputs
self.sequence_len = sequence_len
@abc.abstractmethod
def tokenize_prompt(self, prompt):
pass
@functools.lru_cache(maxsize=128)
def _get_user_token(self):
id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
if isinstance(id_or_ids, (int,)):
return id_or_ids
return False
@functools.lru_cache(maxsize=128)
def _get_assistant_token(self):
id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
if isinstance(id_or_ids, (int,)):
return id_or_ids
return False
def _tokenize(self, prompt: str, add_eos_token=True, strip_bos_token=False):
result = self.tokenizer(
prompt,
truncation=True,
max_length=self.sequence_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != self.tokenizer.eos_token_id
and len(result["input_ids"]) < self.sequence_len
and add_eos_token
):
result["input_ids"].append(self.tokenizer.eos_token_id)
result["attention_mask"].append(1)
if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
result["input_ids"] = result["input_ids"][1:]
result["attention_mask"] = result["attention_mask"][1:]
result["labels"] = result["input_ids"].copy()
return result
class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
"""
Tokenizing strategy for instruction-based prompts.
"""
def parse_instruction_fields(
self, prompt
) -> Union[Tuple[str, str, str], Tuple[str, str, str, str]]:
raise NotImplementedError
def tokenize_prompt(self, prompt):
(
instruction,
input, # pylint: disable=redefined-builtin
response,
) = self.parse_instruction_fields(prompt)
user_prompt = next(
iter(
self.prompter.build_prompt(
instruction,
input,
)
)
)
tokenized_prompt = self._tokenize(user_prompt, add_eos_token=False)
if not self.train_on_inputs:
user_prompt_len = len(tokenized_prompt["input_ids"])
# TODO this could be sped up using numpy array slicing
tokenized_prompt["labels"] = [-100] * user_prompt_len
tokenized_res_prompt = self._tokenize(
response, strip_bos_token=True, add_eos_token=True
)
tokenized_prompt["input_ids"] += tokenized_res_prompt["input_ids"]
tokenized_prompt["attention_mask"] += tokenized_res_prompt["attention_mask"]
tokenized_prompt["labels"] += tokenized_res_prompt["input_ids"]
return tokenized_prompt
def _build_full_prompt(
self, instruction, input, response # pylint: disable=redefined-builtin
):
return next(
iter(
self.prompter.build_prompt(
instruction,
input,
response,
)
)
)
class AlpacaPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for Alpaca prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["instruction"],
prompt["input"] if "input" in prompt else "",
prompt["output"],
)
class AlpacaMultipleChoicePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for Alpaca Multiple Choice prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["question"],
"\n".join(f'- "{choice}"' for choice in prompt["choices"]),
prompt["solution"] if "solution" in prompt else prompt["explanation"],
)
class JeopardyPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for Jeopardy prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["question"],
prompt["category"],
"what is " + prompt["answer"],
)
class OpenAssistantPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for OpenAssistant prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["INSTRUCTION"],
"",
prompt["RESPONSE"],
)
class SummarizeTLDRPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for SummarizeTLDR prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["article"],
"",
prompt["summary"],
)
class GPTeacherPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for GPTeacher prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["instruction"],
prompt["input"] if "input" in prompt else "",
prompt["response"],
)
class NomicGPT4AllPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for NomicGPT4All prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["prompt"],
"",
prompt["response"],
)
class CompletionPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for Completion prompts.
"""
def tokenize_prompt(self, prompt):
full_prompt = self._build_full_prompt(prompt["text"], None, None)
tokenized_full_prompt = self._tokenize(full_prompt)
return tokenized_full_prompt
def _build_full_prompt(
self, instruction, input, response
): # pylint: disable=redefined-builtin
return next(iter(self.prompter.build_prompt(instruction, input, response)))
class ReflectionPromptTokenizingStrategy(PromptTokenizingStrategy):
"""
Tokenizing strategy for Reflection prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str, str]:
raise NotImplementedError
def tokenize_prompt(self, prompt):
(
instruction,
input, # pylint: disable=redefined-builtin
output,
reflection,
corrected,
) = self.parse_instruction_fields(prompt)
full_prompt = self._build_full_prompt(
instruction, input, output, reflection, corrected
)
tokenized_full_prompt = self._tokenize(full_prompt)
if not self.train_on_inputs:
user_prompt = next(
iter(
self.prompter.build_prompt(
instruction,
input,
)
)
)
tokenized_user_prompt = self._tokenize(user_prompt, add_eos_token=False)
user_prompt_len = len(tokenized_user_prompt["input_ids"])
# TODO this could be sped up using numpy array slicing
tokenized_full_prompt["labels"] = [
-100
] * user_prompt_len + tokenized_full_prompt["labels"][user_prompt_len:]
return tokenized_full_prompt
def _build_full_prompt(
self, instruction, input, output, reflection, corrected
): # pylint: disable=redefined-builtin
return next(
iter(
self.prompter.build_prompt(
instruction,
input,
output,
reflection,
corrected,
)
)
)
def _tokenize(self, prompt, add_eos_token=True, strip_bos_token=False):
result = self.tokenizer(
prompt,
truncation=True,
max_length=self.sequence_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != self.tokenizer.eos_token_id
and len(result["input_ids"]) < self.sequence_len
and add_eos_token
):
result["input_ids"].append(self.tokenizer.eos_token_id)
result["attention_mask"].append(1)
result["labels"] = result["input_ids"].copy()
return result
class AlpacaReflectionPTStrategy(ReflectionPromptTokenizingStrategy):
"""
Tokenizing strategy for Alpaca Reflection prompts.
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str, str]:
return (
prompt["instruction"],
prompt["input"] if "input" in prompt else "",
prompt["output"],
prompt["reflection"],
prompt["corrected"],
)
class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
"""
Tokenizing strategy for ShareGPT prompts.
"""
def get_conversation_thread(self, prompt):
return prompt["conversations"]
def tokenize_prompt(self, prompt):
result, current_len = tokenize_prompt_default()
user_token = self._get_user_token()
assistant_token = self._get_assistant_token()
try:
for _, part in enumerate(
self.prompter.build_prompt(self.get_conversation_thread(prompt))
):
if isinstance(part, tuple):
if part[0] == "USER:":
part = part[0] + part[1] if not user_token else part[1]
# this is still the user query, we should
res = self._tokenize(
part.strip(),
add_eos_token=False,
strip_bos_token=True,
)
if user_token:
res["input_ids"] = [user_token, *res["input_ids"]]
# everything from this is masked out from the labels
labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
elif part[0] == "ASSISTANT:":
# TODO label assistant token/tokens w/ IGNORE_TOKEN_ID
part = part[0] + part[1] if not assistant_token else part[1]
# this should be the assistent response, should end with an eos token
res = self._tokenize(
part.strip(),
add_eos_token=True,
strip_bos_token=True,
)
if assistant_token:
res["input_ids"] = [
assistant_token,
*res["input_ids"],
]
# not masked out from labels
labels = copy.deepcopy(res["input_ids"])
elif part[0] == "SYSTEM:":
part = part[1] # Ignore the system role from preamble
# this is only ever the first part, should include the bos token and the user query
res = self._tokenize(
part.strip(), add_eos_token=False, strip_bos_token=False
)
# everything from this is masked out from the labels
labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
else:
logging.warning(f"unhandled role: {part[0]}")
# pylint: disable=duplicate-code
result, current_len = parse_tokenized_to_result(
result,
current_len,
res,
labels,
pad_token_id=self.tokenizer.pad_token_id,
)
return result
except (KeyError, AssertionError, IndexError) as err:
raise InvalidDataException(str(err)) from err
def _tokenize(self, prompt, add_eos_token=True, strip_bos_token=False):
result = self.tokenizer(
prompt,
truncation=True,
max_length=self.sequence_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != self.tokenizer.eos_token_id
and len(result["input_ids"]) < self.sequence_len
and add_eos_token
):
result["input_ids"].append(self.tokenizer.eos_token_id)
result["attention_mask"].append(1)
if result["input_ids"][0] == self.tokenizer.bos_token_id and strip_bos_token:
result["input_ids"] = result["input_ids"][1:]
result["attention_mask"] = result["attention_mask"][1:]
result["labels"] = result["input_ids"].copy()
return result
def tokenize_prompt_default() -> Tuple[Dict[str, List[int]], int]:
"""
Returns the default values for the tokenize prompt function
"""
result: Dict[str, List[int]] = {
"input_ids": [],
"attention_mask": [],
"labels": [],
}
current_len = 0
return result, current_len
def parse_tokenized_to_result(
result: Dict[str, List[int]],
current_len: int,
res: Dict[str, List[int]],
labels: list[int],
pad_token_id: Union[int, None] = None,
) -> Tuple[Dict[str, List[int]], int]:
"""
Parses the tokenized prompt and append the tokenized input_ids, attention_mask and labels to the result
"""
input_ids = res["input_ids"]
input_len = len(input_ids)
result["input_ids"][current_len : current_len + input_len] = input_ids
result["attention_mask"][current_len : current_len + input_len] = [
1 if x != pad_token_id else 0 for x in input_ids
]
result["labels"][current_len : current_len + input_len] = labels
current_len += input_len
return result, current_len
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