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import abc
from transformers import PreTrainedTokenizer
IGNORE_INDEX = -100
LLAMA_DEFAULT_PAD_TOKEN = "[PAD]"
LLAMA_DEFAULT_EOS_TOKEN = "</s>"
LLAMA_DEFAULT_BOS_TOKEN = "<s>"
LLAMA_DEFAULT_UNK_TOKEN = "<unk>"
class InvalidDataException(Exception):
pass
class PromptTokenizingStrategy(abc.ABC):
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
class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str):
raise NotImplementedError
def tokenize_prompt(self, prompt):
instruction, input, response = self.parse_instruction_fields(prompt)
full_prompt = self._build_full_prompt(instruction, input, response)
tokenized_full_prompt = self._tokenize(full_prompt)
if not self.train_on_inputs:
user_prompt = 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, response):
return self.prompter.build_prompt(
instruction,
input,
response,
)
def _tokenize(self, prompt, add_eos_token=True):
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 AlpacaPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str):
return (
prompt["instruction"],
prompt["input"] if "input" in prompt else "",
prompt["output"],
)
class OpenAssistantPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str):
return (
prompt["INSTRUCTION"],
"",
prompt["RESPONSE"],
)
class GPTeacherPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str):
return (
prompt["instruction"],
prompt["input"] if "input" in prompt else "",
prompt["response"],
)
class NomicGPT4AllPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str):
return (
prompt["prompt"],
"",
prompt["response"],
)
class ReflectionPromptTokenizingStrategy(PromptTokenizingStrategy):
def parse_instruction_fields(self, prompt) -> (str, str, str, str, str):
raise NotImplementedError
def tokenize_prompt(self, prompt):
instruction, input, 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 = 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):
return self.prompter.build_prompt(
instruction,
input,
output,
reflection,
corrected,
)
def _tokenize(self, prompt, add_eos_token=True):
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):
def parse_instruction_fields(self, prompt) -> (str, str, str, str, str):
return (
prompt["instruction"],
prompt["input"] if "input" in prompt else "",
prompt["output"],
prompt["reflection"],
prompt["corrected"],
)
class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
def tokenize_prompt(self, prompt):
try:
return self.prompter.build_prompt(prompt["conversations"], self.tokenizer)
except (KeyError, AssertionError, IndexError) as e:
raise InvalidDataException(str(e))
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