"""Module containing PromptTokenizingStrategy and Prompter classes"""
import abc
import copy
import logging
from typing import Dict, List, Tuple, Union
from fastchat.conversation import Conversation
from transformers import BatchEncoding, PreTrainedTokenizer
from axolotl.monkeypatch.fastchat_conversation_turns import (
add_get_turns_to_conversation,
)
from axolotl.prompters import IGNORE_TOKEN_ID
LOG = logging.getLogger("axolotl")
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
add_get_turns_to_conversation()
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
# sequence_len and max_length can be different for CompletionPromptTokenizingStrategy.
# TODO: Document how they are different.
self.sequence_len = sequence_len
self.max_length = sequence_len
@abc.abstractmethod
def tokenize_prompt(self, prompt):
pass
@property
def supports_batched(self):
return False
def _tokenize(
self, prompt: str, add_eos_token: bool = True, strip_bos_token: bool = False
) -> BatchEncoding:
empty = BatchEncoding(data={"input_ids": [], "attention_mask": []})
if not prompt:
LOG.warning("Empty text requested for tokenization.")
return empty
result = self.tokenizer(
prompt,
truncation=True,
max_length=self.max_length,
padding=False,
return_tensors=None,
)
if len(result["input_ids"]) == 0:
LOG.warning("Tokenizer result is empty. You may want to audit your dataset")
return empty
if (
result["input_ids"][-1] != self.tokenizer.eos_token_id
and len(result["input_ids"]) < self.max_length
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"] = [IGNORE_INDEX] * 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 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):
# pylint: disable=duplicate-code
(
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"] = [
IGNORE_INDEX
] * 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):
# Initial values. We will append to these as we go through the conversation.
result, current_len = tokenize_prompt_default()
conversation: Conversation = (
self.prompter._conversation.copy() # pylint: disable=protected-access
)
# support for custom roles from the dataset, only useful for vicuna style prompts/roles
role_remap = []
if (
conversation.name == "vicuna_v1.1"
and "roles" in prompt
and len(prompt["roles"]) >= 2
):
role_remap = [
{"from": conversation.roles[0], "to": prompt["roles"][0]},
{"from": conversation.roles[1], "to": prompt["roles"][1]},
]
try:
for _, part in enumerate(
self.prompter.build_prompt(self.get_conversation_thread(prompt))
):
if not isinstance(part, tuple):
LOG.warning(f"expected tuple, got {part}")
continue
user, assistant = conversation.roles
role, content = part
# Uses "in" because role contains extra characters
if user in role:
role = (
role.replace(role_remap[0]["from"], role_remap[0]["to"])
if role_remap
else role
)
turn = role + content
# this is still the user query, we should
if not content.strip():
LOG.warning(f"user turn has empty text: {prompt}")
res = self._tokenize(
turn,
add_eos_token=False,
strip_bos_token=True,
)
if self.train_on_inputs:
labels = copy.deepcopy(res["input_ids"])
else:
# everything from this is masked out from the labels
labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
elif assistant in role:
role = (
role.replace(role_remap[1]["from"], role_remap[1]["to"])
if role_remap
else role
)
turn = role + content
# this should be the assistant response, should end with an eos token
if not content.strip():
LOG.warning(f"assistant turn has empty text: {prompt}")
add_eos_token = not (
conversation.name == "chatml"
and conversation.sep == self.tokenizer.eos_token
)
res = self._tokenize(
turn,
add_eos_token=add_eos_token,
strip_bos_token=True,
)
role_res = self._tokenize(
role.rstrip(),
add_eos_token=False,
strip_bos_token=True,
)
labels = copy.deepcopy(res["input_ids"])
if not self.train_on_inputs:
# mask out role tokens from the labels
len_role = len(role_res["input_ids"])
labels[:len_role] = [IGNORE_TOKEN_ID] * min(
len_role, len(labels)
)
elif role == "":
turn = content
# this is only ever the first part, should include the bos token and the user query
res = self._tokenize(
turn, add_eos_token=False, strip_bos_token=False
)
if self.train_on_inputs:
labels = copy.deepcopy(res["input_ids"])
else:
# everything from this is masked out from the labels
labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
else:
LOG.warning(f"unhandled role: {role}")
continue
# 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_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