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"""
Prompt Strategy for finetuning Llama2 chat models
see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation.
This implementation is based on the Vicuna PR and the fastchat repo, see also:
https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847
Use dataset type: "llama2_chat" in conig.yml to use this prompt style.
E.g. in the config.yml:
```
datasets:
- path: llama_finetune_train.jsonl
type: llama2_chat
```
The dataset itself should look like this:
```
{'conversations':[{"from": "human", "value": "Who are you?"}, {"from": "gpt", "value": "I am Vicuna"},...]}
```
in a jsonl file. The first message should be from the human, the second from gpt.
For a custom system message, the first "from" can be "system" (followed by alternating "human" and "gpt" turns).
Important: Don't use "special_tokens:" in your config.yml if you are not sure what you are doing!
"""
import logging
from dataclasses import dataclass, field
from typing import Generator, List, Sequence
from axolotl.prompt_tokenizers import PromptTokenizingStrategy
from axolotl.prompters import IGNORE_TOKEN_ID
@dataclass
class Llama2ChatConversation:
"""A class that manages prompt templates and keeps all conversation history.
copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py"""
name: str = "llama2"
# The system prompt
system: str = (
"[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
"Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
"Please ensure that your responses are socially unbiased and positive in nature.\n\n"
"If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
"If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
)
roles: Sequence[str] = ("[INST]", "[/INST]")
messages: List[List[str]] = field(default_factory=list)
offset: int = 0
sep = " "
sep2 = " </s><s>"
stop_token_ids = [2]
def get_prompt(self) -> str:
"""Get the prompt for generation."""
seps = [self.sep, self.sep2]
ret = ""
for i, (role, message) in enumerate(self.messages):
if (i == len(self.messages) - 1) and (role == self.roles[0]):
# last message is from user (due to length),
# return prompt without it for training
return ret
if i == 0:
ret += self.system + message.strip()
else:
ret += role + " " + message.strip() + seps[i % 2]
return ret
def append_message(self, role: str, message: str):
"""Append a new message."""
self.messages.append([role, message])
class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
"""
Tokenizing strategy for ShareGPT prompts.
adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.sequence_len = 4096
self.tokenizer.add_special_tokens({"pad_token": "<pad>"})
# https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/added_tokens.json
def tokenize_prompt(self, prompt):
conv = next(self.prompter.build_prompt(prompt))
conversation_str = conv.get_prompt()
# Tokenize conversations
input_ids = self.tokenizer(
conversation_str,
return_tensors="pt",
padding="max_length",
max_length=self.sequence_len,
truncation=True,
).input_ids[0]
target = input_ids.clone()
# Mask targets. Only compute loss on the assistant outputs.
sep = conv.roles[1]
total_len = int(target.ne(self.tokenizer.pad_token_id).sum())
turns = conversation_str.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_TOKEN_ID
for turn in turns:
if turn == "":
break
turn_len = len(self.tokenizer(turn).input_ids)
parts = turn.split(sep)
if len(parts) != 2:
break
parts[0] += sep
# "-1" is hardcoded for the LLaMA tokenizer to make the offset correct.
instruction_len = len(self.tokenizer(parts[0]).input_ids) - 1
# Ignore the user instructions
target[cur_len - 1 : cur_len + instruction_len] = IGNORE_TOKEN_ID
cur_len += turn_len + 2 # due to length of role token
target[cur_len:] = IGNORE_TOKEN_ID
if cur_len < self.sequence_len:
if cur_len != total_len:
target[:] = IGNORE_TOKEN_ID
logging.warning(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
attention_mask = input_ids.ne(self.tokenizer.pad_token_id).tolist()
input_ids = input_ids.tolist()
target = target.tolist()
# this is a fix for the tokenizer which tokenizes [ differently with eos tokens and
# follows the original llama implementation
for i in range(2, total_len - 2):
if input_ids[i] == 29961:
input_ids[i] = 518
if target[i] == 29961:
target[i] = 518
return {
"input_ids": input_ids,
"labels": target,
"attention_mask": attention_mask,
}
class Llama2ChatPrompter: # pylint: disable=too-few-public-methods
"""
A prompter that generates prompts for Llama2 models.
"""
system_prompt = (
"[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
"Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
"Please ensure that your responses are socially unbiased and positive in nature.\n\n"
"If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
"If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
)
def build_prompt(self, source) -> Generator[Llama2ChatConversation, None, None]:
# see https://github.com/lm-sys/FastChat/blob/da0641e567cf93756b0978ab5a6b092e96f06240/fastchat/train/train.py#L78
source = source["conversations"] # fix data structure for datasets
# if system prompt provided, use it
if source[0]["from"] == "system":
system = f"[INST] <<SYS>>\n{source[0]['value']}\n<</SYS>>\n\n"
source = source[1:]
else:
system = self.system_prompt
conv = Llama2ChatConversation(system=system)
if len(source) < 2:
# If there isn't a back and forth conversation, ignore it
# also happens on the data splitting leaving empty conversations
raise IndexError
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = [] # pylint: disable=R0801
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2]
if sentence["value"]:
conv.append_message(role, sentence["value"])
yield conv
def load(tokenizer, cfg) -> LLama2ChatTokenizingStrategy:
return LLama2ChatTokenizingStrategy(
Llama2ChatPrompter(),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
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