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import json
from typing import Any, Dict
import torch
from torch.utils.data import Dataset
from transformers import PreTrainedTokenizer
class SFTDataset(Dataset):
def __init__(self, data_path: str, tokenizer: PreTrainedTokenizer, template, max_length: int, ):
self.data = self.load_sft_data(data_path)
self.tokenizer = tokenizer
self.max_length = max_length
self.template = template
def load_sft_data(self, file_path):
with open(file_path, "r") as f:
data = json.load(f)
return data
def __len__(self):
return len(self.data)
def __getitem__(self, idx: int) -> Dict[str, Any]:
item = self.data[idx]
rendered_text = self.template.render(
messages=[
{"role": "user", "content": item["input"]},
{"role": "assistant", "content": item["output"]}
],
add_generation_prompt=False
)
tokens = self.tokenizer(
rendered_text,
max_length=self.max_length,
truncation=True,
return_tensors="pt"
)
input_ids = tokens["input_ids"]
attention_mask = tokens["attention_mask"]
instruction_text = self.template.render(
messages=[{"role": "user", "content": item["input"]}],
add_generation_prompt=True, # important
)
instruction_tokens = self.tokenizer(
instruction_text,
max_length=self.max_length,
truncation=True,
return_tensors="pt"
)
labels = input_ids.clone()
instruction_length = instruction_tokens["input_ids"].size(1)
labels[:, :instruction_length] = -100
return {
"input_ids": input_ids.squeeze(),
"attention_mask": attention_mask.squeeze(),
"labels": labels.squeeze(),
}
def collate_fn(self, batch):
input_ids = torch.nn.utils.rnn.pad_sequence(
[item["input_ids"] for item in batch], batch_first=True, padding_value=self.tokenizer.pad_token_id
)
attention_masks = torch.nn.utils.rnn.pad_sequence(
[item["attention_mask"] for item in batch], batch_first=True, padding_value=0
)
labels = torch.nn.utils.rnn.pad_sequence(
[item["labels"] for item in batch], batch_first=True, padding_value=-100
)
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": attention_masks,
}
class RMDataset(Dataset):
def __init__(self, reward_data_path, tokenizer, template, max_length=512):
self.data = self.load_reward_data(reward_data_path)
self.tokenizer = tokenizer
self.template = template
self.max_length = max_length
def load_reward_data(self, file_path):
with open(file_path, "r") as f:
data = json.load(f)
return data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
item = self.data[idx]
reward_value = float(item["value"])
rendered_text = self.template.render(
messages=[
{"role": "user", "content": item["input"]},
{"role": "assistant", "content": item["output"]}
],
add_generation_prompt=False
).strip() # important
tokenized_input = self.tokenizer(
rendered_text,
return_tensors="pt",
max_length=self.max_length,
truncation=True
)
# make sure there is no \n at the end of the inputs
assert tokenized_input['input_ids'][0][-1] == self.tokenizer.eos_token_id
return {
"input_ids": tokenized_input["input_ids"].squeeze(),
"attention_mask": tokenized_input["attention_mask"].squeeze(),
"labels": torch.tensor(reward_value)
}
def collate_fn(self, batch):
input_ids = torch.nn.utils.rnn.pad_sequence(
[item["input_ids"] for item in batch], batch_first=True, padding_value=self.tokenizer.pad_token_id
)
attention_masks = torch.nn.utils.rnn.pad_sequence(
[item["attention_mask"] for item in batch], batch_first=True, padding_value=0
)
labels = torch.stack([item["labels"] for item in batch])
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": attention_masks,
}
class PPODataset(Dataset):
def __init__(self, data_path: str, tokenizer: PreTrainedTokenizer, template, max_length):
self.data = self.load_sft_data(data_path)
self.tokenizer = tokenizer
self.max_length = max_length
self.template = template
def load_sft_data(self, file_path):
with open(file_path, "r") as f:
data = json.load(f)
return data
def __len__(self):
return len(self.data)
def __getitem__(self, idx: int) -> Dict[str, Any]:
item = self.data[idx]
instruction_text = self.template.render(
messages=[{"role": "user", "content": item["input"]}],
add_generation_prompt=True, # important
)
instruction_tokens = self.tokenizer(
instruction_text,
max_length=self.max_length,
truncation=True,
return_tensors="pt"
)
input_ids = instruction_tokens["input_ids"]
return {"input_ids": input_ids.squeeze()}
def collate_fn(self, batch):
input_ids = torch.nn.utils.rnn.pad_sequence(
[item["input_ids"] for item in batch], batch_first=True, padding_value=self.tokenizer.pad_token_id
)
return {"input_ids": input_ids}
class GRPODataset(Dataset):
def __init__(self, data_path: str, tokenizer, template, max_length: int):
self.data = self.load_sft_data(data_path)
self.tokenizer = tokenizer
self.max_length = max_length
self.template = template
def load_sft_data(self, file_path: str):
with open(file_path, "r") as f:
return json.load(f)
def __len__(self):
return len(self.data)
def __getitem__(self, idx: int) -> Dict[str, Any]:
item = self.data[idx]
rendered_prompt = self.template.render(
messages=[{"role": "user", "content": item["input"]}],
add_generation_prompt=True
)
return {
"prompt": rendered_prompt,
"completion": item["output"]
}
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