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"""
Alignment datasets and formatting utilities.
Used for:
1. Supervised Fine-Tuning, SFT
2. Direct Preference Optimization, DPO
Input formats:
SFT JSONL:
{"instruction": "...", "response": "..."}
DPO JSONL:
{"prompt": "...", "chosen": "...", "rejected": "..."}
Output:
PyTorch datasets returning tokenized tensors.
"""
import json
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import Dataset
from tokenizers import Tokenizer
def read_jsonl(path: str) -> list[dict]:
"""
Input:
path: JSONL file path.
Output:
List of dictionaries.
"""
rows = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
rows.append(json.loads(line))
return rows
def format_instruction_prompt(instruction: str) -> str:
"""
Input:
instruction: User instruction text.
Output:
Prompt string used during SFT/alignment. Delegates to the single shared
formatter so SFT, DPO, and inference never drift apart.
"""
from src.common.prompts import format_prompt
return format_prompt(instruction)
def format_response(response: str) -> str:
"""
Input:
response: Assistant response.
Output:
Clean response text.
"""
return response.strip()
def build_sft_example(
tokenizer: Tokenizer,
instruction: str,
response: str,
bos_id: Optional[int],
eos_id: Optional[int],
block_size: int,
) -> Optional[dict]:
"""
Builds one SFT training example.
The model sees:
prompt + response
Loss is calculated only on response tokens.
Input:
tokenizer: custom BPE tokenizer.
instruction: input instruction.
response: expected answer.
bos_id: beginning-of-sequence token id.
eos_id: end-of-sequence token id.
block_size: max sequence length.
Output:
dict with input_ids and labels, or None if invalid.
"""
prompt = format_instruction_prompt(instruction)
response = format_response(response)
prompt_ids = tokenizer.encode(prompt).ids
response_ids = tokenizer.encode(response).ids
if bos_id is not None:
prompt_ids = [bos_id] + prompt_ids
if eos_id is not None:
response_ids = response_ids + [eos_id]
input_ids = prompt_ids + response_ids
# Ignore prompt tokens in loss.
labels = [-100] * len(prompt_ids) + response_ids
if len(input_ids) > block_size:
input_ids = input_ids[:block_size]
labels = labels[:block_size]
# Need at least one supervised response token.
if all(x == -100 for x in labels):
return None
return {
"input_ids": input_ids,
"labels": labels,
}
def build_dpo_pair(
tokenizer: Tokenizer,
prompt: str,
chosen: str,
rejected: str,
bos_id: Optional[int],
eos_id: Optional[int],
block_size: int,
) -> Optional[dict]:
"""
Builds one DPO preference pair.
DPO compares:
log probability of chosen answer
vs.
log probability of rejected answer
Loss is calculated only on answer tokens.
Input:
tokenizer: custom BPE tokenizer.
prompt: user prompt.
chosen: preferred response.
rejected: non-preferred response.
bos_id/eos_id: optional special token ids.
block_size: maximum sequence length.
Output:
dict containing chosen/rejected input_ids and labels.
"""
prompt_text = format_instruction_prompt(prompt)
prompt_ids = tokenizer.encode(prompt_text).ids
if bos_id is not None:
prompt_ids = [bos_id] + prompt_ids
chosen_ids = tokenizer.encode(format_response(chosen)).ids
rejected_ids = tokenizer.encode(format_response(rejected)).ids
if eos_id is not None:
chosen_ids = chosen_ids + [eos_id]
rejected_ids = rejected_ids + [eos_id]
chosen_input = prompt_ids + chosen_ids
rejected_input = prompt_ids + rejected_ids
chosen_labels = [-100] * len(prompt_ids) + chosen_ids
rejected_labels = [-100] * len(prompt_ids) + rejected_ids
if len(chosen_input) > block_size:
chosen_input = chosen_input[:block_size]
chosen_labels = chosen_labels[:block_size]
if len(rejected_input) > block_size:
rejected_input = rejected_input[:block_size]
rejected_labels = rejected_labels[:block_size]
if all(x == -100 for x in chosen_labels):
return None
if all(x == -100 for x in rejected_labels):
return None
return {
"chosen_input_ids": chosen_input,
"chosen_labels": chosen_labels,
"rejected_input_ids": rejected_input,
"rejected_labels": rejected_labels,
}
class SFTDataset(Dataset):
"""
Supervised fine-tuning dataset.
Input:
JSONL file with instruction-response rows.
Output per item:
input_ids
labels
"""
def __init__(
self,
path: str,
tokenizer: Tokenizer,
bos_id: Optional[int],
eos_id: Optional[int],
block_size: int,
):
rows = read_jsonl(path)
examples = []
for row in rows:
example = build_sft_example(
tokenizer=tokenizer,
instruction=row.get("prompt", row.get("instruction", "")),
response=row["response"],
bos_id=bos_id,
eos_id=eos_id,
block_size=block_size,
)
if example is not None:
examples.append(example)
if not examples:
raise ValueError("No valid SFT examples found.")
self.examples = examples
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
class DPODataset(Dataset):
"""
DPO preference dataset.
Input:
JSONL file with prompt/chosen/rejected rows.
Output per item:
chosen_input_ids
chosen_labels
rejected_input_ids
rejected_labels
"""
def __init__(
self,
path: str,
tokenizer: Tokenizer,
bos_id: Optional[int],
eos_id: Optional[int],
block_size: int,
):
rows = read_jsonl(path)
examples = []
for row in rows:
example = build_dpo_pair(
tokenizer=tokenizer,
prompt=row["prompt"],
chosen=row["chosen"],
rejected=row["rejected"],
bos_id=bos_id,
eos_id=eos_id,
block_size=block_size,
)
if example is not None:
examples.append(example)
if not examples:
raise ValueError("No valid DPO examples found.")
self.examples = examples
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
def pad_1d(values: List[int], max_len: int, pad_value: int) -> List[int]:
"""
Pads one integer list to max_len.
"""
return values + [pad_value] * (max_len - len(values))
def collate_sft(batch: list[dict], pad_id: int) -> dict:
"""
Input:
batch of SFT examples.
Output:
padded tensors.
"""
max_len = max(len(x["input_ids"]) for x in batch)
input_ids = []
labels = []
for item in batch:
input_ids.append(pad_1d(item["input_ids"], max_len, pad_id))
labels.append(pad_1d(item["labels"], max_len, -100))
return {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"labels": torch.tensor(labels, dtype=torch.long),
}
def collate_dpo(batch: list[dict], pad_id: int) -> dict:
"""
Input:
batch of DPO examples.
Output:
padded chosen/rejected tensors.
"""
chosen_max = max(len(x["chosen_input_ids"]) for x in batch)
rejected_max = max(len(x["rejected_input_ids"]) for x in batch)
chosen_input_ids = []
chosen_labels = []
rejected_input_ids = []
rejected_labels = []
for item in batch:
chosen_input_ids.append(
pad_1d(item["chosen_input_ids"], chosen_max, pad_id)
)
chosen_labels.append(
pad_1d(item["chosen_labels"], chosen_max, -100)
)
rejected_input_ids.append(
pad_1d(item["rejected_input_ids"], rejected_max, pad_id)
)
rejected_labels.append(
pad_1d(item["rejected_labels"], rejected_max, -100)
)
return {
"chosen_input_ids": torch.tensor(chosen_input_ids, dtype=torch.long),
"chosen_labels": torch.tensor(chosen_labels, dtype=torch.long),
"rejected_input_ids": torch.tensor(rejected_input_ids, dtype=torch.long),
"rejected_labels": torch.tensor(rejected_labels, dtype=torch.long),
}
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