File size: 2,971 Bytes
d5049a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | from __future__ import annotations
from typing import Any, Dict, List
import torch
from torch.utils.data import Dataset
from .common import apply_chat_template, build_messages, normalized_row
class DriveDataset(Dataset):
def __init__(self, rows: List[Dict[str, Any]]) -> None:
self.rows = [normalized_row(row) for row in rows]
def __len__(self) -> int:
return len(self.rows)
def __getitem__(self, index: int) -> Dict[str, Any]:
return self.rows[index]
class RawBatchCollator:
"""Keep raw examples for online generation. Batch size must be one/GPU."""
def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
if len(features) != 1:
raise ValueError("Online OPD requires per-device batch size 1")
return {"row": features[0]}
class SFTCollator:
def __init__(self, processor, num_views: int, max_length: int) -> None:
self.processor = processor
self.num_views = num_views
self.max_length = max_length
def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
if len(features) != 1:
raise ValueError(
"This safe multimodal collator requires per-device batch size 1; "
"use gradient accumulation for the effective batch size"
)
row = features[0]
if not row["question"] or not row["answer"]:
raise ValueError("question and answer must both be non-empty")
prompt_messages = build_messages(
row["question"], row["image_paths"], self.num_views
)
full_messages = build_messages(
row["question"], row["image_paths"], self.num_views, row["answer"]
)
prompt = apply_chat_template(
self.processor,
prompt_messages,
add_generation_prompt=True,
max_length=self.max_length,
)
full = apply_chat_template(
self.processor,
full_messages,
add_generation_prompt=False,
max_length=self.max_length,
)
prompt_ids = prompt["input_ids"]
full_ids = full["input_ids"]
prompt_len = int(prompt_ids.shape[1])
if full_ids.shape[1] <= prompt_len:
raise ValueError(
"Answer was fully truncated. Increase --max-length or shorten input."
)
if not torch.equal(full_ids[:, :prompt_len], prompt_ids):
raise RuntimeError(
"The full chat template is not prefixed by the generation prompt. "
"Refusing to guess the assistant loss mask; inspect the local processor."
)
labels = full_ids.clone()
labels[:, :prompt_len] = -100
attention_mask = full.get("attention_mask")
if attention_mask is not None:
labels = labels.masked_fill(attention_mask.eq(0), -100)
full["labels"] = labels
return full
|