File size: 2,198 Bytes
3270dae | 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 | """RL JSONL dataset with async-only streaming."""
from typing import Dict
import torch
from taoTrain.config import TrainingConfig
from taoTrain.data.jsonl_base import BaseJSONLDataset
class RLJSONLDataset(BaseJSONLDataset):
"""Dataset for RL training with local JSONL files with chunked loading."""
def _preprocess_chunk(self):
"""Prepare prompts for RL from current chunk."""
if not self._current_chunk_data or "text" not in self._current_chunk_data:
return
max_seq_length = self.config.model.max_seq_length
texts = self._current_chunk_data["text"]
# Tokenize all prompts in this chunk
all_token_ids = []
all_attention_masks = []
for text in texts:
tokenized = self.tokenizer(
text,
truncation=True,
max_length=max_seq_length,
padding="max_length",
return_attention_mask=True,
)
all_token_ids.append(tokenized["input_ids"])
all_attention_masks.append(tokenized["attention_mask"])
self._current_chunk_data = {
"input_ids": all_token_ids,
"attention_mask": all_attention_masks,
}
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
"""Get preprocessed prompt, loading chunk if needed."""
# Load appropriate chunk if using streaming
if self.chunk_manager:
chunk_num = self._get_chunk_for_idx(idx)
if chunk_num != self._current_chunk_num:
self._load_chunk(chunk_num)
local_idx = self._get_local_idx_in_chunk(idx)
else:
local_idx = idx
input_ids = torch.tensor(self._current_chunk_data["input_ids"][local_idx], dtype=torch.long)
attention_mask = torch.tensor(self._current_chunk_data["attention_mask"][local_idx], dtype=torch.long)
# For RL, no labels yet (generated during training)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
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