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import torch
import numpy as np
import pickle
import bisect
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
from pathlib import Path
from typing import Optional, Union
class DirectFileDataset(Dataset[dict[str, torch.Tensor]]):
"""Dataset that reads directly from tokenized .npz and latent .npy files."""
def __init__(
self,
token_dir: str,
latent_dir: str,
max_samples: Optional[int] = None,
use_external_latents: bool = True,
verbose: bool = True,
):
self.token_dir = Path(token_dir)
self.latent_dir = Path(latent_dir) if latent_dir else None
self.use_external_latents = use_external_latents
self.token_files = sorted(list(self.token_dir.glob("*.npz")))
self._pkl_data = None
self._pkl_paths = []
self._pkl_lengths = []
self._pkl_cumulative = []
self._pkl_cache_idx = None
self._pkl_cache_data = None
# Support .pkl fallback (all samples in one file) when no .npz found
if len(self.token_files) == 0:
pkl_files = sorted(list(self.token_dir.glob("*.pkl")))
if pkl_files:
if verbose:
print(f"Indexing {len(pkl_files)} pre-tokenized pkl shard(s) from {self.token_dir}...")
remaining = max_samples
total = 0
for pkl_path in pkl_files:
with open(pkl_path, "rb") as f:
shard = pickle.load(f)
shard_len = len(shard)
if remaining is not None:
if remaining <= 0:
break
shard_len = min(shard_len, remaining)
remaining -= shard_len
if shard_len <= 0:
continue
self._pkl_paths.append(pkl_path)
self._pkl_lengths.append(shard_len)
total += shard_len
self._pkl_cumulative.append(total)
if verbose:
print(f"Indexed {total} tokenized samples from pkl shard(s)")
if self._pkl_paths:
first = self._load_pkl_shard(0)[0]
self._seq_len = first["input_ids"].shape[-1]
else:
self._seq_len = 512
self._latent_dim = 32
if max_samples:
self.token_files = self.token_files[:max_samples]
valid_files = []
missing_latents = 0
if self.use_external_latents and self.latent_dir:
for token_file in self.token_files:
latent_name = token_file.stem.replace("_tokens", "")
latent_file = self.latent_dir / f"{latent_name}.npy"
if latent_file.exists():
valid_files.append((token_file, latent_file))
else:
missing_latents += 1
if verbose and missing_latents <= 5:
print(f"Warning: Missing latent for {token_file.name}")
else:
for token_file in self.token_files:
valid_files.append((token_file, None))
self.file_pairs = valid_files
# Infer shapes from first file
self._seq_len = 512
self._latent_dim = 32
if self.file_pairs:
try:
token_f, latent_f = self.file_pairs[0]
token_data = np.load(token_f)
if "input_ids" in token_data:
ids = token_data["input_ids"]
if hasattr(ids, "shape") and len(ids.shape) >= 1:
self._seq_len = int(ids.shape[-1])
if latent_f is not None:
latent = np.load(latent_f)
if hasattr(latent, "shape") and len(latent.shape) >= 1:
self._latent_dim = int(latent.shape[-1])
except Exception:
pass
if verbose:
if self._pkl_data is not None:
print(f"Found {len(self._pkl_data)} tokenized samples (pkl)")
elif self._pkl_paths:
print(f"Found {self._pkl_cumulative[-1]} tokenized samples in {len(self._pkl_paths)} pkl shard(s)")
else:
print(f"Found {len(self.token_files)} token files")
print(f"Found {len(self.file_pairs)} valid file pairs")
if missing_latents:
print(f"Missing latents: {missing_latents}")
def _sample_get(self, sample, key: str, default=None):
if hasattr(sample, "get"):
return sample.get(key, default)
if hasattr(sample, "files") and key in sample.files:
return sample[key]
if hasattr(sample, "__contains__") and key in sample:
return sample[key]
return default
def _optional_response_fields(self, sample, seq_len: int):
fields = {}
response_mask = self._sample_get(sample, "response_mask")
if response_mask is not None:
response_mask = np.asarray(response_mask).astype(bool)
if response_mask.ndim == 0:
response_mask = np.array([bool(response_mask)])
fields["response_mask"] = torch.tensor(response_mask, dtype=torch.float32)
prompt_lengths = self._sample_get(
sample,
"prompt_lengths",
self._sample_get(sample, "prompt_length"),
)
if prompt_lengths is not None:
prompt_lengths_arr = np.asarray(prompt_lengths).reshape(-1)
prompt_len = max(0, min(int(prompt_lengths_arr[0]), seq_len))
fields["prompt_lengths"] = torch.tensor(prompt_len, dtype=torch.int64)
if "response_mask" not in fields:
mask = torch.zeros(seq_len, dtype=torch.float32)
if prompt_len < seq_len:
mask[prompt_len:] = 1.0
fields["response_mask"] = mask
return fields
def __len__(self):
if self._pkl_data is not None:
return len(self._pkl_data)
if self._pkl_paths:
return self._pkl_cumulative[-1]
return len(self.file_pairs)
def _load_pkl_shard(self, shard_idx):
if self._pkl_cache_idx == shard_idx and self._pkl_cache_data is not None:
return self._pkl_cache_data
with open(self._pkl_paths[shard_idx], "rb") as f:
data = pickle.load(f)
limit = self._pkl_lengths[shard_idx]
if len(data) > limit:
data = data[:limit]
self._pkl_cache_idx = shard_idx
self._pkl_cache_data = data
return data
def __getitem__(self, idx):
if self._pkl_data is not None:
sample = self._pkl_data[idx]
input_ids_tensor = torch.tensor(sample["input_ids"].astype(np.int64))
attention_mask_tensor = torch.tensor(sample["attention_mask"].astype(bool), dtype=torch.float32)
item = {
"input_ids": input_ids_tensor,
"attention_mask": attention_mask_tensor,
"latent": torch.zeros(1, self._latent_dim, dtype=torch.float32),
}
item.update(self._optional_response_fields(sample, input_ids_tensor.shape[-1]))
return item
if self._pkl_paths:
shard_idx = bisect.bisect_right(self._pkl_cumulative, idx)
prev = self._pkl_cumulative[shard_idx - 1] if shard_idx > 0 else 0
sample = self._load_pkl_shard(shard_idx)[idx - prev]
input_ids_tensor = torch.tensor(sample["input_ids"].astype(np.int64))
attention_mask_tensor = torch.tensor(sample["attention_mask"].astype(bool), dtype=torch.float32)
item = {
"input_ids": input_ids_tensor,
"attention_mask": attention_mask_tensor,
"latent": torch.zeros(1, self._latent_dim, dtype=torch.float32),
}
item.update(self._optional_response_fields(sample, input_ids_tensor.shape[-1]))
return item
last_error = None
for offset in range(min(16, len(self.file_pairs))):
token_file, latent_file = self.file_pairs[(idx + offset) % len(self.file_pairs)]
try:
token_data = np.load(token_file)
input_ids = token_data["input_ids"].astype(np.int32)
attention_mask = token_data["attention_mask"].astype(bool)
if input_ids.ndim == 0:
input_ids = np.array([input_ids])
if attention_mask.ndim == 0:
attention_mask = np.array([attention_mask])
input_ids_tensor = torch.tensor(input_ids, dtype=torch.int64)
attention_mask_tensor = torch.tensor(attention_mask, dtype=torch.float32)
if self.use_external_latents and latent_file is not None:
latent = np.load(latent_file)
latent_tensor = torch.tensor(latent, dtype=torch.float32)
if latent_tensor.dim() == 1:
latent_tensor = latent_tensor.unsqueeze(0)
elif latent_tensor.dim() == 2 and latent_tensor.shape[0] != 1:
latent_tensor = latent_tensor[0:1]
else:
latent_tensor = torch.zeros(1, self._latent_dim, dtype=torch.float32)
item = {
"input_ids": input_ids_tensor,
"attention_mask": attention_mask_tensor,
"latent": latent_tensor,
}
item.update(self._optional_response_fields(token_data, input_ids_tensor.shape[-1]))
return item
except Exception as e:
last_error = e
print(f"Error loading files {token_file}, {latent_file}: {e}; trying next sample")
raise RuntimeError(f"Failed to load a valid sample near index {idx}: {last_error}")
def _create_empty_sample(self):
return {
"input_ids": torch.zeros(self._seq_len, dtype=torch.long),
"attention_mask": torch.zeros(self._seq_len, dtype=torch.float),
"latent": torch.zeros(1, self._latent_dim, dtype=torch.float),
}
def get_simple_dataloaders(config, tokenizer=None):
data_cfg = config.data
dataset = DirectFileDataset(
token_dir=data_cfg.token_dir,
latent_dir=data_cfg.latent_dir,
max_samples=data_cfg.get("max_samples", None),
use_external_latents=data_cfg.get("use_external_latents", True),
verbose=True,
)
if len(dataset) == 0:
raise ValueError("No usable token/latent pairs found")
val_ratio = float(data_cfg.get("val_ratio", 0.05))
if val_ratio <= 0.0:
val_size = 0
train_size = len(dataset)
else:
val_size = max(1, int(len(dataset) * val_ratio))
train_size = max(1, len(dataset) - val_size)
if train_size + val_size > len(dataset):
train_size = len(dataset) - val_size
train_dataset, val_dataset = torch.utils.data.random_split(
dataset,
[train_size, val_size],
generator=torch.Generator().manual_seed(int(config.training.seed)),
)
latent_dim = int(config.model.get("latent_dim", 32))
def collate_fn(batch):
input_ids = torch.stack([item["input_ids"] for item in batch])
attention_mask = torch.stack([item["attention_mask"] for item in batch])
latents = []
for item in batch:
latent = item["latent"]
if latent is None:
latents.append(torch.zeros(1, latent_dim, dtype=torch.float32))
continue
if latent.dim() == 1:
latent = latent.unsqueeze(0)
elif latent.dim() == 2 and latent.shape[0] != 1:
latent = latent[:1]
latents.append(latent)
latent_tensor = torch.cat(latents, dim=0)
out = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"latent": latent_tensor,
}
if any("response_mask" in item for item in batch):
masks = []
for item in batch:
mask = item.get("response_mask")
if mask is None:
mask = torch.zeros_like(item["attention_mask"], dtype=torch.float32)
masks.append(mask.to(dtype=torch.float32))
out["response_mask"] = torch.stack(masks)
if any("prompt_lengths" in item for item in batch):
if not all("prompt_lengths" in item for item in batch):
raise ValueError(
"Batch mixes samples with and without prompt_lengths. "
"Use a token directory where every SFT sample provides prompt_lengths."
)
prompt_lengths = []
for item in batch:
prompt_lengths.append(item["prompt_lengths"])
out["prompt_lengths"] = torch.stack(prompt_lengths).to(dtype=torch.int64)
return out
if torch.distributed.is_available() and torch.distributed.is_initialized():
train_sampler = DistributedSampler(
train_dataset,
num_replicas=torch.distributed.get_world_size(),
rank=torch.distributed.get_rank(),
shuffle=True,
seed=int(config.training.seed),
)
val_sampler = DistributedSampler(
val_dataset,
num_replicas=torch.distributed.get_world_size(),
rank=torch.distributed.get_rank(),
shuffle=False,
)
else:
train_sampler = None
val_sampler = None
num_workers = int(data_cfg.get("num_workers", 0))
train_loader = DataLoader(
train_dataset,
batch_size=int(config.training.train_batch_size),
sampler=train_sampler,
shuffle=train_sampler is None,
num_workers=num_workers,
collate_fn=collate_fn,
pin_memory=True,
drop_last=True,
persistent_workers=num_workers > 0,
)
val_loader = DataLoader(
val_dataset,
batch_size=int(config.training.eval_batch_size),
sampler=val_sampler,
shuffle=False,
num_workers=num_workers,
collate_fn=collate_fn,
pin_memory=True,
drop_last=False,
persistent_workers=num_workers > 0,
)
return train_loader, val_loader
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