File size: 11,009 Bytes
799d677 |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
import json
from pathlib import Path
from typing import Callable, Optional
import torch
from megatron.core import parallel_state
from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import (
MegatronPretrainingRandomSampler,
MegatronPretrainingSampler,
)
from nemo.collections.nlp.data.language_modeling.megatron.megatron_batch_samplers import (
MegatronPretrainingBatchSampler,
MegatronPretrainingRandomBatchSampler,
)
from nemo.core.classes import Dataset
from nemo.utils import logging
from nemo.utils.get_rank import is_global_rank_zero
from omegaconf import DictConfig
from torch.utils.data import DataLoader
def build_dataloader(
dataset: Dataset,
consumed_samples: int,
micro_batch_size: int,
global_batch_size: int,
collate_fn: Optional[Callable] = None,
seed: Optional[int] = None,
) -> DataLoader:
common_params: dict = {
"total_samples": len(dataset),
"consumed_samples": consumed_samples,
"micro_batch_size": micro_batch_size,
"global_batch_size": global_batch_size,
"data_parallel_rank": parallel_state.get_data_parallel_rank(),
"data_parallel_size": parallel_state.get_data_parallel_world_size(),
"drop_last": True,
"pad_samples_to_global_batch_size": False,
}
if seed is not None and seed >= 0:
batch_sampler = MegatronPretrainingRandomBatchSampler(
**common_params, seed=seed
)
else:
batch_sampler = MegatronPretrainingBatchSampler(**common_params)
return DataLoader(
dataset,
batch_sampler=batch_sampler,
num_workers=0,
pin_memory=True,
collate_fn=collate_fn,
)
def custom_build_dataloader(
dataset: Dataset,
consumed_samples: int,
mbs: int,
gbs: int,
num_workers: int = 0,
drop_last: bool = True,
pad_samples_to_global_batch_size: bool = False,
load_gbs: bool = True,
seed: Optional[int] = None,
use_random_sampler: bool = True,
collate_fn=None,
):
# Common parameters for batch sampler creation
common_params = {
"total_samples": len(dataset),
"consumed_samples": consumed_samples,
"micro_batch_size": mbs,
"data_parallel_rank": parallel_state.get_data_parallel_rank(),
"data_parallel_size": parallel_state.get_data_parallel_world_size(),
"drop_last": drop_last,
"global_batch_size": gbs,
"pad_samples_to_global_batch_size": pad_samples_to_global_batch_size,
}
if use_random_sampler:
cls = (
MegatronPretrainingRandomBatchSampler
if load_gbs
else MegatronPretrainingRandomSampler
)
common_params["seed"] = seed
else:
cls = (
MegatronPretrainingBatchSampler if load_gbs else MegatronPretrainingSampler
)
batch_sampler = cls(**common_params)
return torch.utils.data.DataLoader(
dataset,
batch_sampler=batch_sampler,
num_workers=num_workers,
pin_memory=True,
collate_fn=collate_fn,
)
def load_datasets(cfg: DictConfig) -> tuple[list[dict], list[dict]]:
data_name2num_examples: dict[str, dict[str, int]] = {}
total_train_examples: list[dict] = []
total_dev_examples: list[dict] = []
for data_name, data_info in cfg.datasets.items():
dataset_path: Path = Path(f"{cfg.data_dir}/{data_name}.jsonl")
if not dataset_path.exists():
raise FileNotFoundError(f"{dataset_path} does not exist.")
if data_info.max_train_samples == 0:
if is_global_rank_zero():
logging.info(
f"max_train_samples for {data_name} is set to 0. Skip them."
)
continue
if is_global_rank_zero():
logging.info(f"processing {dataset_path}...")
loaded_examples: list[dict] = []
with dataset_path.open(encoding="utf-8") as f:
for line in f:
loaded_examples.append(json.loads(line))
if data_info.max_train_samples > len(loaded_examples) and is_global_rank_zero():
logging.warning(
f"{data_name} has only {len(loaded_examples)} examples, "
f"but max_train_samples is set to {data_info.max_train_samples}. "
"Use all examples."
)
max_train_samples: int = (
data_info.max_train_samples
if data_info.max_train_samples != -1
else len(loaded_examples)
)
max_dev_samples: int = 0
if data_info.split_dev:
max_dev_samples = min(
cfg.max_dev_samples,
int(len(loaded_examples) * cfg.max_dev_ratio),
)
train_examples: list[dict] = (
loaded_examples[max_dev_samples : max_dev_samples + max_train_samples]
* data_info.upsampling_factor
)
dev_examples: list[dict] = (
loaded_examples[:max_dev_samples] * data_info.upsampling_factor
)
total_train_examples.extend(train_examples)
total_dev_examples.extend(dev_examples)
data_name2num_examples[data_name] = {
"train": len(train_examples),
"dev": len(dev_examples),
"original": len(loaded_examples),
"upsampling_factor": data_info.upsampling_factor,
}
if is_global_rank_zero():
num_total_original_examples: int = 0
logging.info("------------------------------")
logging.info("Dataset summary (original -> train/dev)")
for data_name, num_examples in data_name2num_examples.items():
num_total_original_examples += num_examples["original"]
logging.info(
f"{data_name}: {num_examples['original']} -> {num_examples['train']}/{num_examples['dev']} (upsampling factor: {num_examples['upsampling_factor']})"
)
logging.info(
f"Total: {num_total_original_examples} -> {len(total_train_examples)}/{len(total_dev_examples)}"
)
logging.info("------------------------------")
return total_train_examples, total_dev_examples
class LLMJPSFTDataset(Dataset):
def __init__(
self,
loaded_examples: list[dict],
tokenizer: TokenizerSpec,
use_loss_mask: bool,
max_seq_length: int = 4096,
):
self.tokenizer = tokenizer
self.use_loss_mask: bool = use_loss_mask
self.max_seq_length: int = max_seq_length
self.examples: list[dict[str, list[int]]] = self._process_examples(
loaded_examples
)
def __len__(self) -> int:
return len(self.examples)
def __getitem__(self, idx: int) -> dict[str, list[int]]:
return self.examples[idx]
def _process_examples(
self, loaded_examples: list[dict]
) -> list[dict[str, list[int]]]:
all_input_ids: list[int] = []
all_loss_mask: list[int] = []
for example_idx, loaded_example in enumerate(loaded_examples):
conversation: list[dict[str, str]] = loaded_example["messages"]
assert len(conversation) >= 3
assert conversation[0]["role"] == "system"
input_ids: list[int] = [self.tokenizer.bos_id] + self.tokenizer.text_to_ids(
conversation[0]["content"]
)
loss_mask: list[int] = (
[0] * len(input_ids) if self.use_loss_mask else [1] * len(input_ids)
)
for turn_idx in range(1, len(conversation[1:]) // 2 + 1):
user_message: dict[str, str] = conversation[2 * turn_idx - 1]
assistant_message: dict[str, str] = conversation[2 * turn_idx]
assert user_message["role"] == "user"
assert assistant_message["role"] == "assistant"
if self.use_loss_mask:
prompt_ids: list[int] = self.tokenizer.text_to_ids(
f"\n\n### 指示:\n{user_message['content']}\n\n### 応答:\n"
)[1:]
response_ids: list[int] = self.tokenizer.text_to_ids(
f"\n{assistant_message['content']}"
)[2:] + [self.tokenizer.eos_id]
input_ids.extend(prompt_ids + response_ids)
loss_mask.extend([0] * len(prompt_ids) + [1] * len(response_ids))
else:
prompt_response_ids: list[int] = self.tokenizer.text_to_ids(
f"\n\n### 指示:\n{user_message['content']}\n\n### 応答:\n{assistant_message['content']}"
)[1:] + [self.tokenizer.eos_id]
input_ids.extend(prompt_response_ids)
loss_mask.extend([1] * len(prompt_response_ids))
if is_global_rank_zero() and example_idx < 2:
logging.info(f"{example_idx = }")
logging.info(f"{input_ids = }")
logging.info(f"{loss_mask = }")
all_input_ids.extend(input_ids)
all_loss_mask.extend(loss_mask)
examples: list[dict[str, list[int]]] = []
for i in range(0, len(all_input_ids), self.max_seq_length + 1):
chunked_input_ids: list[int] = all_input_ids[
i : i + self.max_seq_length + 1
]
chunked_loss_mask: list[int] = all_loss_mask[
i : i + self.max_seq_length + 1
]
if len(chunked_input_ids) == self.max_seq_length + 1:
if set(chunked_loss_mask) == {0}: # Skip if all loss_mask is 0
continue
examples.append(
{"input_ids": chunked_input_ids, "loss_mask": chunked_loss_mask}
)
return examples
@torch.no_grad()
def _create_attention_mask(self, seq_length: int) -> torch.Tensor:
attention_mask = torch.tril(torch.ones((seq_length, seq_length))).unsqueeze(
0
) # (1, seq_length, seq_length)
attention_mask = attention_mask < 0.5
return attention_mask
def collate_fn(self, batch: list[dict[str, list[int]]]) -> dict[str, torch.Tensor]:
input_ids: list[list[int]] = [item["input_ids"][:-1] for item in batch]
labels: list[list[int]] = [item["input_ids"][1:] for item in batch]
loss_mask: list[list[int]] = [item["loss_mask"][1:] for item in batch]
pro_batch = {
"tokens": torch.LongTensor(input_ids),
"position_ids": torch.LongTensor(
[list(range(self.max_seq_length)) for _ in batch]
),
"attention_mask": torch.stack(
[self._create_attention_mask(self.max_seq_length) for _ in batch]
),
"labels": torch.LongTensor(labels),
"loss_mask": torch.LongTensor(loss_mask),
}
return pro_batch
|