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multipack w batch sampler (#795)

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* test batch sampler w varying batch lens

* wip

* multipack batchsampler wip

* wip

* fix for prepare data loader to get correct # of steps based on gpues

* lint and clean up

* calculate len estimate

* fix total num steps calc

* add options for dataloader_num_workers and dataloader_pin_memory

* remove gitbook

* support prefetch_factor for dataloader optimization

* fix the kwarg
This commit is contained in:
Wing Lian
2023-11-07 20:27:40 -05:00
committed by GitHub
parent 6dc68a653f
commit 641e6f7e51
9 changed files with 365 additions and 94 deletions
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1
gitbook/README.md
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@@ -1 +0,0 @@
# Page

4
gitbook/SUMMARY.md
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@@ -1,4 +0,0 @@
# Table of contents
* [Page](README.md)
* [Small dev details](small-dev-details.md)

3
gitbook/small-dev-details.md
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@@ -1,3 +0,0 @@
# Small dev details
/

152
src/axolotl/core/trainer_builder.py
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@@ -6,7 +6,6 @@ import abc
import importlib
import logging
import math
import os
import sys
from abc import abstractmethod
from dataclasses import dataclass, field
@@ -18,9 +17,9 @@ import torch
import transformers
from datasets import Dataset
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, DistributedSampler, SequentialSampler
from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
from transformers.trainer_pt_utils import SequentialDistributedSampler
from transformers.trainer_utils import seed_worker
from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
from axolotl.utils.callbacks import (
@@ -31,8 +30,9 @@ from axolotl.utils.callbacks import (
bench_eval_callback_factory,
log_prediction_callback_factory,
)
from axolotl.utils.collators import DataCollatorForSeq2Seq
from axolotl.utils.collators import BatchSamplerDataCollatorForSeq2Seq
from axolotl.utils.dataloader import MultipackDistributedDataloader
from axolotl.utils.samplers import MultipackBatchSampler
from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
try:
@@ -102,6 +102,10 @@ class AxolotlTrainingArguments(TrainingArguments):
bench_source_max_len: int = field(
default=2048, metadata={"help": "Maximum source sequence length for bench."}
)
dataloader_prefetch_factor: Optional[int] = field(
default=None,
metadata={"help": "prefetch_factor argument to the dataloader"},
)
class AxolotlTrainer(Trainer):
@@ -145,46 +149,69 @@ class AxolotlTrainer(Trainer):
return self.lr_scheduler
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
if self.args.world_size > 1 and self.args.sample_packing:
return DistributedSampler(
self.train_dataset,
num_replicas=self.args.world_size,
rank=self.args.process_index,
seed=self.args.seed,
if self.args.sample_packing:
return MultipackBatchSampler(
RandomSampler(self.train_dataset),
self.args.train_batch_size,
drop_last=True,
batch_max_len=self._train_batch_size * self.args.max_seq_length,
lengths=(
self.train_dataset.data.column("position_ids")
.to_pandas()
.apply(lambda x: x[-1] + 1)
.values
),
packing_efficiency_estimate=self.args.sample_packing_efficiency,
)
return super()._get_train_sampler()
def _get_eval_sampler(
self, eval_dataset: Dataset
) -> Optional[torch.utils.data.Sampler]:
if (
self.args.world_size > 1
and self.args.sample_packing
and self.args.eval_sample_packing is not False
):
return SequentialDistributedSampler(
eval_dataset,
num_replicas=self.args.world_size,
rank=self.args.process_index,
batch_size=self.args.per_device_eval_batch_size,
if self.args.sample_packing and self.args.eval_sample_packing is not False:
return MultipackBatchSampler(
SequentialSampler(eval_dataset),
self.args.per_device_eval_batch_size,
drop_last=True,
batch_max_len=self.args.eval_batch_size * self.args.max_seq_length,
lengths=(
eval_dataset.data.column("position_ids")
.to_pandas()
.apply(lambda x: x[-1] + 1)
.values
),
packing_efficiency_estimate=self.args.sample_packing_efficiency,
)
return super()._get_eval_sampler(eval_dataset)
def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
def get_train_dataloader(self) -> DataLoader:
if self.args.sample_packing:
train_sampler = self._get_train_sampler()
return self.accelerator.prepare(
MultipackDistributedDataloader(
self.train_dataset,
batch_size=self._train_batch_size,
seq_max_length=self.args.max_seq_length,
collate_fn=self.data_collator,
sampler=train_sampler,
packing_efficiency_estimate=self.args.sample_packing_efficiency,
sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier,
device_count=int(os.environ.get("WORLD_SIZE", 1)),
num_epochs=self.num_epochs,
)
train_dataset = self.train_dataset
train_dataset = train_dataset.remove_columns(["length"])
data_collator = self.data_collator
dataloader_params = {
"batch_size": self._train_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
}
if self.args.dataloader_prefetch_factor:
dataloader_params[
"prefetch_factor"
] = self.args.dataloader_prefetch_factor
sampler = self._get_train_sampler()
if isinstance(sampler, BatchSampler):
dataloader_params["batch_sampler"] = sampler
del dataloader_params["batch_size"]
else:
dataloader_params["sampler"] = sampler
dataloader_params["drop_last"] = self.args.dataloader_drop_last
dataloader_params["worker_init_fn"] = seed_worker
self.accelerator.even_batches = False
return self.accelerator.prepare_data_loader(
DataLoader(train_dataset, **dataloader_params)
)
return super().get_train_dataloader()
@@ -197,18 +224,29 @@ class AxolotlTrainer(Trainer):
)
eval_sampler = self._get_eval_sampler(eval_dataset)
return self.accelerator.prepare(
MultipackDistributedDataloader(
eval_dataset,
batch_size=self.args.eval_batch_size,
seq_max_length=self.args.max_seq_length,
collate_fn=self.data_collator,
sampler=eval_sampler,
packing_efficiency_estimate=self.args.sample_packing_efficiency,
sample_packing_seq_len_multiplier=self.args.eval_batch_size,
device_count=int(os.environ.get("WORLD_SIZE", 1)),
num_epochs=self.num_epochs,
)
eval_dataset = eval_dataset.remove_columns(["length"])
data_collator = self.data_collator
dataloader_params = {
"batch_size": self.args.eval_batch_size,
"collate_fn": data_collator,
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
}
if self.args.dataloader_prefetch_factor:
dataloader_params[
"prefetch_factor"
] = self.args.dataloader_prefetch_factor
if isinstance(eval_sampler, BatchSampler):
dataloader_params["batch_sampler"] = eval_sampler
del dataloader_params["batch_size"]
else:
dataloader_params["sampler"] = eval_sampler
dataloader_params["drop_last"] = self.args.dataloader_drop_last
self.accelerator.even_batches = False
return self.accelerator.prepare_data_loader(
DataLoader(eval_dataset, **dataloader_params)
)
return super().get_eval_dataloader(eval_dataset)
@@ -229,6 +267,8 @@ class AxolotlTrainer(Trainer):
"num_workers": self.args.dataloader_num_workers,
"pin_memory": self.args.dataloader_pin_memory,
}
if self.args.dataloader_prefetch_factor:
dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
if not isinstance(bench_dataset, torch.utils.data.IterableDataset):
dataloader_params["sampler"] = self._get_bench_sampler(bench_dataset)
@@ -493,6 +533,19 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
"sample_packing_efficiency"
] = self.cfg.sample_packing_eff_est
if self.cfg.dataloader_pin_memory is not None:
training_arguments_kwargs[
"dataloader_pin_memory"
] = self.cfg.dataloader_pin_memory
if self.cfg.dataloader_num_workers is not None:
training_arguments_kwargs[
"dataloader_num_workers"
] = self.cfg.dataloader_num_workers
if self.cfg.dataloader_prefetch_factor is not None:
training_arguments_kwargs[
"dataloader_prefetch_factor"
] = self.cfg.dataloader_prefetch_factor
if self.cfg.eval_steps:
training_arguments_kwargs["evaluation_strategy"] = "steps"
training_arguments_kwargs["eval_steps"] = self.cfg.eval_steps
@@ -672,7 +725,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
args=training_args,
data_collator=DataCollatorForSeq2Seq(
data_collator=BatchSamplerDataCollatorForSeq2Seq(
self.tokenizer,
return_tensors="pt",
**data_collator_kwargs,
@@ -690,4 +743,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
for callback in self.get_post_trainer_create_callbacks(trainer):
trainer.add_callback(callback)
if self.cfg.deepspeed and self.cfg.sample_packing:
trainer.accelerator.state.deepspeed_plugin.deepspeed_config[
"train_micro_batch_size_per_gpu"
] = self.cfg.micro_batch_size
return trainer

27
src/axolotl/utils/collators.py
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@@ -119,3 +119,30 @@ class DataCollatorForSeq2Seq:
features["decoder_input_ids"] = decoder_input_ids
return features
@dataclass
class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
"""
Collator for multipack specific to the using the BatchSampler
"""
def __call__(self, features, return_tensors=None):
chunked_data = {}
for feature in features[0].keys():
if feature == "length":
continue
if feature == "attention_mask":
arrays = [
(1) * np.array(item[feature])
for item in features
if feature in item
]
chunked_data[feature] = np.concatenate(arrays)
else:
arrays = [
np.array(item[feature]) for item in features if feature in item
]
chunked_data[feature] = np.concatenate(arrays)
features = [chunked_data]
return super().__call__(features, return_tensors=return_tensors)

4
src/axolotl/utils/data.py
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@@ -80,11 +80,11 @@ def prepare_dataset(cfg, tokenizer):
)
if cfg.max_steps:
total_num_steps = min(
calculate_total_num_steps(cfg, train_dataset, tokenizer), cfg.max_steps
calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
)
LOG.info(f"Maximum number of steps set at {total_num_steps}")
else:
total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
total_num_steps = calculate_total_num_steps(cfg, train_dataset)
return train_dataset, eval_dataset, total_num_steps, prompters

4
src/axolotl/utils/samplers/__init__.py Normal file
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@@ -0,0 +1,4 @@
"""
axolotl samplers module
"""
from .multipack import MultipackBatchSampler # noqa: F401

193
src/axolotl/utils/samplers/multipack.py Normal file
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@@ -0,0 +1,193 @@
# pylint: skip-file
"""
Multipack Batch Sampler
"""
import logging
import math
import os
from typing import Any, Iterable, List, Union
import numba
import numpy as np
from torch.utils.data import BatchSampler, Sampler
LOG = logging.getLogger("axolotl.utils.samplers.multipack")
@numba.njit
def ffd_check(a: np.ndarray, c: int, n: int):
# First-fit-decreasing bin packing
# Check if a[] could fit in n bins with capacity c
# https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
a = np.sort(a)[::-1]
bins = np.full((n,), c, dtype=a.dtype)
for size in a:
not_found = True
for idx in range(n):
if bins[idx] >= size:
bins[idx] -= size
not_found = False
break
if not_found:
return False
return True
@numba.njit
def ffd_with_result(a: np.ndarray, c: int, start_index: int):
# First-fit-decreasing bin packing (with result return)
indices = np.argsort(a)[::-1]
a = a[indices]
bins: List[Any] = []
bins_result: List[Any] = []
for a_id, size in enumerate(a):
add_new = True
for idx in range(len(bins)):
if bins[idx] >= size:
bins[idx] -= size
bins_result[idx].append(indices[a_id] + start_index)
add_new = False
break
if add_new:
bins.append(c - size)
bins_result.append([indices[a_id] + start_index])
return bins_result
@numba.njit
def allocate(
lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
):
# Dynamic batch allocator, similar to Multifit
# https://en.wikipedia.org/wiki/Multifit_algorithm
# ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
s = 0
start_index = 0
result = []
while True:
# binary search [l, r)
left = 1
right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
while right - left > 1:
mid = (left + right) // 2
if ffd_check(lengths[start_index : start_index + mid], c, n):
left = mid
else:
right = mid
# use length l
batch = ffd_with_result(
lengths[start_index : start_index + left], c, start_index
)
assert len(batch) <= n
if len(batch) < n:
break
start_index += left
s = lengths_cumsum[start_index - 1]
# add local rank
result.append(batch[rank])
return result, s, len(result) * c * n
class MultipackBatchSampler(BatchSampler):
"""
Batch Sampler class for multipack
"""
def __init__(
self,
sampler: Union[Sampler[int], Iterable[int]],
batch_size: int,
drop_last: bool,
batch_max_len: int,
lengths: np.ndarray,
packing_efficiency_estimate: float = 1.0,
):
super().__init__(sampler, batch_size, drop_last)
self.batch_size = None
self.batch_max_len = batch_max_len
self.lengths: np.ndarray = lengths
self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
assert isinstance(self.lengths, np.ndarray)
self.epoch = 0
# statistics
self.eff_total_used = 0
self.eff_total_slots = 0
def set_epoch(self, epoch: int):
self.epoch = epoch
def generate_batches(self, set_stats=False):
indices = [idx for idx in self.sampler]
lengths = self.lengths[indices]
lengths_cumsum = np.cumsum(lengths)
batches, total_used, total_slots = allocate(
lengths=lengths,
lengths_cumsum=lengths_cumsum,
rank=0,
c=self.batch_max_len,
n=1,
)
batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
# statistics
if set_stats:
self.eff_total_used += total_used
self.eff_total_slots += total_slots
return batches
def __iter__(self):
batches = self.generate_batches(set_stats=True)
return iter(batches)
def num_batches(self):
batches = self.generate_batches(set_stats=True)
return len(batches)
def efficiency(self):
return self.eff_total_used / self.eff_total_slots
def __len__(self):
self.num_batches()
return self._len_est()
def _len_est(self):
world_size = int(os.getenv("WORLD_SIZE", "1"))
lengths_sum = np.sum(self.lengths)
lengths_sum_per_device = lengths_sum // world_size
LOG.info(
f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
f"total_num_tokens per device: {lengths_sum_per_device}"
)
# shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
return (
world_size
* math.floor(
0.99
* lengths_sum_per_device
/ self.packing_efficiency_estimate
// self.batch_max_len
)
- 1
)

71
src/axolotl/utils/trainer.py
View File

@@ -8,20 +8,13 @@ from typing import List
import numpy as np
import torch
import torch.cuda
import torch.distributed as dist
from accelerate.logging import get_logger
from datasets import set_caching_enabled
from torch.utils.data import DistributedSampler, RandomSampler
from torch.utils.data import DataLoader, RandomSampler
from axolotl.core.trainer_builder import HFCausalTrainerBuilder
from axolotl.utils.collators import DataCollatorForSeq2Seq
from axolotl.utils.dataloader import MultipackDistributedDataloader
from axolotl.utils.distributed import (
is_distributed,
is_main_process,
reduce_and_broadcast,
zero_first,
)
from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
from axolotl.utils.samplers import MultipackBatchSampler
LOG = get_logger("axolotl")
@@ -148,7 +141,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
return train_dataset, eval_dataset
def calculate_total_num_steps(cfg, train_dataset, tokenizer):
def calculate_total_num_steps(cfg, train_dataset):
if cfg.sample_packing:
# we have to drop anything longer then sequence len otherwise
# flash attention with position ids fails
@@ -196,37 +189,36 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
main_process_only=True,
)
else:
if cfg.world_size > 1 and is_distributed():
sampler = DistributedSampler(
train_dataset,
num_replicas=cfg.world_size,
rank=dist.get_rank(),
seed=cfg.seed or 42,
)
else:
sampler = RandomSampler(train_dataset)
data_loader = MultipackDistributedDataloader(
train_dataset,
sampler = MultipackBatchSampler(
sampler=RandomSampler(train_dataset),
batch_size=cfg.micro_batch_size,
seq_max_length=cfg.max_packed_sequence_len or cfg.sequence_len,
collate_fn=DataCollatorForSeq2Seq(
tokenizer,
return_tensors="pt",
padding="longest",
drop_last=True,
batch_max_len=cfg.micro_batch_size
* (cfg.max_packed_sequence_len or cfg.sequence_len),
lengths=(
train_dataset.data.column("position_ids")
.to_pandas()
.apply(lambda x: x[-1] + 1)
.values
),
sampler=sampler,
packing_efficiency_estimate=cfg.sample_packing_eff_est,
sample_packing_seq_len_multiplier=cfg.micro_batch_size,
device_count=int(os.environ.get("WORLD_SIZE", 1)),
num_epochs=cfg.num_epochs,
)
data_loader_len = data_loader.len_w_stats()
actual_eff = data_loader.efficiency()
data_loader = DataLoader(
train_dataset.remove_columns(["length"]),
batch_sampler=sampler,
)
data_loader_len = len(data_loader)
actual_eff = sampler.efficiency()
LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True)
# FIXME: is there a bug here somewhere? the total num steps depends
# on the agreed on value for sample_packing_eff_est
total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
total_num_steps = int(
math.floor(
data_loader_len
* cfg.num_epochs
/ int(os.environ.get("WORLD_SIZE", 1))
)
)
def calc_sample_packing_eff_est(estimates: List[float]):
LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}")
@@ -246,7 +238,12 @@ def calculate_total_num_steps(cfg, train_dataset, tokenizer):
)
else:
total_num_steps = int(
math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
math.ceil(
len(train_dataset)
* cfg.num_epochs
/ int(os.environ.get("WORLD_SIZE", 1))
/ cfg.batch_size
)
)
LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True)
return total_num_steps