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# 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 max(
1,
(
world_size
* math.floor(
0.99
* lengths_sum_per_device
/ self.packing_efficiency_estimate
// self.batch_max_len
)
- 1
),
)
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