File size: 5,611 Bytes
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import torch.distributed as dist
from torch.utils.data import Sampler
import numpy as np
import numba
@numba.njit
def lpt_check(heap: np.ndarray, A: np.ndarray, c: int, n: int):
# LPT (Longest processing time first scheduling)
# Time: O(|A| log |A| + |A| log n)
A = np.sort(A)[::-1]
heap.fill(0)
for size in A:
# Put into smallest element
heap[1] += size
if heap[1] > c:
return False
# Heapify (Sink)
# https://stackoverflow.com/questions/20397674/replacing-element-in-min-heap
u = 1
while (u << 1) <= n:
v = u << 1 # lch
rch = (u << 1) | 1
if rch <= n and heap[rch] < heap[v]:
v = rch
if heap[u] <= heap[v]:
break
heap[u], heap[v] = heap[v], heap[u]
u = v
return True
@numba.njit
def lpt_with_result(heap: np.ndarray, A: np.ndarray, n: int, rank: int):
# LPT (Longest processing time first scheduling)
# Time: O(|A| log |A| + |A| log n)
result = []
indices = np.argsort(A, kind="mergesort")[::-1] # Stable sort for coherence across machine and versions
A = A[indices]
heap.fill(0)
heap_id = np.arange(-1, n, dtype=A.dtype)
for idx, size in enumerate(A):
# Put into smallest element
heap[1] += size
if heap_id[1] == rank:
result.append(indices[idx])
# Heapify (Sink)
# https://stackoverflow.com/questions/20397674/replacing-element-in-min-heap
u = 1
while (u << 1) <= n:
v = u << 1 # lch
rch = (u << 1) | 1
if rch <= n and heap[rch] < heap[v]:
v = rch
if heap[u] <= heap[v]:
break
heap[u], heap[v] = heap[v], heap[u]
heap_id[u], heap_id[v] = heap_id[v], heap_id[u]
u = v
return np.array(result)
@numba.njit
def allocate(heap: np.ndarray, start_index: int, lengths: np.ndarray, rank: int, c: int, n: int):
# Dynamic batch allocator, binary search + LPT
# ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
# Linear scan
total_len = 0
end_index = start_index
while end_index < lengths.size:
l = lengths[end_index]
total_len += l
end_index += 1
if total_len >= c * n:
break
a = lengths[start_index: end_index]
# binary search [l, r)
l = 1
r = 1 + a.size
while r - l > 1:
m = (l + r) // 2
if lpt_check(heap, a[:m], c, n):
l = m
else:
r = m
# use length l
batch = start_index + lpt_with_result(heap, a[:l], n, rank)
return l >= n, l, batch, np.sum(a[:l])
class MultipackDistributedBatchSampler(Sampler):
"""Unpadded length sampling using Multipack V2, for models with quadratic attention complexity.
It also tries to evenly distribute the sequences using LPT, so that quadratic load is more balanced.
Approximate (at most 1.33x ?) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
Time Complexity: O(n log n log k)
n = maximum number of sequences per batch, k = number of nodes
"""
def __init__(
self,
batch_max_length: int,
lengths: np.ndarray,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
drop_last_batch: bool = False
):
# Get rank
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.num_replicas = num_replicas
self.rank = rank
self.drop_last_batch = drop_last_batch
self.batch_max_length = batch_max_length
self.lengths = lengths
assert isinstance(self.lengths, np.ndarray)
# statistics
self.eff_total_used = 0
self.eff_total_slots = 0
def iter(self, start_index: int = 0):
# Allocate workspace
heap = np.empty(self.num_replicas + 1, dtype=self.lengths.dtype)
# Cursor after the last yielded batch (next batch starts here). O(1) resume.
self.cursor = int(start_index)
while start_index < self.lengths.size:
is_full, global_numseq, batch, batch_totlen = allocate(heap,
start_index, self.lengths,
rank=self.rank, c=self.batch_max_length, n=self.num_replicas)
start_index += global_numseq
self.cursor = int(start_index)
if not is_full: # Skip batch with empty NPUs
break
if self.drop_last_batch and (start_index >= self.lengths.size): # Skip last batch
break
self.eff_total_used += batch_totlen
self.eff_total_slots += self.num_replicas * self.batch_max_length
yield batch
def estimate_num_batches(self):
# Rough estimate, don't consider packing efficiency
return round(np.sum(self.lengths) / (self.num_replicas * self.batch_max_length))
def efficiency(self):
return self.eff_total_used / self.eff_total_slots
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