mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-10-23 07:39:31 +00:00
* Add gradient accumulation, fix lr scheduler * fix FP16 optimizer and adapted torch amp with tensor parallel (#18) * fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes * fixed trainer * Revert "fixed trainer" This reverts commit2e0b0b7699
. * improved consistency between trainer, engine and schedule (#23) Co-authored-by: 1SAA <c2h214748@gmail.com> * Split conv2d, class token, positional embedding in 2d, Fix random number in ddp Fix convergence in cifar10, Imagenet1000 * Integrate 1d tensor parallel in Colossal-AI (#39) * fixed 1D and 2D convergence (#38) * optimized 2D operations * fixed 1D ViT convergence problem * Feature/ddp (#49) * remove redundancy func in setup (#19) (#20) * use env to control the language of doc (#24) (#25) * Support TP-compatible Torch AMP and Update trainer API (#27) * Add gradient accumulation, fix lr scheduler * fix FP16 optimizer and adapted torch amp with tensor parallel (#18) * fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes * fixed trainer * Revert "fixed trainer" This reverts commit2e0b0b7699
. * improved consistency between trainer, engine and schedule (#23) Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: ver217 <lhx0217@gmail.com> * add an example of ViT-B/16 and remove w_norm clipping in LAMB (#29) * add explanation for ViT example (#35) (#36) * support torch ddp * fix loss accumulation * add log for ddp * change seed * modify timing hook Co-authored-by: Frank Lee <somerlee.9@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> * Feature/pipeline (#40) * remove redundancy func in setup (#19) (#20) * use env to control the language of doc (#24) (#25) * Support TP-compatible Torch AMP and Update trainer API (#27) * Add gradient accumulation, fix lr scheduler * fix FP16 optimizer and adapted torch amp with tensor parallel (#18) * fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes * fixed trainer * Revert "fixed trainer" This reverts commit2e0b0b7699
. * improved consistency between trainer, engine and schedule (#23) Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: ver217 <lhx0217@gmail.com> * add an example of ViT-B/16 and remove w_norm clipping in LAMB (#29) * add explanation for ViT example (#35) (#36) * optimize communication of pipeline parallel * fix grad clip for pipeline Co-authored-by: Frank Lee <somerlee.9@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> * optimized 3d layer to fix slow computation ; tested imagenet performance with 3d; reworked lr_scheduler config definition; fixed launch args; fixed some printing issues; simplified apis of 3d layers (#51) * Update 2.5d layer code to get a similar accuracy on imagenet-1k dataset * update api for better usability (#58) update api for better usability Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: ver217 <lhx0217@gmail.com> Co-authored-by: puck_WCR <46049915+WANG-CR@users.noreply.github.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> Co-authored-by: アマデウス <kurisusnowdeng@users.noreply.github.com> Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>
112 lines
2.9 KiB
Python
112 lines
2.9 KiB
Python
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
from functools import partial
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
import torch.multiprocessing as mp
|
|
|
|
from colossalai.context.parallel_mode import ParallelMode
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.initialize import launch
|
|
|
|
CONFIG_PATH = Path(__file__).parent.joinpath('configs/parallel_3d_init.py').absolute()
|
|
|
|
|
|
def check_data_parallel_rank(rank):
|
|
dp_rank = gpc.get_local_rank(ParallelMode.DATA)
|
|
|
|
if rank in list(range(16)):
|
|
assert dp_rank == 0
|
|
elif rank in list(range(16, 32)):
|
|
assert dp_rank == 1
|
|
|
|
|
|
def check_pipeline_parallel_rank(rank):
|
|
ppr = gpc.get_local_rank(ParallelMode.PIPELINE)
|
|
|
|
if rank in list(range(8)):
|
|
assert ppr == 0
|
|
elif rank in list(range(8, 16)):
|
|
assert ppr == 1
|
|
elif rank in list(range(16, 24)):
|
|
assert ppr == 0
|
|
elif rank in list(range(24, 32)):
|
|
assert ppr == 1
|
|
|
|
|
|
def check_tensor_parallel_rank(rank):
|
|
tp_rank = gpc.get_local_rank(ParallelMode.TENSOR)
|
|
|
|
for i in range(8):
|
|
ranks = list(range(i, 32, 8))
|
|
if rank in ranks:
|
|
assert tp_rank == i
|
|
|
|
|
|
def check_3d_parallel_rank(rank):
|
|
ip_rank = gpc.get_local_rank(ParallelMode.PARALLEL_3D_INPUT)
|
|
wp_rank = gpc.get_local_rank(ParallelMode.PARALLEL_3D_WEIGHT)
|
|
op_rank = gpc.get_local_rank(ParallelMode.PARALLEL_3D_OUTPUT)
|
|
|
|
# check for input parallel group
|
|
for i in range(2):
|
|
_ranks = list(range(i * 2, 32, 4))
|
|
_ranks_plus_one = [val + 1 for val in _ranks]
|
|
input_ranks = _ranks + _ranks_plus_one
|
|
if rank in input_ranks:
|
|
assert ip_rank == i
|
|
|
|
# check for weight parallel group
|
|
for i in range(2):
|
|
ranks = list(range(i, 32, 2))
|
|
|
|
if rank in ranks:
|
|
assert wp_rank == i
|
|
|
|
# check for output parallel group
|
|
for i in range(2):
|
|
ranks = []
|
|
for j in range(i * 4, 32, 8):
|
|
ranks.extend([j + k for k in range(4)])
|
|
if rank in ranks:
|
|
assert op_rank == i
|
|
|
|
|
|
def init_3d(rank, world_size, backend, port, host):
|
|
dist_args = dict(
|
|
config=CONFIG_PATH,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
backend=backend,
|
|
port=port,
|
|
host=host,
|
|
verbose=True
|
|
)
|
|
launch(**dist_args)
|
|
check_tensor_parallel_rank(rank)
|
|
check_3d_parallel_rank(rank)
|
|
check_data_parallel_rank(rank)
|
|
check_pipeline_parallel_rank(rank)
|
|
gpc.destroy()
|
|
|
|
|
|
@pytest.mark.cpu
|
|
def test_3d_init():
|
|
"""
|
|
As no computation or communication is done, we can run this test on CPU.
|
|
"""
|
|
world_size = 32
|
|
test_fn = partial(init_3d,
|
|
world_size=world_size,
|
|
backend='gloo',
|
|
port='29502',
|
|
host='localhost'
|
|
)
|
|
mp.spawn(test_fn, nprocs=world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_3d_init()
|