Files
ColossalAI/tests/test_context/test_3d_init.py
Frank Lee da01c234e1 Develop/experiments (#59)
* 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 commit 2e0b0b7699.

* 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 commit 2e0b0b7699.

* 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 commit 2e0b0b7699.

* 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>
2021-12-09 15:08:29 +08:00

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()