ColossalAI/tests/test_zero_tensor_parallel/components.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

77 lines
1.7 KiB
Python

import sys
from pathlib import Path
repo_path = Path(__file__).absolute().parents[2]
sys.path.append(str(repo_path))
try:
import model_zoo.vit.vision_transformer_from_config
except ImportError:
raise ImportError("model_zoo is not found, please check your path")
BATCH_SIZE = 8
IMG_SIZE = 32
PATCH_SIZE = 4
DIM = 512
NUM_ATTENTION_HEADS = 8
SUMMA_DIM = 2
NUM_CLASSES = 10
DEPTH = 6
model_cfg = dict(
type='VisionTransformerFromConfig',
tensor_splitting_cfg=dict(
type='ViTInputSplitter2D',
),
embedding_cfg=dict(
type='ViTPatchEmbedding2D',
img_size=IMG_SIZE,
patch_size=PATCH_SIZE,
embed_dim=DIM,
),
token_fusion_cfg=dict(
type='ViTTokenFuser2D',
img_size=IMG_SIZE,
patch_size=PATCH_SIZE,
embed_dim=DIM,
drop_rate=0.1
),
norm_cfg=dict(
type='LayerNorm2D',
normalized_shape=DIM,
eps=1e-6,
),
block_cfg=dict(
type='ViTBlock',
attention_cfg=dict(
type='ViTSelfAttention2D',
hidden_size=DIM,
num_attention_heads=NUM_ATTENTION_HEADS,
attention_dropout_prob=0.,
hidden_dropout_prob=0.1,
),
droppath_cfg=dict(
type='VanillaViTDropPath',
),
mlp_cfg=dict(
type='ViTMLP2D',
in_features=DIM,
dropout_prob=0.1,
mlp_ratio=1
),
norm_cfg=dict(
type='LayerNorm2D',
normalized_shape=DIM,
eps=1e-6,
),
),
head_cfg=dict(
type='ViTHead2D',
hidden_size=DIM,
num_classes=NUM_CLASSES,
),
embed_dim=DIM,
depth=DEPTH,
drop_path_rate=0.,
)