2022-03-18 07:44:47 +00:00
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from typing import Tuple
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2022-03-17 07:05:41 +00:00
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2022-03-18 08:18:31 +00:00
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import torch
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2022-03-17 07:05:41 +00:00
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import torch.nn as nn
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2022-03-18 07:22:43 +00:00
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from colossalai.logging import get_dist_logger
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2022-03-18 07:44:47 +00:00
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from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2
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from colossalai.zero.sharded_optim.sharded_optim_v2 import ShardedOptimizerV2
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2022-06-02 04:13:15 +00:00
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from .zero_optimizer import ZeroOptimizer
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2022-03-17 07:05:41 +00:00
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2022-03-16 11:29:37 +00:00
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2022-03-18 08:18:31 +00:00
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def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model_config,
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optimizer_config) -> Tuple[ShardedModelV2, ShardedOptimizerV2]:
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2022-03-16 11:29:37 +00:00
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"""
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A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading
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:param model: Your model object
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:type model: :class:`torch.nn.Module`
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:param optimizer_config: Your optimizer object
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:type optimizer_config: :class:`dict`
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:return: (model, optimizer)
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:rtype: Tuple
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"""
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logger = get_dist_logger('convert_to_zero_v2')
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2022-04-20 02:05:39 +00:00
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logger.info(f'optimizer_config is {optimizer_config}', ranks=[0])
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2022-03-17 05:16:22 +00:00
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if optimizer_config is None:
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optimizer_config = dict()
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2022-04-20 02:05:39 +00:00
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logger.info(f'model_config is {model_config}', ranks=[0])
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2022-03-17 05:16:22 +00:00
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if model_config is None:
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model_config = dict()
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2022-03-18 07:44:47 +00:00
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zero_model = ShardedModelV2(model, **model_config)
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2022-03-18 08:18:31 +00:00
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zero_optimizer = ShardedOptimizerV2(zero_model, optimizer, **optimizer_config)
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2022-03-16 11:29:37 +00:00
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return zero_model, zero_optimizer
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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 2e0b0b76990e8d4e337add483d878c0f61cf5097.
* 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 2e0b0b76990e8d4e337add483d878c0f61cf5097.
* 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 2e0b0b76990e8d4e337add483d878c0f61cf5097.
* 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 07:08:29 +00:00
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2022-06-02 04:13:15 +00:00
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__all__ = ['convert_to_zero_v2', 'ShardedModelV2', 'ShardedOptimizerV2', 'ZeroOptimizer']
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