2021-10-28 16:21:23 +00:00
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import inspect
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from colossalai.registry import *
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def build_from_config(module, config: dict):
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"""Returns an object of :class:`module` constructed from `config`.
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2022-03-25 05:02:39 +00:00
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Args:
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module: A python or user-defined class
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config: A python dict containing information used in the construction of the return object
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Returns: An ``object`` of interest
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Raises:
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AssertionError: Raises an AssertionError if `module` is not a class
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2021-10-28 16:21:23 +00:00
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"""
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assert inspect.isclass(module), 'module must be a class'
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return module(**config)
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def build_from_registry(config, registry: Registry):
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2022-03-25 05:02:39 +00:00
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r"""Returns an object constructed from `config`, the type of the object
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2021-10-28 16:21:23 +00:00
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is specified by `registry`.
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2022-03-25 05:02:39 +00:00
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Note:
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2022-03-31 03:36:56 +00:00
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the `config` is used to construct the return object such as `LAYERS`, `OPTIMIZERS`
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and other support types in `registry`. The `config` should contain
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all required parameters of corresponding object. The details of support
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types in `registry` and the `mod_type` in `config` could be found in
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`registry <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/registry/__init__.py>`_.
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2022-03-25 05:02:39 +00:00
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Args:
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config (dict or :class:`colossalai.context.colossalai.context.Config`): information
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used in the construction of the return object.
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registry (:class:`Registry`): A registry specifying the type of the return object
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2022-03-31 03:36:56 +00:00
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Returns:
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A Python object specified by `registry`.
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2022-03-25 05:02:39 +00:00
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Raises:
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2022-03-31 03:36:56 +00:00
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Exception: Raises an Exception if an error occurred when building from registry.
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2021-10-28 16:21:23 +00:00
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"""
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2022-07-12 10:12:07 +00:00
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config_ = config.copy() # keep the original config untouched
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assert isinstance(registry, Registry), f'Expected type Registry but got {type(registry)}'
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2021-10-28 16:21:23 +00:00
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mod_type = config_.pop('type')
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2022-07-12 10:12:07 +00:00
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assert registry.has(mod_type), f'{mod_type} is not found in registry {registry.name}'
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2021-10-28 16:21:23 +00:00
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try:
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obj = registry.get_module(mod_type)(**config_)
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except Exception as e:
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2022-07-12 10:12:07 +00:00
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print(f'An error occurred when building {mod_type} from registry {registry.name}', flush=True)
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2021-10-28 16:21:23 +00:00
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raise e
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return obj
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2022-07-12 10:12:07 +00:00
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2021-10-28 16:21:23 +00:00
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def build_gradient_handler(config, model, optimizer):
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"""Returns a gradient handler object of :class:`BaseGradientHandler` constructed from `config`,
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`model` and `optimizer`.
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2022-03-25 05:02:39 +00:00
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Args:
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config (dict or :class:`colossalai.context.Config`): A python dict or
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a :class:`colossalai.context.Config` object containing information
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used in the construction of the ``GRADIENT_HANDLER``.
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model (:class:`nn.Module`): A model containing parameters for the gradient handler
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optimizer (:class:`torch.optim.Optimizer`): An optimizer object containing parameters for the gradient handler
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Returns:
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An object of :class:`colossalai.engine.BaseGradientHandler`
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2021-10-28 16:21:23 +00:00
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"""
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config_ = config.copy()
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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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config_['model'] = model
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config_['optimizer'] = optimizer
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return build_from_registry(config_, GRADIENT_HANDLER)
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