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[legacy] clean up legacy code (#4743)

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* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci
This commit is contained in:
Hongxin Liu
2023-09-18 16:31:06 +08:00
committed by GitHub
parent 32e7f99416
commit b5f9e37c70
342 changed files with 2919 additions and 4182 deletions
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7
colossalai/nn/optimizer/__init__.py
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@@ -1,10 +1,9 @@
from .colossalai_optimizer import ColossalaiOptimizer
from .cpu_adam import CPUAdam
from .fused_adam import FusedAdam
from .fused_lamb import FusedLAMB
from .fused_sgd import FusedSGD
from .hybrid_adam import HybridAdam
from .lamb import Lamb
from .lars import Lars
from .cpu_adam import CPUAdam
from .hybrid_adam import HybridAdam
__all__ = ['ColossalaiOptimizer', 'FusedLAMB', 'FusedAdam', 'FusedSGD', 'Lamb', 'Lars', 'CPUAdam', 'HybridAdam']
__all__ = ['FusedLAMB', 'FusedAdam', 'FusedSGD', 'Lamb', 'Lars', 'CPUAdam', 'HybridAdam']

44
colossalai/nn/optimizer/colossalai_optimizer.py
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@@ -1,44 +0,0 @@
import torch
import torch.nn as nn
from torch import Tensor
from torch.optim import Optimizer
from colossalai.utils import clip_grad_norm_fp32
class ColossalaiOptimizer(Optimizer):
def __init__(self, optim: Optimizer):
self.optim = optim
@property
def param_groups(self):
return self.optim.param_groups
@property
def defaults(self):
return self.optim.defaults
def add_param_group(self, *args, **kwargs):
return self.optim.add_param_group(*args, **kwargs)
def step(self, *args, **kwargs):
return self.optim.step(*args, **kwargs)
def zero_grad(self, *args, **kwargs):
self.optim.zero_grad(*args, **kwargs)
def load_state_dict(self, *args, **kwargs):
self.optim.load_state_dict(*args, **kwargs)
def state_dict(self):
return self.optim.state_dict()
def backward(self, loss: Tensor):
loss.backward()
def backward_by_grad(self, tensor: Tensor, grad: Tensor):
torch.autograd.backward(tensors=tensor, grad_tensors=grad)
def clip_grad_norm(self, model: nn.Module, max_norm: float):
if max_norm > 0.0:
clip_grad_norm_fp32(model.parameters(), max_norm)