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[cuda] modify the fused adam, support hybrid of fp16 and fp32 (#497)

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LuGY
2022-03-25 14:15:53 +08:00
committed by GitHub
parent 920c5889a7
commit 6a3f9fda83
6 changed files with 253 additions and 143 deletions
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41
colossalai/nn/optimizer/fused_adam.py
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@@ -10,7 +10,7 @@ class FusedAdam(torch.optim.Optimizer):
"""Implements Adam algorithm.
Currently GPU-only. Requires ColossalAI to be installed via
``pip install -v --no-cache-dir --global-option="--cuda_ext" ./``.
``pip install .``.
This version of fused Adam implements 2 fusions.
@@ -18,7 +18,7 @@ class FusedAdam(torch.optim.Optimizer):
* A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
:class:`colossalai.nn.optimizer.FusedAdam` may be used as a drop-in replacement for ``torch.optim.AdamW``,
or ``torch.optim.Adam`` with ``adam_w_mode=False``
or ``torch.optim.Adam`` with ``adamw_mode=False``
:class:`colossalai.nn.optimizer.FusedAdam` may be used with or without Amp.
@@ -36,7 +36,7 @@ class FusedAdam(torch.optim.Optimizer):
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False) NOT SUPPORTED in FusedAdam!
adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
adamw_mode (boolean, optional): Apply L2 regularization or weight decay
True for decoupled weight decay(also known as AdamW) (default: True)
set_grad_none (bool, optional): whether set grad to None when zero_grad()
method is called. (default: True)
@@ -53,7 +53,7 @@ class FusedAdam(torch.optim.Optimizer):
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
adam_w_mode=True,
adamw_mode=True,
weight_decay=0.,
amsgrad=False,
set_grad_none=True):
@@ -62,7 +62,7 @@ class FusedAdam(torch.optim.Optimizer):
raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
defaults = dict(lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay)
super(FusedAdam, self).__init__(params, defaults)
self.adam_w_mode = 1 if adam_w_mode else 0
self.adamw_mode = 1 if adamw_mode else 0
self.set_grad_none = set_grad_none
if multi_tensor_applier.available:
import colossal_C
@@ -109,8 +109,7 @@ class FusedAdam(torch.optim.Optimizer):
group['step'] = 1
# create lists for multi-tensor apply
g_16, p_16, m_16, v_16 = [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
g_l, p_l, m_l, v_l = [], [], [], []
for p in group['params']:
if p.grad is None:
@@ -127,26 +126,16 @@ class FusedAdam(torch.optim.Optimizer):
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if p.dtype == torch.float16:
g_16.append(p.grad.data)
p_16.append(p.data)
m_16.append(state['exp_avg'])
v_16.append(state['exp_avg_sq'])
elif p.dtype == torch.float32:
g_32.append(p.grad.data)
p_32.append(p.data)
m_32.append(state['exp_avg'])
v_32.append(state['exp_avg_sq'])
else:
if p.dtype not in [torch.float16, torch.float32]:
raise RuntimeError('FusedAdam only support fp16 and fp32.')
g_l.append(p.grad.data)
p_l.append(p.data)
m_l.append(state['exp_avg'])
v_l.append(state['exp_avg_sq'])
if (len(g_16) > 0):
multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_16, p_16, m_16, v_16],
group['lr'], beta1, beta2, group['eps'], group['step'], self.adam_w_mode,
bias_correction, group['weight_decay'])
if (len(g_32) > 0):
multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_32, p_32, m_32, v_32],
group['lr'], beta1, beta2, group['eps'], group['step'], self.adam_w_mode,
bias_correction, group['weight_decay'])
multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_l, p_l, m_l, v_l],
group['lr'], beta1, beta2, group['eps'], group['step'], self.adamw_mode,
bias_correction, group['weight_decay'])
return loss