# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
"""
Copyright NVIDIA/apex
This file is adapted from fused adam in NVIDIA/apex, commit 6bd01c4
"""
import torch
from .multi_tensor_apply import MultiTensorApply
multi_tensor_applier = MultiTensorApply(2048 * 32)
from deepspeed.accelerator import get_accelerator
from deepspeed.ops.op_builder import FusedAdamBuilder
[docs]class FusedAdam(torch.optim.Optimizer):
"""Implements Adam algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
This version of fused Adam implements 2 fusions.
* Fusion of the Adam update's elementwise operations
* 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:`apex.optimizers.FusedAdam` may be used as a drop-in replacement for ``torch.optim.AdamW``,
or ``torch.optim.Adam`` with ``adam_w_mode=False``::
opt = apex.optimizers.FusedAdam(model.parameters(), lr = ....)
...
opt.step()
:class:`apex.optimizers.FusedAdam` may be used with or without Amp. If you wish to use :class:`FusedAdam` with Amp,
you may choose any ``opt_level``::
opt = apex.optimizers.FusedAdam(model.parameters(), lr = ....)
model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2")
...
opt.step()
In general, ``opt_level="O1"`` is recommended.
.. warning::
A previous version of :class:`FusedAdam` allowed a number of additional arguments to ``step``. These additional arguments
are now deprecated and unnecessary.
Adam was been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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
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)
.. _Adam - A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
adam_w_mode=True,
weight_decay=0.,
amsgrad=False,
set_grad_none=True):
if amsgrad:
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.set_grad_none = set_grad_none
fused_adam_cuda = FusedAdamBuilder().load()
# Skip buffer
self._dummy_overflow_buf = get_accelerator().IntTensor([0])
self.multi_tensor_adam = fused_adam_cuda.multi_tensor_adam
def zero_grad(self):
if self.set_grad_none:
for group in self.param_groups:
for p in group['params']:
p.grad = None
else:
super(FusedAdam, self).zero_grad()
def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None, grad_scaler=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
The remaining arguments are deprecated, and are only retained (for the moment) for error-checking purposes.
"""
if any(p is not None for p in [grads, output_params, scale, grad_norms]):
raise RuntimeError(
'FusedAdam has been updated. Simply initialize it identically to torch.optim.Adam, and call step() with no arguments.'
)
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
if len(group['params']) == 0:
continue
bias_correction = 1 if group['bias_correction'] else 0
beta1, beta2 = group['betas']
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if 'step' not in group:
group['step'] = 0
# create lists for multi-tensor apply
g_16, p_16, m_16, v_16 = [], [], [], []
g_bf, p_bf, m_bf, v_bf = [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
for p in group['params']:
if p.grad is None:
continue
if p.grad.data.is_sparse:
raise RuntimeError(
'FusedAdam does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
# DeepSpeed ZeRO 3 processes each subgroup a time, so we need to keep tracking step count for each tensor separately.
# While this is not an issue for ZeRO 1 & 2, since they apply a single optimization step to the whole param group at the same time.
# In order to keep backward compatibility for the existing checkpoints, we use group['state'] to initialize state['step'] if it exists.
state['step'] = group.get('step', 0)
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# 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.bfloat16:
g_bf.append(p.grad)
p_bf.append(p)
m_bf.append(state['exp_avg'])
v_bf.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:
raise RuntimeError('FusedAdam only support fp16, bf16 and fp32.')
if len(g_16) > 0:
state['step'] += 1
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'], state['step'], self.adam_w_mode,
bias_correction, group['weight_decay'])
if len(g_bf) > 0:
state['step'] += 1
multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_bf, p_bf, m_bf, v_bf],
group['lr'], beta1, beta2, group['eps'], state['step'], self.adam_w_mode,
bias_correction, group['weight_decay'])
if len(g_32) > 0:
state['step'] += 1
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'], state['step'], self.adam_w_mode,
bias_correction, group['weight_decay'])
return loss